This document provides guidance for managing sediment and water quality data within India's Hydrological Information System (HIS). It summarizes the key HIS manuals that provide procedures for monitoring, data collection, validation, analysis, dissemination and publication of sediment and water quality data. Specifically, it outlines the multi-volume HIS Manuals for Surface Water and Groundwater, which describe the lifecycle of sediment and water quality data within the HIS. It also lists some additional HPI documentation and training modules that are relevant to sediment and water quality monitoring and analysis. The overall aim is to help users navigate and understand the various documents within the HIS library to properly manage sediment and water quality data.
This document provides an overview of guidance materials for the management of surface water data within India's Hydrological Information System (HIS). It describes the lifecycle of hydrometric data from collection through analysis and publication. Key documents that provide procedures for surface water data management are the HIS Manual Surface Water and various training modules developed under the Hydrology Project. The manual and modules cover topics like network design, data collection, entry, validation, processing, analysis, and dissemination of water level, stage-discharge, and flow data. The goal is to standardize surface water data management practices across states and agencies to improve data quality and usability.
This document provides guidance for managing groundwater data as part of a Hydrological Information System (HIS) in India. It discusses the lifecycle of hydrometric data from collection to dissemination. Key points covered include:
- The HIS Manual Groundwater is the primary reference for groundwater data management procedures.
- Groundwater data goes through stages of monitoring, data sensing, validation, analysis, and publication.
- Sections provide guidance on groundwater monitoring networks, data collection, processing, analysis, and dissemination of data.
- Tables list the relevant HIS Manual volumes for groundwater level and rainfall data management.
This document provides a readers' guide to groundwater documents produced by the Hydrology Project (HP). It summarizes the Hydrological Information System (HIS) Manual Groundwater, which consists of 10 volumes covering topics like hydro-meteorology, geo-hydrology, water quality sampling and analysis, data processing, and more. It also describes related groundwater training modules and other documents on standards, maintenance, and more. The guide is intended to help users understand and locate relevant groundwater information resources in the HIS document library.
This document provides a readers' guide to surface water documents related to India's Hydrological Information System (HIS). It summarizes the key documents, including the 10-volume HIS Manual Surface Water, which describes procedures for surface water data collection, analysis and management. It also outlines the surface water training modules available, which cover topics like hydrometry, meteorology and data processing. The guide is intended to help users understand and locate relevant surface water documents on the Hydrology Project website.
This document provides a readers' guide to hydro-meteorological documents related to India's Hydrological Information System (HIS). It summarizes key hydro-meteorology manuals, training modules, and other documents produced by the Hydrology Project to support the collection, processing, and use of rainfall and climate data in India. The primary references are the HIS Manual Surface Water and Groundwater, which describe procedures for network design, data collection, processing, and analysis of hydro-meteorological data. Related training modules cover topics like rainfall data entry, validation, analysis and reporting. The guide aims to help users locate relevant hydro-meteorology documents.
This document provides a guide to water quality documents produced by the Hydrology Project in India. It summarizes the key water quality documents, including the HIS Manual Water Quality, Water Quality Training Modules, and additional technical papers. The guide is intended to help users locate relevant water quality information for surface water and groundwater monitoring and analysis.
This document provides an overview of a Hydrological Information System (HIS) being developed for 9 states in India. It discusses the key components and activities of the HIS, which include: assessing user needs, establishing observation networks, managing historical data, collecting field data, processing and analyzing data, exchanging and reporting data, storing and disseminating data, and developing institutional and human resources. The overall goal of the HIS is to provide reliable hydrological data and information to support long-term water resources planning and management decisions in India.
The document provides guidance on assessing hydrological data needs through stakeholder interviews. Small interview teams will visit existing and potential hydrological data users with a questionnaire. The questionnaire aims to gather information on: 1) The user's organizational profile, current water system use, and current data availability and sources. 2) The user's future hydrological data classification, proposed uses, and parameter requirements. Interview teams will explain the questionnaire and hydrological information system, then review responses to ensure questions are understood and data needs are properly assessed. Results will inform immediate data provision and long-term system adjustments.
This document provides an overview of guidance materials for the management of surface water data within India's Hydrological Information System (HIS). It describes the lifecycle of hydrometric data from collection through analysis and publication. Key documents that provide procedures for surface water data management are the HIS Manual Surface Water and various training modules developed under the Hydrology Project. The manual and modules cover topics like network design, data collection, entry, validation, processing, analysis, and dissemination of water level, stage-discharge, and flow data. The goal is to standardize surface water data management practices across states and agencies to improve data quality and usability.
This document provides guidance for managing groundwater data as part of a Hydrological Information System (HIS) in India. It discusses the lifecycle of hydrometric data from collection to dissemination. Key points covered include:
- The HIS Manual Groundwater is the primary reference for groundwater data management procedures.
- Groundwater data goes through stages of monitoring, data sensing, validation, analysis, and publication.
- Sections provide guidance on groundwater monitoring networks, data collection, processing, analysis, and dissemination of data.
- Tables list the relevant HIS Manual volumes for groundwater level and rainfall data management.
This document provides a readers' guide to groundwater documents produced by the Hydrology Project (HP). It summarizes the Hydrological Information System (HIS) Manual Groundwater, which consists of 10 volumes covering topics like hydro-meteorology, geo-hydrology, water quality sampling and analysis, data processing, and more. It also describes related groundwater training modules and other documents on standards, maintenance, and more. The guide is intended to help users understand and locate relevant groundwater information resources in the HIS document library.
This document provides a readers' guide to surface water documents related to India's Hydrological Information System (HIS). It summarizes the key documents, including the 10-volume HIS Manual Surface Water, which describes procedures for surface water data collection, analysis and management. It also outlines the surface water training modules available, which cover topics like hydrometry, meteorology and data processing. The guide is intended to help users understand and locate relevant surface water documents on the Hydrology Project website.
This document provides a readers' guide to hydro-meteorological documents related to India's Hydrological Information System (HIS). It summarizes key hydro-meteorology manuals, training modules, and other documents produced by the Hydrology Project to support the collection, processing, and use of rainfall and climate data in India. The primary references are the HIS Manual Surface Water and Groundwater, which describe procedures for network design, data collection, processing, and analysis of hydro-meteorological data. Related training modules cover topics like rainfall data entry, validation, analysis and reporting. The guide aims to help users locate relevant hydro-meteorology documents.
This document provides a guide to water quality documents produced by the Hydrology Project in India. It summarizes the key water quality documents, including the HIS Manual Water Quality, Water Quality Training Modules, and additional technical papers. The guide is intended to help users locate relevant water quality information for surface water and groundwater monitoring and analysis.
This document provides an overview of a Hydrological Information System (HIS) being developed for 9 states in India. It discusses the key components and activities of the HIS, which include: assessing user needs, establishing observation networks, managing historical data, collecting field data, processing and analyzing data, exchanging and reporting data, storing and disseminating data, and developing institutional and human resources. The overall goal of the HIS is to provide reliable hydrological data and information to support long-term water resources planning and management decisions in India.
The document provides guidance on assessing hydrological data needs through stakeholder interviews. Small interview teams will visit existing and potential hydrological data users with a questionnaire. The questionnaire aims to gather information on: 1) The user's organizational profile, current water system use, and current data availability and sources. 2) The user's future hydrological data classification, proposed uses, and parameter requirements. Interview teams will explain the questionnaire and hydrological information system, then review responses to ensure questions are understood and data needs are properly assessed. Results will inform immediate data provision and long-term system adjustments.
This document provides guidance on sampling principles for hydrological and hydro-meteorological variables. It discusses key concepts such as units of measurement, basic statistics, measurement error, sampling frequency and spatial sampling. The goal is to design monitoring networks that can estimate important statistical parameters about variables while accounting for various sources of error from sampling. Basic statistical concepts covered include distribution functions, parameters like mean and variance, and how to estimate these from samples along with associated confidence intervals and effects of serial correlation.
This document describes procedures for surface water data processing under the Hydrological Information System (HIS) in India. It discusses various stages of data processing including receipt of data, data entry, validation, completion, compilation, analysis, reporting and transfer. It emphasizes the importance of validation to correct errors and identify data reliability. Validation is done at multiple levels - primary, secondary and hydrological. The document also covers organizing temporary databases, transferring data between databases, and backing up databases.
This document outlines the stages of surface water data processing under the Hydrological Information System (HIS) in India. It discusses: 1) Receipt of data from field stations and storage of raw records; 2) Data entry at sub-divisional offices; 3) Validation of data through primary, secondary, and hydrological checks; 4) Completion and correction of missing or erroneous data; 5) Compilation, analysis, and reporting of validated data; 6) Transfer of data between processing levels from sub-division to division to state centers. The overall goal is to process field data in a systematic series of steps to produce quality-controlled hydrological information.
Mh sw optimisation of g&d stations network of maharashtrahydrologyproject0
This document discusses optimizing the streamgauge and raingauge network for the Upper Bhima Basin in India. It provides background on hydrological information systems and networks in India. The Hydrology Project aims to improve India's capabilities for collecting and analyzing hydrological data. This study was conducted as part of the Hydrology Project to review and optimize the existing hydrometric network in Maharashtra state, which includes streamgauges and raingauges. The goal is to ensure the network is collecting the necessary data to facilitate optimal water resources use and management in the Upper Bhima Basin.
This document describes the design of a hydrological data storage and dissemination system. It discusses the major components of the system including databases to store different types of hydrological data (e.g. field data, processed data, maps), a catalogue to allow users to search and access data, and interfaces to allow external organizations and users to input and retrieve data. It provides specifications for hardware, software, security, and other technical aspects required to build the hydrological information system. The overall aim is to create a centralized, standardized system for permanently storing all types of hydrological data from various agencies and making it accessible to authorized users.
This document provides guidance on using regression analysis for data validation in hydrological data processing. It discusses simple linear regression, multiple linear regression, and stepwise regression. Regression analysis can be used to validate and fill in missing water level, rainfall, and discharge data. It establishes relationships between dependent and independent variables. Both linear and nonlinear regression models are used in hydrological applications. Key applications mentioned include rating curves, spatial interpolation of rainfall, and validating station data against nearby stations.
This document provides guidance on how to carry out primary validation of water level data. It discusses validating data from staff gauges, automatic water level recorders, and digital water level recorders by checking for errors and inconsistencies in single time series, and by comparing data between instruments. Methods include examining data graphically and against physical limits, and viewing hydrographs from adjacent stations. The goal is to flag potentially incorrect values for further validation while replacing others with corrected values based on these initial checks.
The document provides information about a workshop on standards for groundwater monitoring, processing, and data dissemination. It includes the following key points:
1. The workshop aims to review current practices and adopt standard formats, techniques, and procedures for computerized groundwater data acquisition, processing, validation, retrieval and dissemination.
2. Topics to be addressed include computerized techniques, data standards, quality monitoring objectives and procedures, dedicated software demonstrations, and requirements for software.
3. The 3-day workshop program includes sessions on data standards, software discussions, and a visit to an operational digital monitoring network site. Standardizing procedures and using computerization can help establish a reliable hydrological information system.
Achievement of new state under hp 2 - himachal pradesh in integrated water ma...hydrologywebsite1
This document provides an overview of the Hydrology Project Phase II being implemented in Himachal Pradesh. Some key points:
1. The project aims to improve hydrological data collection and management in HP to support water resource planning. It has three main components - institutional strengthening, network expansion, and recurrent costs.
2. Activities under the project include expanding the network of rain gauges, piezometers, weather stations; establishing state and divisional data centers; upgrading water quality labs; and conducting purpose-driven studies.
3. Over 650 officials have been trained so far. MoUs have also been signed for data sharing with other agencies. Workshops and study tours have been held to raise awareness about
This document provides guidance on network design and site selection for hydro-meteorological stations. It discusses the steps for network optimization which include reviewing existing networks, identifying data needs, prioritizing objectives, determining required network density, and cost estimation. Site selection considerations are also outlined, including technical, environmental, logistical, security, legal and financial aspects. Key factors for siting stations include exposure conditions, wind protection, level ground, and integrating with other monitoring networks.
The Hydrology Project established India's Hydrological Information System by developing networks of hydro-meteorological stations, web-based data management systems, and tools for water resources planning and management. It involved 29 agencies across 13 states and 8 central government organizations. Key achievements include establishing surface and groundwater observation networks, databases for water quality and quantity data, decision support systems for integrated planning, and capacity building for water resource professionals. The project helped shift from isolated development to comprehensive basin-scale planning and management of water resources.
The document summarizes efforts to upgrade India's hydrological information system through the Hydrology Project. Key aspects of the upgrade include standardizing data collection procedures, developing infrastructure like new observation stations, and establishing a comprehensive computerized database. Over 1,700 existing rainfall stations were reactivated or upgraded, 650 new river gauging stations were established, and 7,900 new groundwater observation wells were added. The upgraded system aims to provide reliable, accessible hydrological data to support improved water management across nine states in India covering 1.7 million square kilometers.
This document provides guidance on reporting climatic data in India. It discusses the purpose and contents of annual reports on climatic data, including evaporation data. Key points covered include:
- Annual reports summarize evaporation data for the reporting year and compare to long-term statistics.
- Reports include details on the observational network, basic evaporation statistics, data validation processes.
- Network maps and station listings provide details on locations and recorded variables. Statistics include monthly and annual summaries for the current year and historical averages.
- Reports aim to inform users and support planning, while also recognizing data producers and maintaining the climatic observation system.
This document provides guidance on network design and site selection for water level and streamflow stations. It outlines the key steps in the network design process, including defining objectives, evaluating the existing network, identifying gaps, prioritizing stations, and estimating costs. It emphasizes collecting relevant background information and maps. Site selection involves defining objectives, conducting desk studies and field surveys, and selecting locations suitable for specific measurement methods and equipment based on factors like hydrology, access, and costs. The document provides detailed guidelines for selecting sites for water level gauges, current meter measurements, and other streamflow estimation techniques.
The document discusses a hydrology project in India that aims to establish a Hydrological Information System (HIS) across nine states. Key points:
- The project is funded by the World Bank and Government of Netherlands, with a budget of US$122 million and US$15 million for technical consultancy.
- HIS will collect, process, store and disseminate hydrological and meteorological data through a network of monitoring stations, laboratories, data processing centers and storage facilities.
- The network includes 7,900 piezometers, 7,000 water level recorders, 920 river gauging stations, 1,800 rainfall stations and other equipment managed across 25 participating agencies.
- Data will
The document provides an overview of training courses for staff involved in managing and operating a Hydrological Information System for water quality in India. It describes various training courses grouped by instrument, software, and hydrological information system function. For each course it provides information on target group, provider, location, duration, objectives, admission requirements, equipment used, and program/syllabus. The training courses cover a wide range of topics from basic water quality concepts and laboratory practices to more specialized courses on pollution parameters, sampling, and training management. The document is intended to help training beneficiaries, managers and providers in planning and implementing staff training programs.
This document provides instructions for measuring biochemical oxygen demand (BOD) in water samples. It outlines the aim, method, observations and calculations, and report for conducting a BOD laboratory exercise. The method involves collecting various water samples, preparing BOD bottles according to standard procedures, and determining initial and final dissolved oxygen concentrations over a 5-day incubation period to calculate BOD values. The objectives are to measure BOD, understand the need for seeding samples, and check the procedure using a standard sample.
This document provides information on dilution and seeding procedures for biochemical oxygen demand (BOD) measurement. Dilution of samples is necessary when BOD levels are too high, as dissolved oxygen concentrations must remain above 1 mg/L during testing. Procedures for diluting samples using separate containers or direct pipetting are presented. Seeding provides microorganisms to degrade organic matter for some samples. Seed controls and corrections are used to account for any organic matter added from the seed. Worked examples demonstrate how to calculate BOD values from diluted and seeded samples.
The document discusses water quality monitoring networks and mandates in India. It describes the Hydrological Information System (HIS) being set up under the Hydrology Project to coordinate water quality data collection. It identifies the major water quality issues affecting rivers, groundwater, and lakes/reservoirs. Finally, it provides an example of rationalizing the monitoring program for the Cauvery River through establishing objectives and assessing the existing network. The goal is to improve coordination between organizations and optimize limited monitoring resources.
This document summarizes the progress and completion of the Odisha Hydrology Project-II. The key points are:
1) The project had a total revised cost of Rs. 13.46 crore and ran from April 2006 to May 2014 to strengthen surface water data collection and decision support systems in Odisha.
2) Financial progress shows that Rs. 891.04 crore was spent out of the total revised cost of Rs. 1346 crore. Major components included installing a real-time data acquisition system and developing decision support systems for drought monitoring and conjunctive surface and groundwater use.
3) Key achievements were establishing the concept for a real-time data acquisition system,
Hp wq study of ground water quality characteristics in industrially predomina...hydrologyproject2
This document provides an executive summary of a study conducted to assess groundwater quality characteristics in industrially predominant areas of Himachal Pradesh. The study was conducted in two phases: the first involved collecting groundwater samples from deep tube wells and analyzing water quality parameters, while the second involved additional sampling from shallow tube wells to better understand spatial and temporal variations in water quality. Analysis found that groundwater quality varied spatially and some parameters exceeded permissible limits. While direct industrial impacts were not established from deep well samples, shallow well samples provided insight and detected traces of heavy metals at some locations. The study developed GIS-based maps and models to analyze spatial trends in water quality and vulnerability. It concluded that continuous long-term monitoring is
The document describes methods for hydrological observations including rainfall, water level, discharge, and inspection of observation stations. It contains sections on ordinary and recording rainfall observation, ordinary and recording water level observation, observation of discharge using current meters and floats, and inspection of rainfall and water level observation stations. The document was produced by the Ministry of Construction in Japan.
This document provides guidance on sampling principles for hydrological and hydro-meteorological variables. It discusses key concepts such as units of measurement, basic statistics, measurement error, sampling frequency and spatial sampling. The goal is to design monitoring networks that can estimate important statistical parameters about variables while accounting for various sources of error from sampling. Basic statistical concepts covered include distribution functions, parameters like mean and variance, and how to estimate these from samples along with associated confidence intervals and effects of serial correlation.
This document describes procedures for surface water data processing under the Hydrological Information System (HIS) in India. It discusses various stages of data processing including receipt of data, data entry, validation, completion, compilation, analysis, reporting and transfer. It emphasizes the importance of validation to correct errors and identify data reliability. Validation is done at multiple levels - primary, secondary and hydrological. The document also covers organizing temporary databases, transferring data between databases, and backing up databases.
This document outlines the stages of surface water data processing under the Hydrological Information System (HIS) in India. It discusses: 1) Receipt of data from field stations and storage of raw records; 2) Data entry at sub-divisional offices; 3) Validation of data through primary, secondary, and hydrological checks; 4) Completion and correction of missing or erroneous data; 5) Compilation, analysis, and reporting of validated data; 6) Transfer of data between processing levels from sub-division to division to state centers. The overall goal is to process field data in a systematic series of steps to produce quality-controlled hydrological information.
Mh sw optimisation of g&d stations network of maharashtrahydrologyproject0
This document discusses optimizing the streamgauge and raingauge network for the Upper Bhima Basin in India. It provides background on hydrological information systems and networks in India. The Hydrology Project aims to improve India's capabilities for collecting and analyzing hydrological data. This study was conducted as part of the Hydrology Project to review and optimize the existing hydrometric network in Maharashtra state, which includes streamgauges and raingauges. The goal is to ensure the network is collecting the necessary data to facilitate optimal water resources use and management in the Upper Bhima Basin.
This document describes the design of a hydrological data storage and dissemination system. It discusses the major components of the system including databases to store different types of hydrological data (e.g. field data, processed data, maps), a catalogue to allow users to search and access data, and interfaces to allow external organizations and users to input and retrieve data. It provides specifications for hardware, software, security, and other technical aspects required to build the hydrological information system. The overall aim is to create a centralized, standardized system for permanently storing all types of hydrological data from various agencies and making it accessible to authorized users.
This document provides guidance on using regression analysis for data validation in hydrological data processing. It discusses simple linear regression, multiple linear regression, and stepwise regression. Regression analysis can be used to validate and fill in missing water level, rainfall, and discharge data. It establishes relationships between dependent and independent variables. Both linear and nonlinear regression models are used in hydrological applications. Key applications mentioned include rating curves, spatial interpolation of rainfall, and validating station data against nearby stations.
This document provides guidance on how to carry out primary validation of water level data. It discusses validating data from staff gauges, automatic water level recorders, and digital water level recorders by checking for errors and inconsistencies in single time series, and by comparing data between instruments. Methods include examining data graphically and against physical limits, and viewing hydrographs from adjacent stations. The goal is to flag potentially incorrect values for further validation while replacing others with corrected values based on these initial checks.
The document provides information about a workshop on standards for groundwater monitoring, processing, and data dissemination. It includes the following key points:
1. The workshop aims to review current practices and adopt standard formats, techniques, and procedures for computerized groundwater data acquisition, processing, validation, retrieval and dissemination.
2. Topics to be addressed include computerized techniques, data standards, quality monitoring objectives and procedures, dedicated software demonstrations, and requirements for software.
3. The 3-day workshop program includes sessions on data standards, software discussions, and a visit to an operational digital monitoring network site. Standardizing procedures and using computerization can help establish a reliable hydrological information system.
Achievement of new state under hp 2 - himachal pradesh in integrated water ma...hydrologywebsite1
This document provides an overview of the Hydrology Project Phase II being implemented in Himachal Pradesh. Some key points:
1. The project aims to improve hydrological data collection and management in HP to support water resource planning. It has three main components - institutional strengthening, network expansion, and recurrent costs.
2. Activities under the project include expanding the network of rain gauges, piezometers, weather stations; establishing state and divisional data centers; upgrading water quality labs; and conducting purpose-driven studies.
3. Over 650 officials have been trained so far. MoUs have also been signed for data sharing with other agencies. Workshops and study tours have been held to raise awareness about
This document provides guidance on network design and site selection for hydro-meteorological stations. It discusses the steps for network optimization which include reviewing existing networks, identifying data needs, prioritizing objectives, determining required network density, and cost estimation. Site selection considerations are also outlined, including technical, environmental, logistical, security, legal and financial aspects. Key factors for siting stations include exposure conditions, wind protection, level ground, and integrating with other monitoring networks.
The Hydrology Project established India's Hydrological Information System by developing networks of hydro-meteorological stations, web-based data management systems, and tools for water resources planning and management. It involved 29 agencies across 13 states and 8 central government organizations. Key achievements include establishing surface and groundwater observation networks, databases for water quality and quantity data, decision support systems for integrated planning, and capacity building for water resource professionals. The project helped shift from isolated development to comprehensive basin-scale planning and management of water resources.
The document summarizes efforts to upgrade India's hydrological information system through the Hydrology Project. Key aspects of the upgrade include standardizing data collection procedures, developing infrastructure like new observation stations, and establishing a comprehensive computerized database. Over 1,700 existing rainfall stations were reactivated or upgraded, 650 new river gauging stations were established, and 7,900 new groundwater observation wells were added. The upgraded system aims to provide reliable, accessible hydrological data to support improved water management across nine states in India covering 1.7 million square kilometers.
This document provides guidance on reporting climatic data in India. It discusses the purpose and contents of annual reports on climatic data, including evaporation data. Key points covered include:
- Annual reports summarize evaporation data for the reporting year and compare to long-term statistics.
- Reports include details on the observational network, basic evaporation statistics, data validation processes.
- Network maps and station listings provide details on locations and recorded variables. Statistics include monthly and annual summaries for the current year and historical averages.
- Reports aim to inform users and support planning, while also recognizing data producers and maintaining the climatic observation system.
This document provides guidance on network design and site selection for water level and streamflow stations. It outlines the key steps in the network design process, including defining objectives, evaluating the existing network, identifying gaps, prioritizing stations, and estimating costs. It emphasizes collecting relevant background information and maps. Site selection involves defining objectives, conducting desk studies and field surveys, and selecting locations suitable for specific measurement methods and equipment based on factors like hydrology, access, and costs. The document provides detailed guidelines for selecting sites for water level gauges, current meter measurements, and other streamflow estimation techniques.
The document discusses a hydrology project in India that aims to establish a Hydrological Information System (HIS) across nine states. Key points:
- The project is funded by the World Bank and Government of Netherlands, with a budget of US$122 million and US$15 million for technical consultancy.
- HIS will collect, process, store and disseminate hydrological and meteorological data through a network of monitoring stations, laboratories, data processing centers and storage facilities.
- The network includes 7,900 piezometers, 7,000 water level recorders, 920 river gauging stations, 1,800 rainfall stations and other equipment managed across 25 participating agencies.
- Data will
The document provides an overview of training courses for staff involved in managing and operating a Hydrological Information System for water quality in India. It describes various training courses grouped by instrument, software, and hydrological information system function. For each course it provides information on target group, provider, location, duration, objectives, admission requirements, equipment used, and program/syllabus. The training courses cover a wide range of topics from basic water quality concepts and laboratory practices to more specialized courses on pollution parameters, sampling, and training management. The document is intended to help training beneficiaries, managers and providers in planning and implementing staff training programs.
This document provides instructions for measuring biochemical oxygen demand (BOD) in water samples. It outlines the aim, method, observations and calculations, and report for conducting a BOD laboratory exercise. The method involves collecting various water samples, preparing BOD bottles according to standard procedures, and determining initial and final dissolved oxygen concentrations over a 5-day incubation period to calculate BOD values. The objectives are to measure BOD, understand the need for seeding samples, and check the procedure using a standard sample.
This document provides information on dilution and seeding procedures for biochemical oxygen demand (BOD) measurement. Dilution of samples is necessary when BOD levels are too high, as dissolved oxygen concentrations must remain above 1 mg/L during testing. Procedures for diluting samples using separate containers or direct pipetting are presented. Seeding provides microorganisms to degrade organic matter for some samples. Seed controls and corrections are used to account for any organic matter added from the seed. Worked examples demonstrate how to calculate BOD values from diluted and seeded samples.
The document discusses water quality monitoring networks and mandates in India. It describes the Hydrological Information System (HIS) being set up under the Hydrology Project to coordinate water quality data collection. It identifies the major water quality issues affecting rivers, groundwater, and lakes/reservoirs. Finally, it provides an example of rationalizing the monitoring program for the Cauvery River through establishing objectives and assessing the existing network. The goal is to improve coordination between organizations and optimize limited monitoring resources.
This document summarizes the progress and completion of the Odisha Hydrology Project-II. The key points are:
1) The project had a total revised cost of Rs. 13.46 crore and ran from April 2006 to May 2014 to strengthen surface water data collection and decision support systems in Odisha.
2) Financial progress shows that Rs. 891.04 crore was spent out of the total revised cost of Rs. 1346 crore. Major components included installing a real-time data acquisition system and developing decision support systems for drought monitoring and conjunctive surface and groundwater use.
3) Key achievements were establishing the concept for a real-time data acquisition system,
Hp wq study of ground water quality characteristics in industrially predomina...hydrologyproject2
This document provides an executive summary of a study conducted to assess groundwater quality characteristics in industrially predominant areas of Himachal Pradesh. The study was conducted in two phases: the first involved collecting groundwater samples from deep tube wells and analyzing water quality parameters, while the second involved additional sampling from shallow tube wells to better understand spatial and temporal variations in water quality. Analysis found that groundwater quality varied spatially and some parameters exceeded permissible limits. While direct industrial impacts were not established from deep well samples, shallow well samples provided insight and detected traces of heavy metals at some locations. The study developed GIS-based maps and models to analyze spatial trends in water quality and vulnerability. It concluded that continuous long-term monitoring is
The document describes methods for hydrological observations including rainfall, water level, discharge, and inspection of observation stations. It contains sections on ordinary and recording rainfall observation, ordinary and recording water level observation, observation of discharge using current meters and floats, and inspection of rainfall and water level observation stations. The document was produced by the Ministry of Construction in Japan.
This document provides guidance on using the area-slope method to estimate stream discharge indirectly when direct measurement is not possible. It describes the principles and steps of the area-slope method, including selecting a study reach, measuring the cross-sectional area and water surface slope, evaluating velocity using Manning's formula, and computing discharge. Guidelines are given for selecting sites, measuring cross-sections and slope, determining roughness coefficients, and performing calculations. The area-slope method provides a rough estimate of discharge but has limitations due to uncertainties in roughness coefficients.
This document provides guidance on investigating and selecting sites for hydrological observation stations. It discusses the importance of establishing a network of stations to collect hydrological data and assess water resources. The key steps in designing a hydrological observation station network include:
1. Selecting an initial station where a river's mean discharge is highest, such as where a mountain river enters a plain.
2. Establishing subsequent stations where significant changes in flow volume occur, like below a major tributary confluence.
3. Considering other factors like assessing water loss from channels and providing information for various planning purposes.
The collected data is crucial for water resource planning and management activities like utilization, project formulation, and dispute resolution.
This document provides information about a training session on advanced discharge measurement techniques conducted by the Central Training Unit of the Central Water Commission in India. It includes an introduction, objectives, schedule, and content overview of the training. The training covers three advanced techniques: the moving boat method, ultrasonic method, and electromagnetic method. It provides detailed descriptions of the principles and processes involved in discharge measurement using these methods. The document contains several figures to illustrate key concepts.
Ch sw study of reservoir sedimentation, impact assessment and development of ...hydrologywebsite1
The document discusses the importance of summarization for processing large amounts of text. Automatic summarization systems aim to generate concise summaries by identifying the most important concepts and sentences. However, accurately summarizing documents while preserving the key ideas remains a challenging task that current systems have not fully solved.
The document discusses surface water quality planning concepts. It describes two approaches to water quality planning: using fixed emission limits for discharges or controlling quality using water quality objectives. It outlines India's approach which involves river action plans to measure quality, determine uses, and formulate action plans. It also covers water quality monitoring network design, an example network from Himachal Pradesh, and methods for data interpretation including trend analysis, flux calculation, and compliance assessment.
The document summarizes the Hydrology Project-II being implemented in Punjab, India. Key points:
- The Rs. 46.65 crore project aims to improve water resource data collection and management. Around 80% of the work and funding has been used.
- Networks to monitor groundwater, surface water, and rainfall have been installed across 700, 25, and 81 stations respectively. Digital equipment transmits data in real time.
- Three data centers have been constructed to store and analyze water data. A state data center in Mohali will house various water resource offices and laboratories.
- Observed hydrological data will be shared with state agencies, CGWB, and other users to inform water
The document provides an overview of a training module on basic water quality concepts developed by DHV Consultants BV and DELFT HYDRAULICS with funding from the World Bank and Government of The Netherlands. The module aims to create awareness of key water quality parameters and issues. It outlines the session plan, presentations, and handouts which discuss factors influencing water quality, common water quality parameters, types of pollutants, monitoring standards, and agencies involved in water quality monitoring.
The document specifies requirements for bathymetry software. The software is needed to control data collection from a DGPS and echo-sounder during reservoir surveys from a small craft. It must assist the operator and provide navigation information to the helmsman. The software must reliably store, validate, process and present collected depth data to generate charts and assess reservoir volumes and changes over time. It must support various data formats, devices, grids and projections.
The document provides an overview of the World Bank Monitoring Mission for the Hydrology Project Phase II in India from May 06-09, 2014. It summarizes the key achievements and post-project plans for each of the implementing agencies. The agencies include 13 state organizations and 8 central agencies. The objectives of HP-II were to extend and promote the sustained use of hydrological information systems to improve water resources planning and management. The estimated cost was Rs. 631.83 crore with funding from the World Bank. Several agencies had completed construction of data centers, monitoring equipment installations, and pilot studies. Plans after the project included continuing maintenance and operations, staff training, and further developing applications.
This document discusses quality assurance and within-laboratory analytical quality control programs. It emphasizes the need for quality assurance due to errors found in analytical results. A quality assurance program includes sample control, standard procedures, equipment maintenance, calibration, and analytical quality control. Within-laboratory quality control focuses on precision using statistical control charts to monitor performance over time and identify issues. Regular quality control practices help ensure reliability of laboratory results.
This document discusses using HEC-RAS software to analyze a river reach containing a single bridge. It outlines the input data needed, including geometric data and flow data. It then describes the steps to model the bridge, including adding the bridge, defining the geometry, and selecting modeling approaches. The document compares results from modeling the bridge as a pressure/weir and using the energy method. It notes that adjustments to contraction/expansion coefficients and cross section locations can improve results.
This document provides guidance on managing groundwater data within India's Hydrological Information System (HIS). It discusses the lifecycle of hydrometric data from collection to dissemination. The document directs the user to relevant manuals within HIS, particularly the Groundwater manual, for guidance on groundwater level monitoring networks, data collection, processing, analysis and publication. It describes the various types of manuals within HIS - design, field operation, and reference - and lists the specific volumes and parts most pertinent to groundwater level data. The overall aim is to help users locate and understand documentation to standardize high quality groundwater data management and inform water resource planning.
This document provides guidance for managing hydro-meteorological data in India within a Hydrological Information System (HIS). It discusses the data lifecycle, from monitoring networks and data collection to analysis, dissemination and use. It directs the user to relevant manuals on topics like rainfall, snow, climate and evaporation data processing. The goal is to standardize procedures and provide high quality data to inform water resources planning and management.
This document describes procedures for surface water data processing under the Hydrological Information System (HIS) in India. It discusses various stages of data processing including receipt of data, data entry, validation, completion, compilation, analysis, reporting and transfer. It emphasizes the importance of validation to correct errors and identify data reliability. Validation is done at multiple levels - primary, secondary and hydrological. The document also covers organizing temporary databases, transferring data between databases, and backing up databases.
This document describes procedures for surface water data processing under the Hydrological Information System (HIS) in India. It discusses various stages of data processing including receipt of data, data entry, validation, completion, compilation, analysis, reporting and transfer. It emphasizes the importance of validation to correct errors and identify data reliability. Validation is carried out at multiple levels, from primary validation during data entry to secondary and hydrological validation. The document also covers organizing temporary databases, transferring data between databases, and backing up databases.
The document discusses the achievements and objectives of India's Hydrology Project Phases I, II, and III.
Phase I (1995-2003) established hydrological monitoring networks across 9 states. Phase II (2006-2014) expanded these networks to 13 states and strengthened data collection, management, and decision support systems.
Phase III aims to establish integrated water resources management across all Indian states and UTs. It will upgrade groundwater and meteorological monitoring, develop spatiotemporal data and tools to support planning, and strengthen institutions for capacity building. The workshop discussed opportunities for cross-learning and identifying appropriate technologies.
This document provides guidance on using regression analysis for data validation in hydrological data processing. It discusses simple linear regression, multiple linear regression, and stepwise regression. Regression analysis can be used to validate and fill in missing water level, rainfall, and discharge data. It establishes relationships between dependent and independent variables. Both linear and nonlinear regression models are used in hydrological applications. Key applications mentioned include rating curves, spatial interpolation of rainfall, and validating station data against nearby stations.
This document provides guidance on using regression analysis for data validation in hydrological data processing. It discusses simple linear regression, multiple linear regression, and stepwise regression. Regression analysis can be used to validate and fill in missing water level, rainfall, and discharge data. It establishes relationships between dependent and independent variables. Both linear and nonlinear regression models are used in hydrological applications. Key applications mentioned include rating curves, spatial interpolation of rainfall, and validating station data against nearby stations.
1. The document outlines the National Water Policy of India which establishes the need for a standardized national hydrological information system to collect, process, and disseminate reliable water resources data.
2. Key goals of the policy include maximizing water availability, integrating surface and groundwater management, preserving environmental and ecological balances, and involving stakeholders in water management.
3. The hydrological information system described in the document is intended to provide the hydrological data and analysis needed to inform planning, design, management, and policy decisions around India's water resources in accordance with the National Water Policy.
1. The document describes India's Hydrological Information System (HIS) which collects, processes, stores and disseminates hydrological data.
2. The HIS aims to provide reliable data to support long-term water resources planning and management by establishing observation networks, managing historical data, collecting and processing data, and storing and disseminating it to users.
3. Key activities of the HIS include assessing user needs, establishing and maintaining observation networks, managing historical data, collecting field data, processing and analyzing data, exchanging and reporting data, and storing and disseminating data to support water planning in India.
1 - MoWR - Overview of HP-III Workshop-15th Sep 2014indiawrm
This document summarizes the key achievements and objectives of the Hydrology Project in India across its three phases. The project aims to develop a standardized national water resources monitoring and information system to improve water resources planning and management. Key achievements of phase II included upgrading monitoring networks, developing web-based data management and tools for planning, and conducting studies on groundwater management and water quality issues. Phase III will further develop these systems nationwide and introduce tools for flood forecasting, reservoir operation, and assessing climate change impacts. The ultimate goal is to establish a permanent water resources information coordination center.
This document provides an overview of water quality monitoring in India. It discusses key water quality issues for rivers, lakes, and reservoirs, including contamination from faecal matter, organic waste, toxic pollutants, eutrophication, salinization, changes in hydrology, agrochemicals, and mining activities. It also describes the monitoring cycle and key elements of designing a water quality monitoring program, including defining information needs, developing a monitoring strategy, network design, sample collection, laboratory analysis, data handling and analysis, reporting, and information utilization.
This document provides an overview of water quality monitoring in India. It discusses key water quality issues for rivers, lakes, and reservoirs, including contamination from faecal matter, organic waste, toxic pollutants, eutrophication, salinization, changes in hydrology, agrochemicals, and mining activities. It also describes the monitoring cycle and key elements of designing a water quality monitoring program, including defining information needs, developing a monitoring strategy, network design, sample collection, laboratory analysis, data handling and analysis, reporting, and information utilization.
This document provides an operation manual for processing and analyzing groundwater data in India. It details the monitoring networks for water levels, water quality, and hydro-meteorology. It also describes how the data is organized in the Hydrological Information System (HIS) and discusses semi-static and dynamic data collected, including climate/rainfall reviews, groundwater level changes, and resource estimations. The setup of the groundwater agency is explained along with its roles in monitoring, research, and management.
This document provides an operation manual for processing and analyzing groundwater data in India. It details the various networks for monitoring water levels, water quality, and hydro-meteorological data. It also describes the typical geology, soils, lithology, and groundwater issues in the monitored area. Finally, it outlines the organizational setup of the groundwater agency responsible for data collection, processing, and reporting.
This document provides operational details for groundwater data processing and analysis in India. It outlines the monitoring networks for water levels, quality, and hydro-meteorology. It describes the geological structures, soil types, typical groundwater issues, and the organizational setup of the responsible groundwater agency. The agency collects various dynamic data through monitoring networks to estimate groundwater resources and inform management recommendations in an annual groundwater yearbook.
This document provides guidelines for creating a Groundwater Year Book that summarizes key hydrogeological data and monitoring information for a given region. It recommends including details of groundwater level and quality monitoring networks, interpreted trends in groundwater resources, issues of concern, and recommendations. The Year Book should be presented using graphs, maps and pictures with interpreted analysis rather than raw data. It aims to increase awareness and inform various stakeholders about the groundwater system and management needs.
This document provides guidance on reporting discharge data from hydrological monitoring stations. It outlines the contents and purpose of yearly reports, including descriptive summaries of streamflow patterns, basic statistics for selected stations, and comparisons to long-term averages. Periodic long-term reports every 5-10 years are also recommended to analyze trends over longer time periods. The reports aim to inform water resource planning and make hydrological data more accessible and understandable for users.
This document provides guidance on collecting, entering, and validating hydrological data for storage and use in a water resources information system in India. It discusses the mandatory information needed for spatial (e.g. well locations) and temporal (e.g. water level measurements) data. It also describes proper data collection procedures like using field forms, maintaining a data collection register, and entering data directly from field forms to reduce errors. The document emphasizes validation of data at multiple stages and storing data according to standards to ensure long-term usability and reliability of the hydrological information system.
This document provides guidance on designing water quality monitoring programs. It discusses the monitoring cycle, which includes defining information needs, developing a monitoring strategy, network design, sample collection, laboratory analysis, data handling, analysis, reporting, and utilizing information. Key steps in the cycle are identifying water management issues, specifying monitoring objectives, rationalizing existing networks, selecting sites, field techniques, and equipment used for sampling. The document is a manual for technical aspects of developing water quality monitoring programs in India to generate justified, complete and accurate data.
This document provides guidance on designing water quality monitoring programs and networks. It discusses key concepts like the monitoring cycle, identifying management issues and information needs, and objectives for different types of monitoring. In India, several government organizations conduct water quality monitoring, including the Central Ground Water Board, National River Conservation Directorate, and Central Pollution Control Board. Common water quality issues include contamination from fecal matter, toxic pollutants, over-abstraction, and agrochemicals. The document provides detailed recommendations for network design, site selection, field techniques, equipment, and other aspects of effective long-term water quality monitoring.
The document summarizes a water purification system that includes a reverse osmosis unit, storage tank, and ultrapure water purification unit. The system uses a microprocessor to control purification from a municipal water supply to produce at least 35 liters/hour of water with a resistivity of 18 megohm cm or higher that is at least 99% free of bacteria, ions, organics, and particles. The system is also capable of continuous monitoring and will automatically shut off if the feed water is inadequate or the storage tank reaches capacity.
The document describes a water purifier using ion exchange resin columns that produces reagent-grade water for trace analysis. It has separate cation and anion exchange columns with a capacity of 25 liters each and can purify water at a rate over 1 liter per minute. Accessories include spare columns, instructions, a cover, and vacuum hose. Key features are an inline conductivity monitor and the ability to regenerate the resin columns.
This document provides specifications for a double distillation water purification unit. It distills water to a conductivity of 1.0 μS/cm or less at 25°C for generating reagent-grade water. The unit is made of quartz glass with a 1.5 liter/hour capacity. It runs on 220V power and includes a metallic stand, ring clamp, and operation manual. Safety features include over-heating protection and indicators.
This document provides specifications for an automatic water distillation unit. The unit distills water to generate reagent water type III with a maximum conductivity of 0.01 mS/cm at 25°C for use in washing and quantitative analysis. It has a stainless steel construction, 1.5 liter per hour capacity, operates on 220VAC power, and has automatic shutoff when water levels are low.
This document specifies a water geyser (heater) that has a 120 liter capacity, heats water to 90°C with thermostatic control, and requires a 220 VAC ±25%, 47 to 53 Hz power supply for operation in a laboratory setting.
A general purpose water bath has an inner size of approximately 0.4 x 0.3 x 0.1 meters, is made of stainless steel inside and stove enameled outside, and can maintain temperatures from ambient to 100°C with an accuracy of ±2°C. It has a stainless steel cover with 12 holes and features a drain cock or plug, double-walled insulation, and a pilot lamp to indicate thermostat operation.
This document describes the specifications of a water bath used for incubating culture tubes in coliform analysis. The water bath has an inner size of approximately 0.4 x 0.3 x 0.15 meters, is made of stainless steel inside and stove enameled outside, and can maintain temperatures from ambient to 50°C with an accuracy of ±0.1°C. It has a double wall for insulation, a dome lid to hold test tubes vertically, and displays the internal water temperature.
The document specifies the requirements for a wash bottle used to flush glassware. The wash bottle must be made of polythene, hold 500 ml of liquid, and have a bent nozzle and screw cap. It was last reviewed on October 23, 2007 and is used to wash away any sticking sediment from glassware.
The document specifies volumetric flasks that will be used for sediment analyses in a laboratory. The flasks must comply with IS 915-1975, be made of Corning glass or similar material, and come in sizes of 50, 100, 250, and 500 ml with B class accuracy.
The visual accumulation tube is used to assess particle sizes of sediments. It consists of a vertical transparent settling tube through which sediment samples are passed. Particles settle individually based on terminal velocity related to diameter, and accumulated sediment volume in the tube's narrow end relates to solid weight. It is best for uniform, spherical particles. The analysis involves introducing a small sample and recording accumulated sediment height over time.
This document provides specifications for a vacuum pump for general laboratory use. The pump is a single stage pump with a capacity of 50 l/min, capable of reaching a final vacuum of 0.05 mm Hg without ballast or 2 mm Hg with ballast. It has a 200W motor that operates on 220VAC at 47-53 Hz. Accessories include a filter, regulator, gauge, hose, and valve. The pump is designed for noise free operation.
This document provides specifications for a turbidity meter used to directly measure suspended matter in water samples. The turbidity meter has a range of 0 to 1000 NTU in at least 2 ranges, an accuracy of ±2% full scale deflection, and requires a power supply of 220 VAC ±25%, 47 to 53 Hz. Accessories for the turbidity meter include an ambient light shield, 6 spare tubes, a sensor stand, voltage stabilizer, instruction manual, and dust cover.
This tool kit contains basic tools for minor repairs of electrical laboratory equipment, including a set of screwdrivers, pliers, soldering iron, and multi-meter. The tools come in a lockable storage box for organization and security.
The document specifies the requirements for a microprocessor-controlled TOC analyzer. It must be able to directly measure total carbon, total organic carbon, and purgeable organic carbon in water samples. It uses high temperature catalytic combustion up to 900°C and non-dispersive infrared detection. It must have a measuring range of 1-500 mg/L carbon, precision within 3%, and detection limit of at least 500 ppb carbon. The analyzer and auto-sampler require 240V 50Hz power and it uses nitrogen or high purity air as carrier gases. It includes an auto-sampler, syringes, printer, manuals, spare parts, and application software in English.
This document provides specifications for a tissue grinder used to prepare tissue or sediment samples. The grinder must be able to macerate glass fibre filters and is a manual, porcelain device used to homogenize samples for further analysis such as chemical extraction.
This document specifies a set of thermometers for laboratory use, including mercury-filled glass thermometers with three temperature ranges (0-80oC, 10-150oC, 20-250oC) and an accuracy of ±0.5oC, along with a storage box for the thermometers.
This document specifies test tubes for laboratory use in sediment analysis. It outlines that the test tubes should be made of Corning glass or similar material, and lists two standard sizes - 15 x 125 mm and 25 x 200 mm in diameter and height. The test tubes are intended for general use in analyzing sediments in the laboratory.
The document specifies requirements for test sieves with a shaker. It requires stainless steel sieves that are 200mm in diameter, 50mm in height, and have nominal aperture sizes between 63-250 micrometers. It also requires a shaker capable of holding at least 5 sieves that runs on 220V power. Accessories include a sieve brush and wash bottle. Some sieves can be used manually without the shaker.
This document provides specifications for a magnetic stirrer with a hot plate. It can rotate between 0-1200 rpm and heat with a 300 Watt thermostatically controlled element. The stirrer has a stainless steel top and comes with PTFE coated magnets ranging from 10-50mm in 5mm increments, with two of each size included as accessories.
This document provides specifications for a sterilizer autoclave. The autoclave is 0.3 x 0.5 meters in size, made of stainless steel inside and lid with an enameled outside. It operates at a working pressure of 1 bar with a maximum pressure of 1.5 bars. Accessories include a pressure gauge, steam release cock, safety valve, perforated aluminum basket, water level indicator, and lifting arrangement. The autoclave is powered by 220 VAC at 47-53 Hz and uses approximately 2500 Watts.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Essentials of Automations: Exploring Attributes & Automation Parameters
Final wq handbook 180514
1. HP IIIndian Hydrology Project
Technical Assistance
(Implementation Support) and
Management Consultancy
Water Quality Handbook:
Sediment and Water Quality
May 2014
2. Hydrological Information System May 2014
HP II
Last Updated: 19/05/2014 05:02
Filename: WQ Handbook.docx
Water Quality Handbook: Precipitation and Climate
Issue and Revision Record
Revision Date Originator Checker Approver Description
0 21/05/14 Helen Houghton-Carr Version for approval
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3. Hydrological Information System May 2014
HP II
Last Updated: 19/05/2014 05:02
Filename: WQ Handbook.docx
Page i
Contents
Contents i
Glossary iii
1. Introduction
1.1 HIS Manual
1.2 Other HPI documentation
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3
2. The Data Management Lifecycle in HPII 6
2.1 Use of sediment and water quality information in policy and
decision-making
2.2 Sediment and water quality monitoring network design and
development
2.3 Data sensing and recording
2.4 Data validation and archival storage
2.5 Data synthesis and analysis
2.6 Data dissemination and publication
2.7 Real-time data
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3. Sediment and Water Quality Monitoring Stations and Data 11
3.1 Monitoring stations
3.2 Monitoring networks and laboratories
3.3 Inspections, audits and maintenance
3.4 Data sensing and recording
3.5 Data processing
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4. Sediment Data Processing and Analysis 24
4.1 Data entry
4.2 Primary validation
4.3 Secondary validation
4.4 Compilation and analysis
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5. Water Quality Data Processing and Analysis 30
5.1 Data entry
5.2 Primary validation
5.3 Secondary validation
5.4 Analysis
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6. Data Dissemination and Publication 40
6.1 Sediment and water quality products
6.2 Annual reports
6.3 Periodic reports
6.4 Dissemination to hydrological data users
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References 45
Annex I States and agencies participating in the Hydrology Project 46
Annex II Summary of distribution of hard copy of HPI HIS Manual
Surface Water
47
Annex III Summary of distribution of hard copy of HPI HIS Manual
Groundwater
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4. Hydrological Information System May 2014
HP II
Last Updated: 19/05/2014 05:02
Filename: WQ Handbook.docx
Page ii
List of figures
1.1 Hydrometric information lifecycle 1
4.1 Example of S-Q relationship fitted to total suspended sediment
load data 28
5.1 Example of (a) Piper-diagram and (b) Stiff-diagram 34
5.2 Normal distribution of a set of random observations 35
5.3 Cumulative distribution function illustrating percentiles and
proportions 36
5.4 Schematic overview of graphical presentation tools 37
5.5 Example of yearly box and whisper plot (for Cadmium) 38
List of tables
1.1 HPI water quality training modules 5
1.2 HPI water quality “training of trainers” modules 5
2.1 Sediment and water quality data processing timetable for data
for month n 8
3.1 Where to go in the HIS Manual SW/GW for water quality
data management guidance: sediment and water quality 12
6.1 Water quality criteria for various uses of fresh water 43
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Glossary
ADCP Acoustic Doppler Current Profiler
ARG Autographic Rain Gauge
AWS Automatic Weather Station
BBMB Bhakra-Beas Management Board
CGWB Central Ground Water Board
CPCB Central Pollution Control Board
CWC Central Water Commission
CWPRS Central Water and Power Research Station
Div Division
DPC Data Processing Centre
DSC Data Storage Centre
DWLR Digital Water Level Recorder
e-GEMS Web-based Groundwater Estimation and Management System
(HPII)
eHYMOS Web-based Hydrological Modelling System (HPII)
eSWDES Web-based Surface Water Date Entry System in e-SWIS (HPII)
e-SWIS Web-based Surface Water Information System (HPII)
FCS Full Climate Station
GEMS Groundwater Estimation and Management System (HPI)
GW Groundwater
GWDES Ground Water Data Entry System (HPI)
GWIS Groundwater Information System (GPI)
HDUG Hydrological Data User Group
HIS Hydrological Information System
HP Hydrology project (HPI Phase I, HPII Phase II)
HYMOS Hydrological Modelling System (HPI)
IMD India Meteorological Department
Lab Laboratory
MoWR Ministry of Water Resources
NIH National Institute of Hydrology
SRG Standard Rain Gauge
Stat Station
Sub-Div Sub-Division
SW Surface Water
SWDES Surface Water Data Entry System (HPI)
TBR Tipping Bucket Raingauge
ToR Terms of Reference
WISDOM Water Information System Data Online Management (HPI)
WQ Water Quality
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1. Introduction
This Hydrology Project Phase II (HPII) Handbook provides guidance for the management of
sediment and water quality data in rivers, dams/lakes/reservoirs and aquifers. The data are
managed within a Hydrological Information System (HIS) that provides information on the spatial
and temporal characteristics of the quantity and quality of surface water and groundwater. The
information is tuned to the requirements of the policy makers, designers and researchers to
provide evidence to inform decisions on long-term planning, design and management of water
resources and water use systems, and for related research activities. The Indian States and
Central Agencies participating in the Hydrology Project are listed in Annex I. However, this
Handbook is also relevant to non-HP States.
It is important to recognise that there are two separate issues involved in managing sediment and
water quality information. The first issue covers the general principles of understanding monitoring
networks, of collecting, validating and archiving data, and of analysing, disseminating and
publishing data. The second covers how to actually do these activities using the database systems
and software available. Whilst these two issues are undeniably linked, it is the first – the general
principles of data management - that is the primary concern. This is because improved data
management practices will serve to raise the profile of Central/State hydrometric agencies in
government and in the user community, highlight the importance of sediment and water quality
data for the design of water-related schemes and for water resource planning and management,
and motivate staff, both those collecting the data and those in data centres.
This Handbook aims to help HIS users locate and understand documents relevant to sediment and
water quality in the library available through the Manuals page on the Hydrology Project website.
The Handbook is a companion to the HIS manuals. The Handbook makes reference to the six
stages in the hydrometric information lifecycle (Figure 1.1), in which the different processes of data
sensing, manipulation and use are stages in the development and flow of information. The cycle
and associated HIS protocols are explored more fully in Section 2. Subsequent sections cover
different stages of the cycle for different hydro-meteorological variables.
Figure 1.1 Hydrometric information lifecycle (after: Marsh, 2002)
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1.1 HIS Manual
The primary reference sources are the HIS Manual Surface Water (SW) and HIS Manual
Groundwater (GW), two of many hundreds of documents generated during Hydrology Project
Phase I (HPI) to assist staff working in observation networks, laboratories, data processing centres
and data communication systems to collect, store, process and disseminate hydrometric data and
related information. During HPI, special attention was paid to the standardisation of procedures for
the observation of variables and the validation of information, so that it was of acceptable quality
and compatible between different agencies and States, and to facilities for the proper storage,
archival and dissemination of data for the system, so that it was sustainable in the long-term.
Therefore, the majority of the documents produced under HPI, particularly those relating to
fundamental principles, remain valid through and beyond HPII. Some parts of the guides, manuals
and training material relating to HPI software systems (SWDES, HYMOS, WISDOM, GWDES,
GEMS, GWIS) have been partially or wholly superseded as replacement Phase II systems (e-
GEMS, e-SWIS) become active.
The HIS Manual SW and HIS Manual GW describe the procedures to be used to arrive at a sound
operation of the HIS in regard to sediment and water quality data. The HIS Manual SW and HIS
Manual GW each consist of 10 volumes. Each volume contains one or more of the following
manuals, depending on the topic:
• Design Manual (DM) - procedures for the design activities to be carried out for the
implementation and further development of the HIS.
• Field Manual (FM) or Operation Manual (OM) – detailed instructions describing the activities to
be carried out in the field (station operation, maintenance and calibration), at the laboratory
(analysis), and at the Data Processing Centres (data entry, validation, processing,
dissemination, etc). Each Field/Operation Manual is divided into a number of parts, where
each part describes a distinct activity at a particular field station, laboratory or data processing
centre.
• Reference Manual (RM) - additional or background information on topics dealt with or
deliberately omitted in the Design, Field and Operation Manuals.
Those HIS Manual SW/GW volumes relevant to sediment and water quality are:
SW/GW Volume 1: Hydrological Information System: a general introduction to the HIS, its
structure, HIS job descriptions, Hydrological Data User Group (HDUG) organisation and user data
needs assessment. The content of the SW and GW volumes is identical.
• Design Manual
• Field Manual
Part II: Terms of Reference for HDUG
Part III: Data needs assessment
SW/GW Volume 2: Sampling Principles: units, principles of sampling in time and space and
sampling error theory. The content of the SW and GW volumes is identical.
• Design Manual
SW Volume 5: Sediment transport measurements: network design, implementation and
operation.
• Design Manual
• Field Manual
• Reference Manual
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SW/GW Volume 6: Water Quality sampling: network design, implementation, operation and
maintenance. There are some differences in the content of the SW and GW volumes.
• Design Manual
• Field Manual
SW/GW Volume 7: Water Quality analysis: laboratory procedures. The content of the SW and
GW volumes is identical.
• Design Manual
• Operation Manual
SW/GW Volume 8: Data processing and analysis: specification of procedures for Data
Processing Centres (DPCs). The content of SW Operation Manual Part I and GW Operation
Manual Part II is identical.
Surface Water Volume 8: Data processing and analysis
• Operation Manual
Part I: Data entry and primary validation
Part II: Secondary validation
Part III: Final processing and analysis
Part IV: Data management
Groundwater Volume 8: Data processing and analysis
• Operation Manual
Part II: Data entry and primary validation - water quality data
Part V: Groundwater Year Book
SW Volume 10: Surface Water protocols: outline of protocols for data collection, entry, validation
and processing, communication, inter-agency validation, data storage and dissemination, HIS
training and management.
• Operation Manual
Data entry forms
In this Handbook, individual parts of the HIS Manual SW/GW are referred to according to the
nomenclature “SW/GWvolume-manual(part)” e.g. GW Volume 6: “Water Quality sampling” Field
Manual is referred to as GW6-FM, and SW Volume 8: “Data processing and analysis” Operation
Manual Part I: “Data entry and primary processing” is referred to as SW8-OM(I).
A hard copy of the relevant manuals should be available for the locations listed in Annex II. For
example, a hard copy of GW6-FM should be available at all groundwater monitoring stations where
water quality sampling takes place. Similarly, SW8-OM(I) should be available at all Data
Processing Centres where data entry and primary validation of surface water sediment and water
quality data take place.
As noted, there is some inevitable overlap and repetition between the HIS Manual SW and the HIS
Manual GW (e.g. Volume 3). In the following sections of this Handbook, reference is generally
made only to the HIS Manual SW, as the majority of sediment and water quality reference material
is incorporated in here (indeed sediment is only in the HIS Manual SW), unless there is important
additional information in the HIS Manual GW.
1.2 Other HPI documentation
Other HPI documents of relevance to sediment and water quality include:
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• The e-SWIS and e-GEMS software manuals, and the SWDES and GWDES software manuals
- although SWDES and GWDES are being superseded by e-SWIS and e-GEMS, respectively,
in HPII, to promote continuity, e-SWIS contains an eSWDES module and e-GEMS includes
GWDES functionality.
• “Protocol for Water Quality Monitoring” – summary of the design approach and necessary
actions to implement water quality monitoring networks for both surface water and ground
water.
• “Network and Mandates of WQ monitoring” – theme paper discussing water quality monitoring
networks for both surface water and ground water.
• “Standard Analytical Procedures for Water Analyses” – summary of standard analytical
procedures for water quality analysis.
• “Maintenance norms for WQ laboratories” – maintenance guidance for water quality
instrumentation and equipment.
• “Surface Water Yearbook” – a template for a Surface Water Yearbook published at State level.
• Water quality training modules – these are divided into five sets (see Table 1.1):
• Set I: covers surface water and groundwater sampling and on-site analysis, plus chemistry
concepts and laboratory practices for Level I laboratories.
• Set II: covers pollution parameters, plus chemistry concepts and laboratory practices for
Level II and II+ laboratories.
• Set III – Set V: cover chemistry concepts and laboratory practices for Level II and II+
laboratories.
• Surface water “training of trainers” modules also relevant to water quality which may be of
interest to the more advanced user (see Table 1.2).
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Table 1.1 HPI water quality training modules
Topic Module Title
Set I 01 Basic water quality concepts
02 Basic chemistry concepts
03 Good laboratory practices
04 How to prepare standard solutions
05 How to measure colour odour and temperature
06 Understanding hydrogen ion concentration
07 How to measure the pH
08 Understanding EC
09 How to measure EC
10 How to measure solids
11 Chemistry of DO measurement
12 How to measure DO
13 How to sample surface waters
14 How to sample Ground Water
Set II 15 Understanding BOD test
16 Understanding dilution and seeding procedures in BOD test
17 How to measure BOD
18 Understanding COD test
19 How to measure COD
20 Introduction to Microbiology
21 Microbiological Laboratory Techniques
22 Coliforms as Indicator of Faecal Pollution
23 How to measure coliforms
Set III 24 Basic aquatic chemistry concepts
25 Oxygen balance in Surface Waters
26 Basic Ecology Concepts
27 Surface Water Quality Planning Concepts
28 Major Ions in Water
29 Advanced aquatic chemistry solubility equilibria
30 Advanced aquatic chemistry
31 Trace Compounds in the Aquatic Environment
32 Potentiometric Analysis of Water Quality
33 Use of Ion Selective Probes
34 Absorption spectroscopy
35 Emission Spectroscopy and Nephelometry
36 Measurement of Fluoride
37 Measurement of Oxidised Nitrogen
38 Measurement of Ammonia and Organic Nitrogen
39 How to measure Ammonia Nitrogen
40 Measurement of Chlorophyll-a
Set IV 41 Measurement of Phosphorus
42 Measurement of Boron
43 How to Measure Total Iron
44 How to Measure Sodium
45 How to Measure Sulphate
46 How to Measure Silicate
Set V 47 Basic Statistics
48 Applied Statistics
49 Quality Assurance and within Laboratory AQC
50 Inter-Laboratory AQC Exercise
Table 1.2 HPI water quality “training of trainers” modules
Topic Module Title
HIS WQ Training Specifications
Processing of Stream Flow Data
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2. The Data Management Lifecycle in HPII
Agencies and staff with responsibilities for hydrometric data have a pivotal role in the development
of sediment and water quality information, through interacting with data providers, analysts and
policy makers, both to maximise the utility of the datasets and to act as key feedback loops
between data users and those responsible for data collection. It is important that these agencies
and staff understand the key stages in the hydrometric information lifecycle (Figure 1.1), from
monitoring network design and data measurement, to information dissemination and reporting.
These later stages of information use also provide continuous feedback influencing the overall
design and structure of the hydrometric system. While hydrometric systems may vary from country
to country with respect to organisation set-ups, observation methods, data management and data
dissemination policies, there are also many parallels in all stages of the cycle.
2.1 Use of sediment and water quality information in policy and decision-making
The objectives of water resource development and management in India, based on the National
Water Policy and Central/State strategic plans, are: to protect human life and economic functions
against flooding; to maintain ecologically-sound water systems; and to support water use functions
(e.g. drinking water supply, energy production, fisheries, industrial water supply, irrigation,
navigation, recreation, etc). These objectives are linked to the types of data that are needed from
the HIS. SW1-DM Chapter 3.3 presents a table showing HIS data requirements for different use
functions on page 19. In turn, these use functions lead to policy and decision-making uses of HIS
data, such as: water policy, river basin planning, water allocation, conservation, demand
management, water pricing, legislation and enforcement.
Hence, freshwater management and policy decisions across almost every sector of social,
economic and environmental development are driven by the analysis of hydrometric information.
Its wide-ranging utility, coupled with escalating analytical capabilities and information dissemination
methods, have seen a rapid growth in the demand for hydrometric data and information over the
first decades of the 21st century. Central/State hydrometric agencies and international data
sharing initiatives are central to providing access to coherent, high quality hydrometric information
to a wide and growing community of data users. Hydrological data users may include water
managers or policymakers in Central/State government offices and departments, staff and
students in academic and research institutes, NGOs and private sector organisations, and
hydrology professionals. An essential feature of the HIS is that its output is demand-driven, that is,
its output responds to the hydrological data needs of users.
SW1-FM(III) presents a questionnaire for use when carrying out a data needs assessment to
gather information on the profile of data users, their current and proposed use of surface water,
groundwater, hydro-meteorology and water quality data, their current data availability and
requirements, and their future data requirements. Data users can, through Central/State
hydrometric agencies, play a key role in improving hydrometric data, providing feedback
highlighting important issues in relation to records, helping establish network requirements and
adding to a centralised knowledge base regarding national data. By embracing this feedback from
the end-user community, the overall information delivery of a system can be improved.
A key activity within HPII was a move towards greater use of the HIS data assembled under HPI.
Two examples of the use of HIS data include the Purpose-Driven Studies (PDS) and the Decision
Support Systems (DSS) components of HPII. See the Hydrology Project website for more
information about DSS and PDS, and access to PDS reports.
The 38 PDS, which were designed, prepared and implemented by each of the Central/State
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hydrometric agencies, are small applied research projects to investigate and address a wide range
of real-world problems and cover surface water, groundwater, hydro-meteorology and water quality
topics. Some examples of projects include a study of reservoir sedimentation and development of
a catchment area treatment plan for the Kodar Reservoir in Chhattisgarh (PDS number SW-CH-1),
and a study of groundwater quality in the Jabalpur urban area in Madhya Pradesh, with an
emphasis on transport of pathogenic pollutants (PDS number SW-NIH-1). The PDS utilise
hydrometric data and products developed under HPI, supplemented with new data collected during
HPII.
Two separate DSS programmes were set up under HPII. One, for all participating implementing
agencies, called DSS Planning (DSS-P), has established water resource allocation models for
each State to assist them to manage their surface and groundwater resources more effectively.
The other, called DSS Real-Time (DSS-RT) was specifically for the Bhakra-Beas Management
Board (BBMB), although a similar DSS-RT study has also now been initiated on the Bhima River in
Maharashtra. The DSS programmes have been able to utilise hydrological data assembled under
the Hydrology Project to guide operational decisions for water resource management.
2.2 Sediment and water quality monitoring network design and development
Section 3.2 of this Handbook outlines the design and development of sediment and water quality
monitoring networks. Networks are planned, established, upgraded and evolved to meet a range
of needs of data users and objectives, most commonly water resources assessment and
hydrological hazard mitigation (e.g. flood forecasting). It is important to ensure that the hydro-
meteorological, surface water, groundwater and water quality monitoring networks of different
agencies are integrated as far as possible to avoid unnecessary duplication. Integration of
networks implies that networks are complimentary and that regular exchange of data takes place to
produce high quality validated datasets. Responsibility for maintenance of Central/State
hydrometric networks is frequently devolved to a regional (Divisional) or sub-regional (Sub-
Divisional) level.
2.3 Data sensing and recording
Sections 3.1-3.4 of this Handbook review sediment and water quality monitoring networks and
stations, maintenance requirements and measurement techniques. Responsibility for operation of
Central/State hydro-meteorological monitoring stations is frequently devolved to a regional
(Divisional) or sub-regional (Sub-Divisional) level. However, it is important that regular liaison is
maintained between sub-regions and the Central/State agencies through a combination of field site
visits, written guidance, collaborative projects and reporting, in order to ensure consistency in data
collection and initial data processing methods across different sub-regions, maintain strong
working relationships, provide feedback and influence day-to-day working practice. Hence, the
Central/State agencies are constantly required to maintain a balance of knowledge between a
broad-scale overview and regional/sub-region sediment and water quality awareness. Operational
procedures should be developed in line with appropriate national and international (e.g. Indian,
ISO, WMO) standards (e.g. WMO Report 168 “Guide to Hydrological Practices”).
2.4 Data validation and archival storage
The quality control and long-term archiving of sediment and water quality data represent a central
function of Central/State hydrometric agencies. This should take a user-focused approach to
improving the information content of datasets, placing strong emphasis on maximising the final
utility of data e.g. through efforts to improve completeness and fitness-for-purpose of
Centrally/State archived data. Section 3.5 of this Handbook summarises the stages in the
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processing of sediment and water quality data. Sections 4 and 5 of this Handbook cover the
process from data entry through primary and secondary validation, and also compilation and
analysis of data (Section 2.5), for sediment and water quality data, respectively.
During all levels of validation, staff should be able to consult station metadata records detailing the
history of the site and its performance, along with topographical, hydrogeological and isohyetal
maps and previous quality control logs. Numerical and visual tools available at different phases of
the data validation process, such as versatile time series plotting and manipulation software, and
assessment of time series statistics greatly facilitate validation. High-level appraisal by
Central/State staff, examining the data in a broader spatial context, can provide significant benefits
to final information products. It also enables evaluation of the performance of sub-regional data
providers, laboratories, individual stations or groups of stations, which can focus attention on
underperforming sub-regions and encourage improvements in data quality.
A standardised data assessment and improvement procedure safeguards against reduced quality,
unvalidated and/or unapproved data reaching the final data archive from where they can be
disseminated. However, Marsh (2002) warns of the danger of data quality appraisal systems that
operate too mechanistically, concentrating on the separate indices of data quality rather than the
overall information delivery function.
For the Hydrology Project, the timetable for data processing is set out in SW10-OM Protocols and
Procedures for sediment and surface water quality samples and data, and in GW10-OM HIS
activities – Groundwater domain for groundwater quality samples and data, and summarised in
Table 2.1 of this Handbook. Sample analysis is required to be completed at the laboratory within
the allowed time period for the specified water quality parameters, and data entry to be completed
immediately the analysis results are available. Primary validation, also by the laboratory, should
be completed within one week of data entry. Initial secondary validation, in State DPCs for State
data, and CWC/CGWB/CPCB local offices for Central data, should be completed within one month
of data entry. Some secondary validation will not be possible until the end of the hydrological year
when the entire year’s data can be reviewed in a long-term context, and compared with Central
data, so data should be regarded as provisional approved data until then, after which data should
be formally approved and made available for dissemination to external users. At certain times of
year (e.g. during the monsoon season), this data processing plan may need to be compressed, so
that validated sediment and water quality data are available sooner.
Table 2.1 Sediment and water quality data processing timetable for data for month n
Activity Responsibility Deadline
Sediment and water quality data
Sample receipt and analysis Laboratory Within allowed time period
Data entry Laboratory Same day as analysis
Primary validation Laboratory Within 1 week of data entry
Secondary validation State DPC
State DPC
Initial - Within 1 month of data
entry
Final – end of hydrological year +
3 months
Compilation State DPC As required
Analysis State DPC As required
Reporting State DPC At least annually
Data requests State DPC 95% - within 5 working days
5% - within 20 working days
Interagency validation IMD At least 20% of State stations, on
rolling programme, by end of
hydrological year + 6 months
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2.5 Data synthesis and analysis
Central/State hydrometric agencies play a key role in the delivery of large-scale assessments of
sediment and water quality data. Through their long-term situation monitoring, they are often well
placed to conduct or inform scientific analysis at a State, National or International level, and act as
a source of advice on data use and guidance on interpretation of data. This is especially true in
the active monitoring of the State or National situation or the assessment of conditions at times of
extreme events (e.g. monsoonal rains, droughts) which may have a significant impact on water
quality, where agencies may be asked to provide input to scientific reports and research projects,
as well as informing policy decisions, media briefings, and increasing public understanding of the
state of the water environment. Sections 4 and 5 of this Handbook cover compilation and analysis
of data, as well as the process from data entry through primary and secondary validation (Section
2.4), for sediment and water quality data, respectively.
2.6 Data dissemination and publication
One of the primary functions of Central/State hydrometric agencies is to provide comprehensive
access to information at a scale and resolution appropriate for a wide range of end-users.
However, improved access to data should be balanced with a promotion of responsible data use
by also maintaining end-user access to important contextual information. Thus, the dissemination
of user guidance information, such as composite summaries that draw users’ attention to key
information and record caveats (e.g. monitoring limitations, levels of uncertainty regarding data
accuracy, major changes in laboratory setup), is a key stewardship role for Central/State
hydrometric agencies, as described in Section 6 of this Handbook.
For large parts of the 20th century the primary data dissemination route for sediment and water
quality data was via annual hardcopy publications of data tables i.e. yearbooks. However, the last
decade or so has seen a shift towards more dynamic web-based data dissemination to meet the
requirement for shorter lag-time between observation and data publication and ease of data re-
use. Like many countries, India now uses an online web-portal as a key dissemination route for
hydrometric data and associated metadata which provides users with dynamic access to a wide
range of information to allow selection of stations. At least 95% of data requests from users should
be processed within 5 working days. More complex data requests should be processed within 20
working days.
2.7 Real-time data
During HPII many implementing agencies developed low cost real-time data acquisition systems,
feeding into bespoke databases and available on agency websites. Such systems often utilise
short time interval recording of data e.g. 15 minutes, 1 hour, etc. In some instances, agencies are
taking advantage of the telemetry aspect of real-time systems as a cost-effective way of acquiring
data from remote locations. However, for some operational purposes (e.g. real-time flood
forecasting, water abstraction, etc), real-time data may need to be used immediately.
Real-time water quality data should go through some automated, relatively simple data validation
process before being input to real-time models e.g. checking that each incoming data value is
within pre-set limits for the station, and that the change from preceding values is not too large.
Where data fall outside of these limits, they should generally still be stored, but flagged as suspect,
and a warning message displayed to the model operators. Where suspect data have been
identified, a number of options are available to any real-time forecasting or decision support model
being run, and the choice will depend upon the modelling requirements. Whilst suspect data could
be accepted and the model run as normal, it is more common to treat suspect data as missing or to
substitute them with some form of back-up, interpolated or extrapolated data. This is necessary for
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hydrometric agencies to undertake some of their day-to-day functions and, in such circumstances,
all the data should be thoroughly validated as soon as possible, according to the same processing
timetable and protocols as other climate data.
Real-time water quality data should also be regularly transferred to the e-SWIS or e-GEMS
database system, through appropriate interfaces, in order to ensure that all data are stored in a
single location and provide additional back-up for the real-time data, but also to provide access to
the data validation tools available through the software systems.
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3. Sediment and Water Quality Monitoring Stations and Data
3.1 Monitoring stations
For sediment and water quality monitoring stations, Table 3.1 lists the relevant section in the HIS
Manual SW and HIS Manual GW for detailed information on design and installation, maintenance,
measurement/sampling, analysis of samples, data entry, validation, compilation and analysis of
results, and reporting.
A set of specifications for hydrometric equipment was compiled under HPI and updated under
HPII. The specifications, which are downloadable from the Hydrology Project website, provide a
guideline for procurement, some technical guidance for which is offered in SM4-DM Chapter 7
(with examples of some procurement templates and documents also on the Hydrology Project
website).
3.1.1 Sediment monitoring stations
Knowledge of sediment transport in a river is essential for the solution of variety of problems
associated with flow in rivers:
• Estimation of sediment inflow into reservoirs at the planning and design stage - by estimating
the suspended load and bed loads separately
• Studies for river training and river regimes – data may have to be gathered by mounting
intensive observation campaigns for short periods
• Evaluation of basin erosion and identification of conservation measures
• Estimation of regime widths and scour depths for barrages bridges from bed material analysis
Hence, sediment data helps verify existing theories and empirical formulae for computation of
sediment transport, and leads to better problem solving and design of water use facilities. Types of
sediment include:
• Suspended sediment load – sediment maintained in suspension by turbulence in flowing water
for considerable periods of time without contact with the channel bed. It moves with practically
the same velocity as that of the flowing water. Suspended sediment is routinely split into three
class sizes: coarse > 2 mm; medium 0.075-2 mm; and fine <0.075 mm.
• Bed material – material, the participles of which are found in appreciable quantities in that part
of the channel bed affected by transport.
• Bed load – sediment in almost continuous contact with the channel bed, carried forward by
rolling, sliding and/or hopping.
Sediment measurements are relatively difficult to make. The direct measurement method, used in
India, aims at determining the weight or volume of sediment passing a section in a period of time.
The alternative indirect measurement method aims at measuring the concentration of sediment
flowing in the moving water, and needs the measurement of sediment concentrations, the cross-
sectional areas and flow velocities, as well as the sediment being transported as wash load and
bed load. Routine sediment measurements are usually restricted to sampling the suspended load
at flow gauging stations. In this sense, sediment is looked at as a “quality” parameter of the water.
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Table 3.1 Where to go in the HIS Manual SW/GW for water quality data management guidance: sediment and water quality
Instrument
/ Variable
Design &
Installation
Maintenance Measurement/
Sampling
Analysis
samples
Data entry Primary
Validation
Secondary
Validation
Compilation Analysis
results
Reporting
Suspended
sediment
load
SW5-DM
7.2, 8
SW5-FM 5,
6
SW5-DM 5,
6.1-6.4
SW5-FM 1,
2
SW5-FM
4.2, 4.3
SW8-OM(I)
12.2, 12.3
SW8-OM(I)
13.1
SW8-OM(II)
17
SW8-OM(II)
18
SW8-OM(III)
13
Bed
material
SW5-DM
7.3, 8
SW5-FM 5,
6
SW5-DM 5,
6.5, 6.6
SW5-FM 1,
3
SW5-FM 4.4 SW8-OM(I)
12.4
SW8-OM(I)
13.2
Bed load SW5-RM 4 SW5-RM
5.2.3, 5.3.3
SW5-RM 2,
3, 5
WQ-SW SW6-DM 4-
6, 8
SW7-DM 6,
7
SW7-OM 2
SW7-DM 8
SW6-DM 7,
8
SW6-FM 1-5
SW7 OM
SW7-DM 2-
5
SW8-OM(I)
14.4-14.8
SW8-OM(I)
15.2
SW8-OM(III)
8.2
SW8-OM(III)
8.3-8.7
SW8-OM(III)
14, Annex I
WQ-GW GW6-DM 4-
6, 8
GW7-DM 6,
7
GW7-OM 2
GW7-DM 8
GW6-DM 7,
8
GW6-FM 1-
5
GW7 OM
GW7-DM 2-
5
GW8-OM(II)
1.4-1.8
GW8-OM(II)
2
GW8-OM(I)
4.3.4
GW7-DM
2.9.2
GW8-OM(I)
4.4.2
SW8-OM(III)
8.3-8.7
GW8-OM(V)
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3.1.2 Water quality monitoring stations
Water quality is an indicator of the physical, chemical and/or biological state of water, determined
by insitu measurement in the field and/or laboratory analysis for one or more parameters of
interest. Water quality may be related to the suitability of water for a particular use or purpose, and
the most critical step in a successful water quality monitoring programme is a clear definition and
specification of the monitoring objectives and information needs for water management:
• Building up an overall picture of the aquatic environment thus enabling pollution cause and
effect to be judged
• Providing long-term background data against which future changes can be assessed
• Detecting trends
• Providing warnings of potentially deleterious changes
• Checking for compliance or for charging purposes
• Precisely characterising an effluent or water body (possibly to enable classification to be
carried out)
• Investigating pollution
• Collecting sufficient data to perform in-depth analysis (e.g. mathematical modelling) or to allow
research to be carried out
The design of the water quality monitoring programme involves the selection of water sampling
locations, sampling frequency and water quality parameters to generate the required information.
Sampling locations may be many and varied as water quality may be compromised through direct
point sources, diffuse agricultural sources and diffuse urban sources:
• Rivers – issues may include changes in physical characteristics (i.e. temperature, turbidity,
total suspended solids) and river hydrology, contamination by faecal and organic matter,
contamination by toxic pollutants (i.e. organics and heavy metals), river eutrophication,
salinisation, contamination by agrochemicals, and mining activities.
• Dams/lakes/reservoirs – issues may include pollution from riverine, groundwater and/or
atmospheric sources, eutrophication, acidification, and bioaccumulation and biomagnification.
• Aquifers – issues may include contamination by faecal and organic matter, contamination by
toxic pollutants (i.e. organics and heavy metals), overabstraction, contamination by
agrochemicals, and mining activities.
A total of 68 water quality parameters are analysed as standard parameters under the Hydrology
Project. These fall into several groups:
• General - basic parameters many of which can be measured instrumentally either in the field or
in the laboratory e.g. temperature, conductivity, pH, DO
• Nutrients - nitrogen and phosphorus parameters which will measure the nutrients available for
plant growth and eutrophication
• Organic matter - parameters capable of estimating the likely effect on watercourses of the
discharge of organic matter i.e. BOD and COD
• Major ions - the inorganic anions and cations which can describe the chemical composition of
the water and help to assess pollution
• Other inorganics - miscellaneous inorganic species which are important for certain water uses
or for classification purposes
• Metals - metal species which are important because of their toxicity or because they are useful
indicators of the presence of other metals i.e. cadmium, zinc and mercury
• Organics - particular species which are important due to their toxicity, effect on potability of
water or effect on the natural river processes e.g. pesticides
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• Microbiological – total coliforms as an indicator species for the presence of faecal pollution of
water
• Biological – chlorophyll a, present in plants, as an indicator of algal growth and, therefore,
eutrophication of waters
Some potentially important parameters are not in this list: e.g. heavy metals such as lead, copper,
nickel, arsenic, chromium; organic pollutants such as polychlorinated biphenyls (PCBs) and certain
types of pesticide; certain organic solvents; and oils and hydrocarbons. For each basin it may be
necessary to ascertain whether or not any of these pollutants are present in unacceptable
concentrations and should be added to the parameter list for that sampling location.
3.2 Monitoring networks and laboratories
Monitoring networks should be considered to be dynamic entities. It is important that the current
utility of well-established monitoring networks is periodically assessed to ensure that they continue
to meet changing requirements and to optimise the information they deliver. Network reviews
should be done in collaboration with other agencies, including CPCB, CWC and CGWB. SW5-DM
Chapters 3 and 4 describe network design and optimisation for sediment monitoring, and
SW/GW6-DM Chapters 4 to 6 for water quality. Sediment and water quality monitoring sites are
often at flow gauging stations. Whilst priority may be given to the requirements of the surface
water network (Water Level, Stage-Discharge and Flow Handbook, Section 3.2) or groundwater
network (Groundwater Handbook, Section 3.2), there are several advantages: flow or groundwater
level data are available which provides important information for data analysis; generally, the site
will be easily accessible and well-maintained; and any changes at or near the site will be noted.
For more detailed information see: SW/GW2-DM Chapter 7 which provides some generic guidance
on types of network and the steps in network design; SW/GW2-DM Chapters 3.2.1 to 3.2.6 which
describe classification of stations and offer some examples of types of network; and Surface Water
Training Module 45 “How to review monitoring networks”. A good example of a monitoring network
review under HPII is the Purpose Driven Study (PDS) on optimisation of the river gauging station
and raingauge networks in Maharashtra (PDS number SW-MH-1).
3.2.1 Sediment monitoring networks
A sediment monitoring network is a system of flow and sediment gauging stations in a river basin
that provides data needed for the planning, design and management of the water resources in the
basin from the point of view of the sediment. There are no specific requirements in terms of
minimum basin area, but all the sediment sources of importance for establishing sediment
balances should be included in the network. All primary flow stations should have sediment
measured together with the flow, if feasible, but not necessarily at the same location as the flow
gauging station. They should all comprise at least suspended load measurement. However,
sediment monitoring stations are expensive to equip and to operate, so network optimisation
should rather be based on sound judgement and economic consideration (cost-effectiveness).
Possible questions to be addressed when selecting sediment measuring sites include:
• What is the purpose of the station: e.g. monitoring of reservoir sedimentation, planning and
design of structures, setting up sediment balances in river reaches?
• What are the sediment conditions at the site: i.e. how variable are sediment transport rates in
the river reach, are there preferential zones of scour and/or deposition?
• In what range of flow should the sediment be gauged, and which part of the load: e.g. no bed
load during the lean season?
• What size fractions are needed: e.g. the wash load not of interest for a problem of river
morphology?
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• What level of accuracy should be attempted, for the various transport modes and for the
various size fractions?
• What period of record is required and what frequency of measurement is desirable, possibly in
particular phases of the hydrograph: e.g. more frequent sampling during the rising limb of the
hydrograph?
• Who are the possible users and for which kind of data?
• Are there limitations in terms of access to the site and transport of the samples to the
laboratory for analysis?
• What are the constraints in terms of resources (human and financial)?
• What are the possible preferences for equipment and methodology: e.g. existing experience
with one or another type of instrument; proximity of a research centre that can assist in case of
difficulties for implementing the measurements?
SW5-DM devotes Chapter 4.2 to the topic of site surveys for sediment measuring sites, which
comprise three phases: a desk study, a reconnaissance survey and other surveys e.g. trial
sampling. The site survey, which should be carried out in collaboration with CWC, may reveal that
the desired location is unsuitable, and an alternative site may need to be considered. SW5-DM
Chapter 4.2.6 presents a useful list of criteria that should be taken into consideration in selecting a
site to ensure long-term reliable data. Because the majority of sediment measuring sites will be at
flow gauging stations, the station design, construction and installation procedures for flow gauging
stations should be followed (Water Level, Stage-Discharge and Flow Handbook, Section 3.2).
3.2.2 Water quality monitoring networks
A water quality monitoring network is a system of water quality monitoring sites in a river basin,
aligned with the flow monitoring network and/or located at boreholes, that provides information on
the actual status and trends that are relevant for the functions and uses of the river, reservoir or
aquifer. Water quality monitoring networks are classified as:
• Monitoring i.e. long-term standardised measurement in order to define status and trends – with
sub-categories: baseline, trend and flux. See SW6-DM Chapter 4.2.2 or GW6-DM Chapter
4.2.1 for more information.
• Surveillance i.e. continuous specific measurement for the purpose of water quality
management and operational activities – with sub-categories: water use and pollution control.
See SW6-DM Chapter 4.2.3 or GW6-DM Chapter 4.2.1 for more information.
• Survey i.e. a finite duration intensive programme to measure for a specific purpose – with
subcategories: classification, and management and research. See SW6-DM Chapter 4.2.4 or
GW6-DM Chapter 4.2.1 for more information.
New water quality monitoring stations should be a combination of baseline and trend stations with
samples collected every two or three months. After data have been collected for three years, the
stations should be reclassified either as baseline, trend or flux station. A baseline station should
be monitored at least every two or three months, a trend station at least once every month, and a
flux station more regularly e.g. 12 or 24 times per year.
The monitoring network is defined by the density of stations (how many locations on a certain river-
stretch or in a certain aquifer are investigated, and what are the locations?), the frequency of
sampling (how many samples per year are collected from each location?), and the list of water
quality parameters that are analysed for each sample collected. The density, sampling frequency
and parameters for each of the water quality monitoring network classes and sub-categories are
listed in Table 4.1 on page 17 of SW/GW6-DM and the subsequent Tables 4.2-4.4 of the SW
volume.
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Surface water quality monitoring network design and evaluation is a multi-step process,
comprising:
1. Construction of a base map with river basin boundaries, state boundaries, national boundaries,
and rivers, dams and lakes
2. Classification of the main stem and major tributaries (contributing more than 20% of the flow of
the main stem at the confluence point)
3. Construction of overlays with geological information e.g. rock types, and with pollution sources
e.g. major towns, industrial centres, agricultural land
4. Positioning of baseline (monitoring) stations - each major tributary should have a baseline
station to get a good overall picture of the (natural) background concentration of various
constituents of water in rivers in the basin. Baseline stations should be positioned in relatively
unpolluted areas such as upstream of major towns and industrial centres.
5. Positioning of trend (monitoring) stations – trend stations are located on the main stem when
the river flow increases by 20% of the flow at the previous station. In the case of confluence
with a major tributary, trend stations are located both on the tributary and on the main stem of
the river, just above the confluence point.
6. Positioning of flux stations to gauge the load of anthropogenic pollutants passing a sampling
point.
7. Critical review of the monitoring network so far e.g. check that the distance between
successive trend stations on the main stem is not too short
8. Review of existing networks of CWC, CGWB and State agencies – construct an overlay with
the locations of water quality monitoring stations of CWC, CGWB and State agencies e.g.
irrigation department
9. Review of existing network of CPCB - construct an overlay with the locations of water quality
monitoring stations of CPCB
10. Rationalisation of networks – where comparison of monitoring networks reveals a duplication of
effort, dialogue between organisations should be initiated to review the networks compared to:
the information needs of hydrological data users; the station location, sampling frequency and
parameters; the availability of the current and historical data in the public domain; a
comparison of historical data; the validity of the data; and a comparison of the analytical quality
control programmes employed by the laboratories.
11. Adding Surveillance and Survey type stations – if required by the mandate
12. Identifying sampling frequency and water quality parameters for analysis, keeping in mind the
objectives, feasibility of sampling, costs and above all capacity of field staff and laboratories.
Groundwater quality monitoring network design and evaluation has some similarities with the
surface water network. Groundwater quality monitoring networks usually correspond with
groundwater level monitoring networks of observation wells. For water quality monitoring stations,
it is essential that the water is pumped, so that the water in the well represents the aquifer water
and not storage water. A groundwater quality monitoring network should take into account
features of the area or region that are likely to have an impact on the water quality e.g. for a
groundwater quality network, these might include aquifer geology, type of aquifer, land use patter,
geological set up, geomorphological set up, and drainage basin. A simple approach to locating
monitoring stations is to mark the boundaries of the relevant features on a map and locate at least
one station in each intersection. For example, if an area has two aquifer geological formations,
shale and limestone, and two types of land use, agricultural and fallow, their intersection would
yield up to four unique possibilities (shale-agriculture, shale-fallow, limestone-agriculture,
limestone-fallow) and the network should have at least one station in each of the intersections.
Network rationalisation (step 10 under surface water above) is common to any monitoring network
as periodic analysis and review of data is may lead to new or redefined information needs, which
may be translated into different sampling locations, sampling frequency and/or parameters.
Rationalisation within a single organisation, or between networks of multiple organisations, may
free up resources which may be directed towards an increase in frequency of measurement for
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greater reliability and/or introduction of new water quality parameters for characterisation. To
inform rationalisation, the following types of data analyses should be made:
• Is there a correlation between different parameters at one station? If there is a good
correlation between two or more parameters, then possibly one or more parameters could be
dropped from the parameter list, saving analysis effort. The values for the dropped
parameter(s) should then be estimated from the remaining, measured parameter. Examples of
possible parameter correlation are BOD-COD, EC-major cations/anions, etc.
• Is there a correlation between water quality parameter(s) and river flow at a station (surface
water stations)? If there is a good correlation, then water quality information could be
approximated at all times that flow data are available. This can supply additional water quality
information at times between sampling events, without additional sampling effort.
• Is there a correlation in parameters between 2 (or more) stations? If such a correlation exists,
then one of the stations could be dropped, thus saving on sampling and analysis effort. The
water quality information for the dropped station should then be estimated from the parameters
at the measured station. Alternatively, the redundant sampling station could be moved to a
new location where unique water quality information will be gained.
• Is the sampling frequency sufficient to meet the monitoring objectives (e.g. if an objective is
trend detection, is the frequency high enough to be able to detect trends?). Analysis of the
data with respect to the monitoring objectives may result in a raising or lowering of sampling
frequency.
• Does monitoring of surveillance or survey category monitoring indicate any new water quality
issues which should be taken up in monitoring category (i.e. flux or trend type)? If so, new
locations and/or parameters may be added for monitoring, that is to say, the network design
can be adapted.
Rationalisation of a network may usually be done only after 3 to 5 years of data collection when
sufficient historical data are available for analysis. SW6-DM Chapter 4.3 describes design of a
water quality monitoring network in the Mahanadi basin in Madhya Pradesh, Orissa, Bihar and
Maharashtra. SW6-DM Chapter 5.5 describes rationalisation of a surface water quality monitoring
network in the Cauvery basin in Karnataka and Tamil Nadu, and GW6-DM Chapter 5.5 describes
rationalisation of a groundwater quality monitoring network in the Ghataprabha basin in Karnataka.
Because the majority of water quality monitoring sites will be at existing flow gauging stations or
observation wells, the station design, construction and installation procedures for flow gauging
stations or observations wells should be followed (Water Level, Stage-Discharge and Flow
Handbook, Section 3.2 or Groundwater Handbook, Section 3.2, respectively). In addition, SW6-
DM devotes Chapter 6 to the topic of selecting sampling sites for surface water quality monitoring.
The site survey, which should be carried out in collaboration with CWC and/or CPCB, may reveal
that the desired location is unsuitable, and an alternative site may need to be considered. SW5-
DM Chapter 6.1 presents a useful list of criteria that should be taken into consideration in selecting
a site to ensure long-term reliable data.
3.2.3 Laboratories
Water quality laboratories of different levels were established under the Hydrology Project. The
level of the laboratory is an indication of the analytical capacity of the laboratory based on the
equipment available, and not necessarily linked to the actual number of parameters analysed i.e. it
represents the potential capability of the laboratory:
• Level I – small laboratory located at or near the monitoring site (field), generally analysing
temperature, pH, conductivity, total suspended solids, Dissolved Oxygen (DO), colour and
odour (these parameters may also be measured in the field at the time of sampling)
• Level II - laboratory servicing larger area, with facilities to analyse general water quality
parameters, major ions, nutrients, indicators of organic and faecal pollution, etc.
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• Level II+ - laboratory servicing larger area, in possession of advanced equipment such as
Atomic Adsorption Spectrophotometer (AAS), Gas Chromatograph (GC), or UV-Visible
Spectrophotometer etc.
Laboratories should keep records of any malfunctioning and breakdown of equipment (with dates),
dates of equipment maintenance, power failure events and their duration, within-laboratory
analytical quality control (AQC; see Section 3.3.2) results and control chart results for different
parameters, and results of participation in inter-laboratory AQC.
3.3 Inspections, audits and maintenance
Regular maintenance of sampling and analysis equipment, together with periodic inspections and
audits, ensures collection of good quality sediment and water quality data and provides information
that may assist in future data validation queries. Table 3.1 lists the relevant section in the HIS
Manual SW and HIS Manual GW for maintenance of the different types of sediment and water
quality stations and instruments. For sediment, this topic is largely covered in different chapters of
SW5-FM for field inspections and audits, and for routine maintenance and calibration of equipment.
For water quality, this topic is covered in the document “Maintenance norms for WQ laboratories”
which is a maintenance guide for surface water and groundwater quality instrumentation and
equipment.
3.3.1 Sampling equipment
Maintenance and calibration requirements for instruments and equipment are often item-specific.
For example, if suspended sediment is sampled with a bottle sampler, calibration of the equipment
itself is not required, but an inter-comparison of the data obtained with the instrument with results
of other sampling equipment is required for interpretation purposes. A supply of appropriate spare
sampling equipment and parts should be kept on site and/or taken on field visits in case they are
needed. SW/GW6-FM Table 2.1 provides a checklist for a field visit.
For sediment monitoring at flow gauging stations, SW5-FM Chapter 6.3 lists maintenance norms,
including maintenance of civil works, maintenance of equipment, costs of consumable items and
payments to staff (where the costs should be regarded as out of date), in addition to the
maintenance norms for the flow monitoring stations themselves (Water Level, Stage-Discharge
and Flow Handbook, Section 3.3).
3.3.2 Laboratories
The importance of the quality assurance (QA) is recognised in any water quality monitoring
programme, and is an essential part of analytical work. A laboratory contains a variety of
equipment ranging from simple heating devices to extremely sophisticated computer-controlled
analytical equipment. It is a good practice to keep a separate logbook for each major piece of
equipment where details of its use, maintenance schedule, breakdowns and repairs, accessories,
supply of consumables, etc., are carefully entered. Such a record will help in properly maintaining
the instrument and planning for future. Each analyst in each laboratory should follow established
procedures to detect and to correct problems and take every reasonable step needed to keep the
measurement process reliable. The more important features of a QA programme should include:
• Use of documented methods of analyses
• Properly maintained and calibrated equipment
• Properly trained staff
• Effective internal quality control (within-laboratory analytical quality control (AQC))
• Participation in periodic programmes for evaluation of measurement bias (inter-laboratory
AQC)
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• External assessment by accreditation or other compliance schemes
SW/GW7-OM Table 2.3 presents examples of specific sources of error for the analyses of TDS,
TH, EC, fluoride, sulphate, phosphate, nitrate, sodium and boron.
3.4 Data sensing and recording
Table 3.1 lists the relevant section in the HIS Manual SW and HIS Manual GW for operational
instructions on the sampling and subsequent analysis for sediment and water quality.
3.4.1 Sediment sampling and analysis
Selecting appropriate sediment sampling frequency is critical, because the main part of the
sediment fluxes occurs during flood events, when sediment measurements are most difficult to
make. For the lower reaches of flash flood rivers, the fluxes during flood events may be close to
100% of the total yearly sediment discharge. Suspended sediment is likely to be observed even
during the non-monsoon season in the form of wash load, but bed material movement will be
initiated above a certain threshold level and should often be nil or negligible during the non-
monsoon season.
Suspended sediment data comprise of the concentrations of coarse (> 0.2 mm), medium (0.075-
0.2 mm) and fine (< 0.075 mm) material in combination with water level and flow data. The
suspended sediment concentration of the flow is determined by collecting depth integrated
samples that define the mean flow-weighted concentration in the sample vertical, from a sufficient
number of verticals to define the mean flow-weighted concentration in the cross-section. The
suspended sediment concentrations are usually obtained from point samples by the Punjab bottle
sampler (other bottle-type point samplers and alternative fixed volume depth-integrating samplers
and point-integrating samplers are also available) taken at 0.6 of the water depth in a number of
verticals in the cross-section, and normally collected during flow gauging (alternative approaches
take several sediment samples in each vertical, at 0.2d, 0.5d, 0.6d and 0.8d where samples are
subsequently combined). SW5-FM Chapter 2 discusses the various different sampling instruments
for suspended sediment and bed material, including instructions for use and precautions before,
during and after sampling, and advantages and limitations.
The two methods for determining the concentration in the cross-section are the equal discharge
increment (EDI) method where the cross-section is divided into unequally spaced segments of
equal discharge describing the cross-sectional variation in concentration, and the equal width
increment (EWI) method where the cross-section is divided into equally spaced segments.
Recommendations for the minimum number of verticals given by the Bureau of Indian Standards
are at least 3 vertical for rivers < 30 m in width, at least 5 vertical for rivers between 30 m and 300
m wide, and at least 7 verticals for rivers > 300 m in width. Sampling may take place by wading,
from a bridge, from a boat, or from a cableway. Per fraction, a flow-weighted average
concentration is computed in the field laboratory and entered in the field data sheet. If samples are
collected with only water level measurement, the corresponding flows are computed later using the
station rating curve. Suspended sediment samples are analysed in the water quality laboratory by
a gravimetric procedure.
Bed material samples are generally collected thrice in a water year: pre-monsoon, monsoon and
post-monsoon. Surface samples are collected using scoop-type, grab-type and dredge-type
devices and pumps. SW5-FM Chapter 2 discusses the various different sampling instruments for
suspended sediment and bed material, including instructions for use and precautions before,
during and after sampling, and advantages and limitations. Sub-surface samples are collected
using (piston) corers or from a pit in a dry bed. Bed-material samples are often collected in
conjunction with flow gauging and/or a set of suspended sediment samples. By taking these
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samples at the same stationing points, any change in bed material or radical change in flow across
the stream that would affect the sediment discharge computations can be accounted for by
subdividing the stream cross-section at one or between two of the common verticals. As samples
are obtained across the channel, they should be visually checked and compared with the previous
samples to see if the material varies considerably in size from one location to the next. Bed
material samples should be labelled properly for future identification, and to provide important
information useful in the laboratory analysis and the preparation of records. Bed material samples
are analysed in the water quality laboratory in two ways: for particle sizes > 0.6 mm by dry sieving,
and for particle sizes < 0.6 mm by siltometer.
Bed load samples are difficult to obtain, requiring extensive training in proper operation and
maintenance of bed load instruments and gauging strategies to get the most representative
samples and measurements, and are not covered further in this Handbook. See SW5-RM for
more information.
Observations of sediment should be daily for at least one or two years, until the stability of the site
(i.e. the relationship between suspended sediment concentration and flow, and an understanding
of the size and nature of the bed material and bed load) is established, after which sampling
frequency may be reduced during the non-monsoon season. However, in cases such as flash
flood rivers, irregular sediment input downstream from mines, landslide-prone areas , artificial
drainage systems, etc, sampling may be more times a day, up to hourly.
3.4.2 Water quality sampling and analysis
SW6-FM Figure 3.4 provides a sample identification form for surface water samples which records
all important information concerning the sample collected, including local conditions at the time of
sampling. GW6-FM Table 4.1 provides an equivalent sample identification form for groundwater
samples. The sample identification form should be given to the laboratory with the water sample.
Sampling location, sampling frequency and the required water quality parameters depends upon
the class and sub-category of the water quality monitoring network (Section 3.2.2).
For surface water in rivers and dams/lakes/reservoirs, grab samples are collected in allocated
containers from approximately 20 cm below the water surface, taking care not to catch any floating
material or bed material into the container. If the water is less than 40 cm deep, the sample should
be collected at half the actual water depth though, if possible, sampling from shallow waters (less
than 40 cm deep) should be prevented by moving, within the site, to a deeper part of the river. If
the sampling strategy prescribes a collection method other than grab sampling, samples should
subsequently be mixed to integrate them over time (samples taken at the same location at different
times) and space (samples taken at different depths of widths at the same time).
For groundwater, samples may be collected from:
• Open dug wells in use for domestic or irrigation water supply – a weighted sample bottle is
used to collect a sample from about 30 cm below the surface
• Tubewells or boreholes fitted with a hand pump or power-driven pump in use for domestic or
irrigation water supply – the well should be run for at least 5 minutes before collecting the
sample
• Piezometers purpose-built for water level recording and water quality monitoring – the well
should be purged for at least 10 minutes using a submersible pump (purged water volume
should be 4-5 times the standing water volume) before collecting the sample
When using a submersible pump, the sampler should be cleaned and rinsed, and should also be
briefly checked for functioning, closing of caps, if applicable, and condition of the cable by which
the submersible pump will be lowered inside the well. To take a representative sample, the
sampling procedure should: allow removal of stagnant water from the well (called purging) by
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means of a submersible pump so that the sampled water represents the water in the aquifer; avoid
degassing of the sample and volatilisation of components in it; prevent oxidation caused by contact
with the atmosphere; and avoid contamination of the sample and the well. Three types of
submersible pump are recommended: electric centrifugal pump (gears or rotor-assembly), piston
pump (gas-operated plunger) and bladder pump (gas-operated diaphragm).
The type of container and the number of containers needed for water quality samples depends on
the parameters specified for monitoring. SW/GW6-DM Table 7.1 gives the required type of
container, the suggested volume of sample and the recommend sample-pre-treatment for most
common parameters on page 54. The number of containers is high, because of the large number
of combinations of container material (PE, Glass, Teflon), container specifics (special containers
for DO, pesticides, coliforms) and pre-treatments (different acids to be added). Combining
containers risks cross-contamination of samples and is not recommended.
Preparation of equipment for water quality samples is usually carried out in the laboratory where
the samples are sent to for analysis. In general, bottles which are to be used for collecting
samples should be thoroughly washed and rinsed before use, by hand or by machine. Bottles to
be used for collecting microbiological samples should be thoroughly washed and sterilised before
use, in an autoclave or sterilising oven. Bottles to be used for the collection of pesticides should be
rinsed with organic solvent (e.g. hexane) before use. All sampling bottles should be checked to
see if the caps/seals close properly. Labels for bottles should be prepared or special pens for
labelling bottles used. Sample labels should include a sample code number, the name of the
sample collector, location, date and time of sampling, source and type of sample, and fixing or
preserving carried out on the sample, and any special notes for the analyst.
It is necessary to measure some water quality parameters in the field rather than in the laboratory
because these parameters are likely to change their value before they can be analysed in a
laboratory. On-site analysis should be carried out from the 1000 ml PE container used for
sampling the general parameters group. Contamination with suspended solids or chemicals
(calibration standards) should be avoided by pouring a part into a separate bowl or container:
• Colour is assessed in the file by the sample collector
• Odour is assessed in the file by the sample collector
• Temperature is measured in the field using a thermometer or a thermistor
• pH is measured in the field using either indicator paper (which changes colour depending upon
the pH of the water) or a purpose-built meter which is the preferred method as it is more
accurate than indicator papers
• Conductivity is measured in the field using a purpose-built conductivity meter
• Oxygen reduction potential (groundwater samples) is measured in the field using a purpose-
built meter
Rather than use separate meters for temperature, pH and conductivity, it is possible to purchase a
single instrument which will measure all three parameters, but may be more expensive than single
parameter meters.
Other samples should be chemically “fixed” to ensure that the water quality parameter
concentration determined in the laboratory is as near as possible to that which prevailed in the
water body. All necessary reagent solutions for fixing and/or preserving should be prepared in the
laboratory and brought to the field by the sample collector:
• DO (surface water samples) – the sample, collected using a DO sampler, should be chemically
“fixed” in the field. Then, the analytical determination may be carried out up to 8 hours later
with no loss of accuracy.
• Chemical oxygen demand (COD), ammoniacal nitrogen, total oxidised nitrogen and presence
of metals – samples should be preserved below pH 2 in the field. Then, the analytical
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determination may be carried out up to 6 months later with no loss of accuracy, though mercury
determinations should be carried out within 5 weeks.
The maximum permissible storage times for water quality parameters are given in SW/GW7-OM
Table 3.2.
Water quality samples returning to the laboratory for analysis should be preserved, transported and
stored correctly to preserve the characteristics of the by reducing the reaction rate of all bio-
chemical reactions taking place in the sample and, thus, slowing down undesired changes in the
quality of the sample to ensure the correct analysis results. All water quality samples should be
stored at a temperature below 4o
C and in the dark as soon as possible after sampling. In the field,
this usually means placing samples in an insulated cool box together with ice or cold packs. In the
laboratory, this means transferring samples to a refrigerator as soon as possible.
SW/GW7-OM presents the relevant procedures for sampling and laboratory analysis of common
water quality parameters, some of which are only relevant for either surface water or groundwater.
The procedures are based on “Standard Methods for the Examination of Water and Wastewater”
(www.standardmethods.org) which should be consulted for the most up-to-date information and
techniques. For each parameter, the information includes the full name, the abbreviated name, the
full name, version number and unique identification code for the analysis technique, the apparatus
and reagents required for the analysis, the analytical procedure and calculation, any notes, and
how the results should be reported. To avoid ambiguity in reporting results or in presenting
directions for a procedure, it is the customary to use significant figures only. Results should be
transferred from the personal laboratory journal of the analyst performing the analysis to the data
record and validation register (SW/GW7-OM Figure 6.1), from where they are entered onto the
database through the e-SWIS or e-GEMS (Section 5.1).
3.5 Data processing
SW8-OM(IV) Chapter 2 sets out the steps in data processing, which starts with receipt, preliminary
checking, analysis, data entry and primary validation in the laboratory, through successively higher
levels of validation in State and Central DPCs, before data are fully validated and approved in the
National database. Validation ensures that the data stored are as complete and of the highest
quality as possible by: identifying errors and sources of errors to mitigate them occurring again,
correcting errors where possible, and assessing the reliability of data.
Data validation is very much a two-way process, which feeds back any comments or queries
relating to the data provided. The diverse hydrological environments found in India mean that staff
conducting data validation should be familiar with the expected climate, flow patterns of individual
rivers and behaviour of aquifers, in order to identify potentially anomalous behaviour in sediment
and water quality data. The data processing steps comprise:
1. Receipt and analysis of data according to prescribed target dates. Rapid and reliable transfer
of samples is essential, using the optimal method based on factors such as volume, frequency,
speed of transfer/transmission and cost. Maintenance of a strict time schedule is important
because it ensures the quality of the analysis results, it gives timely feedback to field staff, it
encourages regular exchanges between field staff, Sub-Divisional offices, State and Central
agencies, it creates continuity of processing activities at different offices, and it ensures timely
availability of final (approved) data for use in policy and decision-making.
2. Entry of data to computer, using the e-SWIS or e-GEMS software, is primarily done by the
laboratories who have analysed the samples. Historical data, previously only available in
hardcopy form, may also be entered this way by laboratories or DPCs. Each Central/State
agency should have a programme of historical data entry.
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3. Data validation – Primary validation should be carried out by the laboratories within one week
of data entry, again using e-SWIS or e-GEMS. This ensures that any obvious problems (e.g.
indicating an instrument malfunction, observer error, etc) are spotted at the earliest opportunity
and resolved. Other problems may not become apparent until more data have been collected,
and data can be viewed in a longer temporal context during secondary validation which should
be carried out in State DPCs for State data and CWC/CGWB/CPCB local offices for Central
data. States should have access to Central data during secondary validation, and may receive
support from Central agencies in this activity.
4. Data storage. The e-SWIS and e-GEMS HIS databases, of both approved data and
unapproved data undergoing primary and secondary validation, are backed up automatically.
Therefore, there is no need to make regular back-ups, unless any data are stored outside the
HIS database, for instance in Excel files or other formats awaiting data entry, or in stand-alone
real-time databases – such files should be securely backed up, ideally onto an external back-
up device and/or backed up network server, so that there is no risk of data loss. All PCs
should have up-to-date anti-virus software.
Raw field data and laboratory notebooks and files should also be stored in a secure manner
after database entry to ensure that original field/laboratory data remain available should any
problems be identified during validation and analysis. Such hardcopy data should ultimately be
securely archived, in the State DPC for State data or local office of Central agencies for Central
data, possibly by scanning documents and storing them digitally.
5. Interagency data validation by IMD – IMD should aim to validate at least 20% of current and
historic data from State hydro-meteorological monitoring stations every year, on a rolling
programme, so that IMD has independently validated the data from every State gauge at least
once every 5 years. Interagency validation is a 2-way process and IMD should discuss any
identified issues and agree final datasets with State DPCs through a 2-way consultative
process, to build capacity for data validation within the States.
For sediment and water quality data, Sections 4 and 5 of this Handbook, respectively, cover the
process from data entry through primary and secondary validation, to compilation and analysis of
data.
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4. Sediment Data Processing and Analysis
4.1 Data entry
4.1.1 Overview
Entry of sediment data to computer is primarily done in the laboratory where the sediment samples
are analysed. Data entry is carried out using e-SWIS, the data entry module of which replicates
the SWDES software from HPI, and is referred to as eSWDES. Prior to analysis, two manual
activities are essential: registration of receipt of the samples from the field, and manual inspection
of the forms and notebooks from the field, for complete information and obvious errors. Analysis of
sediment samples should be completed within the allowed time period, and data entry (see Table
3.1) of analysis results should be done on the same day as the analysis, ready for primary
validation at Sub-Divisional level.
4.1.2 Entry of suspended sediment data
Using the eSWDES module in e-SWIS, the user selects the correct station and suspended
sediment series. The screen for entry of suspended sediment data is displayed. The sediment
data is entered through a tabular form, with one row for each reading and column fields as follows:
• Column 1: Day of sampling
• Column 2: Time of sampling
• Column 3: Observation number within day
• Column 4: Water level
• Column 5: not used
• Column 6: Flow
• Column 7: Whether flow is observed (from gauging) or calculated (from rating curve)
• Column 8: Concentration of sediment in coarse fraction (>0.2 mm)
• Column 9: Concentration of sediment in medium fraction (0.075-0.2 mm)
• Column 10: Total coarse-medium (sand-silt) fraction (sum of columns 8 and 9) – automatically
calculated by software
• Column 11: Concentration of sediment in fine fraction (<0.075 mm)
• Column 12: Total suspended sediment concentration (sum of columns 8, 9 and 11) –
automatically calculated by software
One data entry check is performed immediately. The total suspended sediment concentration
calculated by the software is compared with that on the input data document. A difference may
mean that in one or more of the concentrations of a particular day, typing errors have been made.
It may also be due to an incorrect calculation in the laboratory. In the latter case, feedback has to
be given to the field/laboratory, as it may indicate a transcription error in the field/laboratory
documents.
To further verify the data, graphs should be plotted of the various suspended sediment
concentrations against flow. Though a log-log scale is often suitable for analysis of sediment data,
for data entry checking, a linear scale for the concentration with a linear or logarithmic scale for the
flow is more appropriate as particular attention should be given to zero concentration entries:
• Coarse fraction against flow
• Medium fraction against flow
• Fine fraction against flow
• Coarse-medium (sand-silt) fraction against flow
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• Total suspended sediment concentration against flow
The graphs show that suspended sediment concentration of whatever fraction shows a wide
variation with flow. Hence, it is important that the flow data are properly validated. These graphs
are examined further during primary validation.
4.1.3 Entry of bed material data
Using the eSWDES module in e-SWIS, the user selects the correct station and bed material series.
The screen for entry of bed material data is displayed. The user enters:
• Day of sampling
• Location of sampling i.e. location of cross-section and location in cross-section with respect to
river bank
• Type of sampler – this is needed for further interpretation of the results
• Results of sieve analysis for grain sizes >0.6 mm – the total weight of the sample Ws used, the
weight of the quantity Wi retained by each sieve of aperture I of a total N sieves, and the weight
of quantity Wm passing through the sieve with aperture 0.6 mm, where:
• Results of siltometer analysis for grain sizes <0.6 mm – the temperature of the water used in
the tube of the 20--pocket siltometer, the weight of the sediment added to the siltometer (i.e.
from the Wm g of bed material passing through the 0.6 mm sieve, a quantity Wa of 10 g is
taken for the siltometer), and the dry weight of sediments caught in each of the 20 pockets,
summing up to Wp (the difference Wa – Wp is the loss i.e. a quantity having a size less than
the lowest size measureable with a siltometer)
One data entry check is performed immediately. The loss calculated by the software is compared
with that on the input data document. Next, to bring the results back in line with the results of the
sieve analysis, all weights from the siltometer analysis are multiplied Wm/Wa, and the quantity of
each fraction as a percentage of the total and a cumulative percentage finer (starting off from the
largest particle size in the sample and putting it at 100%). Finally, the software determines the
characteristic particle sizes D10, D16, D35, D50, Dm (i.e. mean diameter), Dg (i.e. geometric mean
diameter), D65, D84, D90 and σg (i.e. geometric standard deviation), where:
Where: Di = arithmetic mean diameter of particles in class i
Pi = percentage of mass of the sample in class i
To further verify the data, graphs should be plotted at each stage.
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4.2 Primary validation
4.2.1 Overview
Primary validation of sediment data is primarily done at a Sub-Divisional office level where staff are
in close contact to field staff who have collected the samples and laboratory staff who have
analysed the samples. Primary validation is carried out using e-SWIS, the data entry module of
which replicates the SWDES software from HPI, and is referred to as eSWDES. Prior to primary
validation, the forms and notebooks from the field should be again inspected for complete
information and obvious errors. Primary validation (see Table 3.1) of analysis results is required to
be completed at Sub-Divisional office level within one week of data entry at the laboratory, ready
for secondary validation by State offices. This time schedule ensures that any obvious problems
(e.g. indicating an instrument malfunction, observer error, etc) are spotted at the earliest
opportunity and resolved. Other problems may not become apparent until more data have been
collected, and data can be viewed in a longer-term context during secondary validation.
4.2.2 Primary validation of suspended sediment data
Primary validation of suspended sediment data comprises:
• Inspection of inconsistency in zero concentration entries - particularly for the coarse and
medium fractions, zero concentrations will occur annually a number of times when the flow
velocities are very low. However, if the zero entries also occur when the flow velocities are
significant either the field/laboratory entry or data entry was incorrect. Zeros should only be
observed below a certain threshold value, as may be observed from a semi-log plot of
concentration versus flow for individual months of the year and for the whole year.
• Investigation of any anomaly in the concentration versus flow plots – the plots of the coarse,
medium and fine sediment concentrations against flow, and also the sand-silt fraction and the
total suspended sediment concentration against flow (Section 4.1.2) should be examined for
anomalies i.e. outliers. In view of the high scatter of these graphs, only extreme outliers may
be readily detected. Outliers should be marked and checked against the field/laboratory
documents. If clearly erroneous, the entry should be eliminated from the data set. However,
should be done with some care as the concentration of fine sediment, in particular, may vary
with the season.
4.2.3 Primary validation of bed material data
Primary validation of bed material data comprises:
• The weight of the total sample as measured should comply with the sum of the weight of the
various fractions retained by the sieves and passing the 0.60 mm sieve, as computed by the
software
• The weight of the sample passing the 0.60 mm sieve as entered in the input data document for
the siltometer analysis should be verified
• The totals as shown in the final result with their actual measured values should be verified
• Results of samples for the same cross-section at different distances from the bank should be
compared. Where the flow velocities are highest, the particle sizes will be highest, and this
should be reflected in the characteristic particle sizes. Note that in a river bend a clear
difference should be visible between the grain sizes along the outer bend (coarser) and the
inner bend (finer)
• Results of samples for the same location in the cross-section for different moments of time
should be compared. Samples taken during the monsoon are likely to be coarser than
thereafter, whereas the finest samples should be found before the onset of the monsoon, as in
the lean season many fine sediments may have settled in almost stagnant water.
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4.3 Secondary validation
4.3.1 Overview
Secondary validation of sediment data is primarily carried out at State DPCs using e-SWIS, the
validation module of which replicates the HYMOS software from HPI, and is referred to as
eHYMOS. Data may also be exported to Excel for secondary validation. For the Hydrology
Project, initial secondary validation (see Table 3.1) done at State level should be completed within
one month of data entry. Some secondary validation (including comparison with CWC/CPCB data)
will not be possible until the end of the hydrological year when the entire year’s data can be
reviewed in a long-term context, so data should be regarded as provisional approved data until
then (e.g. for June data by the end of the hydrological year plus 3 months), after which data should
be formally approved and made available for dissemination to external users. Secondary
validation, and the remainder of this section, applies only to suspended sediment data.
4.3.2 Secondary validation of suspended sediment data
The secondary validation of suspended sediment data includes comparisons of a single fraction for
various seasons or months within a year and between years, and comparisons for multiple
fractions within a year and between years, to detect anomalies:
• Single fraction, multiple seasons – for coarse and medium sized fractions there should be a
distinct relationship with the flow velocity and, hence, with the flow in the river, which should not
differ much from season to season, unless bank erosion or river bed mining takes place just
upstream of the sampling location. For the fine fraction, a seasonal dependency is expected,
in a manner that the concentrations will be highest at the start of the wet season, due to the
supply from the basin: when the rains start, the concentration of the fine fraction will be high as
the first rains bring a lot of sediment from the basin into the river and their concentration is fairly
independent of the flow velocity, but at the end of the rainy period (end of monsoon) the
concentration of the fine fraction is generally much less as supply has been utilised. Plots of
suspended sediment concentration against flow should use different symbols for each month.
For the coarse fraction, both linear and semi-log plots should be used as there should be a
distinct flow range for zero concentrations irrespective of the season best observed on the
semi-log plot. For the medium fraction, semi-log plots should be used, and for the fine fraction
log-log plots.
• Multiple fractions, single year – for coarse and medium-sized fractions, the concentration as
a function of flow should show a similar pattern, with the observation that the threshold flow
value for non-zero concentrations is less for the medium fraction. Long-log plots should be
used, with different symbols for different fractions. When the combined sand-silt fraction and
the fine fraction are plotted against flow, it should be possible to determine the relative
importance of the various fractions in the total load, which is important for later use, when the
concentrations have to be transformed into sediment loads. If the concentration of the fine
fraction is much smaller than of the sand-silt fraction, then one sediment-discharge
relationship, valid for the whole hydrological year, will be sufficient to derive the sediment
loads. However, if the concentration of the fine fraction constitutes an important part of the
sediment concentration, separate curves for the sand-silt fraction and the fine fraction will be
required as no single relationship between the concentration of the fine fraction and the flow in
a single year will exist.
• Single/multiple fractions, multiple years - for coarse and medium-sized fractions, the
concentration as a function of flow should show a similar pattern from year to year, although
considerable deviations may occur at times, possibly at particular flow ranges. Long-log plots
should be used, with different symbols for different years. Comparison of concentrations of the
fine fractions from year to year will show a variable picture, due to the changing supply from the
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basin. When making comparisons for successive years, the same seasons should be
compared. A comparison of total suspended sediment concentrations for successive years
makes sense when the concentration of the fine fraction is small compared to those of the
coarse and medium fractions, where a pattern similar to the sand-silt fraction will occur.
4.4 Compilation and analysis
4.4.1 Overview
Compilation of sediment data (see Table 3.1) refers to the process to develop a suspended
sediment load-discharge relationship which may be used to derive a suspended sediment load
time series from a flow time series.
4.4.2 Computation of suspended sediment load time series
The 8-step process to derive suspended sediment load S is:
• Compute the flow time series Q
• Plot concentration versus flow for the 3 distinguished fractions, with different symbols and
colours for selected periods
• Identify clear outliers and eliminate wrong entries
• Repeat the concentration-flow plot for multiple fractions
• Transform the concentrations into loads and determine for which fractions or aggregation of
fractions an S-Q relationship can be established
• Fit a power type curve to the S-Q plot (e.g. Figure 4.1):
Where: as, b = coefficients
The equation is identical to the single power law stage-discharge relationship and the same
procedure may be applied (XXX Handbook, Section XXX)
• If a considerable amount of wash load (fine material in suspended sediment load controlled by
rate at which material becomes available in basin) is available, add a time variable load to the
S-Q relationship derived from fitted relationships for short periods of time
• Create an S(t) time series using the S-Q relationships and the Q(t) time series
Figure 4.1 Example of S-Q relationship fitted to total suspended sediment load data
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If more sediment sampling stations are available on the same river, a sediment balance should be
made for river stretches to estimate the sedimentation or erosion rate in the reach. Such
information may be compared with data on bed levels for the reach for the balance period, when
available.
If the river is entering a reservoir, which is regularly surveyed, a comparison should be made with
the sedimentation rate in the reservoir. For this a percentage has to be added to the suspended
load to account for bed load transport. The match will further be dependent on the trap efficiency
of the reservoir.
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5. Water Quality Data Processing and Analysis
5.1 Data entry
5.1.1 Overview
Entry of water quality data to computer is primarily done in the laboratory where the water samples
are analysed, though some parameters may have been measured in the field. For surface water
data, data entry is carried out using e-SWIS, the data entry module of which replicates the SWDES
software from HPI, and is referred to as eSWDES. For groundwater data, data entry is carried out
using e-GEMS which has GWDES water quality functionality. Prior to analysis, two manual
activities are essential: registration of receipt of the samples from the field or laboratory, and
manual inspection of the forms and notebooks from the field, for complete information and obvious
errors. Analysis of water quality samples should be completed within the allowed time period, and
data entry (see Table 3.1) of analysis results should be done on the same day as the analysis,
ready for primary validation also by the laboratory.
5.1.2 Entry of sample reference information
Using the appropriate screen in e-SWIS or e-GEMS, the user enters information about when,
where and how the water sample was collected and analysed. The sample reference information
is entered for every water quality sample, and specifies:
• Location (i.e. station code for flow gauging station, observation well or other sampling site)
• Date/time information for the sampling
• Laboratory conducting analysis and laboratory sample ID
• Agency collecting the sample from the field and delivering it to the laboratory
• Source of water sample (e.g. river, lake, rain, groundwater, etc)
• Medium (e.g. water, suspended matter, solid, etc) and matrix (e.g. fresh water, brackish, salt,
effluent, etc) of water sample
• Type of water sample (e.g. grab sample, time-composite, flow-composite, depth-integrated,
etc)
• Depth at which the water sample was collected
• Optional project or programme name, and type of monitoring (e.g. baseline, trend, flux,
surveillance, survey, etc)
• Name of person collecting the sample in the field
• Conditions of water and weather before and during the sampling which might influence the
sample
• From sample specification form, the type of sample container (e.g. PE, Glass, Teflon, etc),
sample specification (i.e. type of analysis to be performed), volume of sample, sampling device
(e.g. bottle, depth sampler, DO sampler, etc), preservation in field and treatment at laboratory.
The user is prompted should any of the entered data not be in the correct alphanumeric format or
expected range. Each water quality sample is assigned a unique reference number.
Many fields in the sample reference information will not change frequently, especially for those
samples collected for routine monitoring. Therefore it is possible to save information about a
specific sampling location as a template, which can be loaded when a new sample is created.
Alternatively, many fields have a default value that is entered automatically when a new sample is
created, and that the user may edit manually.