This document is a reference manual on hydro-meteorology that describes a pilot study conducted to design rain gauge networks for two sub-basins in India. The study involved collecting 26 years of monthly rainfall data from existing stations, validating the data, performing statistical analysis on the data, and assessing spatial correlation between stations to determine optimal network design. Key steps included grouping stations into climatically homogeneous areas, computing basic statistics such as mean rainfall and variability for each area, and calculating spatial correlation coefficients between station pairs to establish relationships between correlation and distance between stations. The goal of the network design was to estimate mean areal rainfall with less than 10% error on average for each area on both monthly and annual timescales.
Determination of homogenous regions in the Tensift basin (Morocco).IJERA Editor
The aim of this study is to determine homogenous region in the Tensift basin within which the hydrological behavior is similar. In order to do this we used two methods: The Principal components analysis on the monthly precipitation registered at the 23 rainfall stations. This resulted in setting apart 4 groups of stations. The second method is analysis of land use map, geological map, pedagogical map, vegetation map and slope map of the studied area. This method allowed us to delineate 4 homogenous areas. The two methods yielded complementary results and the superposition of groups and regions obtained allowed us to retain 4 homogenous regions corresponding to 3 groups of stations.
Derivation Of Intensity Duration Frequency Curves Using Short Duration Rainfa...Mohammed Badiuddin Parvez
The estimation of rainfall intensity is commonly required for the design of hydraulic and water resources engineering control structures. The intensity-duration-frequency (IDF) relationship is a mathematical relationship between the rainfall intensity, the duration and the return period. The present study aimed the derivation of IDF curves of Yermarus Raingauge Station of Raichur District with 19 years of rainfall data (1998 to 2016). The Normal Distribution, Log Normal Distribution, Gumbel distribution, Pearson Type III Distribution and Log Pearsons Type III Distribution techniques are used to Find the rainfall intensity values of 2, 5, 10, 15, 30, 60, 120, 720, 1440 minutes of rainfall duration with different return period. Chi Square test was conducted to find the goodness of fit the short duration IDF using daily rainfall data are presented, which is input for water resources projects.
Intensity-Duration-Frequency Curves and RegionalisationAM Publications
Storm sewers make up a large percentage of drainage system in an urban setup. The design of these
components are based on rainfall intensities of a specific design period for that location. These can be derived from
intensity-duration-frequency (IDF) relationship. These IDF relationships are derived from historical rainfall, using
an extreme value distribution for maximum rainfall intensity. In the present study the IDF curves and parameter
regionalisation were studied for various kinds of basins. These equation parameters can be then used to understand
the spatial variation of rainfall intensity in the study area. The parameter contour maps subsequently generated using
various interpolation method are then used for plotting IDF curves for any ungauged station in the basin.
Analysis of river flow data to develop stage discharge relationshipeSAT Journals
Abstract For investigation and design of river valley projects, assessment of the water resources potential of river basins and peak discharge is to be made. For these, the collection of daily discharge data is necessary. But direct measurement of daily discharge in a number of points in all streams is not only prohibitive in cost, but also very much time consuming, which can be best achieved by using stage-discharge relationship. In the present work, field data of three gauging sites, i.e. Theni (Basin: Mahanadi to Kanyakumari), Pingalwada (Basin: Mahi, Sabarmati and others) and Ghatsila (Basin: Subarnarekha, Burhabalang & Baitarni) were analysed to develop the steady-state stage-discharge relationship. Stage and Discharge data with the time of the gauging stations were available. Erroneous values were identified by comparative examination of the stage-hydrograph and discharge-hydrograph plotted one above the other. These values were rectified before developing stage-discharge equations. Keywords: Basins, Discharge, Gauge, Hydrograph, Rating curve and Stage.
Analysis of river flow data to develop stage discharge relationshipeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Determination of homogenous regions in the Tensift basin (Morocco).IJERA Editor
The aim of this study is to determine homogenous region in the Tensift basin within which the hydrological behavior is similar. In order to do this we used two methods: The Principal components analysis on the monthly precipitation registered at the 23 rainfall stations. This resulted in setting apart 4 groups of stations. The second method is analysis of land use map, geological map, pedagogical map, vegetation map and slope map of the studied area. This method allowed us to delineate 4 homogenous areas. The two methods yielded complementary results and the superposition of groups and regions obtained allowed us to retain 4 homogenous regions corresponding to 3 groups of stations.
Derivation Of Intensity Duration Frequency Curves Using Short Duration Rainfa...Mohammed Badiuddin Parvez
The estimation of rainfall intensity is commonly required for the design of hydraulic and water resources engineering control structures. The intensity-duration-frequency (IDF) relationship is a mathematical relationship between the rainfall intensity, the duration and the return period. The present study aimed the derivation of IDF curves of Yermarus Raingauge Station of Raichur District with 19 years of rainfall data (1998 to 2016). The Normal Distribution, Log Normal Distribution, Gumbel distribution, Pearson Type III Distribution and Log Pearsons Type III Distribution techniques are used to Find the rainfall intensity values of 2, 5, 10, 15, 30, 60, 120, 720, 1440 minutes of rainfall duration with different return period. Chi Square test was conducted to find the goodness of fit the short duration IDF using daily rainfall data are presented, which is input for water resources projects.
Intensity-Duration-Frequency Curves and RegionalisationAM Publications
Storm sewers make up a large percentage of drainage system in an urban setup. The design of these
components are based on rainfall intensities of a specific design period for that location. These can be derived from
intensity-duration-frequency (IDF) relationship. These IDF relationships are derived from historical rainfall, using
an extreme value distribution for maximum rainfall intensity. In the present study the IDF curves and parameter
regionalisation were studied for various kinds of basins. These equation parameters can be then used to understand
the spatial variation of rainfall intensity in the study area. The parameter contour maps subsequently generated using
various interpolation method are then used for plotting IDF curves for any ungauged station in the basin.
Analysis of river flow data to develop stage discharge relationshipeSAT Journals
Abstract For investigation and design of river valley projects, assessment of the water resources potential of river basins and peak discharge is to be made. For these, the collection of daily discharge data is necessary. But direct measurement of daily discharge in a number of points in all streams is not only prohibitive in cost, but also very much time consuming, which can be best achieved by using stage-discharge relationship. In the present work, field data of three gauging sites, i.e. Theni (Basin: Mahanadi to Kanyakumari), Pingalwada (Basin: Mahi, Sabarmati and others) and Ghatsila (Basin: Subarnarekha, Burhabalang & Baitarni) were analysed to develop the steady-state stage-discharge relationship. Stage and Discharge data with the time of the gauging stations were available. Erroneous values were identified by comparative examination of the stage-hydrograph and discharge-hydrograph plotted one above the other. These values were rectified before developing stage-discharge equations. Keywords: Basins, Discharge, Gauge, Hydrograph, Rating curve and Stage.
Analysis of river flow data to develop stage discharge relationshipeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Application of mathematical modelling in rainfall forcast a csae study in...eSAT Journals
Abstract Malaysia receives rainfall from 2000 mm to 4000 mm annually where it is greatly influenced by two monsoon periods in November to March and May to September. The state of Sarawak is well known for its long and wide rivers. Numerous activities such as commercial, industrial and residential can always be found in the vicinity of the rivers. The activities have started since decades ago and still continue to grow and spatially expanding through times providing incomes ranging from small farmers to the largest corporations. Unfortunately, these areas are expected to experience frequent flood events as well as possible receding water level in rivers based on the findings of previous studies. If the projections are accurate, the productivity of these activities will be reduced, hence, in a longer term may affect the economy of the state as whole as well. Therefore, there is an urgent need for existing knowledge on rainfall behavior to be revised as effects of climate change with the intention that the state can fully utilize the favorable conditions and make scientific based decisions in the future. Recent study reveals that the Fourier series (FS), has the ability to simulate long-term rainfall up to 300 years is viewed as an important finding in the study of rainfall forecast. Long-term rainfall forecasting is viewed to be beneficial to the state of Sarawak in its future planning in various sectors such as water supply, flood mitigation, river transportation as well as agriculture. The main goal of the study is to apply a mathematical modeling in rainfall forecasting for the Sungai Sarawak basin. Data from eight rain gauge stations was analyzed and prepared for missing data, consistency check and adequacy of number of stations. Simple statistical analysis was conducted on the data such as maximum, minimum, mean and standard deviation. 27 years of annual rainfall data were simulated with the Fourier Series equation using spreadsheet. Hence, the result was compared with the Fitting N-term Harmonic Series. The model result reveals that the Fourier Series has the ability to simulate the observed data by being able to describe the rainfall pattern and there is a reasonable relationship between the simulation and observed data with p-value of 0.93. Keywords: Fourier series, Mathematical
Delineation of Mahanadi River Basin by Using GIS and ArcSWATinventionjournals
Precipitation is the significant segment of hydrologic cycle and this essential wellspring of overflow. In hydrological models precipitation as information, release is mimicked at the outlet of a watershed. Exactness of release re-enactment relies on drainage zone of the watershed. Therefore in the present work Mahanadi river basin lying within Odisha (drainage area approximately 65000 sq. km.) has been delineated in to five subbasins based on the five CWC operated discharge sites in Odisha. In the present work Arc-Swat has been used to delineate the watershed with the help of the (digital elevation model) DEM. At last as indicated by area of release locales, the aggregate study range was isolated into five sub-basins in particular Kesinga, Kantamal, Salebhata, Sundergarh and Tikarpada. It was observed that number of sub-watersheds into which the study area is being depicted relies on number of outlets and density of drainage. For a specific number of outlets, the thick is the density of drainage the more is the quantity of sub-watershed and the other way around.
Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...Dr Ramesh Dikpal
Rainfall studies are of utmost utility for understanding nature & hence the behaviour of climate changes. Time series is a set of observations taken at specified times usually at equal interval. The inherent variability displayed by many hydrological time series usually mask trends and periodic patterns. This situation has often led to “something” the original time series so that the effects of random variations are reduced and trends or cyclical patterns enhanced. Thus a set of data depending on time is called a Time series. Here, Rainfall series represent the time series. The time series analysis is helpful to compare the actual performance and analyse the cause of variations. By comparing different time series we can draw important conclusion. Graphical method implies in increasing trend for pre-monsoon, south-west monsoon, north-east monsoon and annually.Geo- informatics module consists of GIS mapping for Location map, Geomorphology map and Season wise Rainfall maps are generated. Autocorrelation indicates the periodicity observed as 37,16 & 6 years (PM), 12, 37 & 16 years (SWM), 8, 18 & 6 years (NEM) and 16, 22 & 8 years (Annual) respectively. Power spectral depicts the cyclicity of 37, 4 & 3 years (PM), 2, 4& 2 years (SWM), 3, 7 & 2 years (NEM) and 2, 4 & 2 years (Annual) respectively. Moving average displays prominent positive correlation coefficients at lags of 18 to 42 years in PM & SWM and 12 to 24 years in NEM & Annual. The southwest and southeast parts of the study area experience the heavy rainfall whereas the least rainfall areas are the northern parts of the study area.The short term and long term cyclicity observed in Autocorrelation, power spectrum and Moving Average. Spatial variation of rainfall for the three seasons and annual has been studied
An Attempt To Use Interpolation to Predict Rainfall Intensities tor Crash Ana...IJMERJOURNAL
ABSTRACT: This study uses different interpolation techniques to predict rainfall intensity at locationsthat are not directly located near a rainfall gauges. The goal of being able to interpolate the rainfall intensity is to study its impact on traffic crashes. To perform the study, a collection of rainfall gauges in Alabama were used as subject locations where rainfall intensity was predicted from surrounding gauges, while also providing validation data to compare the predictions. Essentially, the actual rainfall intensities at existing gauges were interpolated using nearby gauges and the results were analyzed.The interpolation techniques used in the study included proximal, averaging and a distance weighted average. The results of the study indicated that none of the interpolation methodologies were sufficient to accurately predict the rainfall intensity values any significant distance from the actual gauges.
On March 11, 2016, ICLR held a Friday Forum workshop entitled 'Mapping extreme rainfall statistics for Canada', led by Dr. Slobodan Simonovic of Western University.
Climate change is expected to increase the frequency and intensity of extreme rainfall events, affecting rainfall intensity-duration-frequency (IDF) curve information used in the design, maintenance and operation of water infrastructure in Canada. Presented in this lecture are analyses of precipitation data from 567 Environment Canada hydro-meteorological stations using the IDF_CC tool. Results for the year 2100 based on Canadian climate model and an ensemble of 22 GCMs have been generated. A spatial interpolation method was used to produce Canadian precipitation maps for events of various return periods. Results based on the Canadian climate model indicate a reduction in extreme precipitation in central regions of Canada and increases in other regions. Relative to the ensemble approach, the Canadian climate model results (a) suggest more spatial variability in change of IDFs, and (b) the ensemble approach generated generally lower values than the Canadian climate model.
Dr. Simonovic has extensive research, teaching and consulting experience in water resources systems engineering. He teaches courses in water resources and civil engineering systems. He actively works for national and international professional organizations. Dr. Simonovic’s primary research interest focuses on the application of systems approach to management of complex water and environmental systems. Most of his work is related to the integration of risk, reliability, and uncertainty in hydrology and water resources management. He has received a number of awards for excellence in teaching, research and outreach. He has published over 450 professional publications and three major textbooks. He was inducted to the Canadian Academy of Engineering in June of 2013.
A comparative study of different imputation methods for daily rainfall data i...journalBEEI
Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.
Fitting Probability Distribution Functions To Discharge Variability Of Kaduna...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Application of mathematical modelling in rainfall forcast a csae study in...eSAT Journals
Abstract Malaysia receives rainfall from 2000 mm to 4000 mm annually where it is greatly influenced by two monsoon periods in November to March and May to September. The state of Sarawak is well known for its long and wide rivers. Numerous activities such as commercial, industrial and residential can always be found in the vicinity of the rivers. The activities have started since decades ago and still continue to grow and spatially expanding through times providing incomes ranging from small farmers to the largest corporations. Unfortunately, these areas are expected to experience frequent flood events as well as possible receding water level in rivers based on the findings of previous studies. If the projections are accurate, the productivity of these activities will be reduced, hence, in a longer term may affect the economy of the state as whole as well. Therefore, there is an urgent need for existing knowledge on rainfall behavior to be revised as effects of climate change with the intention that the state can fully utilize the favorable conditions and make scientific based decisions in the future. Recent study reveals that the Fourier series (FS), has the ability to simulate long-term rainfall up to 300 years is viewed as an important finding in the study of rainfall forecast. Long-term rainfall forecasting is viewed to be beneficial to the state of Sarawak in its future planning in various sectors such as water supply, flood mitigation, river transportation as well as agriculture. The main goal of the study is to apply a mathematical modeling in rainfall forecasting for the Sungai Sarawak basin. Data from eight rain gauge stations was analyzed and prepared for missing data, consistency check and adequacy of number of stations. Simple statistical analysis was conducted on the data such as maximum, minimum, mean and standard deviation. 27 years of annual rainfall data were simulated with the Fourier Series equation using spreadsheet. Hence, the result was compared with the Fitting N-term Harmonic Series. The model result reveals that the Fourier Series has the ability to simulate the observed data by being able to describe the rainfall pattern and there is a reasonable relationship between the simulation and observed data with p-value of 0.93. Keywords: Fourier series, Mathematical
Delineation of Mahanadi River Basin by Using GIS and ArcSWATinventionjournals
Precipitation is the significant segment of hydrologic cycle and this essential wellspring of overflow. In hydrological models precipitation as information, release is mimicked at the outlet of a watershed. Exactness of release re-enactment relies on drainage zone of the watershed. Therefore in the present work Mahanadi river basin lying within Odisha (drainage area approximately 65000 sq. km.) has been delineated in to five subbasins based on the five CWC operated discharge sites in Odisha. In the present work Arc-Swat has been used to delineate the watershed with the help of the (digital elevation model) DEM. At last as indicated by area of release locales, the aggregate study range was isolated into five sub-basins in particular Kesinga, Kantamal, Salebhata, Sundergarh and Tikarpada. It was observed that number of sub-watersheds into which the study area is being depicted relies on number of outlets and density of drainage. For a specific number of outlets, the thick is the density of drainage the more is the quantity of sub-watershed and the other way around.
Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using...Dr Ramesh Dikpal
Rainfall studies are of utmost utility for understanding nature & hence the behaviour of climate changes. Time series is a set of observations taken at specified times usually at equal interval. The inherent variability displayed by many hydrological time series usually mask trends and periodic patterns. This situation has often led to “something” the original time series so that the effects of random variations are reduced and trends or cyclical patterns enhanced. Thus a set of data depending on time is called a Time series. Here, Rainfall series represent the time series. The time series analysis is helpful to compare the actual performance and analyse the cause of variations. By comparing different time series we can draw important conclusion. Graphical method implies in increasing trend for pre-monsoon, south-west monsoon, north-east monsoon and annually.Geo- informatics module consists of GIS mapping for Location map, Geomorphology map and Season wise Rainfall maps are generated. Autocorrelation indicates the periodicity observed as 37,16 & 6 years (PM), 12, 37 & 16 years (SWM), 8, 18 & 6 years (NEM) and 16, 22 & 8 years (Annual) respectively. Power spectral depicts the cyclicity of 37, 4 & 3 years (PM), 2, 4& 2 years (SWM), 3, 7 & 2 years (NEM) and 2, 4 & 2 years (Annual) respectively. Moving average displays prominent positive correlation coefficients at lags of 18 to 42 years in PM & SWM and 12 to 24 years in NEM & Annual. The southwest and southeast parts of the study area experience the heavy rainfall whereas the least rainfall areas are the northern parts of the study area.The short term and long term cyclicity observed in Autocorrelation, power spectrum and Moving Average. Spatial variation of rainfall for the three seasons and annual has been studied
An Attempt To Use Interpolation to Predict Rainfall Intensities tor Crash Ana...IJMERJOURNAL
ABSTRACT: This study uses different interpolation techniques to predict rainfall intensity at locationsthat are not directly located near a rainfall gauges. The goal of being able to interpolate the rainfall intensity is to study its impact on traffic crashes. To perform the study, a collection of rainfall gauges in Alabama were used as subject locations where rainfall intensity was predicted from surrounding gauges, while also providing validation data to compare the predictions. Essentially, the actual rainfall intensities at existing gauges were interpolated using nearby gauges and the results were analyzed.The interpolation techniques used in the study included proximal, averaging and a distance weighted average. The results of the study indicated that none of the interpolation methodologies were sufficient to accurately predict the rainfall intensity values any significant distance from the actual gauges.
On March 11, 2016, ICLR held a Friday Forum workshop entitled 'Mapping extreme rainfall statistics for Canada', led by Dr. Slobodan Simonovic of Western University.
Climate change is expected to increase the frequency and intensity of extreme rainfall events, affecting rainfall intensity-duration-frequency (IDF) curve information used in the design, maintenance and operation of water infrastructure in Canada. Presented in this lecture are analyses of precipitation data from 567 Environment Canada hydro-meteorological stations using the IDF_CC tool. Results for the year 2100 based on Canadian climate model and an ensemble of 22 GCMs have been generated. A spatial interpolation method was used to produce Canadian precipitation maps for events of various return periods. Results based on the Canadian climate model indicate a reduction in extreme precipitation in central regions of Canada and increases in other regions. Relative to the ensemble approach, the Canadian climate model results (a) suggest more spatial variability in change of IDFs, and (b) the ensemble approach generated generally lower values than the Canadian climate model.
Dr. Simonovic has extensive research, teaching and consulting experience in water resources systems engineering. He teaches courses in water resources and civil engineering systems. He actively works for national and international professional organizations. Dr. Simonovic’s primary research interest focuses on the application of systems approach to management of complex water and environmental systems. Most of his work is related to the integration of risk, reliability, and uncertainty in hydrology and water resources management. He has received a number of awards for excellence in teaching, research and outreach. He has published over 450 professional publications and three major textbooks. He was inducted to the Canadian Academy of Engineering in June of 2013.
A comparative study of different imputation methods for daily rainfall data i...journalBEEI
Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.
Fitting Probability Distribution Functions To Discharge Variability Of Kaduna...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Analysis of rainfall intensity of kunigal taluk, tumkur district, karnataka u...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Unit Hydrograph (UH) is the most famous and generally utilized technique for analysing and deriving flood hydrograph resulting from a known storm in a basin area. For ungauged catchments, unit hydrograph are derived using either regional unit hydrograph approach. Central Water Commission (CWC) derived the regional unit hydrograph relationships for different sub-zones of India relating to the various unit hydrograph parameters with some prominent physiographic characteristics. In this study, the lately developed UH model is applied located between Latitude 15º54′2′′ N to 16º16′19′′ N Latitude and 76º48′40′′ E to77º4′21′′ E Longitude. The study area covers an area of 466.02 km2, having maximum length of 36.5 km. The maximum and minimum elevation of the basin is 569 m and 341 m above MSL, respectively. The Peak discharge of unit hydrograph obtained is 171.58m3/s. The final cumulative discharge is 1669.05 m3/s.
This study aims to detect the trend in seasonal rainfall of four rainy months i.e. June, July, August and
September. To determine the trend of rainfall, non-parametric Mann-Kendall test and non-parametric Sen’s
Slope estimator for determination of magnitude of trend was used. Linear regression analysis, which is a useful
parametric model used to developed functional relationships between variables was also applied to determine
trend of rainfall for the study area. From all statistical test results it was indicated that there was some change in
the trend of rainfall of the rainy months
Adequate knowledge about the hydrology is very much required for the proper planning and management of water resources in an area. Rainfall and runoff are the important constituents in determining the hydrology of an area to determine the water management strategies. SCS- CN method is a widely used method for the calculation of surface runoff considering the land use pattern, soil type and antecedent moisture condition. In the present study runoff of the Palar watershed, Karnataka state, South India has been calculated using the SCS-CN method. The watershed consists of a total area of 2872.357 km2. The maximum rainfall of 1231.67 mm in the year 2005 and a minimum of 418.7 mm in the year 2003. The average annual runoff is calculated as 218.26 mm and 626.91MCM. The rainfall- runoff correlation value is 0.8253. The study results can be effectively coordinated for the watershed management activities.
Flood frequency analysis of river kosi, uttarakhand, india using statistical ...eSAT Journals
Abstract In the present study, flood frequency analysis has been applied for river Kosi in Uttarakhand. The river Kosi is an important tributary of Ganga river system, which arising from Koshimool near Kausani, Almora district flows on the western side of the study area and to meet at Ramganga River. The annual flood series analysis has been carried out to estimate the flood quantiles at different return period at Kosi barrage site of river Kosi. The statistical approach provided a significant advantage of estimation of flood at any sites in the homogenous region with very less or no data. In the at –site analysis of annual flood series the Normal, Log normal, Pearson type III, Log Pearson type III, Gumbel and Log Gumbel distribution were applied using method of moments . From the analysis of different goodness of fit tests, it has been found that the Log Gumbel distribution with method of moment as parameters estimation found to be the best-fit distribution for Kosi River and other sites in the region. It is recommended that the regional parameters for Kosi Basin may be used only for primary estimation of flood and should be reviewed when more regional data available. Keywords: Flood Frequency Analysis, River Kosi, Annual Peak Flood discharge, Return Period, Goodness of fit Test.
As basic data, the reliability of precipitation data makes a significant impact on many results of environmental applications. In order to obtain spatially distributed precipitation data, measured points are interpolated. There are many spatial interpolation schemes, but none of them can perform best in all cases. So criteria of precision evaluation are established. This study aims to find an optimal interpolation scheme for rainfall in Ningxia. The study area is located in northwest China. Meteorological stations distribute at a low density here. Six interpolation methods have been tested after exploring data. Cross-validation was used as the criterion to evaluate the accuracy of various methods. The best results were obtained by cokriging with elevation as the second variable, while the inverse distance weighting (IDW) preform worst. Three types of model in cokriging were compared, and Gaussian model is the best.
DELINEATION OF FLOOD-PRONE AREAS THROUGH THE PERSPECTIVE OF RIVER HYDRAULICSDasapta Erwin Irawan
Flash floods in the Saka River (part of the KUSW) struck Muara Dua District with a population of 177.47 people/km2 on May 8th, 2020, due to increased rainfall intensity and land cover changes upstream. Based on this incident, this research will examine hydraulic parameters that directly implications for potential flooding. The rainfall intensity analysis was based on calculations from the Gumbel-Sherman equation in the baseline period 2011-2020. Then the parameters of the runoff coefficient consisting of the slope, land cover, and type of lithology are analyzed by the Hassing method. The results of the rainfall intensity analysis showed that the lowest intensity occurred in August while the highest power occurred in November and April. The runoff coefficient of 53% has implications for peak flow discharge which has an average increase of 11.6%. Flood simulation in KUSW modeled with Hydrologic Engineering Center-River Analysis System (HEC-RAS) software shows 174.4 km2 potential flooding in the five years of the return period and 200 km2 in the ten years of the return period. This analysis model is used as a preventive effort and reduces the negative impact around KUSW.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Estimation of morphometric parameters and runoff using rs & gis techniqueseSAT Journals
Abstract
Land and water are the two vital natural resources, the optimal management of these resources with minimum adverse environmental
impact are essential not only for sustainable development but also for human survival. Satellite remote sensing with geographic
information system has a pragmatic approach to map and generate spatial input layers of predicting response behavior and yield of
watershed. Hence, in the present study an attempt has been made to understand the hydrological process of the catchment at the
watershed level by drawing the inferences from moprhometric analysis and runoff. The study area chosen for the present study is
Yagachi catchment situated in Chickamaglur and Hassan district lies geographically at a longitude 75⁰52’08.77”E and
13⁰10’50.77”N latitude. It covers an area of 559.493 Sq.km. Morphometric analysis is carried out to estimate morphometric
parameters at Micro-watershed to understand the hydrological response of the catchment at the Micro-watershed level. Daily runoff
is estimated using USDA SCS curve number model for a period of 10 years from 2001 to 2010. The rainfall runoff relationship of the
study shows there is a positive correlation.
Keywords: morphometric analysis, runoff, remote sensing and GIS, SCS - method
Similar to Download-manuals-ground water-manual-gw-volume3referencemanualhydro-meteorology (20)
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
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- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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Send an interactive Slack channel message (using buttons)
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In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
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And...
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💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
1. Government of India & Government of The Netherlands
DHV CONSULTANTS &
DELFT HYDRAULICS with
HALCROW, TAHAL, CES,
ORG & JPS
VOLUME 3
HYDRO-METEOROLOGY
REFERENCE MANUAL
2. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page i
Table of Contents
1 PILOT STUDY: DESIGN OF RAIN GAUGE NETWORK 1-1
1.1 INTRODUCTION 1-1
1.2 DATA COLLECTION AND VALIDATION 1-2
1.3 STATISTICAL ANALYSIS 1-3
1.4 ASSESSMENT OF SPATIAL CORRELATION STRUCTURE 1-5
1.5 ESTIMATION ERROR ASSESSMENT 1-11
1.6 NETWORK DESIGN 1-12
2 ESTIMATION ERROR IN LONG TERM MEAN AREAL RAINFALL 2-1
3 SUMMARY OF MONTHLY AND ANNUAL STATISTICS OF CLIMATIC VARIABLES 3-1
3.1 INTRODUCTION 3-1
3.2 ANDHRA PRADESH 3-2
3.3 GUJARAT 3-5
3.4 KARNATAKA 3-8
3.5 KERALA 3-11
3.6 MADHYA PRADESH 3-14
3.7 MAHARASHTRA 3-17
3.8 ORISSA 3-20
3.9 TAMIL NADU 3-23
4 REFERENCES 4-1
3. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-1
1 PILOT STUDY: DESIGN OF RAIN GAUGE NETWORK
1.1 INTRODUCTION
A Pilot Study for designing a rain gauge network was made for two sub-basins in the Mahanadi river
basin in Orissa. The objective of the Pilot study was to provide an example of the theory and related
practical aspects on network design as presented in Chapter 3 of Volume 3, Design Manual, Hydro-
meteorology, including the integration of hydro-meteorological network with the hydrometric network.
The procedure presented in the Design Manual is general and can be applied to any basin
irrespective of the climatic conditions. Furthermore, the procedure is also applicable for the design of
evaporation networks.
For the Pilot Study, the rainfall networks of two tributaries of river Mahanadi namely: the Ib and Tel
rivers have been considered. The catchment areas of these two rivers are 7,627 km2
and 30,426 km2
respectively. Data of 12 rain gauge stations in Ib basin and 54 rain gauge stations in Tel basin, for
which monthly rainfall data for the period 1970-1995 have been made available by Orissa state.
These 66 stations have been listed in Table 1.1 and their locations are shown in Figure 1.1.
The selection of the Ib and Tel basins for the Pilot Study was first of all based on their general
representativeness with respect to climatic and drainage conditions. Furthermore, the readily
availability of computerised series of historical data was considered a prerequisite for taking up any
such study in the beginning of the Hydrology Project. The study was carried out in the period April to
December 1997.
Figure 1.1: Rainfall network in Tel river basin, Orissa
4. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-2
Basin No Station No Station No Station
Ib 1
2
3
4
Balisankara
Bamra
Baragoan
Jharsuguda
5
6
7
8
Kochinda
Laikera
Rengali
Subdega
9
10
11
12
Sundergarh
Gurundia
Hemgiri
Jamunkira
Tel 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Agalpur
Attabira
Bijipur
Binka
Dunguripally
Ghaisilat
Jharabandh
Padampur
Paikmal
Rairakhol
Sambalpur
Sohela
Sonepur
Urlanda
Dahugoan
Dharmgarh
Jayapatna
Junagarh
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Kalampur
Koksara
Bhawanipatna
Kesinga
Lanjigarh
Narla
Nawpara
Sinapally
Bangamunda
Golamunda
Khariar
Buden
Belpara
Khaprakhol
Komna
Muribahal
Patnagarh
Tureikel
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
Bolangir
Deogoan
Kantamal
Luisinga
Saintala
Tarabha
Titlagarh
Baragoan
Karlamunda
Kotagarh
Madanpurampur
Tumlibandha
Baliguda
Daringpadi
G. Udayagiri
Phulbani
Raikeda
Tikabali
Note: in bold stations selected for regression analysis
Table 1.1: Summary of rainfall stations in Ib and Tel basin
1.2 DATA COLLECTION AND VALIDATION
Data collection
Time series of monthly rainfall data of the 12 rain gauge stations in Ib basin and the 54 rain gauge
stations in Tel basin as listed in Table 1.1 were made available by the State Surface Water
Department of Orissa on diskette. The monthly series have been created by aggregation from daily
observations in the period 1970-1995. For further analysis, the time series of monthly data with the
station latitude, longitude and altitude were transferred to the database of HYMOS software package
for processing and storage of hydrological data.
Data validation
No information was available on any kind of validation or completion carried out on the data.
Therefore, all data were subjected to an extensive data validation process, including:
• screening of data by tabulation and flagging of outliers,
• comparison of time series plots of neighbouring stations,
• application of the nearest neighbour technique to each of the series; this techniques compares
the observed monthly value at a station with an estimate based on surrounding stations and flags
the value if the difference with the estimate exceeds a pre-set absolute value or a relative one
derived from the standard error of estimate, and
• double mass analysis on annual values for the 26 year period of data for each of the series.
5. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-3
The validation showed that a large amount of the data was of doubtful quality unfit for further analysis.
The stations that passed the tests are summarised in Table 1.2 (see also Table 1.1 stations printed in
bold letter type) presented per homogeneous area.
Ib Tel, Northwest Tel, Southeast Tel, Southwest
Bamra
Balisankara
Gurundia
Jamunkira
Rengali
Agalpur
Attabira
Bolangir
Gaisilet
Deogan
Dungipally
Komna
Sambalpur
Tilagarh
Baliguda
Bijipur
Kalampur
Kantamal
Karlamunda
Kotagarh
Pulbani
Tumlibund
Madanpurampur
Golamunda
Dharmgarh
Kaksara
Dahugaon
Table 1.2: Selected rainfall stations per region used in statistical analysis
The unreliable data in the selected series have been eliminated. The series with missing data have
not been completed; this is essential in view of the determination of basic statistics and spatial
correlation analysis to be carried as a next step. By filling in missing data using regression without
applying a random component for the unexplained part in regression, the variance of the series will be
reduced and the spatial correlation coefficient will be increased.
In all, only 40% of the series were found reliable. This shows that proper validation of the data prior to
any further statistical analysis is a necessity. Once it is known that the data have been subjected to
the validation process as presented in the Data Processing Manual, which are conformable to the
methods applied in this study, the network design process can continue with further statistical
analysis.
1.3 STATISTICAL ANALYSIS
Basic statistics and grouping of stations
The basic statistics of the monthly and annual series of all finally selected point rainfall stations have
been computed. The statistics of the point rainfall series are displayed in the Figures 1.2 to 1.5 and
Tables 1.3 and 1.4. The latter table includes the statistics of the annual series; here also the monsoon
rainfall as a percentage of the annual rainfall is presented as well.
Based on these data the stations were grouped according to climatically homogeneous areas. The
following areas were discerned:
• Ib basin,
• Tel basin, Northwest of the Tel river,
• Tel basin, Southeast of the Tel river, and
• Tel basin, in the south-western part of the basin, covering the upper reaches.
The tables and figures show that the areas nearest to the coast receive most rainfall, particularly in
mid-monsoon. It is observed that, generally, August is the wettest month with slightly more rainfall
than July. As indicated in the last column of Table 1.4 the rainfall is almost entirely concentrated in the
monsoon period; 86-89 percent of the annual total is received in that period on average.
Consequently, the coefficient of variation Cv is lowest in these months and is maximum in December
when hardly any rain is experienced. In the Tables and Figures also the 90% reliable Cv-value is
presented to analyse the variability of this coefficient among the various station records. Generally,
these values are about 30% larger than the average values, with slightly larger ones in the post-
monsoon period.
6. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-4
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Ib
Mean (mm) 12.1 17.1 13.1 15.6 35 209.6 387.8 407.3 204.8 50.1 9.4 3.4
Cv-average 1.70 1.62 1.74 1.29 1.07 0.69 0.39 0.41 0.52 1.05 1.90 3.24
Cv-90% 1.98 2.05 2.13 1.46 1.46 0.78 0.49 0.46 0.65 1.17 2.26 4.50
Tel NW
Mean (mm) 10.0 13.0 11.2 15.5 29.5 196.3 358.0 364.4 184.7 44.8 13.1 1.8
Cv-average 2.17 1.41 1.72 1.37 1.14 0.66 0.36 0.47 0.63 1.27 2.08 3.30
Cv-90% 2.98 1.94 2.32 1.94 1.53 0.87 0.48 0.63 0.84 1.70 2.74 4.65
Tel SE
Mean (mm) 10.7 15.6 16.4 26.3 46.4 220.3 382.3 418.2 215.1 65.7 16.7 1.3
Cv-average 2.11 1.39 1.64 1.03 1.30 0.60 0.44 0.48 0.56 1.14 2.00 3.41
Cv-90% 2.81 1.81 2.17 1.41 1.72 0.80 0.58 0.64 0.74 1.60 2.69 5.28
Tel SW
Mean (mm) 6.7 9.8 12.2 31.1 49.2 219.1 365.9 363.5 211.9 67.6 13.3 0.6
Cv-average 2.13 1.79 1.67 1.35 1.29 0.51 0.39 0.47 0.52 1.33 2.00 4.44
Cv-90% 2.97 2.46 2.29 1.88 1.89 0.70 0.54 0.65 0.74 1.89 2.73 6.25
Table 1.3: Basic statistics of monthly point rainfall series, period 1970-1995
Basin Mean
(mm)
Cv-average Cv-90% ∑ (June-Sept)/Year (%)
Ib 1365.3 0.27 0.28 89
Tel NW 1242.3 0.25 0.33 89
Tel SE 1435.0 0.29 0.38 86
Tel SW 1350.9 0.27 0.39 86
Table 1.4: Basic statistics of annual point rainfall series, period 1970-1995
Figure 1.2: Basic statistics Ib basin Figure 1.3: Basic statistics Tel NW basin
7. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-5
m,jm,i
n
1k
m,jk,m,jm,ik,m,i
ij
)hh)(hh(
n
1
r
σσ
−−
=
∑=
Figure 1.4: Basic statistics Tel SE basin Figure 1.5: Basic statistics Tel SW basin
A comparison of the monthly average values and coefficients of variation for the various areas is
presented in Figure 1.6 and 1.7.
Figure 1.6: Monthly average rainfall in Figure 1.7: Average Cv-values in Ib
Tel and Ib basins and Tel basins
Orographic effects
To investigate if orographic effects could play a role in the rainfall observed in the Ib and Tel basins,
the altitudes of all stations have been collected, either taken from files or estimated from topographical
maps. Most stations are at an elevation less than 500 m, whereas a few are located between 500 and
600 m height. In tropics like in India, orographic influence is prominent above an elevation of 800 m.
Hence, it is safe to state that transformations for orographic effects are not required in this case to
arrive at homogeneous series.
1.4 ASSESSMENT OF SPATIAL CORRELATION STRUCTURE
Measure of effectiveness
From the analysis of basic statistics it is revealed that the rainfall in the Ib and Tel basins is almost
entirely concentrated in the months June to September. Hence, for water resources assessment it is
sufficient to concentrate on these months and on the annual total. Since no Hydrological Data User
need inventory was made at the time of the analysis, it was assumed that the network should be able
to provide monthly and as an alternative seasonal or annual areal rainfall. Consequently, we will use
as a measure of effectiveness the estimation error in the areal average monthly and annual rainfall in
the distinguished areas, which value should not be more than 10% on average.
Spatial correlation coefficients have therefore been computed for all the monsoonal months
individually and for the annual series.
Spatial correlation coefficient
The spatial correlation coefficient between two stations is computed as follows:
(1.1)
8. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-6
where: rij = correlation coefficient between series at station i and station j
n = number of data in series i and j ≠0
hj,m,k = rainfall at station i in month m and year k
h,m = (time) average rainfall at station i in month m
σi,m = standard deviation of rainfall series of station i in month m
In this way for each region the spatial correlation coefficient has been determined for each pair of
stations. Subsequently the distance d in km between the stations was derived from the co-ordinates of
the sites. For N stations this leads to N(N-1)/2 pairs and values for rij(d).
When all rij -values are plotted against d a large scatter is generally observed. To reduce the scatter
per distance interval of 10 km the average correlation coefficient and average distance was
computed. The results are shown in Table 1.5. The technique presented in the Design Manual
requires the correlation-distance relationship to be modelled by the following relation:
(1.2)
where: r(d) = correlation as a function of distance
r0 = correlation at d=0
d0 = characteristic correlation distance: at distance d0, the correlation is r0e-1
Basically, r(0) should be 1, but measuring errors and microclimatic variability at small values of d
create r0 values less than 1, see Design Manual. The average correlation coefficients and distance
per distance interval have been plotted using a semi-logarithmic scale to fit a straight line through the
observations, after elimination of outliers. From this the values of r0 and d0 have been obtained.
The estimates for r0 and d0 are listed in Table 1.6. The fit of function (1.2) to the observations is shown
for each month and for the annual series in:
• for Ib: Figures 1.8 to 1.13
• for Tel NW: Figures 1.14 to 1.19
• for Tel SE: Figures 1.20 to 1.25
No estimates for r0 and d0 could be made for the south-western region in the Tel basin as the scatter
in the r(d) data was too large and the data amount too small.
Figure 1.8: r(d) for June, Ib basin Figure 1.9: r(d) for July, Ib basin
)d/dexp(r)d(r 00 −=
10. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-8
Figure 1.10: r(d) for August, Ib basin Figure 1.11: r(d) for September, Ib basin
Figure 1.12: r(d) for Year, Ib basin Figure 1.13: r(d) for monthly and annual
data, Ib basin
Figure 1.14: r(d) for June, Tel NW basin Figure 1.15: r(d) for July, Tel NW basin
11. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-9
Figure 1.16: r(d) for August, Tel NW basin Figure 1.17: r(d) for September, Tel NW
basin
Figure 1.18: r(d) for Year, Tel NW basin Figure 1.19: r(d) for monthly and annual data,
Tel NW basin
Figure 1.20: r(d) for June, Tel SE basin Figure 1.21: r(d) for July, Tel SE basin
12. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-10
Figure 1.22: r(d) for August, Tel SE basin Figure 1.23: r(d) for September, Tel SE basin
Figure 1.24: r(d) for Year, Tel SE basin Figure 1.25: r(d) for monthly and annual data,
Tel SE basin
Basin June July August Sept Year
Ib
r0
d0 (km)
0.98
180
0.70
125
0.87
150
0.99
80
0.76
200
Tel NW
r0
d0 (km)
0.92
200
0.73
140
0.95
225
0.92
200
0.85
210
Tel SE
r0
d0 (km)
0.98
140
0.85
150
0.95
125
0.95
150
0.90
200
Table 1.6: Parameters in spatial correlation function
From Table 1.6 and Figures 1.13, 1.19 and 1.25 the following is observed:
• r0 is smallest for July; a small value for July also brings down the r0 for annual data
• d0 is generally largest for the annual values.
The observation that the characteristic correlation distance increases with the aggregation level was
to be expected. However, the low value for r0 in July is not understood. It may either be due to
measuring errors and/or microclimatic disturbances. Storm data (days with 500 mm of rainfall in one
13. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-11
+−=
N
S
d
23.0
r1
N
1
CvZ
0
0areal
day) of this century do not show any anomaly for the month July compared to other monsoon months.
There is also no evidence to ascribe it to measuring errors as there is no reason why the
measurements in July would be less accurate than in the rest of the monsoon period. One is tempted
to attribute it to the school holiday season, which starts in July, since many of the observers are
schoolteachers. However, the holiday season also includes August, but an equally low r0-value for
that month is not observed. It might well be, that part of the data is not original, as can be observed
from e.g. Figure 1.14; the high correlation coefficients at large distance in June do suggest that at
least part of the series is artificial (obtained by regression analysis). Without having access to the
original data the low r0-value for July remains a mystery.
1.5 ESTIMATION ERROR ASSESSMENT
The error made in estimation of the areal rainfall of individual months in the monsoon period and of
the annual total was taken as the measure of effectiveness of the rainfall observation network. This
criterion was taken in view of absence of information on Hydrological Data User requirements at the
time of the execution of the study. Since monthly and annual rainfall data are usually considered in
water balance studies together with flow data, the estimation error in both types of data should be of
the same order of magnitude. Since the error in individual discharge data derived from water levels
will be in the order of 10% a similar estimation value is applied for rainfall data.
The root mean square error in the areal rainfall estimate relative to the point (time) average value
Zareal is computed from (see Chapter 6 of Volume 2, Design Manual, Sampling Principles):
(1.3)
The estimation error is seen to depend strongly on the point rainfall characteristic Cv, whereas
furthermore the measuring error (1-r0) and the characteristic correlation distance d0 play a role. In
equation (1.3) N is the number of gauges per area S. Essential in the application of equation (1.3) is
the choice of area S. Since, the hydro-meteorological network should be integrated with the
hydrometric network and/or the groundwater network the value of S should coincide with a
characteristic unit of drainage area or aquifer. Considering surface runoff, a logical choice for S is the
minimum area upstream of a stream gauging station. For the hydrometric network in the Hydrological
Information System typically for the plain areas one stream gauging station per 2,000 km2
drainage
area is applied. Hence the design surface area S for the hydro-meteorological network in the plains is
taken as S=2,000 km2
. The estimation error Z for each month and for annual data for each area is
presented in Table 1.7 and Figures 1.26 to 1.28.
In Table 1.7 the area S/N to be covered by one rain gauge, to reach on average an areal rainfall
estimate with an estimation error of not larger than 10% in a single month in a design area of 2,000
km2
, is presented. The S/N values are rounded to the nearest 50 km2
. In the Figures 1.26 to 1.27 the
relationship between the estimation error and the network density is displayed. Interesting features
are observed from e.g. Figure 1.26. The required network density for July would be four times as large
if the acceptable estimation error would be halved from 10% to 5%. Apparently, highly accurate areal
rainfall estimates can only be obtained with a disproportionate increase in network density. This
observation stresses the need for a careful assessment of the acceptable estimation error.
14. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-12
Basin June July August Sept Year
Ib
Cv
r0
d0 (km)
S/N (km
2
)
FWMO (%)
0.69
0.98
180
750
8
0.39
0.70
125
400
11
0.41
0.87
150
700
8
0.52
0.99
80
800
7
0.24
0.76
200
1250
6
Tel NW
Cv
r0
d0 (km)
S/N (km
2
)
FWMO (%)
0.66
0.92
200
450
11
0.36
0.73
140
500
10
0.47
0.95
225
1100
6
0.63
0.92
200
500
10
0.25
0.85
210
1650
5
Tel SE
Cv
r0
d0 (km)
S/N (km
2
)
FWMO (%)
0.60
0.98
140
800
7
0.44
0.85
150
550
9
0.48
0.95
125
850
7
0.56
0.95
150
700
8
0.29
0.90
200
1650
5
Table 1.7: Network density for 10% estimation error in average
rainfall for a design area of 2,000 km2
.
1.6 NETWORK DESIGN
The objective was to arrive at a network capable in estimating the areal rainfall for catchment areas of
2,000 km2
and beyond with an error less than 10% of the long term average rainfall for the month or
year. From Table 1.7 it is observed that for the various distinguished areas the area per gauge ranges
from 400 to 1100 km2
.
Figure 1.26: Est. error as function Figure 1.27: Est. error as function of
of S/N, Ib basin S/N, Tel NW basin
A high density is generally required for estimating the July areal rainfall with the desired accuracy,
mainly because of the low r0 value for that month. To reach for all months the required accuracy, the
network density should be one gauge per 400 and 500 km2
for Ib and Tel, respectively. If the
objectives are not applied to the last percent admissible error then for both basins a density of one
15. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 1-13
gauge per 500 km2
will do. To get a proper estimate of the annual value the demands on the network
density are strongly reduced; the density can then be reduced with a factor 2 to 3.
Figure 1.28:
Estimation error as function of S/N, Tel SE basin
It is noted that in the preceding analysis the computed values should not be considered to be very
accurate, given the scatter in the spatial correlation plots used to estimate the values for r0 and d0.
Rather, the analysis gives a first estimate of the estimation errors involved. A somewhat better
estimate of the errors in the areal rainfall is obtained from kriging as here the assumption of an equally
spaced network is not a prerequisite was the case in the above analysis. Nevertheless the
uncertainties in the estimation of the spatial correlation structure applies here as well.
WMO minimum network density requirement
WMO uses as standard for plain areas a minimum network density of one gauge per 500 km2
. In
Table 1.7 it is indicated what the consequences would with respect to estimation error if this norm is
applied. It is seen that the estimation error would vary for the monthly values between 6 and 11% and
between 5 and 6% for the annual values. Given the various uncertainties it is observed that the WMO
norm perfectly fits to the accuracy requirements for monthly values in the Ib and Tel basins.
Hence, assuming that the spatial correlation structure and the temporal variation of the point rainfall
processes in areas outside Mahanadi do not differ too much from the variability in the Ib and Tel
basins, as a first guess the WMO norm can be applied for preliminary network design.
Summing up
The total catchment areas of Ib and Tel are respectively 7,627 and 30,426 km2
. Applying a network
density of one gauge per 500 km2
the requirement becomes respectively 15 rainfall stations for the Ib
basin and 61 stations for Tel. At present the network contains 12 and 54 stations respectively, hence
some 10% increase in the number of stations in both catchments would be required.
It is stressed though, that much effort has to be put in proper upgrading of the stations. The analysis
of the historical data has shown that some 60% of the stations have series of doubtful quality.
Hence, station conditions, equipment and operational practice has to be given serious attention.
16. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 2-1
n
1
r1
r1
.
n
1
)T(F
1,T
1,T
1 ≈
−
+
=
s
s
2 r
N
r1
)r,N(F +
−
=
2 ESTIMATION ERROR IN LONG TERM MEAN AREAL
RAINFALL
The estimation or standard error of long term mean areal rainfall can be split into 3 multiplicative
components (Rodriguez-Iturbe, 1974), namely:
1. the standard deviation of the point rainfall process σh
2. the temporal reduction factor F1(T), dependent on the period used for estimating the average
areal rainfallhA , and the serial correlation coefficient
3. the spatial reduction factor F2(N,r), which depends on the number of stations used in the
determination ofhA and the spatial correlation structure.
The error variance of the mean σ2
(hA) is defined by:
(2.1)
Its root is the estimation error σ(hA). To be consistent with Chapter 1, the error will be expressed
relative to the long term mean rainfall. Hence, the relative error in the long term mean areal rainfall
Z(hA). becomes:
or since σh/h = Cv
(2.2)
with:
(2.3)
and:
(2.4)
where: Z(hA)= relative estimation error in the long term mean areal rainfall
h = average of point rainfall series
σh = standard deviation of the point rainfall series
Cv = coefficient of variation of point rainfall series
rT,1 = auto-correlation coefficient at lag 1
n = length of rainfall series; here the number of years in the series
N = number of gauging stations
rs = mean spatial correlation coefficient over the area considered
Two remarks are made here:
• the autocorrelation coefficient of monthly or annual rainfall series is generally about zero, hence
the approximation made in equation (2.3) will often be applicable
]])h[Eh[(E)h( 2
AAA
2
−=σ
)r,N(F).T(F.
hh
)h(
)h(Z s21
h
A
A
A
σ
=
σ
=
)r,N(F).T(F.Cv)h(Z s21A =
17. Reference Manual – Hydro-meteorology (GW) Volume 3
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• the mean spatial correlation coefficient can be approximated by spatial correlation coefficient at
the average distance, if the distance between two randomly chosen points in the area of concern
is considered as a stochastic variable (see Buishand, 1977):
(2.5)
The distance E(d) or dr is called the characteristic correlation distance of the homogeneous and
isotropic area S. Values for dr for some typical shapes of basins with unit surface are given in Table
2.1.
Type of basin Circumference dr
Circle
Regular hexagon
Square
Equilateral triangle
Rectangle L/W=2
Rectangle L/W=4
Rectangle L/W=16
3.545
3.722
4.000
4.559
4.243
5.000
8.500
0.5108
0.5126
0.5214
0.5544
0.5691
0.7137
1.3426
Source: Matern, 1960
L/W = length-width ratio
Table 2.1: Characteristic correlation distance dr for basins with area 1
To apply Table 2.1, the value for dr in the table is multiplied with the root of the basin area. so if the
area is a square with area S=5,000 km2
, then for dr it follows: 0.5214*√(5000) ≈ 37 km.
Application
The above procedure is applied to the areal annual rainfall series of the south-eastern part of Tel
basin. From Chapter1, Table 1.7 it follows that the coefficient of variation of the annual point rainfall
series is 0.29 and that the spatial correlation structure for annual data is well described by the
following relation:
(2.6)
From Figure 1.1 in Chapter1 it is observed that the catchment area of the south-easter part can be
approximated by a rectangle with a length-width ratio of about 2 and its size is roughly 12,000 km2
. So
the characteristic correlation distance dr becomes:
(2.7)
With equation (2.6) then the mean correlation for the area will be:
(2.8)
It is further assumed that the auto-correlation at lag1 of the annual series is not significant, hence rT,1
= 0. Filling in these values in equation 2.2, Z(hA) as a function of the number of years n and the
number of gauges N can be computed. The effects of F1(T) and F2(N,rs) on Z(hA) separately is
shown in Figure 2.1.
[ ] [ ])d(Err)d(rE sss ≈=
)200/dexp(90.0)d(rs −=
km62120005691.0dr ≈=
ss r66.0)200/62exp(90.0)62(r ≈=−=
18. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 2-3
Figure 2.1:
Reduction of estimation error by F1(T) and
F2(N,rs) for areas in Tel basin of size 2,000 and
12,000 km2
It is observed from Figure 2.1 that to reduce the error in the long term areal mean rainfall, addition of a
few more years of observation is much more effective than extending the network. This effect is
stronger the smaller the catchment area, as is shown in the same figure by comparing F2(N, rs) with
the same function for an area of 2,000 km2
.
19. Reference Manual – Hydro-meteorology (GW) Volume 3
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3 SUMMARY OF MONTHLY AND ANNUAL STATISTICS OF
CLIMATIC VARIABLES
3.1 INTRODUCTION
In Chapter 2 of Volume 3, Design Manual on Hydro-meteorology the rainfall and evaporation
processes have been described. To give insight in the variation of the relevant climatic variables, in
this chapter a summary is given of the monthly and annual statistics of these variables for selected
locations in peninsular India. The data are taken from published summaries by IMD (IMD, 1990:
Climatological Tables of Observatories in India) and refer to the period 1931-1960. The following
variables are presented:
• Temperature
• Daily maximum
• Daily minimum
• Highest
• Lowest
• Humidity
• Rainfall
• Monthly/annual totals
• Highest 24 hour rainfall
• Windspeed
The variables are given for the following locations:
1. Andhra Pradesh: Hyderabad and Kakinada
2. Gujarat: Ahmedabad and Surat
3. Karnataka: Bangalore and Honavar
4. Kerala: Trivandrum and Calicut
5. Madhya Pradesh: Bhopal and Pachmarhi
6. Maharashtra: Mumbai and Mahabaleshwar
7. Orissa: Cuttack and Sambalpur
8. Tamil Nadu Chennai and Madurai
Above stations are the state capitals and a representative coastal/hill station. Their locations are
shown in Figure 3.1
Figure 3.1:
Location of selected climatic stations.
20. Reference Manual – Hydro-meteorology (GW) Volume 3
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3.2 ANDHRA PRADESH
The monthly and annual statistics of selected climatic variables for Hyderabad and Kakinada are
presented in the Tables 3.1 and 3.2 and Figures 3.2 to 3.5.
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr
mm
Km/ h
Hyderabad January 28.6 14.6 35.1 6.1 36 1.7 93.2 8.1Andhra
Pradesh
February 31.2 17.6 37.2 8.9 35 11.4 42.9 8.9
March 34.8 20.1 42.2 13.2 30 13.4 103.1 9.6
April 36.9 23.7 43.3 16.1 31 24.1 60.7 10.9
May 38.7 26.2 44.4 19.4 33 30.1 65.1 12.4
June 34.1 24.1 43.9 17.8 54 107.4 122.7 23.8
July 29.8 22.3 37.2 19.4 69 165.1 109.2 22.1
August 29.5 22.1 36.1 19.4 70 146.9 190.5 18.3
September 29.7 21.6 36.1 17.8 71 163.3 153.2 12.6
October 30.3 19.8 36.7 12.2 58 70.8 117.1 8.9
November 28.7 16.1 33.9 7.8 48 24.9 95.5 8.1
December 27.8 13.4 33.3 7.2 42 5.5 44.5 7.4
ANNUAL 31.7 20.1 44.4 6.1 48 764.4 190.5 12.6
Table 3.1: Summary statistics of climatic variables for Hyderabad
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr
mm
Km/ h
Kakinada January 27.3 19.1 32.8 14.4 70 3.6 78.2 10.3Andhra
Pradesh
February 29.6 20.7 37.8 15.6 67 11.9 45.1 8.8
March 33.1 23.1 38.9 17.2 62 11.8 71.6 8.3
April 35.3 25.8 42.8 18.9 65 22.1 61.1 9.1
May 36.9 27.7 46.7 21.1 64 45.7 109.7 11.1
June 35.9 27.1 47.2 21.7 61 126.1 501.4 12.1
July 31.8 25.4 41.7 21.1 73 218.2 127.1 12.3
August 31.8 25.6 37.8 21.7 74 151.6 146.1 11.1
September 32.1 25.5 37.2 21.7 76 158.2 285.7 8.6
October 30.8 24.5 37.2 17.2 74 306.6 281.9 9.5
November 28.7 21.6 33.9 15.6 69 107.1 276.3 12.1
December 27.1 19.2 32.2 13.9 69 9.1 130.3 11.3
ANNUAL 31.7 23.8 47.2 13.9 69 1171.9 501.4 10.4
Table 3.2: Summary statistics of climatic variables for Kakinada
21. Reference Manual – Hydro-meteorology (GW) Volume 3
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Figure 3.2: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Hyderabad
Figure 3.3: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Kakinada
22. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-4
Figure 3.4: Monthly rainfall totals and humidity for Hyderabad and Kakinada
Figure 3.5: Monthly highest 24 hour rainfall and average windspeed for Hyderabad
and Kakinada
23. Reference Manual – Hydro-meteorology (GW) Volume 3
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3.3 GUJARAT
The monthly and annual statistics of selected climatic variables for Ahmedabad and Surat are
presented in the Tables 3.3 and 3.4 and Figures 3.6 to 3.9.
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Gujarat Ahmedabad January 28.7 11.9 36.1 3.3 28 3.9 30.7 5.4
February 31.1 14.5 40.6 2.2 24 0.3 26.4 5.1
March 35.7 18.6 43.9 9.4 20 0.9 12.2 7.3
April 39.7 23.1 46.2 12.8 18 1.9 21.6 7.8
May 40.7 26.3 47.8 19.4 21 4.5 46.2 9.5
June 38.1 27.4 47.2 19.4 41 100.1 130.8 10.8
July 33.2 25.7 42.2 21.1 68 316.3 414.8 10.8
August 31.8 24.6 38.9 21.7 69 213.3 150.6 8.3
September 33.1 24.2 41.7 21.6 60 162.8 257.8 7.1
October 35.6 21.2 42.8 14.4 35 13.1 52.8 4.6
November 33.1 16.1 38.9 9.4 29 5.4 53.3 4.1
December 29.6 12.6 35.6 6.1 29 0.7 14.1 2.6
ANNUAL 34.2 20.5 47.8 2.2 37 823.1 414.8 6.9
Table 3.3: Summary statistics of climatic variables for Ahmedabad
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Gujarat Surat January 31.4 14.8 38.3 4.4 41 0.6 43.7 6.1
February 33.1 16.4 41.7 8.6 35 1.1 38.1 6.1
March 36.1 20.1 43.9 10.6 33 0.7 8.1 6.6
April 37.3 23.7 45.6 15.1 40 5.1 97.8 7.6
May 36.2 26.6 45.6 19.4 57 5.5 48.8 11.1
June 33.7 27.1 45.6 21.8 70 209.8 260.1 12.2
July 30.5 25.7 38.9 20.6 81 448.1 459.2 11.8
August 30.3 25.4 37.2 21.1 79 254.1 228.9 10.7
September 31.6 24.1 41.1 20.6 72 217.9 389.4 7.3
October 35.5 23.1 41.1 14.4 48 43.1 257.1 5.9
November 34.9 19.2 39.4 10.6 40 15.7 148.3 6.2
December 32.8 16.1 38.9 6.7 41 2.1 42.2 6.1
ANNUAL 33.6 21.9 45.6 4.4 53 1203.5 459.2 8.1
Table 3.4: Summary statistics of climatic variables for Surat
24. Reference Manual – Hydro-meteorology (GW) Volume 3
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Figure 3.6: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Ahmedabad
Figure 3.7: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Surat
25. Reference Manual – Hydro-meteorology (GW) Volume 3
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Figure 3.8: Monthly rainfall totals and humidity for Ahmedabad and Surat
Figure 3.9: Monthly highest 24 hour rainfall and average windspeed for
Ahmedabad and Surat
26. Reference Manual – Hydro-meteorology (GW) Volume 3
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3.4 KARNATAKA
The monthly and annual statistics of selected climatic variables for Bangalore and Honavar are
presented in the Tables 3.5 and 3.6 and Figures 3.10 to 3.13.
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Karnataka Bangalore January 26.9 15.1 32.2 7.8 40 3.3 65.8 10.4
February 29.7 16.5 34.4 9.4 29 10.2 67.3 7.9
March 32.3 19.1 37.2 11.1 24 6.1 50.8 9.4
April 33.4 21.2 38.3 14.4 34 45.7 90.7 9.1
May 32.7 21.1 38.9 16.7 46 116.5 153.9 11.8
June 28.9 19.7 37.8 16.7 62 80.1 101.6 17.1
July 27.2 19.2 33.3 16.1 68 116.6 105.4 17.5
August 27.3 19.2 33.3 14.4 66 147.1 162.1 15.2
September 27.6 18.9 33.3 15.1 62 142.7 124.7 12.1
October 27.5 18.9 32.2 13.3 64 184.9 116.8 8.2
November 26.3 17.2 31.1 10.6 59 54.3 114.5 8.5
December 25.7 15.3 31.1 8.9 51 16.2 67.3 9.6
ANNUAL 28.8 18.4 38.9 7.8 50 923.7 162.1 11.5
Table 3.5: Summary statistics of climatic variables for Bangalore
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Karnataka Honavar January 31.9 20.1 36.1 15.6 57 1.4 20.3 5.1
February 31.3 20.5 37.2 14.2 64 0.1 36.3 5.2
March 31.9 22.7 37.8 17.8 67 1.1 29.2 5.1
April 32.4 25.2 35.1 20.6 70 24.1 107.9 5.4
May 32.3 25.8 35.1 20.6 72 137.5 238.5 6.3
June 29.3 24.1 33.9 21.1 86 1038.6 378.5 6.8
July 28.2 23.5 32.2 21.1 90 1176.2 330.2 7.2
August 28.3 23.5 31.8 19.4 88 638.5 282.5 6.1
September 28.8 23.2 32.2 20.6 84 349.3 181.1 4.4
October 30.6 23.2 35.6 18.3 79 174.7 209.1 4.2
November 32.5 21.9 36.7 15.6 66 47.4 118.4 4.4
December 32.7 20.9 37.2 16.1 59 7.4 102.6 5.2
ANNUAL 30.9 22.9 37.8 15.6 74 3596.3 378.5 5.4
Table 3.6: Summary statistics of climatic variables for Honavar
27. Reference Manual – Hydro-meteorology (GW) Volume 3
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Figure 3.10: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Bangalore
Figure 3.11: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Honavar
28. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-10
Figure 3.12: Monthly rainfall totals and humidity for Bangalore and Honavar
Figure 3.13: Monthly highest 24 hour rainfall and average windspeed for Bangalore and Honavar
29. Reference Manual – Hydro-meteorology (GW) Volume 3
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3.5 KERALA
The monthly and annual statistics of selected climatic variables for Trivandrum and Calicut are
presented in the Tables 3.7 and 3.8 and Figures 3.14 to 3.17.
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Kerala Trivandrum January 31.3 22.3 35.5 18.9 63 20.1 52.1 5.1
February 31.7 22.9 35.1 18.9 63 20.3 88.1 5.9
March 32.5 24.2 36.2 20.6 66 43.5 80.1 6.6
April 32.4 25.1 35.3 21.7 73 122.1 129.8 7.8
May 31.6 25.1 35.2 21.2 77 248.6 277.9 9.2
June 29.4 23.6 34.4 20.1 82 331.2 154.7 9.6
July 29.1 23.2 31.7 21.1 81 215.4 151.6 10.9
August 29.4 23.3 32.8 20.6 78 164.1 102.4 11.2
September 29.9 23.3 33.3 21.1 77 122.9 125.5 10.4
October 29.9 23.4 32.8 21.1 80 271.2 215.9 7.3
November 30.1 23.1 33.9 18.9 78 206.9 162.8 5.5
December 30.9 22.5 34.4 18.9 69 73.1 148.8 4.8
ANNUAL 30.7 23.5 36.2 18.9 74 1839.3 277.9 7.9
Table 3.7: Summary statistics of climatic variables for Trivandrum
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Kerala Calicut January 31.7 22.1 35.6 17.2 64 5.9 104.4 9.4
February 31.9 23.1 35.6 16.1 66 11.1 150.1 11.4
March 32.6 24.7 35.1 19.4 69 21.1 83.3 12.3
April 32.9 25.8 35.6 21.1 71 111.1 143.3 12.6
May 32.5 25.6 37.2 20.1 76 322.5 268.5 12.5
June 29.5 23.8 33.9 20.6 85 870.9 250.2 9.8
July 28.2 23.3 32.2 21.1 89 860.1 264.2 9.2
August 28.7 23.6 32.2 20.6 86 404.9 204.5 8.9
September 29.5 23.7 33.9 21.1 82 215.1 179.1 8.7
October 30.4 23.8 34.4 20.1 78 290.4 189.2 8.8
November 31.1 23.4 34.4 16.1 72 140.1 192.3 8.1
December 31.6 22.2 34.8 16.1 64 29.9 115.1 8.3
ANNUAL 30.9 23.7 37.2 16.1 75 3282.7 268.5 10.1
Table 3.8: Summary statistics of climatic variables for Calicut
30. Reference Manual – Hydro-meteorology (GW) Volume 3
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Figure 3.14: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Trivandrum
Figure 3.15: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Calicut
31. Reference Manual – Hydro-meteorology (GW) Volume 3
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Figure 3.16: Monthly rainfall totals and humidity for Trivandrum and Calicut
Figure 3.17: Monthly highest 24 hour rainfall and average windspeed for Trivandrum and Calicut
32. Reference Manual – Hydro-meteorology (GW) Volume 3
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3.6 MADHYA PRADESH
The monthly and annual statistics of selected climatic variables for Bhopal and Pachmarhi are
presented in the Tables 3.9 and 3.10 and Figures 3.18 to 3.21.
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Bhopal January 25.7 10.4 32.2 0.6 35 16.8 34.3 5.8Madhya
Pradesh
February 28.5 12.5 36.1 1.7 23 4.5 15.5 6.4
March 33.6 17.1 40.1 7.8 17 9.8 35.1 7.2
April 37.8 21.2 44.2 12.2 14 3.3 13.5 8.6
May 40.7 26.4 45.6 19.4 16 11.1 72.6 11.9
June 36.9 25.4 43.9 19.5 41 136.6 120.9 13.1
July 29.9 23.2 40.6 19.1 72 428.5 218.2 13.2
August 28.6 22.5 35.1 19.4 76 307.7 188.5 11.3
September 30.1 21.9 36.1 17.2 66 232.1 233.2 9.1
October 31.3 18.1 37.8 11.7 41 36.9 123.7 5.1
November 28.5 13.3 33.3 6.1 33 14.7 68.3 4.3
December 26.1 10.6 32.8 3.3 39 7.1 31.7 4.4
ANNUAL 31.5 18.5 45.6 0.6 39 1208.9 233.2 8.3
Table 3.9: Summary statistics of climatic variables for Bhopal
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Pachmarhi January 22.4 8.7 27.8 -1.1 49 27.9 94.2 3.3Madhya
Pradesh
February 24.7 10.4 31.7 -0.6 37 25.3 52.1 4.2
March 28.9 14.8 36.1 3.3 25 13.3 55.1 4.5
April 33.4 20.1 40.1 8.9 22 10.4 38.6 5.1
May 36.1 24.3 40.6 15.1 23 15.8 35.3 6.8
June 31.4 22.5 40.6 15.6 55 201.9 201.9 7.2
July 24.3 19.9 37.4 16.1 87 753.4 338.3 9.1
August 23.8 19.6 30.1 15.1 88 646.7 458.7 8.3
September 25.2 19.1 35.6 12.8 82 435.7 350.3 6.2
October 26.2 14.8 31.7 6.7 56 84.1 164.1 3.6
November 24.1 9.6 28.3 2.2 50 26.1 99.1 2.8
December 22.6 7.5 27.8 -1.1 47 4.2 63.1 2.7
ANNUAL 26.9 15.9 40.6 -1.1 52 2244.6 458.7 5.3
Table 3.10: Summary statistics of climatic variables for Pachmarhi
33. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-15
Figure 3.18: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Bhopal
Figure 3.19: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Pachmarhi
34. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-16
Figure 3.20: Monthly rainfall totals and humidity for Bhopal and Pachmarhi
Figure 3.21: Monthly highest 24 hour rainfall and average windspeed for Bhopal and Pachmarhi
35. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-17
3.7 MAHARASHTRA
The monthly and annual statistics of selected climatic variables for Mumbai and Mahabaleshwar are
presented in the Tables 3.11 and 3.12 and Figures 3.22 to 3.25.
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Mumbai January 29.1 19.4 35.1 11.7 63 2.1 49.3 9.1Maharash
tra
February 29.5 20.3 38.3 11.7 62 1.1 41.7 9.3
March 31.1 22.7 39.7 16.7 63 0.4 34.3 10.4
April 32.3 25.1 40.6 20.1 66 2.8 37.3 10.5
May 33.3 26.9 36.2 22.8 68 16.1 126.2 10.1
June 31.9 26.3 37.2 21.1 78 520.3 408.9 12.8
July 29.8 25.1 35.6 21.7 85 709.5 304.8 14.8
August 29.5 24.8 32.2 21.7 84 439.3 287.1 13.4
September 30.1 24.7 35.1 20.1 80 297.1 548.1 10.1
October 31.9 24.6 36.6 20.6 74 88.1 148.6 8.5
November 32.3 22.8 36.2 17.8 67 20.6 122.7 8.2
December 30.9 20.8 35.1 12.8 64 2.2 24.4 8.5
ANNUAL 31.1 23.6 40.6 11.7 71 2099.2 548.1 10.5
Table 3.11: Summary statistics of climatic variables for Mumbai
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
January 24.1 13.8 28.9 6.1 49 3.1 22.1 7.7Maharash
tra
Mahabaleshw
ar
February 26.1 14.9 31.1 3.9 40 1.8 29.1 8.1
March 28.8 17.4 33.9 9.4 43 6.8 33.1 9.7
April 29.3 18.8 36.1 11.1 55 29.6 95.1 10.7
May 28.8 18.2 34.4 13.9 67 55.4 62.2 11.6
June 21.9 16.9 32.2 12.8 94 898.3 310.4 14.8
July 19.1 16.7 25.9 13.9 100 2521.3 381.2 20.1
August 18.9 16.3 26.7 13.9 100 1714.7 339.9 17.3
September 20.3 15.7 27.8 12.2 98 709.2 327.1 11.8
October 24.1 16.1 30.6 11.1 78 179.3 191.5 10.3
November 24.1 14.9 28.9 10.1 62 56.7 148.6 10.3
December 23.5 13.9 28.2 8.3 52 6.1 39.6 9.7
ANNUAL 24.1 16.1 36.1 3.9 70 6182.3 381.2 11.8
Table 3.12: Summary statistics of climatic variables for Mahabaleshwar
36. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-18
Figure 3.22: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Mumbai
Figure 3.23: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Mahabaleshwar
37. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-19
Figure 3.24 Monthly rainfall totals and humidity for Mumbai and Mahabaleshwar
Figure 3.25: Monthly highest 24 hour rainfall and average windspeed for Mumbai and
Mahabaleshwar
38. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-20
3.8 ORISSA
The monthly and annual statistics of selected climatic variables for Cuttack and Sambalpur are
presented in the Tables 3.13 and 3.14 and Figures 3.26 to 3.29.
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Orissa Cuttack January 28.9 15.7 35.6 7.8 48 10.4 61.1 2.9
February 31.5 18.2 38.9 10.6 43 28.5 98.1 3.8
March 35.9 22.1 42.8 14.4 41 19.5 99.1 5.6
April 38.3 25.3 45.1 17.2 50 27.1 94.5 7.7
May 38.8 26.9 47.7 20.6 58 71.8 142.7 9.1
June 35.8 26.5 47.2 21.7 69 214.6 205.7 7.2
July 31.6 25.6 40.1 21.1 81 355.1 210.8 6.7
August 31.6 25.6 37.2 21.7 81 364.5 320.8 6.1
September 32.2 25.5 36.7 21.7 80 252.1 249.2 4.8
October 32.1 23.7 38.9 16.7 72 167.6 292.6 4.3
November 30.1 18.8 35.1 10.6 59 41.4 195.6 3.3
December 28.4 15.5 33.3 8.9 52 4.7 54.9 2.6
ANNUAL 32.9 22.5 47.7 7.8 61 1557.2 320.8 5.3
Table 3.13: Summary statistics of climatic variables for Cuttack
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Orissa Sambalpur January 28.2 12.6 33.9 4.4 45 18.8 90.7 3.1
February 30.5 14.9 37.8 5.6 38 24.7 55.1 3.7
March 35.4 18.7 43.3 11.1 29 23.1 46.2 4.3
April 39.6 23.8 45.6 14.4 27 11.7 45.2 5.3
May 42.1 27.6 47.2 20.6 29 24.5 107.9 6.7
June 37.2 26.9 46.7 19.4 57 237.6 254.5 8.1
July 30.7 24.9 40.6 18.3 82 503.1 401.3 8.3
August 30.7 24.9 35.1 21.1 83 476.5 290.7 7.4
September 31.5 24.8 36.1 20.6 79 262.4 200.9 5.5
October 31.5 22.1 36.1 12.8 67 67.7 173.7 3.8
November 29.3 15.8 33.9 7.8 55 9.5 106.2 3.1
December 27.7 12.2 32.2 4.4 49 2.1 39.1 2.6
ANNUAL 32.9 20.8 47.2 4.4 53 1661.5 401.3 5.1
Table 3.14: Summary statistics of climatic variables for Sambalpur
39. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-21
Figure 3.26: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Cuttack
Figure 3.27: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Sambalpur
40. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-22
Figure 3.28: Monthly rainfall totals and humidity for Cuttack and Sambalpur
Figure 3.29: Monthly highest 24 hour rainfall and average windspeed for Cuttack
and Sambalpur
41. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-23
3.9 TAMIL NADU
The monthly and annual statistics of selected climatic variables for Chennai and Madurai are
presented in the Tables 3.15 and 3.16 and Figures 3.30 to 3.33.
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Chennai January 28.8 20.3 32.8 13.9 67 23.8 212.9 9.1Tamil
Nadu
February 30.6 21.1 36.7 15.1 63 6.8 123.2 9.2
March 32.7 23.1 40.6 16.7 64 15.1 88.1 10.2
April 34.9 26.1 42.8 20.1 68 24.7 96.3 10.5
May 37.6 27.8 45.1 21.1 66 51.7 214.9 13.1
June 37.3 27.6 43.3 20.6 59 52.6 59.2 16.4
July 35.2 26.3 41.1 21.7 61 83.5 116.3 14.6
August 34.5 25.8 40.1 20.6 64 124.3 91.7 13.6
September 33.9 25.4 38.9 20.6 69 118.1 100.3 11.1
October 31.8 24.4 39.4 16.7 76 267.1 233.7 9.2
November 29.2 22.5 34.4 13.1 76 308.7 236.2 11.7
December 28.2 21.1 32.8 13.9 71 139.1 261.6 12.6
ANNUAL 32.9 24.3 45.1 13.9 67 1215.3 261.6 11.8
Table 3.15: Summary statistics of climatic variables for Chennai
State Station Month Temperature Humidity Rainfall Wind
speed
Daily
maximum
o
C
Daily
minimum
o
C
Highest
o
C
Lowest
o
C
% Total
mm
Highest
24 hr mm
Km/ h
Madurai January 30.2 20.9 34.4 15.6 54 26.2 152.4 7.3Tamil
Nadu
February 32.4 21.6 38.3 16.1 44 16.1 188.1 6.6
March 35.1 23.4 41.7 17.2 37 21.3 100.3 5.8
April 36.3 25.4 41.7 19.4 46 80.8 166.4 5.1
May 37.5 26.3 41.7 17.8 47 58.9 99.6 6.3
June 36.7 26.3 42.2 17.8 45 30.9 105.4 9.6
July 35.7 25.7 40.6 19.4 45 47.8 124.5 9.3
August 35.3 25.2 40.1 20.6 50 117.1 112.3 7.1
September 35.1 24.8 39.4 20.1 51 122.7 154.2 6.4
October 33.1 24.1 38.3 18.9 63 179.2 128.8 4.5
November 30.6 23.1 36.1 17.2 68 161.2 169.7 5.2
December 29.7 21.6 35.1 16.7 62 42.8 165.6 6.9
ANNUAL 33.9 24.1 42.2 15.6 51 904.9 188.1 6.7
Table 3.16: Summary statistics of climatic variables for Madurai
42. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-24
Figure 3.30: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Chennai
Figure 3.31: Monthly daily maximum and minimum temperatures and highest and lowest
temperatures for Madurai
43. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 3-25
Figure 3.32: Monthly rainfall totals and humidity for Chennai and Madurai
Figure 3.33: Monthly highest 24 hour rainfall and average windspeed for Chennai and
Madurai
44. Reference Manual – Hydro-meteorology (GW) Volume 3
Hydro-meteorology March 2003 Page 4-1
4 REFERENCES
• Buishand, T. (1977)
The variance of the amount of rainfall on an area related to point rainfall amounts.
(In Dutch with English summary) Mededelingen Landbouwhogeschool Wageningen, 77-10
• Matérn, B. (1960)
Spatial Variation. Stochastic models and their application to some problems in forest surveys and
other sampling investigations.
Meddelanden fran Statens Skogsforskningsinstitut Band 49-Nr 5.
• Rodriguez-Iturbe, I. and J. Mejia (1974)
On the Design of Rainfall Networks in Time and Space.
Water Resources Research, Vol. 10, No 4, pp. 713-728.