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     Download-manuals-ground water-training-dwlrg-wdatahandling Download-manuals-ground water-training-dwlrg-wdatahandling Document Transcript

    • World Bank & Government of The Netherlands funded Training module # 1 Understanding Conventional and DWLR Assisted Water Level Monitoring New Delhi, March 2000 CSMRS Building, 4th Floor, Olof Palme Marg, Hauz Khas, New Delhi – 11 00 16 India Tel: 68 61 681 / 84 Fax: (+ 91 11) 68 61 685 E-Mail: dhvdelft@del2.vsnl.net.in DHV Consultants BV & DELFT HYDRAULICS with HALCROW, TAHAL, CES, ORG & JPS
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 1 Table of contents Page 1. Module context 2 2. Module profile 3 3. Session plan 4 4. Main text 5 5. Overhead/flipchart master 6 6. Handout 7 7. Additional handout 8
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 2 1. Module context While designing a training course, the relationship between this module and the others, would be maintained by keeping them close together in the syllabus and place them in a logical sequence. The actual selection of the topics and the depth of training would, of course, depend on the training needs of the participants, i.e. their knowledge level and skills performance upon the start of the course. This is an independent module.
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 3 2. Module profile Title : Understanding Conventional and DWLR Assisted Water Level Monitoring Target group : Hydrogeologists, Asst-Hydrogeologists, Senior Technical Assistant Duration : One Session of 30 minutes Objectives : After the training the participants will be able to: • Differentiate between conventional water level monitoring and high frequency level monitoring. Key concepts : • Prevalent water level monitoring • High Frequency water level Monitoring • True hydrographs Training methods : Lecture Training tools required : OHS Handouts : As provided in this module Further reading and references :
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 4 3. Session plan No Activities Time Tools 1 • Discuss the prevailing water level monitoring, • Show the nature of the hydrograph emerging from conventional monitoring • Discuss the nature of aquifers being monitored, • List the deficiencies of the conventional monitoring, • Describe the advantages of dedicated piezometers over hand dug wells • Explain the need for high frequency monitoring and role of DWLR, • Explain a true hydrograph 15 min OHS 2 • Illustration 5 min OHS 3 Feedback 5 min 4 Wrap up 5 min
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 5 4. Main text Contents 1. Prevalent Monitoring 1 2. High Frequency Monitoring 2 3. True Hydrograph – What to do with it? 2
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 1 Understanding Conventional and DWLR Assisted Water Level Monitoring 1. Prevalent Monitoring Conventionally the groundwater monitoring in India has been conducted on the following lines: • Water levels are usually monitored in privately owned open dug wells tapping the upper unconfined aquifers. These levels reveal the piezometric head/water table elevation of the semi-confined/unconfined aquifers. However, the necessary well-aquifer hydraulic connection is not always beyond suspicion. • The frequency of monitoring has generally been restricted to four times in a year. These times are rather arbitrarily selected during pre-monsoon, monsoon, post-monsoon and winter seasons. It is presumed that these water levels represent the troughs and peaks of the water table hydrograph. However, many a time these data may be too sparse to yield reliable and credible water table hydrograph, as illustrated in figure 1. The figure shows the true hydrograph (derived from high frequency DWLR data) superposed over the corresponding hydrograph based upon four annual observations. Fig 1: True Hydrograph from phreatic aquifer (Granitic rock), in Kattangur Village, Nalgonda District Andhra Pradesh
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 2 • Limited monitoring of the piezometric head of the deeper confined/leaky confined aquifers has been carried out by some agencies, usually by observing the water level in the deep production tube wells. The tube wells, many a time may not be adequately isolated from (or worse, may even be tapping) the unconfined aquifer. Dedicated piezometers tapping only the deeper aquifers and duly isolated from the unconfined aquifer are almost non-existent. The historical monitoring programmes though quite extensive and commendable in many ways, have been deficit in several respects. The practising hydrogeologists have been conducting the resource evaluations quite credibly in spite of these deficits. They have been circumventing the problem by certain subjective practices based upon norms/past experience or intuitive reasoning. Nevertheless, this has restricted their practice in many ways. For example, no norms have been developed for estimating resource of the deeper aquifers. (This estimation is no doubt difficult due to uncertainties regarding the recharge zone, but lack of the piezometric head data has pre-empted its solution.) Similarly, since the practitioners have never been able to view a true water table hydrograph, recharge estimation by water balance of the unconfined aquifer gets uncertain in many ways. Further, time series analysis of the water level data is not routinely done because the data at necessary frequency are usually not available. 2. High Frequency Monitoring The Hydrology Project has enabled construction of a large number of scientifically designed piezometers tapping unconfined and the deeper aquifers. These piezometers have the necessary hydraulic connection with the targeted aquifers and are suitably isolated from overlying/underlying aquifers. Further, digital automatic water level recorders (DWLRs) are installed in these piezometers. This ensures measurement of undistorted piezometric head at the desired frequency, which may be much larger than the present frequency. In fact, the frequency may be so high that the resulting piezometric hydrograph may almost be continuous. 3. True Hydrograph – What to do with it? With the high frequency and credible piezometric head data emanating from the DWLRs, the groundwater practitioners in India shall have an access to true piezometric head hydrographs, possibly for the first time. This is bound to inspire the practitioners to enhance the scientific/technical content of their prevailing practice and also to incorporate in it many new analyses. Some of the possibilities shall be discussed subsequently.
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 6 5. Overhead/flipchart master
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 7 6. Handout
    • HP Trng Module File: “ 1 Conventional and DWLR assisted water level monitoring.doc” Version 10/10/02 Page 8 7. Additional handout These handouts are distributed during delivery and contain test questions, answers to questions, special worksheets, optional information, and other matters you would not like to be seen in the regular handouts. It is a good practice to pre-punch these additional handouts, so the participants can easily insert them in the main handout folder.
    • World Bank & Government of The Netherlands funded Training module # 2 Role of DWLR data in Groundwater Resource Estimation New Delhi, March 2000 CSMRS Building, 4th Floor, Olof Palme Marg, Hauz Khas, New Delhi – 11 00 16 India Tel: 68 61 681 / 84 Fax: (+ 91 11) 68 61 685 E-Mail: dhvdelft@del2.vsnl.net.in DHV Consultants BV & DELFT HYDRAULICS with HALCROW, TAHAL, CES, ORG & JPS
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 1 Table of contents Page 1. Module context 2 2. Module profile 3 3. Session plan 4 4. Main text 5 5. Overhead/flipchart master 6 6. Handout 7 7. Additional handout 8
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 2 1. Module context While designing a training course, the relationship between this module and the others, would be maintained by keeping them close together in the syllabus and place them in a logical sequence. The actual selection of the topics and the depth of training would, of course, depend on the training needs of the participants, i.e. their knowledge level and skills performance upon the start of the course.
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 3 2. Module profile Title : Role of DWLR data in Groundwater Resource Estimation Target group : Hydrogeologists, Asst-Hydrogeologists, Senior Technical Assistant Duration : One Session of 60 minutes Objectives : After the training the participants will be able to: • Appreciate the utility of high frequency DWLR water level monitoring. • Correlate the rainfall hyetograph to the corresponding water level hydrograph. • Appreciate the utility of high frequency ground water levels for systematic assessment of ground water resources. Key concepts : • Understanding the recharge process • Lumped water balance • Role of DWLR data Training methods : Lecture Training tools required : OHS Handouts : As provided in this module Further reading and references :
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 4 3. Session plan No Activities Time Tools 1 • Discuss the prevailing Water balance computations for unconfined aquifers • Scope for improvement of water balance computations by using DWLR data • Methodology for selection of water balance periods • Identification of effective rainfall events • Estimation of evapotranspiration loss 30 min OHS 2 • Illustration 10 min OHS 3 Feedback 15 min 4 Wrap up 5 min
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 5 4. Main text Contents 1. Understanding the Recharge Process 1 2. Lumped Water Balance 2 3. Role of DWLR data 3
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 1 Role of DWLR data in Groundwater Resource Estimation An important activity of many of groundwater practitioners in India is to estimate the groundwater resource (mainly monsoon recharge) by a lumped water balance of the unconfined aquifer, in accordance with GEC-84/97 norms. The high frequency water table data from the DWLRs can assist a practitioner in conducting such estimations more rationally and credibly. A few possible applications of such data are described in the following paragraphs. 1. Understanding the Recharge Process The high frequency DWLR water table data can assist a groundwater practitioner in understanding the recharge process. Such an understanding can lead to quantification of recharge parameters, which may be useful in conducting the resource estimation. The water infiltrated into the ground surface has to follow a circuitous path through the unsaturated zone extending from ground to the water table. As the water flows through the unsaturated zone, a part of it may be held back in soil storage and another part transpired by the vegetation. The conductivity of dry soil is very low. Thus, at the beginning of a rainy season (when the soil may be dry), most part of (or all of) the infiltrated water may be held back in the soil and there may practically be no recharge. However, as the initial rainfall events build up the soil moisture, the recharge process may be initiated. Thus, the rainfall has to accumulate to a certain level (RC), before a rainfall event starts producing recharge. Further, since the water has to flow through the unsaturated zone before it appears as recharge at the water table, there would be an inevitable lag (Tg) between a rainfall event and the consequent recharge. As discussed in the following section, recharge parameters RC and Tg need to be estimated for a proper resource assessment There are two ways of determining these recharge parameters. First is to simulate the flow of water through the unsaturated zone extending from the ground surface to the water table. Unsaturated flow is a complex phenomenon and its simulation requires intensive data of soil, which may not be available on a regional scale. The other way is to study the impact of the recharge derived from a rainfall event, on the water table. With the advent of the high frequency water level data emanating from the DWLRs, it is literally possible to see the signatures of a rainfall event upon the water table hydrograph and identify the above mentioned recharge parameters. This is accomplished in the following steps: • Superpose the hyetographs of all the rainfall events of a rainy season over the corresponding water table hydrograph. • Study the water level response following each rainfall event, starting from the beginning of the rainy season. • Identify the first rainfall event (say X) which is followed by a conspicuous water table rise. • Compute the cumulative rainfall preceding the X rainfall event. This provides the RC • Study the time lags between the rainfall events (starting from X) and the following water table rises. Determine an average time lag. This provides Tg.
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 2 For an illustration, refer to the figure 1 showing the rainfall events superposed over the high frequency water table data observed at Yeldurthy from May 27 to Oct 12, 1999. It is clear that the infiltration from all the rainfall events prior to Aug 1 does not lead to any recharge. It apparently is used up in building the soil moisture and providing the evapotranspiration. Thus RC may be estimated by adding up all the rainfall events preceding Aug 1. Similarly, the time lag between a productive rainfall event and the consequent recharge is clearly visible in the figure. Fig 1 Hydrograph of high frequency water table data observed at Yeldurthy, Medak district Andhra Pradesh with rainfall events superposed 2. Lumped Water Balance The current practice of recharge assessment typically is based upon water balance study of the unconfined aquifer. A water balance study involves an application of the continuity equation to the unconfined aquifer. The continuity equation in this context is a statement to the effect that the difference between the net recharge volume (I) and net discharge volume (O) equals the change of groundwater storage ( S). I, O and S must be in respect of the same aquifer area and the time period. I - O = S ∆S = Sy.∆h Where Sy is the Specific yield of the aquifer, and ∆h is the change in the spatially averaged water table elevation in the chosen period. This equation can be used to estimate anyone of the recharge or discharge components, or storage parameter. Typically, this approach is adopted for estimating the Specific yield and the recharge from monsoon rainfall. This involves first dividing a hydrologic year into the monsoon and non- monsoon periods. The Specific yield is estimated by carrying out the water balance of the non-monsoon period. Subsequently, the rainfall recharge is estimated by carrying out the water balance study of the monsoon season, employing the pre-computed value of the Specific yield.
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 3 3. Role of DWLR data The above stated strategy of water balance studies can be improved upon by employing the high frequency water table data emanating from DWLRs. These data shall permit a more realistic water balance study and hence a more accurate assessment of recharge. 3.1 Periods of water balance For estimation of a specific component of the water balance, it is necessary to select a period during which the component is just fully generated. Any period less than that shall lead to an underestimation of the component. A longer period shall attenuate the predominance of the component and hence shall lead to a less reliable estimate of the component. Thus for estimating the rainfall recharge by carrying out water balance study of a rainy season, it shall be desirable to define a period during which the entire rainfall recharge just occurs. This period could be different from the period of the monsoon rainfall because of the inevitable time lag between the occurrence of the rainfall and the consequent recharge. The period must span between the discrete times of the lowest and the highest water table and not the start and end of the rainy season. Similarly while estimating the specific yield by carrying out the water balance of the dry period, the duration of the water balance should incorporate the maximum possible decline from the viewpoint of activation of the specific yield. This implies that the duration should span between the discrete times of the highest and the lowest water table. Thus, for optimal identification of the specific yield it is necessary to carry out the water balance study from the highest (peak) to the lowest (trough) water table. Similarly for optimal estimation of rainfall recharge, it is necessary to carry out the water balance study from the lowest (trough) to the highest (peak) water table. This calls for an identification of the peaks and troughs and their times of occurrence. The frequency of manual monitoring of water table is generally not adequate to estimate the peaks and troughs accurately. The high frequency data from the DWLRs shall permit identification of true hydrograph of water level. A true hydrograph can lead to estimation of the pre-monsoon (troughs) and the post-monsoon water table elevations (peaks), and their times of occurrences; with a far higher resolution. Thus, the identified peak may be higher and the trough lower, than the corresponding estimates derived from the manually monitored hydrographs. This is illustrated in figure 2. The figure shows two years’ six hourly DWLR data, and manually monitored four-yearly data, from Nuthangal village in Nalgonda district of Andhra Pradesh. The hydrograph of the manually monitored data underestimates the peak by about a meter.
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 4 Fig 2. Hydrograph showing two years’ six hourly DWLR data, and manually monitored four-yearly data, from Nuthangal village in Nalgonda district of Andhra Pradesh 3.2 Selection of water balance years As already discussed, the rainfall recharge during a rainy season starts occurring only after a cumulative rainfall RC has occurred from the beginning of the season. Thus, if the total rainfall in a rainy season is equal to or less than RC, there may not be any rainfall recharge. As such, the years selected for estimation of recharge should have rainfall well above RC. As already discussed, this parameter can be estimated from the DWLR-derived water table hydrographs and the corresponding rainfall hyetographs. 3.3 Identification of effective rainfall events Identification of unambiguous times of the peak and the trough leads to an explicit determination of the time period of a water balance study. Thus, all other components of recharge and discharge in the water balance equation must correspond to this period. In this context, a special reference is called for in respect of the rainfall. Rainfall does not appear explicitly in the water balance equation. However, it does appear implicitly as rainfall- recharge. Thus, only such rainfall should be included in the water balance study, whose recharge occurs within the identified period. This could mean inclusion of a rainfall event, which occurred before the period, or exclusion of an event occurring towards the end of the period. This can be accomplished knowing the lag parameter of recharge Tg. As already discussed, this parameter can be estimated from the DWLR derived water table hydrographs and the corresponding rainfall hyetographs Fig 3.
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 5 Fig3. Water table hydrograph and the corresponding rainfall hyetograph from Garedapally village, Nalgonda district, Andhra Pradesh 3.4 Estimation of evapotranspiration loss Evapotranspiration from the unconfined aquifer forms a component of the net discharge from the aquifer. This loss could be quite significant during rainy seasons. Its estimation essentially requires identification of such periods during which the depth to water table falls below a shallow critical depth – dependent upon the capillary rise/root zone depth. Identifying such periods on the basis of just pre and post monsoon depths could be quite subjective and could lead to distorted estimates. Some periods may be missed altogether; others may be overestimated or underestimated. On the other hand, water table depth hydrographs derived from the DWLR data shall have much higher resolution and thus, shall permit a far more accurate identification of such periods.
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 6 5. Overhead/flipchart master
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 7 6. Handout
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 8 7. Additional handout These handouts are distributed during delivery and contain test questions, answers to questions, special worksheets, optional information, and other matters you would not like to be seen in the regular handouts. It is a good practice to pre-punch these additional handouts, so the participants can easily insert them in the main handout folder.
    • HP Trng Module File: “ 2 Role of DWLR data in GW Resource Estimation.doc” Version 10/10/02 Page 1
    • World Bank & Government of The Netherlands funded Training module # 3 Other applications of DWLR data New Delhi, March 2000 CSMRS Building, 4th Floor, Olof Palme Marg, Hauz Khas, New Delhi – 11 00 16 India Tel: 68 61 681 / 84 Fax: (+ 91 11) 68 61 685 E-Mail: dhvdelft@del2.vsnl.net.in DHV Consultants BV & DELFT HYDRAULICS with HALCROW, TAHAL, CES, ORG & JPS
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 1 Table of contents Page 1. Module context 2 2. Module profile 3 3. Session plan 4 4. Main text 5 5. Overhead/flipchart master 6 6. Handout 7 7. Additional handout 8
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 2 1. Module context While designing a training course, the relationship between this module and the others, would be maintained by keeping them close together in the syllabus and place them in a logical sequence. The actual selection of the topics and the depth of training would, of course, depend on the training needs of the participants, i.e. their knowledge level and skills performance upon the start of the course. This module is related to module 2 and should be referred during the discussions.
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 3 2. Module profile Title : Other applications of DWLR data Target group : Hydrogeologists, Asst-Hydrogeologists, Senior Technical Assistant Duration : One Session of 60 minutes Objectives : After the training the participants will be able to: • Appreciate the utility of high frequency DWLR water level monitoring • Enhance the professional practice beyond the primary task of Ground water Resource Assessment Key concepts : Conjunctive use planning • Identification of over-exploited areas • Scheduling of Pumpages • Calibration of aquifer response models • Identification of Cycles Training methods : Lecture Training tools required : OHS Handouts : As provided in this module Further reading and references :
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 4 3. Session plan No Activities Time Tools 1 • Discuss the utility of high resolution water level data in water logged areas, over-exploited areas, coastal areas. • Discuss the utility of high resolution data for reliable and credible model calibration. 30 min OHS 2 • Illustrations 10 min OHS 3 Feedback 15 min 4 Wrap up 5 min
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 5 4. Main text Contents 1. Conjunctive use Planning 1 2. Identification of Over-Exploited areas 2 3. Scheduling of Pumpage 2 4. Calibration of Aquifiers Response Models 3 5. Identifications of Cycles 3
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 1 Other applications of DWLR data The high frequency water level data from DWLRs, apart from permitting a more rational and credible lumped water balance studies, can be useful in many other ways such as follows: 1. Conjunctive use Planning Conjunctive use of the canal water and the groundwater in a canal command area is essentially aimed at avoiding water logging of the land. A land is considered to be water logged if the water table depth (below ground) is lower than a stipulated critical depth. Thus, a check for water logged conditions essentially involves a study of the water table depth hydrograph at a few key points with in the study area. The study leads to identification of such periods (if any) during which the depth is found to be less than the critical value. The manually monitored water table hydrographs are generally not of high enough resolution to identify such periods of water logging. Some water logging periods may be missed altogether; others may be overestimated or underestimated. On the other hand, water table depth hydrographs derived from the DWLR data shall have much higher resolution and thus, shall permit a far more accurate identification of water logging periods. This is illustrated in figure 1. The figure shows a DWLR-derived water table hydrograph from a canal command area. The water table rise coinciding with canal opening, and decline after its closure, and the consequent waterlogged periods are quite visible in the hydrograph. Such an identification of the waterlogged conditions can lead to much better planning of the groundwater development in the command area. Fig 1 shows the Water level hydrograph from canal command area
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 2 2. Identification of Over-Exploited areas Over exploited areas are characterised by falling annual peaks and troughs (refer figure 2). Thus, to identify such areas, it would be necessary to identify annual peaks and troughs of successive years. As already mentioned, the hydrograph derived from manually monitored water levels may miss either peak or trough or both. The high frequency data from the DWLRs shall permit identification of true hydrograph of water level and hence the peaks and troughs. Fig 2 shows the Water level hydrograph from an over exploited area 3. Scheduling of Pumpage The high frequency data from DWLRs may provide useful prompts regarding opportunity times for pumpage. A few examples are as follows: 3.1 Coastal Aquifers In coastal aquifers the times of daily peaks and troughs may to a large extent be governed
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 3 by the tidal cycle (refer figure 3). The DWLR data can permit its identification, which may assist in designing daily pumping schedules. 3.2 Canal Commands Seepage from canal may recharge the water table and may lead to its rise in the vicinity of the canal. However, there would be a time lag between the beginning of the discharge in the canal and the rise (refer figure 4). The rise may sustain for a while even after the closure of the canal discharge. The DWLR data can assist in identifying such time lags and hence the opportunity times for the pumpage. Fig 4 shows recharge to the water table hydrograph showing recharge contributions after canal openings 4. Calibration of Aquifiers Response Models Aquifer response modelling is a powerful tool to check the feasibility of a given spatial and temporal pattern of discharge and/or recharge. Thus, such models are being increasingly put to use to plan various activities like groundwater development, artificial recharge, conjunctive use etc. These models are very data intensive and require among others, spatially distributed aquifer parameters. Such data are almost never available, and thus, have to be derived by calibration. Calibration implies running the model in the historical period and arriving at such distributions of the parameters which lead to the closest possible match between the observed and the computed water level hydrographs/contours. It is evident that the high frequency data from the DWLRs shall permit far more reliable and credible model calibrations. 5. Identifications of Cycles A water level hydrograph represents the resultant effect of a number of phenomenon many of which may be periodic (that is, self repeating). Each periodic phenomenon imparts a periodicity to the hydrograph. However, due to their superposition, all these
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 4 periodicities may not be visible. Usually the hydrograph derived from manually monitored data, may comprise only an annual cycle displaying a relatively fast rise from trough to peak, followed by a short fast recession and finally a prolonged slow recession till the trough. (However, some exceptional phenomena like extreme exploitation, artificial recharge, discontinuation of pumpage may modify this trend.) On the other hand, a hydrograph derived from DWLR data shall comprise apart from an annual cycle, many cycles of shorter durations like seasonal, barometric, daily, tidal etc. 5.1 Harmonic Analysis Harmonic analysis is essentially a numerical algorithm capable of breaking a time series of a periodic attribute into these hidden periodicities (or say cycles). (The analysis however, is applicable only to stationary time series i.e., to time series devoid of any long-term trend.) The analysis reveals periodicities hidden in the time series. This may ultimately facilitate identification of significant or dominant cycles. A hydrograph is essentially a time series of water levels and cycles hidden in it may be identified by Harmonic analysis. The details of Harmonic analysis shall be discussed subsequently.
    • HP Trng. Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 6 5. Overhead/flipchart master
    • Hydrology Project Training Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 8 6. Handout
    • Hydrology Project Training Module File: “ 3 Other applications of DWLR data.doc” Version 10/10/02 Page 8 7. Additional handout These handouts are distributed during delivery and contain test questions, answers to questions, special worksheets, optional information, and other matters you would not like to be seen in the regular handouts. It is a good practice to pre-punch these additional handouts, so the participants can easily insert them in the main handout folder.
    • World Bank & Government of The Netherlands funded Training module # 4 How to identify the cycles using Harmonic Analysis New Delhi, March 2000 CSMRS Building, 4th Floor, Olof Palme Marg, Hauz Khas, New Delhi – 11 00 16 India Tel: 68 61 681 / 84 Fax: (+ 91 11) 68 61 685 E-Mail: dhvdelft@del2.vsnl.net.in DHV Consultants BV & DELFT HYDRAULICS with HALCROW, TAHAL, CES, ORG & JPS
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 1 Table of contents Page 1. Module context 2 2. Module profile 3 3. Session plan 4 4. Main text 5 5. Overhead/flipchart master 1 6. Handout 2 7. Additional handout 3
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 2 1. Module context While designing a training course, the relationship between this module and the others, would be maintained by keeping them close together in the syllabus and place them in a logical sequence. The actual selection of the topics and the depth of training would, of course, depend on the training needs of the participants, i.e. their knowledge level and skills performance upon the start of the course. This module is related to module 2 & 3 and should be referred during the discussions.
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 3 2. Module profile Title : How to identify the cycles using Harmonic Analysis Target group : Hydrogeologists, Asst-Hydrogeologists, Senior Technical Assistant Duration : One Session of 45 minutes Objectives : After the training the participants will be able to: • Understand the concept of Harmonic analysis, Key concepts : Components of a hydrologic time series • The Harmonics • Variance or power of a harmonic • Perodogram • Identifiable Cycles • What to do with Cycles • Analysis of hydrograph recession • Estimation of tidal efficiency • Estimation of barometric efficiency Training methods : Lecture Training tools required : OHS Handouts : As provided in this module Further reading and references :
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 4 3. Session plan No Activities Time Tools 1 • Discuss the periodicities in time series. • Describe the harmonics and periodogram • Discuss the methodology of harmonic analysis, isolation of dominant cycles. • Describe the estimation of T&S by analysing the hydrograph recession • Describe the estimation of Tidal Efficiency and barometric efficiency using the hydrograph 30 min OHS 2 • Illustration 5 min OHS 3 Feedback 10 min
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 5 4. Main text Contents 1. Components of a Hydrologic Time Series 1 2. The Harmonics 1 2.1 Identifiable Harmonics 2 3. Periodogram 2 4. An Illustration 2 5 What to do with cycles 5
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 1 How to identify the cycles using Harmonic Analysis 1. Components of a Hydrologic Time Series A hydrologic time series (say of groundwater level) represents the resultant effect of a number of phenomenon many of which may be periodic (that is, self repeating). Each periodic phenomenon imparts a periodicity to the time series. However, due to their superposition, all these periodicities may not be visible in the time series. Harmonic analysis is essentially aimed at breaking up the time series into these hidden periodicities (or say cycles). This reveals the hidden periodicities. This may ultimately facilitate identification of significant or dominant cycles. Each periodicity is represented by an independent time series of the form of a sinusoid wave (also known as a harmonic) and having its own parameters. The parameters include wavelength (time period), amplitude and starting point. Thus, first step of the analysis involves computation of the parameters. The parameters are so computed that superposition (summation) of the sinusoids leads to the original time series. Further sum of variances of individual sinusoids equals the variance of the original time series. The computed parameters permit an identification of the predominant cycles. 2. The Harmonics Consider a time series comprising n attribute data (Yi, i varying from 0 to n-1) at a uniform time interval (say ∆t). Thus, the total span of the time series is ∆t.(n-1). Spectral analysis describes the departure of the attribute (Y) from its arithmetic mean [say, (a0/2)], as a sum of (n-1)/2 harmonics [Hj, j varying from 1 to (n-1)/2]. Thus. Y at any time t since the beginning of the series is given by the following equation: ∑ − = += 2/)1( 12 1 )( n j jo HatY where: ∑ − = = 1 0 2 n i io Y n a )()( θθ jSinbjCosaH jjj += where: tn t ∆ = π θ 2 ∑ − =       = 1 0 22 n i ij n ij CosY n a π ∑ − =      π = 1n 0i ij n ij2 SinY n 2 b
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 2 The jth harmonic represents a phenomenon of a time period equal to (n-1). ∆t/j. Since j varies from 1 to (n – 1)/2, the time period of the identifiable harmonics varies from (n-1) ∆t to 2∆t. 2.1 Identifiable Harmonics Thus, the smallest identifiable cycle is of time period 2∆t, and time period of the longest identifiable cycle is the time domain (that is, length) of the time series. However, in practice only the cycles of time period lying in the range 4∆t to one fourth (or even one sixth) of the time domain may be identified reliably. For example, for correctly identifying an annual cycle the length of the time series must be at least four years. Further, if a daily cycle is to be identified, the interval must be 6 hours or less. 2.2 Variance or power of a harmonic The variance (or power) of the jth harmonic is given by the following expression: 22 jjj baA += As pointed out earlier, the variance of the time series equals sum of the variances of the individual harmonics. Variance is a measure of the scatter of a time series around the mean. Thus, it can be inferred that variance of a harmonic is a proportional to its relative dominance in the original time series and therefore, may be viewed as a measure of the relative significance of the associated phenomenon. 3. Periodogram A plot of the variance versus the harmonic number (j) is known as periodogram of the time series. Since this plot comprises discrete number of variance values, it is also known as discrete power spectrum. By a visual inspection of the periodogram, one can identify the dominant harmonic numbers, i.e., serial numbers of the harmonics displaying conspicuously high variance. Knowing the harmonic numbers, the corresponding time periods can be computed. 4. An Illustration As an illustration, harmonic analysis is performed on six days’ 144 hourly data of water level monitored at OUAT campus, Bhubaneswar, from Oct 22 to 28, 1999 (refer figure 1). Subsequent data were not included in the analysis, since they are rendered non-stationary by the infamous cyclone. (That is, perform the analysis only such part of the time series which does not display a long term trend.) The outcome of the analyses revealed that the time series comprises three dominant cycles of time periods 8, 12 and 24 hours (refer figure 2). The identified cycles are shown in figures 3. The possible composite daily cycle obtained by superposing these three cycles is shown in figure 4.
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 3 Fig 1. DWLR Data monitored at OUAT campus, Bhubaneswar, Orissa Fig;2 Periodogram showing the result of the harmonic analysis
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 4 Fig.3. Amplitude of fluctuations in different cycles Fig.4 Cumulative fluctuation for 6hr, 8hr, 12hr and 24 hr cycle
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 5 5. What to do with cycles? Harmonic analysis is essentially a mathematical tool facilitating isolation of dominant cycles from a stationary time series. Such isolated cycles shall be free from the noise and thus, could be viewed as intrinsic cycles. Being a mathematical tool, harmonic analysis does not lead to the physics of the identified cycles. The physics has in fact to be identified by the practitioner of the analysis. This essentially implies identification of phenomenon (such as, daily pumping/recovery, tidal effects, seasonal rainfall/pumpage/irrigation recharge) responsible for the identified cycles. The groundwater practitioners can infer the relative influence of the cycles on the groundwater system and can prioritise the field-work for their identification accordingly. This can be professionally very gratifying and could result in improvement of existing practices or evolution of new practices. A few suggestions are as follows: 5.1 Analysis of hydrograph recession The recession of a hydrograph comprises its declining phase, that is, from peak to the trough. The recession may result from processes like natural drainage to the hydraulically connected streams, pumpage and evapotranspiration. A recession predominantly resulting from the natural drainage is related to the aquifer geometry and the diffusivity (T/S). Thus, an analysis of such a recession can provide a preliminary estimate of the diffusivity, which in turn may lead to estimation of transmissivity or the storage coefficient, knowing the other. The steps of the computation are as follows: • Isolate the intrinsic annual cycle from the time series. Derive the corresponding annual cycle of the driving head by shifting the datum to stage of the draining stream during the period of the recession. • Plot the recession curve (log of the driving head versus the time). The curve may reveal two or more straight lines segments. The first one, usually steep and short, may represent a fast recession. This is usually followed by a relatively flat and long segment, representing a moderate/slow recession. • Assuming a linear relation between log of the driving head and the time, carry out a regression analysis of the dominant segment of the recession. Hence compute the depletion time. The time for one log cycle (to the base ten) change in the driving head, divided by 2.3, is termed as the depletion time. • The depletion time in general can be expressed as (k.L2 .T/S); where L is the distance of the sampled well from the draining stream along the flowline and k is a constant depending upon the boundary conditions. It could vary from 0.405 (drainage on both sides of the well) to 1.0 (drainage only on one side). The analysis described above holds for the recession of the outflow hydrograph also. The outflow may manifest as stream flow during dry season (when the entire stream flow may be derived from groundwater drainage) or as spring flow. The flow time series if available, may also be analysed in a similar way. This may provide a means of corroborating the estimate of diffusivity arrived at by analysing the driving head time series. 5.2 Estimation of tidal efficiency Tidal efficiency (TE) is defined as the ratio of the piezometric head fluctuation resulting exclusively from tides, to the causative fluctuation of the tide level. It’s estimate can provide a tentative value of specific storage (Ss) in accordance with the following equation:
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 6 )1/( TESs −= γθβ Where γ is the specific weight of water, θ is the porosity of aquifer and β is the inverse of the bulk modulus elasticity of water. Tidal efficiency may be estimated by separating tidal cycles from the water level hydrograph of a piezometer tapping a confined aquifer in coastal region. Knowing the tidal variations in the sea in the vicinity, the tidal efficiency can be computed. 5.3 Estimation of barometric efficiency Barometric efficiency (BE) is defined as the ratio of the piezometric head fluctuation resulting exclusively from the atmospheric pressure fluctuations, to the causative fluctuation of the atmospheric pressure expressed as head of water. It’s estimate can also provide a tentative value of specific storage (Ss) in accordance with the following equation: BESs /γθβ= Barometric efficiency may be estimated by first identifying the time period of a barometric cycle from the atmospheric pressure data and subsequently separating a cycle of the identified period from the water level hydrograph of a piezometer tapping a confined aquifer in that area. 5.4 Software The technical consultants to the Hydrology Project have conceptualised an approach for Harmonic analysis. The approach has been assimilated in a software, christened as DWLR- ANALYST.
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 1 5. Overhead/flipchart master
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 2 6. Handout
    • HP Trng. Module File: “ 4 How to identify the cycles using Harmonic Analysis.doc” Version 10/10/02 Page 3 7. Additional handout These handouts are distributed during delivery and contain test questions, answers to questions, special worksheets, optional information, and other matters you would not like to be seen in the regular handouts. It is a good practice to pre-punch these additional handouts, so the participants can easily insert them in the main handout folder.
    • World Bank & Government of The Netherlands funded Training module # 5 Understanding the Concept of Optimal Monitoring frequency of DWLR New Delhi, March 2000 CSMRS Building, 4th Floor, Olof Palme Marg, Hauz Khas, New Delhi – 11 00 16 India Tel: 68 61 681 / 84 Fax: (+ 91 11) 68 61 685 E-Mail: dhvdelft@del2.vsnl.net.in DHV Consultants BV & DELFT HYDRAULICS with HALCROW, TAHAL, CES, ORG & JPS
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 1 Table of contents Page 1. Module context 2 2. Module profile 3 3. Session plan 4 4. Main text 5 5. Overhead/flipchart master 6 6. Handout 7 7. Additional handout 8
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 2 1. Module context While designing a training course, the relationship between this module and the others, would be maintained by keeping them close together in the syllabus and place them in a logical sequence. The actual selection of the topics and the depth of training would, of course, depend on the training needs of the participants, i.e. their knowledge level and skills performance upon the start of the course. This module is related to module 2, 3 & 4 and should be referred during the discussions.
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 3 2. Module profile Title : Understanding the Concept of Optimal Monitoring frequency of DWLR Target group : Hydrogeologists, Asst-Hydrogeologists, Senior Technical Assistant Duration : One Session of 45 minutes Objectives : After the training the participants will be able to: • Arrive at optimal monitoring frequency for any given intended use of the DWLR data. Key concepts : • Credibility of derived attribute(s) • Preserving the hydrograph shape • Identification of Cycles • Correlation Training methods : Lecture Training tools required : OHS Handouts : As provided in this module Further reading and references :
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 4 3. Session plan No Activities Time Tools 1 • Describe the strategies to be adopted for arriving at optimal monitoring frequency, ensuring the credibility of the attributes, preserving the shape of hydrograph. 30 min 2 • Illustration 5 min OHS 3 Feedback 10 min
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 5 4. Main text Contents 1. Objective based Optimal Frequency 1 2. Optimizing Criteria 1 3. Optimizing Strategy 2 4. Correlation 3 5 Software 4
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 1 Understanding the Concept of Optimal Monitoring frequency of DWLR 1. Objective based Optimal Frequency The high frequency data emanating from the DWLRs shall assist the hydrogeologists in performing their professional activities more objectively and hence more credibly. The requirement of the frequency can apparently not be uniquely defined and shall depend upon the intended use of the high frequency data as well as upon the local hydrogeological and hydrological characteristics. For example, an aquifer having low specific yield may display faster water level variations and hence may need a higher frequency of monitoring. Similarly, if the objective is to estimate only the peaks and troughs, a higher frequency may be adopted around the beginning and the end of the rainy seasons and a lower frequency may be adopted at other times. On the other hand if the objective is to arrive at a true hydrograph, a uniformly high frequency may have to be adopted. A few strategies for arriving at the optimal monitoring frequency are described in the following paragraphs: 2. Optimizing Criteria It follows from the preceding paragraph that there does not exist any unique optimal monitoring frequency. The optimal monitoring frequency would depend upon the expectation from (or intended use/uses of) the hydrograph to be monitored. Some criteria could be as follows: 2.1 Credibility of Derived Attribute(s) There may normally be a few well-defined objectives of monitoring the water table/piezometric head. The objectives may relate to deriving one or more of the following attributes from the observed hydrograph. • Peak of the hydrograph • Trough of the hydrograph • Range of water level fluctuation • Time of shallow water level, i.e., time during which the water level rises above a stipulated shallow critical level • Time of deep water level, i.e., time during which the water level falls below a stipulated deep critical level Thus, the criteria would be to arrive at such monitoring frequency that the desired attribute(s) as derived from the observed hydrograph is (are) close enough to the true values (i.e., the values derived from the true hydrograph). 2.2 Preserving the Hydrograph Shape This implies that the selected monitoring interval should be such that the monitored hydrograph resembles closely with the true hydrograph. This is indeed the most stringent and all-encompassing expectation requiring uniformly small monitoring intervals. The intervals would depend upon the degree of the desired resemblance. As discussed subsequently, correlation is an index of similarity between the shapes of two time series. It shall be an index of resemblance if the two time series display the variation of the same variable. Thus, the criteria could be to arrive at such an interval which ensures a high
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 2 enough correlation between the true and the monitored hydrographs. 2.3 Identification of Cycles As already stated, the cycle of smallest time period that can be identified (or separated) by the Spectral analysis is 4∆t, where ∆t is the interval between two successive water level data. Thus, the monitoring interval has to be at lone fourth of the time period of the smallest cycle intended to be identified. 3. Optimizing Strategy It follows from the above discussion that for optimising the monitoring frequency, we shall require the true hydrograph. The hydrograph of smallest feasible interval (say hourly) could be deemed as the true hydrograph. Therefore, it is necessary to first procure a time series of water level with small enough monitoring interval. Subsequently under-sampled hydrographs of increasing monitoring intervals, are simulated as follows: • Knock off the intermittent data from the true hydrograph to simulate the under-sampled series of the chosen larger interval. • Simulate the under-sampled hydrograph by estimating the knocked off data by linear interpolation. The simulated under-sampled hydrographs may be analysed to arrive at the optimal monitoring frequency as follows: 3.1 Credible Attribute Estimation Compute the desired attribute from the true hydrograph and from each of the simulated under-sampled hydrographs. Terming the attribute value as computed from the true hydrograph as X and the values computed from ith simulated under-sampled hydrograph as Ai, compute the loss array [Li = ABS(X - Ai)/S]. Here, S is a relevant quantity for normalizing the error. Thus the array represents the loss of information (expressed as a fraction of the chosen S) on account of increasing the time interval from the minimum feasible to the one incorporated in ith simulated under-sampled hydrograph. This array could be interpreted for specific attributes as follows: Peak, Trough and Range: (Over-estimation of depth to peak, under-estimation of depth to trough, and consequent under-estimation of the range) Selecting S as the true range (that is, vertical distance from trough to peak in the true hydrograph), the array shall represent the loss of information expressed as a fraction of the true range. Time of shallow/deep water level: Selecting S as true period, the array shall represent the loss expressed as a fraction of the true time. Plot the computed array [Li ] versus the corresponding intervals of the simulated under- sampled hydrographs. Assigning an acceptable level of the loss, pick up the optimal interval of monitoring. 3.2 Preservation of Hydrograph Shape The simulated under-sampled hydrographs of increasing time interval may be successively compared visually with the true hydrograph. This may lead to a threshold interval beyond which the two series may cease to resemble each other. Alternatively, this could be done more objectively in the following steps: • Compute the correlation (described in the following section) between the true hydrograph and each of the simulated under-sampled hydrographs.
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 3 • Plot the computed correlations against the time interval. • Pick up the optimal interval corresponding to the minimum desired similarity between the two hydrographs. • 3.3 Identification of Cycles The longest permissible interval for identifying a cycle of any time period, by Harmonic analysis is half the time period. For a more reliable identification of the cycle, it may be desirable to further restrict the longest permissible interval say to one fourth of the time period. Thus, for identifying a daily cycle, it may be desirable to have an interval no bigger than 6 hours. 4. Correlation This statistic determines the degree of linear interrelation (that is, scaled similarity) between two time series. A direct linear relation (that is, as one series rises, the other also rises and vice versa) is termed as positive correlation. An inverse linear relation (that is, as one series rises, the other declines and vice versa) is termed as negative correlation. If the rise of one series has apparently no effect on the other, the two series are known to be uncorrelated. The two series must have the same data frequency and an adequately long overlap. A correlation between two series falling in different time spans can be computed by analysing the overlapping period only, that is, by curtailing one or both the series. If there is no overlapping period, the correlation can not be estimated. If the two series comprise data at different frequencies, it is necessary to manipulate one of the two series to ensure frequency-compatibility. Thus, either the series of higher frequency (say series of DWLR data) may be pruned, or the missing data in the series of low frequency (say series of manual data) may be interpolated. This correlation is essentially a normalised covariance between the pivotal and the derived series. Thus, the correlation ® between two series (xi, i = 1, 2, ……., n) and (yi, i = 1, 2, ………, n) shall be given by the following equation: COR = yx ii n i SS yyxx . )).(( 1 −−∑= ;1 n x x i n i ∑= = n y y i n i ∑= = 1 Sx = 2 1 )( xxi n i −∑= ; Sx = 2 1 )( yyi n i −∑= ;
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 4 Two positively correlated time series shall have a correlation greater than zero and it may go up to +1. A correlation of +1 indicates a perfect positive correlation, that is, a direct proportionality of the fluctuations in the two series and hence a perfect linearity with positive gradient between the two variables. Similarly, two negatively correlated time series shall have a correlation less than zero and it may go up to –1. A correlation of –1 indicates a perfect negative correlation, that is, an inverse proportionality and hence a perfect linearity with negative slope. Two uncorrelated series shall have a zero correlation. 5 Software The above stated approach for optimising the monitoring frequency was conceptualised by technical consultants to the Hydrology Project. The approach has been assimilated in a software, christened as DWLR-ANALYST.
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 6 5. Overhead/flipchart master
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 7 6. Handout
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 8 7. Additional handout These handouts are distributed during delivery and contain test questions, answers to questions, special worksheets, optional information, and other matters you would not like to be seen in the regular handouts. It is a good practice to pre-punch these additional handouts, so the participants can easily insert them in the main handout folder.
    • HP Trng. Module File: “ 5 Concept of Optimal Monitoring frequency of DWLR.doc” Version 10/10/02 Page 1