CHAPTER 5 GROUNDWATER RECHARGE …shodhganga.inflibnet.ac.in/bitstream/10603/10201/14/14...Groundwater Recharge Estimates for Study Villages 225 CHAPTER 5 GROUNDWATER RECHARGE ESTIMATES

Groundwater Recharge Estimates for Study Villages 225

CHAPTER 5

GROUNDWATER RECHARGE ESTIMATES BY DIFFERENT

METHODS

The Saurashtra groundwater recharging movement has been examined from two points of

view: one, whether there has been significant enhanced recharge that could provide

enhanced agricultural returns to the farmer; two, whether the recharge could be quantified

at the village level so as to connect with the agricultural returns. This chapter attempts to

address these two aspects. How the enhanced recharge could have contributed to

enhanced agricultural returns is analyzed in the following chapter.

This Chapter is divided into three sections: section 1 deals with computation of recharge

values by three methods, namely, WL & SY Method, the Regression Method and the

CRU Method. The theoretical aspects of these methods have been discussed in the

preceding Chapter 4. Section 2 examines the Uncertainty in Recharge Estimates by

different methods so as to be aware of not only the relative merits and demerits of

recharge estimations but also the limitations. Section 3 compares the recharge values

obtained from the three methods and draws conclusions on the usefulness of employing

more than one method.

SECTION 1

ESTIMATION OF GROUNDWATER RECHARGE FOR STUDY VILLAGES

[1] Water Level & Specific Yield Method

In India, as per the GWRE 2002 guidelines, the CGWB adopts (and recommends) use of

the WL & SY method and the Regression Method (CGWB, 2004:27) for computing


water balance and to notify the stage of groundwater development135

for the purposes of

monitoring. However, it also emphasizes cross checking of results between these two

methods and stipulates that the variation lie within 20% only (CGWB 2004:33). The

guidelines also strongly recommend budgeting for environmental flows and allocations

for drinking and industrial purposes136

. The water balance estimate is done by the CGWB

at the level of taluka; a taluka comprises many villages and an average figure for such a

large geographical area with diverse geological and meteorological conditions is often

misleading. The study district Rajkot comprises 14 talukas, 846 villages, and 10 towns

and cities137

. The level of groundwater development for Gujarat State was estimated

between 65-85% during 1997138

(GoG, 1997) while it was estimated at 76.47% for the

year 2004 (CGWB, 2004). Current indications are that the transition is from semi critical

to critical stage (Safe: <70% stage of groundwater development; Semi critical: 70-90%;

Critical: >90%; Overexploited: >100% (CGWB & GoG, 2005). Table 5.1 gives the stage

of groundwater development for the study talukas of Rajkot district.

Table 5.1: Groundwater development in study talukas

District Taluka Groundwater development

(as of 31st March 2004) (%)

Rajkot Gondal 97.35

Rajkot Jamkandorna 74.48

Rajkot Wankaner 56.37

Rajkot Morbi 77.09

Source: GoG, 2004139

.

The following Table 5.2 gives the detailed steps in this process of computation of

recharge for one village Ambaredi. A similar procedure is adopted for all the 6 study

villages. The values of recharge obtained by all the three methods are presented in Table

5.12.

135

Measured as the ratio of gross groundwater draft for all uses to the net available groundwater

availability expressed in percentage (CGWB, 1997). 136

These are expected to take care of factors other than the water level and specific yield in the WL & SY

equation. 137

http://www.gec.gov.in/envis/SoER_Table_htm/DisPro.htm. 138

www.cgwb.gov.in accessed Feb 10, 2009. 139

The reference year 2004 is used as the study year for primary data collection was 2003-4.

http://www.gec.gov.in/envis/SoER_Table_htm/DisPro.htm

http://www.cgwb.gov.in/


Table 5.2: Computation of Groundwater Balance and Stage of GW development for

Ambaredi village (Mudrakartha, 2008)

WL & SY Regression Method

1 Gross GW Recharge 2.2241 1.8331

2 For Environmental Flows @5% 0.1112 0.0917

3 Available GW recharge/year (1-2) 2.1129 1.7414

4 GW Draft for irrigation from primary data 0.4661 0.4661

5 Domestic and Industrial draft (15% of 1) 0.3346 0.2750

6 Gross GW Draft for all uses (4+5) 0.7997 0.7411

7 Regeneration Recharge @30% of (4) 0.1398 0.1398

8 Net GW balance (3-6+7) 1.4530 1.1402

9 Level (stage) of GW Development (%) 37.9 42.6

[Note : All figures in Million Cubic meters except for sl.no.9;

: Minor mismatch in totals due to rounding off may exist]

The average annual water-level fluctuation (denoted by the difference between the

maximum and the minimum water levels in the wells) for Ambaredi is estimated as 8.1 m

for the reference year from June 2003 to May 2004. These maximum and minimum water

levels in arid and semi arid regions, where unimodal rainfall pattern exists, are obtained

generally during peak monsoon and subsequent pre monsoon periods. The water levels

actually represent net values as there is extraction as well as indirect or localised recharge

possible even after the monsoon duration. Thus, the groundwater recharge computed

would almost always be underestimated. Before proceeding for computation, it is useful

to understand the terms used.

(i) Gross groundwater recharge

In general, it is assumed (although not quantitatively measured), that 70% of the natural

recharge to groundwater can be extracted (Athavale, 2003). The CGWB guidelines for

categorization of groundwater assessment units for water balance also consider up to 70%

groundwater development as safe (GoG, 1998; CGWB, 2004:18; Planning Commission,

2007).

Table 5.2 gives the detailed steps for computing the groundwater balance. The specific

yield of basalt, the predominant rock type in the study areas, ranges from 1 to 3 (Sinha &

Sharma, 1988). Using equation the WL & SY equation (1) [NR = Area (sq. km) x


average of difference of maximum and minimum water levels from wells (m) x specific

yield (%) of the geological formation], the volume of rainwater that has recharged into

the ground (NR) is computed as 2.224 MCM and 6.67 MCM for specific yields 1 and 3

respectively for basalt which is a very wide range. Which value of the specific yield

would represent the study village is discussed in section 3? For our present purpose, we

assume that the specific yield 1 may be appropriate, also being on the lowest side, in

order to reduce uncertainty due to reasons explained in section 2 of this Chapter. Using

the second method of Regression equation, [natural recharge NR (or RE) = 0.174

(Rainfall in mm)-62], the volume of water that has recharged into the ground is computed

as 1.83 MCM. In terms of recharge, the two methods yielded values given in columns B,

C and F of Table 5.12.

(ii) Allocation for environmental flows

GoG guidelines of 1997 & 2002 (GoG, 1998; CGWB, 2004) provide for 5% of gross

groundwater recharge for surface flows in river bodies for inter-basin transfers. Concerns

during the eighties and the nineties were that many important basins in India were either

closing140

or closed141

. Some studies have also expressed concern about many sub basins

also being in the process of closing or closed due to the large number of water harvesting

activities promoted by various agencies. These activities intercept not only the surface

flow to downstream areas but also impact groundwater recharge adversely. The GWRE

guidelines (2002) provide for a 5% allocation of gross groundwater recharge as

environmental flows to check, and pre-empt, partial or complete closure of basins. As can

be seen from Table 5.2, the computations here consider this allocation.

(iii) Groundwater draft

Groundwater draft represents the total volume of water pumped out from all the well

structures in a particular year. The draft can be computed based on the well inventory

data comprising, inter alia, the number, and type, of pumpsets and their horsepower, their

running hours and discharges, for all wells in a given village. Table 5.2 shows the total

140

Closing basins are those that have inter basin flow during wet season, but no usable flow during dry

season. Even the Indus, the Ganges and the Yellow River are closing by this definition (Seckler et al.

2003). 141

Closed basins are those that do not have any usable flows during any part of the year, not even during

wet season (Seckler et al. 2003).


annual groundwater draft for Ambaredi village computed as 0.4661 MCM for the year

2003-04.

(iv) Allocation for Drinking Water and Industrial needs

GoG guidelines of 1997 & 2002 (GoG, 1998; CGWB, 2004) also provide for 15% of the

annual gross groundwater recharge towards drinking water and industrial needs. Under

these guidelines, the competent authority142

could regulate extraction of groundwater in

times of water scarcity, thus meeting essential drinking water needs. Where industries do

not exist, which is usually the case in rural areas, the water is supposed to be available for

environmental purposes. In practice, it may also be overextracted.

(v) Regeneration Recharge or Return Flow

The irrigation provided to crops is not fully utilised by the crop. There is return flow143

into the water bearing formations or aquifers. It is estimated that one-third of the total

water used for irrigation percolates and adds to the groundwater reserve (Athavale, 2003).

For the purpose of computations for the study villages, thirty percent of return flow is

considered also because of the shallow groundwater level (near surface during monsoon)

with a high at 20 m for Ambaredi and 13 m for Jalsikka cluster of villages. The irrigation

practice is through field channels covering the whole farm; the channels are of course not

too wide to be classified as suitable for flood irrigation.

[2] The Regression Method

The regression equation for basalt is Recharge RE=0.174 (Rainfall in mm)-62 (Athavale,

2003). Parameters other than rainfall are subsumed to be taken account of. On the face of

it, mathematically, rainfall is the only input given. For the study villages, average rainfall

of 740 mm for Rajkot district is considered for the year 2003 as obtained from official

sources for computations. This gives a uniform value of recharge of 66.76 mm for all the

study villages.

142

The competent legal authority is the Central Ground Water Authority, which functions through its

Regional Offices; often, the district administration is notified as local authority. 143

Seepage and percolation losses are about 60 percent of the applied water in a canal command area

(Reddi and Reddy 2006).


[3] CRU-NUT_MONTH Method

Chapter 4 has described the Climatic Research Unit (CRU), University of East Anglia

proposed method of estimating recharge using climatic variables such as precipitation,

temperature, diurnal temperature, vapour pressure, cloud cover, sunshine duration and

wet days. The procedure and steps for estimation of recharge have also been described in

detail therein.

The CRU and NUT_MONTH method is very useful as „in the absence of actual

hydrological data such as observations of river flow data at a number of points along a

river and its tributaries; long period basic meteorological data like rainfall, temperature,

humidity, etc., can be used for estimating water potential of a region or a basin and its

variation in space and time by using suitable technique‟ (Kulkarni, 2003 as quoted in

Ramesh & M. G. Yadava, 2005). The method is also useful to assess groundwater

recharge when water level data is not available.

The following steps are adopted for running the CRU Model:

The location coordinates of the areas for which climate variables are sought to be

extracted are identified; the coordinates can be obtained from a toposheet,

published by the Survey of India.

The coordinate referencing in CRU program is made in the form of degrees.

Therefore, the coordinates are converted into degrees for easier working as shown

in the 5. 3 (last two columns, (5) and (6)) for the study villages. As can be seen

from the same Table 5.3, the study villages are falling in two distinct nodes in the

CRU global database. Ambaredi village falls in first node (hereafter referred to as

Ambaredi cluster), and Jalsikka, Vithalpar, Haripar, Kerala and Bella (hereafter

referred to as Jalsikka cluster), fall in second node (column 2).


Table 5.3: Study Villages and Coordinates

S.

no.

Village Latitude

(in deg.,

min., sec)

Longitude

(in deg.,

min., sec)

Cell

coordinates

(in degrees)

Reference node

on CRU global

database

(1) (2) (3) (4) (5) (6)

a. Ambaredi

22_21-71_70

70031‟30” E 21

058‟00”N 70.50-71.00E,

21.50-22.00N

70.750E,

21.750N

b. Jalsikka

23_22-71_70

71000‟00” E 22

030‟00”N 71.00E,

22.50-23.00N

70.750E,

22.750N

c. Vithalpar

23_22-71_70

70059‟00” E 22

071‟00”N 70.50-71.00E,

22.50-23.00N

70.750E,

22.750N

d. Haripar-Kerala-

Bella

23_22-71_70

70045‟00” E 22

032‟00”N 70.50-71.00E,

22.50-23.00N

70.750E,

22.750N

In the CRU input file, the location coordinates along with village name are

provided as shown in the (CRU_input file), and the years for which the climate

data is desired.

Running the program CRU_TS21_READ_METEO by double clicking would use

the CRU_input file and generate an output file that contains data organised in four

files corresponding to four sub-cells. Consider an area represented by latitude and

longitude of one degree by one degree; the CRU program divides each such area

into a cell of 0.5 deg. by 0.5 deg. Thus, we have four sub-cells in one degree by

one degree. The data is organised around the mid-point of each sub-cell; the

outputs from CRU are also generated around the mid-values of these four sub

cells.

Thereafter, the sub-cell that represents the study area needs to be identified since the

resolution of the CRU program is 0.5 deg by 0.5 deg. The last column of the Table 5.3

represents the sub-cells for the study villages in Rajkot district. As already mentioned

elsewhere, the output file of CRU contains month-wise precipitation and number of

stations corresponding to the years specified, for the four sub-cells. The data of the sub-

cell that comprises the location of the study area becomes an input file for the

NUT_MONTH program that computes precipitation recharge.


LONG TERM RAINFALL-RECHARGE ANALYSIS OF STUDY VILLAGES

BASED ON CRU DATA

In a Research Report (No: 2/2006) published by the National Climate Centre, P.

Guhathakurta and M. Rajeevan (2006) analysed the Trends in the rainfall pattern over

India. They cite several studies that revealed no particular trend, rather it was random

behaviour of the Indian monsoonal rainfall. But on spatial scale, trends were noticed. On

an all India level, the months of June, July, August and September contributed 13.8, 24.2,

21.2, and 14.2% to the total rainfall respectively. The post and pre monsoon rainfall

contributed 11% each. Although broadly the monsoon rainfall is categorised as a

systematic event occurring every year, it shows considerable variation during individual

years. The important aspects of the variations are (Guhathakurta, P & M. Rajeevan,

2006):

The timing of the onset or the commencement of the rainy season;

The pattern of distribution of rainfall including the timing;

The timing of withdrawal of the monsoon from the different parts of the country,

and

The total amount of rainfall of the season.

For a farmer, both the total rainfall and its distribution during the rainy season are

important. The common challenges faced by farmers include delayed or early

commencement of the monsoon rains, long breaks comprising no rains, and early

withdrawal. High intensity spells or excessive spells also result in flooding and water

logging, and consequently, loss of crops, partially or fully. As discussed earlier, the

pattern of rainfall also determines the rate of recharge.

Based on the analysis of long term rainfall data for Gujarat for the years 1901 to 2002,

Patel, K. I et al. (2004) indicated that the consecutive years of receiving negligible to

below normal rainfall never exceeded more than three years; whereas the consecutive


years of having received near normal to above normal ranged from one to five years in

Saurashtra and Kutchh region144.

Further, Indian monsoonal rainfall has a high coefficient of variation, CV, (standard

deviation expressed as percentage of the mean) exceeds 30% over large areas of the

country and is over 40-50% in parts of Saurashtra, Kutch, and Rajasthan. In some places,

the variability is as high as 100% implying that these places are particularly liable to very

heavy rainfall in some years and very scanty rainfall in others (Jagannathan & Bhalme,

1973). For example, the lowest and highest rainfall recorded during the period 1901-2002

respectively were 144 mm and 1361 mm for Ambaredi, and 120 mm and 1328 mm for

Jalsikka clusters. In addition, there is high yearly fluctuation as can be seen from Figures

5.2 and 5.8. Therefore, these variations should be kept in mind while drawing

conclusions, acknowledging the inaccuracy and uncertainty elements that will be

introduced, in particular, during computation of recharge values.

This section examines the long term trend analysis of rainfall and recharge, and then the

rainfall-recharge relationship for both clusters of Ambaredi and Jalsikka villages. Further,

the factors Actual Evapo-transpiration (AET) and Potential Evapo-transpiration (PET)

would also be considered and their influence on the natural recharge process will also be

examined.

As can be seen from Table 5.3, the study villages are falling in two distinct nodes in the

CRU global database. Ambaredi village falls in first node, and Jalsikka, Vithalpar,

Haripar, Kerala and Bella fall in second node. The rainfall data for Ambaredi and

Jalsikka clusters for 102 years-from 1901 to 2002 is extracted from the public access

domain www.uea.ac.uk using the CRU_TS-READ_METEO program as explained

earlier. The rainfall and recharge analysis are carried out for both the clusters as

described in the following section.

144

For other regions in Gujarat: one to seven years in North and Middle Gujarat region, and one of

seventeen years in South Gujarat region. The Kutchh, Surendranagar, Banaskantha, Patan, Mehsana,

Jamnagar, Kheda, Anand, Rajkot and Bhavnagar districts experienced drought conditions two to three

times during the period 1991 to 2002.

http://www.uea.ac.uk/


The data for Ambaredi shows that the lowest rainfall was 144 mm corresponding to the

year 1987 and a high 1361 mm in the year 1959. The average long term rainfall based on

the 1901-2002 rainfall data for Ambaredi works out to 625.9 mm. Table 5.4 shows that

once in 12.8 years, we have a situation of rainfall less than 300 mm. On an average,

Ambaredi receives rainfall between 301-600 mm once in 2.7 years, and between 601-

1000 mm, once in two years. Put differently, once in 2.3 years, there is a probability of

rainfall between 301 and 1000 mm (see also Figure 5.3).

Table 5.4: Rainfall data for 1901-2002 for Ambaredi cluster

Rainfall mm

No. of

years

Years

in %

Frequency

(once in ..

years)

0 – 300 8 7.8 12.8

301 – 600 38 37.3 2.7

601 – 1000 52 51.0 2.0

1000 – 1361 4 3.9 25.5

Total 102 100

301-1000 45 88.3 2.3

Figure 5.1 is scatter diagram between rainfall and recharge for Ambaredi for the long

term data of 102 years. The rainfall on the x-axis is sorted in ascending order and

recharge values plotted indicates that there is a broad correlation in the trend between

rainfall and recharge. A detailed analysis shows that recharge is generated only under

certain conditions of rainfall; soil constants and climate parameters also play a role. This

section examines these aspects for the study villages.


Figure 5.1: Scatter diagram between rainfall and recharge for Ambaredi

Figure 5.1 shows that for rainfall of up to around 350-400 mm, the recharge generated is

almost negligible-in other words, close to zero. The number of such zero-recharge years

is 25 out of 102 implying a frequency of once in 4.1 years as seen from Table 5.5.

Further, the figure also shows that between 400 – 640 mm, there are many years with

recharge around 60mm, although interspersed with zero recharge. Beyond a rainfall of

500 mm, the recharge tends to become more certain. For a rainfall range of 550-750 mm,

which is quite populous, the recharge is around 100 mm-something very significant for

agriculture in Ambaredi, given its semi arid climatic conditions. Beyond 640 mm, the

correlation between rainfall and recharge becomes much more positive, with no „zero‟

recharge years; the recharge on the contrary tends to become significant.

0

100

200

300

400

500

600

700

0 200 400 600 800 1000 1200 1400

Rainfall-Recharge for Ambaredi


Figure 5.2: Rainfall-Recharge relation for Ambaredi

Figure 5.3: Rainfall versus probability of RF occurrence and frequency years.

0

200

400

600

800

1000

1200

1400

1600

19

01

19

07

19

13

19

19

19

25

19

31

19

37

19

43

19

49

19

55

19

61

19

67

19

73

19

79

19

85

19

91

19

97

Rainfall-Recharge for Ambaredi

Rainfall, mm

Recharge, mm

7.8

37.3

51.0

3.9

12.8

2.7 2.0

25.5

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0 - 300 301 - 600 601 - 1000 1000 - 1361

Rainfall in mm

Rainfall versus prob of RF occurrence, frequency- Ambaredi

% years

frequency: once in …years


Table 5.5: Recharge and frequency of recharge occurrence for Ambaredi

Recharge

mm

No. of

years %

Frequency

(1 in ..

years)

0 25 24.5 4.1

1 -60 19 18.6 5.4

61 – 100 11 10.8 9.3

101-150 10 9.8 10.2

151 – 200 14 13.7 7.3

200 – 250 6 5.9 17.0

251 – 300 8 7.8 12.8

301 – 350 3 2.9 34.0

351-400 4 3.9 25.5

401-600 1 1.0 102.0

601-609 1 1.0 102.0

102 100.0 1.0

61-300 49 48.0 2.1

>100mm 47 46.1 2.2

Let us analyse from the point of view of the recharge pattern in Ambaredi. As can be seen

from Table 5.5, „zero‟ or negligible recharge occurs in 25 years out of 102 years: this

means that once in 4.1 years, there will be at least one zero or negligible recharge year

(corroborates with the point that the frequency of rainfall is also zero in one out of four

years). The zero or negligible recharge year also corroborates with water scarcity or

drought year. The highest recharge of 609 mm has occurred in the year 1959 for the

highest rainfall of 1361. About 60 mm recharge is possible once in 5.4 years. If we

consider recharge between 61 and 300 mm, which is a significant quantum of recharge to

occur in semi arid areas, the probability works out to once in two years. Higher recharge

is always welcomed. What is the frequency of occurrence of more than 100 mm

recharge? Table 5.5 further shows that 47 out of 102 years have generated recharge

greater than 100 mm, which translates roughly as once in 2.2 years. Very high recharge

of 300 mm and above occurs once in 8 years; although spaced, such „wet‟ years tend to

build up groundwater storage, as can be seen from the rainfall-recharge relationship

(Figure 5.1), in some way compensating for secular declines in the local water levels.


Thus, it may be generally concluded that, for Ambaredi, a rainfall of 500 mm and beyond

will generate a recharge of at least 60 mm, and a rainfall beyond 640 mm will generate

100 mm. Since such measures of recharge also imply abundant soil moisture, agriculture

is expected to benefit significantly, subject to water-intensive crops not raised majorly.

During the other years, for rainfall between 340-500 mm, some recharge does take place.

Similarly, although Figure 5.1 indicates that the possibility of recharge to groundwater in

Ambaredi is almost nil below say, 340-350 mm, in reality one could expect some amount

of recharge seen in the form of quick build up of water levels in response to even say a

couple of high intense spells when occurring, in particular in hard rock areas such as

Ambaredi, where water tables are shallow. This aspect indicates that the values obtained

as recharge also have an inherent element of uncertainty, sometimes interpretation errors

adding to this uncertainty; this element however seems to be very small and negligible.

Similar discussion is valid for Jalsikka cluster as described in the following section.

Figure 5.4: Recharge versus Rainfall probability-Ambaredi

24.518.6

10.8 9.8 13.75.9 7.8

2.9 3.9 1.0 1.04.1 5.4 9.3 10.2 7.317.0 12.8

34.025.5

102.0102.0

0.0

20.0

40.0

60.0

80.0

100.0

120.0

Recharge in mm

Recharge versus RF probability-Ambaredi

Recharge in mm


Note: Curves PET and AET overlap, shown as the top curve.

Figure 5.5: Relationship between rainfall, recharge and PET and AET for Ambaredi

The Actual Evapotranspiration (AET) and the Potential Evapotranspiration (PET) are

important climatic factors in groundwater recharge. Figure 5.5 indicates that the PET and

AET overlap completely because the values are exactly the same every year; the ratio of

AET/PET therefore is equal to one. This implies that the field capacity is achieved and

recharge taking place on a year to year basis. However, Table 5.5 also shows that there is

nil recharge once in four years. Which implies that there should be at least one water

deficit year out of 4.1 years. However, when we analyse the rainfall data from Table 5.4,

it is clear that less than 300 mm rainfall occurs once in 12.8 years. Table 5.5 also shows

that there is recharge between 61-300 mm happening once every 2.1 years, more

specifically, more than 100 mm once every 2.2 years. So, we have a situation where there

is some soil moisture retention taking place even during low rainfall years (say 300 or

400 mm), and no recharge being generated. However, as discussed in the foregoing, the

rainfall 550-750 mm range is quite populous (Figure 5.1), and contributing to generation

of recharge. All these factors point to the soil moisture availability of some degree at the

end of the year which is most probably carried forward to the next hydrological year.

Whether this soil moisture contributes to the recharge directly is not known, but it does

hasten the recharge by way of achieving quicker saturation of soil (that is, field capacity

that includes root zone) during the succeeding rainfall events. The available soil moisture

1717 1727

0

919

370

-500

0

500

1000

1500

2000

19

01

19

07

19

13

19

19

19

25

19

31

19

37

19

43

19

49

19

55

19

61

19

67

19

73

19

79

19

85

19

91

19

97

Ambaredi

Potential ET in mm Actual ET in mm

Recharge in mm Rainfall in mm


may not be of any help to the farmer in making sowing decisions as he would not have

methods of knowing or estimating soil moisture content in the root zone. However, when

the sowing decision is taken at the advent of timely first rains, this soil moisture would

help by prolonging the wilting point of the crop.

Alternatively, it may be also be concluded that though the soil moisture condition in

general is good in Ambaredi, and recharge does happen during 3 out of 4 years to support

crops, there is some inadequacy in the soil moisture balance method itself to reflect

accurately the ET variations on a year to year basis. The AET and PET values are

estimated by the Thornthwaite-Mather method in the NUT_MONTH programme as the

values were broadly matching with the values already referred to in literature. Also

because the normal method of estimating the ET basing on assumptions of crop type,

area, soil conditions etc. which are generally inadequate introduce inaccuracy in the

recharge estimation.

In short, if we consider a four year cycle, two years could be with a rainfall of 600-

1000mm, one between 300-600 mm and one less than 300 mm. In terms of recharge, one

could be a year with zero recharge, two with recharge between 60 and 300 mm, and one

could be with minimal or recharge less than 60 mm. This is a broad trend in terms of

rainfall and recharge pattern based on long term analysis.

It is interesting to note that the farmers of Ambaredi during focus group discussions have

found that their recharge activities have helped them take at least two crops every year-

one among them being the 6-month cotton crop.

Rainfall and Recharge Analysis for Jalsikka Cluster

The rainfall data for Jalsikka cluster of villages (Jalsikka, Vithalpar, Haripar, Kerala and

Bella) is sourced from the public access domain www.uea.ac.uk. The long term average

computed from the long term data works out to 584.05 mm. The data reveals that Jalsikka

cluster registered a low rainfall of 120 mm for the year 1987 to a high of 1328 in 1956.

Analysis of rainfall shown in Table 5.6 indicates that once in 6.8 years, Jalsikka cluster


receives rainfall less than 300 mm. The rainfall incident is between 301-600 mm once in

2.6 years on an average, and between 601-1000 mm once in 2.5 years. Put differently,

once in 2.5 years approximately, there is a probability of rainfall between 301 and 1000

mm.

Table 5.6: Analysis for Rainfall trend 1901-2002 for Jalsikka

Cluster

Rainfall mm No. of years Years in % Frequency

(1 in .. years)

0 – 300 15 14.7 6.8

301 – 600 39 38.2 2.6

601 – 1000 41 40.2 2.5

1001 – 1328 7 6.9 14.6

Figure 5.6: Rainfall vs. rainfall frequency and probability of occurrence for Jalsikka

When we consider rainfall and recharge pattern of Jalsikka cluster as shown in Figure

5.7, it is difficult to draw a strictly linear correlation between rainfall and recharge on a

year to year basis, just like in the case of Ambaredi. However, there appears to be a broad

correlation as can be seen from Figure 5.8 for the given soil and climatic conditions of

Jalsikka.

14.7

38.240.2

6.96.8

2.6 2.5

14.6

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

0 - 300 301 - 600 601 - 1000 1001 - 1328

Rainfall in mm for Jalsikka

Rainfall vs prob of RF and frequency-Jalsikka

% probability

frequency: once in …..years


Fig 5.7: Rainfall-Recharge relation for Jalsikka

Figure 5.8: Rainfall-Recharge correlation for Jalsikka

Further analysis of the rainfall in relation with recharge from Figure 5.7 (Rainfall-

recharge correlation) would reveal that a rainfall up to 560 mm approximately, has

produced negligible recharge. The non-zero recharge years for Jalsikka cluster below 560

mm of rainfall are very few, unlike in the case of Ambaredi cluster. Put differently, the

number of zero or negligible recharge years for Jalsikka cluster is 40 out of 102, which

works out to once in 2.6 years (Table 5.7) as compared to Ambaredi which is once in 4.1

years. Figure 5.8 shows that rainfall above 560 mm and below 620 mm has produced an

0

100

200

300

400

500

600

700

0 200 400 600 800 1000 1200 1400

Re

char

ge in

mm


0

200

400

600

800

1000

1200

1400

1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001

Re

char

ge in

mm


Rainfall in mm

Recharge mm


annual recharge of around 60 mm. Between 620 mm and 750 mm, the recharge is

generally around 80-100 mm. For a rainfall of above 750 mm, the recharge generated is

above 100 mm.

If we look at recharge quantity and frequency, less than 60 mm recharge is possible once

in 7.8 years, excluding the zero recharge years, as can be seen from Table 5.7. If we

consider recharge between 60 and 300 mm, which is a very significant quantum of

recharge to occur in semi arid areas, the probability works out to once in 2.6 years.

Further, as can be seen from Appendix 3, the highest recharge of 593 mm occurred

during the year 1956 which is also the year of highest rainfall of 1328 mm.

What is the frequency of occurrence of more than 100 mm recharge? Analysis indicates

that 37 out of 102 years have generated recharge greater than 100 mm, which transforms

into a frequency of roughly once in 2.8 years; between 61-100 mm recharge occurs once

in 8.5 years. The graph (Figure 5.8) indicates that approximately 80-120 mm of recharge

is produced for rainfall range of 580-750 mm. Higher recharge of 300 mm and above

occur infrequently; however these „wet‟ years tend to build up groundwater storage,

compensating for long term declines in water levels.

Table 5.7: Recharge in Jalsikka

Recharge mm

No. of

years %

Frequency

(once in ..

years)

0 40 39.2 2.6

1 – 60 13 12.7 7.8

61-100 12 11.8 8.5

101-150 16 15.7 6.4

151-200 4 3.9 25.5

200-250 1 1.0 102.0

251-300 6 5.9 17.0

301-350 5 4.9 20.4

351-400 2 2.0 51.0

401-593 3 2.9 34.0

102 100.0 1.0

>100 mm

37 36.8 2.8


Groundwater Recharge and Crops

Groundwater recharge is a function of the soil properties, temperature and evapo-

transpiration (that includes vegetative cover/landuse-in the case of Jalsikka, the presence

of crop, mostly) some of which keep altering on a year to year basis.

For agriculture crops to be raised, a combination of rainfall and recharge (in the form of

water in the wells) are important. The rainfall helps in land preparation and timely

sowing while the recharged water from wells supports supplementary irrigation during

rabi and during long intervals of rainfall, or low rainfall during kharif. The common crops

raised in the study villages are cotton, groundnut, and winter wheat. Cotton is taken as a

6-month crop-sown in kharif and goes upto rabi.

It is interesting to note that the recharge (when occurring as discussed in the previous

section) for a rainfall window is always a percentage lower for Jalsikka compared to

Ambaredi.

In Jalsikka, Vithalpar, Haripar, Kerala and Bella cluster of villages, the wells are of

around 13 m depth; lithomarge of 1-2 m thickness occurs anywhere between 10-13

metres. The top soil is clay. Conditions here seem to facilitate recharge only during long-

duration, high intensity spells, and aided by soil and moisture conservation structures,

both in-land and across the streams (constructed as part of the watershed programme)

during which the soil reaches its field capacity and soil moisture adds to the shallow

water table. In case of smaller duration, high intensity spells, there is soil moisture that is

added. In other rainfall conditions such as when it is of very high intensity, more runoff is

indicated perhaps indicated by the large number of zero recharge years. The addition of

soil moisture during smaller duration, high intensity spells increases the available soil

moisture, discussed and corroborated in later sections. The „wet years‟, though

interspersed at longer intervals of time and whenever occurring, contribute to

groundwater recharge by raising water levels. This can be seen in the fact that recharge

greater than 100 mm is possible once in 2.8 years for Jalsikka cluster, which is 2.2 years

for Ambaredi.


The soil moisture balance is a critical factor in recharge generation. The occurrence of

recharge is also dependent upon the carried forward soil moisture balance. Farmers in

Haripar, Kerala and Bella take one crop mostly, the 6-month cotton crop and groundnut.

Half of the farmer community goes in for low water requiring crop as second crop.

Further local situations also could be influencing the soil moisture balance, and the water

levels in the wells, through stream-aquifer connectivity such as in the case of Jalsikka and

Vithalpar, mainly due to availability of water in the river for longer time duration.

What about the evapotranspiration in Jalsikka? Figure 5.9 shows the potential and actual

evapotranspiration graphs drawn from the values obtained from the NUT_MONTH

programme for Jalsikka.

Note: Potential and Actual ET values are same and hence only one curve is seen.

Figure 5.9: Potential and Actual Evapotranspiration trend for Jalsikka

It can be seen from Figure 5.9 that the AET and PET values for Jalsikka are exactly

same-identical every year although year to year variation exists. Because of this, the two

curves coincide perfectly. The year to year variation, however, is not very large. Same

values of PET and AET, or the ratio of AET/PET equal to one, indicate that the soil

moisture supply is sufficient (Tallaksen et al. 2004). This is also corroborated during

focus group discussions with Jalsikka farmers when they said that they are able to take

only one or two crops. It is common for Jalsikka group of farmers to go in for cotton

0

500

1000

1500

2000

2500

19

01

19

07

19

13

19

19

19

25

19

31

19

37

19

43

19

49

19

55

19

61

19

67

19

73

19

79

19

85

19

91

19

97

mm

Year

Jalsikka

Precipitation in mm

Potential ET in mm

Actual ET in mm

Recharge in mm


which is a six months crop; in effect this is like taking two crops. Though there is some

water left in the wells, farmers feel that it is insufficient for irrigating a whole water

intensive third crop. A small percentage of farmers raise crops such as vegetables and

small millets etc.

Sensitivity Analysis for soil parameters

In situ weathering of pre-existing rocks produces sand, silt and clay in combination that is

dependent upon the composition of the parent rock type. The shape, size and roundness

of the resultant particles depends upon the degree of weathering and extent of

transportation they have been subjected to. Depending upon the combination of the sand,

silt and clay, the soil types are categorised as fine sand, sandy loam, silt loam, clay loam

and clay. The soil textural classification chart has been developed by USDA which shows

clay on one extreme, with 40% of clay particles and 45% sand, and the rest silt; in the

intermediate level, loam contains equal composition of sand, silt and clay. There are

many types of the clay mineral; depending upon the type of clay mineral, the soil displays

characteristics of swelling or shrinking with changes in water content. The other

combination would have sand and silt in higher percentages. Loam soils are considered

to be most favourable for plant growth as they can hold more water than sand and better

aerated than clay; they can be worked easier for land preparation for agriculture. The

composition of the soil, in other words, the texture, determines the composition of the

other two phases, namely, the soil water and soil air phases as described earlier (Michael,

1983). These aspects have been discussed in section 1 of Chapter 4 on Groundwater

Recharge Literature Review. The USDA classification chart (given in standard text

books) provides a method of identifying the type of soil depending upon the composition.

The water holding capacity of the soils is as follows (Michael, 1983):


Table 5.8: Water holding capacity and soil type

Soil type % moisture, based on dry weight of

soil

Depth of

available water

per unit of soil

Field Capacity

(FC)

Permanent

Wilting point

(WP)

cm/ m depth of

soil

Fine sand 3-5 1-3 2-4

Sandy loam 5-15 3-18 4-11

Silt loam 12-18 6-10 6-13

Clay loam 15-30 7-16 10-18

Clay 25-40 12-20 16-30

The objective of this section is to understand the influence of the soil parameters and the

rooting depth on the available soil moisture and recharge. Tables 5.8 and 5.9 basically

give output data from the program NUT_MONTH for various inputs of rooting depth and

the soil type described by its clay, sand and silt composition. The input parameters also

include number of soil layers and the FC, CP and WP in percentage appropriate for the

crops raised in Ambaredi and Jalsikka clusters. The output data includes the FC, CP, WP

in mm, the climate data, and the recharge. The available soil moisture can be calculated

as the difference between FC and the WP, as shown in the Tables 5.8 and 5.9.

Relation between Rooting depth, FC and Recharge

In order to study the relationship between rooting depth, FC and recharge, let us consider

Table 5.9, and Figure 5.10 generated from it for the Jalsikka cluster. Various rooting

depth values (10, 50, 90, 120 cm) were used in the NUT_MONTH programme to

compute FC, recharge etc.

Table 5.9: Rooting Depth, soil constants, recharge and available soil moisture

relationship for Jalsikka for the year 2003 for actual rainfall (740 mm) Roo

ting

dept

h

Cla

y

San

d

Sil

t

F

C

C

P WP FC CP WP

Rai

nfal

l PET AET

Rech

arge

Avl

soil

moist

ure

Cm % % % % % mm mm mm mm mm mm mm mm mm

a. 10 28 42 30 31 6 18.1 31 6 18 739 1797 1797 260 13

b. 50 28 42 30 31 6 18.1 155 30 91 739 1797 1797 157 64

c. 90 28 42 30 31 6 18.1 279 54 163 739 1797 1797 57 116

D 100 28 42 30 31 6 18.1 310 60 181 739 1797 1797 44 129

e. 120 28 42 30 31 6 18.1 372 72 217 739 1797 1797 18 155


Figure 5.10: Rooting depth, FC and Recharge for Jalsikka cluster

Figure 5.10 shows that as the rooting depth increases, recharge decreases. This implies

that the thicker the root zone the higher is the demand for soil moisture. Which implies

that after saturation of the rooting depth, the soil zone would tend to achieve field

capacity under conditions of adequate water supply and result in soil moisture balance

and recharge. Recharge would also depend upon the depth to the water table. Here in the

case of Jalsikka, the recharge is occurring, which indicates that field capacity is achieved

and the water is reaching the groundwater table. Well inventory indicates that the depth

to the water level during monsoon in wells is near surface or shallow depending upon the

physical elevation of the well. Further, the rooting depth and recharge are inversely

related, which also implies that the recharge will depend upon the type of crops grown,

because different crops have different rooting depths depending not only on the crop but

also on crop variety (Michael, 1983). Put differently, this implies that the field capacity

should increase with rooting depth which is corroborated by the rooting depth versus FC

curve in the Figure 5.10. Again, increased rooting depth also implies increased available

soil moisture readily accessible for the plant, which is computed as the difference

between the field capacity (FC) and the permanent wilting point (WP). This is

corroborated by the Figure 5.11 for Jalsikka for the year 2003.

260

157

57 441831

155

279310

372

0

50

100

150

200

250

300

350

400

0 20 40 60 80 100 120

Rooting depth in cm

Rooting depth vs FC andrecharge-JalsikkaActual RF 2003

Recharge in mm

FC in mm


Figure 5.11: Rooting depth versus available moisturefor Jalsikka cluster.

The available soil moisture is also calculated for all the situations. Fine-textured soils

have a wide-range of water between FC and permanent WP than coarse-textured soils

unlike sandy soils which have mostly non-capillary water that tends to release most of it

within a narrow range of potential due to predominance of large pores (Michael, 1983).

As can be seen from Table 5.8, the range of water available as soil moisture is 160-300

mm for clay and 100-180 mm (16-30 cm) per metre depth for clay loam. This availability

reduces as the silt and sand composition increase. Fine sand at the end of USDA chart

opposite clay has just 20-40 mm of available soil moisture. Put differently, the

Poiseuille‟s law comes to play where the rate of flow of water (through a pipe) is

proportional to the fourth power of the pore size. Assuming saturated conditions, since

the pore size increases as the transition happens from sand to loam to clay, the rate of

flow in soils of various textures is also more or less in that order (Michael, 1983). In

short, soils with fine texture serve not only as good storage zones but also yield larger

quantities of water.

What is the relationship of WP, recharge and available soil moisture? While these are

touched upon in the above discussion for Jalsikka, we will discuss based on the output

data obtained from the NUT_MONTH programme for various inputs of wilting point for

the study villages. Although the pattern of response is similar for all the study villages,

for the purpose of discussion, let us consider the Table 5.9 again for Jalsikka. The Table

and the Figure 5.12 shows that as the rooting depth increases, the wilting point increases

and consequently, the available soil moisture increases as also the recharge.

260

157

57 441813

64

116 129155

0

50

100

150

200

250

300

0 20 40 60 80 100 120Rooting depth, cm

Rooting depth vs Recharge and Soil moisture-Jalsikka: Actual RF 2003

Recharge, mm


Figure 5.12: Relation between Rooting Depth, Recharge, soil moisture and WP for

Jalsikka for Actual rainfall 2003

Another example is that of Jalsikka for the year 2002; all other factors remaining the

same, the wilting point here is changed as can be seen in the Table 5.10. As the wilting

point is reduced, the available soil moisture has increased. The recharge in all cases is

zero and hence the impact could not be qualified as increasing or decreasing. But as seen

in the previous graph, as the soil moisture increases, recharge decreases.

Table 5.10: Wilting point versus available soil moisture Cla

y Sand

Sil

t FC CP WP FC CP WP Prec PET AET

Rech

arge

Avl soil

moisture

% % % % % % mm mm mm mm mm mm mm Mm

a. 28 42 30 31 6 18.1 279 54 163 249 1829 1829 0 116

b. 28 42 30 31 6 12.4 279 54 112 249 1829 1829 0 167

c. 28 42 30 31 6 10.8 279 54 97 249 1829 1829 0 182

Since the FC, CP and WP influence the available soil moisture and recharge, it will be

interesting to see how the soil constants would influence when their values for one layer

are the same as when distributed as two layers. In the Table 5.10, row c shows that

irrespective of whether the soil is composed of one layer or two layers, the FC, CP and

WP are the same in two cases. If there are two layers, then the soil constants are present

as average of the two layers as can be seen in row c of Table 5.10.

260

157

5744

1813

64

116129

155

0

50

100

150

200

250

300

0 20 40 60 80 100 120

Rooting depth, cm

Rooting depth vs Wilting Point, Recharge and Soil moisture-Jalsikka: Actual RF 2003

Recharge, mm

Soil moisture, mm

Wilting point, mm


However, reducing the rooting depth or thickness by half has resulted in increase of

recharge by three times and decrease in available soil moisture by half (see Table 5.11).

This is because the amount of soil moisture stored in the rooting zone also is reduced by

half, as can be seen in the FC, which is also reflected as the increase in the available soil

moisture. This response is for the given conditions of water table (which is shallow) and

all other climate variables remaining the same. This indicates that rooting depth makes a

significant difference in the process of groundwater recharge.

Table 5.11: Impact of Rooting Depth layer on recharge and available soil moisture in

Jalsikka S.

no.

RD Lay

ers

Cla

y

San

d

Sil

t

FC

C

P WP FC CP WP Rai

nfa

ll*

PET AET Rec

har

ge

Av

l so

il

mo

istu

re

mm No. % % % % % % mm mm mm mm mm mm mm mm

a. 100 1

2

8 42 30 31.1 10 20.1 311 100 201 739 1797 1797 66 110

b. 50 1

2

8 42 30 31.1 10 20.1 156 50 101 739 1797 1797 176 55

The amount of increase in the recharge is also a function of the depth to water table. The

“losses” where the water tables are shallow include ET losses due to evaporation and

transpiration requirements by plants and crops. Deeper water tables would show lower

quantum of recharge due to the need for meeting with the moisture requirements of the

intermediate layers.

Table 5.10, for example, also shows that soil moisture could be available even when the

recharge is zero. This however depends upon so many factors which have been discussed

in section 1, Chapter 4 as part of literature review; what is important now is to recognise

that provided the annual rainfall is above a certain limit, there is a likelihood of recharge.

While rainfall lower than 300 mm is most likely to produce no recharge, anything above

that level, depending upon the vegetation and climatic factors, may first result in

enhancing soil moisture, and more or less after soil moisture saturation, adds to the

groundwater table. The threshold limit from rainfall analysis already described earlier is

around 640 mm for Ambaredi and around 600 mm for Jalsikka. Rainfall analysis, as

described earlier has also indicated that for Ambaredi, an annual rainfall of 400-640 mm


is likely to produce an annual recharge of around 60 mm, which for Jalsikka is between

560-620 mm of rainfall. Hence, rainfall above 300 mm and below 400 mm for Ambaredi,

and above 300 mm and below 560 mm for Jalsikka is likely to add to soil moisture.

How do the AET and PET impact on the soil moisture? Analysis of the AET and PET

data for the years 1901-2002 for both Ambaredi and Jalsikka has shown that AET and

PET are both equal for all the years on a year to year basis. The output files of both

Ambaredi and Jalsikka from NUT_MONTH also indicate that AET is equal PET when

sufficient soil moisture supply is available.

Tallaksen and Henny (2004) show that the wet years help carry forward not only soil

moisture but also lead to higher groundwater levels. Further, they found that the

difference in the AET during consecutive years in 95% of the case is not more than 35-40

mm/yr. The same has been found for both the clusters of study villages. For example,

Figure 5.13 for Jalsikka shows that the variance in AET in consecutive years has been

mostly between +50 and -50 mm/yr for all the 102 years from 1901-2002 except for a

value of 132 mm (difference for the years 1986 and 1987); this is because the highest

AET was 1962 mm for the year 1987 as per the CRU data.

Figure 5.13: Year to year change in AET for Jalsikka

-150

-100

-50

0

50

100

150

19

01

19

07

19

13

19

19

19

25

19

31

19

37

19

43

19

49

19

55

19

61

19

67

19

73

19

79

19

85

19

91

19

97AET

, mm

Year

Year to year change in AET-Jalsikka

Year to year change in AET


Figure 5.14: Year to year change in AET for Ambaredi

In fact, the highest and lowest AET that occurred during the years 1901-2002 as per CRU

data were 1725 mm and 1885 mm, with an exception of 1962 mm in the year 1987 which

was a very severe drought year in Rajkot district and in the Saurashtra region. When seen

in the light of soil moisture capacity and recharge, it may be concluded that in arid and

semi arid regions, the AET is not of much significance. Figure 5.14 shows a very similar

trend and the above interpretation applies for Ambaredi.

SECTION 2

UNCERTAINTY IN RECHARGE ESTIMATION METHODS

(a) Uncertainty in Water Level & Specific Yield Method

In the WL & SY method, water level and specific yield are important input parameters.

The water level is normally assumed to represent the top level of a fully saturated

formation, and the zone above as unsaturated. However, the wet zone occurring due to

capillarity above the saturated zone introduces an element of inaccuracy about the

measurement of water level or water table. A similar phenomenon occurs in the form of

-100

-50

0

50

100

150

19

01

19

07

19

13

19

19

19

25

19

31

19

37

19

43

19

49

19

55

19

61

19

67

19

73

19

79

19

85

19

91

19

97

Year

Year to year recharge, Ambaredi

year to year recharge, mm


varying water levels because of formation properties, in particular, contrasting formation

properties at the lithological discontinuities. The irregular pathways rendered by varying

or contrasting lithologies, and the presence of fractures, faults and joint like structures,

results in occurrence of lateral flows. This adds complication to the determination of

water levels too. Further, the water table reaches its peak when the outflows and lateral

movements of groundwater exceed the rate at which water is reaching the water table

(Lerner et al. 1990). An intermediate lower conductivity layer (below the surface and

above a water table) may often result in surface flows giving rise to „perched‟ water table.

This happens also in deep seated unsaturated zones overlying a low conductivity layer.

Finally, variations in groundwater recharge with time and in space (both laterally and

vertically) are well known in arid and semi arid regions due to direct consequence of

facts such as differing precipitation, soil characteristics, vegetation, land use and

topography (Lerner et al. 1990).

The water demand of crops as per groundwater draft used in the WL & SY method is

estimated based on crop irrigation standards, and assessment of area under various crops.

The assessment of area under crop itself has not only errors of measurement but also

errors due to scaling up of smaller measurements.

To a certain extent, allocation of discounts for environmental flows, and industrial and

domestic draft in the WL & SY procedure compensates for the other factors ignored in

the computation such as the climate factors. But this may not be the proper approach.

Furthermore, from the point of view of water level fluctuations, two characteristics of

hardrocks assume importance: (i) the groundwater storage in the weathered zone below

the surface, and (ii) decline of specific yield with depth (Moench, 1995). Generally, the

water level fluctuations in hard rock areas are quite high; often the wells are totally

dewatered. As the water level declines, the specific yield also reduces with depth145

.

145

This is because the volumetric water content and flow mechanisms in the unsaturated zone vary in a

complex manner, the main problem being that the parameters moisture content, matrix potential and

hydraulic conductivity are sensitively interrelated. A change in the volumetric water content of 5% often


When the well bottom is reached, which is often the fresh rock, there are few fractures

and hardly any effective storage space at this point. The specific yield is very low or

negligible at this depth. Any recharge in such conditions, would result in quick build up

of water levels in wells146

. “Stabilisation” of such peak water levels in wells would take

time, depending upon the porosity and permeability of the weathered zone. There would

be a series of inverted conical structures around wells representing peak or built up water

levels. If the water level fluctuation is taken to represent the aquifer conditions, then the

recharge would tend to be overestimated, especially when short duration water level data

are considered.

Another category of uncertainty exists on whether or not the water levels are influenced

by externalities such as changes in atmospheric pressure, trapped air or other phenomena

(Scanlon, 2002).

The WL & SY method does not presuppose flow mechanisms. It is also not dependent

upon the flow in the vadose zone (Chand et al. 2005) which is both complex and

important. The WL & SY method does not take into consideration subsurface inflows and

outflows, but assumes that every inflow and outflow is uniformly distributed over the

area. For example, the lateral flow in the aquifer and the vertical leakage from the water

table aquifer through clay layers into the underlying aquifers. While the assumption of

uniform distribution of flows may hold for rainfall, it does not hold for abstraction.

During abstraction, there is a redistribution of hydraulic heads so that part of the observed

increase in water level may be due to normal well recovery. These aspects contribute to

the errors in estimation of recharge in practice. The presence of low conductivity layer

corresponds to a change in the hydraulic conductivity by two or more orders of magnitude (Rushton, 1988

quoted in Lerner at al., 1990). 146

Dinesh et al (http://www.iwmi.cgiar.org/Publications/Other/PDF/NRLPProceeding-Paper013.pdf)

describe results from a research study on the Ghelo River basin in Saurashtra made in the context of

intensive water-harvesting since 1995. The study looked at the rainfall-water level relationship through

time series data, comprising before and after monsoon water level data on wells. The water levels in wells

close to and far away from water harvesting structures demonstrated both quick build after first wet spell

and second wet spell. The conclusion is that the wells with poor specific yield showed steep rise in water

levels irrespective of the distance from the water harvesting structures, and importantly, even when the

rainfall was quite small, of the order of 200 mm.


below a river for example would result in a „perched‟ water level that would continue to

„leak‟ even after the river stops flowing into the deeper layers as a base flow.

Regarding the specific yield, two aspects need to be kept in view: one, since the specific

yield varies with depth, consideration of any one specific yield value for use in the WL &

SY equation would be erroneous. It should also be noted that part of the water table

fluctuation occurs in the partially saturated zone and hence the specific yield would be

different from within a saturated zone; two, the specific yield for most of the formations

is determined to lie in a range, yielding a wide range of recharge values thus creating an

uncertainty over which value to choose from for recharge estimations147

.

The recharge values obtained from use of stabilised water levels and standard specific

yields in this method are helpful for simple, straightforward aquifers and treated as first

approximations; it is always useful to cross check with other methods (Lerner et al.,

1990).

(b) Uncertainty in Regression Method

The major criticism of the regression method is that the recharge is not simply dependent

on rainfall alone; there are many factors that affect recharge such as hydrogeology,

antecedent soil moisture, characteristics of the vadose zone, vegetation, rainfall

distribution within and between years, local topography and watershed characteristics

(Simmers, 1997). Even between villages, often there is a variation in rainfall pattern and

intensity. For example, the rainfall of Ambaredi and Jalsikka village clusters (from global

database) for the year 2003 was 625.90 and 584.05 mm respectively although they are

separated by a radial distance of about 100 kms. Such differences, when aided by

variation in monthwise distribution pattern, would introduce significant accuracy.

147

Variations in the specific yield are also explained due to the non-homogeneous and anisotropic

character of aquifer formations. Specific yield, which is defined as the quantity of water yielded per unit

area of storage for a unit draw down thus tends to vary significantly. For instance, the specific yield for

basalt, given as 1-3%, and 0.02-0.40% for an alluvial formation, provide widely ranging natural recharge

estimates using the WL & SY formula. Only long duration pumping tests would provide dependable values

of specific yields (Lerner et al. 1990).


Nevertheless, such estimates are helpful in obtaining an approximate idea about the

recharge; this is also because, the same rainfall in different years would produce different

quantum of recharge due to variation in monthwise rainfall distribution. This

automatically implies that as a percentage of rainfall, recharge does vary from year to

year; therefore recharge cannot be estimated as a percentage of rainfall. Analysis based

on large data helps to reduce this uncertainty, providing scope for deducing rainfall-

recharge relationship, and in turn broad values of recharge. In addition, it also helps

identify the threshold value of rainfall after which the runoff would be generated. This is

a useful indicator.

(c) Uncertainty in Soil Moisture Balance Method

A lot of uncertainty remains in the estimation of evapotranspiration, one of the key

components needed in the soil moisture balance method, due to the need for a large data

to represent actual ground scenarios. Further, the rooting depth for different types of

vegetation, including crops, varies significantly in a given area. Landuse changes in fact

introduce a lot of complexity as they are difficult to measure accurately. Recharge is

influenced both by the amount of soil moisture moving beyond the rooting depth and the

soil thickness, and reaching the water table. The recharge is also controlled by the

evapotranspiration (both by direct evaporation and through transpiration) occurring at the

surface.

In arid and semi arid regions, recharge values are generally small numbers and are

residual-that is a small difference between large numbers. Recharge has a direct relation

with evapotranspiration and rainfall. Evapotranspiration cannot be measured easily, but

comprises the largest outflow. For example, the error percentage in estimation of river

flows is often +/- 25%; which means that if a recharge comprises 25% of the flow, then

the error is 100%. Similarly, a change in the volumetric water content of 5% often

corresponds to a change in the hydraulic conductivity by two or more orders of

magnitude (Rushton, 1988 quoted in Lerner at al. 1990). Therefore, even small

inaccuracies in the two parameters, evapotranspiration and rainfall, lead to large variation

in the recharge estimates. Further, the annual evapotranspiration in Indian arid and semi


arid areas is much higher than the rainfall (including in the study villages). When we

consider on a monthly basis, except for the 3 or 4 monsoon months, the ET is generally

higher throughout the year.

Climate variables such as temperature, sunshine hours, vapour pressure play a critical

role in the recharge process. These critical elements are not taken into consideration in

the WL & SY and Regression methods, but are considered in the soil moisture methods

such as the CRU-NUT_MONTH method. However, the estimation of these parameters as

well as their accuracy of estimation, such as described in the above points, introduces

element of inaccuracy. However, in comparison to the WL & SY and Regression

Methods, the recharge values obtained by soil moisture balance method should be more

accurate.

Recharge values should be estimated by more than one method and should be within 20%

of variation (GoG, 1997; CGWB, 2004). Comparison by more methods is recommended

should be viewed at best as first cut approximations.

SECTION 3

COMPARISON OF RECHARGE VALUES

This section deals with comparison of the recharge values obtained by the three methods.

The uncertainty aspects are kept in mind while carrying out analysis.

Table 5.12 gives recharge values computed by different methods. Method 1 under

column B gives recharge estimates obtained by WL & SY method described as procedure

described in Table 5.2. Column E under Method 2 gives recharge values obtained by

Regression method. Column C gives the long term taluka average rainfall obtained for

the years 1901-2002; column D gives the recharge calculated using regression method for

long term average rainfall. Column E again gives the recharge obtained by the same

regression method but proportionately rectified for the 2003 year rainfall of 740 mm.

Method 3 is the CRU-NUT_MONTH method; the recharge values obtained are given in


column F. The actual RF of 2003 sourced from official sources, monthwise, is

substituted in the output of a CRU file which is used as an input to the NUT_MONTH

method to estimate recharge for the year 2003 [discussed in more detail in the next

section]. The recharge values of the two clusters are different although the rainfall is

same; the difference is perhaps due to the soil characteristics and climate parameters.

Method 4 estimates the recharge values as given in column G using the Regression

equation for the district average rainfall. Columns F and G also bring in the variation

possible in the recharge value estimation.

Table 5.12: Comparison of Recharge values for the year 2003 Method 1 Method 2 – Regression Method Method 3 Method 4

Village WL & SY

method

(mm)

Taluka

long term

average

Rainfall

(mm)

Recharge

for taluka

average

rainfall in

col. C

(mm)

Recharge for

rainfall

proportionate

to actual

total RF of

year 2003

Recharge

from CRU

data

(substituted)

Regression

equation for

Rajkot total

RF of 740

mm for

2003

A B C D E F G

Ambaredi 81 625.90 46.9 55.45 73 66.8

Jalsikka 70 584.05 39 49.41 57 66.8

Vithalpar 63 584.05 39 49.41 57 66.8

Haripar 55 584.05 39 49.41 57 66.8

Kerala 30 584.05 39 49.41 57 66.8

Bela 53 584.05 39 49.41 57 66.8

Source: Computed from field data

The values of recharge obtained by various methods and shown in Table 5.12 can be

considered to be agreeing reasonably well given the complexity of the recharge process

and the physical systems.

Let us examine how recharge values obtained above compare with the findings from the

long term rainfall-recharge relationship discussed in section two.

The long term analysis has revealed that for a rainfall of 550-750 mm, the most probable

recharge estimated for Ambaredi was around 100 mm, while for Jalsikka, it was 80-120

mm for the corresponding rainfall in column B of Table 5.12. Clearly, these values are

higher than the recharge values estimated by the above methods for the corresponding


rainfall: for Ambaredi, it ranges from 55.45 to 81mm, and for Jalsikka cluster, it is 30 to

70 mm. The difference is contributed by the uncertainty factors described in section 2

under Uncertainty in Recharge Methods. The difference is by and large less than 20%

between any two methods which is acceptable as per the norms of the Government of

India. In particular, the WL & SY method differs from the CRU by less than 20% for all

villages except for Kerala. The hydrogeological conditions in Kerala are quite different,

including the clay soil conditions that affected recharge adversely.

To sum up, it is seen that the general rainfall pattern for both the clusters of villages is

similar. The rainfall and recharge seem to be closely correlated, although there is

variation in terms of quantity in both the clusters. The long term average rainfall for

Ambaredi and Jalsikka clusters is 625.9 and 584.05 mm respectively.

There is however, difference in the recharge amounts not only due to rainfall pattern,

distribution and quantity, but also due to soil constants. The number of zero or negligible

recharge years and rates of recharge on an annual basis also support this. The rainfall-

recharge analysis also showed that recharge of 100 mm is most likely for a rainfall range

550-750 for Ambaredi, while recharge of 80-120 mm is likely for rainfall range of 580-

750 mm for Jalsikka cluster. Similarly, a rainfall of 500 mm and beyond for Ambaredi

will generate a recharge of at least 60 mm; while a rainfall of 560-620 mm for Jalsikka

cluster will produce a similar recharge of 60 mm. The quantum of recharge for Ambaredi

is found to be more than that of Jalsikka for the same rainfall windows due to

comparatively more favourable soil conditions and rainfall frequency cycle. The

frequency cycle of rainfall for Ambaredi is also shorter compared to Jalsikka cluster as

discussed elsewhere. Further, the recharge is found to be more consistent for Ambaredi

than Jalsikka in view of favourable soil constants already discussed.

Analysis also shows that the AET and PET ratio is 1, and is the same for both clusters.

Therefore, factors that should be affecting recharge would be the soil characteristics,

discussed in a previous section. When we consider the soil composition, the more silty

composition of Ambaredi soils facilitated enhanced recharge compared to the clayey soils


of the Jalsikka cluster. In addition, the presence of a 2 m-thick lithomarge layer as shown

by well inventory in Jalsikka, Haripar, Kerala and Bella between 10-13 metres depth

below ground level has created well digging problems as well as hindered recharge. This

is evidenced by the total depth of wells in Ambaredi which is around 20 m as compared

to that of Jalsikka cluster of 10-13 m below ground level. However, within the Jalsikka

cluster, Jalsikka and Vithalpar villages are again comparatively better in terms of soil

characteristics or soil constants; however, due to all these villages falling in the same

CRU node, separate analysis was not possible. Other socio economic data (discussed in

chapter 6) indicates that Jalsikka and Vithalpar have shown more productive agriculture

and animal husbandry activities among the Jalsikka cluster mainly because of two

reasons: (i) the wells of depths are around 20 m in Jalsikka and Vithalpar, while in the

other villages, namely, Haripar, Kerala and Bella, the depth of wells is 10-13 m; (ii) there

is direct pumping of water from the rivers in the case of Jalsikka and Vithalpar which to a

certain extent countered the disadvantage of shortage of water due to lower recharge.

While Jalsikka and Vithalpar are able to take two crops, Haripar, Kerala and Bella are

able to take only one crop (including that of the six-month cotton crop).

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