Factors controlling groundwater recharge in the ...

Hydrology in Mountain Regions: Observations, Processes and Dynamics (Proceedings of Symposium HS1003 at IUGG2007, Perugia, July 2007). IAHS Publ. 326, 2009.

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Factors controlling groundwater recharge in the mountainous hard-rock Aravalli terrain C. BHUIYAN1, R. P. SINGH1 & W. A. FLÜGEL2 1 Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur – 208016, India [email protected] 2 Institute of Geography, Friedrich Schiller University Jena, Löbdergraben 32, Jena 07743, Germany

Abstract Recharge is one of the key hydrological parameters for assessment, budgeting, management, and modelling of groundwater resources. To have a measure of groundwater recharge, it is necessary to obtain precise information on the factors governing infiltration and loss from the groundwater system. The present study focused on the Aravalli terrain of India covering ~ 25 000 km2 area in between N23º30′–N26º18′ and E72º24′–E74º36′. Evaluation of individual and combined influence of hydrogeological and non-geological parameters on groundwater recharge was set as the main objective, and linear multiple-regression was chosen as the technique. Groundwater recharge is manifested by seasonal fluctuations of groundwater level, and is used as the dependent variable in the regression analysis. Standardised partial regression coefficients have been used as a measure of factor-influence and as a tool for sensitivity analysis. With accumulation of temporal data, the mean value of water-table fluctuation varies, and with the variation of the responsive parameter, the partial regression-coefficients of the regressors also vary. Therefore, confidence intervals of regression coefficients have been used in the present study to check model uncertainty. The multipleregression established that frequency of “deficient rainfall” i.e. amount of rainfall required to ensure normal aquifer-recharge (mean water-table rise) is the most important recharge-controlling parameter. Therefore, it has been interpreted that inadequacy of groundwater recharge is largely determined by the frequency of deficient-rainfall. Depth, elevation, vegetation, lineament buffer, and density of lineament count and lineament-intersection are found to be other parameters positively influencing seasonal water-level fluctuations. Key words hard-rock; Aravalli; groundwater recharge

INTRODUCTION Assessment of groundwater recharge-potential is neither easy nor straightforward. Understanding the relationship and degree of influence of geological and non-geological parameters on groundwater regime is very important for evaluation of recharge-potential of a region. Siddiqui & Parizek (1971) through graphical analysis and parametric and non-parametric statistical tests assessed the influence of geological factors on well yield. Moor et al. (2000) used multipleregression for identification of relationship and evaluation of factor-influence on well yield in the fractured-bedrock aquifer of New Hampshire. Several researchers (Holtschlag, 1996; Cherkauer, 2004; Cherkauer & Ansari, 2005; Lorenz & Delin, 2007) have attempted multiple-regression approaches for regional groundwater recharge estimation. In the present study, a linear multipleregression has been used to evaluate the relative importance and influence of various geologic and non-geologic parameters over seasonal groundwater recharge in the mountainous hard-rock Aravalli terrain of India. The standardised partial regression coefficients have been used as a tool for sensitivity analysis, and the confidence interval of regression coefficients are used to check model uncertainty.

STUDY AREA Rajasthan, the largest state of India, is situated in the northwest of India and is largely an arid region for most of its parts. Scarcity of surface-water bodies has made the state dependent mostly on groundwater resources. The Aravalli Range, one of the oldest mountains of the world, situated in the south–central part of the state separates the eastern plain of the Malwa Plateau from the western Thar Desert (Fig. 1). Scanty rainfall and over-extraction of ground water cause depletion Copyright  2008 IAHS Press

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of water in many parts of the terrain. Moreover, frequent occurrence of drought and wide variations in water-table fluctuation from well to well and from year to year are characteristics of the region. The present study is focused on the major part of the Aravalli region comprising of about 25 000 km2 in between N23º30′–N26º18′ latitude and E72º24′–E74º36′ longitude.

Fig. 1 The study area: (a) India, (b) Rajasthan, (c) Aravalli.

METHODOLOGY Parameter influence Multiple-regression has been carried out to assess the relative importance and influence of various parameters over seasonal groundwater recharge. Water levels in wells through time exhibit normal distributions (Krumbein & Graybill, 1965). Also in the present study, the skewness and kurtosis values of the water-level fluctuation data are found to be negligible. Furthermore, a one-sample Kolmogorov-Smirnov test confirms that the mean seasonal water-level fluctuation (WLF) follows a normal distribution. The mean of seasonal fluctuation varies with time, thereby producing different sets of regression coefficients. Therefore, multiple-regression is carried out for the same set of wells with different values of dependent variables. In the first regression, mean WLF (WLF_rand) of 16 years (after elimination of four years randomly) of seasonal fluctuation has been used as the dependent variable. In the second regression, mean WLF (WLF_8400) of 17 years (1984–2000) has been considered, and in the final regression, mean WLF (WLF_8403) of 20 years (1984–2003) has been used. The independent variables also have been selected accordingly. The geological variables include soil classes (SOIL), rock classes (ROCK), geomorphic classes (GMORPH), lineament buffer (LB: 150 m on both side of lineaments), lineament intersection buffer (ISB: 300 m radius), drainage buffer (DB: 100 m on both sides of drainage lines), subsurface fractured zones (SSF), lineament count density (LCD), lineament length density (LLD), lineament intersection density (ISD), drainage density (DD), soil thickness (STHIC), weatheredzone thickness (WEATH), shallow fractured-zone thickness (SFT), and deep fractured-zone thickness (DFT). The soil classes include sandy soil (SANDY), coarse loamy soil (C-LOAM), fine loamy soil (F-LOAM), clay soil (CLAY), and rocky skeleton soil (ROCKY). The rock classes are: carbonates (R1), gneiss (R2), granite (R3), quartzite (R4), metavolcanics (R5), phyllite (R6), and schist (R7). The geomorphic classes are alluvial plain (G1), buried pediment (G2), hills (G3), pediment (G4), and valley fills (G5). The physiographic variables used are surface elevation

Factors controlling ground-water recharge in the mountainous hard-rock Aravalli terrain

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(ELEV) and surface slope (SLOPE). Well parameters involved in the regression are well depth (DEPTH), depth of static water level (STWL), saturated-zone thickness (SATUR), and well yield (YIELD). The eco-physical parameters involved are land use classes (LULC) and mean monsoonal vegetation health index (mVHI). The eight LULC classes involved are: barren rock (L1), double crop (L2), dry channels (L3), fallow land (L4), forest (L5), habitation (L6), scrub (L7), and single crop (L8). The climatic parameters included as regressors are mean monsoonal rainfall (mRAIN) and frequency of weighted deficient rainfall (dRFRQ). Potential evapotranspiration and groundwater draft lack sufficient data, and hence are not considered for the analysis. The presence of multi-collinearity leads to misinterpretation of results of a regression analysis (Landau & Everitt, 2003). “Collinearity diagnostics” (of SPSS software) revealed the presence of multi-collinearity among LCD, LLD, and ISD. Therefore, LLD is eliminated, and LCD and ISD are combined to produce LCISD. The analyses involved 475 cases with 42 predictors, of which 27 were categorical and therefore participated as dummy or indicator variables. Among the above parameters, soil, rock, geomorphic, and land use classes, and subsurface fracture zones have been used as indicator variables. Of the indicator variables, fine loamy soil, schist, pediment, single crop, and zone devoid of fractures (SSF0) have been considered as the reference groups of the respective parameters. Lineament buffer, lineament-intersection buffer and drainage buffer have been incorporated in the regression analysis as dummy variables. Parameter sensitivity and model stability In the case of multiple-regression with numerous predictors, the standardised partial regression coefficient (ß) states the rate of change in the dependent variable for unit change in the particular predictor variable. Hence, there is a direct correspondence between correlation and sensitivity. Again, sensitivity of a variable is a measure of its importance, as is the correlation coefficient for a linear model (McCuen, 2002). Thus, ß values have been used for sensitivity analysis, the higher the value (irrespective of sign) of ß is, the higher is the parameter sensitivity. Note that this is true because we work with standardised variables. Stability of a model is important for its applicability. A regression model can be declared stable if it consistently produces similar results for different time intervals. The confidence interval of the regression coefficients (b) demarcates the limit for every parameter, stating that the true population coefficient lies within the confidence interval with specific probability (Davis, 2002). If and when the different sets of partial regression coefficient of a parameter lies within this interval with high probability (commonly 0.95), a regressor in the regression is considered as stable. Therefore, confidence interval of the regression coefficients has been used to check model stability and model uncertainty.

RESULTS AND DISCUSSIONS In the first step of multiple-regression, the value of R was 0.704 (R2 =0.496) producing a standard error of estimate 1.142. Elimination of outliers resulted in a substantial increase of R (0.760) and R2 (0.577) and a decrease in standard error of estimate (0.918). The P-value (50 mm, 3 to >100 mm, and 4 to >200 mm). The dRFRQ is computed by multiplying the number of years (of deficient-rainfall) with weights, and dividing it by 80, the maximum possible score (4 × 20 = 80) in 20 years. A high value of ß indicates significant positive influence of well depth and elevation on recharge. Among the geological factors, lineament buffer, and lineament count and intersection density (ß = 0.109) are found highly influential in recharge. Soil thickness is found to have moderate positive influence on water-table fluctuation. Weathered-zone thickness has small ß and t values. The apparent lack of influence of weathering could be due to association of the wells with shallow aquifers, whereas weathered zones extend to deeper layers. Drainage density and slope with small ß and small t indicates inverse but small influence on recharge. The positive and negative coefficients of shallow fracture thickness (SFT) and deep fracture thickness (DFT), respectively, indicate that while subsurface fractures at shallow depth promote infiltration, deeper fractures cause water-loss due to downward drainage. Among rocks: granite and quartzite have higher influence on recharge. Although clay soil shows big values of b and t, small ß (0.046) implies its little influence on recharge. Except valley fill, other landform classes display an inverse relation with fluctuation. Among land use classes: double cropland and shrub have high ß and t values, and are positively associated with recharge. A positive relation between vegetation health and recharge is evident from the ß value of mVHI. Sandy soil and mean monsoon-rainfall both have ß = 0.05 and t values above 1.0. Clay soil, metavolcanics and shallow subsurface fractures have small ß values below 0.05, but have t values above 1.0, and hence are statistically significant. Parameters with high ß and t values (at least 0.05 and 1.0, respectively) are shown in bold in Table 2.

PARAMETER SENSITIVITY Relative importance and sensitivity of various recharge-controlling parameters is reflected in the standardised partial regression coefficients (ß). Thus, dRFRQ emerged as the most important recharge controlling parameter on the basis of b, ß, and t values obtained through the multipleregression analysis. The graph of cumulative frequency of wells under various deficient-rainfall ranges vs water-level fluctuation (Fig. 2) confirms it. The fluctuation statistics (Table 3) also confirm a perfect inverse relationship between them through the mean, the median, the 75th centile, and the Avg75 (average of best 75% fluctuation values in a class) values.

MODEL STABILITY The confidence intervals obtained using WLF_8403 as dependent variable, have been used as the reference for the regression coefficients (b) at 95% confidence level. Partial regression coefficients obtained through other sets of multiple-regressions have been compared with the reference. Multiple-regression using WLF_rand as the dependent variable generates partial regression coefficient 0.109 for DEPTH that falls slightly above the upper limit (0.102) of the confidence belt. However, all other variables have partial regression coefficients within the range of confidence intervals (Table 2). This clearly states that the true population coefficient lies within the confidence intervals with a probability of 0.95 (Davis, 2002) and the regression model can be declared stable.


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Table 2 Confidence interval for regression coefficients. 95 % Confidence Lower Upper –0.098 0.556 –0.346 1.865 –0.114 0.986 0.121 0.925 –0.447 1.439

Variable Description

ß b b b WLF_rand WLF_8400 WLF_8403

t

Sig.

SANDY CLAY R3 R4 R5

0.266 0.887 0.533 0.333 0.467

0.163 0.729 0.140 0.221 0.537

0.229 0.760 0.436 0.523 0.496

0.050 0.046 0.056 0.090 0.035

1.375 1.351 1.559 2.556 1.035

0.170 0.177 0.120 0.011 0.301

–0.579

–0.606

–0.573

–0.112

–2.657

0.008 –0.996

–0.149

0.214 0.374 0.333

0.215 0.374 0.450

0.201 0.394 0.359

0.065 0.060 0.109

1.690 1.739 2.844

0.092 –0.033 0.083 –0.051 0.005 0.111

0.434 0.840 0.608

–0.261

–0.218

–0.217

–0.057

–1.698

0.090 –0.469

0.034

0.418

1.743

1.429

0.109

2.319

0.021 0.218

2.641

0.002

0.002

0.001

0.194

3.645

0.000 0.001

0.002

–0.048

–0.029

–0.035

–0.055

–1.435

0.152 –0.082

0.013

0.068 0.010

0.102 0.005

0.072 0.010

0.276 0.066

5.604 1.833

0.000 0.047 0.068 –0.001

0.098 0.020

0.007

0.005

0.004

0.041

1.083

0.279 –0.003

0.012

–0.010

–0.005

–0.006

–0.086

–2.081

0.038 –0.012

0.000

–0.332

–0.292

–0.301

–0.089

–1.799

0.073 –0.630

0.028

0.004 0.059

0.005 0.068

0.005 0.061

0.103 0.138

2.410 3.459

0.016 0.001 0.001 0.026

0.008 0.095

0.361

0.614

0.559

0.050

1.214

0.225 –0.346

1.465

–5.241

–5.052

–4.583

–0.387

–10.591

0.000 –5.433

–3.732

0.361

–0.602

0.427

0.438

0.662 –1.488

2.342

G1 L2 L7 LB DB LCISD

ELEV SLOPE DEPTH STWL SFT DFT SSF3 YIELD Mvhi mRAIN dRFRQ CONST

Sandy soil Clay soil Granite Quartzite Metavolcani cs Alluvial Plain Double crop Shrub Lineament buffer Drainage buffer Lineament count and intersection density Surface elevation Surface slope Well depth Static waterlevel Shallowfracture thickness Deepfracture thickness Only deep fractures Well yield Mean monsoonal VHI Mean monsoonrainfall Freq. deficientrainfall Constant

ESTIMATION OF SEASONAL RECHARGE Probable mean seasonal water-level fluctuation in a basin or terrain can be estimated following the results of the multiple-regression analysis. An algorithm can be developed for computation of water-level fluctuation using the b values of the regressor described in section Parameter Influence (under Methodology) and Table 2. The regression model developed can be used in GIS to generate grid maps of distributed water-table fluctuation, which when multiplied with specific yield values of the associated rock types, will produce a distributed surface of seasonal recharge, which can be classified further into ground water recharge-potential zones.

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Water Level Fluctuation (m)

10 9 8 7 6

Frequency of Weighted Deficient Rainfall 0.5 > 0.6 > 0.65 > 0.7 > 0.75 > 0.8

5 4 3 2

1 0.9 0.8 1

2

5

10

20

30 40 50 60 70

80

90

95

98 99

Relative % of Wells

Fig. 2 Fluctuation of water level with different frequencies of weighted deficient rainfall. Table 3 Statistics of fluctuation for different weighted deficient-rainfall frequency. Frequency of weighted deficient rainfall ≤0.50 >0.50 >0.60 >0.65 >0.70 >0.75 >0.80

n 30 69 88 87 77 53 71

Min 2.65 2.05 1.18 0.98 0.78 0.24 0.05

Max 10.39 8.03 6.61 8.78 7.45 6.94 5.21

Mean 4.98 4.29 3.51 3.39 3.20 2.71 2.13

Median 4.68 4.24 3.50 3.19 3.07 2.40 1.92

75% 3.54 3.37 2.62 2.55 2.17 1.75 1.45

Avg75 5.50 4.72 3.95 3.89 3.70 3.19 2.50

SD 1.93 1.29 1.14 1.40 1.41 1.48 1.05

CONCLUSIONS Analysis and evaluation of parameter-influence on an event is challenging as well as important for assessment and prediction of a future event. Among various widely used techniques, the graphical analysis method offers a qualitative idea, whereas multiple-regression is found to efficiently quantify relative influences of various controlling factors. In the present study, multiple-regression establishes a linear relationship of seasonal water-table fluctuation with different parameters. The regression coefficients of multiple observations fall within the confidence intervals, indicating consistency in influence. The multiple-regression established that in the Aravalli region frequency of weighted deficient-rainfall is the most important parameter influencing recharge. This eventually infers that groundwater recharge in arid and semi-arid regions is most sensitive to rainfall deficiency rather than mean or total rainfall. Depth, elevation, vegetation, density of lineaments and lineament-intersections are also found influential in seasonal groundwater recharge. Hence, in an aquifer of the Aravalli region, recharge will be higher in wells with greater depth, higher elevation, higher densities of lineaments, lineament-intersections, and vegetation, and most importantly having low frequency of deficient-rainfall.

REFERENCES Cherkauer, D. S. (2004) Quantifying ground water recharge at multiple scales using PRMS and GIS. Ground Water 42(1), 97–110. Cherkauer, D. S. & Ansari, S. A. (2005) Estimating ground water recharge from topography, hydrogeology, and land cover. Ground Water 43(1), 102–112.


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Davis, J. C., (2002) Statistics and Data Analysis in Geology. 3rd edn. John Wiley & Sons, New York, USA. Krumbein, W. C. & Graybill, F. A. (1965) An Introduction to Statistical Models in Geology. International Series in the Earth Sc, McGraw-Hill Inc. Landau, S. & Everitt, B. S. (2003) A Handbook of Statistical Analyses using SPSS. Chapman & Hall/CRC, Florida, USA. Lorenz, D. L. & Delin, G. N. (2007) A regression model to estimate regional ground water recharge. Ground Water 45(2), 196–208. McCuen, R. H. (2002) Modelling Hydrologic Change. Lewis Publishers, Florida, USA. Moore, R. B., Schwarz, G. E., Clark, S. E., Jr., Walsh, G. J. & Degnan, J. R. (2002) Factors related to well yield in the fractured-bedrock aquifer of New Hampshire. US Geological Survey Professional Paper 1660. Rangarajan, R. & Athavale, R. N. (2002) Annual replenishable ground water potential of India – an estimate based on injected tritium studies. J. Hydrol. 234, 38–53. Siddiqui, S. H. & Parizek, R. R. (1971) Hydrogeological factors influencing well yields in folded and faulted carbonate rocks in central Pennsylvania. Water Resour. Res. 7(5), 1295–1312. Sophocleous, M. A. (2000) Quantification and regionalization of groundwater recharge in south-central Kansas: integrating field characterization, statistical analysis, and GIS. The Compass 75(2–3), 101–115. Townend, J.,(2001) Practical Statistics for Environmental and Biological Scientists. John Wiley & Sons, New York, USA.