地下水

RESEARCH COMMUNICATIONS*For correspondence. (e-mail: rameshtyagi@) 19. Price, G. P., Preferred orientations in quartzites. In Preferred Orien-tation in Deformed Metals and Rocks: An Introduction to Modern Texture Analysis (ed. Wenk, H. R.), Academic Press, New York, 1985, pp. 385–406.20. Ramsay, J. G. and Huber, M. I., The Techniques of Modern Struc-tural Geology , Folds and Fractures . Academic Press, New York, 1987, vol. 2.21. Park, R. G., Shear zone deformation and bulk strain in granite–greenstone terrain of the Western Superior Province, Canada . Pre-cambrian. Res ., 1981, 14, 31–47.22. Anderson, E. M., The Dynamics of Faulting , Oliver and Boyd Edin-burgh, 1951.ACKNOWLEDGEMENTS. The research was supported by Depart-ment of Science and Technology, New Delhi.Received 16 July 2004; revised accepted 6 November 2004Reliable natural recharge estimates in granitic terrainRamesh Chand*, G. K. Hodlur, M. Ravi Prakash, N. C. Mondal and V. S. SinghNational Geophysical Research Institute, Uppal Road, Hyderabad 500 007, IndiaThe average natural recharge to the aquifer of Kongal river basin located in Nalgonda district, Andhra Pradesh, India due to the monsoons is estimated using injected tritium technique at a few selected sites. Kriging and Thiessen Polygon methods are employed to interpolate recharge estimates and arrive at more realistic values. The estimate is made from the spatial distribution of recharge values. The spatial distribution of recharge values is derived from 25 site-specific measurements. The quantum of groundwater recharge through vertical infiltration has been estimated as 27.5 and 30.0 mm (l –2) by Kriging and Thiessen polygon methods respectively; a value of 25.0 mm (l –2) was arrived at using average recharge values in the area. The total rainfall during the corresponding period is 566.67 mm (l –2) and thus the fractional amount of recharge is 5% of the rainfall.G ROUNDWATER abstraction has increased to complement the increasing water demand by the continuously growing population. Groundwater recharge to the aquifer system is thus the most important variable to be estimated. Its estima-tion is thus an important input for a rational exploitation and management of groundwater resources. A reliable estima-tion of recharge in hard-rock aquifers is a difficult task in view of wide spatial–temporal variations in the hydro-logical and hydrometrological conditions. It may be estimated by several conventional methods such as groundwaterbalance, lysimeter, etc. These methods require analysis of large volumes of hydrological data (precipitation, surface run-off, evapo-transpiration, change in groundwater storage, etc.) accumulated over a considerable time span, which is generally inadequate, lacking or unreliable in many areas 1–3. The tracer technique is a direct method for estimation of groundwater recharge 4–6 at the site-specific point. Extensive research has been done in the allied fields during the last four decades 7–16. Here an attempt has been made to estimate recharge in the area by the injected tritium technique and to also estimate the spatial variation of recharge by Kriging and Thiessen methods.Integrated hydrogeological and geophysical studies were carried out by National Geophysical Research Institute (NGRI), Hyderabad in the Kongal river basin, located in Nalgonda district, Andhra Pradesh, India (Figure 1 a ). The aim was to develop an appropriate methodology for groundwater management and development. Estimation of natural re-charge from precipitation was an important component of this work. Natural recharge to the shallow aquifer takes place through the movement of percolated rain water through the vadose zone, till it reaches the water table. Hence it depends upon many parameters, namely porosity, permeability, infil-tration capacity of the soil, amount of rainfall, and its variation in space and time domains. Recharge estimates are arrived at by averaging the recharge values estimated at a few repre-sentative sites in the region. Hence, in any given area, if the number of data points gives the recharge values at the representative sites, the accuracy level of the recharge esti-mate of the area improves and tends to become a more realistic evaluation of the parameters. An attempt has been made to utilize the Krige estimates to obtain recharge values for the sites, using neighbouring field-estimated values falling with a certain distance. Using such statistically generated large values, the total recharge of the study area has been arrived at. The study area is about 50 km east of Hyderabad. It covers an area of 180 km 2 of the Kongal river (Figure 1 a ) and lies between 78°48′38″–79°03′25″E long. and 17°07′48″–17°16′48″N lat. Most of the area is covered by pink granite. The area is devoid of major strike like folds and faults. Joint patterns are not common in exposures. Many basic dykes, which are fine-grained dolerite, traverse the granite. These dykes are spread throughout the basin. These are NS, NW–SE, NE–SW and EW trending dykes. A small strip of kankar, which is silty and clayey, is exposed in the southwest-ern zone and also the northeastern part. The area is drained by Kongal river, and its main network of streams is shown in Figure 1 a . The river flows NW to SE and passes through Palwela village. Surface water bodies such as lakes are spread out in the southeastern part of the basin. The area is in general a flat terrain without major topographical relief like hills and valleys. The drainage of the area clearly indica-tes the slope of the terrain, which is NW to SE. Groundwater occurs in shallow-weathered and deep-fractured granite in the western and northwestern parts, whereas in the remaining area it occurs in weathered gneisses. The depth to water levelRESEARCH COMMUNICATIONSFigure 1. Geological map of the study area (a ) and location map of tritium-injected sites (b ).Table 1. Estimated recharge values at 25 sites in Kongal river basinSite no. Villages Recharge (cm)1 Choutuppal-I 0.52 Talasingaram 1.63 Pantangi-I (irrigated field) 11.4 4 Koyalagudem 0.5 5 Yellagiri 0.76 Choutuppal-II 0.57 Golagudem 1.38 Aregudem 1.29 Gundrampally-I 5.010 Yepur-I 2.1 11 Gundrampally-II 1.7 12 Gundrampally-III 1.413 Veliminedu-I 6.414 Ipparti 6.215 Palwela 2.016 Krishnapuram 1.4 17 Tangedpally 3.8 18 Lakkaram 1.819 Dharmojigudem 0.520 Pantangi-II 2.021 Veliminedu-II 6.422 Lingojigudem 4.7 23 Katreuvugudem-I 1.4 24 Katreuvugudem-II 1.525 Yepur-II 2.1Total number of injected points = 45, Total number of sites = 25.varies from a few metres to about 25 m below ground level. The groundwater is mostly exploited through dug wells, dug-cum-bore-wells and bore wells for irrigation, as well as domestic purposes 17. The main source of groundwater is pre-cipitation and to a lesser extent infiltration of water from the tanks and surface water during monsoon periods. Rain gauge stations were established at Gundranpally, Chou-tuppal village, Choutuppal Observatory of NGRI for moni-toring the daily rainfall. The average rainfall recorded from the stations is found to be 57 cm during the study period. Locations where natural recharge was estimated by using injected tracer technique are shown in Figure 1 b .The injected tracer method of recharge estimation takes care of many of the variables and gives the net amount ofwater that joins the water table after contributions to evapora-tion (evapo-transpiration) and run-off. Recharge is esti-mated at representative sites and average recharge in the area under study is computed. To estimate the natural recharge in the Kongal basin, 25 representative sites were selected(Figure 1 b ). Tritium is tagged below the shallow root zone before the onset of monsoon rains and soil samples were collected after monsoon. The displaced position of the tracer is indicated by the peak in its concentration distri-bution, which corresponds to spot natural recharge over thetime interval between the injection of tritium and the collec-tion of the soil core profiles. Moisture content and tritium activity of samples of each site were plotted against depth. The displacement of the tracer was determined and used to estimate the recharge.A small quantity (2.5 ml) of tritiated water (activity 10 µC/ml) was injected at 80 cm depth below the root zone in a 1.2 cm diameter hole made using a drive rod. The hole was filled back with local soil. The location of injec-tion points is shown in Figure 1 b . Results are shown in theTable 1.Soil samples were weighed immediately after collection in the field and double-sealed in a plastic bag. Hoffer-type augers of varying lengths were used. Soil moisture was extracted in the laboratory using the vacuum distillation set-up. The moisture content in about 25 g of soil taken from each sample was determined on a torsion balance, heated by an infrared oven at 105°C. Four millilitre distil-late was mixed with 10 ml of insta-gel (a universal liquid scintillation cocktail for aqueous and non-aqueous samples, manufactured by Packard Instrument Company, USA) in low potash glass vials, and tritium activity was counted us-ing liquid scintillation counter having background of about 25 counts per minute. The tritium activity of every 10 cm sec-RESEARCH COMMUNICATIONSFigure 2. Profile of recharge at (a ) Aregudem and (b ) Lingojigudem.Table 2. Parameters of fitted variogram for recharge variable Mean square Model type Nugget Sill Range (km) errorCubic 0.6 2.70 1.90 3.352 Spheric 0.6 2.77 1.60 3.486 Exponential 0.6 2.25 0.50 3.595tion was plotted against the sample depth for all the profiles. Typical examples of such plots along with moisture varia-tion are shown in Figure 2 a and b .The estimated recharge due to rainfall has varied from 0.5 to 6.4 cm at different sites, with an average value of 2.5 cm in the study area. The total natural recharge to the basin was estimated using point recharge values at each site by Kriging and Theissen polygons.Kriging method is an interpolation technique based on the theory of regionalized variables 18,19. There have been several studies on the spatial variability to groundwater hydro-logy 17,20–22. Spatial distribution of the natural recharge due to monsoon is computed after carrying out variogram analysis of the site-specific recharge measurements. A cubic variogram model, which yielded minimum mean square error (Table 2) is fitted to the experimental variogram. The fitted model revea-led a zone of influence of 1.9 km, which implies that the field values are correlated in space up to a distance of 1.9 km. In the present case, the area was divided into uniform square grids of 1.5 × 1.5 km 2 and the recharge values were estimated at the central node of each grid. The contours of Kriged recharge values and their estimated error variance are shownin Figure 3 a and b respectively. The quantum of natural recharge through vertical infiltration has been estimated to be 4.95 MCM by this method for a complete season. This computed spatial distribution of recharge is helpful in demarcating potential zones of recharge; this in turn helps in selecting sites for future drilling for resources.The area was divided into polygons of various sizes based on the Thiessen polygon method (Figure 3 c ). The location at the centre of each polygon was considered to arrive at the average recharge estimate for each polygon. Finally the recharge values of all the polygons were summed up to arrive at the total natural recharge. The quantum of natural recharge through vertical infiltration has been estimated to be 5.40 MCM by the polygon method. The total input estimated by taking average value of recharge using tracers has been estimated as 4.5 MCM over the study area of 180 km 2. The quantum of groundwater recharge through vertical infiltration has been estimated as 25, 27.5 and 30 mm (l –2) using average value of recharge, Kriging and Theissen polygons methods respectively. The total rainfall during the corresponding monsoon period is 102 MCM and thus the fractional amount of recharge is 5% of the rainfall. The Krige method of estimation provides estimates with error bounds. The Kriged values are obtained using optimal grid size, by variogram analysis. The optimal grid size is a vital parameter provided by the Kriging method for interpolating the spatial recharge values. The estimate is unbiased and has minimum estimation variance; hence is more reliable than the polygon and the averaging method of recharge values. Recharge contours obtained through Kriging is generally smooth, because the Krige estimates are the weighted averageRESEARCH COMMUNICATIONSFigure 3. Kriged recharge values (a), estimated error variance (b), recharge estimation by Theissen polygons (c).of neighbouring field values. Interpolation errors are usually large where the data are sparse, especially when one moves towards the boundary compared to the interior portion of the basin. The estimation error can therefore be reduced if the num-ber of observations is increased at those locations. The result also focuses attention on the large number of scientists enga-ged in groundwater development and management for the application of Kriging method for estimating average as well as the quantum of groundwater recharge over the study area.1. 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C., Watermovement in the unsaturated zone of high and low permeabilitystrata by measuring natural tritium. Isotope Hydrology, IAEA, Vienna, 1970, pp. 73–87.8. Sukhija, B. S. and Rama, Evaluation of groundwater recharge in semi-arid region of India using environmental tritium. Proc. IndianAcad. Sci. Sect. A, 1973, 77, 279–292.9. Dincer, T., Al-Murgin, A. and Zimmerman, U., Study of infiltra-tion and recharge through the sand dunes in arid zones with special tothe stable isotopes and thermonuclear tritium. J. Hydrol., 1974,23, 79–109.10. Allison, G. B. and Huges, M. W., The use of environmental chlorideand tritium to estimate total local recharge to an unconfined aquifer.Aust. J. Soil Res., 1978, 16, 181–195.11. Athavale, R. N., Murthi, C. S. and Chand, R., Estimation of rechargeto phreatic aquifers of Lower Maner Basin by using the tritium injec-tion method. J. Hydrol., 1980, 45, 185–202.12. Athavale, R. N., Chand, R. and Rangarajan, R., Ground water rechargeestimation for two basins in the Deccan Trap Bassalt formation.Hydrol. Sci. J., 1983, 28, 525–538.13. Sharma, M. L., Bari, M. and Byrne, J., Dynamics of seasonal re-charge beneath semi-arid vegetation on the gnangara mound,Western Australia. Hydrol. Process., 1991, 5, 383–398.14. Sharma, M. L., Measurement and prediction of natural groundwaterrecharge – An overview. J. Hydrol., 25, 1987, 49–56.15. Ragarajan, R., Natural recharge evaluation in selected basins ofsemiarid India using injected tritium method, Ph D thesis, Osma-nia University, Hyderabad, 1997, p. 80.16. Rangarajan, R. and Athavale, R. N., Annual replenishable groundwa-ter potential of India – An estimate based on injected tritium stud-ies. J. Hydrol., 2000, 234, 38–53.17. Prakash, M. R. and Singh, V. S., Network designs for groundwatermonitoring – A case study. Environ. Geol., 2000, 39, 628–632. 18. Matheron, G., Principles of Geo-statistics. Econ. Geol., 1963, 58,1246–1266.19. Matheron, G., Les Variables regionalisees et leur estimation, Mas-sion, Paris, 1965, p. 306.20. Delhome, J. P., Spatial variability and uncertainty in groundwaterflow parameters. A geostatistical approach. Water Resour. Res.,1979, 15, 269–280.21. Gambolati, G. and Volpi, G., Groundwater contour mapping inVenice by stochastic interpolator. Water Resour. Res., 1979, 15,281–290.22. Ahmed, S. and Marsily, G. de, Multivariate geostatistical methodsfor estimating transmissivity using data on transmissivity and specificcapacity. Water Resour. Res., 1987, 23, 1717–1737.ACKNOWLEDGEMENTS. We thank the Director, Dr V. P. Dimri, NGRI, Hyderabad, for encouragement and permission to publish this paper. We also thank the anonymous reviewers for valuable suggestions to improve the paper.Received 1 September 2004; revised accepted 6 November 2004c。