Skip to main content
Log in

Estimating aquifer recharge in Mission River watershed, Texas: model development and calibration using genetic algorithms

  • Original Article
  • Published:
Environmental Geology

Abstract

Soil moisture balance studies provide a convenient approach to estimate aquifer recharge when only limited site-specific data are available. A monthly mass-balance approach has been utilized in this study to estimate recharge in a small watershed in the coastal bend of South Texas. The developed lumped parameter model employs four adjustable parameters to calibrate model predicted stream runoff to observations at a gaging station. A new procedure was developed to correctly capture the intermittent nature of rainfall. The total monthly rainfall was assigned to a single-equivalent storm whose duration was obtained via calibration. A total of four calibrations were carried out using an evolutionary computing technique called genetic algorithms as well as the conventional gradient descent (GD) technique. Ordinary least squares and the heteroscedastic maximum likelihood error (HMLE) based objective functions were evaluated as part of this study as well. While the genetic algorithm based calibrations were relatively better in capturing the peak runoff events, the GD based calibration did slightly better in capturing the low flow events. Treating the Box-Cox exponent in the HMLE function as a calibration parameter did not yield better estimates and the study corroborates the suggestion made in the literature of fixing this exponent at 0.3. The model outputs were compared against available information and results indicate that the developed modeling approach provides a conservative estimate of recharge.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Adeli H, Hung SL (1995) Machine learning neural networks, genetic algorithms, and fuzzy systems. Wiley, New York

    Google Scholar 

  • Alley WM (1984) On the treatment of evapotranspiration, soil moisture accounting and aquifer recharge in monthly water balance models. Water Resour Res 20:1137–1149

    Google Scholar 

  • Alley WM (1985) Water balance models in one month-ahead streamflow forecasting. Water Resour Res 21:597–606

    Google Scholar 

  • Bras RL, Cordova JR (1981) Intra-seasonal water allocation in deficit irrigation. Water Resour Res 17:866–874

    Google Scholar 

  • Campbell GS (1974) A simple method for determining unsaturated conductivity from moisture retention data. Soil Sci 117:311–314

    Article  Google Scholar 

  • Chowdhury AH, Wade S, Mace RE, Ridgeway C (2004) Groundwater availability model of the Central Gulf Coast aquifer system: numerical simulations through 1999. Texas Water Development Board, Austin, TX 108

  • Clapp RB, Hornberger GM (1978) Empirical equations for some soil hydraulic properties. Water Resour Res 14:601–604

    Google Scholar 

  • CWP (2004) Crop weather program. http://www.cwp.tamu.edu Accessed 07/04

  • Dingman L (2002) Physical hydrology. Prentice Hall, New Jersey

    Google Scholar 

  • Dutton AR, Richter BC (1990) Regional hydrogeology of the gulf coast aquifer in Matagorda and Wharton counties, Texas—development of a numerical flow model to estimate the impact of water management strategies: report prepared for the Lower Colorado River Authority under contract (88–89) 0910. Bureau of Economic Geology, University of Texas at Austin, p 118

  • Eagleson PS (1978) Climate, soil and vegetation 3. A simplified model of soil moisture movement in the liquid phase. Water Resour Res 14:722–730

    Google Scholar 

  • Gee GW, Hillel D (1988) Groundwater recharge in arid regions: review and critique of estimation methods. Hydrol Process 2:255–266

    Article  Google Scholar 

  • Ghosh RK (1983) A note on infiltration equation. Soil Sci 136:333–338

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic Algorithms in search, optimization, and machine learning. Addison-Wesley Professional, Lebanon

    Google Scholar 

  • Groschen GE (1985) Simulated effects of projected pumping on the availability of freshwater in the Evangeline aquifer in an area southwest of Corpus Christi, Texas. US Geological Survey water resources investigation report 85–4182. US Geological Survey, p 103

  • Haan CT (1972) A water yield model for small watersheds. Water Resour Res 8:58–69

    Google Scholar 

  • Hamilton L (1991) Regression with graphics, a second course in applied statistics. Brooks/Cole Publishers, Belmont

    Google Scholar 

  • Hay R (1999) A numerical groundwater flow model of the Gulf Coast Aquifer along the South Texas Gulf Coast. MS Thesis, Texas A&M University—Corpus Christi, p 47

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Kasmarek MC, Robinson JL (2004) Hydrogeology and simulation of groundwater flow and land surface subsidence in the northern part of the Gulf Coast Aquifer System, US geological survey scientific investigations report, 2004–5102. US Geological Survey, p 111

  • Khazaei E, Spink AEF, Warner JW (2003) A catchment water balance model for estimating groundwater recharge in arid and semi-arid regions of south-east Iran. Hydrogeol J 11:333–342

    Google Scholar 

  • Lasdon LS (2002) Optimization theory for large systems. Dover Publications, Mineola

    Google Scholar 

  • LGWSP (2004) Lower Guadalupe water supply project. http://www.gbra.org/ Accessed 07/04

  • Misirli F, Gupta HV, Sooroshian S (2002) Bayesian recursive estimation of parameter and output uncertainty for watershed models. In: AGU monograph: advances in model calibration

  • Moorthy S (2005) Hydrologic field investigations in Refugio County, TX. MS Thesis, Texas A&M University, Kingsville, p 73

  • Muthukrishnan S, Lim KJ, Harbor J, Engel BA (2005) iSep: a simple GIS enabled, Internet-based hydrograph separation tool. Comput Geosci (in revision)

  • NCDC (2003) National Climatic Data Center. http://www.ncdc.noaa.gov Accessed 07/03

  • Norwine JR (1995) The changing climate of Texas: predictability and implications for the Future. Geobooks, College Station

    Google Scholar 

  • NWIS (2004) National Water Information System. http://www.ncdc.noaa.gov Accessed 07/04

  • Philip JR (1957) The theory of infiltration: 1,2 and 4. Soil Sci 83, 84:345–357, 435–448, 257–264

    Google Scholar 

  • Rodriguez-Iturbe I, D’Odorico P, Porporato A, Ridolfi L (1999) On the spatial and temporal links between vegetation, climate, and soil moisture. Water Resour Res 35:3709–3722

    Article  Google Scholar 

  • Ronan AD, Prudic DE, Thodal CE, Constantz J (1998) Field study and simulation of diurnal temperature effects on infiltration and variably saturated flow beneath an ephemeral stream. Water Resour Res 34:2137–2153

    Article  Google Scholar 

  • Rutledge AT, Daniel CC III (1994) Testing an automated method to estimate groundwater recharge from streamflow records. Groundwater 32:180–189

    Google Scholar 

  • Ryder PD (1988) Hydrogeology and pre-development flow in the Texas Gulf Coast aquifer systems: US Geological Survey water resources investigation report 87–4248, US Geological Survey, p 109

  • Ryder PD, Ardis AF (2002) Hydrology of the Texas Gulf Coast aquifer systems: US Geological Survey professional paper 1416-E. US Geological Survey, p 77

  • Scanlon BR, Healy RW, Cook PG (2002) Choosing appropriate techniques for quantifying groundwater recharge. Hydrogeol J 10:18–39

    Article  Google Scholar 

  • Shamir E, Imam B, Morin E, Gupta HV, Sorooshian S (2005) The role of hydrograph indices in parameter estimation of rainfall-runoff models. Hydrol Process 19:2187–2207

    Article  Google Scholar 

  • Sorooshian S, Dracup J (1980) Stochastic parameter estimation procedures for hydrologic rainfall-runoff models: correlated and heteroscedastic cases. Water Resour Res 16:430–442

    Article  Google Scholar 

  • Stephens DB (1995) Vadose zone hydrology. CRC, Boca Raton

    Google Scholar 

  • Swartzendruber D, Youngs EG (1974) A comparison of physically-based infiltration equations. Soil Sci 117:165–167

    Article  Google Scholar 

  • Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55–94

    Article  Google Scholar 

  • Wright KA, Xu Y (2000) A water balance approach to the sustainable management of groundwater in South Africa. Water SA 26:167–170

    Google Scholar 

  • Xu C-Y, Singh VP (1998) A review on monthly water balance models for water resource investigations. Water Resour Manage 12:31–50

    Article  Google Scholar 

  • Xu CY, Vandewiele GL (1995) Parsimonious monthly rainfall-runoff models for humid basins with different input requirements. Adv Water Resour 18:39–48

    Article  Google Scholar 

  • Xu CY, Seibert J, Halldin S (1996) Regional water balance modeling in the NOPEX area: development and application of monthly water balance models. J Hydrol 180:211–236

    Article  Google Scholar 

Download references

Acknowledgments

Financial support from South Texas Alliance of Groundwater Conservation Districts and the National Science Foundation is greatly appreciated.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. Uddameri.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Uddameri, V., Kuchanur, M. Estimating aquifer recharge in Mission River watershed, Texas: model development and calibration using genetic algorithms. Environ Geol 51, 897–910 (2007). https://doi.org/10.1007/s00254-006-0453-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00254-006-0453-4

Keywords

Navigation