This study aims to adapt the Soil and Watershed Assessment Tool (SWAT), a ubiquitously used watershed model, for groundwater dominated surface waterbodies by accounting for recharge from the aquifers. Using measured flow to a headwater slope wetland in Alabama’s coastal plain region as a case study, we present challenges and relatively simple approaches in using the SWAT model to predict flows from the draining watershed and relatively simple approaches to model groundwater upwelling. SWAT-simulated flow at the study watershed was limited by precipitation, and consequently, simulated flows were several times smaller in magnitude than observed flows. Thus, our first approach involved a separate stormflow and baseflow calibration which included the use of a regression relationship between observed and simulated baseflow (ENASH = 0.67). Our next approach involved adapting SWAT to simulate upwelling groundwater discharge instead of deep aquifer losses by constraining the range of deep losses, βdeep parameter, to negative values (ENASH = 0.75). Finally, we also investigated the use of artificial neural networks (ANN) in conjunction with SWAT to further improve calibration performance. This approach used SWAT-calibrated flow, evapotranspiration, and precipitation as inputs to ANN (ENASH = 0.88). The methods investigated in this study can be used to navigate similar flow calibration challenges in other groundwater dominant watersheds which can be very useful tool for managers and modelers alike.
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Abbaspour, K. C., Yang, J., Reichert, P., Vejdani, M., Haghighat, S., & Srinivasan, R. (2008). SWAT-CUP. Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Zurich, Switzerland: SWAT calibration and uncertainty programs.
Amatya, D. M., & Jha, M. K. (2011). Evaluating the SWAT model for a low-gradient forested watershed in coastal South Carolina. Transactions of the American Society of Agricultural and Biological Engineers, 54(6), 2151–2163.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment: part I. Model development. Journal of the American Water Resources Association, 34(1), 73–89.
Barksdale, W. F., Anderson, C. J., & Kalin, L. (2014). The influence of watershed run-off on the hydrology, forest floor litter and soil carbon of headwater wetlands: run-off effects on hydrology, leaf litter and soils of headwater wetlands. Ecohydrology, 7, 803–814.
Bosch, D. D., Sheridan, J. M., Batten, H. L., & Arnold, J. G. (2004). Evaluation of the SWAT model on a coastal plain agricultural watershed. Transactions of the ASAE, 47(5), 1493–1506.
Brinson, M. M. (1993). Changes in the functioning of wetlands along environmental gradients. Wetlands, 13, 65–74.
Cibin, R., Athira, P., Sudheer, K. P., & Chaubey, I. (2013). Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty. Hydrological Processes, 28, 2033–2045.
Gassman, P. W., Reyes, M. R., Green, C. H., & Arnold, J. G. (2007). The Soil and Water Assessment Tool: historical development, applications, and future research directions. Center for Agricultural and Rural Development: Iowa State University.
Gomi, T., Sidle, R. C., & Richardson, J. S. (2002). Understanding processes. and downstream linkages of headwater systems. BioScience, 52(10), 905–916.
Guzman, J. A., Moriasi, D. N., Gowda, P. H., Steiner, J. L., Starks, P. J., Arnold, J. G., & Srinivasan, R. (2015). A model integration framework for linking SWAT and MODFLOW. Environmental Modelling and Software, 73, 103–116.
Hamon, W. R. (1961). Estimating potential evapotranspiration. Journal of Hydraulics Division, 871, 107–120.
Isik, S., Kalin, L., Schoonover, J. E., Srivastava, P., & Lockaby, B. G. (2013). Modeling effects of changing land use/cover on daily streamflow: an artificial neural network and curve number based hybrid approach. Journal of Hydrology, 485, 103–112.
Kalin, L., & Hantush, M. M. (2006). Hydrologic modeling of an eastern Pennsylvania watershed with NEXRAD and rain gauge data. Journal of Hydrologic Engineering, 11, 555–569.
Kalin, L., Isik, S., Schoonover, J. E., & Lockaby, B. G. (2010). Predicting water quality in unmonitored watersheds using artificial neural networks. Journal of Environment Quality, 39, 1429.
Kim, R. J., Loucks, D. P., & Stedinger, J. R. (2012). Artificial neural network models of watershed nutrient loading. Water Resources Management, 26, 2781–2797.
Lam, Q. D., Schmalz, B., & Fohrer, N. (2010). Modelling point and diffuse source pollution of nitrate in a rural lowland catchment using the SWAT Model. Agricultural Water Management, 97, 317–325.
Leopold, L. B., Wolman, M. G., & Miller, J. P. (1964). Fluvial processes in geomorphology W. San Francisco, California: H. Freeman and Co..
Lim, K. J., Engel, B. A., Tang, Z., Choi, J., Kim, K.-S., Muthukrishnan, S., & Tripathy, D. (2005). Automated Web GIS based Hydrograph Analysis Tool, WHAT. Journal of the American Water Resources Association, 41, 1407–1416.
Lu, J., Sun, G., McNulty, S. G., & Amatya, D. M. (2005). A comparison of six potential evapotranspiration methods for regional use in the Southeastern United States. Journal of the American Water Resources Association, 41(3), 621–633.
Makarewicz, J. C., Lewis, T. W., Rea, E., Winslow, M. J., & Pettenski, D. (2015). Using SWAT to determine reference nutrient conditions for small and large streams. Journal of Great Lakes Research, 41, 123–135.
McBride, E. H., & Burgess, L. H. (1964). Soil survey of Baldwin County, Alabama. USDA-SCS Soil Survey Report 12:110. Washington (DC): USDA-SCS.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulation. Transactions of the ASABE, 50(3), 885–900.
Murgulet, D., & Tick, G. (2007). The extent of saltwater intrusion in Southern Baldwin County, Alabama. Environmental Geology, 55, 1235–1245.
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models: Part I. A discussion of principles. Journal of Hydrology, 10, 282–290.
Neitsch, S. L., Arnold, J. C., Kiniry, J. R., & Williams, J. R. (2001). Soil and Water Assessment Tool (SWAT) user’s manual: version 2000. U.S. Department of Agriculture, Agricultural Research Service, Grassland, Soil, and Water Research Laboratory, Temple, Texas.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2009). Soil and Water Assessment Tool (SWAT) theoretical documentation: version 2000. U.S. Department of Agriculture, Agricultural Research Service, Grassland, Soil, and Water Research Laboratory, Temple, Texas.
Noble, C. V., Wakeley, J. S., Roberts, T. H., & Henderson, C. (2007). Regional guidebook for applying the hydrogeomorphic approach to assessing the functions of headwater slope wetlands on the Mississippi and Alabama coastal plains. US Army Corps of Engineers ERDC/EL TR-07–9. Vicksburg (MS): US Army Corps of Engineers.
Noori, N., & Kalin, L. (2016). Coupling SWAT and ANN models for enhanced daily streamflow prediction. Journal of Hydrology, 533, 141–151.
Pechlivanidis, I. G., Jackson, B. M., McIntyre, N. R., & Wheater, H. S. (2011). Catchment scale hydrological modelling: a review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications. Global NEST Journal, 13, 193–214.
Rantz, S. E., et al. (1982). Measurement and computation of streamflow: U.S. Geological Survey Water-Supply Paper 2175, 2 v., 631 p.
Rezaeianzadeh, M., Kalin, L., & Anderson, C. J. (2015). Wetland water-level prediction using ANN in conjunction with base-flow recession analysis. Journal of Hydrologic Engineering, 22, D4015003.
Rheinhardt, R. D., Rheinhardt, M. C., Brinson, M. M., & Faser, K. (1998). Forested wetlands of low order streams in the inner coastal plain of North Carolina, USA. Wetlands, 18, 365–378.
Rheinhardt, R. D., Rheinhardt, M. C., Brinson, M. M., & Faser,Jr. K. E. (1999). Application of reference data for assessing and restoring headwater ecosystems. Restoration Ecology 7(3):241–251.
Robinson, J. L., Moreland, R. S., & Clark, A. E. (1996). Ground-water resources data for Baldwin County, Alabama. In US Geological Survey. Branch of Information: Services.
Roy, A. H., Dybas, A. L., Fritz, K. M., & Lubbers, H. R. (2009). Urbanization affects the extent and hydrologic permanence of headwater streams in a midwestern US Metropolitan area. Journal of the North American Benthological Society, 28, 911–928.
Salas, J. D., Markus, M., & Tokar, A. S. (2000). Streamflow forecasting based on artificial neural networks. Artificial Neural Networks in Hydrology 23–51.
Shaneyfelt, R. C., & Metcalf, C. (2014). Coastal Alabama pilot headwater stream survey study, ADEM-ACNPCP, MCSWCD and U.S. EPA-R4; 53 pp.
Sophocleous, M., & Perkins, S. P. (2000). Methodology and application of combined watershed and ground-water models in Kansas. Journal of Hydrology, 236, 185–201.
Srivastava, P., McNair, J. N., & Johnson, T. E. (2006). Comparison of process-based and artificial neural network approaches for streamflow modeling in an agricultural watershed. Journal of the American Water Resources Association, 42, 545–563.
Talebizadeh, M., Morid, S., Ayyoubzadeh, S. A., & Ghasemzadeh, M. (2010). Uncertainty analysis in sediment load modeling using ANN and SWAT model. Water Resources Management, 24, 1747–1761.
Vis, M., Knight, R., Pool, S., Wolfe, W., & Seibert, J. (2015). Model calibration criteria for estimating ecological flow characteristics. Water, 7, 2358–2381.
Wang, R., & Kalin, L. (2011). Modelling effects of land use/cover changes under limited data. Ecohydrology, 4, 265–276.
Winter, T. C., Rosenberry, D. O., & LaBaugh, J. W. (2003). Where does the ground water in small watersheds come from? Ground Water, 41, 989–1000.
Zeng, R., & Cai, X. (2014). Analyzing streamflow changes: irrigation-enhanced interaction between aquifer and streamflow in the Republican River basin. Hydrology and Earth System Sciences, 18, 493–502.
The work reported in this document was funded by the U.S. Environmental Protection Agency (EPA or the Agency) under Work Assignment WA 1-57 of contract no. EP-C-15-010 through its Office of Research and Development. EPA funded and managed, or partially funded and collaborated in, the research described herein.
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Ramesh, R., Kalin, L., Hantush, M. et al. Challenges Calibrating Hydrology for Groundwater-Fed Wetlands: a Headwater Wetland Case Study. Environ Model Assess 25, 355–371 (2020). https://doi.org/10.1007/s10666-019-09684-8
- Headwater slope wetland
- High baseflow
- Artificial neural networks