Literature DB >> 35574564

Challenges Calibrating Hydrology for Groundwater-Fed Wetlands: a Headwater Wetland Case Study.

R Ramesh1, L Kalin1, M Hantush2, M Rezaeinzadeh3, C Anderson1.   

Abstract

This study aims to adapt the Soil and Watershed Assessment Tool (SWAT), a ubiquitously used watershed model, for ground-water 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 (E NASH = 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, β deep parameter, to negative values (E NASH = 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 (E NASH = 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.

Entities:  

Keywords:  Artificial neural networks; Headwater slope wetland; High baseflow; Model; SWAT; Wetland

Year:  2020        PMID: 35574564      PMCID: PMC9104761          DOI: 10.1007/s10666-019-09684-8

Source DB:  PubMed          Journal:  Environ Model Assess (Dordr)        ISSN: 1420-2026            Impact factor:   2.016


  1 in total

1.  Predicting water quality in unmonitored watersheds using artificial neural networks.

Authors:  Latif Kalin; Sabahattin Isik; Jon E Schoonover; B Graeme Lockaby
Journal:  J Environ Qual       Date:  2010 Jul-Aug       Impact factor: 2.751

  1 in total

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