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

机译:挑战地下水液湿地校准水文:一个落水湿地案例研究

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摘要

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.
机译:本研究旨在通过算用于从含水层的充值来调整土壤和流域评估工具(SWAT),一个普遍存在的流域模型,用于地下水主导的地表水域。在阿拉巴马州沿海普通地区的沿着沿着落地湿地的测量流程作为案例研究,我们呈现挑战和使用SWAT模型的挑战和相对简单的方法来预测从排水流域的流动和相对简单的模型地下水升值方法。在研究流域的SWAT模拟流动受到沉淀的限制,因此,模拟流量比观察到的流动幅度较小几倍。因此,我们的第一种方法涉及单独的暴风流和基础流校准,包括使用观察和模拟的基础(E_(NASH)= 0.67)之间的回归关系。我们的下一个方法涉及调整SWAT以模拟升高的地下水排放而不是深度含水层损失。通过约束深损耗范围,β_(深)参数,对负值(e_(nash)= 0.75)。最后,我们还调查了人工神经网络(ANN)与SWAT结合使用,以进一步提高校准性能。这种方法使用了SWAT校准的流动,蒸散蒸腾和降水作为ANN的输入(E_(纳什)= 0.88)。该研究中调查的方法可用于在其他地下水中的流域中导航类似的流动校准挑战,这可能是管理者和建模者的非常有用的工具。

著录项

  • 来源
    《Environmental Modeling & Assessment》 |2020年第3期|355-371|共17页
  • 作者单位

    School of Forestry and Wildlife Sciences Auburn University 602 Duncan Drive Auburn AL 36849 USA;

    School of Forestry and Wildlife Sciences Auburn University 602 Duncan Drive Auburn AL 36849 USA;

    Center for Environmental Solutions and Emergency Response U.S.Environmental Protection Agency 26 West Martin Luther King Dr. Cincinnati OH 45268 USA;

    Lynker 3002 Bluff St Suite 101 Boulder CO 80301 USA;

    School of Forestry and Wildlife Sciences Auburn University 602 Duncan Drive Auburn AL 36849 USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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

    机译:湿地;模型;扑打;下坡湿地;高基流;人工神经网络;

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