Addressing data challenges in riverine nutrient load modeling of an intensively managed agro‐industrial watershed
Addressing data challenges in riverine nutrient load modeling of an intensively managed agro‐industrial watershed
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DOI：
10.1111/1752-1688.13097
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
S. Niroula;Kevin Wallington;Ximing Cai
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文献类型：
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作者：
S. Niroula;Kevin Wallington;Ximing Cai
数据的局限性往往对水质模型的可靠性提出挑战，特别是在集中管理的流域。虽然许多研究报告成功的水文模型的设置和校准，很少有详细的多站点和多变量模型校准到一个集中管理的流域的数据挑战。在这项研究中，我们解决了一些这些挑战的基础上，我们的反思经验校准土壤和水资源评估工具（SWAT）上桑格蒙河流域在伊利诺伊州中部的基础上，每日流量，每年的作物产量，每月的沉积物，硝酸盐和总磷负荷。我们强调了SWAT校准过程中的一些挑战，由于数据错误和不一致，以及降水和水质观测不足。接下来，我们展示了额外的天气和水质观测的优点，可以帮助减少输入的不确定性，我们提供了选择合适的观测模型校准的建议。在处理数据问题后，我们表明，SWAT模型可以进行校准与可接受的结果的案例研究流域。
Data limitations often challenge the reliability of water quality models, especially in intensively managed watersheds. While numerous studies report successful hydrological model setup and calibration, few have addressed in detail the data challenges for multisite and multivariable model calibration to an intensively managed watershed. In this study, we address some of these challenges based on our reflective experience calibrating the Soil and Water Assessment Tool (SWAT) to the Upper Sangamon River Watershed in central Illinois based on daily flow, annual crop yield, and monthly sediment, nitrate, and total phosphorus loads. We highlight some challenges in SWAT calibration processes due to data errors and inconsistencies, and insufficient precipitation and water quality observations. Following, we demonstrate the merits of additional weather and water quality observations that could help reduce input uncertainties, and we provide suggestions for selecting appropriate observations for the model calibration. After dealing with the data issues, we show that the SWAT model could be calibrated with acceptable results for the case study watershed.