利用国家水文模型获取区域气候变化对未体现过程的河川流域的影响

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-09-27 DOI:10.1016/j.envsoft.2024.106234
Patience Bosompemaa , Andrea Brookfield , Sam Zipper , Mary C. Hill
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引用次数: 0

摘要

气候变化对水资源供应的影响越来越大。国家尺度水文模型模拟了许多重要过程所产生的河水流量,但往往忽略了人类用水和管理活动等过程。这项工作利用一个国家尺度的水文模型,探索并测试了考虑此类遗漏过程的方法。在美国地质调查局国家水文模型(NHM-PRMS)模拟的溪流上测试了流量持续时间曲线(FDC)和自回归综合移动平均(ARIMA)这两种偏差校正方法,该模型忽略了灌溉抽水。采用的是半干旱农业案例研究。FDC 和 ARIMA 分别在修正低流量和高流量方面表现较好。一种混合方法在低流量和高流量时均表现良好;典型的 Nash-Sutcliffe 值从 <-1.00 增加到约 0.75。研究结果表明,全国尺度的水文模型可以根据遗漏过程进行偏差校正,以改进区域性的流量估算。讨论了这些校正方法在模拟未来预测中的实用性。
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Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes
Climate change is increasingly impacting water availability. National-scale hydrologic models simulate streamflow resulting from many important processes, but often without processes such as human water use and management activities. This work explores and tests methods to account for such omitted processes using one national-scale hydrologic model. Two bias correction methods, Flow Duration Curve (FDC) and Auto-Regressive Integrated Moving Average (ARIMA), are tested on streamflow simulated by the US Geological Survey National Hydrologic Model (NHM-PRMS), which omits irrigation pumping. A semi-arid agricultural case study is used. FDC and ARIMA perform better for correcting low and high flows, respectively. A hybrid method performs well at both low and high flows; typical Nash-Sutcliffe values increased from <-1.00 to about 0.75. Results suggest methods with which national-scale hydrologic models can be bias-corrected for omitted processes to improve regional streamflow estimates. Utility of these correction methods in simulation of future projections is discussed.
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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