Enhancing the representation of water management in global hydrological models

IF 4 3区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, Hong-Yi Li
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引用次数: 0

Abstract

Abstract. This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5∘ spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling–Gupta efficiency (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44 % of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human–earth system perspective.
加强水管理在全球水文模型中的代表性
摘要本研究通过增加一个新的水管理模块来增强现有的全球水文模型Xanthos,该模块可以区分灌溉、水电和防洪水库的运行特征。我们将全球水库和水坝(GRanD)数据库中的水库重新映射为Xanthos中的0.5°空间分辨率,以便每个网格单元存在一个集总水库,从而产生3790个大型水库。我们根据每种油藏的主要用途,对其实施了独特的操作规则。特别是,在以往的GHM研究中,水电水库被视为防洪水库,而在这里,我们通过优化来确定水电水库的运行规则,以最大化长期水电产量。我们使用增强的Xanthos进行了全球模拟,并验证了91个大型河流流域的月流量,这些流域有高质量的观测流量数据。在91个流域的3790个水库中,共有1878个(水电296个,灌溉486个,防洪和其他1096个)是我们报告结果的一部分。39个流域和81个流域的克林-古普塔效率(KGE)值≥0.5和≥0.0。在加入新的水管理模块后,91个流域中有75个流域的模型性能得到改善,只有7个流域的模型性能恶化。为了衡量显式表示水电水库与将水电水库表示为防洪水库之间的相对差异(在其他ghm中通常是这样做的),我们使用归一化均方根误差(NRMSE)和决定系数(R2)。在296个水电站中,NRMSE为;当比较两种模拟的水库释放和储存时间序列时,296个水库中超过44%的水库为0.25(即考虑0.25表示中等差异)。我们认为,在ghm中正确地表示水电水库可能对我们在区域到全球尺度上理解和管理淡水资源挑战具有重要意义。这种增强的全球水管理建模框架将允许从人地耦合系统的角度分析未来全球水库的开发和管理。
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来源期刊
Geoscientific Model Development
Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
8.60
自引率
9.80%
发文量
352
审稿时长
6-12 weeks
期刊介绍: Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.
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