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Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia

机译:通过河流模型评估可用的全球径流数据集,以支持南亚和东南亚的跨界水管理

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Numerical models have become essential tools for simulating and forecasting hydro-meteorological variability, and to help better understand the Earth’s water cycle across temporal and spatial scales. Hydrologic outputs from these numerical models are widely available and represent valuable alternatives for supporting water management in regions where observations are scarce, including in transboundary river basins where data sharing is limited. Yet, the wide range of existing Land Surface Model (LSM) outputs makes the choice of dataset challenging in the absence of detailed analysis of the hydrological variability and quantification of associated physical processes. Here we focus on two of the world’s most populated transboundary river basins – the combined Ganges-Brahmaputra-Meghna (GBM) in South Asia and the Mekong in Southeast Asia – where downstream countries are particularly vulnerable to water related disasters in the absence of upstream hydro-meteorological information. In this study, several freely-available global LSM outputs are obtained from NASA’s Global Land Data Assimilation System (GLDAS) and from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-interim/Land (ERA-interim/Land) and used to compute river discharge across these transboundary basins using a river network routing model. Simulations are then compared to historical discharge to assess runoff data quality and identify best-performing models with implications for the terrestrial water balance. This analysis examines the effects of meteorological inputs, land surface models and their spatio-temporal resolution, as well as river network fineness and routing model parameters on hydrologic modeling performance. Our results indicate that the most recent runoff datasets yield the most accurate simulations in most cases, and suggest that meteorological inputs and the selection of the LSM are together the most influential factors affecting discharge simulations. Conversely, the spatial and temporal resolution of the LSM and river model have the least impact on the quality of simulated discharge, although the routing model parameters affect the timing of hydrographs.
机译:数值模型已经成为模拟和预测水文气象变异性的必要工具,并有助于更好地了解时空尺度上的地球水循环。这些数值模型的水文产出可广泛获得,并代表了宝贵的替代方案,以支持缺乏观测的地区的水管理,包括数据共享受限的跨界河流域。然而,由于缺乏对水文变异性的详细分析和相关物理过程的量化,现有陆面模型(LSM)输出的范围很广,因此选择数据集具有挑战性。在这里,我们重点介绍两个世界上人口最多的跨境流域,即南亚的恒河-布拉马普特拉-梅格纳河(GBM)和东南亚的湄公河流域,这两个下游国家在没有上游水电的情况下特别容易遭受与水有关的灾害-气象信息。在这项研究中,从NASA的全球土地数据同化系统(GLDAS)和欧洲中距离天气预报中心(ECMWF)重新分析-中期/土地(ERA-interim / Land)中获得了一些免费的全球LSM输出。 ),并使用河网路由模型来计算这些跨界流域的河流量。然后将模拟与历史流量进行比较,以评估径流数据质量,并确定对地下水平衡具有影响的最佳模型。该分析检查了气象输入,土地表面模型及其时空分辨率以及河网精细度和路径模型参数对水文建模性能的影响。我们的结果表明,在大多数情况下,最新的径流数据集可产生最准确的模拟,并且表明气象输入和LSM的选择共同是影响排放模拟的最有影响力的因素。相反,LSM和河流模型的时空分辨率对模拟流量的质量影响最小,尽管路由模型参数会影响水文图的时间。

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