Improving global hydrological simulations through bias-correction and multi-model blending
Improving global hydrological simulations through bias-correction and multi-model blending
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DOI：
10.1016/j.jhydrol.2023.129607
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发表时间：
2023-05
影响因子：
6.4
通讯作者：
A. Chevuturi;Maliko Tanguy;Katie Facer-Childs;Alberto Martínez-de la Torre;Sunita Sarkar;Stephan Thober;L. Samaniego;O. Rakovec;M. Kelbling;E. Sutanudjaja;N. Wanders;E. Blyth
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作者：
A. Chevuturi;Maliko Tanguy;Katie Facer-Childs;Alberto Martínez-de la Torre;Sunita Sarkar;Stephan Thober;L. Samaniego;O. Rakovec;M. Kelbling;E. Sutanudjaja;N. Wanders;E. Blyth
鉴于未来在不断变化的气候下易受水文灾害和水资源短缺的影响，迫切需要制定准确可靠的全球水文预报。作为世界气象组织（WMO）全球水文状况和展望系统（HydroSOS）计划的一部分，我们研究了混合多模型模拟以开发整体业务全球预报的不同方法。ULYSSES（多模型水文SeaSonal prEditionS系统）数据集，将发表为“全球季节预报和再预报河流流量和相关水文变量集合从四个国家的最先进的陆地表面和水文模型”在这项研究中使用。改进这些预测的第一步是研究改进模型模拟的方法，因为全球模型没有针对当地条件进行校准。该分析在1981-2019年的基线期内对全球119个不同的集水区进行了三个变量的分析：蒸散量，表层土壤水分和径流量。这项研究评估了混合方法与性能指标为基础的（加权）平均的多模型模拟，使用集水区的克林古普塔效率（KGE）的变量来定义的权重。水文模型模拟也进行了偏差校正，以提高多模型混合输出。加权混合结合偏差校正提供了最好的改善性能的集水区调查。在混合原始模拟过程中应用建模权重可提高未测量集水区的性能。结果表明，有可能成功地和容易地实施偏差校正加权混合方法，以改善全球业务预报。这项工作可用于改善水资源管理和减轻水文灾害，特别是在数据稀少的地区。
There is an immediate need to develop accurate and reliable global hydrological forecasts in light of the future vulnerability to hydrological hazards and water scarcity under a changing climate. As a part of the World Meteorological Organization’s (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we investigated different approaches to blending multi-model simulations for developing holistic operational global forecasts. The ULYSSES (mULti-model hYdrological SeaSonal prEdictionS system) dataset, to be published as “Global seasonal forecasts and reforecasts of river discharge and related hydrological variables ensemble from four state-of-the-art land surface and hydrological models” is used in this study. The first step for improving these forecasts is to investigate ways to improve the model simulations, as global models are not calibrated for local conditions. The analysis was performed over 119 different catchments worldwide for the baseline period of 1981–2019 for three variables: evapotranspiration, surface soil moisture and streamflow. This study evaluated blending approaches with a performance metric based (weighted) averaging of the multi-model simulations, using the catchment’s Kling-Gupta Efficiency (KGE) for the variable to define the weight. Hydrological model simulations were also bias-corrected to improve the multi-model blending output. Weighted blending in conjunction with bias-correction provided the best improvement in performance for the catchments investigated. Applying modelled weights during blending original simulations improved performance over ungauged catchments. The results indicate that there is potential to successfully and easily implement the bias-corrected weighted blending approach to improve operational forecasts globally. This work can be used to improve water resources management and hydrological hazard mitigation, especially in data-sparse regions.