Alfieri Lorenzo, Lorini Valerio, Hirpa Feyera A, Harrigan Shaun, Zsoter Ervin, Prudhomme Christel, Salamon Peter
European Commission, Joint Research Centre (JRC), Ispra, Italy.
School of Geography and Environment, University of Oxford, Oxford, UK.
J Hydrol X. 2020 Jan;6:100049. doi: 10.1016/j.hydroa.2019.100049.
Global and continental scale hydrological reanalysis datasets receive growing attention due to their increasing number of applications, ranging from water resources management, climate change studies, water related hazards and policy support. Until recently, their use was mostly limited to qualitative assessments, due to their coarse spatial and temporal resolution, large uncertainty and bias in the model output, and limited extent of the dataset in space and time. This research reports on the setup of a gridded hydrological model with quasi-global coverage, able to reproduce a seamless 39-year streamflow simulation in all world's medium to large river basins. The model was calibrated at 1226 river sections with a total drainage area of 51 million km within 66 countries, using ECMWF's latest atmospheric reanalysis ERA5. A performance assessment revealed large improvements in reproducing past discharge observations, in comparison to the calibration used in the current operational setup of the hydrological model as part of the Copernicus - Global Flood Awareness System (GloFAS, www.globalfloods.eu), with median scores of Kling-Gupta Efficiency KGE = 0.67 and correlation r = 0.8. The simulation bias was also dramatically reduced and narrowed around zero, with more than 60% of stations showing percent bias within ±20%. Pronounced regional differences in the simulation results remain, pointing out the need for detailed investigation of the hydrological processes in specific regions, including parts of Africa and South Asia. In addition, observed discharges with high data quality is key to achieving skillful model output. The new calibrated model will become part of the operational runs of GloFAS in the next system release foreseen for Spring 2020, together with a near real time extension of the streamflow reanalysis.
全球和大陆尺度的水文再分析数据集因其应用数量不断增加而受到越来越多的关注,其应用范围涵盖水资源管理、气候变化研究、与水相关的灾害以及政策支持等领域。直到最近,由于其空间和时间分辨率粗糙、模型输出存在较大不确定性和偏差,以及数据集在空间和时间上的范围有限,其使用大多局限于定性评估。本研究报告了一个具有准全球覆盖范围的网格化水文模型的设置情况,该模型能够在世界所有中大型流域再现无缝的39年径流模拟。该模型在66个国家的1226个河段进行了校准,总排水面积为5100万平方千米,使用了欧洲中期天气预报中心(ECMWF)最新的大气再分析数据ERA5。性能评估显示,与作为哥白尼全球洪水预警系统(GloFAS,www.globalfloods.eu)一部分的水文模型当前业务设置中所使用的校准相比,在再现过去流量观测方面有了很大改进,克林 - 古普塔效率(KGE)中位数得分 = 0.67,相关性r = 0.8。模拟偏差也大幅降低并趋近于零,超过60%的站点偏差百分比在±20%以内。模拟结果仍存在明显的区域差异,这表明需要对特定区域(包括非洲和南亚部分地区)的水文过程进行详细研究。此外,高质量的观测流量数据是获得准确模型输出的关键。新校准的模型将在预计于2020年春季发布的下一个系统版本中成为GloFAS业务运行的一部分,同时还将对径流再分析进行近实时扩展。