Assessment of Hydrologic Data Estimates From ERA5 Reanalyses in Benin, West Africa

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
René Bodjrènou, Luc Ollivier Sintondji, Yekambessoun M' Po N'Tcha, Diane Germain, Francis Esse Azonwade, Fernand Sohindji, Gilbert Hounnou, Edid Amouzouvi, Arthur Freud Segnon Kpognin, Françoise Comandan
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Abstract

In West Africa, the validation of distributed models is limited by the quality and availability of point station data measured in situ. ERA5 is a climate reanalysis product produced by the European Centre for Medium-range Weather Forecasts (ECMWF) and is suggested to overcome this constraint. This study assessed and compared the quality of ERA5 and its variant ERA5-Land (namely, LAND) over Benin at spatial and monthly time scales. ERA5 relies on a single-level version with a 0.25° × 0.25° resolution, while LAND is a land surface version with a 0.1° × 0.1° resolution. Four variables were collected, namely, surface runoff (SRO), evapotranspiration (PET), water table depth (WTD) and soil water content (SWC). Single nearest pixel (SNP) and inverse distance weighting (IDW) selection methods were used to compare the reanalyse data to point station data based on the correlation (c), mean absolute error (MAE) and relative mean absolute error (RMAE). With the SNP method, both reanalyses showed a best peak simulation in mean SRO. Their performance in terms of correlation ranged from 0.26 to 0.65 for ERA5 vs. 0.34 to 0.60 for LAND. The reanalyses showed high correlations (generally > 0.80) for SWC and for the PET (sometime greater than 0.90). The correlations were below 0.5 in both reanalyses for the WTD, with slight overestimations (4.73 m for ERA5 vs. 3.13 m for LAND). Similar results were reported with the IDW selection method. One or the other of the two reanalyses can be recommended for model calibration/validation, but care must be taken in the choice because the one chosen may be better in terms of correlation even though it has significant biases and vice versa. Correcting the variables of these reanalysis datasets could also improve their performance.

Abstract Image

西非贝宁ERA5再分析水文数据估算的评估
在西非,分布式模型的验证受到现场测量的点站数据的质量和可用性的限制。ERA5是欧洲中期天气预报中心(ECMWF)制作的气候再分析产品,建议克服这一限制。本研究在空间和月时间尺度上评估并比较了贝宁地区ERA5及其变体ERA5- LAND(即LAND)的质量。ERA5依赖于分辨率为0.25°× 0.25°的单级版本,而LAND是分辨率为0.1°× 0.1°的陆地表面版本。收集地表径流(SRO)、蒸散发(PET)、地下水位深度(WTD)和土壤含水量(SWC) 4个变量。基于相关性(c)、平均绝对误差(MAE)和相对平均绝对误差(RMAE),采用单最近像元(SNP)和逆距离加权(IDW)选择方法将再分析数据与点站数据进行比较。使用SNP方法,两次再分析都显示出对平均SRO的最佳峰值模拟。它们在相关性方面的表现,ERA5为0.26至0.65,而LAND为0.34至0.60。再分析显示,SWC和PET的相关性很高(通常为>; 0.80)(有时大于0.90)。在WTD的两次重新分析中,相关性都低于0.5,略有高估(ERA5为4.73 m, LAND为3.13 m)。IDW选择方法也得到了类似的结果。两种重新分析中的一种或另一种可以推荐用于模型校准/验证,但必须谨慎选择,因为所选择的一种可能在相关性方面更好,即使它具有显著的偏差,反之亦然。纠正这些再分析数据集的变量也可以提高它们的性能。
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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
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