The performance of a high-resolution satellite-derived precipitation product over the topographically complex landscape of Eswatini

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Wisdom M. D. Dlamini, Samkele S. Tfwala
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引用次数: 0

Abstract

The study evaluated the use of Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) data for monitoring rainfall data in Eswatini. Various statistical metrics such as Bias, correlation coefficient (r), mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the CHIRPS 2.0 data against 14 rain gauge observations acquired during 1981–2020. CHIRPS 2.0 rainfall agrees well with rain gauge precipitation at monthly (r = 0.73, Bias = 1.02, RMSE = 50.44 and MAD = 31.44), seasonal (r = 0.77, Bias = 1.01, RMSE = 36.99 and MAD = 24.15) and annual scales (r = 0.65, Bias = 2.46, RMSE = 500.78 and MAD = 468.06). Moreover, areas characterized by complex topography and land use, and areas in transition zones (to a different agroecological zone) had generally poor correlations. Nonetheless, CHIRPS 2.0 captures well the spatial distribution of rainfall in the different agroecological zones of Eswatini, even in areas with no rain gauge data. In conclusion, CHIRPS 2.0 can be a very valuable tool in filling gaps created by poor spatial coverage of ground-based rain gauges, especially in the developing world where this is often the case.

Abstract Image

高分辨率卫星衍生降水产品在地形复杂的斯瓦蒂尼景观上的表现
该研究评估了气候灾害组红外降水观测站(CHIRPS)数据用于监测斯瓦蒂尼降雨数据的使用情况。利用偏差、相关系数(r)、平均绝对误差(MAE)和均方根误差(RMSE)等统计指标对CHIRPS 2.0数据与1981-2020年14个雨量计观测数据进行了比较。CHIRPS 2.0降雨量在月尺度(r = 0.73, Bias = 1.02, RMSE = 50.44, MAD = 31.44)、季节尺度(r = 0.77, Bias = 1.01, RMSE = 36.99, MAD = 24.15)和年尺度(r = 0.65, Bias = 2.46, RMSE = 500.78, MAD = 468.06)上与雨量计降水吻合良好。此外,地形和土地利用复杂的地区与过渡区(向不同的农业生态区)的相关性一般较差。尽管如此,即使在没有雨量计数据的地区,CHIRPS 2.0也能很好地捕捉到斯瓦蒂尼不同农业生态区降雨的空间分布。总之,CHIRPS 2.0是一个非常有价值的工具,可以填补由于地面雨量计空间覆盖率低而造成的空白,特别是在经常出现这种情况的发展中国家。
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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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