Abstract The daily rainfall is the most important and demanded input of water resources studies, which are challenged by typical low density and/or poor quality of in-situ observations. However, the satellite earth observation, through freely available, web-based products, can provide complementary rainfall data. Such data is however, typically affected by substantial error, particularly at daily temporal resolution. Therefore, effective methods and protocols of rainfall downscaling, validation, and bias-correction are needed. The aims of this study were to: i) validate two downscaled satellite-derived rainfall products, CHIRPS and MPEG, against in-situ observations; ii) merge the downscaled products with in-situ observations and evaluate them to select better performing one. This study rainfall assessment was conducted on daily basis, at topographically complex, Upper Tekeze Basin (UTB), separately for the wet and dry seasons, within 1 January 2015–31 December 2018. Validation of the products, downscaled by nearest-neighbor (NN) and bilinear (BL) methods, was carried out using descriptive statistics, categorical statistics and bias decomposition methods, introducing novel protocol with new bias indicators for each of the evaluation methods. It showed large biases of CHIRPS and of MPEG, larger for CHIRPS than for MPEG, larger in dry than in wet season and slightly larger for NN than for BL. To correct biases of the downscaled CHIRPS and MPEG, each was merged with the in-situ observed rainfall, applying Geographically Weighted Regression (GWR) algorithm, using rainfall dependence on altitude as explanatory variable. The GWR-merging method substantially improved the accuracy of the MPEG and CHIRPS, with slightly better final accuracy of MPEG than of CHIRPS and better in wet than in dry season. This study confirmed that GWR-merged method could substantially reduce daily bias of satellite rainfall products, even in such topographical complex area as the UTB. Further improvement of the method application, can be achieved by densifying rain-gauge network and eventually by adding accuracy-effective explanatory variable(s).

中文翻译：

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将每日卫星降雨与实地降雨相结合的新方法，埃塞俄比亚上特克泽盆地

摘要 日降雨量是水资源研究中最重要和最需要的输入，其面临典型的低密度和/或实地观测质量差的挑战。然而，卫星地球观测可以通过免费提供的基于网络的产品提供补充降雨数据。然而，此类数据通常会受到大量误差的影响，尤其是在每日时间分辨率下。因此，需要降雨量降尺度、验证和偏差校正的有效方法和协议。本研究的目的是： i) 对照原位观测验证两种缩小的卫星衍生降雨产品 CHIRPS 和 MPEG；ii) 将缩小的产品与原位观察合并并评估它们以选择性能更好的产品。这项研究降雨评估是每天进行的，2015 年 1 月 1 日至 2018 年 12 月 31 日期间，在地形复杂的上特克泽盆地 (UTB)，分别针对雨季和旱季。通过最近邻 (NN) 和双线性 (BL) 方法对产品进行了验证使用描述性统计、分类统计和偏差分解方法，为每种评估方法引入具有新偏差指标的新协议。它显示出 CHIRPS 和 MPEG 的较大偏差，CHIRPS 的偏差大于 MPEG，旱季大于雨季，NN 略大于 BL。为了纠正缩减后的 CHIRPS 和 MPEG 的偏差，将每个都与现场观测到的降雨合并，应用地理加权回归 (GWR) 算法，使用降雨对海拔的依赖作为解释变量。GWR合并方法大大提高了MPEG和CHIRPS的精度，MPEG的最终精度略高于CHIRPS，雨季优于旱季。本研究证实，即使在 UTB 等地形复杂地区，GWR-merged 方法也可以显着降低卫星降雨产品的日偏差。方法应用的进一步改进可以通过增密雨量计网络并最终添加准确有效的解释变量来实现。