Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.

中文翻译：

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评估也门高地地区几种网格化降水产品在水资源管理中的性能。

在许多干旱和半干旱地区，气候变化下的水资源管理是最具挑战性的任务之一。也门等国家的主要挑战是，缺乏足够的长期气候数据来推动水文模型来更好地管理水资源。在这项研究中，我们根据1998-2007年Al Mahwit省（也门高地）的观测数据，评估了可获取的卫星和基于再分析的降水产品的准确性。在这里，我们评估了带站（CHIRPS）数据的气候灾害组红外降水，国家环境预测中心（NCEP）的气候预测系统重新分析（CFSR），使用人工神经网络从气候信息中估算降水的准确性-气候数据记录（PERSIANN-CDR），热带降雨测量任务（TRMM 3B42），基于统一量规的全球每日降水分析（CPC）和欧洲大气再分析（ERA-5）。通过将每个站点的数据与每个产品的最近网格框中的数据直接进行比较，以每日，每月和每年的时间进行评估。在每天的时间尺度上，CHIRPS可以最好地捕获每天的降雨特征，例如潮湿的天数，与潮湿持续时间的平均偏差约为11.53％。TRMM 3B42是日常评估中表现第二好的产品，平均偏差约为34.7％。但是，CFSR（85.3％）和PERSIANN-CDR（103％）和ERA-5（−81.13％）显示出对湿天的高估和低估，并且没有反映研究区域的降雨变化。此外，CHIRPS是每月最准确的网格产品，具有较高的相关性和较低的偏差。观测值与CHIRPS，TRMM 3B42，PERSIANN-CDR，CPC，ERA-5和CFSR之间的平均每月相关性分别为0.78、0.56、0.53、0.15、0.20和0.51。CHIRPS，TRMM 3B42，PERSIANN-CDR，CPC，ERA-5和CFSR的平均每月偏差分别为-2.9，-5.25、7.35，-25.29，-24.96和16.68 mm。CHIRPS很好地显示了年度降雨模式的空间分布，在五个验证点的偏差百分比（Pbias）约为-8.68％，而TRMM 3B42，PERSIANN-CDR和CFSR的偏差大于15.30、22.90和66.21％ ， 分别。CPC和ERA-5从观察到的数据显示出大约-88.6％的偏倚。总体而言，如果没有更好的数据，