Estimating precipitation at high spatial-temporal resolution is vital in manifold hydrological, meteorological and water management applications, especially over areas with un-gauged networks and regions where water resources are on the wane. This study aims to evaluate five downscaling methods to determine the accuracy and efficiency of which on generating high-resolution precipitation data at annual and monthly scales. To establish precipitation-Land surface characteristics relationship, environmental factors, including Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Digital Elevation Model (DEM), were considered as proxies in the spatial downscaling procedure. The downscaling algorithms, namely support vector machine (SVM), random forest (RF), geographically weighted regression (GWR), multiple linear regression (MLR) and exponential regression (ER), were implemented to downscale the version 7 of TRMM (Tropical Rainfall Measuring Mission) precipitation (3B43 V7 product) over Lake Urmia Basin (LUB) from 0.25° to 1 km spatial resolution. The downscaled precipitation data was validated against observations from meteorological stations. Monthly fractions derived from TRMM 3B43 were used to disaggregate 1 km annual precipitation to 1 km monthly precipitation. Furthermore, the best method was selected for calibration based on Geographical Difference Analysis (GDA) to assess the effectiveness of the calibration as a viable option. The results indicate that SVM not only outperforms the other methods, but also has good agreements with in-situ measurements compared to the original TRMM. The results confirm that inclusion of LST and geographic information along with NDVI can improve the downscaling performance. Downscaling and GDA calibration significantly improve the accuracy of TRMM 3B43 product at both spatial and temporal resolution and should be considered as an essential step in calibration of TRMM precipitation. Calibration at monthly scale yields slightly better results than calibration at annual scale and then disaggregating into monthly maps in terms of accuracy assessment.

在多种水文，气象和水管理应用中，以高时空分辨率估算降水是至关重要的，尤其是在无约束网络和水资源匮乏地区的地区。这项研究旨在评估五种缩减尺度的方法，以确定其在生成年度和月度尺度高分辨率降水数据时的准确性和效率。为了建立降水与土地表面特征的关系，环境因素，包括归一化植被指数（NDVI），地表温度（LST）和数字高程模型（DEM），被视为空间缩减过程的代理。缩减算法，即支持向量机（SVM），随机森林（RF），地理加权回归（GWR），实施了多元线性回归（MLR）和指数回归（ER），以将URmia湖盆地（LUB）上TRMM（热带降雨测量任务）降水（3B43 V7产品）的版本7从0.25°降到1 km。根据气象站的观测结果验证了降尺度的降水数据。来自TRMM 3B43的月度分数用于将1 km年降水量分解为1 km月降水量。此外，基于地理差异分析（GDA）选择了最佳校准方法，以评估校准的可行性。结果表明，与原始TRMM相比，SVM不仅性能优于其他方法，而且在现场测量方面也具有良好的一致性。结果证实，将LST和地理信息与NDVI一起包含可以改善降级性能。缩小尺寸和GDA校准可在空间和时间分辨率上显着提高TRMM 3B43产品的精度，应将其视为校准TRMM降水的重要步骤。与按年尺度进行校准相比，按月尺度进行校准所产生的结果要稍好一些，然后就准确性评估而言分解为按月地图。