High spatiotemporal resolution evapotranspiration (ET) data are very important for end users to manage water resources. The global ET product always has a high temporal resolution, but the spatial resolution is too low to meet the requirements of most end users. In this study, we developed a deep neural network (DNN)-based global ET product downscaling algorithm by combining remotely sensed and meteorological data sets as the input data. The relationship between global ET product and input data was built at a low spatial resolution using the DNN. Then, this relationship was applied at high spatial resolution to generate high spatial resolution ET derived from the input data with high spatial resolution. Taking the Global Land Evaporation Amsterdam Model (GLEAM) ET product as an example, downscaled ET was found to be highly consistent with the original GLEAM ET product, but to have high spatial resolution. Field validations showed that the overall coefficient of correlation and root mean square error (bias, Nash–Sutcliffe efficiency coefficient) of the downscaled GLEAM ET is 0.90 and 0.87 mm/d (−0.32 mm/d, 0.62), respectively, indicating high quality. The proposed method bridged the gaps between the global ET product and the requirements of local end users. This will benefit end users in charge of water resources management.

Error estimation of precipitation products is an important procedure in the data quality evaluation. It is a challenging task due to the lack of the in situ ground observations and the variations of the geophysical characteristics in regions with complex terrain. Compared with the traditional methods, the double instrumental variable (DIV) method has the merits of being able to estimate the errors between two products. In this study, the DIV method for data error estimation is applied and validated on precipitation products in regions with complex terrain. The DIV-based errors for two state-of-the-art precipitation products Integrated Multisatellite Retrievals for Global Precipitation Measurement Mission (IMERG) and Soil Moisture to Rain (SM2RAIN) are being further verified by using another high-accuracy ground-based precipitation products China Merged Precipitation Analysis (CMPA). The results indicate that the DIV-based errors of IMERG and SM2RAIN range from 0 to 25 mm per day and from 0 to 15 mm per day, respectively. The root-mean-square errors (RMSEs) of IMERG and SM2RAIN compared with CMPA, which are defined as CMPA-based errors, are ranging from 0 to 23 and 0 to 22 mm, respectively. It is concluded that the spatial distribution of the DIV-based errors shows the consistency with the CMPA-based errors, which further demonstrates the potential of using the DIV method for precipitation products fusion.

In order to survey the possible periodic, uncertainty and common features in runoff with multi-temporal scales, the empirical mode decomposition (EMD) method combined with the set pair analysis (SPA) method was applied, with data observed at Zhangjiashan hydrological station. The results showed that the flood season and annual runoff time series consisted of four intrinsic mode function (IMF) components, and the non-flood season time series exhibited three IMF components. Moreover, based on the different coupled set pairs from the time series, the identity, discrepancy, and contrary of different periods at multi-temporal scales were determined by the SPA method. The degree of connection μ between the flood season and annual runoff periods were the highest, with 0.94, 0.77, 0.7 and 0.73, respectively, and the μ between the flood periods and the non-flood periods were the lowest, with 0.66, 0.46, 0.24 and 0.24, respectively. Third, the maximum μ of each SPA appeared in the first mode function. In general, the different extractive periods decomposed by the EMD method reflected the average state of Jinghe River. Results also verified that runoff suffered from seasonal and periodic fluctuations, and fluctuations in the short-term corresponded to the most important variable. Therefore, the conclusions drawn in this study can improve water resources regulation and planning.