Accurate precipitation data at a high spatial resolution are essential for hydrological, meteorological, and ecological research at regional scales. This study presented a geostatistical downscaling-calibration procedure to derive the high spatial resolution maps of precipitation over a mountainous watershed affected by a monsoon climate. Based on the relationships between precipitation and other environmental variables, such as the Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM), a regression model with a residual correction method was applied to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 product from coarse resolution (25 km) to fine resolution (1 km). Two methods, geographical difference analysis (GDA) and geographical ratio analysis (GRA), were used to calibrate the downscaled TRMM precipitation data. Monthly 1 km precipitation data were obtained by disaggregating 1 km annual downscaled and calibrated precipitation data using monthly fractions derived from original TRMM data. The downscaled precipitation datasets were validated against ground observations measured by rain gauges. According to the comparison of different regression models and residual interpolation methods, a geographically-weighted regression kriging (GWRK) method was accepted to conduct the downscaling of TRMM data. The downscaled TRMM precipitation data obtained using GWRK described the spatial patterns of precipitation reasonably well at a spatial resolution of 1 km with more detailed information when compared with the original TRMM precipitation. The results of validation indicated that the GRA method provided results with higher accuracy than that of the GDA method. The final annual and monthly downscaled precipitation not only had significant improvement in spatial resolution, but also agreed well with data from the validation rain gauge stations (i.e., R² = 0.72, RMSE = 161.0 mm, MAE = 127.5 mm, and Bias = 0.050 for annual downscaled precipitation during 2001 to 2015; and R² = 0.91, RMSE = 22.2 mm, MAE = 13.5 mm, and Bias = 0.048 for monthly downscaled precipitation during 2001 to 2015). In general, the downscaling-calibration procedure is useful for complex mountainous areas with insufficient ground gauges.

Quantifying the effects of climate variability and human activities on runoff changes will contribute to regional water resource planning and management. This study aims to separate the effects of climate variability and human activities on runoff changes in the upper catchment of the Red River Basin in China. The Mann–Kendall test and Pettitt’s test methods were applied to identify the trends and change points of the hydro-meteorological variables. The hydrological sensitivity, climate elasticity and hydrological simulation methods were adopted to estimate the contributions of climate variability and human activities to runoff changes. Results showed that annual runoff significantly decreased by 1.57 mm/year during the period of 1961–2012. A change point in annual runoff coefficient occurred in 2002. Accordingly, the annual runoff series were divided into the baseline period (1961–2002) and the impacted period (2003–2012). Mean annual runoff of the impacted period decreased by 29.13% compared with the baseline period. Similar estimates of the contributions of climate variability and human activities were obtained by the three different methods. Climate variability was estimated to be responsible for 69%–71% of the reduction in annual runoff, and human activities accounted for 29%–31%. Climate variability was the main driving factor for runoff decrease in the catchment.

Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash–Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1–28.0%) and overestimated low flow (18.9–49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management.