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.

Tonle Sap Lake is the largest freshwater lake in Southeast Asia. Water development infrastructures are increasingly being constructed in the Lancang–Mekong River Basin, which is a major concern considering its potential impact on Tonle Sap Lake. This study aimed to investigate variations in the area of the lake and discuss their possible linkage to runoff alterations in the Lancang River (Upper Mekong) by comparing runoff at the Yunjinghong hydrological station before and after significant changes in runoff trends that occurred in 2008. First, four commonly used water body extraction methods (MNDWI, NDWI, NDVI, and EVI) were compared and MNDWI was found to provide a better and more stable performance. Based on MOD09A1 data, MNDWI was used to extract the water area of the lake from 2000 to 2014, and characteristics of variations in the area before and after 2008 were analyzed. The water area of Tonle Sap Lake displayed an overall decreasing trend, and specifically decreased by 8.3% during the flood season and by 1.5% on average during the dry season after 2008. Seasonal variations in the water area of Tonle Sap Lake were dominantly influenced by runoff from the Mekong River. Compared with the period 2000–2007, runoff at Yunjinghong station were increased during the dry season (20.74%) and decreased during the flood season (34.25%) between 2008 and 2014. Changes in upstream runoff contributed to runoff at the Stung Treng station in the lower Mekong River by 6.17% (dry season) and −2.41% (flood season). Evidently, the operation of dams in the Lancang River does not primarily account for the area decrease of Tonle Sap Lake during the flood season. In contrast, runoff increase during the dry season mitigates the area decrease of Tonle Sap Lake to a certain extent.

Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data is challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) estimates in the Yarlung Tsangpo–Brahmaputra River basin (YBRB) using two linear and two nonlinear methods, and their influence on extreme precipitation indices was assessed by cross-validation. Bias correction greatly improved the performance of extreme precipitation analysis. The ability of four methods to correct wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Local intensity scaling (LOCI) and quantile–quantile mapping (QM) performed better than linear scaling (LS) and power transformation (PT). This study would provide a reference for using gridded precipitation data in extreme precipitation analysis and selecting a bias-corrected method for rainfall products in data-sparse regions.

In the Lancang–Mekong River Basin, monsoon fluctuation affects profoundly the spatial and temporal distributions of precipitation, which is the major cause of the uncertainty in hydrological processes and water resources. This study investigated the implications of monsoon fluctuation and regional topography on precipitation variation in the Lancang–Mekong River Basin, and it examined the potential link between monsoons and river flow. The results indicated that the fluctuations of the western North Pacific summer monsoon (WNPSM) and the Indian summer monsoon (ISM) played different roles in precipitation variation. The areas in which precipitation variation was found synchronous with the WNPSM were much larger than those associated with the ISM. Significant positive correlation was found between precipitation during June–September (JJAS) and the WNPSM index (WNPMI) and the ISM index (IMI) over 29.3% and 12.8% of the basin, respectively, and that these areas were distributed mainly on the left and right bank downstream, respectively. A strong (weak) WNPSM was found to increase (decrease) moist westerlies that caused excess (deficient) orographic precipitation through the interaction with the Annamite Mountains. During strong (weak) WNPSM years, observed river flow during JJAS at the Mukdahan, Pakse, and Stung Treng stations in the downstream area were 8.0% (5.0%), 8.2% (12.6%), and 12.1% (19.5%) higher (lower) than the mean, respectively, showing that downstream river flow is modulated by the WNPSM intensity. These findings could support long-term hydrological predictions, and be beneficial for optimal flood control and water resource utilization in the basin.

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.

Twenty-five climate indices based on daily maximum and minimum temperature and precipitation at 15 meteorological stations were examined to investigate changes in temperature and precipitation extremes over the Yarlung Tsangpo River Basin (1970–2017). The trend-free prewhitening (TFPW) Mann–Kendall test and Pettitt’s test were used to identify trends and abrupt changes in the time series, respectively. The results showed widespread significant changes in extreme temperature indices associated with warming, most of which experienced abrupt changes in the 1990s. Increases in daily minimum and maximum temperature were detected, and the magnitude of daily minimum temperature change was greater than that of the daily maximum temperature, revealing an obvious decrease in the diurnal temperature range. Warm days and nights became more frequent, whereas fewer cold days and nights occurred. The frequency of frost and icing days decreased, while summer days and growing season length increased. Moreover, cold spell length shortened, whereas warm spell length increased. Additionally, changes in the precipitation extreme indices exhibited much less spatial coherence than the temperature indices. Spatially, mixed patterns of stations with positive and negative trends were found, and few trends in the precipitation extreme indices at individual stations were statistically significant. Generally, precipitation extreme indices showed a tendency toward wetter conditions, and the contribution of extreme precipitation to total precipitation has increased. However, no significant regional trends and abrupt changes were detected in total precipitation or in the frequency and duration of precipitation extremes.