Water resources are essential to ecosystems and social economies worldwide, especially in the deserts and oases of the Tarim River Basin, whose water originates largely from alpine mountains characterized by complicated hydrological processes and scarce hydrometeorological observations. This paper presents multi‐site and multi‐objective modeling of hydrological processes in the whole Tarim River Basin, covering 32 catchments in total. The study uses the Soil and Water Assessment Tool (SWAT), extended by incorporating a degree‐day glacier melt module to enable modeling of glacier melt in the alpine mountains. The multi‐objective calibration approach of ε‐NSGAII (Nondominated Sorting Genetic Algorithm‐II) was implemented with the two objective functions of the Nash‐Sutcliffe value of daily streamflow (NS) and the bias of simulated glacier melt contribution to streamflow (BIAS_g). Based on the combined use of the Morris sensitivity technique and hierarchy cluster, the 32 catchments in the study area are divided into six groups according to their dominating hydrological processes, e.g., glacier melt, snowmelt, groundwater and routing. The multi‐objective calibration was satisfactory, with 22 of the 32 catchments showing NS values larger than 0.6 and BIAS_g values smaller than 0.05. Model performance was highly dependent on meteorological data availability, in that low data availability led to poor model performance, while factors such as catchment area and mean annual snowfall had little influence on model performance. The results indicate that multi‐site and multi‐objective calibration enables consistent and comprehensive examination of the spatially‐different hydrological processes in a large basin and provides information for further assessment of the impact of climate change on water availability.

Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management.