Quantifying the impact of climate change on hydrologic features is essential for the scientific planning, management and sustainable use of water resources in Northwest China. Based on hydrometeorological data and glacier inventory data, the Spatial Processes in Hydrology (SPHY) model was used to simulate the changes of hydrologic processes in the Upper Shule River (USR) from 1971 to 2020, and variations of runoff and runoff components were quantitatively analyzed using the simulations and observations. The results showed that the glacier area has decreased by 21.8% with a reduction rate of 2.06 km²/a. Significant increasing trends in rainfall runoff, glacier runoff (GR) and baseflow indicate there has been a consistent increase in total runoff due to increasing rainfall and glacier melting. The baseflow has made the largest contribution to total runoff, followed by GR, rainfall runoff and snow runoff, with mean annual contributions of 38%, 28%, 18% and 16%, respectively. The annual contribution of glacier and snow runoff to the total runoff shows a decreasing trend with decreasing glacier area and increasing temperature. Any increase of total runoff in the future will depend on an increase of rainfall, which will exacerbate the impact of drought and flood disasters.

Streamflow generation was investigated using isotopic and geochemical tracers in semiarid, glacier‐covered, montane catchments in the upper Shule River, northeastern Tibetan Plateau. Samples from stream water, precipitation, glacier meltwater, and groundwater were collected at the Suli and Gahe catchments along the Shule River, with an area of 1908 and 4210 km², respectively. The samples were analysed for stable isotopes of water and major ions. Results of diagnostic tools of mixing models showed that Ca²⁺, Mg²⁺ and Cl⁻, along with δ¹⁸O and δ²H, behaved conservatively as a result of mixing of three endmembers. The three endmembers identified by the mixing analysis were surface runoff directly from precipitation, groundwater, and glacier meltwater. Streamflow was dominated by groundwater, accounting for 59% and 60% of streamflow on average in the Suli and Gahe catchments, respectively, with minimum groundwater contribution in July (47% and 50%) and maximum contribution in October (69% and 70%). The contributions of surface runoff were slightly higher in the Suli catchment (25%) than in the Gahe catchment (19%). However, the contributions of glacier meltwater were higher in the Gahe catchment (21%) compared to the Suli catchment (17%), as a result of a higher percentage of glacier covered area in the Gahe catchment. This difference followed well the non‐linear power–law trend of many glacier‐covered catchments around the world. As glacier retreat continues in the future, the reduction of streamflow in glacier‐covered upper Shule catchment likely will be accelerated and possibly elsewhere in the Tibetan Plateau. This study suggests that it is critical to define the turning point of an accelerated reduction in glacier meltwater for glacier‐covered catchments around the world in order to better assess and manage water resources.

Tropical moist forests have been severely affected by natural and anthropogenic disturbances, leading to substantial changes in global carbon cycle and climate. These effects have received great attention in scientific research and debates. Here we review recent progress on drivers and ecological impacts of tropical moist forest disturbances, and their monitoring and modeling methods. Disturbances in tropical moist forests are primarily driven by clearcutting, selective logging, fire, extreme drought, and edge effects. Compound disturbances such as fire and edge effects aggravate degradation in the edge forests. Drought can result in terrestrial carbon loss via physiological impacts. These disturbances lead to direct carbon loss, biophysical warming and microclimate change. Remote sensing observations are promising for monitoring forest disturbances and revealing mechanisms, which will be useful for implementing disturbance processes in dynamic vegetation models. Yet, constrained spatiotemporal coverages and resolutions limit the application of these data in process-based models. It is also challenging to represent physical processes derived from fine-resolution remote sensing data in coarse-resolution models. We highlight the need to continuously integrate new datasets and physical processes in forest disturbance modeling to advance understanding of disturbance patterns and impacts. Interactions and impacts of climate change and anthropogenic activities should also be considered for modeling and assessing feedbacks of tropical moist forest disturbances.