Assessments of climate change impacts on streamflow and sediment processes are essential for developing science‐based sustainable watershed management plans. We assessed climate change impacts on streamflow and sediment load in the upstream of the Mekong River Basin, as a case study. Future climate scenarios including an ensemble‐mean climate scenario (EnM scenario) were generated based on 20 GCMs in CMIP5, using a stochastic weather generator (LARS‐WG) coupled with a distribution‐free shuffle procedure. The SWAT model was applied to simulate changes in streamflow and sediment load for the future period 2071–2100 under RCP8.5 with respect to the baseline period 1971–2000. Results show that mean monthly maximum and minimum temperature were projected to increase by all the 20 GCMs, with an ensemble‐mean increase of 4.6–5.7°C and 4.2–5.8°C across the 12 months, respectively. An increase in mean annual precipitation (3.4–55.8%) and streamflow (1.0–72.7%) was also projected by all GCMs. However, projected changes in sediment load were not consistent. One half of the GCMs projected an increase (5.2–53.2%) in annual sediment load while the other half projected a decrease (5.1%–43.2%). In each month, at least three‐quarters of the GCMs projected an increase in monthly streamflow. For monthly sediment load, an increase in May to July was projected by over half of the GCMs, while a decrease was projected by a majority of the GCMs for other months. Our results indicate large uncertainties in streamflow and sediment projections under climate change, demonstrating the need to use multi‐model ensembles in climate change impact studies. Moreover, it was found that the streamflow and sediment loads simulated using the EnM scenario were often close to the ensemble means simulated using the 20 GCMs, which implies that the single EnM scenario has the potential of effectively and efficiently estimating the ensemble means of projections in a multi‐model ensemble.

Many hydrological models incorporate vegetation-related parameters to describe hydrological processes more precisely. These parameters should adjust dynamically in response to seasonal changes in vegetation. However, due to limited information or methodological constraints, vegetation-related parameters in hydrological models are often treated as fixed values, which restricts model performance and hinders the accurate representation of hydrological responses to vegetation changes. To address this issue, a vegetation-related dynamic-parameter framework is applied on the Xinanjiang (XAJ) model, which is noted as Eco-XAJ. The dynamic-parameter framework establishes the regression between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration parameter K. Two routing methods are used in the models, i.e., the unit hydrograph (XAJ-UH and Eco-XAJ-UH) and the Linear Reservoir (XAJ-LR and Eco-XAJ-LR). The original XAJ model and the modified Eco-XAJ model are applied to the Ou River Basin, with detailed comparisons and analyses conducted under various scenarios. The results indicate that the Eco-XAJ model outperforms the original model in long-term discharge simulations, with the NSE increasing from 0.635 of XAJ-UH to 0.647 of Eco-XAJ-UH. The Eco-XAJ model also reduces overestimation and incorrect peak flow simulations during dry seasons, especially in the year 1991. In drought events, the modified model significantly enhances water balance performance. The Eco-XAJ-UH outperforms the XAJ-UH in 9 out of 16 drought events, while the Eco-XAJ-LR outperforms the XAJ-LR in 14 out of 16 drought events. The results demonstrate that the dynamic-parameter model, in regard to vegetation changes, offers more accurate simulations of hydrological processes across different scenarios, and its parameters have reasonable physical interpretations.

Accurate drought assessment is crucial for effective regional water resource management. While reanalysis and remote sensing products enable high-resolution drought assessment, their regional application requires rigorous local validation. This study evaluates nine data combinations, pairing three precipitation products with three evapotranspiration products, to identify the optimal combination for robust SPEI estimation and subsequently to investigate the spatiotemporal variations in drought conditions during 1980–2020 in Zhejiang Province, China. The results indicate that the choice of precipitation product is the dominant factor influencing SPEI accuracy, with the combination of CMFD V2.0 precipitation and GLEAM v4.2a evapotranspiration identified as the most reliable for SPEI estimation across multiple timescales (SPEI1/3/6/12). The long-term trend analysis of the SPEI derived from this optimal data combination reveals significant spatiotemporal heterogeneity: temporally, a pronounced “wetter winters, drier springs” seasonal pattern emerges, posing a substantial threat to agricultural water security; spatially, a distinct divergence shows central/northeastern areas wetting while southern/southeastern regions experience a significant drying trend, particularly for long-term hydrological drought (SPEI12). Additionally, the prevalence of light droughts across the province suggests a sustained baseline of water stress. Attribution analysis further demonstrates that precipitation is the dominant driver of droughts across all timescales. This study contributes both a validated, high-resolution data foundation for regional drought assessment and a scientific basis for targeted drought adaptation strategies.

Land use/land cover (LULC) exerts a substantial influence on hydrological processes, notably runoff generation, flow routing, and evapotranspiration. To evaluate the impacts of LULC change on different flood types in the Jinhua River Basin, LULC data from four periods (2005, 2015, 2025, and 2035) were incorporated into the Distributed Hydrology–Soil–Vegetation Model (DHSVM). Using a fuzzy decision tree approach, thirty-six flood events were identified and classified as flash floods (FFs), short-rainfall floods (SRFs), and long-rainfall floods (LRFs). Projections of LULC for 2035 under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were generated by coupling System Dynamics (SD) with the Patch-based Land Use Simulation (PLUS) model. DHSVM was calibrated separately for each flood category, with the 2005 LULC dataset serving as the baseline for parameter estimation. The results revealed a progressive decline in forest land and water from 2005 to 2035, together with an expansion of construction land. By contrast, cultivated land exhibited a fluctuating pattern, expanding initially, then contracting, and recovering thereafter. These land-cover changes increased flood volumes and peak flows across FF, SRF, and LRF alike, largely owing to the growth of construction land and the loss of forest cover and water. Across the three future scenarios, the largest increases were projected under SSP5-8.5, followed by SSP2-4.5 and SSP1-2.6. With respect to flood type, LULC change exerted the most pronounced effects on flood volume and peak flow for LRF, followed by SRF and FF. Under SSP5-8.5, the maximum relative changes in LRF reached 6.74% in flood volume and 8.56% in peak flow. These findings suggest that LRF warrant greater attention in future assessments given their comparatively higher potential risk. Such divergent responses of the three flood types to LULC change can be attributed primarily to intrinsic differences in their flood characteristics.