The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.

Ecological zonation research is typically conducted in the eastern margin of the Tibetan Plateau. In order to enhance the structure and function of regional ecosystems and monitor their quality, it is crucial to investigate shifts in the coverage of vegetation and the factors that contribute to these shifts. The goal of this study is to assess the spatial and temporal variations in vegetation covering and the partitioning of its drivers in the Minjiang River Basin on the eastern edge of the Tibetan Plateau between 2000 and 2022. The Mann-Kendall test, Hurst index, Theil-Sen median trend analysis, and other techniques were used to look at the features of temporal and geographical changes in regional vegetation coverage as well as potential development trends. The climatic influences leading to the spatial differentiation of vegetation NDVI (Normalized Difference Vegetation Index) were quantified through partial and complex correlation analyses of NDVI with temperature and precipitation. The results of the study showed that (1) the NDVI of the watershed performed well with a stable upward trend, indicating that the vegetation growth was generally good; (2) the spatial analysis showed that the coefficient of variation of the NDVI reached 0.092, which highlighted the stability of the vegetation change in the region; (3) the future development trend of the vegetation coverage in the watershed is low, and there is a certain degree of ecological risk; and (4) the main driver of the vegetation coverage is the non-climate factor, distributed in most parts of the watershed; (5) the climate driver shows localized influence, especially concentrated in the southwest, downstream and part of the upstream areas of the watershed.

Runoff is greatly influenced by changes in climate conditions. Predicting runoff and analyzing its variations under future climates are crucial for ensuring water security, managing water resources effectively, and promoting sustainable development within the catchment area. As the key step in runoff modeling, the calibration of hydrological model parameters plays an important role in models’ performance. Identifying an efficient and reliable optimization algorithm and objective function continues to be a significant challenge in applying hydrological models. This study selected new algorithms, including the strategic random search (SRS) and sparrow search algorithm (SSA) used in hydrology, gold rush optimizer (GRO) and snow ablation optimizer (SAO) not used in hydrology, and classical algorithms, i.e., shuffling complex evolution (SCE-UA) and particle swarm optimization (PSO), to calibrate the two-parameter monthly water balance model (TWBM), abcd, and HYMOD model under the four objective functions of the Kling–Gupta efficiency (KGE) variant based on knowable moments (KMoments) and considering the high and low flows (HiLo) for monthly runoff simulation and future runoff prediction in Tunxi basin, China. Furthermore, the identified algorithm and objective function scenario with the best performance were applied for runoff prediction under climate change projections. The results show that the abcd model has the best performance, followed by the HYMOD and TWBM models, and the rank of model stability is abcd > TWBM > HYMOD with the change of algorithms, objective functions, and contributing calibration years in the history period. The KMoments based on KGE can play a positive role in the model calibration, while the effect of adding the HiLo is unstable. The SRS algorithm exhibits a faster, more stable, and more efficient search than the others in hydrological model calibration. The runoff obtained from the optimal model showed a decrease in the future monthly runoff compared to the reference period under all SSP scenarios. In addition, the distribution of monthly runoff changed, with the monthly maximum runoff changing from June to May. Decreases in the monthly simulated runoff mainly occurred from February to July (10.9–56.1%). These findings may be helpful for the determination of model parameter calibration strategies, thus improving the accuracy and efficiency of hydrological modeling for runoff prediction.

Climate change and human activities are intensifying the hydrologic cycle and increasing extreme events, challenging accurate prediction. This study builds on the Transformer architecture by introducing a sliding time window and runoff classification mechanism, enabling high-precision long-term runoff forecasting and significantly improving the simulation of extreme floods. However, the generalization ability of data-driven models remains limited in non-stationary environments. To address this issue, we further propose a hybrid framework that couples the process-based GBHM with the enhanced Transformer via bias correction. This fusion leverages the strengths of both models: the process-based model explicitly captures topographic heterogeneity, the spatial distribution of meteorological forcings, and their temporal variability, while the data-driven model excels at uncovering latent relationships among hydrological variables. The results demonstrate that the coupled model significantly outperforms traditional approaches in peak-flow prediction and exhibits superior robustness and generalizability under changing environmental conditions.

Hydrological processes and the sustainable use of water resources in a river basin are altered by climate change and changes in human variables. This study examined the significant effects of vegetation and hydrological, climatic, and human activity changes on the basin’s biological environment and usage of water resources. The Min River Basin (MRB) in the upper Yangtze River served as the study location. Mann–Kendall and Pettitt mutation test techniques were used to examine the features of runoff changes in the basin. The effects of meteorological and anthropogenic factors on runoff and vegetation changes in the MRB from 1982 to 2020 were quantitatively evaluated using the expanded Budyko equation. Following this, spatial and temporal variations in land use and the NDVI in the basin were studied. The results of the research demonstrated the following: (1) The MRB yearly runoff trended downward and that an abrupt change in runoff happened in 1994. (2) Precipitation (Pr) showed a decreasing tendency from the base period (S1) to the change period (S2), but potential evapotranspiration (ET₀) showed an increasing trend. (3) From 1985 to 2020, the land use area of the MRB changed rapidly, and the construction land and water area increased by 322% and 58.85%, respectively, while the cultivated land area decreased by 11.72%. (4) From S1 to S2, there was a rising trend in both the NDVI and the Budyko parameter n. The contributions of Pr, ET₀, NDVI, and n to the runoff change were 32.41%, 9.43%, 27.51%, and 30.65%, respectively.

Bias correction of global climate model (GCM) simulations is usually required for hydrological impact studies due to the coarse resolution and systematic biases of these simulations. Commonly used bias correction methods are applied at sites independently while ignoring the spatial correlation of variables, which may cause unreasonable hydrological simulation. To solve this problem, a stochastic multisite bias correction (SMBC) method is proposed for hydrological impact studies. It first uses the Daily Bias Correction (DBC) method to correct the distribution of variables, and then the distribution-free shuffle algorithm and Markov chain are used to generate spatial correlation of variables. The performance of this method is compared with the DBC method for hydro-meteorological impact studies in the Xiangjiang River Basin. The results show that the DBC method inherits the bias of the temporal sequence of precipitation and spatial correlation of GCM simulated variables. The mean absolute error (MAE) of the spatial correlation is between 0.25 and 0.38 and between 0.36 and 0.39 for the simulated precipitation occurrence and daily precipitation amount, respectively, while the MAE for the probability of two stations having wet days/dry days simultaneously is around 0.07. The SMBC method effectively reproduces spatial correlation of observations, with the MAE of the above indexes around 0.02. In hydrological simulation, the SMBC method has much better performance in reproducing the return period of the maximum consecutive day for streamflow and the maximum consecutive day’s streamflow. The averaged streamflow process is also well represented. Overall, the SMBC method efficiently reproduces the distribution and spatial correlation of variables, thereby generating more accurate hydrology simulations.