Bing Yan

The evaluation and diagnosis of water resources carrying capacity are crucial for optimizing regional water resources management. This study focuses on the Yishusi River Basin over the period 2008–2017, using a combined single-element and multi-element approach to assess the region’s water resources carrying capacity. Subjective weights were derived from a fuzzy analytic hierarchy process (FAHP) informed by a panel of 30 anonymous peer experts, and objective weights were derived from a fuzzy entropy weight method (FEWM) applied to the 2007–2017 odd-year official records. An improved Accelerated Genetic Algorithm (AGA) was used to determine both subjective and objective weights, which are then integrated using the Principle of Minimum Entropy Production (PMEP). We therefore develop the FAHP-AGA-FEWM-PMEP diagnostic model, which identifies the main driving indicators of water resources carrying capacity in the region. The framework is applied to screen and diagnose the key driving indicators of each element of water resources carrying capacity, and its stability is verified by comparison with single-method weighting and by parameter sensitivity analysis. The results show that, in terms of water resource carrying capacity evaluation, the Yishusi River Basin experienced severe overload in 2017 with respect to water volume. Henan was critically overloaded, Jiangsu was severely overloaded, and Anhui and Shandong were not overloaded. Regarding the water resource carrying capacity diagnosis, key diagnostic driving indicators for the “regional available water” capacity representation include large and medium-sized reservoir storage, surface water resources, inter-basin water transfer, non-conventional water utilization, and groundwater resources. For the “regional water consumption” load representation, the key diagnostic driving indicators are water consumption per 10,000 CNY of GDP, ecological environment water consumption, water consumption per 10,000 CNY of industrial added value, per capita GDP, the proportion of water used by the secondary industry, and irrigation water consumption per unit area.

The Yalong and Dadu River basins (Yangtze headwaters) serve as critical “water towers” for ecosystems and strategic diversions. However, climate warming may decouple atmospheric inputs from hydrological responses, exposing the limitations of traditional runoff-centric assessments that overlook terrestrial water storage dynamics. To address this gap, this study develops a probability-based diagnostic framework integrating the SWAT model with a novel Water Balance Resilience Index (WBRI) to evaluate basin health evolution (1961–2018). Annual water storage change was quantified via the hydrological water balance, standardized into a Normalized Storage Index (NSI), and transformed into the WBRI by fitting probability distributions to absolute anomaly magnitudes and mapping them to hydrological return periods on a continuous 0–1 health scale. Results reveal a significant non-linear divergence: while precipitation and evapotranspiration in the Dadu basin declined markedly, runoff remained relatively stable. This stability may be associated with cryospheric and storage-related buffering, which partly compensates for precipitation deficits through enhanced cryospheric contributions and reduced evapotranspiration under water-limited conditions. Phase-space diagnosis identified a recurring flux–state decoupling pattern, with the “High Flux–Low State” anomaly occurring at comparable frequencies in the Source Region and Sink group (both 19.8%) and varying across subbasins from 13.8% to 22.4%. This suggests that stable discharge may exert a “masking effect,” concealing potential depletion of terrestrial water storage and legacy cryospheric reserves. Furthermore, the WBRI indicates that since 2010, both basins have tended toward a more vulnerable or tighter water-balance state, with reduced resilience to hydro-climatic variability. These findings support a flux–state collaborative monitoring framework. For the West Route Diversion Project, a hierarchical “Mainstem Control + Tributary Quotas” mode is suggested, in which transferable water limits consider storage-state indicators rather than runoff abundance alone. This may help reduce long-term storage-depletion risk and support alpine ecosystem sustainability.

Climate change and human activities are intensifying the water cycle processes, exerting a certain degree of impact on the spatiotemporal distribution characteristics of water resources, thereby increasing the uncertainty in water resource development and utilization. This has led to situations where in some regions, the degree of water resources development and utilization has approached or even exceeded the carrying capacity of water resources, creating a mismatch between the spatial layout of water resource carrying capacity and the economic and social structure. This study focuses on the Yishusi River Basin, constructing a system dynamics coupled orthogonal experimental optimization method for predicting and regulating water resources carrying capacity. By setting various scenarios combining climate change and human activities, and applying the constructed model for predicting and regulating water resources carrying capacity under climate change, the study conducted analysis on predicting and regulating water resources carrying capacity during different future development periods in the region. The results include predictions of water resources carrying capacity and regulatory measures for this basin. The results indicate that: (1) Under different scenarios, the natural runoff in the Yishusi River Basin is expected to increase in the future. (2) By 2030, 2035, and 2050, under current water supply conditions, the water quantity and quality in the Yishusi River Basin will be all in a state of overload for each climate scenario.(3) By selecting regulatory indicators and formulating control measures, the predicted results for different scenarios in the Yishusi River basin were regulated. After regulation, all scenarios are in a critically overloaded state.

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.

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 warming accelerates the hydrological cycle, especially in high-latitude and high-altitude areas. The increase in temperature will increase the amount of snow and glacier melting and change the runoff, which will affect the operations of cascade reservoirs significantly. Therefore, taking the upper reaches of the Yellow River with an alpine climate as an example, we propose an improved SIMHYD-SNOW, which considers the snowmelt runoff process. The impacts of climate changes on the runoff process were revealed based on the SIMHYD-SNOW model using the precipitation and temperature data predicted by the SDSM model. A model for the maximum power generation of the cascade reservoirs in the upper reaches of the Yellow River was constructed to explore the impacts of climate changes on the inter-annual and intra-annual hydropower generation of the cascade reservoirs at different periods in the future. The results show that climate change has changed the spatial and temporal allocation of water resources in this area. The future runoff will decrease during the flood period (July to September) but increase significantly during the non-flood period. The inter-annual and intra-annual hydropower generation under the RCP8.5 climate change scenario is significantly lower than the RCP2.6 and RCP4.5 climate change scenarios, and as the CO₂ emission concentration increases, this gap increases significantly. This study can provide technical references for the precise formulation of water resources management under climate change.

Base flow, as an important component of runoff, is the main recharge source of runoff during the dry period, especially in the Yellow River Basin located in a semiarid area. However, the process of obtaining base flow has great uncertainty when considering hydrological simulations. Thus, in this study, a three-step framework is proposed, i.e., the particle swarm optimization (PSO) algorithm is used to calibrate model parameters under different subbasin partitioning schemes; then, the hydrograph separation (HYSEP), Improved United Kingdom Institute of Hydrology (IUKIH) and Lyne and Hollick filter (Lyne-Hollick) methods are used to separate the baseflow from the total runoff process, thereby exploring the uncertainty impacts of baseflow segmentation methods on the hydrological simulation process. The subsample-variance-decomposition method is used to quantify the independent and interactive uncertainty in the hydrological simulation process. The results show that the Topmodel model can be better applied to the source area of the Yellow River (the KGE values in the Sub5, Sub13, Sub21, Sub29, Sub37 and Sub13 scenarios were 0.91 and 0.65, 0.94 and 0.86, 0.94 and 0.88, 0.92 and 0.82, 0.95 and 0.89, and 0.92 and 0.83, respectively). The subbasin division uncertainty had less impact on simulated streamflow during the dry season and had a significant impact in the wet season, such as, the subbasin division uncertainty caused the difference between the median of the simulated streamflow to be as high as 213.09 m³/s in August but only 107.19 m³/s in January; Meanwhile, the baseflow segmentation method uncertainty has a significant impact on the annual mean streamflow values under different subbasin segmentation schemes. In addition, the baseflow values estimated by the Lyne-Hollick and HYSEP methods were obviously higher than those estimated by the IUKIH method during the wet season. The uncertainty influence of subbasin partitioning schemes and baseflow segmentation methods had significant differences on hydrological processes in different periods. The uncertainty influence of subbasin partitioning schemes was dominant in the dry season, accounting for 86%, and the baseflow segmentation methods took second place, accounting for approximately 12%. In the wet season, the uncertainty influence of the baseflow segmentation methods was gradually weakened, which may have been due to the uncertainty influence of the hydrological model. These results provide a reference for the calibration and validation of hydrological model parameters using baseflow components.

Water resources carrying capacity (WRCC) is an important component of the carrying capacity of regional natural resources. It is a restrictive index indicating whether a region can support the coordinated development of its population, economy, and environment. In order to solve the problem that WRCC varies with the supporting capacity of, load on, and capacity for regulation of the water resources, this study establishes the relationship between WRCC and the level of socio-economic development (carrying level) and then develops a WRCC evaluation model by analyzing the connotations, characteristics and influencing factors of WRCC. The model is applied to calculate the population and economic scales supportable by water resources in Jiangsu Province under different carrying levels. Predictions are made of the effects of varying water consumption quotas and the industrial structure so as to explore methods of regulating and controlling WRCC in the region. The evaluation, prediction, and control results provide an important reference for the rational allocation of water resources in Jiangsu Province so as to further improve the regional WRCC and achieve sustainable development of regional water resources. The regional WRCC evaluation model has strong applicability and good application effects.