Hydrological models are widely used in regional drought assessments. Usually, the spatiotemporal development of droughts is evaluated only via drought indexes based on simulated soil moisture. However, the physiological status of crops is key to accurately assessing the effect of drought, and hydrological models either do not consider crop physiological status or oversimplify it. In addition, the effects of reservoirs, irrigation, and crop rotation are generally not considered. To overcome the oversimplification of crop and irrigation modules in hydrological models, the variable infiltration capacity (VIC) model was coupled with the Environmental Policy Integrated Climate model. Irrigation and reservoir modules were introduced into the coupled hydrology‐crop growth (CHC) model. The CHC model was built for the watershed upstream of Heilin hydrological station on the Qingkou River on the border of Jiangsu and Shandong Provinces, China. The model performance was compared with simulation results of the Soil and Water Assessment Tool and VIC, which showed that the CHC model was more accurate. The temporal variation of water consumption and demand of three crop rotations, namely spring peanuts, summer peanuts‐winter wheat, and summer corn‐winter wheat, in the study area was analyzed. Drought area data obtained from Junan County yearbooks were consistent with the simulated water deficit and drought index considering the effect of irrigation. Crop irrigation increased soil water content and evapotranspiration and decreased crop water deficit. Therefore, crop irrigation was considered in hydrological simulations. The CHC model reliably simulated crop water consumption, soil moisture, and drought index, and the model simulated data could be used as basic data for water resources management and drought mitigation measures.

To promote the application of entropy concepts in uncertainty analysis of water resources complex system, a quantitative evaluation and obstacle factor diagnosis model of agricultural drought disaster risk was proposed using connection number and information entropy. The results applied to Suzhou City showed that the agricultural drought disaster risks in Suzhou during 2007–2017 were all in middle-risk status, while it presented a decreasing trend from 2010. The information entropy values of the difference degree item bI were markedly lower than those of the difference degree b, indicating that bI provided more information in the evaluation process. Furthermore, the status of drought damage sensitivity and drought hazard were improved significantly. Nevertheless, high exposure to drought and weak drought resistance capacity seriously impeded the reduction of risk. Thus, the key to decreasing risk was to maintain the level of damage sensitivity, while the difficulties were to reduce exposure and enhance resistance. In addition, the percentage of the agricultural population, population density, and percentage of effective irrigation area were the main obstacle factors of risk and also the key points of risk control in Suzhou. In short, the results suggest that the evaluation and diagnosis method is effective and conducive to regional drought disaster risk management.

HIGHLIGHTS. Northern part of Huaihe River in Anhui Province is located in the transition zone between Meiyu region and non-Meiyu region.The ending date of Meiyu

The extent of water deficit and drought loss mitigation, through human activities such as defense, mitigation, and resistance to drought risk, is revealed by its effects on agriculture. To analyze the distribution regularities of the effects of drought mitigation on agriculture and to provide better insight on drought mitigation actions, an expectation index of drought mitigation effects (EDRE) was formulated based on the definitions of the drought system. The crop yield when no drought mitigation measures were implemented was calculated via tests and simulations, and expectations of drought-related yield loss with and without drought mitigation measures (EDRL and EDRM, respectively) were calculated in the drought loss risk assessment model. Then, a quantitative assessment model for the effects of drought mitigation on regional agriculture was built. Using a case study from the Huaibei Plain in the Anhui Province of China, it was found that drought mitigation effects decrease gradually from North to South. Moreover, small values of drought mitigation effects correspond to large EDRM and small EDRL values. It is necessary to urgently improve drought mitigation measures in locations where EDRE is small and EDRL is large, or where EDRE is small and EDRM is large. The main drought mitigation measures were identified through correlation analysis. Additionally, the adaptation of drought mitigation measures to local conditions leads to a spatial distribution regularity.

Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood and drought risk prediction. This study proposes a Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The framework enhances estimation accuracy for hydrological simulations. The BMA framework synthesizes four precipitation products—Climate Hazards Group Infrared Precipitation with Station (CHIRPS), the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 to 2020. We evaluated the merged dataset’s performance against its constituent datasets and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) at daily, monthly, and seasonal scales. Evaluation metrics included the correlation coefficient (CC), root mean square error (RMSE), and Kling–Gupta efficiency (KGE). The Variable Infiltration Capacity (VIC) hydrological model was further applied to assess how these datasets affect runoff simulations. The results indicate that the BMA-merged dataset substantially improves precipitation estimation accuracy when compared with individual inputs. The merged product achieved optimal daily performance (CC = 0.72, KGE = 0.70) and showed superior seasonal skill, notably reducing biases in autumn and winter. In hydrological applications, the BMA-driven VIC model effectively replicated observed runoff patterns, demonstrating its efficacy for regional long-term predictions. This study highlights BMA’s potential for optimizing hydrological model inputs, providing critical insights for sustainable water management and risk reduction in complex basins.

An agricultural drought assessment is the basis for formulating agricultural drought mitigation strategies. Traditional agricultural drought assessment methods reflect agricultural drought degree by using the soil water deficit, e.g., Soil Moisture Anomaly Percentage Index (SMAPI). However, due to varying water demands for different crops, a given soil water deficit results in varying crop water deficits and agricultural droughts. This variation often leads to a misinterpretation of agricultural drought classification when one only considers the soil water deficit. To consider the influence of crop growth, this study proposes an agricultural drought assessment method by coupling hydrological and crop models (variable infiltration capacity-environmental policy integrated climate, VIC-EPIC). Agricultural drought in Jiangsu Province, China was evaluated using the VIC-EPIC model and crop water anomaly percentage index (CWAPI). The validation results based on the actual drought records showed that the correlation coefficients (0.79 and 0.82, respectively) of the statistical values and CWAPI simulated values of light and moderate drought area rates were greater than those for SMAPI (0.72 and 0.81, respectively), indicating that the simulation results of the VIC-EPIC model in Jiangsu Province were highly reasonable. The temporal and spatial variation characteristics of the drought grade in typical large-scale drought events in Jiangsu Province were also analyzed.