Global climate change has led to an increase in the number of drought events. Agricultural droughts, which are influenced by crops, climate, and soil, are complex and challenging to identify. This study proposed a new index, the available water content of the soil index, based on the soil reservoir theory, which focuses on soil water and considers both crops and soil structure. We constructed the MODCYCLE model to depict soil water dynamics and evaluate agricultural drought by using the available water content of the soil index in a typical North China Plain region. The results showed that the soil in the western and southern regions was relatively dry and that the soil in the eastern and northern regions was humid. Drought occurred owing to the increased water demand during the critical growth period of winter wheat. Irrigation alleviated the drought. When the AWCI is greater than 0.6 in early April, the degree of drought is relatively mild. When the AWCI is less than 0.4, it is very easy to experience severe drought in mid-April.The central and eastern regions were susceptible to drought after mid-April, and the western region experienced drought until late May. Varying soil moisture, particle composition, irrigation, and precipitation conditions influenced the evolution of the drought. Enhancing soil moisture and timely irrigation before the critical growth period of winter wheat effectively prevented severe droughts in the region. The results provide guidance for regional drought monitoring and forecasting, water resource management, and scientific irrigation, effectively ensuring food security.

With the impacts of climate change and human activities, agricultural drought disaster losses have increased remarkably. Drought disaster risk assessment is a prerequisite for formulating disaster reduction strategies and ensuring food security. However, drought disaster risk is a complex system, and quantitative assessment methods reflecting the risk formation mechanism are still rarely reported. This study presented a chain transmission system structure of drought disaster risk, which meant that drought disaster loss risk R was derived from drought hazard H by the transformation of drought disaster vulnerability V. Based on this point, firstly, a drought hazard curve between drought intensity and drought frequency was established using remote sensing data and the copula function. Then, a crop loss calculation approach under various drought events and drought resistance capacity scenarios was achieved by two-season field experiments and the AquaCrop model. Finally, a loss risk curve cluster of “drought frequency–drought resistance capacity–yield loss rate” was proposed by the composition of the above two quantitative relationships. The results of the case study for summer maize in Bengbu City indicated that the average yield loss rate under 19 droughts occurring during the growth period of maize from 1982 to 2017 was 24.51%. High risk happened in 1988, 1992, 1994, 2001, and 2004, with the largest loss rate in 2001, up to 65.58%. Overall, droughts with a return period less than two years occurred frequently during the growth period of summer maize in Bengbu, though the loss risk was still controllable. In conclusion, the results suggest that the loss risk curve provides a new effective method of drought disaster risk quantitative assessment from a physical mechanism perspective, which lays a scientific foundation for decision-making in risk management.

Flooding is the most pervasive hydrological disaster globally. This study presents a comprehensive analysis of torrential rain and flood characteristics across three major urban agglomerations (CY, MRYR, and YRD) in the Yangtze River Basin from 1991 to 2020. Utilizing satellite-derived microwave SSM/I data and CHIRPS precipitation datasets, this study examines the impacts of urbanization and climate change on flood risk patterns. The results showed: (1) In 1998, the MRYR had the highest flood risk due to heavy rainfall and poor flood control, but by 2020, risk shifted to the CY with rapid urbanization and more rainfall, while the YRD maintained the lowest risk due to advanced flood control. (2) The relationship between impervious surface area and flood risk varied by region. The CY showed a negative correlation (−0.41), suggesting effective flood mitigation through topography and infrastructure; the MRYR had a slight positive correlation (0.12), indicating increased risks from urban expansion; and the YRD’s weak negative correlation (−0.18) reflected strong flood control systems. This research underscores the imperative of strategic urban planning and effective water resource management to mitigate future flood risks and contributes valuable insights to ongoing efforts in flood disaster prevention and control within the Yangtze River Basin.

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.

Precipitation plays a vital role in the hydrological cycle, directly impacting water resource management and the prediction of flood and drought risks. This study explores an approach by applying the Bayesian Model Averaging (BMA) algorithm to merge multiple precipitation datasets, aiming to improve the accuracy of precipitation estimates for hydrological simulations. The BMA framework combines four widely used precipitation datasets—CHIRPS, ERA5, GSMaP, and IMERG—across the Ganjiang River Basin in China from 2008 to 2020. To evaluate the performance of the merged dataset, researchers analyzed it alongside its individual components and the MSWEP dataset at daily, monthly, and seasonal scales. Several key metrics, including CC, RMSE, and KGE, were used for assessment. Additionally, the Variable Infiltration Capacity (VIC) hydrological model was employed to examine how these datasets influence runoff simulation. The results indicate that the BMA-merged dataset significantly enhances precipitation estimation accuracy compared to individual datasets. It achieved the highest CC (0.72) and KGE (0.70) at the daily scale and demonstrated superior seasonal performance, particularly in minimizing biases during autumn and winter. When applied to hydrological simulations, the BMA-driven VIC model closely mirrored observed runoff patterns, proving its effectiveness for long-term runoff predictions in the region. Overall, this study underscores the potential of the BMA approach to refine precipitation inputs for hydrological models, offering valuable insights for sustainable water resource management and risk mitigation in complex hydrological environments.