To quantitatively evaluate and diagnose the carrying capacity of regional water resources under uncertain conditions, an index system and corresponding grade criteria were constructed from the perspective of carrying subsystem. Meanwhile, an improved entropy weight method was used to determine the objective weight of the index. Then, an evaluation model was built by applying set pair analysis, and a set pair potential based on subtraction was proposed to identify the carrying vulnerability factors. Finally, an empirical study was carried out in Anhui Province. The results showed that the consistency among objective weights of each index was considered, and the uncertainty between the index and grade criterion was reasonably dealt with. Furthermore, although the carrying situation in Anhui was severe, the development tended to be improved. The status in Southern Anhui was superior to that in the middle area, and that in the northern part was relatively grim. In addition, for Northern Anhui, the fewer water resources chiefly caused its long-term overloaded status. The improvement of capacity in the middle area was mainly hindered by its deficient ecological water consumption and limited water-saving irrigation area. Moreover, the long-term loadable condition in the southern part was due largely to its relatively abundant water resources and small population size. This evaluation and diagnosis method can be widely applied to carrying issues in other resources and environment fields.

Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. The 3B42 product shows the best hydrological performance, while PERSIANN shows unsatisfactory hydrological performance. Therefore, 3B42 performs better than the other two satellite precipitation products over the study area.

With the changing environment, a number of researches have revealed that the assumption of stationarity of flood sequences is questionable. In this paper, we established univariate and bivariate models to investigate nonstationary flood frequency with distribution parameters changing over time. Flood peak Q and one-day flood volume W₁ of the Wangkuai Reservoir catchment were used as basic data. In the univariate model, the log-normal distribution performed best and tended to describe the nonstationarity in both flood peak and volume sequences reasonably well. In the bivariate model, the optimal log-normal distributions were taken as marginal distributions, and copula functions were addressed to construct the dependence structure of Q and W₁. The results showed that the Gumbel-Hougaard copula offered the best joint distribution. The most likely events had an undulating behavior similar to the univariate models, and the combination values of flood peak and volume under the same OR-joint and AND-joint exceedance probability both displayed a decreasing trend. Before 1970, the most likely combination values considering the variation of distribution parameters over time were larger than fixed parameters (stationary), while it became the opposite after 1980. The results highlight the necessity of nonstationary flood frequency analysis.

Global climate change and rapid urbanization have caused increases in urban floods. Urban flood risk assessment is a vital method for preventing and controlling such disasters. This paper takes the central region of Cangzhou city in Hebei Province as an example. Detailed topographical information, such as the buildings and roads in the study area, was extracted from GF-2 data. By coupling the two models, the SWMM and MIKE21, the spatial distribution of the inundation region, and the water depth in the study area under different return periods, were simulated in detail. The results showed that, for the different return periods, the inundation region was generally consistent. However, there was a large increase in the mean inundation depth within a 10-to-30-year return period, and the increase in the maximum inundation depth and inundation area remained steady. The comprehensive runoff coefficient in all of the scenarios exceeded 0.8, indicating that the drainage system in the study area is insufficient and has a higher flood risk. The flood risk of the study area was evaluated based on the damage curve, which was obtained from field investigations. The results demonstrate that the loss per unit area was less than CNY 250/m² in each return period in the majority of the damaged areas. Additionally, the total loss was mainly influenced by the damaged area, but, in commercial areas, the total loss was highly sensitive to the inundation depth.

Estimating crop evapotranspiration under drought stress provides the theoretical foundation for optimizing irrigation schedule and reducing agricultural drought risk. Winter wheat water deficit plot experiments were conducted based on large-scale weighing lysimeters in the Huaibei Plain. The response of evapotranspiration to water deficit during several growth stages was analyzed; four crop coefficients were optimized and then evapotranspiration under drought stress was estimated with the dual crop coefficient method and the genetic algorithm. Drought stress not only reduced evapotranspiration during the current stage, but this influence was also transferred to the following periods. Evapotranspiration could basically return to the normal status after rewatering if the plant did not previously suffer from serious drought stress. Therefore, severe water deficit during continuous wheat growth periods should be avoided, and the deficit intensity during each stage should be controlled. The optimized K_{cb ini}, K_{cb mid}, K_{cb end} and K_{c max} were 0.32, 1.38, 0.33 and 1.39, and the root mean square error, mean absolute error, and mean relative error of the corresponding estimated evapotranspiration under drought stress were 22.83%, 25.36%, and 11.78% less than those using recommended coefficients, respectively. These optimized crop coefficients provide references for the practical application in the Huaibei Plain, this method for estimating crop evapotranspiration under drought stress can be applied to field circumstances and other regions.