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.

Simple analytical storage–reliability–yield relationships have traditionally only considered a single reliability for a single yield, yet many reservoirs supply water of different priorities. Simulation models may be used to handle such multiple‐priority water rights but these models are complex and usually system specific. Here we propose a simple analytical method based on Gould‐Dincer to estimate yields in dual and triple priority allocation systems from a carryover storage. This allows rapid assessment of changes in water resource availability for different water priorities, potentially over large spatial scales. We use a dam simulation model to assess this method at 15 sites across six continents and find that the “dual‐priority” and “triple‐priority” G‐D methods can reproduce the results of the dam simulation model. Thus, the method could be generalized for multiple priority allocation systems use. We demonstrate the potential utility of the “dual‐priority” G‐D method through an evaluation of the optimum yield between high and low‐priority water rights (HPWR and LPWR) from hypothetical (but realistic) carryover systems. It confirms the possibility that “dual‐priority” water allocation may be beneficial overall compared to a single‐priority water right. By balancing the yield of HPWR and LPWR, the optimum marginal value of available water (i.e., sum of high‐ and low‐priority water) can be achieved. Overall, the method provides a simple way for rapid assessment across multiple sites allowing insights into optimal allocation practices and the interacting driving factors that affect them at a regional‐to‐global scale.

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This study investigates the water supply risk of Panjiakou reservoir in the Luanhe River basin of China during 1956–2016 under environmental change. Since the monthly runoff series during 1956–2016 is a time series with change points, it is necessary to find a new stochastic streamflow series generation approach to preserve the statistical characteristics of the original series and to refine the reliability of water supply risk analysis. This paper improves a known stochastic streamflow simulation method of previous research to better reflect the characteristics of series with change points. And this paper simulates the monthly runoff series with change point of Panjiakou reservoir during 1956–2016 by three different methods, including Thomas–Fiering model, copula function stochastic simulation method, and copula function stochastic simulation method with the mixed distribution model. Among the three methods, the copula function stochastic simulation method with the mixed distribution model which is improved on the basis of copula function stochastic simulation method in this study performs best in simulating the observed monthly runoff series during 1956–2016, and the water supply risk indices including reliability (time-based and volume-based), resilience, vulnerability, drought risk index (DRI), and sustainability index (SUI) are evaluated for Panjiakou reservoir and analyzed by using the stochastic simulation results. By comparing with the previous studies, all indicators are between the corresponding results of 1956–1979 and 1980–2016 with stationary inflows; it can be seen that change point seriously affects the water supply risk of Panjiakou reservoir. These results make it easy to formulate water supply strategies and schemes in changing environment for water resources managers.