Understanding the relation between land-use types and baseflow mean response time (BMRT) is important to explore the response mechanism of baseflow processes in watersheds. BMRT was determined using an instantaneous unit hydrograph. The instantaneous unit hydrograph parameters were estimated by autocorrelation functions. The relative importance of land-use types in determining BMRT dynamics was assessed by hydrological model and partial least-squares regression. Our study suggests greater effects of urban area on BMRT than the effects of forest and agricultural land. This may be because the urban interception impervious area may impede baseflow generation over a short timescale. The effects of agricultural land are greater than those of forest in areas with steeper hillslopes, but lower than those of the forest in areas with more plains, reflecting the varied ability of forest and agricultural lands with different topography to hinder overland flow. Variations of BMRT are strongly linked to land use in the watershed. Overall, our study provides insight into the BMRT and dominant factors of land-use types in watersheds, planning of sustainable water resource use, and ecological protection in watersheds.

Although inflow forecasts are useful information that can be used for improving reservoir operation efficiency, uncertainty is still a challenge for getting sound operation results. Ensemble precipitation forecasts can take uncertainties under consideration, so they have been a research topic for improving reservoir operations. In this paper, a rainfall–runoff model, combined of a multiple linear regression model (for non-flood seasons) and the Xinanjiang model (for flood seasons), for inflow forecasts and a stochastic dynamic programming model for reservoir operations are developed in order to effectively use the ensemble precipitation forecast. To explore the best way for using the ensemble precipitation forecast, two post-processing techniques, i.e., ensemble forecast averaging (EFA) and interval value (IV), are tested. The ensemble precipitation forecast from the European Centre for Medium-Range Weather Forecasts (ECMWF) was chosen due to its high accuracy, and the Huanren reservoir, located in China, was used to test the newly developed models. The results show that, compared with the traditional rule curve, hydropower generation increases by 4.86% and 4.55%, respectively, when EFA and IV are used, which indicates that the use of ensemble forecasts facilitates considerable improvements in operating performance.

Converting floodwater into power without increasing flood risk is critical for energy-stressed regions. Over the past decades, numerous methods have been proposed to solve this problem. However, few studies have investigated the theoretical explanation of the trade-offs between power generation and flood risk. This study establishes an analytical framework to derive optimal hedging rules (OHR) and explains the economic insights into flood risk reduction and power generation improvement. A two-stage model based on the concept of dynamic control of carryover storage (DCCS) was developed as part of the framework, considering forecast uncertainty and risk tolerance. The results illustrated that hedging and trade-offs between power generation and flood risk during DCCS only occurs when the forecasted inflow and forecast uncertainty are within certain ranges, beyond which there is no hedging and trade-offs analysis; either power generation or flood risk becomes the dominant objective. The OHR was divided into three cases under different levels of forecast uncertainty and risk tolerance. Compared to forecast uncertainty, downstream risk tolerance plays a more important role in determining which case of the OHR is adopted in real-world operations. The analysis revealed what and how intense trade-offs are between power generation and flood risk under different scenarios of forecasted inflow, forecast uncertainty, and risk tolerance. The framework serves as a guideline for less abundant water resources or energy-stressed areas of operational policy. Nierji Reservoir (located in northeast China) was taken as a case study to illustrate the analysis, and the application results showed that OHR increases the average annual power generation by 4.09% without extra flood risk compared to current operation rules.

The operation of the cascaded reservoirs is a complex problem, and lots of algorithms have been developed for optimal cascaded reservoirs operation. However, the existing algorithms usually have the disadvantages such as the ‘curse of dimensionality’ and prematurity. This study proposes a grey discrete differential dynamic programming (GDDDP) algorithm for effectively optimizing the cascaded reservoir operation model, which is a combination of the grey forecasting model and discrete differential dynamic programming (DDDP). Additionally, a modification of the grey forecasting model is presented for better forecast accuracy. The proposed method is applied to optimize the Baishan-Fengman cascaded reservoir system in the northeast of China. The results show that GDDDP obtains more power generation than DDDP with less computing time in three cases, i.e., dry years, wet years and the whole series. Especially in the case of the whole series, the power generation of GDDDP is 2.13 MWH more than that of DDDP, while the computing time is decreased by 66,161 ms. Moreover, the power generation of GDDDP is comparable with that of dynamic programming but the computing time is much less. All these indicate GDDDP has high accuracy and efficiency, which implies that it is practicable for the operation of cascaded reservoir system.