• Energy Research

Abstract The joint and optimal impoundment operation of cascade reservoirs can dramatically boost the efficiency of water resource utilization. However, most existing techniques fail to conquer the curse of dimensionality in mega multi-objective reservoir system. To overcome this obstacle, this study proposes a novel framework that integrates aggregation-decomposition (AGDP), parameterization simulation optimization (PSO), and the parallel progressive optimization algorithm (PPOA). In detail, it involves three main steps: (1) reservoir grouping and application of AGDP in the same group; (2) derivation of the initial impoundment solution by using the non-dominated sorting genetic algorithm-II to solve the PSO model; and (3) further improvement of the impoundment policy via PPOA. The proposed framework is tested on a mega reservoir system in the upper Yangtze River basin. Results demonstrate that our hybrid method can generate a series of impoundment policies to adapt to different flood event scenarios. Compared to the conventional operating rule, the optimal policy can increase impoundment efficiency from 89.50% to 94.21%, increase hydropower generation by 6.63 billion kWh/year (3.26%) and reduce CO2 emissions by 5.21 billion kg/year while maintaining the flood control risk at a low level. These findings verify the applicability and effectiveness of the novel framework in high-dimensional multi-objective impoundment, and also highlight the substantial potential benefits of sustainable water resources.

Hydro-meteorological datasets are key components for understanding physical hydrological processes, but the scarcity of observational data hinders their potential application in poorly gauged regions. Satellite-retrieved and atmospheric reanalysis products exhibit considerable advantages in filling the spatial gaps in in-situ gauging networks and are thus forced to drive the physically lumped hydrological models for long-term streamflow simulation in data-sparse regions. As machine learning (ML)-based techniques can capture the relationship between different elements, they may have potential in further exploring meteorological predictors and hydrological responses. To examine the application prospects of a physically constrained ML algorithm using earth observation data, we used a short-series hydrological observation of the Hanjiang River basin in China as a case study. In this study, the prevalent modèle du Génie Rural à 9 paramètres Journalier (GR4J-9) hydrological model was used to initially simulate streamflow, and then, the simulated series and remote sensing data were used to train the long short-term memory (LSTM) method. The results demonstrated that the advanced GR4J9–LSTM model chain effectively improves the performance of the streamflow simulation by using more remote sensing data related to the hydrological response variables. Additionally, we derived a reservoir operation model by feeding the LSTM-based simulation outputs, which further revealed the potential application of our proposed technique.

AbstractGlobal warming increases the atmospheric water‐holding capacity, consequently altering the frequency, and intensity of extreme hydrological events. River floods characterized by large peak flow or prolonged duration can amplify the risk of social disruption and affect ecosystem stability. However, previous studies have mostly focused on univariate flood magnitude characteristics, such as flood peak or volume, and there is still limited understanding of how these joint flood characteristics (i.e., magnitude and duration) might co‐evolve under different warming levels. Here, we develop a systematical bivariate framework to project future flood risk in 11,528 catchments across the globe. By constructing the joint distribution of flood peak and duration with copulas, we examine global flood risk with a bivariate framework under varying levels of global warming (i.e., within a range of 1.5–3.0°C above pre‐industrial levels). The flood projections are produced by driving five calibrated lumped hydrological models (HMs) using the simulations with bias adjustment of five global climate models (GCMs) under three shared socioeconomic pathways (SSP126, SSP370, and SSP585). On average, global warming from 1.5 to 3.0°C tends to amplify flood peak and lengthen flood duration across almost all continents, but changes are not unidirectional and vary regionally around the globe. The joint return period (JRP) of the historical (1985–2014) 50‐year flood event is projected to decrease to a median with 36 years under a medium emission pathway at the 1.5°C warming level. Finally, we evaluate the drivers of these JRP changes in the future climate and quantify the uncertainty arising from the different GCMs, SSPs, and HMs. Our findings highlight the importance of limiting greenhouse gas emission to slow down global warming and developing climate adaptation strategies to address future flood hazards.