Bias correction methods are routinely used to correct climate model outputs for hydrological and agricultural impact studies. Even though superior bias correction methods can correct the distribution of daily precipitation amounts, as well as the wet-day frequency, they usually fail to correct the temporal sequence or structure of precipitation occurrence. To solve this problem, we presented a hybrid bias correction method for simulating the temporal sequence of daily precipitation occurrence. We did this by combining a first-order two-state Markov chain with a quantile-mapping (QM) based bias correction method. Specifically, a QM-based method was used to correct the distributional attributes of daily precipitation amounts and the wet-day frequency simulated by climate models. Then, the sequence of precipitation occurrence was simulated using the first-order two-state Markov chain with its parameters adjusted based on linear relationships between QM-corrected mean monthly precipitation and the transition probabilities of precipitation occurrence. The proposed Markov chain-based bias correction (MCBC) method was compared with the QM-based method with respect to reproducing the temporal structure of precipitation occurrence over 10 meteorological stations across China. The results showed that the QM-based method was unable to correct the temporal sequence, with the cumulative frequency of wet- and dry-spell length being considerably underestimated for most stations. The MCBC method can could reproduce the temporal sequence of precipitation occurrence, with the generated cumulative frequency of wet- and dry-spell lengths fitting that of the observation well. The proposed method also performed reasonably well with respect to reproducing the mean, standard deviation, and the longest length of observed wet- and dry-spells. Overall, the MCBC method can simulate the temporal sequence of precipitation occurrence, along with correcting the distributional attributes of precipitation amounts. This method can be used with crop and hydrological models in climate change impact studies at the field and small watershed scales.

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.

To balance the water demands of different departments and produce a win–win result for reservoir operation, a series of conflict-resolution methods have been developed to define the socio-optimal operation strategy for specific conflict problems. However, given the inherent uncertainty of reservoir operation brought by climate change, the compromised strategies selected by conflict-resolution methods can vary. Therefore, quantifying the impacts of climate change on the decision characteristics of conflict-resolution methods can help to address questions about whether conflict-resolution decisions are sustainable given unforeseen changes. In this study, the Yangtze River is regarded as study area. As a world-class hydropower project located on the midstream of Yangtze River, Three Gorges Hydroelectric Power Station can transfer plenty of water energy into electricity. To alleviate the ecological water shortage caused by hydropower operation, sustainable and balanced operation strategies considering the water demands of two departments needs to be studied. In the context of hydropower-environmental conflict-resolution management, the decision behaviors of two fuzzy social choice methods and four game-theoretical bargaining methods under 25 kinds of future climate scenarios are analyzed. Comparing the strategy selection results of different methods for a future period (2021–2082) shows that in all proposed climate scenarios, the decisions of the Nash bargaining method, alternating offer method, and unanimity fallback bargaining method in game-theoretical bargaining methods are more stable than other studied methods, which means that climate change affects the decision behaviors of these three methods slightly. In addition, balanced strategies selected by these three methods could formulate adaptable reservoir operation policies that would satisfy the interests of hydropower and environmental stakeholders equally, and avoid a very low satisfaction level of individual stakeholder and whole stakeholders in the water-conflict year. Therefore, against the background of an increasing demand for environmental protection, these three methods can provide socio-optimal strategies considering social and economic benefits for water resource management.