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[Display omitted] • A model for multi-objective optimization operation of multiple water sources under uncertainties was proposed. • Uncertainties in inflow and water demand forecasts can be considered simultaneously, and the correlation between uncertainties can be considered quantitatively. • The impact of inflow-water demand forecast uncertainties and their correlations on water resource operations are explored for water resources managers. Uncertainties in inflow and water demand forecasts bring risks to water resources management. Therefore, the research on multi-objective optimization operation of multiple water sources under uncertainties holds critical research significance and application value. This paper proposes a model for the multi-objective optimization operation of multiple water sources that can consider the inflow and water demand forecast dual uncertainties. In handling uncertainties, this paper adopts a scenario generation method based on joint distribution-Monte Carlo (JDMC), which allows for a quantitative consideration of the correlation between uncertainties. Moreover, the variable weight method and LINGO 20.0 are employed for solving multi-objective optimization issues to obtain stable Pareto frontiers. The proposed model is applied to the water receiving area inside Jiangsu Province of the South-to-North Water Transfer East Route Project (SNWTERP) in China. The main findings are as follows: 1) The impact of the inflow and water demand forecast uncertainties on the operation results is synergistic rather than antagonistic. Considering only a single uncertainty may result in water receiving area facing more severe water shortages or purchasing spot water at high prices. 2) There is a strong spatial correlation between uncertainties. If we ignore or only qualitatively consider the correlation between uncertainties, the impact of uncertainties on operation results will be underestimated or overestimated. 3) The Pareto frontiers are convex functions, indicating that decision-makers should choose a solution in the middle part. [ABSTRACT FROM AUTHOR]

[Display omitted] • Quantifies water consumption of 135 sectors caused by tourism activities in China. • Illustrates direct and indirect water used driven by direct tourism sector in China. • Compared the accommodation water consumption per guest per day in different regions. • China exported a net 898 million m3 tourism water footprint to worldwide. • Assesses the global scarce water footprint transferred from Chinese tourist. Tourism depends largely on a high water consumption. Previous studies have primarily focused on virtual water embodied in goods or products through trade. Our study provides novel insights into the virtual water embodied in goods and services purchased by tourists. We identified the tourism blue water footprint (TWF) of China according to the environmentally extended input–output table of 135 sectors (11 direct + 124 indirect tourism sectors) together with tourism satellite account data. The results show that the agriculture, electricity, and petroleum sectors majorly contribute to the indirect TWF, while the accommodation and air transport sectors majorly contribute to the direct TWF and were water intensive. Using a constant ratio of 1.5 relating local water footprints to tourist water footprints, we then estimate that China's outbound tourists consume more water than inbound tourists (net TWF of 906 million m3). Nearly half of the net TWF from Chinese tourists is consumed in water deficient countries. Thus, the pressure caused by Chinese tourists on global water resources should be considered. This paper quantitatively evaluates the impact of China's tourism-induced water transfer on global water resources from the perspective of direct and indirect tourism consumption. The results of this paper enrich the research cases of the impact of final consumption on water resources utilization. [ABSTRACT FROM AUTHOR]

• A preference decision method (PDM) is developed to assist in making trade-offs. • The PDM provides solid quantitative support for subjective preferences. • The PDM draws on economic theories and has physical and mathematical connotations. • The PDM is based on the Pareto set itself and can be extended into other areas. The key to formulating a multi-objective reservoir operation scheme is coordinating the high/low priorities and achieving balance among various targets accordingly in the decision-making process. However, the traditional decision-making methods are either completely subjective or neglect the decision-maker's preferences, making it essential to optimize these methods to address such unsatisfactory aspects. Therefore, through drawing on some universally acknowledged economic theories and studying the geometric relationships among the Pareto solutions to such multi-objective decision-making problems, the concepts of "replacement rate", "profit-loss sensitivity ratio", and "preference equilibrium degree" are introduced, deduced, and elaborated on in this paper. On the basis of them, the preference decision method (PDM), entrusted with strict mathematical and physical connotations, is constructed and derived in detail, especially for addressing the bi-objective and tri-objective scenarios. The PDM can quantify the complex internal feedback relationship among various targets, providing objective support for decision-makers in the subjective trade-offs of the interests of multiple subjects and achieving the unity of subjective and objective. Furthermore, the proposed method is applied to the Wujiang cascade reservoirs in China, through which the benefits of power generation, water supply and ecological protection are enabled to be considered simultaneously. The application shows that the PDM can effectively shrink the Pareto set, which greatly reduces the difficulty of decision-making, and the obtained results prove feasible and satisfactory. In addition, the PDM is based entirely on the Pareto set itself, making it simple to utilize, and can be extended into solving other multi-objective decision-making problems. [ABSTRACT FROM AUTHOR]

• A bed-steadying discharge is proposed. • A flow-sediment model and an LSTM model are integrated. • The riverbed deformation is predicted. River morphology plays an important role in water environment and resources. The dominant discharge (Q D) is a crucial indicator for understanding river morphology and bed evolution under the impact of various interacting processes. At present, the identification of Q D mainly depends on the analysis of a large number of hydrological data derived from measuring stations, leading to difficulty in obtaining detailed Q D distributions along the study reach. In this paper, Q D is approximately expressed as the bed-steadying discharge (Q S) which is based on major factors of water level and sediment-carrying capacity. Subsequently, an integrated model combining a numerical fluid-flow and sediment model with a deep-learning algorithm is applied to analyze the changing process of the Q S. The flow and sediment transport processes are simulated by the calibrated mathematical model, which are then adopted as the input sequences for the long short-term memory (LSTM) module. The verification results of the established LSTM model show robustness and accuracy in predicting the flow and sediment transport processes under multi-stage incoming flow and sediment conditions. Furthermore, the proposed integrated model is applied to identify the detailed process of Q S in the Middle Huaihe River. Results show that the changing process of Q S along the study reach is characterized by a particular trend of "increase-decrease-rapid increase" due to natural changes and human activities. Additionally, the Q S agrees well with Q D at the hydrological station, showing that Q S can be applied as an approximation for Q D along the study reach. By analyzing longitudinal and transverse profiles, the rationality of using Q S as obtained by the newly presented model is demonstrated. Its temporal variation is also consistent with the cross-sectional changes for the specified stations. [ABSTRACT FROM AUTHOR]

• A bed-steadying discharge is proposed. • A flow-sediment model and an LSTM model are integrated. • The riverbed deformation is predicted. River morphology plays an important role in water environment and resources. The dominant discharge (Q D) is a crucial indicator for understanding river morphology and bed evolution under the impact of various interacting processes. At present, the identification of Q D mainly depends on the analysis of a large number of hydrological data derived from measuring stations, leading to difficulty in obtaining detailed Q D distributions along the study reach. In this paper, Q D is approximately expressed as the bed-steadying discharge (Q S) which is based on major factors of water level and sediment-carrying capacity. Subsequently, an integrated model combining a numerical fluid-flow and sediment model with a deep-learning algorithm is applied to analyze the changing process of the Q S. The flow and sediment transport processes are simulated by the calibrated mathematical model, which are then adopted as the input sequences for the long short-term memory (LSTM) module. The verification results of the established LSTM model show robustness and accuracy in predicting the flow and sediment transport processes under multi-stage incoming flow and sediment conditions. Furthermore, the proposed integrated model is applied to identify the detailed process of Q S in the Middle Huaihe River. Results show that the changing process of Q S along the study reach is characterized by a particular trend of "increase-decrease-rapid increase" due to natural changes and human activities. Additionally, the Q S agrees well with Q D at the hydrological station, showing that Q S can be applied as an approximation for Q D along the study reach. By analyzing longitudinal and transverse profiles, the rationality of using Q S as obtained by the newly presented model is demonstrated. Its temporal variation is also consistent with the cross-sectional changes for the specified stations. [ABSTRACT FROM AUTHOR]

• A hydrological modeling approach was proposed to distinguish the relative contribution of climate changes and human activities on Daihai Lake's recession. • In the absence of natural runoff and human-induced water withdrawal observations, the natural state's lake water storage changes were reconstructed. • Without considering the high water years, the anthropogenic factor took the responsibility for the lake shrinkage about 58%, while the climatic factor took roughly about 42% in period 1974–2018. • This investigation serves as a useful example of applying a distributed model to the assessment of lake hydrology, especially for the lake catchments with limited observations. In recent decades, water levels in many closed inland lakes over north-western China experienced severe declines, among which the Daihai Lake in Inner Mongolia, China was particularly significant. Remotely sensed investigations indicated that the Daihai Lake had shrunk approximately 64% in an area with the dramatic decline of water level approximately 9 m from the 1980 s to 2010 s, which has caused serious concern on the sustainable development of the surrounding region. Consequently, several previous studies have concentrated on the possible causes of lake shrinkage. However, predicting climate variability and anthropogenic activities' relative contributions to lake water decline is challenging due to the lack of direct water consumption upstream and watershed-scale natural runoff observations. In this paper, a hydrological modeling approach was presented to quantitatively evaluate and separate the relative contributions of climate variations and intensifying anthropogenic activities. The simulation accuracy was evaluated through comparisons with observed data and cross-validation with previous studies. Based on a baseline period, the natural streamflow draining into the lake was reconstructed using the model. The results revealed that the water loss of the Daihai Lake was caused predominately by human activities rather than climate variability. Specifically, for wet conditions, the climate factors posed a negative effect on the water level decline of the lake and delayed the shrinking process of the lake area. The anthropogenic factors took a dominant role (roughly 58%) of lake shrinkage at the annual average scale for normal and dry conditions, while the climate factors accounted for approximately 42%. Hence, the rational policy for water resource management, ecological restoration, and dam and reservoir constructions/operations should be the essential precautions for sustainable development of the Daihai Lake and its surroundings. Additionally, the hydrological modeling approach adopted in this investigation serves as a useful example of the quantitative assessment of climate variations and human activities, especially for the ungauged catchments. [ABSTRACT FROM AUTHOR]

• The mean groundwater table depth in China dropped by approximately 0.2 m (P < 0.01) during 1979–2018. • The Noah-MP land model forced with reanalysis climate data can reasonably capture terrestrial water and radiation processes across China. • Incorporating the groundwater lateral flow process enhances the seasonality of modeled terrestrial water and radiation processes on a regional scale. Faced with intensified climate change and ever-increasing water demand, the groundwater table depth is vital to sustaining terrestrial environments in a country such as China; however, on a regional scale, insufficient research has been conducted on its variability thus far. Using a land model forced with reanalysis climate data, this paper investigates groundwater table depth changes across China for the 1979–2018 period. The results show a significant overall reduction in the groundwater table depth in China during the past 40 years without explicit consideration of direct human influence. There is an approximately 0.2 m decrease on average, but it is approximately 0.5 m in southern China and 0.8 m to the north of 35°N despite a 1.2 m rise in part of this region due to an increasingly wet climate and a topographic water convergence. The findings provide a first-order pattern of historical groundwater table responses to climate change, highlighting inherent links among subsurface-surface-atmosphere water cycles and worsened terrestrial water availability in China. A model evaluation compared with in situ observations has demonstrated the model's ability to realistically portray terrestrial water and energy balances on regional scales, as well as the enhancement of their seasonality with incorporated lateral groundwater flow processes. This ability gives us reasonable confidence in the simulated trends and spatial variability. Nonetheless, the mean groundwater table still shows large biases relative to observations, and the problems of human influence, two-way interactions between groundwater and surface water, and local topographical complexity have yet to be properly addressed in the present study and deserve further research. [ABSTRACT FROM AUTHOR]

• Energy consumption is introduced in the optimization model. • Eliminate the negative influence caused by weight distribution. • Optimal planting schemes were obtained. In order to multi objectives and uncertainties in the optimization allocation of irrigation water resources, this paper developed an improved interval multi-objectives programming method. The proposed model was then applied to a case study in Jinghuiqu irrigation district, Shaanxi Province, China. Maximum economic benefit and minimum energy consumption are regarded as planning objectives. A series of planting structure optimal schemes were obtained under different representative hydrological years. From the result, shortage of water resources has a negative effect on the development of Jinghuiqu irrigation district. Moreover, the remaining water resources prefer vegetable to wheat after meeting the minimum demand of the developed model, which characterized with high economic benefit and lower energy consumption. Moreover, due to the introduction of effective precipitation, a lot of irrigation water resources have been saved, such as 47.14% of total water consumption was saved when P = 25%. Furthermore, the desired optimal schemes can be obtained by the decision-makers based on the results of the developed model. [ABSTRACT FROM AUTHOR]