Deceleration of China's human water use and its key drivers.

Increased human water use combined with climate change have aggravated water scarcity from the regional to global scales. However, the lack of spatially detailed datasets limits our understanding of the historical water use trend and its key drivers. Here, we present a survey-based reconstruction of China's sectoral water use in 341 prefectures during 1965 to 2013. The data indicate that water use has doubled during the entire study period, yet with a widespread slowdown of the growth rates from 10.66 km3⋅y-2 before 1975 to 6.23 km3⋅y-2 in 1975 to 1992, and further down to 3.59 km3⋅y-2 afterward. These decelerations were attributed to reduced water use intensities of irrigation and industry, which partly offset the increase driven by pronounced socioeconomic development (i.e., economic growth, population growth, and structural transitions) by 55% in 1975 to 1992 and 83% after 1992. Adoptions for highly efficient irrigation and industrial water recycling technologies explained most of the observed reduction of water use intensities across China. These findings challenge conventional views about an acceleration in water use in China and highlight the opposing roles of different drivers for water use projections.

Large-scale prediction of stream water quality using an interpretable deep learning approach.

Deep learning methods, which have strong capabilities for mapping highly nonlinear relationships with acceptable calculation speed, have been increasingly applied for water quality prediction in recent studies. However, it is argued that the practicality of deep learning methods is limited due to the lack of physical mechanics to explain the prediction results of water quality changes. A knowledge gap exists in rationalizing the deep learning results for water quality predictions. To address this gap, an interpretable deep learning framework was established to predict the spatiotemporal variations of water quality parameters in a large spatial region. Mereological, land-use, and socioeconomic variables were adopted to predict the daily variations of stream water quality parameters across 138 sub-catchments in a total of over 575,250 km2 in southern China. The coefficients of determination of chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) predictions were over 0.80, suggesting a satisfactory prediction performance. The model performance in terms of prediction accuracy could be improved by involving land-use and socioeconomic predictors in addition to hydrological variables. The SHapley Additive exPlanations method used in this study was demonstrated to be effective for interpreting the prediction results by identifying the significant variables and reasoning their influencing directions on the variation of each water quality parameter. The air temperature, proportion of forest area, grain production, population density, and proportion of urban area in each sub-catchment as well as the accumulated rainfall within the previous 3 days were identified as the most significant variables affecting the variations of dissolved oxygen, COD, ammoniacal nitrogen(NH3-N), TN, TP, and turbidity in the stream water in the case area, respectively.

Release process identification of non-instantaneous point source pollution in rivers via reverse flow and pollution routing.

Source identification is fundamental for managing sudden river water pollution; however, it is a challenging task. Although numerous studies have investigated this issue, most involve optimization or statistical models for instantaneous pollution and do not consider the reverse propagation and release processes. Herein, we propose an approach for identifying the release process of non-instantaneous point source pollution in rivers, based on reverse flow and pollution routing. The identification approach can trace the historical trajectory of pollutants and their release processes, providing the necessary information for treating accidental pollution. The effectiveness and efficiency of the proposed approach were tested and demonstrated using hypothetical and real-world river cases. The results indicated that the approach identified the release process with high accuracy, and second-round identification using the ensemble Kalman filter could generally improve the identification results from the reverse routing model. This approach was feasible in different cases of observation error, although the error considerably reduced its accuracy. The identification results were also found to be substantially influenced by release duration, with a shorter release time corresponding to an inferior identification result. Nevertheless, the approach worked well in real-world river cases and was generally not affected by the release location, pollutant diffusion, or river geomorphology. In addition, the new approach has advantages in computational efficiency and applicability over traditional methods.

Comparing administered and market-based water allocation systems through a consistent agent-based modeling framework.

Water allocation can be undertaken through administered systems (AS), market-based systems (MS), or a combination of the two. The debate on the performance of the two systems has lasted for decades but still calls for attention in both research and practice. This paper compares water users' behavior under AS and MS through a consistent agent-based modeling framework for water allocation analysis that incorporates variables particular to both MS (e.g., water trade and trading prices) and AS (water use violations and penalties/subsidies). Analogous to the economic theory of water markets under MS, the theory of rational violation justifies the exchange of entitled water under AS through the use of cross-subsidies. Under water stress conditions, a unique water allocation equilibrium can be achieved by following a simple bargaining rule that does not depend upon initial market prices under MS, or initial economic incentives under AS. The modeling analysis shows that the behavior of water users (agents) depends on transaction, or administrative, costs, as well as their autonomy. Reducing transaction costs under MS or administrative costs under AS will mitigate the effect that equity constraints (originating with primary water allocation) have on the system's total net economic benefits. Moreover, hydrologic uncertainty is shown to increase market prices under MS and penalties/subsidies under AS and, in most cases, also increases transaction, or administrative, costs.

Reconstructed eight-century streamflow in the Tibetan Plateau reveals contrasting regional variability and strong nonstationarity.

Short instrumental streamflow records in the South and East Tibetan Plateau (SETP) limit understanding of the full range and long-term variability in streamflow, which could greatly impact freshwater resources for about one billion people downstream. Here we reconstruct eight centuries (1200-2012 C.E.) of annual streamflow from the Monsoon Asia Drought Atlas in five headwater regions across the SETP. We find two regional patterns, including northern (Yellow, Yangtze, and Lancang-Mekong) and southern (Nu-Salween and Yarlung Zangbo-Brahmaputra) SETP regions showing ten contrasting wet and dry periods, with a dividing line of regional moisture regimes at ~32°-33°N identified. We demonstrate strong temporal nonstationarity in streamflow variability, and reveal much greater high/low mean flow periods in terms of duration and magnitude: mostly pre-instrumental wetter conditions in the Yarlung Zangbo-Brahmaputra and drier conditions in other rivers. By contrast, the frequency of extreme flows during the instrumental periods for the Yangtze, Nu-Salween, and Yarlung Zangbo-Brahmaputra has increased by ~18% relative to the pre-instrumental periods.

Scenario analysis for the sustainable development of agricultural water in the Wuyuer River basin based on the WEP model with a reservoir and diversion engineering module.

Agricultural water use is increasing quickly with the rapid socioeconomic development observed in the Wuyuer River basin. Water withdrawal for agriculture over the past decade have seriously depleted the ecological water requirements in the basin and damaged the channel and downstream wetland ecosystems. Achieving sustainable development in the basin will require a balance between agricultural water exploitation and ecological water demands. In this paper, a reservoir and diversion engineering module was integrated with a dualistic distributed hydrological model (WEP model) to investigate the effects of agricultural water use on river discharge. Agricultural water shortages and yearly minimum river discharges between 2020 and 2050 under six agricultural water exploitation scenarios and one natural scenario were estimated based on the proposed model. The results showed that the dualistic hydrological process model was more suitable for basins with agricultural water resource exploitation and that the river discharge was significantly less than the natural discharge due to irrigation and reservoir filling, especially in drought years. Under the scenarios of high, middle and low water resource exploitation without ecological operations, agricultural development was unsustainable because of agricultural water shortages and ecological water scarcity. The evaluation of the guaranteed rates for the agricultural water supply and environmental flows showed that the low water resource exploitation scenario with ecological operations was the best option and that sustainable development could be achieved in the basin under this exploitation scenario in the future. However, implementing water management practices in the basin could result in certain economic losses. These losses could be offset by ecological protection funds for downstream wetlands. Overall, the model results could help to inform planning and investment decisions within the basin to improve the sustainable management of water resources.

Influence of River Discharge on the Transport of the Saltwater Group from the North Branch in the Yangtze River Estuary.

The Yangtze River Estuary (YRE) is the largest estuary in China. Recently, due to the increase of extent and frequency, saltwater intrusion has received more and more attention. In this paper, with the adoption of hydrodynamic and salinity transport mode, quantitative research of the influence of river discharge to the North Branch (NB) of the Yangtze River on the saltwater group migration law is conducted. Tide and salinity data are used to validate the model effectively. In different paths, the changes in flow and the movement of the saltwater group are similar. The saltwater group starts to move downward from the sixth day. In the staged downward movement, the larger the runoff volume, the further the distance of the core of the saltwater group, and converges to around 90 km gradually. At different flow rates, the relationship between the average location of each waterway saltwater group core tide cycle and time is consistent with the Gompertz model, and its parameters had a nonlinear relationship with the flow rate. A function is constructed to calculate the length and time of the saltwater group migration. As the flow rate increases, the faster the core of the saltwater group reaches the entrance. The downwards movement takes 3-8 days. Quantitative research on the influence of the saltwater spilling from NB to the three major reservoirs in the South Branch (SB)is conducted. The simulation results are consistent with the function calculation. River discharge has a direct impact on saltwater transport and diffusion in the YRE.

South-to-North Water Diversion stabilizing Beijing's groundwater levels.

Groundwater (GW) overexploitation is a critical issue in North China with large GW level declines resulting in urban water scarcity, unsustainable agricultural production, and adverse ecological impacts. One approach to addressing GW depletion was to transport water from the humid south. However, impacts of water diversion on GW remained largely unknown. Here, we show impacts of the central South-to-North Water Diversion on GW storage recovery in Beijing within the context of climate variability and other policies. Water diverted to Beijing reduces cumulative GW depletion by ~3.6 km3, accounting for 40% of total GW storage recovery during 2006-2018. Increased precipitation contributes similar volumes to GW storage recovery of ~2.7 km3 (30%) along with policies on reduced irrigation (~2.8 km3, 30%). This recovery is projected to continue in the coming decade. Engineering approaches, such as water diversions, will increasingly be required to move towards sustainable water management.

New approach for point pollution source identification in rivers based on the backward probability method.

Pollution risk from the discharge of industrial waste or accidental spills during transportation poses a considerable threat to the security of rivers. The ability to quickly identify the pollution source is extremely important to enable emergency disposal of pollutants. This study proposes a new approach for point source identification of sudden water pollution in rivers, which aims to determine where (source location), when (release time) and how much pollutant (released mass) was introduced into the river. Based on the backward probability method (BPM) and the linear regression model (LR), the proposed LR-BPM converts the ill-posed problem of source identification into an optimization model, which is solved using a Differential Evolution Algorithm (DEA). The decoupled parameters of released mass are not dependent on prior information, which improves the identification efficiency. A hypothetical case study with a different number of pollution sources was conducted to test the proposed approach, and the largest relative errors for identified location, release time, and released mass in all tests were not greater than 10%. Uncertainty in the LR-BPM is mainly due to a problem with model equifinality, but averaging the results of repeated tests greatly reduces errors. Furthermore, increasing the gauging sections further improves identification results. A real-world case study examines the applicability of the LR-BPM in practice, where it is demonstrated to be more accurate and time-saving than two existing approaches, Bayesian-MCMC and basic DEA.