MASR: A novel monitoring method coupled with interpretation platform for near-term management in thermal stratified reservoirs.

Good water quality is critical to public health and aquatic ecological security of global reservoirs. In stratified reservoirs, increasing near-term management demands foster extremely high monitoring and forecasting needs. In this study, a management assistant for stratified reservoirs (MASR) was developed, including a wave-driven monitoring platform and interpretation platform for multiple reservoir water quality variables. The wave-driven platform can adapt to the characteristics of water level changes and transmit the monitoring data through a mobile network to the reservoir manager, which are processed by the interpretation platform in real time for near-term management. After several months of application, MASR monitored 1237 groups of valid profile water quality data with an acceptable error, which showed a strong capacity for capturing the water quality dynamics in a stratified reservoir. The effective identification of thermal stratification structures and anoxic zones can help managers to design withdrawal schemes for reservoirs. Moreover, the prediction of algae state based on the back propagation (BP) neural network provided the basis for making operation plans to proactively control algae blooms. Our study provides an economical and convenient solution for stratified reservoirs to address near-term management issues.

Cumulative effects of experimental nitrogen deposition on soil chemistry in a desert steppe: A 12-year field study.

Although the effects of human-enhanced atmospheric nitrogen (N) deposition are well documented, the response of dryland soils to N deposition remains unclear owing to the divergence in hydrological outputs and soil heterogeneity. We selected a typical desert steppe in western China to simulate the effects of long-term N deposition by applying 0 (CK), 3.5, 7, and 14 g N m-2 yr-1 for 12 consecutive years. We found that, compared with the CK plots, the total N content of the upper (0-10 cm) and lower (10-20 cm) soil layers in fertilized plots increased by 8.3-14.6 % and 2.4-8.2 %, respectively. Correspondingly, the available, NH4+-, and NO3--N contents in the upper soil significantly increased by 25.5-68.3 %. However, in the lower soil, available and NO3--N contents were significantly lower than those in the CK plots, and their variation trend was opposite to that of NH4+-N, implying N turnover and leaching. As a result, the upper and lower soil pH in fertilized plots significantly decreased by 0.36-0.53 and 0.31-0.37 units; however, their CaCO3 content significantly increased by 9.8-22.8 % and 7.2-30.3 %, respectively. The total phosphorus (P) content in the upper and lower soil layers in fertilized plots significantly increased and decreased by 3.6-51.3 % and 16.7-62.5 %, respectively, however, both significantly decreased along the N fertilization gradient. Furthermore, the upper and lower soil organic carbon (SOC) content in the fertilized plots significantly increased by 57.7-78.1 % and 19.2-27.4 %, respectively. Pearson's correlation analysis revealed that available soil P was significantly negatively correlated with plant shoot Mn content (a proxy for rhizosphere carboxylates), whereas dissolved OC, SOC, and CaCO3 were significantly positively correlated, suggesting that Ca cycling is involved in P cycling and SOC sequestration. Our study suggests that long-term N input exacerbates P limitation in desert steppes, however, enhances SOC sequestration.

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures.

Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031-2065 and 2066-2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB.

Quantifying the effects of submerged aquatic vegetation on internal loading in lake: A modeling study of the largest shallow lake in North China.

Shallow lakes are greatly influenced by submerged aquatic vegetation (SAV), which affects hydraulic and water quality during their entire life cycle. An integrated model was developed based on the Environmental Fluid Dynamics Code (EFDC), which considers the dynamic bottom roughness and sediment release flux related to SAV growth and decomposition. Model results of hydrodynamics, water quality, and sediment-P release in Baiyangdian Lake (BL) were analyzed with and without the SAV module. The results showed that SAV played a critical and alterable role in regulating the internal loading in lakes. During the period of exponential growth, SAV reduced the velocity and sediment-P release in Zaozhadian by 20 % and 12 %, respectively. During the period of senescence, SAV reduced the velocity by 19 % and increased sediment-P release by 49 %, which was mainly attributed to dissolved oxygen (DO) consumption during residue decomposition. To mitigate the adverse effects of SAV on internal loading, measures should be taken to control the growth of SAV and ensure timely salvage before decomposition.

Laboratory investigation on the influence of factors on the outflow temperature from stratified reservoir regulated by temperature control curtain.

A temperature control curtain can effectively mitigate the negative effect of outflow temperature on the river eco-environment downstream. To investigate the response of outflow temperature to influence factors (i.e., installation position of temperature control curtain, submerged depth, temperature distribution, and outflow discharge), experiments were conducted in a nonlinearly stratified fluid. The important degree of influence factors was determined by entropy weight method. The results indicated that the effect extent of influence factors on the outflow temperature was temperature distribution, submerged depth, outflow discharge, and installation position in turn. The installation position had little effect on the outflow temperature. Increasing the outflow discharge could withdraw more warm water near the surface and increase the outflow temperature. The outflow temperature also rose with decreasing submerged depth, and more warm water above the temperature control curtain level tended to be extracted when the submerged depth was enough. Although the outflow temperature increased, its variation amplitude depended on the temperature gradient of temperature distribution and was not affected by the structural form of selective withdrawal. From the point of operation management, the minimum submerged depth was determined using sensitivity analysis to obtain maximum improvement of outflow temperature.

Water shortage risk assessment considering large-scale regional transfers: a copula-based uncertainty case study in Lunan, China.

The risk of water shortage caused by uncertainties, such as frequent drought, varied precipitation, multiple water resources, and different water demands, brings new challenges to the water transfer projects. Uncertainties exist for transferring water and local surface water; therefore, the relationship between them should be thoroughly studied to prevent water shortage. For more effective water management, an uncertainty-based water shortage risk assessment model (UWSRAM) is developed to study the combined effect of multiple water resources and analyze the shortage degree under uncertainty. The UWSRAM combines copula-based Monte Carlo stochastic simulation and the chance-constrained programming-stochastic multiobjective optimization model, using the Lunan water-receiving area in China as an example. Statistical copula functions are employed to estimate the joint probability of available transferring water and local surface water and sampling from the multivariate probability distribution, which are used as inputs for the optimization model. The approach reveals the distribution of water shortage and is able to emphasize the importance of improving and updating transferring water and local surface water management, and examine their combined influence on water shortage risk assessment. The possible available water and shortages can be calculated applying the UWSRAM, also with the corresponding allocation measures under different water availability levels and violating probabilities. The UWSRAM is valuable for mastering the overall multi-water resource and water shortage degree, adapting to the uncertainty surrounding water resources, establishing effective water resource planning policies for managers and achieving sustainable development.