An ensemble modeling framework to study the effects of climate change on the trophic state of shallow reservoirs

Development and application of a comprehensive evaluation index system for groundwater quality evolution patterns

In recent years, driven by rapid socio-economic development and intensified human activities, the groundwater quality has exhibited a concerning trend of degradation. The challenge lies in integrating the impacts of both natural and anthropogenic factors to establish a scientific evaluation framework for the evolution of groundwater quality. This study adopts the model of driving forces - pressures - state - impacts - responses (DPSIR) proposed by the European Environment Agency, in conjunction with the Analytic Hierarchy Process (AHP) and Information Entropy Theory (IET), and the Water Quality Index (WQI) evaluation methods, to construct an evaluation index system for groundwater quality evolution that encompasses driving forces, state, and response systems. Initially, twelve indicators relevant to groundwater quality are quantified by screening across three systems, and a functional relationship between the categorization and scoring of each indicator is established. Subsequently, the weights for each system and indicator are obtained through the AHP, and the objective weights of the indicators are determined using the IET. The scores of each indicator are then comprehensively calculated. Finally, based on the defined types of groundwater quality evolution patterns, an integrated assessment of the evolution of groundwater quality over various time periods is conducted. Taking the Shijiazhuang region as a case study and analyzing the hydrochemical data of groundwater from 1985 to 2015, the results indicate a shift in the groundwater quality evolution pattern from one dominated by natural factors to one primarily influenced by human activities (The comprehensive score of the evaluation index system has increased from 1.84 to 3.25). Among these, the application of fertilizers emerges as the most important driving factors affecting groundwater quality. Particularly, nitrate and total hardness (TH) have emerged as the most salient indicators of quality degradation, with a significant escalation in their composite scores. At the outset, nitrate registered a score of 0.408, while TH scored 0.326; yet, these values have sharply ascended to 0.716 and 0.467, respectively, by the advanced stage. The study concludes with a discussion on the accuracy, strengths, limitations, and applicability of the evaluation index system. The establishment of this evaluation framework provides a scientific basis for the management and protection of groundwater resources and serves as a reference for identifying groundwater quality evolution patterns in other regions.

A critical analysis of parameter choices in water quality assessment

The determination of water quality heavily depends on the selection of parameters recorded from water samples for the water quality index (WQI). Data-driven methods, including machine learning models and statistical approaches, are frequently used to refine the parameter set for four main reasons: reducing cost and uncertainty, addressing the eclipsing problem, and enhancing the performance of models predicting the WQI. Despite their widespread use, there is a noticeable gap in comprehensive reviews that systematically examine previous studies in this area. Such reviews are essential to assess the validity of these objectives and to demonstrate the effectiveness of data-driven methods in achieving these goals. This paper sets out with two primary aims: first, to provide a review of the existing literature on methods for selecting parameters. Second, it seeks to delineate and evaluate the four principal motivations for parameter selection identified in the literature. This manuscript categorizes existing studies into two methodological groups for refining parameters: one focuses on preserving information within the dataset, and another ensures consistent prediction using the full set of parameters. It characterizes each group and evaluates how effectively each approach meets the four predefined objectives. The study presents that the minimal WQI approach, common to both categories, is the only approach that has successfully reduced recording costs. Nonetheless, it notes that simply reducing the number of parameters does not guarantee cost savings. Furthermore, the group of studies classified as preserving information within the dataset has demonstrated potential to decrease the eclipsing problem, whereas studies in the consistent prediction group have not been able to mitigate this issue. Additionally, since data-driven approaches still rely on the initial parameters chosen by experts, they do not eliminate the need for expert judgment. The study further points out that the WQI formula is a straightforward and expedient tool for assessing water quality. Consequently, the paper argues that employing machine learning solely to reduce the number of parameters to enhance WQI prediction is not a standalone solution. Rather, this objective should be integrated with a more comprehensive set of research goals. The critical analysis of research objectives and the characterization of previous studies lay the groundwork for future research. This groundwork will enable subsequent studies to evaluate how their proposed methods can effectively achieve these objectives.

Integrating impacts of climate change on aquatic environments in inter-basin water regulation: Establishing a critical threshold for best management practices

Inter-basin water diversion (IBWD) is a viable strategy to tackle water scarcity and quality degradation due to climate change and increasing water demand in headwaters regions. Nevertheless, the capacity of IBWD to mitigate the impacts of climate change on water quality has rarely been quantified, and the underlying processes are not well understood. Therefore, this study aims to elucidate how the IBWD manipulated total phosphorus (TP) loading dilution and conveying patterns under climate change and determine a critical threshold for the quantity of water entering downstream reservoirs (WIN) for operational scheduling. To resolve this issue, climate-driven hydrologic variability over a 60-year period was derived utilizing the least square fitting approach. Subsequently, six scenarios evaluating the response of in-lake TP concentrations (TPL) to increased temperatures and IBWDs of 50 %, 100 %, and 150 % from the baseline water volume in 2030 and 2050 were studied by employing a calibrated hydrological-water quality model (SWAT-YRWQM). In the next stage, three datasets derived from mathematical statistics based on the observed data, the Vollenweider formula, and modeled projections were integrated to formulate best management practices. The results revealed that elevated air temperatures would lead to reduced annual catchment runoff but increased IBWD. Additionally, our study quantified the IBWD potential for mitigating water quality degradation, indicating the adverse effects of climate change on TPL would be weakened by 4.2-14.4 %. A critical threshold for WIN was also quantified at 617 million m3, maintaining WIN at or near 617 million m3 through optimized operational scheduling of IBWD could effectively restrict external inflow TP loading to lower levels. This study clearly illustrates the intricate interactive effects of climate change and IBWD on aquatic environments. The methodology elucidated in this study for determining the critical threshold of WIN could be applied in water management for analogous watershed-receiving waterbody systems.

Identification of surface water - groundwater nitrate governing factors in Jianghuai hilly area based on coupled SWAT-MODFLOW-RT3D modeling approach

A comprehensive understanding of the key controlling factors on NO3-N spatiotemporal distribution in surface and groundwater is of great significance to nitrogen pollution control and water resources management in watershed. Hence, the coupled SWAT-MODFLOW-RT3D model was employed to simulate nitrate (NO3-) fate and transport in Huashan watershed system. The model was calibrated using a combination of stream discharge, groundwater levels, NO3-N in-stream loading and groundwater NO3-N concentrations. The simulation revealed the significant spatiotemporal variations in surface water-groundwater nitrate interactions. The annual average percolation of NO3- from rivers to groundwater was 171.5 kg/km2 and the annual average discharge NO3- content from groundwater into rivers was 451.9 kg/km2 over the simulation period. The highest percolation of NO3- from rivers to groundwater occurred in April and the highest discharge NO3- content from groundwater into rivers occurred in July. Grassland and agriculture land contributed more nitrate contents in river water and groundwater compared to bare land and forest in the study area and the water exchange was the primary driving force for nitrate interactions in the surface water-groundwater system. Sensitivity analysis indicated that river runoff and groundwater levels were most influenced by the SCS runoff curve number f (CN2) and aquifer hydraulic conductivity (K), which, in turn, significantly affected nitrate transport. Regarding water quality parameters, the denitrification exponential rate coefficient (CDN) had the most pronounced impact on NO3-N in-stream loading and groundwater NO3-N concentrations. This study underscores the central role of surface-groundwater (SW-GW) interactions in watershed-scale nitrate research and suggests that parameters with higher sensitivity should be prioritized in analogous watershed modeling.

How a reservoir modulates downstream water quality under declining upstream loading and progressing climate change

Reservoirs regulate water flow and pollutant transport in catchments. However, climate change can significantly impact their ability to perform this function. This study analysed a multi-decadal time series of data to examine the complex relationship between climate and nutrient pollution trends in the Möhne reservoir catchment. The study aimed at understanding the effect of the reservoir on downstream nutrient pollution in the face of a changing climate. The analysis revealed that upstream nutrient concentrations were higher than downstream, indicating a general nutrient-trapping effect of the reservoir. Upstream stations exhibited a declining trend in total nitrogen (TN) and total phosphorus (TP) concentrations. This was due to improved wastewater management and reduced nutrient mobilisation resulting from decreasing surface runoff and streamflow. At the downstream station, whereas TN concentrations decreased, TP concentrations mildly increased. These opposite downstream trends were likely due to rising temperatures and declining dissolved oxygen concentration within the reservoir, which might have favoured nitrogen denitrification and internal phosphorus loading, causing the decline and increase in downstream TN and TP concentrations, respectively. The contrasting downstream TN and TP trends alter the nutrient stoichiometry, which can profoundly affect the ecosystem's biogeochemical functioning. Therefore, in a warming climate, reservoirs may modulate nitrogen and phosphorus nutrients differently, leading to ecological discontinuities along river networks due to changes in TN-to-TP ratios. The study highlights the need to develop adaptable and precise nutrient pollution management strategies in reservoir catchments to address the challenges of climate change effectively.

Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins

Information on water availability in basins can be crucial for making decisions for effective water resource management in basins. As the operation of hydrometric stations in Korea is mainly focused on flood season and large rivers, most basins have lack or no observed data. Consequently, this complicates water resource planning and management. Remote sensing data is emerging as a powerful alternative to hydrological information in ungauged basins. This study investigated the applicability of Satellite-Remote Sensed Data (SRSD) as a source for model calibration in Prediction in Ungauged Basins (PUB) through modeling. Remote sensed leaf area index (LAI), actual evapotranspiration, and soil moisture data were used. Each SRSD was used alone to calibrate a hydrologic model to predict the daily streamflow for 28 basins in Korea. A vegetation module was added to the existing hydrologic model to use LAI. Among the SRSDs tested, the model calibrated with LAI had the most robust performance, predicting streamflow with acceptable accuracy compared to the traditional calibration based on streamflow. In particular, since the model account for vegetation actively interacting with evapotranspiration and soil moisture in the season of low flow, the LAI-calibrated model showed an advantage in improving the flow prediction performance. Although further research is required to utilize evapotranspiration and soil moisture data, the overall results of the LAI-based calibration were promising for predicting streamflow in ungauged basins where observations are scarce or absent, given that the satellite-derived LAI data were used alone without any preprocessing such as a bias correction. However, the prediction performance of the LAI-calibrated model was found to have a statistically significant relationship with local conditions. Therefore, by evaluating and improving the potential of SRSD in different region and climatic conditions, it is expected that the application of the SRSD-only calibration method can be extended to various ungauged basins.

The contribution of typhoon local and remote forcings to storm surge along the Makou-Dahengqin tidal reach of Pearl River Estuary

Due to the interaction between upstream discharge and astronomical tides in tidal reaches, the typhoon-induced storm surge processes are quite different from that in other coastal regions. Investigating the contributions of driving factors is essential to deepen the understanding of storm surges in tidal reaches. In this study, a coupled hydrological-hydrodynamic storm surge model is first developed to explore the main driving factors of storm surges in Makou-Dahengqin tidal reach during the three most influential typhoon events (Hagupit, Hato and Mangkhut). After that, the machine learning method is integrated to assess the water level in response to storm surges. The driving factors of storm surge are decomposed into remote forcing (upstream discharge, astronomical tide) and direct local forcing (wind stress, atmospheric pressure). The relative contributions of remote forcing are the highest near the estuary mouth. The relative contributions of local forcing to water levels are higher in the sections 40-80 km away from the estuary mouth. The most impacting period of the local forcing is about 48 h, while the relative contributions of remote forcing increase before and after the period. The local forcing-induced surges are highest at the upper reach during Hagupit, while it causes extreme surges at the estuary mouth during more powerful typhoons (Hato, Mangkhut). The maximum water levels and remote forcing-induced maximum surges invariably appear at the upper reach. However, when local and remote forcings are in the same phase, the maximum storm surge appears in the lower reaches during Hato. If local and remote forcings are in the same phase, the peak water levels would be amplified by up to 15.04 %, 36.23 % and 40.68 % during Hagupit, Hato and Mangkhut, respectively. Moreover, Remote forcing contributes more to the amplification of peak water levels than local forcing does, accounting for 68.5 % to 100 %.

Rainstorm sediment events in heterogeneous karst small watersheds: Process characteristics, prediction modeling and management enlightenment

Frequent rainstorms caused by climate change are causing significant stresses and impacts on karst zones and even global hydrological systems. However, few reports have focused on rainstorm sediment events (RSE) based on long series, high-frequency signals in karst small watersheds. Present study assessed the process characteristics of RSE and analyzed the response of specific sediment yield (SSY) to environmental variables using random forest and correlation coefficients. Management strategies are then provided based on revised index of sediment connectivity (RIC) visualizations, sediment dynamics and landscape patterns, and modeling solutions for SSY are explored through the innovative use of multiple models. The results showed that the sediment process showed high variability (CV > 0.36), and the same index had obvious watershed differences. Landscape pattern and RIC show highly significant correlation with mean or maximum suspended sediment concentration (p<0.01, |r|>0.235). Early rainfall depth was the dominant factor affecting SSY (Contribution = 48.15 %). The hysteresis loop and RIC infer that the sediment of Mahuangtian and Maolike mainly comes from downstream farmland and riverbeds, while Yangjichong comes from remote hillsides. The watershed landscape is centralized and simplified. In the future, patches of shrubs or herbaceous plants should be added around the cultivated land and at the bottom of the sparse forest to increase the sediment collection capacity. The backpropagation neural network (BPNN) is optimal for modeling SSY, particularly for running the variables preferred by the generalized additive model (GAM). This study provides insight into understanding RSE in karst small watersheds. It will help the region to cope with future extreme climate change and develop sediment management models that are consistent with regional realities.

Eutrophication risk assessment of a large reservoir in the Brazilian semiarid region under climate change scenarios

The present study assesses the risk of eutrophication of a large semiarid reservoir under SSP2-4.5 and SSP5-8.5 scenarios for three future periods and different conditions of influent total phosphorus (TP) concentration and reservoir withdrawal. An integrated approach coupling climate, hydrological and water quality models was proposed for forecasting the climate change impacts on the trophic condition of the reservoir. The projected TP concentrations were organized as probability-based cumulative distribution functions to quantify the risk of eutrophication. The results indicated changes of eutrophication status in the three future periods, with the end of the 21st century experiencing the highest impacts on water quality. On the other hand, major reductions both in the inlet TP concentration and the reservoir withdrawal are necessary to significantly improve the trophic status and minimize the risk of eutrophication. The results also showed that the dry period is more susceptible to eutrophication than the rainy period, suggesting that tropical semiarid reservoirs are more vulnerable to eutrophication under climate change than reservoirs in other regions of the world. The proposed approach and model results are important to better understand the impact of climate change on reservoir water quality and improve water resources management in tropical semiarid regions.

Century-Long Homogenization of Algal Communities Is Accelerated by Nutrient Enrichment and Climate Warming in Lakes and Reservoirs of the North Temperate Zone

Anthropogenic pressures can threaten lake and reservoir ecosystems, leading to harmful algal blooms that have become globally widespread. However, patterns of phytoplankton diversity change and community assembly over long-term scales remain unknown. Here, we explore biodiversity patterns in eukaryotic algal (EA) and cyanobacterial (CYA) communities over a century by sequencing DNA preserved in the sediment cores of seven lakes and reservoirs in the North Temperate Zone. Comparisons within lakes revealed temporal algal community homogenization in mesotrophic lakes, eutrophic lakes, and reservoirs over the last century but no systematic losses of α-diversity. Temporal homogenization of EA and CYA communities continued into the modern day probably due to time-lags related to historical legacies, even if lakes go through a eutrophication phase followed by a reoligotrophication phase. Further, algal community assembly in lakes and reservoirs was mediated by both deterministic and stochastic processes, while homogeneous selection played a relatively important role in recent decades due to intensified anthropogenic activities and climate warming. Overall, these results expand our understanding of global change effects on algal community diversity and succession in lakes and reservoirs that exhibit different successional trajectories while also providing a baseline framework to assess their potential responses to future environmental change.

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.

Early warning of water quality degradation: A copula-based Bayesian network model for highly efficient water quality risk assessment

In the context of global climate change and increasingly severe environmental pollution, drinking water quality risk assessments to provide crucial early warnings have become essential routine work. At present, traditional water quality assessment methods are commonly used without considering the correlation among different indicators and the substantial uncertainty from multiple sources, which limit their applications. To address this issue, a copula-based Bayesian network (CBN) method was proposed in this study to concretely evaluate the water quality risk with multiple environmental risk indicators in a large drinking water reservoir in Tianjin city, China. Taking rainfall and water temperature (WT) as external environmental risk indicators and pH, ammonia nitrogen (NH₃-N), total nitrogen (TN), total phosphorus (TP), and permanganate index (COD_Mn) as internal environmental risk indicators, the CBN model was constructed to investigate the interaction between the indicators and water quality state and assess the contingent risk. Our results showed that TN and NH₃-N should be considered key risk indicators. Additionally, we performed forward and backward risk analyses to assess water quality risk during different seasons and determined the distributions of key indicators under different water quality risk grades. From a time perspective, the reservoir's water quality risk is much higher in winter and spring than in other seasons affected by winter snowfall. From a spatial perspective, the water quality risk is much higher at the reservoir's entrance than at other locations affected by water diversion. Furthermore, we found that the probability of water quality risk events may be relatively high when the TN concentration is 3.6 mg/L to 6.4 mg/L at the reservoir's entrance. The results reveal that the CBN method could be an invaluable decision-support tool for reservoir managers and scientists, which could provide an early warning of water quality degradation by only inputting monitoring data.

An Ensemble Kalman Filter approach to assess the effects of hydrological variability, water diversion, and meteorological forcing on the total phosphorus concentration in a shallow reservoir

Total phosphorus (TP) is a vitally important water quality index in shallow reservoirs and is closely connected with hydrological variability, anthropogenic water diversion and meteorological forcing. However, it is still unclear to what extent the TP concentration in a complex shallow reservoir system attributes to each type of forcing. To resolve this issue, this study proposed a TP concentration contribution index (TPI) to assess the contribution of each forcing, using the data assimilation (DA) method, the Ensemble Kalman Filter (EnKF), which was applied in the shallow Yuqiao Reservoir, China. The EnKF model was conducted based on the Vollenweider model and logistic regression models with datasets of 1989-2015. The results showed that human-originated activities forcing (water diversion) contributed the maximum TPI (40%), followed by hydrological variability forcing (37%). Finally, meteorological forcing (air temperature and wind included) only accounted for 23%. Furthermore, the seasonal analyses also showed that the TPI of hydrological variability dominated in spring and winter, with 65% and 73% respectively. However, the contributions of meteorological forcing (air temperature and wind) accounted for a larger proportion of 63% and 57% in summer and autumn. The benefit of our EnKF model denoises the Gaussian noise contained in observation and simulation, which offers a chance to isolate and identify even a minor driving factor (i.e., meteorological forcing) from a complex river and lake system with limited data. The study provides a method to assess the influence of direct and indirect forcing on TP concentration in shallow reservoirs from a quantitative perspective. Thus, it may serve as a useful tool for water quality management in water-receiving systems.

The effects of turbulence on phytoplankton and implications for energy transfer with an integrated water quality-ecosystem model in a shallow lake

Turbulence has significant influences on the growth rate and community structure of phytoplankton in large shallow lakes. Phytoplankton in moving water may be influenced by turbulence and nutrient concentration gradients on a short time scale. To assess this issue, our research used an ensemble water quality and ecological model by internally coupling the three-dimensional hydrodynamic model, the Environmental Fluid Dynamics Code (EFDC), and the one-dimensional ecosystem model, PCLake. The results showed that turbulence dramatically inhibited phytoplankton growth, while nutrients had the opposite effect. In addition, turbulence was the key factor contributing to phytoplankton growth. However, the effects of turbulence on phytoplankton correlated with nutrient concentrations. For lower nutrient concentrations, phytoplankton growth was controlled by nutrients. Logistic regression models were established with the modeled chlorophyll a, total nitrogen (TN), total phosphorus (TP) and turbulent kinetic energy (K_e). The results also showed that turbulence could improve nutrient uptake by phytoplankton, especially at low nutrient levels. The effects of turbulence on phytoplankton may imply that energy transfer occurs between water turbulence and phytoplankton. Our study will provide insight into management and remediation strategies of ecosystems based on energy processes in the future.