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Lentic waters integrate atmosphere and catchment processes, and thus ultimately capture climate signals. However, studies of climate warming effects on lentic waters usually do not sufficiently account for a change in heat flux from the catchment through altered inflow temperature and discharge under climate change. This is particularly relevant for reservoirs, which are highly impacted by catchment hydrology and may be affected by upstream reservoirs or pre‐dams. This study explicitly quantified how the catchment and pre‐dams modify the thermal response of Rappbode Reservoir, Germany's largest drinking water reservoir system, to climate change. We established a catchment‐lake modeling chain in the main reservoir and its two pre‐dams utilizing the lake model GOTM, the catchment model mHM, and the stream temperature model Air2stream, forced by an ensemble of climate projections under RCP2.6 and 8.5 warming scenarios. Results exhibited a warming of 0.27/0.15°C decade−1 for the surface/bottom temperatures of the main reservoir, with approximately 8%/24% of this warming attributed to the catchment warming, respectively. The catchment warming amplified the deep water warming more than at the surface, contrary to the atmospheric warming effect, and advanced stratification by about 1 week, while having a minor impact on stratification intensity. On the other hand, pre‐dams reduced the inflow temperature into the main reservoir in spring, and consequently lowered the hypolimnetic temperature and postponed stratification onset. This shielded the main reservoir from climate warming, although overall the contribution of pre‐dams was minimal. Altogether, our study highlights the importance of catchment alterations and seasonality when projecting reservoir warming, and provides insights into catchment‐reservoir coupling under climate change.

High‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an effective approach, conventional methods struggle to consistently provide accurate retrievals over diverse regions and seasons. In this study, we introduce reservoir‐ and monthly‐specific classification models to enhance the integration of Sentinel‐1 SAR backscatters with optical‐based water dynamics. Our method covers 721 reservoirs with a capacity greater than 0.1 km3 in China during 2017–2021. Furthermore, we leverage multisource satellite altimetry records (e.g., ICESat‐2, CryoSat‐2, and GEDI) and digital elevation models to derive hypsometry relationship (i.e., water level–water area relationship) for reservoirs, enabling the transformation of inundated areas into monthly water storage changes for 662 reservoirs, representing 93% of the total storage capacity of large reservoirs. Validation against in‐situ measurements at 80 reservoirs reveals improved monthly inundated area monitoring compared to existing data sets. Additionally, our reservoir water storage change estimates exhibit an average R2 of 0.79 and a mean relative root mean square error (rRMSE) of 21%. Our findings highlight reservoir water increases from May/June to November and declines in winter–spring in most regions. However, the inter‐annual patterns vary among regions, with increases in Northeast China, the Yellow River basin (YR), and Southwest China, contrasted by declines in Eastern and Northwest China. Inter‐ and intra‐annual variability in reservoir water storage is mainly influenced by natural inflow in Northeast and Northwest China, while anthropogenic factors dominate in the YR, Eastern, and Southwest China. Plain Language Summary Reservoirs can supply water, control flood and provide electricity. Without high temporal frequency data on inundated area and water storage over a large spatial extent, we could not clearly understand how and to which degree numerous reservoirs have altered the natural water cycle and provided multiple benefits to people all across the world. The development of remote sensing technology has facilitated the long‐term monitoring of inundated areas and water levels of large quantities of reservoirs. However, high‐temporal‐resolution monitoring remains challenging. In this study, by developing a novel method which integrates active microwave remote sensing data with optical remote sensing observations, we largely improved the satellite‐based monthly inundated area and water storage change monitoring at almost all large reservoirs in China. The results reveal distinct seasonal patterns and interannual variations of reservoir water in different regions of China. Specifically, we found that reservoirs in Northeast China, the Yellow River basin, and Southwest China generally expanded, whereas those in Eastern and Northwest China generally shrank during 2017–2021. The climatic and anthropogenic impacts on the inter‐ and intra‐annual variations in reservoir water across different regions were also analyzed. Key Points A new methodology for integrating synthetic aperture radar backscattering coefficients with optical remote sensing‐based inland surface water dynamics is developed Monthly inundated area and water storage changes of 721 large reservoirs in China during 2017–2021 are retrieved at improved accuracy Impacts of natural climate and human control on the inter‐ and intra‐annual variations in reservoir water across different regions in China are analyzed

Arid region water reservoirs have different characteristics and solutions from humid regions with the most water shortage in the world socio-economically. This paper outlines possible implementation methodologies, procedures and guidance for water storage in natural and artificial reservoirs for better operation and management rules taking into account the impacts of climate change. The literature is full of methodological applications regarding the impact of climate change on the hydro-meteorological records, but the same is not available in reservoirs (surface and underground), which is the scope of this paper. In addition, reservoir structures offer the necessary mitigation and adaptation activities against the effects of climate change to design, construct, maintain, operate or increase their existing capacity. To increase groundwater reservoir capacity in local aquifers, precipitation, associated flooding and flash flooding should be diverted to artificial groundwater recharges through precipitation and surface runoff harvesting activities. Definitions of fully or partially penetrating underground dams are also explained. The real groundwater feeding application is offered from the Kingdom of Saudi Arabia as arid region representative. Finally, a series of recommendations are presented for the future design and management of reservoirs.

During the past decades, the increased impact of anthropogenic interventions on river basins has prompted hydrologists to develop various approaches for representing human–water interactions in large-scale hydrological and land surface models. The simulation of water reservoir storage and operations has received particular attention, owing to the ubiquitous presence of dams. Yet, little is known about (1) the effect of the representation of water reservoirs on the parameterization of hydrological models, and, therefore, (2) the risks associated with potential flaws in the calibration process. To fill in this gap, we contribute a computational framework based on the Variable Infiltration Capacity (VIC) model and a multi-objective evolutionary algorithm, which we use to calibrate VIC's parameters. An important feature of our framework is a novel variant of VIC's routing model that allows us to simulate the storage dynamics of water reservoirs. Using the upper Mekong river basin as a case study, we calibrate two instances of VIC – with and without reservoirs. We show that both model instances have the same accuracy in reproducing daily discharges (over the period 1996–2005), a result attained by the model without reservoirs by adopting a parameterization that compensates for the absence of these infrastructures. The first implication of this flawed parameter estimation stands in a poor representation of key hydrological processes, such as surface runoff, infiltration, and baseflow. To further demonstrate the risks associated with the use of such a model, we carry out a climate change impact assessment (for the period 2050–2060), for which we use precipitation and temperature data retrieved from five global circulation models (GCMs) and two Representative Concentration Pathways (RCPs 4.5 and 8.5). Results show that the two model instances (with and without reservoirs) provide different projections of the minimum, maximum, and average monthly discharges. These results are consistent across both RCPs. Overall, our study reinforces the message about the correct representation of human–water interactions in large-scale hydrological models.

The depletion of groundwater resources in the water-stressed regions has led to the overuse of surface water reservoirs. Recharging groundwater by rejuvenating dried surface reservoirs using recycled water is a new sustainable solution. To ensure the prevention of groundwater contamination and associated health risks (as recycled water is used), it is crucial to assess the surface reservoir water quality. The study for the first time suggests the Lake2K model, a one-dimensional mechanistic mass-balance model, to simulate the changes in water quality in a series of man-made surface water reservoirs where recycled water flows under an indirect groundwater recharge scheme (soil aquifer treatment system). The model was developed, calibrated, and validated using field observations to estimate degradation/reaction rate constants for various water quality parameters. The observed average degradation/reaction rate constants for parameters including ammonia-N, nitrate–N, total nitrogen, total organic carbon, and organic phosphorous were 0.043 day −1 , 0.04 day −1 , 0.043 day −1 , 0.055 day −1 , and 0.056 day −1 , respectively, which were found to be relatively high compared to existing literature, indicating a greater degradation of these parameters in warmer climates. The results showed that the water quality improved significantly as the water progressed through the reservoirs, aligning with field observations. Additionally, the simulated seasonal variations revealed that the maximum growth rate of phytoplankton occurred during July, August, and September for each reservoir, while the nutrient pool (nitrate–N and orthophosphates) experienced the greatest depletion during this growth period. These findings shed light on the dynamics of surface water quality in regions facing water scarcity and contribute to the development of sustainable groundwater management strategies. Graphical Abstract

Located in reservoir area of Dahuaqiao Hydropower Station in Lancang River, the Dahua ancient deposit landslide exhibits high possibility of reactivation due to reservoir impoundment. In this study, physical model tests are conducted to investigate the variations of groundwater, deformation, and failure process of the landslide under different fluctuation speeds of reservoir water level. Influence of groundwater on landslide stability when reservoir water level fluctuating is analyzed then. Results indicate that the seepage pressure caused by water level difference can increase landslide displacement. During the dropping process of reservoir water level, the relationship between landslide displacement and water level difference can be described by a power function model. Groundwater has negative effects on stability of landslides, and the damage is characterized by traction landslide. More attentions should be paid on the displacement of the front edge of the landslide during the first rise and drop of reservoir water level. The study provides indispensable information for scheduling reservoir water level in the Dahuaqiao and others similar reservoir areas, thus having vital importance.

Water pollution is a concern in the management of water resources. This paper presents a statistical approach for data mining of patterns of water pollution in reservoirs. Genetic programming (GP), artificial neural network (ANN), and support vector machine (SVM) are applied to reservoir quality modeling. Input data for GP, ANN, and SVM were derived with the CE-QUAL-W2 numerical water quality simulation model. A case study was carried out using measured reservoir inflow and outflow, temperature, and nitrate concentration to the Amirkabir reservoir, Iran. Data mining models were evaluated with the MAE , NSE , RMSE , and R 2 goodness-of-fit criteria. The results indicated that using the SVM model for determining nitrate pollution is time saving and more accurate in comparison with GP, ANN, and particularly CE-QUAL-W2. The SVM model reduces the runtime of nitrate concentration simulation by 581, 276, and 146 s compared with CE-QUAL-W2, GP, and ANN, respectively. The goodness-of-fit results showed that the highest values (R 2 = 0.97, NSE = 0.92) and the lowest values ( MAE = 0.034 and RMSE = 0.007) corresponded to SVM predictions, indicating higher model accuracy. This study demonstrates the potential for application of data mining tools to solute concentration simulation in reservoirs.

Water temperature forecasting in lakes and reservoirs is a valuable tool to manage crucial freshwater resources in a changing and more variable climate, but previous efforts have yet to identify an optimal modeling approach. Here, we demonstrate the first multi‐model ensemble (MME) reservoir water temperature forecast, a forecasting method that combines individual model strengths in a single forecasting framework. We developed two MMEs: a three‐model process‐based MME and a five‐model MME that includes process‐based and empirical models to forecast water temperature profiles at a temperate drinking water reservoir. We found that the five‐model MME improved forecast performance by 8%–30% relative to individual models and the process‐based MME, as quantified using an aggregated probabilistic skill score. This increase in performance was due to large improvements in forecast bias in the five‐model MME, despite increases in forecast uncertainty. High correlation among the process‐based models resulted in little improvement in forecast performance in the process‐based MME relative to the individual process‐based models. The utility of MMEs is highlighted by two results: (a) no individual model performed best at every depth and horizon (days in the future), and (b) MMEs avoided poor performances by rarely producing the worst forecast for any single forecasted period (<6% of the worst ranked forecasts over time). This work presents an example of how existing models can be combined to improve water temperature forecasting in lakes and reservoirs and discusses the value of utilizing MMEs, rather than individual models, in operational forecasts. Key Points Aggregated lake temperature forecast skill was higher for multi‐model ensemble (MME) forecasts than individual model forecasts Including baseline empirical models (day‐of‐year, persistence) with process models improved MME forecast performance MME forecasts improved forecast skill by “hedging,” as no individual model performed best at all horizons or depths

Stormflow runoff is an important non-point source of pollution in drinking water reservoirs. Storm runoff is usually very turbid and contains a high concentration of organic matter, therefore affecting water quality when it enters reservoirs. In order to investigate the impact of storm runoff on spatial–temporal variation and stratification of water quality during this rainstorm event, the inflow process of the storm runoff was studied through a combination of field investigation and simulation using the Delft3D-Flow model. Water samples were collected from Biliuhe Reservoir at four different periods: before storm runoff, storm runoff flood peak period, 1 week after storm runoff, and 5 weeks after storm runoff. The results showed that the input of storm runoff resulted in a significant increase in the nitrogen (N) and phosphorus (P) in the reservoir water, especially in the reservoir entrance. The concentrations of total nitrogen (TN) and total phosphorus (TP) gradually decreased after the flood peak period; however, the average concentrations of TN and TP in the entire reservoir remained higher than those before the storm runoff levels for an extended duration. The storm runoff will greatly contribute to the contamination of water quality in a reservoir, and the water quality cannot be quickly restored by self-purification in the short term. During the flood peak period, under the influence of density current, the electrical conductivity (EC) and turbidity increased significantly in the water depth of 10–15 m, so that the reservoir water had obvious stratification between 10 and 15 m. The form of pollutants in storm runoff was mostly in particle phosphorus. Total particulate phosphorus (TPP) concentration was 0.015 ± 0.011 mg/L, accounting for 44.12% of total phosphorus (TP) concentration in storm runoff flood peak period. The process of a rainstorm caused runoff, which carried high levels of turbidity, particulate phosphorus, and organic matter. The storm runoff disrupts the stratification of the reservoir water. In terms of vertical distribution, the turbidity in the reservoir area increased to 73.75 NTU. Therefore, the occurrence of significant turbidity density flow in the reservoir is frequently accompanied by intense rainfall events. Gaining insights into the impact of storm runoff on the vertical distribution of reservoir turbidity can help managers in selecting an appropriate inlet height to mitigate high turbidity outflow.

The delineation of favourable areas of water potentials and their management must be based on rigorous scientific studies. Thus, geographic information system (GIS) and remote sensing (RS) techniques are extremely important in predicting and mapping favourable groundwater zones. This paper aims to map potential areas of groundwater in Waddai region, eastern Chad. A region which has experienced successive droughts over the last two decades, causing the drying of most of the rivers in the area. This study focuses on combining GIS, RS, and analytical hierarchy process; in addition to the factors controlling the movement and retention of groundwater. Six factors (rainfall, slope, land use/land cover, drainage density, lineament density, and lithology) were used to integrate the spatial analysis of areas likely to hold groundwater. The results indicate that potential groundwater areas are unevenly distributed throughout the study area. For instance, the northwestern part is characterized by a low groundwater potential. This low potential in this part of the study area as well as a small portion of the eastern area is explained by a low density of lineaments and drainage, the presence of moderate precipitations, and a semi-permeable lithology (alternating hard rocks and loose sediments). While every low and moderate area occupy most of the middle of the region, good ground water reservoirs occupy a large part of the region. This distribution is explained by good fracturing, permeability, lineament density, high drainage, gentle slope, and precipitation. Therefore, areas of the northwestern part are highly suitable for groundwater exploration and exploitation. Hence; these results would be a guide for future explorations and will maximizes the economic efficiency of the ground water exploitation processes. Furthermore, this map will be useful as a guide in decision-making and on water policy planning.