Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098-0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Multi-purpose reservoir operation oncomitant with estimating hydropower potential using multifarious hydrological models

The research aims at determining the optimal release rule to increase the capacity of Rib reservoir. The reservoir inflow using HBV-light hydrological model embracing optimal reservoir operation through HEC-ResSim model were used to prepare an optimum operational plan. The potential of the river for hydropower generation prioritise the demand at a specified level regarding storage capacity (m3), level of reservoir (m), and the relation between inflow and outflow of the reservoir. From the model performance features, the coefficient of correlation (R2) and Nash Sutcliffe Efficiency (NSE) were determined to be, respectively, 0.77 and 0.73 for calibration and 0.72 and 0.70 for validation. The Sobol approach was used for detailed sensitivity analysis of DROP model parameters based on the performance of C2M on outflows and volumes. The results suggest that the threshold coefficient characterizing the demand-controlled release level is the most significant parameter. According to the simulation's findings, the reservoir's average regulated release is calculated to be 22.86 m3/s, and its average monthly hydropower output is 6.73 MW. Average annual hydropower energy was estimated as 58.955 GW h/year and mean annual inflow of reservoir volume of water to be 223.54 Mm3. This volume of water is adequate to accommodate total annual irrigation demand, environmental obligation, and other respective requirements in the downstream. The demand for hydropower and irrigation and supply from reservoir capacity can be counterbalanced from the simulated result without any hindrance.

Reservoir operation affects propagation from meteorological to hydrological extremes in the Lancang-Mekong River Basin

Hydrological extremes intensified by meteorological extremes are threatening water security in the Lancang-Mekong River Basin (LMRB), and reservoir operation may mitigate hydrological extreme through regulating hydrological processes during meteorological extreme. However, the capacity of reservoirs in modulating propagation from meteorological extremes to hydrological extremes has seldom been quantified. This study adopted the VIC-Reservoir hydrological model to assess the impact of reservoir operation on the propagation at multi-timescales in the LMRB. The Standardized Precipitation Index and Standardized Streamflow Index were adopted to characterize meteorological extreme and hydrological extreme, respectively, on a range of timescales. The results indicate that reservoir operation has effectively delayed the propagation from meteorological to hydrological extremes during the period of 2008-2016 with rapid reservoir development in the LMRB, compared with the period of 1984-2007 with natural condition. The transmission process of extreme events with a duration of no more than 6 months has been suppressed during the reservoir impact period. However, the influence of reservoir regulation on long-term extreme events that last more than 12 months is generally low. In the upstream basin where reservoir impact is largest, reservoirs can exert a weak mitigation effect on long-term dry extremes. This study provides quantitative assessment of the role of reservoirs in regulating propagation between meteorological and hydrological extremes in the LMRB, and facilitate decision making for the management of water hazards under changing environment.

Disproportional erosion of the middle-lower Yangtze River following the operation of the Three Gorges Dam

The operation of the Three Gorges Dam (TGD) modifies downstream flow and sediment regimes, triggering disproportional fluvial responses at different distances downstream. However, our understanding of the downstream geomorphic changes in the middle-lower Yangtze River remains incomplete due to the complexity of the river responses across temporal and spatial scales. Here, we leverage data on discharge, suspended sediment concentration (SSC), riverbed grain size, cross-sectional profiles and high-resolution channel bathymetric maps at different locations downstream of the TGD to investigate geomorphic responses. The results show that the magnitude of fluvial erosion decreases downstream, with the Yichang-Luoshan Reach (the first ~500 km downstream) experiencing the most severe erosion in 2003-2020 (~9.05 × 10⁴ t/km/yr). Local changes in riverbed morphology include channel bar erosion, channel incision (~0.43 m/yr in CS1 near the dam site over 2002-2019), riverbank retreat and bed material coarsening (an increase in D₅₀ from 0.175 to 43.1 mm at Yichang station from 2002 to 2017). Such marked erosion is caused by the sharply reduced SSC in the dominant discharge range (10,000-30,000 m³/s) and the extended duration of this dominant discharge range. The sediment erosive magnitude in the Luoshan-Datong Reach is relatively small (3.85 × 10⁴ t/km/yr) in 2002-2020. The Luoshan-Hukou Reach (~500-1000 km downstream) exhibits moderate channel incision, minor bed material coarsening and moderate mid-channel bar lateral erosion. The Hukou-Datong Reach (below 1000 km downstream) experienced minor geomorphic change without significant evidence of bed material coarsening. The relatively small impact of the TGD on the lower reach from Luoshan to Datong can be mainly attributed to the progressive SSC recovery along the river induced by upstream channel erosion providing sediment replenishment. These findings have significant implications for estimating geomorphic changes in response to upstream damming and thus could inform better river management and ecological assessment in other similar alluvial rivers.

Influence of the impoundment of the Three Gorges Reservoir on hydrothermal conditions for fish habitat in the Yangtze River

The thermal regimes of rivers play an important role in the overall health of aquatic ecosystems. Modifications to water temperature regimes resulting from dams and reservoirs have important consequences for river ecosystems. This study investigates the impacts of the impoundment of the Three Gorges Reservoir (TGR) on the water temperature regime of fish spawning habitats in the middle reach of the Yangtze River, China. Mike 11 model is used to analyze the temporal and spatial variation of water temperatures of the expanse of 400 km along the river, from Yichang to Chenglingji. The water temperature alterations caused by the operation of the TGR are assessed with river temperature metrics. The impact on spawning habitats due to water temperature variation was also discussed in different impoundments of the TGR. The results show that the TGR has significantly altered the downstream water temperature regime, affecting the baseline deviation and phase shift of the water temperature. Such impacts on the thermal regime of the river varied with the impoundment level. The effects of the TGR on the water temperature regime decreased as the distance from the structure to the sample site increased. The water temperature regime alterations have led to the delay of the spawning times of the four famous major carp (FFMC) species. The results could be used to identify the magnitudes of water temperature alterations induced by reservoirs in the Yangtze River and provide useful information to design ecological operations for the protection of river ecosystem integrity in regulated rivers.

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.

Unravelling the potential of global streamflow reanalysis in characterizing local flow regime

While global streamflow reanalysis provides valuable information for environmental modelling and management, it is not yet known how effective they are in characterizing the local flow regime. This paper presents a novel evaluation of the potential of streamflow reanalysis in the flow regime analysis by accounting for the effects of reservoir operation. Specifically, the indicators of hydrologic alteration (IHA) are used to characterize the five components of flow regime for both reservoir inflow and outflow; the performance of raw reanalysis is evaluated and the raw reanalysis is furthermore corrected by using the quantile mapping for improved flow regime analysis. The results of 35 major reservoirs in California show that raw reanalysis tends to be effective in characterizing the regime of reservoir inflow and that it is generally less effective in capturing outflow. For both inflow and outflow, the performance of raw reanalysis is beset by the existence of systematic errors. The quantile mapping is effective in error correction and therefore considerably improves the performances of reanalysis in characterizing the regime of not only reservoir inflow but also outflow. Nevertheless, for both reservoir inflow and outflow, the low flow part tends to be more difficult to handle than the high flow part. The evaluation conducted in this paper can serve as a roadmap for further exploitations of the potential of global streamflow reanalysis for flow regime analysis at regional and even continental scales.

Integrated scheduling-assessing system for drought mitigation in the river-connected lake

Lakes are important inland surface water resources and have great influence on the ecological environment as well as the surrounding residential life. However, global lake water resources showed a depleting tendency over the past decades because of the climate change and human activities. To mitigate the drought of lakes linked to a regulated main river, this study proposes an integrated scheduling-assessing system (ISAS) based on the machine learning methodology for a large river-lake system controlled by upstream reservoirs. Closely calibrated to observational data, the ISAS was applied to the middle Yangtze River to mitigate the Poyang Lake drought. The results show that the drought situation in the downstream lake could be improved through the reservoir optimal operation. For the Poyang Lake case, the lowest lake level is not obviously improved, while the starting data of the drought could be delayed by 12, 11, and 17 days, comparing to the conventional scheme in typical dry, normal, and wet years, respectively. Moreover, the duration of the drought could be 20, 19, and 21 days less. It is illustrated that accelerating the reservoir filling speed and decelerating the emptying speed is beneficial to alleviate the drought situation of downstream river-connected lakes.

Water-energy-ecosystem nexus modeling using multi-objective, non-linear programming in a regulated river: Exploring tradeoffs among environmental flows, cascaded small hydropower, and inter-basin water diversion projects

Small hydropower (SHP) possesses significant economic, technical, and environmental advantages, and accounts for a large proportion of hydropower development in China. However, the concentrated, cascaded, and diversion-type development of SHP has resulted in long-distance dewatering of river sections, and inter-basin water transfers have led to severe exploitation of water resources and damage to river ecosystems. In this paper, the Datong River Basin, a secondary sub-basin of the Yellow River Basin in China, was selected as the illustrative case, which includes 22 hydropower projects (HPPs) and three inter-basin water diversion projects (WDPs). A nexus system model was established that used weighted multi-objective programming to consider three main objectives: the water resources utilization (local water withdrawal and inter-basin water transfer), energy production (by cascaded HPPs), and riverine environmental conservation. The Tennant method was used to estimate the environmental flows (e-flows) at the cross-sections immediately downstream of the dam/sluice gate and immediately downstream of the hydropower plant of diversion-type HPPs. The decreased percentage of regulated flow in comparison with runoff and the guaranteed rate of e-flow at the control cross-section were introduced to assess the degree of environmental impact to the river. Using a historical series of runoff data during 1956-2016 as the model input (i.e., implicit stochastic method), the Multi-start solver of nonlinear programming of LINGO software was used to conduct optimizations and analyses for multiple scenarios (with/without e-flow, with consideration of various levels of e-flow, and with/without water resources utilization). The sectoral linkages relating to the water-energy-ecosystem (WEE) nexus were quantitatively identified. The possible influences of different boundary conditions (i.e., initial/final reservoir storage, inter-basin water diversion capacity, and climate change) on the WEE nexus were further explored. The present study aims to provide an exemplar for the optimal operation and scientific management of a complicated water resources system in a regulated river with cascaded SHP and inter-basin WDPs.

An improved MOPSO algorithm for multi-objective optimization of reservoir operation under climate change

Gradually, the previously proposed water resource management schemes and reservoir operating policies adjusted to the historically experienced climatic conditions are losing their validity and efficacy, urging building up the models compatible with the likely climatic change conditions at the future. This paper aims at optimizing the reservoir operation under climate change conditions targeting the objectives including (1) minimizing the shortages in meeting the reservoir downstream water demands and (2) maximizing the sustainability of the reservoir storage. For evaluating the effects of the climate change, six general circulation models (GCMs) built up under the representative concentration pathway (RCP4.5) emission scenario are adopted and utilized to predict the climate variables over a 30-year planning period. To solve this problem, an improved version of our recently proposed fuzzy multi-objective particle swarm optimization (f-MOPSO) algorithm, named f-MOPSO-II, is proposed. The f-MOPSO takes a novel approach to handle multi-objective nature of the optimization problems. In this approach, the common concept of "diversity" is replaced with "extremity," to choose the better guides of the search agents in the algorithm. The f-MOPSO-II is based on the f-MOPSO. However, it is aimed at simultaneously mitigating the f-MOPSO computational complexity and enhancing the quality of the final results presented by this algorithm. The results obtained by the f-MOPSO-II were then compared with those yielded by the popular non-dominated sorting genetic algorithm-II (NSGA-II). As the results suggest, the f-MOPSO-II is capable of simultaneously meeting the water demands and holding the reservoir storage sustainable, much better than the NSGA-II.

GA-based implicit stochastic optimization and RNN-based simulation for deriving multi-objective reservoir hedging rules

Management of reservoir systems is a complicated process involving many uncertainties regarding future events and the diversity of purposes these reservoirs serve; therefore, an effective management of these systems could help improve resource utilization and avoid stakeholder disputes. The aim of this paper was to build an optimization-simulation framework based on implicit stochastic optimization (ISO), genetic algorithms (GA), and recurrent neural network (RNN) for addressing the issue of reservoir operation. Inflow scenarios were generated synthetically based on a monthly scale to be used as an input to a multi-objective genetic programming model to construct an optimal operating rules database. Such database was subsequently used simultaneously with the output of the inflow forecasting model to simulate monthly reservoir hedging rules using RNN. Our results demonstrate the effectiveness of the GA-ISO-RNN model for simulating and predicting optimal reservoir release with consistent accuracy. Results from both the training and testing phases clearly proved the usefulness of RNN in predicting optimal reservoir release with relatively higher values of the Nash-Sutcliffe model efficiency coefficient, correlation coefficient, and lower values of root mean squared error and mean absolute deviation. Furthermore, by comparing the historical releases and the output of the proposed model, the results show that the proposed model was less vulnerable than standard operating rules. The proposed methodology was applied to the Bigge reservoir in Germany, as it features an extensive management infrastructure, but this methodology can also be easily adopted in other similar cases.

Linking water environmental factors and the local watershed landscape to the chlorophyll a concentration in reservoir bays

The frequency of harmful algal blooms caused by eutrophication is increasing globally, posing serious threats to human health and economic development. Reservoir bays, affected by water environment and local watershed landscape, are more prone to eutrophication and algal blooms. The chlorophyll a (Chl a) concentration is an important indicator for the degree of eutrophication and algal bloom. Exploring the complex relationships between water environment and landscape background, and Chl a concentration in the reservoir bays are crucial for ensuring high-quality drinking water from reservoirs. In this study, we monitored Chl a concentrations of 66 bays in Danjiangkou Reservoir and the related water quality parameters (e.g., water temperature, turbidity, nutrients) in waterbodies of these reservoir bays in the storage and discharge periods from 2015 to 2018. Partial least squares-structural equation modeling (PLS-SEM) was used to quantify the relationship between water environmental factors and watershed landscapes, and Chl a concentrations in reservoir bays. The results showed that mean Chl a concentration was higher in storage period than that in discharge period. Two optimal PLS-SEMs explained 66.8% and 53.6% of Chl a concentration variation in the storage and discharge periods, respectively. The net effect of water chemistry on Chl a concentration was more pronounced during the discharge period (total effect = 0.61, 37% of the total effect on Chl a), while the net effect of land-use composition on Chl a concentration was more significant during the storage period (total effect = 0.57, 30% of the total effect on Chl a). The landscape pattern had significant indirect effects on Chl a concentration, especially during the discharge period (indirect effect = -0.31, 19% of the total effect on Chl a). Our results provide valuable information for managers to make rational decisions, thereby contributing to the prevention of eutrophication and algal blooms in reservoir bays.

Mismatch between critical and accumulated temperature following river damming impacts fish spawning

Dam operations considerably influence water temperature regimes in rivers, which affects fish spawning activities. Previous studies have focused on the effects of critical temperature (CT) alterations during the spawning period, and largely ignored the effects of accumulated temperature (AT) alterations on gonadal development. Successful spawning relies on the simultaneous achievement of the two thermal requirements at appropriate times. River damming may cause a mismatch between the times of achieving CT and AT thresholds, and in turn influence fish reproduction. In the present study, spawning events of Coreius heterodon (C. heterodon) from 2009 to 2015 in the upper reaches of the Yangtze River, which are under the influence of cascade dams, were analysed based on the times of achievement of CT and AT thresholds. The CT and AT thresholds for C. heterodon spawning were 18.4 °C and 1324.9 °C·d, respectively. Under pre-impoundment conditions, the time of achievement of the AT threshold was 23 d on average later than that under post-impoundment conditions; however, the time of achievement of the CT threshold was similar under both conditions. The time of achievement of the AT threshold was 10 d earlier than that of achievement of the CT threshold in post-impoundment conditions. Earlier achievement of AT thresholds was followed by reduced spawning. The alteration of temperature rhythm caused by reservoir operations could be the major factor decreasing spawning abundance after river damming. The results of the present study could facilitate sustainable reservoir operations with regards to water temperature management, and thereby improve the conservation of fish resources.

Modeling impacts of future climate change on reservoir storages and irrigation water demands in a Mediterranean basin

Water storage requirements in the Mediterranean region vary in time and are strongly affected by the local geography and climate conditions. The objective of this study is to assess the implications of climate change on the water balance of an agricultural reservoir in a Mediterranean-climate basin in Turkey throughout the 21st century. A monthly dynamic water balance model is developed to simulate the historical and future water availability in the reservoir. The model is driven by the fine-resolution dynamically downscaled climate data from four GCMs from the CMIP5 archive, namely CCSM4, GFDL-ESM2M, HadGEM2-ES, and MIROC5, under two different representative concentration pathway scenarios (RCP4.5 and RCP8.5), and the hydrologic data projected under the same scenarios. The reservoir outflows, including the reservoir evaporation and downstream irrigation water demands, are also modeled using the projected climate variables. The net irrigation water requirement of the crops in the irrigation system, seasonal evapotranspiration rates, and reservoir evaporation rates are estimated based on the Penman-Monteith Evapotranspiration method (FAO-56 Method). The study investigates whether the future water supply in the reservoir will be sufficient to meet the future irrigation water demands for the years from 2017 to 2100. The results show that under all eight modeled climate change projections, statistically significant increasing trends for the annual irrigation water demands are expected throughout the 21st century. Moreover, higher evapotranspiration rates are predicted under the ensemble average of the RCP8.5 projections, compared to those of the RCP4.5 projections. Ultimately, seven out of eight projections projected insufficient reservoir water levels during the 21st century, especially during the irrigation seasons when higher water demands are expected. These impacts indicate the importance of sustainable water resources management in the region to provide irrigation water from reservoirs, and to sustain agricultural productivity under projected water limitations due to climate change.

Hydropower dam operation strongly controls Lake Victoria's freshwater storage variability

River impoundments strongly modify the global water cycle and terrestrial water storage (TWS) variability. Given the susceptibility of global water cycle to climate change and anthropogenic influence, the synthesis of science with sustainable reservoir operation strategy is required as part of an integrated approach to water management. Here, we take advantage of new approaches combining state-of-the-art computational models and a novel satellite-based reservoir operation scheme to spatially and temporally decompose Lake Victoria's TWS, which has been dam-controlled since 1954. A ground-based lake bathymetry is merged with a global satellite-based topography to accurately represent absolute water storage, and radar altimetry data is integrated in the hydrodynamic model as a proxy of reservoir operation practices. Compared against an idealized naturalized system (i.e., no anthropogenic impacts) over 2003-2019, reservoir operation shows a significant impact on water elevation, extent, storage and outflow, controlling lake dynamics and TWS. For example, compared to Gravity Recovery and Climate Experiment (GRACE) data, reservoir operation improved correlation and root mean square error of basin-wide TWS simulations by 80% and 54%, respectively. Results also show that lake water storage is 20% higher under dam control and basin-wide surface water storage contributes 64% of TWS variability. As opposed to existing reservoir operation schemes for large-scale models, the proposed model simulates spatially distributed surface water processes and does not require human water demand estimates. Our proposed approaches and findings contribute to the understanding of Lake Victoria's water dynamics and can be further applied to quantify anthropogenic impacts on the global water cycle.

Reservoir water quality simulation with data mining models

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 R² 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 (R² = 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.

Climate-environment-water: integrated and non-integrated approaches to reservoir operation

Integrated water planning and management face multiple challenges, among which are the competing interests of several water-using sectors and changing climatic trends. This paper presents integrated and non-integrated climate-environment-water approaches for reservoir operation, illustrated with Karkhe reservoir, Iran. Reservoir operation objectives are meeting municipal, environmental, and agricultural water demands. Results show the integrated approach, which relies on multi-objective optimization of municipal, environmental, and agricultural water supply, improves the municipal, environmental, and agricultural objectives by 70, 32, and 65% compared with the objectives' values achieved with the non-integrated approach, which implements a standard operating policy.

Multi-objective future rule curves using conditional tabu search algorithm and conditional genetic algorithm for reservoir operation

Multi-objective future rule curves are imperative recommendations for operating multipurpose reservoirs throughout long term periods. This research utilized the conditional tabu search algorithm (CTSA) and conditional genetic algorithm (CGA) combining to the reservoir simulation model through contemplating the multiple-purpose functionals when exploring processes for finding adaptable rule curves of a single reservoir. The historic inflow data during 1966–2016 (51 years) including the future inflow during 2017–2041 (25 years) in case of the B2 scenario of IPCC for the Ubolrat Reservoir in Thailand were applying to create the searching conditions. The 500 synthetic events of historical inflow and 25 years of future inflow were used to calculate the reservoir operation process for assessing the obtained rule curves. As a result, the predicament of water scarcity and spill water were illustrated in terms of frequency scale and duration along with the maintained water at the edge of the rainy period. The operation outcomes suggest that the multi-objective rule curves developed by the CGA can alleviate the frequency of flooding and drought situations appropriately than the CTSA during the future period. However, the rule curves obtained from both optimization techniques indicate better performance correlated to the actual rule curves along with having more maintained water at the end of the rainy period (November), which has continued benefits betwixt the dry period because the reservoir can discharge water in sufficient quantities to the downstream area.

A multi-time-scale power prediction model of hydropower station considering multiple uncertainties

Hydropower, as one of renewable energies, has been widely used all over the world. The uncertainties such as reservoir inflows and electricity price cause random changes in the output power and the hydropower generation benefit. Thus, it is important to research on the power prediction of hydropower station considering the uncertainties. This study proposes a multi-time-scale power prediction model of hydropower station based on dynamic Bayesian network theory, considering the uncertainties of reservoir inflow, electricity price, and hydropower consumption rate. The proposed model consists of three components: a multi-time-scale coupling operation (MCO) model, a dynamic Bayesian network (DBN) model, and a probability-based prediction (PBP) model for decision making. The MCO model provides training data inputs for the DBN model, which is established based on expert knowledge and the relationships among the uncertainties. The PBP model performs power prediction of the hydropower station for decision making using the trained DBN. We apply the proposed model to the Tankeng hydropower station in China. The results show that the model not only quantitatively predicts the multi-time-scale output power and benefit of the hydropower station considering the uncertainties, but also provides the risks of power generation deficiency and power output deficiency.

Development of a bacteria-based index of biotic integrity (Ba-IBI) for assessing ecological health of the Three Gorges Reservoir in different operation periods

It is urgently needed to quantitatively assess ecological health of the Three Gorges Reservoir (TGR) when considering its special environmental conditions and temporal variations caused by reservoir operation. This study developed a bacteria-based index of biotic integrity (Ba-IBI) based on sediment samples collected along the TGR in low water level period, impoundment period and sluicing period, respectively. Reference conditions were defined using 8 ecological variables describing the hydromorphology and anthropogenic disturbances around the sites. Five core metrics, including % Acidobacteria, % Gemmatimonadetes, % Geobacter, Methanotroph and Phototroph, were selected after the screening processes. The developed index could clearly discriminate reference and impaired conditions and exhibited significant relationship with environmental parameters according to the redundancy (p < 0.01) and multivariable linear regression analysis (R² = 0.76). By implementing Ba-IBI in the TGR, the ecological health of the sampling sites was defined as "Excellent" (25%), "Good" (50%) and "Fair" (25%) separately. The spatial variation of biotic integrity was closely associated with environmental and ecological changes, especially the increase of nutrient concentrations. This study revealed a significant tendency that the ecological health in the low water level and sluicing periods was better than that in the impoundment period, which could be attributed to the hydrodynamic changes due to water level fluctuation. This study provides a comprehensive understanding of ecological health of the TGR in different operation periods and the index offers a guideline for the reservoir regulation in the similar areas.

A new method and a new index for identifying socioeconomic drought events under climate change: A case study of the East River basin in China

Drought is a complex natural hazard that may have destructive damages on societal properties and even lives. Generally, socioeconomic drought occurs when water resources systems cannot meet water demand, mainly due to a weather-related shortfall in water supply. This study aims to propose a new method, a heuristic method, and a new index, the socioeconomic drought index (SEDI), for identifying and evaluating socioeconomic drought events on different severity levels (i.e., slight, moderate, severe, and extreme) in the context of climate change. First, the minimum in-stream water requirement (MWR) is determined through synthetically evaluating the requirements of water quality, ecology, navigation, and water supply. Second, according to the monthly water deficit calculated as the monthly streamflow data minus the MWR, the drought month can be identified. Third, according to the cumulative water deficit calculated from the monthly water deficit, drought duration (i.e., the number of continuous drought months) and water shortage (i.e., the largest cumulative water deficit during the drought period) can be detected. Fourth, the SEDI value of each socioeconomic drought event can be calculated through integrating the impacts of water shortage and drought duration. To evaluate the applicability of the new method and new index, this study examines the drought events in the East River basin in South China, and the impact of a multi-year reservoir (i.e., the Xinfengjiang Reservoir) in this basin on drought analysis is also investigated. The historical and future streamflow of this basin is simulated using a hydrologic model, Variable Infiltration Capacity (VIC) model. For historical and future drought analysis, the proposed new method and index are feasible to identify socioeconomic drought events. The results show that a number of socioeconomic drought events (including some extreme ones) may occur in future, and the appropriate reservoir operation can significantly ease such situation.

Air-water CO2 and CH4 fluxes along a river-reservoir continuum: Case study in the Pengxi River, a tributary of the Yangtze River in the Three Gorges Reservoir, China

Water surface greenhouse gas (GHG) emissions in freshwater reservoirs are closely related to limnological processes in the water column. Affected by both reservoir operation and seasonal changes, variations in the hydro-morphological conditions in the river-reservoir continuum will create distinctive patterns in water surface GHG emissions. A one-year field survey was carried out in the Pengxi River-reservoir continuum, a part of the Three Gorges Reservoir (TGR) immediately after the TGR reached its maximum water level. The annual average water surface CO₂ and CH₄ emissions at the riverine background sampling sites were 6.23 ± 0.93 and 0.025 ± 0.006 mmol h^-1 m^-2, respectively. The CO₂ emissions were higher than those in the downstream reservoirs. The development of phytoplankton controlled the downstream decrease in water surface CO₂ emissions. The presence of thermal stratification in the permanent backwater area supported extensive phytoplankton blooms, resulting in a carbon sink during several months of the year. The CH₄ emissions were mainly impacted by water temperature and dissolved organic carbon. The greatest water surface CH₄ emission was detected in the fluctuating backwater area, likely due to a shallower water column and abundant organic matter. The Pengxi River backwater area did not show significant increase in water surface GHG emissions reported in tropical reservoirs. In evaluating the net GHG emissions by the impoundment of TGR, the net change in the carbon budget and the contribution of nitrogen and phosphorus should be taken into consideration in this eutrophic river-reservoir continuum.

An improved multi-objective optimization model for supporting reservoir operation of China's South-to-North Water Diversion Project

An improved multi-objective optimization model based on goal programming (GP) for supporting reservoir operation was developed under inflow senarios of multiple runoff guarantee rates (i.e., 25%, 75%, perennial mean, and 95%) and ecological goals with the combination of steady- and pulse-state ecological water demands. Under these four scenarios, discharge flows of Danjingkou Reservoir would be 358.40, 369.67, 268.91 and 98.14×10⁸m³/a, and those at Taocha Canal headwork would be 104.61, 86.62, 95.08 and 64.00×10⁸m³/a, respectively. The generated results for stream flows could successfully meet the predetermined operational goals for the project. Comparatively, under the scenario of 95% runoff guarantee rate, the obtained strategies could not satisfy the ecological water demands. The modeling results indicated that the capacity of water diversion and storage for Danjiangkou Reservoir would be enhanced due to the operation of the South-to-North Water Diversion Project. The results showed the risks associated with possible flooding would be comparatively low under those four runoff guarantee rates. This represents the current priority for flood control in Danjiangkou Reservoir needs to be changed into multiple ones including ecological water supply, water transfer, as well as downstream water security maintenance.

Application of PSO algorithm in short-term optimization of reservoir operation

The optimization of the operation of existing water systems such as dams is very important for water resource planning and management especially in arid and semi-arid lands. Due to budget and operational water resource limitations and environmental problems, the operation optimization is gradually replaced by new systems. The operation optimization of water systems is a complex, nonlinear, multi-constraint, and multidimensional problem that needs robust techniques. In this article, the practical swarm optimization (PSO) was adopted for solving the operation problem of multipurpose Mahabad reservoir dam in the northwest of Iran. The desired result or target function is to minimize the difference between downstream monthly demand and release. The method was applied with considering the reduction probabilities of inflow for the four scenarios of normal and drought conditions. The results showed that in most of the scenarios for normal and drought conditions, released water obtained by the PSO model was equal to downstream demand and also, the reservoir volume was reducing for the probabilities of inflow. The PSO model revealed a good performance to minimize the reservoir water loss, and this operation policy can be an appropriate policy in the drought condition for the reservoir.

Selective withdrawal optimization in river-reservoir systems; trade-offs between maximum allowable receiving waste load and water quality criteria enhancement

In this paper, a new systematic approach is designed to maximize the demand coverage and receiving waste load by river-reservoir systems while enhancing water quality criteria. The approach intends to control the reservoir eutrophication while developing a trade-off between the maximum receiving load and shortage on demand coverage. To simulate the system, a hybrid process-based and data-driven model is tailored. Initially, the two-dimensional hydrodynamics and water quality simulation model (CE-QUAL-W2) is linked with an effective single and/or multiple optimization algorithms (PSO) to evaluate the proposed scenarios. To increase the computational efficiencies, the simulation model is substituted with a surrogate model (ANN) in an adaptive-dynamically refined routine. The proposed method is illustrated by a case study in Iran, namely, Karkheh River Reservoir, for 180-monthly periods. The results showed the applicability of the methodology especially to solve high-dimensional multi-period complex water resource optimization problems. Also, the results demonstrated that eutrophication could be reduced under the optimal inflow phosphate control and reservoir operation, regulating the total phosphorous concentration in the reservoir.

Succession of phytoplankton assemblages in response to large-scale reservoir operation: a case study in a tributary of the Three Gorges Reservoir, China

The Three Gorges Dam (TGD) has greatly altered ecological and environmental conditions within the reservoir region, but it is not known how these changes affect phytoplankton structure and dynamics. Here, a bimonthly monitoring program was implemented from 2007 to 2009 to study the impact of damming on phytoplankton assemblages in the backwater area of the Pengxi River (PBA). By application of the phytoplankton functional group (C strategists, competitive species; S strategists, stress-tolerant species; R strategists, rapid propagation species), seasonal changes in phytoplankton relative to environmental variations were evaluated using ordination analysis. Seasonal patterns of phytoplankton dynamics were detected during this study, with CS/S strategists causing algal blooms from mid-spring to early summer, CS/CR strategists often observed during flood season, and CS strategists dominant during mid-autumn. CR/R groups dominated during winter and caused algal blooms in February. Our results indicated that phytoplankton assemblages were directly related to reservoir operation effects. Generally, the TGD had a low water level during flood season, resulting in a relatively short hydraulic retention time and intensive variability, which supported the cooccurrence of CS and CR species. During the winter drought season, water storage in the TGD increased the water level and the hydraulic retention time in the PBA, enabling R/CR strategists to overcome the sedimentation effect and to out-compete S/CS species in winter. As expected, these diversity patterns were significantly correlated with the hydraulic retention time and nutrient limitation pattern in the PBA. This study provides strategic insight for evaluating the impacts of reservoir operations on phytoplankton adaptation.

Grid-wide subdaily hydrologic alteration under massive wind power penetration in Chile

Hydropeaking operations can severely degrade ecosystems. As variable renewable sources (e.g. wind power) are integrated into a power grid, fluctuations in the generation-demand balance are expected to increase. In this context, compensating technologies, notably hydropower reservoir plants, could operate in a stronger peaking scheme. This issue calls for an integrated modeling of the entire power system, including not only hydropower reservoirs, but also all other plants. A novel methodology to study the link between the short-term variability of renewable energies and the subdaily hydrologic alteration, due to hydropower reservoir operations is presented. Grid operations under selected wind power portfolios are simulated using a short-term hydro-thermal coordination tool. The resulting turbined flows by relevant reservoir plants are then compared in terms of the Richard-Baker flashiness index to both the baseline and the natural flow regime. Those are then analyzed in order to: i) detect if there is a significant change in the degree of subdaily hydrologic alteration (SDHA) due to a larger wind penetration, and ii) identify which rivers are most affected. The proposed scheme is applied to Chile's Central Interconnect System (SIC) for scenarios up to 15% of wind energy penetration. Results show a major degree of SDHA under the baseline as compared to the natural regime. As wind power increases, so does the SDHA in two important rivers. This suggests a need for further ecological studies in those rivers, along with an analysis of operational constraints to limit the SDHA.