Reservoir Release Research Articles

Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin

AbstractStreamflow in the Colorado River Basin (CRB) is significantly altered by human activities including land use/cover alterations, reservoir operation, irrigation, and water exports. Climate is also highly varied across the CRB which contains snowpack‐dominated watersheds and arid, precipitation‐dominated basins. Recently, machine learning methods have improved the generalizability and accuracy of streamflow models. Previous successes with LSTM modeling have primarily focused on unimpacted basins, and few studies have included human impacted systems in either regional or single‐basin modeling. We demonstrate that the diverse hydrological behavior of river basins in the CRB are too difficult to model with a single, regional model. We propose a method to delineate catchments into categories based on the level of predictability, hydrological characteristics, and the level of human influence. Lastly, we model streamflow in each category with climate and anthropogenic proxy data sets and use feature importance methods to assess whether model performance improves with additional relevant data. Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high‐resolution reservoir release data sets at a global scale. On the other hand, the classification approach reduced the complexity of the data and has the potential to improve streamflow forecasts in human‐altered regions.

Adaptive assessment of reservoir scheduling to hydrometeorological comprehensive dry and wet condition evolution in a multi-reservoir region of southeastern China

The role of reservoirs in water resource management is becoming crucial for flood control and drought mitigation in any basin because of the frequent occurrence of extreme weather events attributed to global climate change and human activities. Therefore, evaluating the relationship between reservoir storage (discharge) and wet (dry) evolution is crucial. This study explores the time-delay effect and spatial heterogeneity of reservoir discharge and storage on dry and wet conditions in several basins of Lin’an District (LAD) in southeastern China. An integrated methodology is developed in this study to assess the relationship by a monthly streamflow simulation model, the meteorological and hydrological comprehensive drought index (CDI) using a Frank Copula function, and an eXtreme Gradient Boosting (XGBoost) model and Shapley Additive exPlanations (SHAP) framework were used to develop a model to forecast dry and wet conditions and to evaluate the key factors affecting their changes. Results from the study indicate that the monthly water balance model can simulate the monthly hydrological processes with relatively high accuracy in the LAD region. The CDI reflects the intensity of wet and dry events more precisely, thoroughly, sensitively, and consistently by combining the benefits of hydrological and meteorological drought indicators. Precipitation, evaporation, streamflow, the Pacific Decadal Oscillation (PDO), and the Indian Ocean Dipole (IOD) were the main contributing factors influencing the above 80% accuracy of the wet and dry forecast models. The average correlation between the outflow of each reservoir in LAD and CDI is 0.47, which is higher than the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI). Moreover, the delay in months of dry (wet) events based on SPI, SRI, and CDI are 0.45 (0.41), 1.07 (0.65), and 0.87 (0.60), respectively. It suggests reservoirs are less capable of adaptive scheduling for drought events than for wet events, and they respond most quickly to SPI defined events. The results can provide scientific and technological support for water safety and security in the study area.

Assessment of irrigation performance trends using selected comparative indicators over consecutive periods: A case study of the Koga Irrigation Scheme in the Amhara region, Ethiopia

AbstractThe Koga Irrigation Scheme is a modern irrigation scheme in Ethiopia, and furrow irrigation is a common water application method. The main purpose of this study is to show in which year it performed well and what the problems were and to identify the best alternatives that may be essential for managing the scheme effectively and efficiently. The performance trends of the Koga Irrigation Scheme were evaluated using four selected performance indicators, namely, physical, agricultural, water use and economic performance indicators. Primary and secondary data, including geographic locations, water supply, harvested yield, market prices, irrigated crops, actual irrigated area, planned irrigable area, total management and operation cost, water fees and historical climate data, were collected. Land use performance analysis indicates that the scheme operates below its potential. The maximum irrigated area was 6,327.7 ha less than the designed 7,000 ha in 2015 and 3,619.9 ha in 2016. Water consumption peaked in 2015 and decreased in 2016. The dominant crops were wheat (55%), potato (19%), barley (7.6%), onion (7.4%), maize (5.6%) and other crops (5.4%). This suggests a preference for low‐value, water‐intensive crops. Water use by crops is lower than reservoir release, indicating significant water loss. Economic water productivity is low, primarily due to the dominance of low‐value, water‐intensive crops. To enhance scheme performance, the study recommends adjusting cropping patterns, estimating actual water demand for the respective crops and irrigation scheduling, adopting efficient water application methods and strengthening the market chain.

Temporal Variation and Risk Assessment of Typical Fluoroquinolone Antibiotic Residues in a River-Reservoir System During the Reservoir Discharge and Filling Periods

Temporal Variation and Risk Assessment of Typical Fluoroquinolone Antibiotic Residues in a River-Reservoir System During the Reservoir Discharge and Filling Periods

Environmental consequences of the creation and discharge of reservoirs in the steppe zone

The article examines the environmental consequences of the construction and operation of reservoirs – the responses of aquatic and terrestrial ecosystems and their components to changes in the water regime. Such reactions depend on the surrounding landscape and are zonally specific. They are especially pronounced in the steppe zone. The authors systematized the consequences, natural and anthropogenic factors determining their evolution, measures to minimize negative environmental consequences that can be traced from the upper reaches of the reservoir to the reservoir – receiver of the river flow, where it is built. In particular, the decrease in bioproductivity and biodiversity caused the problem of reservoir drainage. As a result of lengthy discussions, an opinion has been formed that the descent of small reservoirs, which have lost their importance, as well as orphan ones, is currently environmentally justified. At the same time, it is necessary to carry out special hydro-reclamation and phytomeliorative measures aimed at stabilizing the hydrological and ecological situation.

Evaluation of Seasonal Reservoir Water Treatment Processes in Southwest Florida: Protection of the Caloosahatchee River Estuary

In southwest Florida, the Caloosahatchee River flows from Lake Okeechobee into a biologically productive tidal estuarine system. A combination of excess water during the wet season, insufficient fresh water in the dry season, and poor quality of the river water are damaging the estuarine ecosystem. To better control the quality and quantity of the water entering the estuary, reservoirs are being constructed to store excess, poor quality water during the wet season and return it to the river for discharge into the estuary at an appropriate time. This stored water is enriched in nutrients and organic carbon. Because of the subtropical nature of the climate in southwest Florida and potential increases in temperature in the future, the return flow of water from the reservoirs must be treated before it can be returned to the river. Hence, an experimental water treatment system was developed and operated to compare biological treatment processes consisting of solely wetland plants versus adding some engineered processes, including slow sand filtration and a combination of slow sand filtration and ultraviolet (UV) treatment. These three treatment trains were operated and monitored through a seasonal cycle in 2021–2022. All three treatment methods significantly reduced the concentrations of nutrients and total organic carbon. While the enhanced engineered wetlands’ treatment trains did slightly outperform the wetland train, a comparison of the three process trains showed no statistically significant difference. It was concluded that upscaling of the slow sand filtration and UV process could improve the treatment efficiency, but this change would have to be evaluated within a framework of long-term economic benefits. It was also concluded that the Caloosahatchee River water quality is quite enriched in nutrients so that reservoir storage would increase the organic carbon concentrations, making it imperative that it be treated before being returned to the river. It was also discovered that the green alga Cladophora sp. grew rapidly in the biological treatment tubs and will present a significant challenge for the treatment of the reservoir discharge water using the currently proposed alum treatment.

Optimization of reservoir release operation using genetic algorithm method

Genetic Algorithms (GA) are highly effective in optimizing hydropower release parameters, identifying global optima in complex systems, and adapting to varying circumstances, making them ideal for managing hydropower conditions. This study employs GA to optimize hydropower release at Wonorejo Reservoir, Indonesia, aiming to balance power generation with flood control, irrigation, and environmental sustainability. The application of GA improved operational efficiency, resulting in a 10.24 % increase in electricity production. Notably, during the rainy season, the improvement was 27.3 % higher than in the dry season, highlighting GA responsiveness to seasonal variations. These findings demonstrate GA potential as a valuable tool for reservoir management.

Fuzzy rule–based control of multireservoir operation system for flood and drought mitigation in the Upper Mun River Basin

Strategic reservoir operation, a primary water management measures, plays a significant role in mitigating floods and droughts. Since the reservoir operation involves making complicated decisions on uncertain hydrological variables driven by climate variability, therefore, constructive tool for decision making like fuzzy logic is essential to optimize reservoir management and ensure water security. This study demonstrated fuzzy logic application to multiple reservoir operation in tropical region like Thailand. A Fuzzy Rule–Based Model (FRBM) exploiting FL approach was developed to control the upstream reservoir operation in the Upper Mun River Basin (UMRB) using the data from 2008 to 2021. Implementing FRBM for UMRB was conducted by identifying two key variables; available water storage and 7–day ahead predicted inflow, as fuzzy inputs. The fuzzy output of the system is the release fraction determined by three operational condition modules; flood, neutral, and drought. For flood module, fuzzy release is primarily determined by the predicted inflow. However, the determination of reservoir release for drought and neutral modules is influenced by the targeted water demand. The results of base case illustrate the capability of FRBM in increasing reservoir storages at the start of dry season by 123.56 MCM/yr in UMRB due to the new daily release schemes generated. This allows supplying water closer to the theoretical agricultural needs and gross irrigation water requirement potentially reducing the risk of water shortfall during consecutive dry years. Whereas, the maximum fuzzy release is constrained corresponding to safe channel capacity of tributaries and Upper Mun river, therefore, downstream flooding is accordingly prevented.

Timing of Volatile Degassing From Hydrous Upper‐Crustal Magma Reservoirs With Implications for Porphyry Copper Deposits

AbstractThe timing and duration of volatile generation from crystallizing magma reservoirs and fluid release across the magmatic‐hydrothermal interface depend on complex coupled interactions controlled by non‐linear, dynamic properties of magmas, rocks and fluids. Understanding these mechanisms is essential to explain the rare formation of economic porphyry copper deposits. For this study, we further developed a coupled numerical model that can simultaneously resolve magma and hydrothermal flow by introducing a description of fluid transport within the magma reservoir and volatile release to the host rock. Our simulations use realistic magma properties derived from published experimental and modeling studies and cover different magma compositions and water contents. We show that magma convection at melt‐dominated states leads to homogenization, which delays fluid release and promotes a rapid evolution toward a mush state. The onset of magmatic volatile release can be near‐explosive with a tube‐flow outburst event lasting <100 years for high initial water contents of >3.5 wt% H2O that could result in the formation of hydrothermal breccias and vein stockworks or trigger eruptions. This event can be followed by sustained fluid release at moderate rates by volatile flushing caused by magma convection. Subsequent fluid release from concentric tube rings by radial cooling of non‐convecting magma mush with a volume of ∼100 km3 at ∼5 km depth is limited to remaining water contents of ∼3.1 wt% H2O and lasts 50–100 kyr. Ore formation from hydrous magmas may thus involve distinct phases of volatile release.

Regulating flow to maximize riverine nitrogen removal by riparian zones downstream of reservoirs

Flood waves generated by reservoir discharge play a crucial role in the potential for riparian denitrification downstream. The efficiency of nitrogen (N) removal from river water by riparian zones is typically enhanced with greater wave amplitude (A) and duration (T). However, a knowledge gap exists with respect to the impact of flood waves on N removal when considering a fixed-value integral over time of flood waves (IOFW). This study explored N transport and transformation in the riparian zone downstream of the dammed Inbuk River, Korea under various wave conditions with a fixed IOFW. The results revealed a logarithmic enhancement in solute infiltration into the riverbank with an increasing wave amplitude/duration ratio (A/T ratio). The solute residence time within the riparian zone displays an initial increase followed by a decrease with the increment in the A/T ratio. We identified a threshold for the wave A/T ratio that maximizes riverine N removal by the riparian zone, a phenomenon observed across various dammed rivers. Our findings indicated that riparian denitrification is reaction time-limited when the wave A/T ratio exceeds the threshold; contrarily, it becomes transport capacity-limited. Additionally, we observed a significant relationship between the net nitrate removal and the return/infiltration time ratio of water (r2 = 0.93, p < 0.05). The research results contribute to a better understanding of the impact of water level fluctuations on the N cycle in riparian zones, and provide valuable insights for developing sustainable reservoir operation strategies.

Climate Change Scenarios for Impact Assessment: Lower Zab River Basin (Iraq and Iran)

Selecting appropriate climate change scenarios is crucial, as it influences the outcomes of climate change impact studies. Several storylines could be used to investigate the sensitivity of water resource schemes to weather variability and improve policymakers’ adaptation strategies. This study proposes a comprehensive and generic methodology for assessing the future climate change impact on semi-arid and arid zones at the basin scale by comparing delta perturbation scenarios to the outcomes of seven collections of GCMs (general circulation models). The findings indicate that the two scenarios predicted nearly identical declines in average reservoir discharges over a monthly timescale. Consequently, their maximum values are almost similar. The projected decrease in the streamflow for the period 2080–2099 is approximately 48%—the same as the ratio from the delta perturbation scenario of Future16 (a 30% precipitation decrease and a 30% potential evapotranspiration increase). Furthermore, delta perturbation scenarios allow the impacts of model sensitivity to climate change to be clearly identified in relation to GCM scenarios. Delta perturbation scenarios allow for an extensive collection of possible climate changes at the regional scale. In addition, delta perturbation scenarios are simpler to create and use; therefore, they might complement GCM scenarios.

Constraint programming for reservoir operation optimization of Bhumibol dam

The modern constraint programming (CP) was adopted to minimize water scarcity and excessive water which are the critical issues in reservoir operation of Bhumibol Dam (BB) to solve consecutive droughts in the Chao Phraya River Basin (CPYRB), Thailand. The situations of the severe droughts have been frequently occurred in a broad area of CPYRB due to an extremely low rainfall leading to unusually low water levels and inflow in major reservoirs. Therefore, multi-objective optimization was conducted to characterize the actual operation and physical reservoir system of BB Dam. Two different CP models with seasonal and yearly constraints were manipulated using MiniZinc programming language and the constraint solver IPOPT to find the optimal daily release scheme from 2000 to 2018 of BB Dam. The potential of downstream flow conditions was also considered to partially supply downstream water demand and store savable water in a reservoir for subsequent use during possible future droughts. The results reveal that CP models can diminish some extent of yearly reservoir release, while daily long-term release scheme conforms well with the actual operation particularly during dry and wet seasons in specific drought years. These mean that amount of reservoir water of approximately 47.12–103.83 MCM/year can be saved and stored in reservoir for subsequent use in CPYRB when CP models are deployed. This results in a reservoir storage increase of 7.10–7.94% at the end of the wet season for seasonal and yearly CP models, respectively. When potential side flow is considered, the increase climbs up to 10.49%. This envisages the higher possibility of supplying reservoir water for agricultural water needs over the dry season in the Greater Chao Phraya Irrigation Scheme. As the potential hydraulic head is subject to increased reservoir water storage, therefore, the increase in hydropower production is definitely found of ranging from + 6.10% to + 13.79% by these two sorts of CP models. In addition, handling with huge volumes of flood and drought by CP models is well managed as the specific release constraints during refilled and drawdown periods are used to define the constraint satisfaction problem.

Characterizing the impact of reservoir storage and discharge on nitrogen dynamics in an upstream wetland using a δ15N and δ18O dual-isotope approach

The identification of nitrate sources in reservoir water is important for watershed-scale surface pollution management. Significant fluctuations in river water levels arising from reservoir storage and discharge influence nitrate sources and transport processes. The Sanmenxia Reservoir, in the middle reaches of the Yellow River in China, undergoes significant water level changes (290–316 m), altering the composition of the nitrogen sources. This study employed a δ15N and δ18O dual-isotope method and MixSIAR modeling to quantify the contributions of nitrate sources. This reveals the impact of reservoir water impoundment and discharge on nitrogen dynamics in the upstream region of the wetland and the model sensitivity for each nitrate source. The results showed that the average concentrations of nitrate‑nitrogen (NO- 3-N) were elevated during the impoundment period compared to the discharge period. Nitrogen sources exhibited varying proportions in surface water, groundwater, and soil water during both the impoundment and discharge periods. The predominant sources include manure and sewage (MS), with a maximum proportion of 57.4 % in surface water. Soil nitrogen (SN) accounted for 25.8 % of groundwater nitrogen and 32.1 % of soil water nitrogen during the impoundment period, whereas, during the discharge period, soil nitrogen made up 41.4 % of surface water nitrogen, manure and sewage contributed 44.8 % of groundwater nitrogen, and manure and sewage dominated with 56.7 % of soil water nitrogen. Sensitivity analysis of the MixSIAR model revealed that the isotopic composition of the manure and sewage primary source most significantly influenced the apportionment results of the riverine nitrate source. Reservoir discharge facilitates the dissimilatory nitrate reduction to ammonium (DNRA). The migration of NO- 3 from surface water to soil water and groundwater occurred from the impoundment period to the discharge period.

A robust decision-making framework to improve reservoir water quality using optimized selective withdrawal strategies

In arid locations such as Oman, improving the quality of water released from a reservoir can be daunting. This is particularly true for Wadi Dayqah Dam, which struggles to provide sufficient, high-quality water for downstream needs. The study's objective was to establish more reliable operational guidelines, as well as identify vulnerable outlets and release ratios. To do so, a multi-objective optimization algorithm (NSGA-II) was combined with a selective withdrawal approach, using CE-QUAL-W2 and an Artificial Neural Network to simulate water quality in a reservoir. Then, a Robust Decision Making (RDM) approach was utilized to diagnose solutions that maintain acceptable water quality according to users’ requirements. Finally, scenario discovery models (Patient Rule Induction Method (PRIM) and Classification and Regression Tree (CART)) were used to calculate vulnerable outlets and release ratios based on the established threshold. The study adopted an XLRM framework to manage a proposed model under climate change scenarios and specify the effects of involved factors. XLRM stands for eXogenous factors, Levers used by the policy-makers, Relationships between L and X, and Measurement matrix to evaluate scenarios. This study found that a higher-quality reservoir release correlated with more activated outlets and decreased pollutant concentration. Evaluating robust solutions showed that vulnerability increased under scenarios with lower annual rainfall, and water released from low and mid-level outlets was more vulnerable than that from the surface. To enhance operational stability and reduce vulnerability, the suggestions advocate for a strategic approach in releasing varying water ratios from outlets situated at different levels. Specifically, the proposal emphasizes the controlled release of both smaller and larger volumes of water from outlets positioned at lower and higher levels within the dam structure, respectively. This deliberate variation in water release aims to bolster the overall resilience of the operational framework, contributing to a more robust and reliable system.

Applicability of seasonal forecasts from dynamical models for reservoir management practices

Despite the availability of reliable seasonal forecasts of Indian Summer Monsoon Rainfall (ISMR), the use of dynamical models driven by these forecasts for reservoir level management is limited. Reservoir water management can specially be useful if it can be done several months in advance, in view of an impending drought/flood scenario. The applicability of seasonal forecasts from the Monsoon Mission (MM) seasonal forecast model for seasonal and monthly inflow forecasts for tropical Indian reservoirs (Mula and Kangsabati) is studied using the Soil and Water Assessment Tool (SWAT) hydrological model, at a lead time of 3 months. Long-term observed inflow datasets are used for calibration and validation of SWAT-Calibration and Uncertainty Procedure (CUP) with Sequential Uncertainty Fitting (SUFI)-2 algorithm using insitumeteorological data. Observed inflows and inflow simulations are compared with simulated inflow using SWAT with same calibrated parameters, but with forcing derived from reforecasts from the MM model. The SWAT-CUP calibrated well with reasonable Nash Sutcliffe Efficiency (NSE) (Mula = 0.75, Kangsabati = 0.79) and Percentage Bias (PBIAS) (Mula = -28%, Kangsabati = 17%) for both reservoirs. The skill scores for streamflow predictions vary from 0.6-0.70 during the monsoon season, indicating reasonable accuracy for these predictions. The SWAT-MM model has a reasonable skill with 0.52-0.53 NSE and 26%-40% PBIAS. Therefore, SWAT-MM-based model has a good potential to forecast monthly and seasonal reservoir inflow for various agro-climatic zones of India. These forecasts when used in real-time, can serve as a guideline for managing the reservoir storage and release, and hence proving to be of great socio-economic importance.

Downstream Nutrient Concentrations Depend on Watershed Inputs More Than Reservoir Releases in a Highly Engineered Watershed

AbstractIn this study, we characterized the impact of regulatory water releases relative to watershed inputs on the quality of receiving waters to identify if and how managed releases could be scheduled to mitigate nutrient export and downstream water quality impairment. We specifically investigated freshwater flow partitioning to the Caloosahatchee River and Estuary (CRE) from a large managed lake, Lake Okeechobee, and the CRE's upstream watershed, the C‐43 basin, in southwest Florida (USA). A water balance was developed to identify dominant freshwater inflow sources (i.e., Lake Okeechobee vs. watershed inputs) over time. From the water balance, analyses of historical trends were performed to detect changes in freshwater inflow contributions to the CRE. Further, seasonal and annual concentration variations and long‐term concentration‐discharge (C‐Q) relationships were analyzed to better understand biogeochemical and hydrological processes in the system in relation to freshwater source. Since 1966, we found the duration and magnitude of flows from the C‐43 basin were higher than those from Lake Okeechobee releases. However, recent increases in the annual water volume and proportion of inflow coming from Lake Okeechobee to the CRE were observed. The C‐Q analysis revealed that nitrate and ammonium concentrations in the CRE were responsive to changes in discharge, while total phosphorus and orthophosphate concentrations were chemostatic. While modifications to the Lake Okeechobee operation schedule could potentially mitigate downstream inorganic nitrogen loading, this potential is limited by complex, seasonal C‐Q relationships and confounding effects from surrounding watersheds.

Refined machine learning modeling of reservoir discharge water temperature

Water temperature is a controlling factor for physical, biological, and chemical processes in rivers, and is closely related to hydrological factors. The construction of reservoirs interferes with natural water temperature fluctuations. Hence constructing a model to accurately and efficiently predict the reservoir discharge water temperature (DWT) is helpful for the protection of river water ecology. Although there have been studies on constructing efficient and accurate machine learning prediction models for DWT, to our knowledge, there is currently no research focused on hourly scales. The study proposed in this paper is based on high-frequency monitoring data of vertical water temperature in front of a dam, water level, discharge flow, and DWT. In this study, six types of machine learning algorithms, namely, support vector regression, linear regression, k-nearest neighbor, random forest regressor, gradient boosting regression tree, and multilayer perceptron neural network, were used to construct a refined prediction model for DWT. The results indicated that the SVR model using the radial basis function as the kernel function had the best modeling performance. Based on the SVR model, we constructed a 1–24 h early warning model and optimized the scheduling of DWT based on changing discharge flow. In summary, a machine learning model for DWT that can provide short-term forecasting and decision support for reservoir managers was refined in this study.

An integrated reservoir operation framework for enhanced water resources planning

Climate change induced spatiotemporal variation in global water availability modifies the proposed design criteria of water infrastructure structures like dams and reservoirs. Although reservoir operation is treated as a potential adaptation option, obsolescence of existing operation rules in the climate change scenarios could cause devastating situation through faulty water management practices. Presently onboard simulation–optimization based reservoir operation schemes fail to capture the uncertainty involved in the climate change scenario. Hence, there is a need to identify the limiting application scenario of the existing reservoir operation rule, and subsequently, revise the operation framework to address the future supply–demand uncertainty adequately. This research develops an integrated Soil and Water Assessment Tool (SWAT) (hydrologic), HEC-ResSim (hydraulic), and genetic algorithm (GA) (optimization) based adaptive reservoir operation framework, which is competent enough in accounting the future supply–demand uncertainty. Incorporation of the newly proposed environmental flow assessment approach in the reservoir operation would assist the decision makers in guiding the reservoir release for maintaining the water quality and sustenance of the downstream aquatic species. Certainly, corresponding to the existing operation rules under both the baseline and future climate change scenarios of RCP 4.5 and 8.5, the developed SWAT-HEC-ResSim-GA based reservoir operation scheme could improve the performance of the Kangsabati reservoir with the time and volume reliability estimates of 0.631 and 0.736, respectively. Conclusively, the developed approach in this study could be the best feasible alternative for hydrologic characterization in complex reservoir catchment-command regions with the option for enhanced reservoir planning in global catchment-command regions.

Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin

Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy.Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes.Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model.The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data).Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture.The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate.

Multi-Dimensional Collaborative Operation Model and Evaluation of Cascade Reservoirs in the Middle Reaches of the Yellow River

Reservoir operation optimization is a technical measure for flood control and is beneficial owing to its reasonable and reliable control and application of existing water conservancy and hydropower hubs, while ensuring dam safety and flood control, as well as the normal operation of power supply and water supply. Considering the beneficial functions of reservoirs, namely flood control and ecological protection, this paper firstly established a two-objective optimal operation model for the reservoir group in the middle reaches of the Yellow River. We aim to maximize the average output of the cascade reservoir group and minimize the average change in ecological flow during the operation period under efficient sediment transport conditions, with the coordination degree of water and sediment as the constraints of reservoir discharge flows. The paper aims to construct an evaluation index system for reservoir operation schemes, apply a combined approach of objective and subjective evaluations, and introduce the gray target and cumulative prospect theories. By uniformly quantifying the established scheme evaluation index system, screening the reservoir operation schemes with the fuzzy evaluation method, and selecting the recommended scheme for each typical year, this paper provides a new scientific formulation of the operation schemes of reservoirs in the middle reaches of the Yellow River. The selected schemes are compared with actual data, demonstrating the effectiveness of joint reservoir operation and for multidimensional benefits in terms of power generation, ecology, and flood control.