Search Results (60)

This study aimed to quantify and assess the spatial distribution of ²³⁸U, ²³²Th, and ⁴⁰K in the soils of the Espora Hydroelectric Power Plant (Espora HPP) and Queixada Small Hydroelectric Power Plant (Queixada SHPP) watershed (model hydraulic development areas) and their relationship with the geological, chemical, physical, and biological aspects of the soil. The study areas are located in the Corrente River drainage basin, in the southwestern portion of the state of Goiás, Brazil. Radionuclides were quantified using a PGIS-2 portable gamma spectrometer, with measurements taken at 21 sampling points. Soil samples were collected from the surface layer (0–20 cm) for particle-size and chemical analyses. The results indicated that the average radionuclide contents in the soils were 64.49 Bq/kg for ⁴⁰K, 45.44 Bq/kg for ²³⁸U, and 4.53 Bq/kg for ²³²Th. When comparing these values with the global average established by UNSCEAR, it was observed that ²³²Th and ⁴⁰K concentrations were below the global reference, whereas ²³⁸U concentration exceeded the world average of 33 Bq/kg. Particle-size characterization revealed significant variability in soil texture, with sand content ranging from 51.46 to 90.91%, clay content from 7.45 to 30.64%, and silt content from 1.64 to 17.90%. Organic matter content had an average of 10.09 g/kg, while soil pH ranged from 4.67 to 6.54. The results of this study have demonstrated the relevance of integrating radiometric and geochemical data for assessing environmental safety in hydroelectric development areas. The approach adopted can support monitoring programs and decision-making processes related to soil management and land-use planning in regions influenced by hydraulic infrastructures. Full article

Meteorological droughts have been occurring with greater frequency and intensity, impacting water security in various regions. Between 2013 and 2015, the Paraopeba River Basin in southeast Brazil experienced its most severe drought in the last 70 years, resulting in low levels in the Paraopeba system reservoirs, which supplies 53% of the Metropolitan Region of Belo Horizonte, the third largest metropolitan area in Brazil. This study evaluated the climate models’ performance from the NEX-GDDP-CMIP6 through drought indices projections, specifically the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The results showed that seven climate models can represent the current climate in the basin. For the drought’s projection, the indices were used in two time scales (six and twelve months) for both the current climate and two future scenarios (SSP245 and SSP585). Our results highlight the intensification of droughts throughout the twenty-first century, with greater intensification in the SSP585 scenario. The SPEI indicated trends towards drier conditions, particularly under the SSP585 scenario and on the twelve-month timescale. These findings demonstrate the relevance of climate change and drought indices on the projections, supporting public policies for mitigation and adaptation, especially in strategic regions for water supply and hydro-electric generation. Full article

The present study examines the possibilities for developing the use of small hydropower plants (SHP) in Slovakia, focusing on the principles of sustainability and compliance with European and national legislation. At present, there is a tendency for the construction of hydroelectric power plants to intervene in the river environment, with the potential to exert a substantial impact on the flow of the river and disrupt the surrounding ecosystem. A potential strategy for minimizing environmental impact would be the construction of SHPs, which require less construction work. The Hornád river sub-basin, located in eastern Slovakia, was selected as the study area. The spatial and hydrological data were processed using Geographic Information System (GIS) tools. The hydrological characteristics of the area were determined through the utilization of a digital terrain model (DMR 5.0). The results of the hydrological analyses were then combined with environmental constraints to identify suitable locations for small hydropower plants. The theoretical and technical potential and gradient were calculated for individual sections of watercourses. It is estimated that approximately 61% of watercourse sections have a gradient greater than or equal to 10 m, which represents suitable conditions for the development of small hydropower plants. The presence of a stable flow regime engenders optimal conditions for the utilization of hydropower in the designated location. The study emphasizes the importance of environmental protection of the area, the resolution of property rights issues, and the streamlining of permitting processes. The results of the study contribute to energy planning at the regional level and confirm the effectiveness of using GIS in determining locations for small hydropower plants. Concurrently, emphasis is placed on the necessity to incorporate environmental and legislative imperatives within the overarching strategy for water energy development. Full article

Hydroelectricity, one of the oldest and most potent forms of renewable energy, not only provides low-cost electricity for the grid but also preserves nature through flood control and irrigation support. Forecasting hydroelectricity generation is vital for utilizing alleviating resources effectively, optimizing energy production, and ensuring sustainability. This paper provides an innovative approach to hydroelectricity generation forecasting (HGF) of a 138 MW hydroelectric power plant (HPP) in the Eastern Mediterranean by taking electricity productions from the remaining upstream HPPs on the Ceyhan River within the same basin into account, unlike prior research focusing on individual HPPs. In light of tuning hyperparameters such as number of trees and learning rates, this paper presents a thorough benchmark of the state-of-the-art tree-based machine learning models, namely categorical boosting (CatBoost), extreme gradient boosting (XGBoost), and light gradient boosting machines (LightGBM). The comprehensive data set includes historical hydroelectricity generation, meteorological conditions, market pricing, and calendar variables acquired from the transparency platform of the Energy Exchange Istanbul (EXIST) and MERRA-2 reanalysis of the NASA with hourly resolution. Although all three models demonstrated successful performances, LightGBM emerged as the most accurate and efficient model by outperforming the others with the highest coefficient of determination (R²) (97.07%), the lowest root mean squared scaled error (RMSSE) (0.1217), and the shortest computational time (1.24 s). Consequently, it is considered that the proposed methodology demonstrates significant potential for advancing the HGF and will contribute to the operation of existing HPPs and the improvement of power dispatch planning. Full article

There is a consensus that climate change has affected society. The increase in temperature and reduction in precipitation for some regions of the world have had implications for the intensity and frequency of extreme events. This scenario is worrying for various sectors of water use, such as hydroelectric power generation and agriculture. Reduced flows in river basins, coupled with increased water consumption, can significantly affect energy generation and food production. Within this context, this paper presents an analysis of climate change impacts in a large basin of Brazil between the Amazon and Cerrado biomes, considering the effects of water demands. Inflow projections were generated for seven power plant reservoirs in the Tocantins–Araguaia river basin, using projections from five climate models. The results indicate significant reductions in flows, with decreases of more than 50% in the average flow. For minimum flows, there are indications of reductions of close to 85%. The demand for water, although growing, represents a smaller part of the effects, but should not be disregarded, since it impacts the dry periods of the rivers and can generate conflicts with energy production. Full article

The São Francisco River provides water for agriculture, urban areas, and hydroelectric power generation, benefiting millions of people in Brazil. Its Basin supports various species, some of which are endemic and rely on its unique habitats for survival. Currently, monitoring maximum air temperature in the São Francisco River Basin is limited due to sparse weather stations. This study proposes three linear regression models to estimate maximum air temperature using satellite-derived land surface temperature from the Aqua’s moderate resolution imaging spectroradiometer across the Basin’s three main biomes: Caatinga, Cerrado, and Mata Atlântica. With over 94,000 paired observations of ground and satellite data, the models showed good performance, accounting for 46% to 54% of temperature variation. Cross-validation confirmed reliable estimates with errors below 2.7 °C. The findings demonstrate that satellite data can improve air temperature monitoring in areas with limited ground observations and suggest that the proposed biome-specific models could assist in environmental management and water resource planning in the São Francisco River Basin. This includes providing more informed policies for climate adaptation and sustainable development or analyzing variations in maximum air temperature in arid and semi-arid regions to contribute to desertification mitigation strategies in the São Francisco River Basin. Full article

The equitable use and distribution of shared water resources is a topic of renewed regional debate in Latin America, especially given the recent review of the Binational ITAIPU Treaty between Brazil and Paraguay. Building more equitable and transparent agreements in this context requires an understanding of the historical trends of negotiations. This study analyzes five decades of negotiations on the shared use of water resources in the Paraná River Basin, drawing on interviews with former negotiators and officials from Argentina, Brazil, and Paraguay. The complex interaction between internal dynamics and geopolitical factors in establishing state-owned transboundary hydroelectric plants is highlighted. Based on these findings, we propose a conceptual framework that identifies the key elements to consider when negotiating strategic resources at national and regional levels. This study, extending beyond the Paraná basin, offers an applicable model for managing other shared natural resources, providing useful insights into negotiation strategies for transboundary resource and infrastructure management. Full article

In this review paper, we perform a comprehensive review of the current state of the art, worldwide applications, and modifications of the Snowmelt Runoff Model (SRM). Snow is a significant element of the hydrologic cycle and is sometimes regarded as the primary source of streamflow in watersheds at high latitudes and altitudes. Quantitative assessment of snowmelt runoff is crucial for real-world applications, including runoff projections, reservoir management, hydro-electricity production, irrigation techniques, and flood control, among others. Numerous hydrological modeling software have been developed to simulate snowmelt-derived streamflow. The SRM is one of the well-known modeling software developed to simulate snowmelt-derived streamflow. The SRM simulates snowmelt runoff with fewer data requirements and uses remotely sensed snow cover extent. This makes the SRM appropriate for use in data-scarce locations, particularly in remote and inaccessible mountain watersheds at higher elevations. It is a conceptual, deterministic, semi-distributed, and degree-day hydrological model that can be applied in mountainous basins of nearly any size. Recent advancements in remote sensing integration and climate model coupling have significantly enhanced the model’s ability to estimate snowmelt runoff. Additionally, numerous studies have recently improved the traditional SRM, further enhancing its capabilities. This paper highlights some of the global SRM research, focusing on the working of the model, input parameters, remote sensing data availability, and modifications to the original model. Full article

Southwestern China has abundant hydropower networks, wherein neighboring cascade hydropower stations within the same river basin are typically connected to the power system in a chain-structured configuration. However, when such chain-structured cascade hydroelectric power plants (CC-HPPs) participate in automatic voltage control (AVC), problems such as reactive power interactions among stations and unreasonable voltage gradients frequently arise. To address these issues, this study proposes an optimized multi-station coordinated response control strategy based on sensitivity analysis and hierarchical AVC. Firstly, based on the topology of the chain-structured hydropower sending-end network, a reactive power–voltage sensitivity matrix is constructed. Subsequently, a regional-voltage-coordinated regulation model is developed using sensitivity analysis, followed by the establishment of a mathematical model, solution algorithm, and operational procedure for multi-station AVC-coordinated response optimization. Finally, case studies based on the actual operational data of a CC-HPP network validate the effectiveness of the proposed strategy, and simulation results demonstrate that the approach reduces the interstation reactive power pulling up to 97.76% and improves the voltage gradient rationality by 16.67%. These results substantially improve grid stability and operational efficiency while establishing a more adaptable voltage control framework for large-scale hydropower integration. Furthermore, they provide a practical foundation for future advancements in multi-scenario hydropower regulation, enhanced coordination strategies, and predictive control capabilities within clean energy systems. Full article

Sustainable water management requires integrated approaches balancing competing demands and environmental sustainability. This study introduces the Sustainable Water Optimization Tool (SUWO), an open-source, Python-based simulation-optimization framework for basin-scale surface-water-resources management. SUWO employs the water–energy–food–ecosystem (WEF-E) nexus approach, utilizing a multi-objective genetic algorithm (MOGA) to generate Pareto-optimal solutions and facilitate a trade-off analysis among water uses through simulations of reservoir operations, hydro-energy production, irrigation, and flow regulation. SUWO integrates scenario analysis with multi-criteria decision making (MCDM), enabling the evaluation of various management, climate, and environmental scenarios. The framework was applied to the Sakarya River Basin (SRB) in Türkiye, a rapidly developing region pressured by water infrastructure development, hydroelectric power plants (HEPPs), and irrigation expansion. The SUWO-SRB model showed that while Non-dominated Sorting Genetic Algorithm II (NSGA-II) generally exhibited superior performance, NSGA-III presented a competitive alternative. The optimization results were analyzed across four management scenarios under varying hydrological conditions and environmental management classes (EMCs) for the near future. The model results highlight WEF-E nexus trade-offs. Maximizing energy production often impacts irrigation and the ecosystem, while prioritizing sustainable irrigation can reduce energy output. Dry conditions reduce hydropower and irrigation capacity, emphasizing water scarcity vulnerabilities. Ecological deviation negatively correlates with anthropogenic factors. Full article

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin. Full article

Integrated water resource management (IWRM) has been globally recognized as a key strategy for advancing toward water security; however, Chile has not yet implemented it. While water governance in the country has been predominantly analyzed through documents and laws, integrating empirical insights from local actors’ perspectives is essential. This study applied the trialogue model of governance to understand stakeholders’ perspectives and relationships, to identify barriers to achieving water security, and to explore the roles of different actors in enhancing governance systems. The research design focused on the Biobío River Basin (BRB) as a case study, employing a qualitative strategy for data collection through semi-structured interviews. Qualitative data analysis consisted on a thematic analysis, where interview transcripts were coded to identify relevant topics. The results reveal that Chile’s highly centralized governance structure, along with inadequate information management and socioeconomic conflicts related to the construction of hydroelectric power plants, pose significant barriers to achieving water security in the BRB. Key opportunities to address barriers include legal reforms, improved information management, and strengthened enforcement and supervision. Achieving effective governance relies heavily on legal reforms, mobilizing resources and creating spaces for raising social awareness. Transparent and accessible data-sharing mechanisms are also crucial for better information management. Although the trialogue model is a valuable framework for analyzing river basin governance, it is necessary to emphasize the need to account for the intricate nature of the society cluster in future studies. Full article

The Congo River Basin is the second-largest watershed globally, flowing through nine countries before reaching the Atlantic Ocean. The Kasai River Basin (KARB), containing about one-fourth of Congo’s freshwater resources, plays a strategic role in sustaining navigation, food production, and hydroelectricity generation in Central Africa. This study applies a multi-model framework suited for data-scarce regions to assess climate change impacts on water availability in the KARB. Using two conceptual hydrological models calibrated with four reanalysis datasets and fed with bias-corrected outputs from 19 climate models under two representative climate pathways (RCPs), we project changes in the mean annual discharge ranging from −18% to +3%, highlighting the sensitivity of impact assessments to model and input data choices. Additionally, streamflow signatures (Q10, Q50, Q90) are projected to decline by approximately 9%, 18%, and 13%, respectively, under RCP 8.5. Annual hydropower potential is estimated to decrease by 14% and 5% under RCPs 4.5 and 8.5, respectively. These findings provide actionable insights for water management practices in the KARB, including guiding the development of adaptive strategies to optimize water allocation, mitigate risks of scarcity, and support sustainable agricultural and industrial activities in the region. Full article

To enhance the efficiency of solving the optimal operation model for cascade reservoirs, this paper first constructed an optimal operation model of cascade reservoirs. The model comprehensively considered the ecological flow, the guaranteed output of hydroelectric power plants, and the relaxation constraints of the water level at the end of water supply and storage period. The relaxation constraints refer to two relaxation variable constraints, which are used to ensure that the water levels decline in the water supply period and rise in the water storage periods. At the same time, to avoid the challenges of “dimension disaster” and susceptibility to local optima commonly encountered in existing optimization algorithms when resolving the above model, a novel optimization algorithm, M-IWO-ODDDP, derived from the optimization principles of the Invasive Weed Optimization (IWO) and Discrete Differential Dynamic Programming (DDDP) algorithms, was proposed in this paper. The 11 cascade hydropower stations in the Wujiang River basin were used as a case study, and the results showed that the water-level dispatching process exhibited a high degree of conformity with the actual dispatching process during both the water supply and storage periods. Furthermore, the output calculation results based on the M-IWO-ODDDP algorithm were 3.94% and 0.30% higher than the actual output and ODDDP calculation results, respectively, while reducing water abandonment by 21.58% and 4.07%. Full article

Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area. Full article