Brazilian hydropower plants have been facing increasing challenges during periods of hydrological instability associated with climate change, making adaptation imperative. One promising approach to achieving adaptation is to learn from nature, given its inherent capacity for adaptability and resilience to environmental change. Accordingly, this study proposes an integrated framework that combines Animal Decision-Making (ADM) theory with a successful example of natural adaptation, namely Amazon rainforest ant colonies. The proposed methodology comprises the following stages: (1) identification; (2) definition; (3) alternative generation; (4) solution selection; and (5) implementation and testing. A simulated case study and a real-world case study involving the Ponte de Pedra Hydropower Plant, located in the state of Mato Grosso, Brazil, were investigated. Human operators and ant colonies exhibited similar probabilities of shifting towards adaptation (human operators, pR = 0.7088; ant colonies, pR = 0.7091). However, the adaptation strategy adopted by the ant colonies proved to be more focused, concentrating on a smaller number of performance areas. Human operators identified Operation and Maintenance (O&M), Finance, and Plant as the most affected performance areas, with similar levels of importance (12%, 11%, and 11%, respectively). In contrast, the ant colonies prioritised O&M, Plant, and Environmental Impact, with corresponding importance levels of 18%, 13%, and 10%, respectively. Furthermore, the ant colonies demonstrated a dynamic and integrated decision-making strategy that prioritised different activities according to local environmental conditions. Future research should focus on evaluating, calibrating, and validating the proposed framework using historical data.
Climate change has interrupted the global hydrological cycle affecting water resource management and hydropower production. This study conducts a quantitative risk analysis of the Modi Khola Hydroelectric Power Plant. The study analyzes risks on existing hydropower potential for the duration of 2026 to 2100 due to climate change. From eight selected coupled model inter comparison project phase 6 global climate models representing four extreme climate corners, under moderate Shared Socioeconomic Pathways (SSP) 2-4.5 and high emission SSP 5 8.5 scenarios by using advanced envelope method, future streamflow projections at the dam site were adapted. The baseline energy potential of 92.50 GWh and 81 GWh were utilized to identify the energy production risk. Energy generation, compared with 92.50 GWh, shows declines by 0.41% to 16.16%, except NESM3 increases from 5.57% to 7.73% and with 81 GWh baselines, improves from-4.26% to 23.03%. The risk rises with higher energy production, particularly under SSP 5-8.5 however, SSP 2-4.5 consistently reveals lower risk for the tenure. The greatest significant risks are seen during the near future then slightly declines by the mid and far future. The quantitative risk analysis shows that at 81 GWh production target, failure risk ranges from 6% to 38% across scenarios. At 92.50 GWh target, risk increased drastically to 53% to 86%. The 81 GWh level generally represents moderate to significant risk, whereas 92.50 GWh target represents the significant risk category across all future periods and models.
Khuwa Prasad Paudel, Shankar Lamichhane, Rabi Marasini et al.· Everest Advances in Science...· 0 citations
ABSTRACT This study developed an optimized operating rule for the Colônia River reservoir (BA) via a hybrid DP-ANN approach, aiming to mitigate regional water vulnerability. DP was applied to a 20-year historical series (2000-2020) to minimize supply deficits and ecological flow violations, with its operating policy emulated by an MLP neural network to ensure practical applicability. The model's effectiveness was validated in AcquaNet software, using the 2015-2016 extreme drought as a stress scenario across eight distinct water availability and initial storage configurations. Results demonstrate that the ANN Rule outperformed manual management, increasing ecological compliance from 58.3% to 95.8% in the most critical scenario, without compromising urban supply reliability. Achieving regional water resilience requires management that combines operational optimization, ecological integrity, and diversification of water sources.
This study developed and applied an artificial intelligence (AI)-based index, termed the climate resilience index for community aqueducts in tropical Andean watersheds (CRI-CATAW), to assess the climate resilience (CR) of these systems.
Daniel David Montenegro-Murillo, Viviana Vargas-Franco, Diego Fernando Rosero-Portilla· Journal of water resources p...· 0 citations
Efficient reservoir operation is an important aspect of sustainable water resources management in monsoon-driven river basins such as India, where hydrological variability and competing water demands present complex operating challenges. The present study is aimed to evaluate the comparative performance of evolutionary and nature inspired metaheuristic optimization approaches for the optimal operation of Indira Sagar Reservoir. The reservoir operation problem is formulated as a constrained multi-objective optimization problem to minimize water-supply deficits and improve hydropower related performance under the constraints of storage, dead-storage, release, spillway-capacity and storage-change. The selected algorithms represent different population-based evolutionary and nature-inspired search mechanisms and are evaluated under a common modelling framework using representative hydrological years. Performance is assessed based on deficit reduction, convergence behavior, reliability, resilience, vulnerability, and sustainability indicators. The existing operating policy exhibited low time-based reliability (0.4627) and volumetric reliability (0.6854), while resilience reached 1.0000, indicating rapid recovery following detected failures but frequent shortage occurrence. The comparative results showed that CSA achieved the lowest mean deficit of 966.8 MCM among the evaluated approaches, compared with approximately 14,220.14 MCM for the existing policy, followed by IWO and CA. The observed ranking was CSA > IWO > CA > TV-EM-MOPSO > EM-MOPSO > MO-PSO. These findings identify CSA as the most promising candidate among the evaluated approaches for further development and validation of an improved operating policy for Indira Sagar Reservoir.
Recent research has emphasized the need to adapt life cycle inventories and life cycle impact assessments to account for changes in future scenarios. This is mostly achieved by using Integrated Assessment Models (IAMs), which combine environmental and economic data to develop prospective life cycle inventories (LCIs). However, running complex IAMs to simulate scenarios is computationally expensive, and the resulting data is not always aligned with the geographical scope of background inventories. This study explores the potential of machine learning (ML) to create prospective water-scarcity characterisation factors by forecasting the AWARE factor for ~ 9700 watersheds globally. Historical time series of water-scarcity characterisation factors are generated using the global freshwater model WaterGAP v2.2d and the AWARE method. Several ML models are trained on these historical datasets and benchmarked using symmetric mean absolute percentage error (sMAPE). The best-performing model, N-Beats, is then used to forecast AWARE values through 2032. The results demonstrate that ML can produce spatially and temporally explicit forecasts with reasonable accuracy (median sMAPE ~ 28%). However, the models primarily capture seasonal patterns rather than long-term structural trends and the results are sensitive to the quality and representativeness of the training data. This study highlights both the potential and the limitations of time series forecasting for developing prospective characterisation factors in life cycle assessment.
N. Engberg, C. Blanco, V. Barbarossa et al.· Journal of Industrial Ecolog...· 0 citations
Climate change and urbanization are intensifying pressures on the sustainable development of water-scarce regions. Exploring adaptive management strategies and models is therefore critical. While previous studies have applied socio-hydrological balance theory, synergetics, or random forest separately, to our knowledge, this study is the first to integrate all three methods into a unified framework for analyzing stage-based adaptive mechanism in a single basin. Furthermore, the proposed ‘Pressure-State-Response-Regulation-Pressure’ feedback cycle provides a novel conceptual model for understanding self-regulation in socio-hydrological systems. This study focuses on China’s Yiluo River Basin, integrating the socio-hydrological balance framework, synergetics theory, and random forest models to assess adaptive development pathways in the context of evolving socio-hydrological cycles. Key results from the period 2013–2023 indicated: (1) Mann–Kendall trend tests and Sen’s slope estimation analysis indicated that the hydrological variables in the Yiluo River Basin still did not show a significant trend. (2) Trend tests for 19 social hydrological cycle indicators suggested that the water usage for daily life and ecological environment showed an increasing trend, while industrial water usage showed a decreasing trend (P < 0.05), and wastewater discharge showed a decreasing trend (P < 0.05). It can be seen that the social hydrological cycle in the Yiluo River Basin in the past 10 years has presented different characteristics from the changes in water resources. (3) Although the key potential factors for the adaptability of water resource systems have been constantly changing over different periods, there has been an increasingly orderly evolution of the socio-hydrological balance. Synergy‑degree analysis further demonstrated the indicators with higher compatibility with the water resource system have increased from 5 to 11 in the study period. (4) The dominant statistically important predictors evolved dynamically: initially integrated and domestic water conservation, then industrial water saving, followed by combined industrial-agricultural water saving, and ultimately, unconventional water utilization. (4) Adaptation operates through a “Pressure–State–Response–Regulation–Pressure” feedback mechanism.
Yuan Fang, Hongwei Pan, Yang Li et al.· Discover Water· 0 citations
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