Oct 2026· Journal of water resources planning and management· Vol 152· 0 citations· 53 references
TL;DR
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.
Abstract
Community-managed water supply systems, or community aqueducts, are complex socioecological systems that are highly vulnerable to climate variability and change. Assessing climate resilience is therefore essential to inform decision-making processes aimed at strengthening socioenvironmental sustainability. 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. The CRI-CATAW is grounded in three climate resilience capacities (absorption, adaptation, and transformation) and integrates four cross-cutting dimensions: environmental, sociocultural, administrative management, and structural–operational. The methodological framework comprised three stages: (1) indicator selection using the Delphi method, (2) indicator operationalization through fuzzy logic, and (3) application of the index to 13 community aqueducts within the Bolo River watershed, Valle del Cauca, Colombia. The Delphi process resulted in the definition of 28 qualitative and quantitative indicators that were integrated using an AI-based fuzzy logic model. The index incorporated 486 decision rules, modeled with trapezoidal, triangular, and singleton fuzzy membership functions. Climate resilience was estimated by processing inference rules in MATLAB, yielding a classification into three levels: low, medium, and high. Application of the CRI-CATAW to 13 aqueducts in the Bolo River basin indicated that 69.2% exhibited low levels of climate resilience, with significant implications for their socioenvironmental sustainability, and produced results consistent with observed local socioenvironmental conditions. CRI-CATAW constitutes a novel, comprehensive, generic, and replicable AI-based tool for assessing the climate resilience of community-managed water supply systems. Further applications across diverse socioenvironmental and cultural contexts are required to strengthen the validation of indicators, membership functions, and decision rules.
Transportation infrastructure and systems in the United States (US) are increasingly challenged by climate change, aging infrastructure, and rapid urbanization, necessitating planning approaches that integrate both long-term sustainability and short-term resilience. However, most existing frameworks have evaluated these concepts independently, limiting their effectiveness for holistic decision-making. This study proposed a quality prioritization index (QPI) framework that unified sustainability and resilience into a single quantifiable index using the fuzzy analytic hierarchy process (FAHP). Through an extensive literature review, 49 resilience factors were identified and systematically categorized into four dimensions: absorptive capacity, adaptive capacity, restorative capacity, and equitable access. A structured FAHP-based survey was administered to transportation professionals across diverse geographic regions, and their opinions were analyzed using FAHP to obtain factor weights and to prioritize them. These weights were used to develop the resilience prioritization index (RPI). The RPI was subsequently integrated with an existing sustainability prioritization index (SPI) to construct the unified QPI framework. The proposed framework was demonstrated through a case study of five US Gulf of Mexico–adjacent states—Texas, Louisiana, Mississippi, Alabama, and Florida—and its robustness was evaluated using sensitivity analysis. The results revealed that financial preparedness, interagency coordination, strategic resource allocation, and multichannel communication were the most influential factors for transportation system resilience. The case study demonstrated notable regional variations, with Florida exhibiting the highest QPI value, indicating comparatively lower overall transportation system quality, while Alabama and Louisiana showed higher overall quality. Sensitivity analysis further revealed that QPI outcomes were highly responsive to varying sustainability and resilience weights. The QPI framework provides transportation planners and policy makers with a practical decision–support tool for identifying high-impact intervention areas and strategically allocating resources to simultaneously enhance long-term sustainability and short-term resilience.
S. Baral, Behzad Rouhanizadeh, Elnaz Safapour· Journal of Transportation En...· 0 citations
Integrated watershed sustainability assessments are still incipient in Ecuador, where scarce and heterogeneous data are limiting factors. This study introduces the Watershed Sustainability Index (WSI) to the Chibunga Basin in Chimborazo, Ecuador, marking its first application in this region. A key aspect of this study is the development of a clear and reproducible method for applying the index, particularly in inter-Andean basins where data are limited. The four dimensions—Hydrology, Environment, Life, and Policy—were assessed through the Pressure–State–Response (PSR) framework by combining secondary statistics, field sampling, GIS land-use analysis, and a two-round Delphi consultation with ten experts to operationalize the institutional (Policy) component. The basin scored 0.38 (low sustainability), with a critical Hydrology dimension (0.08), poor Policy dimension (0.33), moderate–low Life dimension (0.42), and moderate Environment dimension (0.67). The central finding goes beyond these scores: the operationalized PSR framework pinpointed where the management cycle breaks down in practice. Systematically null response scores reveal that unsustainability stems from the failure to translate an existing regulatory framework into formal institutional action, rather than from regulatory absence or physical water scarcity alone, further constrained by unfavorable socioeconomic conditions. The procedure turns the WSI from a scoring tool into a diagnostic one, providing a replicable reference for data-scarce Andean basins.
Julia Calahorrano-González, Franco Delgado, César Cisneros-Vaca et al.· Water· 0 citations
The savanna ecological zones of Nigeria are vulnerable to eco-climatic anomalies driven by climate change and land degradation. Despite previous studies on individual climate stressors, few have developed sub-national spatial Eco-Climatic Resilience Indices (ECRI) that integrate both biophysical and socio-economic dimensions across the region, thereby limiting targeted climate adaptation planning. This study applied Mann–Kendall rainfall trend analysis and the Sen slope test to rainfall data acquired from the Nigeria Meteorological Agency (NIMET) across 19 stations from 1971 to 2023. A four-stage cluster sampling design was employed to sample 2400 farming households from 48 communities in the study regions. A Principal Component Analysis (PCA) was applied to reduce the dimensionality of the data from 46 variables across climatic anomalies, ecological anomalies, exposure, sensitivity, adaptation capacity, and transformative adaptation capacity. Community PCA scores were interpolated and integrated into the composite ECRI, which was then classified into five resilience zones. The findings revealed a spatial mixed trend in rainfall as stations in the Sudan-Sahelian savanna showed both significant and insignificant upward trends, with the highest rate of change recorded in Kano (+17.89 mm/year), while stations in the Guinea Savanna indicated a significant and insignificant downward trend, with the highest rate of change recorded in Makurdi (−26.43 mm/year). Although the stations in the Sudan-Sahelian showed upward rainfall trends, the ECRI map depicted a north–south resilience gradient, with very low resilience across the Sahelian savanna to the north and very high resilience across the Guinea Savanna to the south. Overall, the climate and ecological stressors were strongly related (r = 0.82). These findings imply that increased rainfall does not necessarily translate into improved climate resilience. The varying resilience levels depicted by ECRI signal the need for state and location-specific transformation/adaptation strategies to build individual, community, and state capacity for more resilient livelihoods across the study region and similar regions with comparable socio-economic and climate characteristics.
Aishetu Abdulkadir, A. Jibrin, I. Ibrahim et al.· Discover Environment· 0 citations
Understanding and managing the impacts of climate change on ecologically and economically important plant species requires integrated modelling approaches. In this study, we developed an environmental modelling and decision-support framework for assessing the current and future habitat suitability of Nepeta persica Boiss. in Fars Province, Iran. The framework combines bivariate models (FR, WofE, IofE) and machine learning algorithms (GLM, GAM, ANN, MaxEnt, XGBoost, ENET) with fuzzy Multi-Criteria Decision Analysis (AHP, TOPSIS, VIKOR), enabling both quantitative habitat forecasting and structured decision support. Results indicated that temperature and elevation are the dominant drivers shaping species distribution. Projections under SSP245 and SSP585 scenarios suggest up to 30% contraction of suitable habitats by 2100, accompanied by an eastward and upslope shift. These outcomes provide critical insights for sustainable management, highlighting climatically buffered highlands as potential refugia for conservation and climate-resilient cultivation. By linking model-based ecological forecasting with participatory decision analysis, this research contributes to the development of adaptive management strategies aligned with the Sustainable Development Goals (SDGs 2, 13, and 15), supporting both biodiversity conservation and rural livelihood resilience under global change.
E. Dastres, A. Sonboli, Ghazal Shafiee Sarvestani et al.· Scientific Reports· 1 citation
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.
Welitom Ttatom Pereira da Silva, Izabelly Aguiar Palmeira Bulhões, Alexandre Puls Ferretti et al.· Hydropower· 0 citations
Designing resilient irrigation infrastructure under hydrological uncertainty is a critical challenge in climate-vulnerable, sediment-laden Himalayan basins. This study develops an integrated engineering framework through comprehensive assessment of Nepal's Bagmati River at Pandhera Dobhan, combining forty-one years of discharge data (1979–2019), historical and contemporary sediment records (1990–2024), and seasonal water quality monitoring. The Log-Normal distribution provided the best fit for flood frequency analysis (χ² goodness-of-fit, p < 0.05), yielding a 100-year design flood of 7,638 m³/s (95% CI: 6,110–9,166 m³/s). Applying a recommended climate safety margin of 15–20% to address non-stationarity raises the practical design value to 8,800–9,200 m³/s. Mann-Kendall analysis revealed no significant trend in annual peak flows (τ = 0.08, p = 0.32) but identified increasing pre-monsoon flows (τ = 0.22, p = 0.03; Sen's slope: +15.3 m³/s/yr). The flow duration curve establishes Q₉₅=11 m³/s as the environmental flow requirement. The river exhibits extreme seasonality, with suspended sediment concentrations ranging from 40.5 ppm (±12 SD) in the dry season to monsoon peaks of 2,214 ppm (±458 SD) in a supply-limited transport regime (R² < 0.3). Post-2015 Gorkha earthquake monsoon sediment concentrations increased by 48.7% relative to the 1990–1997 baseline (p = 0.008). From particle size analysis, D50 = 0.4 mm was determined for the settleable sediments. The phosphate values measured were 3.5 mg/L (±0.7 SD), which is very much higher than the 0.1 mg/L eutrophication limit value. The electrical conductivity stayed lower than 200 μS/cm, meeting FAO. These interconnected constraints are synthesized into a quantitative "Resilience Trilemma" framework, from which specific design parameters are derived: a settling basin of 3,347–6,300 m² surface area (overflow rate: 0.77 cm/s), Manning's n of 0.0225 for unlined canals, freeboard of 0.8 m for flows exceeding 10 m³/s, and a design duty of 0.88 lps/ha. Validated against operational data from the Bagmati Irrigation Project, this framework provides a transferable, climate-smart model for irrigation infrastructure design in rapidly changing mountain basins worldwide.
Ramesh Singh, Pradip Bantawa, Satendra Ray et al.· Journal of Advanced Academic...· 0 citations
