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Advanced Hydrological Modeling Approach for Assessing Climate-Induced Watershed Vulnerability Trends
Subject area: Science,Engineering and Technology · Area of research: Assessing Climate-Induced Watershed
Abstract
Climate change continues to intensify hydrological extremes, altering precipitation regimes, runoff dynamics, soil moisture patterns, and groundwater recharge processes across diverse watershed systems. These shifts pose significant challenges for accurately diagnosing vulnerability trends and supporting adaptive water-resource decision-making. This study presents an advanced hydrological modeling approach designed to assess climate-induced watershed vulnerability with improved spatial, temporal, and process-based resolution. The framework integrates high-resolution climate projections, physically based hydrological models, machine learning algorithms, and multi-criteria vulnerability metrics to capture complex watershed responses under evolving climate scenarios. Downscaled climate datasets derived from CMIP6 ensemble projections were incorporated into a coupled surface?subsurface hydrological model to quantify variations in streamflow, evapotranspiration, soil moisture deficits, and baseflow contributions. A machine-learning-guided sensitivity analysis was employed to identify the dominant climatic and physiographic drivers shaping watershed susceptibility to hydrological stress. Furthermore, a composite vulnerability index was developed using hydroclimatic indicators, ecological thresholds, and socio-environmental exposure parameters to characterize both biophysical and anthropogenic risks. Model outputs were validated using long-term observational records and remote sensing datasets to ensure robustness and reliability. Results demonstrate pronounced spatial heterogeneity in vulnerability trajectories, with headwater catchments, semi-arid basins, and rapidly urbanizing watersheds showing heightened sensitivity to projected climate forcing. The integrated modeling approach reveals increasing trends in seasonal water deficits, higher flood frequencies in precipitation-intensive regions, and long-term reductions in groundwater recharge across several climate scenarios. These findings underscore the necessity of adopting advanced, data-enhanced hydrological models that capture non-linear watershed feedbacks and enable early identification of at-risk hydrological units. The proposed framework offers a scalable and transferable methodology for evaluating future watershed vulnerabilities, supporting proactive climate adaptation planning, sustainable water allocation, and resilience-focused watershed management. By combining physical modeling with data-driven insights, the study strengthens the scientific basis for climate-informed hydrological assessments and contributes to developing robust adaptation strategies for climate-impacted watersheds.
Keywords
Hydrological Modeling, Climate Change, Watershed Vulnerability, CMIP6, Machine Learning, Hydroclimatic Indicators, Spatial Analysis, Climate Adaptation, Water Resources Management, Vulnerability Assessment.
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How to cite this paper
@article{1713081,
author = {Omolola Badmus, Azeez Lamidi Olamide},
title = {Advanced Hydrological Modeling Approach for Assessing Climate-Induced Watershed Vulnerability Trends},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {5},
pages = {388-410},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713081.pdf},
abstract = {Climate change continues to intensify hydrological extremes, altering precipitation regimes, runoff dynamics, soil moisture patterns, and groundwater recharge processes across diverse watershed systems. These shifts pose significant challenges for accurately diagnosing vulnerability trends and supporting adaptive water-resource decision-making. This study presents an advanced hydrological modeling approach designed to assess climate-induced watershed vulnerability with improved spatial, temporal, and process-based resolution. The framework integrates high-resolution climate projections, physically based hydrological models, machine learning algorithms, and multi-criteria vulnerability metrics to capture complex watershed responses under evolving climate scenarios. Downscaled climate datasets derived from CMIP6 ensemble projections were incorporated into a coupled surface?subsurface hydrological model to quantify variations in streamflow, evapotranspiration, soil moisture deficits, and baseflow contributions. A machine-learning-guided sensitivity analysis was employed to identify the dominant climatic and physiographic drivers shaping watershed susceptibility to hydrological stress. Furthermore, a composite vulnerability index was developed using hydroclimatic indicators, ecological thresholds, and socio-environmental exposure parameters to characterize both biophysical and anthropogenic risks. Model outputs were validated using long-term observational records and remote sensing datasets to ensure robustness and reliability. Results demonstrate pronounced spatial heterogeneity in vulnerability trajectories, with headwater catchments, semi-arid basins, and rapidly urbanizing watersheds showing heightened sensitivity to projected climate forcing. The integrated modeling approach reveals increasing trends in seasonal water deficits, higher flood frequencies in precipitation-intensive regions, and long-term reductions in groundwater recharge across several climate scenarios. These findings underscore the necessity of adopting advanced, data-enhanced hydrological models that capture non-linear watershed feedbacks and enable early identification of at-risk hydrological units. The proposed framework offers a scalable and transferable methodology for evaluating future watershed vulnerabilities, supporting proactive climate adaptation planning, sustainable water allocation, and resilience-focused watershed management. By combining physical modeling with data-driven insights, the study strengthens the scientific basis for climate-informed hydrological assessments and contributes to developing robust adaptation strategies for climate-impacted watersheds.},
keywords = {Hydrological Modeling, Climate Change, Watershed Vulnerability, CMIP6, Machine Learning, Hydroclimatic Indicators, Spatial Analysis, Climate Adaptation, Water Resources Management, Vulnerability Assessment.},
month = {November},
}