Hydrological Modeling of Rainfall–Runoff Processes for Improved Flood Forecasting in Semi-Arid Regions
Keywords:
: Rainfall–runoff modeling, flood forecasting, semi-arid regions, hydrological modeling, conceptual models, physically based models, data-driven models, machine learning, Geographic Information SystemsAbstract
Flood forecasting in semi-arid regions remains a complex and highly uncertain task due to the inherent characteristics of these environments, including highly variable and infrequent rainfall events, elevated evapotranspiration rates, heterogeneous land surface conditions, and limited availability of long-term hydrometeorological observations. These constraints significantly affect the reliability of rainfall–runoff relationships and pose major challenges for accurate flood prediction and water resource planning. Despite these difficulties, rainfall–runoff modeling continues to serve as a fundamental approach for understanding catchment hydrological responses and improving flood forecasting capabilities in data-scarce regions.
This review provides a comprehensive assessment of recent advances in rainfall–runoff modeling techniques used for flood prediction in semi-arid environments. It examines traditional conceptual models, such as lumped parameter approaches, physically based distributed models that simulate detailed hydrological processes, and modern data-driven models powered by machine learning algorithms. Each modeling approach is evaluated in terms of its applicability, advantages, and limitations under semi-arid climatic conditions.
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