Modeling Streamflow under Data Scarcity: Regionalization of IHACRES Using Catchment Attributes in a Mediterranean Setting

Rainfall-runoff (RR) models are fundamental tools in hydrology, enabling the simulation and prediction of watersheds responses to precipitation and other climatic inputs. These models represent key hydrological processes (e.g., infiltration, surface runoff, evapotranspiration) and are widely applied in water resource management, flood forecasting, drought assessment, and climate change impact analysis. Regardless of whether they are conceptual, empirical, or physically based, RR models rely on a set of parameters that regulate the representation of hydrological processes within the model. Parameters must be calibrated to ensure models can reproduce streamflow dynamics accurately, minimizing discrepancies between simulated and observed discharge. However, the calibration process is highly dependent on the availability, continuity and quality of long-term hydrometeorological records, including precipitation, temperature, and streamflow data. In gauged catchments, where discharge observations are available, calibration can be performed directly. In contrast, ungauged basins lack such data, making conventional calibration unfeasible. In these cases, model parameters must be estimated through alternative strategies, such as regionalization techniques, empirical relationships, or analogies with hydrologically similar gauged basins (Samuel et al., 2011; Arsenault et al., 2019). These approaches assume that catchments sharing similar climatic, topographic, and land-use characteristics also exhibit similar hydrological behavior, allowing parameter values to be transferred using similarity metrics. Although such indirect estimation methods extend the applicability of RR models to data-scarce regions, they inherently introduce uncertainty due to lack of direct observations. Among these approaches, regionalization techniques specifically aim to mitigate this uncertainty by establishing statistical or conceptual relationships between model parameters and measurable catchment attributes (e.g., basin area, slope, soil and vegetation types, climatic conditions). The analysis presented in this study was carried out using the IHACRES model (Identification of Hydrographs And Components from Rainfall, Evaporation, and Streamflow; Jakeman & Hornberger, 1993), a lumped conceptual rainfall-runoff model specifically designed to simulate streamflow responses using limited climatic input data. IHACRES is based on the transformation of rainfall into streamflow through a simplified yet effective representation of catchment-scale hydrological processes. Its structure is particularly suited for applications in data-scarce environments, due to its parsimony, flexibility, and low computational demand (Ye et al., 1997; Lotfirad et al., 2019). The model consists of two main modules: (i) a non-linear loss module, which converts raw rainfall into effective rainfall by accounting for soil moisture dynamics and temperature-dependent evapotranspiration losses, and (ii) a linear routing module, based on unit hydrographs, which translates the effective rainfall into streamflow at the catchment outlet. IHACRES has demonstrated strong adaptability across a wide range of climatic and hydrological settings, and it is especially effective in semi-arid and arid regions, including catchments with intermittent or ephemeral streamflow (Croke et al., 2007). These characteristics make it a particularly relevant choice for modeling hydrological processes in Mediterranean environments. In this study, the IHACRES model was applied to a selection of gauged catchments distributed across Sicily (Italy), chosen to represent the diversity of climatic, morphological, and hydrological conditions characterizing the region. For the purposes of calibration and validation, a comprehensive and long-term hydrometeorological dataset was employed. This dataset includes daily time series of precipitation, air temperature, and streamflow spanning the period 1951-1997, and was made available by the River Basin Authority of the Hydrographic District of the Sicily Region. The calibration of the IHACRES model was performed using a multi-objective approach designed to achieve a balanced performance across multiple hydrological evaluation criteria. To determine the optimal set of model parameters, a ranking-based strategy was employed: for each simulation, model results were ranked according to each performance metric, and the final solution was selected as the best compromise among the top-performing configurations. The adopted approach yielded reliable results during the calibration phase, with accurate reproduction of observed hydrographs, flow volumes, and FDCs. Furthermore, the robustness of the calibrated parameter sets was confirmed during the validation phase, demonstrating the model’s ability to generalize to independent data. Following the successful calibration and validation of the IHACRES model, the aim was to extend its applicability to ungauged Sicilian basins (i.e., model regionalization), where direct streamflow observations are unavailable (Figure 1). As a fundamental preliminary step, a correlation analysis was conducted to explore relationships both among the calibrated model parameters themselves and between parameters and key physiographic and climatic characteristics of the catchments (i.e., catchment area, mean slope, elevation, soil texture, land cover types, and climatic indicators, such as mean annual precipitation and temperature). Building upon these insights, the study considered some regionalization approaches. The performance of each technique was assessed using statistical metrics (Mihret et al., 2025). This comparative analysis provided useful insights into the relative strengths of the different methods in terms of predictive reliability and robustness, with the goal to identify the most effective regionalization technique for a reliable model parameter estimation in the ungauged Sicilian catchments.