Evaluating multiscale performance of the TETIS distributed hydrological model using satellite and in situ observations in the Tugela basin

The spatial resolution at which distributed hydrological models are implemented plays a critical role in their ability to represent dominant hydrological processes and to close the water balance consistently across scales (Blöschl & Sivapalan, 1995). Despite the increasing availability of satellite-based observations, their integration into multiscale hydrological modelling frameworks remains challenged by scale dependency, parameter transferability (Barrios & Francés, 2012; Medici et al., 2008), and computational constraints.

This study presents a multiscale performance assessment of the distributed hydrological model TETIS (Francés et al., 2007; GIMHA - Grupo de Investigación en Modelación Hidrológica y Ambiental Distribuida, 2021) by coupling in situ observations from 27 gauging stations with satellite-derived(García-García et al., 2026) state variables, including evapotranspiration (ET) and surface soil moisture (SSM), in the Tugela River basin (South Africa) (Droppers et al., 2024). The model is implemented at four spatial resolutions (250 m, 500 m, 1 km, and 5 km) to evaluate the sensitivity of key water balance components—ET, SM, and discharge (Q)—to spatial discretization.

A set of mono-objective (5 km) and multi-objective (1 km) calibration experiments is conducted using Q, ET, and SSM as target variables, supported by both satellite products and ground observations. Model performance is assessed using complementary efficiency metrics (correlation, variability ratio, bias ratio, KGE, and SPAEF), enabling a detailed analysis of scale-dependent behavior and spatial pattern consistency.

The results reveal systematic trends in model performance across spatial resolutions, highlighting scale-dependent sensitivities of individual water balance components. According to the KGE metric, model performance is consistently higher at finer resolutions and progressively degrades toward coarser ones, a behavior observed across all experiments regardless of the calibration scale. Furthermore, the integration of satellite data with ground observations leads to improved model performance across scales, as reflected by higher KGE values and a more balanced contribution of the correlation, variability, and bias components.

Overall, this work contributes to the ongoing discussion on scale dependency in hydrology and directly relates to several open questions identified by Blöschl et al. (2019), particularly those addressing the consequences of spatial heterogeneity in hydrological fluxes, the existence of hydrological laws across catchment scales, and the effective use of innovative observation technologies to characterize hydrological states and fluxes across resolutions. By integrating satellite-derived state variables into a multiscale distributed modelling framework, this study establishes a methodological baseline for future research on calibration transferability, multiscale equifinality, and synthetic basin experimentation.