Although Essential Climate Variables (ECVs) have been widely adopted as important metrics for guiding scientific and policy decisions, the Earth Observation (EO) and Land Surface and Hydrologic Model (LSM/HM) communities have yet to treat terrestrial ECVs in an integrated manner. To develop consistent terrestrial ECVs at regional and continental scales, greater collaboration between EO and LSM/HM communities is needed. An essential first step is assessing the LSM/HM simulation uncertainty. To that end, we introduce a new hydrological reference dataset that comprises a range of 19 existing LSM/HM simulations that represent the current state-of-the-art of our LSM/HMs. Simulations are provided on a daily time step, covering Europe, notably the Rhine and Po river basins, alongside the Tugela river basin in Africa, and are uniformly formatted to allow comparisons across simulations. Furthermore, simulations are comprehensively validated with discharge, evapotranspiration, soil moisture and total water storage anomaly observations. Our dataset provides valuable information to support policy development and serves as a benchmark for generating consistent terrestrial ECVs through the integration of EO products.

En muchas cuencas del planeta donde el riego constituye un elemento significativo del ciclo del agua, no existe información al respecto. Este trabajo evalúa los efectos de la inclusión del riego, estimado por satélite, en la modelación hidrológica de la cuenca del río Po. Se utilizó el modelo TETIS v9.1 para el periodo 2016-2021. El riego se incluyó sumándolo a la precipitación satelital y considerando sus abstracciones, extrayendo del caudal simulado un valor acorde con las demandas estimadas por la autoridad ambiental. Tras la incorporación del riego, el modelo muestra sensibilidad en el área irrigada y en toda la cuenca, dependiendo del experimento, reflejándose en el balance hídrico y en la mejora de la representación del caudal observado. Además, la evapotranspiración y humedad comparadas con datos satelitales sugieren una capacidad de mejora de la representación espaciotemporal del modelo, a pesar de las diferencias con los datos de referencia.

Spatial scaling and the reconditioning of Digital Elevation Models (DEMs) are fundamental to hydrological modeling, as they directly affect the accuracy of geomorphological parameters and runoff simulation results. This study analyzes two basins, the Po River (Europe) and the Tugela River (Africa), using base DEMs with a resolution of 30 m, scaled to resolutions of 200, 500, 1000, 2500, and 5000 m. The DEMs were reconditioned using the AGREE method (both locally and globally) to evaluate variations in parameters such as flow direction, flow accumulation, slope, and hillslope and river network flow velocity. These variations were analyzed in the hydrological modeling of a precipitation event using TETIS v9.1 software, under the assumption of impermeable soil and reproducing the unit hydrograph principle. In addition, an exploratory analysis of the fractal dimension (FD) was conducted. To identify patterns in the scalar interactions of the results, primarily using the Box Counting methodology. Recognizing the notion that fractal dimension can be mathematically interpreted as regressions of data sets, FD was estimated by clustering sets of 2, 3, 4, 5, and 6 data points for each of the study scenarios, focusing on variables such as the total basin area, the area covered by the drainage network, the network length, and the drainage density.The results indicate that the total area of the basins increases with scaling: from 28,955 km² to 31,225 km² for Tugela, and from 67,021 km² to 95,925 km² for Po. Flow direction alterations were observed at intermediate scales (1000 and 2500 m) reaching up to 60%, while the percentage of unaltered flow velocity decreased to 0% for scales between 500 and 1000 m . The slope exhibited a substantial decrease, from mean values of 1.84 and 3.16 to 0.07 and 0.11 for Tugela and Po, respectively. In the modeling, scale variations could amplify simulated peak flows by up to 10% or reduce them by up to 26%, while simulated peak times could be delayed by up to 12% or advanced by up to 20%. Regarding FD, it was observed that the variable "area covered by the drainage network" exhibited a tendency to converge at a value of 1.0 when the dataset corresponded to two fine scales. Conversely, when the dataset corresponded to two coarse scales, the results exhibited a tendency to approach a value of 2.0. Finally, the analysis indicated that clustering 6 data points minimizes uncertainty in the regressions. For instance, the mean values for the "area covered by the drainage network" converged to 1.17, while for total area, network length, and drainage density, the mean values were 1.97, 0.14, and 0.17, respectively.In conclusion, each spatial scale requires specific adjustments to achieve precise calibration in hydrological modeling. These adjustments are essential to ensure that the results are consistent and reliable, allowing the model to accurately reflect the actual basin conditions and flow dynamics.

Earth observation (EO) products provide unprecedented spatial and temporal detail on key water cycle processes, yet their systematic use in hydrological and land surface models remains limited. Here, we evaluate the added value of high-resolution EO data for daily streamflow simulations at $\approx$1\,km resolution across contrasting hydro-climates in Europe and Africa, using five state-of-the-art model structures. We explore two complementary pathways: (i) replacing conventional meteorological forcing with EO-based precipitation, and (ii) calibrating models against EO variables such as soil moisture, evapotranspiration, and snow cover, either alone or jointly with streamflow. Model skill was quantified with the Kling–Gupta Efficiency (KGE) and its components, based on paired gauge comparisons. Results show that EO precipitation forcing improved streamflow in some basins (e.g. Po, Tugela) but reduced skill in others (Rhine). Calibration against EO variables alone yielded mixed outcomes, whereas joint calibration with EO and streamflow consistently delivered more robust improvements, particularly for TETIS in Tugela and mHM in Po. The decomposition of KGE revealed that improved variability alignment was the dominant contributor to the gains, with bias correction and timing playing secondary roles. Despite differences across models and basins, our findings demonstrate that EO integration can substantially enhance kilometre-scale hydrological simulations when combined with discharge information. This highlights the potential of EO data to strengthen operational hydrology, flood and drought early warning systems, and climate-resilient water management.

Irrigation is one of the most widespread anthropogenic water uses worldwide, yet it is often excluded from Land Surface Models (LSMs) and Hydrological Models (HMs) due to the limited availability of data. Recent advances in Earth Observation (EO) provide valuable irrigation information, enabling its integration into model simulations. This study assesses whether incorporating high-resolution EO-based irrigation products into LSMs/HMs improves the representation of the water budget, fluxes, and storages at 5 km and 1 km spatial resolutions. Three experiments were performed: a baseline (SC40), representing a no-irrigation scenario calibrated against observed discharge; irrigation and abstractions included without recalibration (SC41); and irrigation and abstractions included with recalibration against "naturalized" discharge (SC42). Simulations were run for 2016-2021, evaluating evapotranspiration (ET), surface soil moisture (SSM), total water storage (TWS), and discharge at several stations. Results indicate that irrigation influences all variables. In SC41, impacts are concentrated in irrigated areas, while recalibration in SC42 propagates effects across the basin. ET generally increases from SC40 to SC41, then decreases in SC42, likely correcting baseline overestimation. Discharge performance improves in all models (except one) when irrigation is included, and recalibration applied. Spatio-temporal analyses suggest a space for better representation of SSM and ET. Overall, these patterns were consistent across spatial resolutions, models are sensitive to irrigation, and recalibration enhances water budget estimates, discharge simulation, and spatio-temporal representation. Such improvements may also be relevant for other basins influenced by irrigation practices.

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.