Ruben Imhoff

Accurate representation of terrestrial Essential Climate Variables (ECVs) is fundamental for understanding the hydroclimate system and informing policy decisions. Despite progress in hydrological modelling, coordinated multi-model intercomparisons across diverse regions at kilometre-scale resolution remain limited. This study presents a multi-scale analysis of six state-of-the-art models (CLM, GEOframe, mHM, PCR-GLOBWB, TETIS, and wflow$\_$sbm) using unified meteorological and observational datasets, benchmarking their model performance and flux consistency against a common reference. This study assessed the models in three hydroclimatically contrasting basins (the Rhine, Po, and Tugela) at multiple spatial resolutions. A multi-basin calibration was implemented for each model and compared against its default configuration, alongside a diagnostic assessment of evapotranspiration consistency between 1\;km and 5\;km resolutions. Results from 48 simulations indicate that calibration relative to default parameters improves discharge performance across spatial resolutions for most models, with median Kling-Gupta Efficiency (KGE) gains of up to 0.2 in the Po and Tugela. In contrast, model performance in the Rhine improved only slightly after calibration, likely because the default setups already performed well and dense observational data were available. The mHM and wflow$\_$sbm showed consistent annual ET totals between 1\;km and 5\;km spatial resolutions in the Po, while other models diverged more strongly. These results establish a benchmark of current discharge performance across resolutions and calibration states, and indicate that 1\;km resolution modelling is computationally feasible for the two models for which runtime comparisons were available. These insights may support further development of high-resolution hydrological modelling and the integration of Earth observation data.

The relationship between initial soil moisture and floods is well studied in sloping areas, but not in lowland catchments, where the saturated zone, unsaturated zone and surface water are strongly coupled. The aim of this study was to determine the importance of initial groundwater depth (representing soil wetness) on flood peaks in lowland catchments and to examine if and how this affects the magnitude and timing of floods in the future. We used the rainfall-runoff model WALRUS to investigate the relation between initial groundwater depth (48 h before the peak), effective rainfall sum (over the 48 h before the peak) and the resulting peak discharge and peak volume in 12 lowland catchments, for 109 years of forcing in the current climate and four climate scenarios for both 2050 and 2085. We found that this relation is strong in these catchments, with a stronger dependence on initial groundwater depth in flatter catchments. When climate changes, less precipitation and more evapotranspiration are projected in summer, resulting in deeper groundwater in summer and autumn, reducing flood frequency and magnitude. More rain in autumn, winter and spring will lead to more severe floods in winter and spring only. Averaged over all catchments, scenarios and seasons, the effective rainfall sum is projected to increase with 1.5 % in 2050 and 5.6 % in 2085, while the initial groundwater depth increases with 0.7 % in 2050 and 0.3 % in 2085. This combination leads to more frequent and severe floods, with 1 % more floods and 3 % larger peak volumes in 2050 and 9 % more floods and 21 % larger peak volumes in 2085. Without the mitigating effect of the deeper initial groundwater tables, the higher rainfall sums would have led to more frequent and more severe floods in the future.

Recent advances in machine learning (ML) techniques show promise for estimating soil hydraulic properties from soil data sets. Pedo‐transfer functions (PTFs) can facilitate the mapping of the complex relationship between soil properties and soil hydraulic properties, for example, lateral hydraulic conductivity—a necessity for estimating lateral subsurface flow in distributed hydrological models. In wflow_sbm model, the horizontal‐to‐vertical saturated hydraulic conductivity ratio is a sensitive parameter, but no established PTF exists. Our objective is to investigate the potential of ML algorithms in estimating PTFs for prediction. In this study, publicly available calibrated (i.e., optimized) across Great Britain were utilized to train two ML algorithms: Random Forest (RF) and Boosted Regression Trees (BRT), employing SoilGrids data set. Both algorithms effectively predicted in 92 of the 115 tested sub‐basins (i.e., 80%), demonstrating a high correlation with the optimized values, with RF slightly outperforming BRT. As a next step, we compared wflow_sbm simulated discharge results using uncalibrated (default value) and our predicted values. The predictions notably improved discharge simulations, with a median Kling‐Gupta Efficiency (KGE) increasing from 0.55 to 0.75. Subsequently, we generated two globally distributed maps to investigate the transferability of the ML‐based PTFs in the Loire basin, France. ML‐based PTFs improved performance in 75% of sub‐basins, with an average KGE increase of 0.06. Finally, we assessed the uncertainty in predictions, confirming the robustness of the ML‐based PTFs. Our study highlights the potential of ML methods for estimating soil hydraulic properties, aiding parameter estimation for distributed hydrological models.

In lowland catchments, the severity of pluvial floods is determined by both the magnitude of rainfall events and the initial catchment wetness. The aim of this study was to determine the importance of initial wetness on flood peaks in lowland catchments and to examine if and how this affects the magnitude and timing of floods in the future. We used the rainfall-runoff model WALRUS to investigate the relation between initial groundwater depth (48 hours before the peak), effective rainfall sum (over the 48 hours before the peak) and the resulting peak discharge and peak volume in 12 lowland catchments, for 109 years of forcing in the current climate and four climate scenarios for both 2050 and 2085. We found that this relation is strong in these catchments, with a stronger dependence on initial groundwater depth in flatter catchments. When climate changes, less precipitation and more evapotranspiration are projected in summer, resulting in deeper groundwater in summer and autumn, reducing flood frequency and magnitude. More rain in autumn, winter and spring will lead to more severe floods in winter and spring only. Averaged over all catchments, scenarios and seasons, the effective rainfall sum is projected to increase with 1.5 % in 2050 and 5.6 % in 2085, while the initial groundwater depth increases with 0.7 % in 2050 and 0.3 % in 2085. This combination leads to more frequent and severe floods, with 1 % more floods and 3 % larger peak volumes in 2050 and 9 % more floods and 21 % larger peak volumes in 2085. Without the mitigating effect of the deeper initial groundwater tables, the higher rainfall sums would have led to more frequent and more severe floods in the future.

In lowland catchments, the severity of pluvial floods is determined by both the magnitude of rainfall events and the initial catchment wetness. The aim of this study was to determine the importance of initial wetness on flood peaks in lowland catchments and to examine if and how this affects the magnitude and timing of floods in the future. We used the rainfall-runoff model WALRUS to investigate the relation between initial groundwater depth (48 hours before the peak), effective rainfall sum (over the 48 hours before the peak) and the resulting peak discharge and peak volume in 12 lowland catchments, for 109 years of forcing in the current climate and four climate scenarios for both 2050 and 2085. We found that this relation is strong in these catchments, with a stronger dependence on initial groundwater depth in flatter catchments. When climate changes, less precipitation and more evapotranspiration are projected in summer, resulting in deeper groundwater in summer and autumn, reducing flood frequency and magnitude. More rain in autumn, winter and spring will lead to more severe floods in winter and spring only. Averaged over all catchments, scenarios and seasons, the effective rainfall sum is projected to increase with 1.5 % in 2050 and 5.6 % in 2085, while the initial groundwater depth increases with 0.7 % in 2050 and 0.3 % in 2085. This combination leads to more frequent and severe floods, with 1 % more floods and 3 % larger peak volumes in 2050 and 9 % more floods and 21 % larger peak volumes in 2085. Without the mitigating effect of the deeper initial groundwater tables, the higher rainfall sums would have led to more frequent and more severe floods in the future.

To mitigate the impact of severe storms, accurate and timely high-resolution precipitation forecasts are crucial. In the tropics, however, many low- and middle-income countries typically lack the near surface rainfall sensors to provide such a nowcast. Weather radars are largely unavailable, and rain gauge networks are often sparse or poorly maintained, and not available in (near) real-time. Satellite precipitation products do provide valuable precipitation information in these regions, but often come with the drawback of having a spatial or temporal resolution too low to nowcast convective storms at the kilometer scale.A viable and ‘opportunistic’ source of near-surface high-resolution space-time rainfall estimates is based on the rain-induced signal attenuation experienced by commercial microwave links (CMLs) in cellular communication networks. This idea exploits the fact that the EM waves travelling between two antennas on different cell phone towers are scattered and absorbed by raindrops causing the strength of the signal to be partially attenuated. Using the open source algorithm RAINLINK we convert this specific attenuation to path-averaged rainfall intensities, essentially creating a network of virtual rain gauges which we then interpolate to create 2D rainfall maps, every 15 minutes.In this work we investigate the potential to use these CML derived rainfall maps as input into a conventional nowcasting algorithm, pySTEPS. The analysis is based on a CML network from Sri Lanka. The data set spans 15 months across 2019 and 2020. For each of the four monsoon seasons represented in the data set we define extreme events of different duration, ranging from 1 to 24 hours. These events are used as input to create probabilistic nowcasts in pySTEPS for lead times up to three hours. The nowcasts are evaluated spatially against the QPE at multiple catchments, and using 21 hourly rain gauges as an independent point reference source. Based on our findings we point out the opportunities and limitations of using CML data for nowcasting tropical storms. Finally, we highlight where CMLs can complement other remotely sensed rainfall estimates, for example from geostationary satellites, to provide more accurate nowcasts and as such potentially have impact in an operational setting too.

In lowland catchments, the severity of pluvial floods is determined by both the magnitude of rainfall events and the initial catchment wetness. The aim of this study was to determine the importance of initial wetness on flood peaks in lowland catchments and to examine if and how this affects the magnitude and timing of floods in the future. We used the rainfall-runoff model WALRUS to investigate the relation between initial groundwater depth (48 hours before the peak), effective rainfall sum (over the 48 hours before the peak) and the resulting peak discharge and peak volume in 12 lowland catchments, for 109 years of forcing in the current climate and four climate scenarios for both 2050 and 2085. We found that this relation is strong in these catchments, with a stronger dependence on initial groundwater depth in flatter catchments. When climate changes, less precipitation and more evapotranspiration are projected in summer, resulting in deeper groundwater in summer and autumn, reducing flood frequency and magnitude. More rain in autumn, winter and spring will lead to more severe floods in winter and spring only. Averaged over all catchments, scenarios and seasons, the effective rainfall sum is projected to increase with 1.5 % in 2050 and 5.6 % in 2085, while the initial groundwater depth increases with 0.7 % in 2050 and 0.3 % in 2085. This combination leads to more frequent and severe floods, with 1 % more floods and 3 % larger peak volumes in 2050 and 9 % more floods and 21 % larger peak volumes in 2085. Without the mitigating effect of the deeper initial groundwater tables, the higher rainfall sums would have led to more frequent and more severe floods in the future.

In lowland catchments, the severity of pluvial floods is determined by both the magnitude of rainfall events and the initial catchment wetness. The aim of this study was to determine the importance of initial wetness on flood peaks in lowland catchments and to examine if and how this affects the magnitude and timing of floods in the future. We used the rainfall-runoff model WALRUS to investigate the relation between initial groundwater depth (48 hours before the peak), effective rainfall sum (over the 48 hours before the peak) and the resulting peak discharge and peak volume in 12 lowland catchments, for 109 years of forcing in the current climate and four climate scenarios for both 2050 and 2085. We found that this relation is strong in these catchments, with a stronger dependence on initial groundwater depth in flatter catchments. When climate changes, less precipitation and more evapotranspiration are projected in summer, resulting in deeper groundwater in summer and autumn, reducing flood frequency and magnitude. More rain in autumn, winter and spring will lead to more severe floods in winter and spring only. Averaged over all catchments, scenarios and seasons, the effective rainfall sum is projected to increase with 1.5 % in 2050 and 5.6 % in 2085, while the initial groundwater depth increases with 0.7 % in 2050 and 0.3 % in 2085. This combination leads to more frequent and severe floods, with 1 % more floods and 3 % larger peak volumes in 2050 and 9 % more floods and 21 % larger peak volumes in 2085. Without the mitigating effect of the deeper initial groundwater tables, the higher rainfall sums would have led to more frequent and more severe floods in the future.

In lowland catchments, the severity of pluvial floods is determined by both the magnitude of rainfall events and the initial catchment wetness. The aim of this study was to determine the importance of initial wetness on flood peaks in lowland catchments and to examine if and how this affects the magnitude and timing of floods in the future. We used the rainfall-runoff model WALRUS to investigate the relation between initial groundwater depth (48 hours before the peak), effective rainfall sum (over the 48 hours before the peak) and the resulting peak discharge and peak volume in 12 lowland catchments, for 109 years of forcing in the current climate and four climate scenarios for both 2050 and 2085. We found that this relation is strong in these catchments, with a stronger dependence on initial groundwater depth in flatter catchments. When climate changes, less precipitation and more evapotranspiration are projected in summer, resulting in deeper groundwater in summer and autumn, reducing flood frequency and magnitude. More rain in autumn, winter and spring will lead to more severe floods in winter and spring only. Averaged over all catchments, scenarios and seasons, the effective rainfall sum is projected to increase with 1.5 % in 2050 and 5.6 % in 2085, while the initial groundwater depth increases with 0.7 % in 2050 and 0.3 % in 2085. This combination leads to more frequent and severe floods, with 1 % more floods and 3 % larger peak volumes in 2050 and 9 % more floods and 21 % larger peak volumes in 2085. Without the mitigating effect of the deeper initial groundwater tables, the higher rainfall sums would have led to more frequent and more severe floods in the future.