The Surface Water and Ocean Topography (SWOT) satellite mission introduces unprecedented 2-D radar interferometry to inland hydrology, aiming to evaluate water bodies down to 0.0625 km2. Standard operational products, such as LakeSP, are designed for global applicability but may exhibit suboptimal design choices for particular hydrological regimes. To address this, a customized processing workflow utilizing low-level SWOT L2 HR PIXC vD data was evaluated using 37 small Danish lakes (0.25 to 40 km2) against daily in-situ gauge data from July 2023 to May 2026. The workflow achieves a median root-mean-square error (RMSE) of 5.57 cm and a Pearson correlation coefficient (PCC) of 0.91, similar to LakeSP. Crucially, this approach retains a median of 125 valid timestamps per lake compared to 60 timestamps preserved by the filtered LakeSP product, effectively doubling the temporal resolution and enabling the capture of higher-frequency hydrological variations. Additionally, we find indications that SWOT's PIXC data contains time-varying residual roll-errors over Danish lakes that affect ca 18 % of all passes with more than 10 observed lakes covering Denmark.
The French Alps, characterised by complex topography and diverse climatic influences, are highly vulnerable to extreme precipitation events, which can trigger floods and landslides with significant socio-economic impacts. While local stakeholders report a perceived increase in sub-daily extreme precipitation events, such a trend has not yet been statistically confirmed based on observations. In this study, we apply extreme value theory to long time series of in situ observations of daily and hourly precipitation to provide an updated climatology and trend analysis of precipitation extremes across the region. We focus on 20-year return levels to analyse rare, potentially high-impact events and show that precipitation extremes exhibit distinct spatial patterns with marked differences between daily and hourly timescales. By examining trends at monthly and seasonal levels and making use of an existing weather pattern classification, we gain a deeper understanding of the dynamics underlying these trends. For extremes of daily precipitation intensity, we observe strong positive trends in the southern French Alps and negative trends in the north, primarily driven by a stronger influence of southerly (Mediterranean) flows in autumn. The spatial patterns for hourly extremes show more spatially variable trends, and seem to be primarily influenced by local thermodynamic effects potentially linked to e.g. changing moisture availability or changes in surface albedo due to shorter snow cover persistence. This detailed analysis provides important insights for stakeholders who are working on prevention and protection from precipitation-related hazards, underscoring the need for adaptive strategies in response to changing precipitation patterns. It also highlights the importance of region-specific and timescale-specific approaches in future studies of precipitation extremes.
This technical note presents a physically based post-processing workflow for correcting coarse-resolution regional climate model output used to force high-resolution hydrological models in high-elevation, glacierized catchments. The Weather Research and Forecasting (WRF) model was used to dynamically downscale the 20th Century Reanalysis Version 3 (20CRv3; 1° × 1°, 3-hourly) to a spatial resolution of 2 km and an hourly temporal resolution for the period 1969–2015. Evaluation of WRF output in the upper Kauner Valley (Austria) revealed a pronounced cold bias in the diagnosed 2 m temperature at elevations above 2600 m above sea level, which was attributed to the coarse representation of snow- and ice-covered surfaces and their influence on land-atmosphere interactions. To address this limitation, we developed a novel post-processing workflow that derives hourly temperature lapse rates directly from the WRF atmosphere using temperature and geopotential height fields from multiple eta levels. These lapse rates are subsequently used to reconstruct near-surface temperatures from the first atmospheric model level, thereby reducing the influence of surface-related biases. In addition, a sequential thermodynamically consistent correction of relative humidity was applied using the corrected temperature fields. The proposed approach was evaluated against observations from four meteorological stations spanning an elevation gradient of nearly 900 m and through hydrological simulations with the physics-based WaSiM model. Corrected hourly temperatures showed substantially improved agreement with observations, whereas simulations driven by uncorrected WRF data failed to adequately reproduce observed cryospheric and hydrological dynamics. The temperature correction significantly improved the simulation of snow accumulation and melt, glacier mass balance, snow cover dynamics, and streamflow. Furthermore, the thermodynamically consistent correction of relative humidity enhanced late-summer streamflow during periods of maximum glacier melt. Evaluation against homogenized station observations demonstrated that the corrected temperature product preserves long-term regional warming trends over a period of 42 years, indicating its suitability for climate impact assessments. The results show that the proposed workflow effectively reduces temperature biases associated with coarse-resolution land-cover representation while maintaining physical consistency among meteorological variables. Because the method relies on atmospheric lapse rates derived from the regional climate model itself rather than long-term local observations, it provides a transferable alternative to empirical bias-correction approaches and a robust framework for hydrological and climate impact studies in glacierized mountain environments.
Water bodies such as lakes and reservoirs can play a crucial role in reducing estimated flood peaks, at both daily and hourly time resolution. While the effect of water bodies on flood peaks has been demonstrated in the past at different time resolutions, it remains unclear how water bodies affect the ratio between estimated daily and hourly quantiles, that is, to which degree their attenuation effect is scale dependent. Here, we analyse how water bodies influence this ratio using two approaches: (1) by comparing estimated daily and hourly flood peak quantiles upstream and downstream of reservoirs of four local case studies, and (2) by comparing daily and hourly flood peak quantiles across a large sample of catchments in Switzerland with various degrees of water-body influence. Our results show that reservoirs dampen hourly peak discharge much more strongly than daily peak discharge, which leads to similar daily and hourly flood peaks downstream of reservoirs. Specifically, our case study analysis highlights that (sub-)hourly peak discharge is attenuated by up to 70 % downstream of reservoirs during flood events with a 10-year return period. We also find that the attenuation effect is particularly pronounced in catchments that are heavily influenced by water bodies, i.e. those catchments where more than 60 % of the area contributes to water body inflow. This implies that daily flood peaks are a good proxy for hourly peaks in strongly regulated catchments in contrast to natural ones. We conclude that considering water body influence on flood peaks is crucial to understand the similarity between daily and hourly flood peaks downstream of water bodies. This suggests that water body influence should also be considered in large-sample studies for which the exact influence may be unknown, and quantified using suitable metrics such as the fraction of the total catchment above the water body.
Soil-Vegetation-Atmosphere Transfer (SVAT) models are essential tools for simulating and underploting the dynamic interactions governing water balance components within forest ecosystems. These models are widely employed to predict hydrological responses to environmental change, including the impacts of shifting meteorological conditions on forested landscapes. Despite their usefulness, the reliability of SVAT models is frequently compromised by uncertainties arising from incomplete or imprecise input data. These limitations often result in model assumptions that may lead to over- or underestimation of critical water balance components such as groundwater recharge. In order to improve the accuracy of SVAT models, observed soil moisture data are integrated to enhance parameterization processes by aligning simulated outputs with measured values. However, uncertainties remain regarding the selection of representative soil moisture profiles for calibration and the extent of measurements necessary to robustly characterize a forest plot. To address these challenges, the present study explores the spatial variability of soil moisture across two forested plots with contrasting soil and vegetation conditions by the deployment of an extensive network of soil moisture probes in 11 profiles per plot. The influence of soil moisture variability on the adjustment of model input parameters during the calibration process and its subsequent impact on the computation of groundwater recharge is evaluated. The findings reveal that soil moisture variability at the plot characterized by a heterogeneous soil was greater, both horizontally and in depth, throughout the study period. These patterns of variability are also mirrored in the different parameter sets obtained from the calibration of the LWF Brook90 model, based on the recorded soil moisture time series in each of the 11 profiles per plot. The most significant variation is observed in the infiltration and hydraulic soil parameters, whereby this is more pronounced at the plot with heterogeneous soil structure. When examining the groundwater recharge rates calculated using the 30 best-performing parameter sets for each of the 11 profiles, both plots exhibited comparable temporal patterns but substantial differences in total groundwater recharge volumes across profiles. The results also suggest that model-inherent uncertainties, including parameter interactions, equifinality and dimensional simplifications, have a stronger impact on model outputs than uncertainties arising from variability in soil moisture caused by spatial heterogeneity of soil texture and hydraulic properties within the plot. Taking into account both sources of uncertainty, the application of Monte Carlo based subsampling yielded contrasting results. While groundwater recharge at the Kienhorst plot could be reliably estimated using data from only 3 soil profiles at the investigated 20×20 m plot, eleven soil profiles were required at the Tharandt site to adequately capture the spatial variability of the system. These numbers are indicative rather than universal thresholds, as the required sampling density is strongly site-specific and depends on local soil structure and model related uncertainties.
