River floods are among the most disastrous and costly extreme weather events around the world. Atmospheric blocking events (persistent, slow-propagating and self-preserved weather systems that 'get stuck' and 'slow down' the atmospheric circulation) are a key feature of the persistent weather regimes over the Euro-Atlantic, playing a central role in shaping the weather and extreme weather of Europe. Notwithstanding this socioeconomic importance, the covariability between atmospheric blocking and river flooding has rarely been examined at the climate and continental scales. Our study explores the hydrological way in which atmospheric blocking propagates into river floods, and how this relationship varies across space and time. We analyse observed flood discharge across the continent together with atmospheric and terrestrial variables derived from reanalysis, resulting in >6000 rivers analysed over the last 60 years. We combine process-based hydrological analysis and climate analytics to obtain the influence of atmospheric blocking on floods in Europe across spatial and temporal scales, characterising the seasonal behavior, temporal dynamics over decades, and spatial patterns of this relationship. Our results clearly demonstrate the large-scale signature of blocking on flood behavior, and how atmospheric blocking has influenced the hydrometeorological drivers of river floods at the regional and continental scales, inducing robust patterns of variability across different flood attributes over the last 60 years. Despite strong decadal variability in the hydrological propagation of atmospheric blocking and blocking activity itself, our analyses reveal significantly changing impacts of atmospheric blocking on floods in Europe. Furthermore, our results demonstrate how the effects of atmospheric blocking interact locally with the regional hydrological characteristics. Overall, our findings link a dynamical mechanism with flood behavior within the climate system, improving the understanding of multiple features of flood variability in Europe over the last 60 years.

How to cite: Hernandez, D., Bertola, M., and Blöschl, G.: The hydrological propagation of atmospheric blocking into river floods in Europe, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-698, https://doi.org/10.5194/ems2026-698, 2026.

Atmospheric Rivers (ARs) are recognised as important drivers of extreme precipitation and flooding in many mid-latitude regions. However, their hydrological impacts over the complex terrain of the Italian peninsula remain relatively underexplored. In this study, we investigate the ground-level impacts associated with two extreme AR-related events affecting Italy: Vaia storm in October 2018 and Alex storm in October 2020. These events impacted different regions, characterised by diverse physiographic settings, thus providing an opportunity to analyse how AR-driven precipitation translates into river response under various orographic and hydrological conditions.

The analysis is based on distributed hydrological simulations performed using the CETEMPS Hydrological Model (CHyM) at high spatial resolution. CHyM control simulations were forced with hourly rainfall and air temperature observations from the Italian Civil Protection network and used as comparison for numerical experiments forced with hourly precipitation fields produced by the MOLOCH convection-permitting model at different spatial resolutions. Additional sensitivity experiments were conducted using meteorological simulations in which the AR contribution was largely weakened, allowing the assessment of the role of AR-related moisture transport in the hydrological response.

Hydrological impacts were evaluated using two stress indices derived from CHyM simulations: the Best Discharge-based Drainage index (BDD), which reflects river discharge conditions along the drainage network, and the CETEMPS Alarm Index (CAI), which highlights rapid runoff responses associated with intense precipitation.

The results highlight the important role of AR-driven precipitation in shaping the hydrological response during both events, also emphasizing the strong modulation exerted by regional orography, basin morphology, and drainage network structure. The comparison between simulations with and without AR forcing further illustrates the relevance of accurately representing AR-related processes in coupled meteorological–hydrological modelling frameworks.

Overall, this study contributes to improving the understanding of the links between AR dynamics and hydrological impacts and supports the development of more reliable approaches for forecasting AR-related hydro-meteorological hazards in complex terrain environments.

How to cite: Tomassetti, B., Di Antonio, L., Verdecchia, M., Colaiuda, V., Lombardi, A., Mastrangelo, D., and Davolio, S.: Ground impacts of Atmospheric Rivers over Italy: the extreme events of Vaia (2018) and Alex (2020) storms., EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-528, https://doi.org/10.5194/ems2026-528, 2026.

Challenges and risks related to flood prediction and warning, which became evident during and in the aftermath of devastating flood events in recent years in Germany, call for a new level of cooperation between weather and flood forecasting and their respective operational services. While these domains have historically been structurally quite separate in Germany, several collaborative projects are now laying the foundation for increased exchange and joint development.

The first phase of the project “Co-Design of Innovations between Weather and Flood Forecasting” (Italia–Deutschland science-4-services, IDEA-S4S), initiated directly in response to the lessons learned from the Ahrtal flood in 2021, addresses multiple topics. One focus is on improving mutual knowledge and understanding of forecast evaluation and verification, especially given the constantly increasing number of weather forecasting systems. Another emphasis has been placed on analyzing and training communication along the entire warning chain down to disaster management authorities, in particular regarding the handling of forecast uncertainty. The upcoming second phase of Co-Design aims to deepen this communication aspect by testing decision-making strategies that incorporate probabilistic criteria in multi-stage alert and readiness plans, in direct collaboration with emergency response units. Moreover, the second project phase will, among other topics, explore ML-based approaches to combine multiple ensemble models with different lead times into a best-guess ensemble as input for a hydrological model, thereby facilitating the use of the growing number of models in downstream flood applications.

The joint research project KI-HopE-De aims to test new machine-learning-based approaches and brings together meteorologists and hydrologists from both academia and operational services. First, a new high-resolution hourly hydrometeorological dataset (CAMELS-DE-1h) will be compiled and published for around 1,600 catchments across Germany. Second, various training strategies—from both hydrological and meteorological perspectives—for a regionally trained LSTM flood forecasting model will be developed to provide a benchmark model that is compared with the current operational models in Germany.

This contribution provides an overview of ongoing projects and highlights related presentations at the conference. We will share results and provide an outlook on upcoming work, and look forward to exchanging experiences with other initiatives at the intersection of meteorology and hydrology.

How to cite: Bondy, J., Keller, J., Fundel, V., Blumenstein-Weingartz, I., Kiseleva, O., Wolff, S., Fundel, F., Lambert, A., Rauthe-Schöch, A., Khosravi, S., Valmassoi, A., Keller, J., Loritz, R., Ehret, U., Dolich, A., Demuth, N., and Badde, U.: Towards a new level of collaboration and communication between weather and flood forecasting: an overview of ongoing efforts in Germany, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-733, https://doi.org/10.5194/ems2026-733, 2026.

Small and medium-size catchments respond rapidly to intense rainfall and are therefore especially challenging for flood forecasting, as short warning times coincide with large meteorological and hydrological uncertainties. Within the KI-HopE-De (AI-based flood prediction in small river basins in Germany) project, which aims to develop AI-based flood forecasting for small catchments, we investigate how probabilistic numerical weather prediction can be transformed into hydrologically more useful inputs for machine-learning-based runoff prediction. The project focuses on short-range forecasts up to 48 hours and on catchments smaller than 500 km².

Our contribution addresses the meteorological side of this challenge through catchment-based post-processing of ensemble precipitation forecasts. We use hourly operational ICON-D2 ensemble precipitation forecasts with 3-hourly initializations and lead times up to 48 hours, aggregated from the native model grid to catchments. The resulting catchment-scale ensemble time series are post-processed using statistical and machine-learning-oriented methods designed to improve both calibration and hydrological relevance. In particular, we examine ensemble model output statistics (EMOS) and isotonic distributional regression (IDR) for marginal adjustment, and reconstruct temporal dependence using approaches such as ensemble copula coupling and the Schaake shuffle. This setup is designed to retain the benefits of probabilistic weather forecasts while generating physically and hydrologically more consistent forcing data for downstream flood-prediction models.

The presentation will show how these post-processing strategies perform at two connected levels. First, we will compare post-processed precipitation forecasts against the raw ensemble using probabilistic verification metrics and event-based diagnostics, with particular emphasis on extremes. Second, we will assess whether improved meteorological inputs translate into improved flood forecasts in selected severe-flood cases. In this way, the study highlights not only whether post-processing improves rainfall forecasts, but also whether these improvements are relevant for downstream hydrological AI applications in small, fast-responding catchments.

How to cite: Sharifi, E., Lerch, S., Perschke, M., and Knippertz, P.: Catchment-based post-processing of probabilistic weather forecasts for AI-supported flood prediction in Germany, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-507, https://doi.org/10.5194/ems2026-507, 2026.

In Germany, flooding in small catchments, driven by locally intense convective precipitation, has caused significant damage and in some cases fatalities in recent years. To ensure more accurate and timely operational flood forecasts and warnings, the regional German Flood Forecasting Centres require improved precipitation forecasts for short-term forecast ranges.

In this context, the ICON Rapid Update Cycle (RUC) of the well-established, high-resolution (2.2 km) regional German numerical weather prediction model ICON-D2 represents a promising approach. The RUC forecasts have been developed by the German Meteorological Service (Deutscher Wetterdienst, DWD) within the framework of SINFONY (Seamless Integrated Forecasting System). They are operationally available since summer 2024. Compared to the standard ICON-D2 model, the RUC— both deterministic and probabilistic — feature an improved two-moments microphysics scheme to better represent strong convective events, hourly updates (instead of three-hourly), a forecast horizon of 14 hours (compared to 48h), and earlier availability for users due to a reduced computation time.

Evaluating the impact of these “rapid update” characteristics on flood forecasting is one of the key objectives of the project “Co-Design of Innovations between Weather and Flood Forecasting”, which aims to strengthen the collaboration between the DWD and the regional German Flood Forecasting Centres. As part of this initiative, the Flood Forecast Centre of the State Institute for Environment Baden-Württemberg (HVZ LUBW) has been investigating the use of the RUC products for hourly operational discharge forecasting, using the distributed water balance model LARSIM (Large Area Runoff Simulation Model).

This contribution presents a verification of discharge forecasts for a large number of gauging stations in Baden-Württemberg during the summer 2024 flood events in south-western Germany. Discharge hindcasts driven by ICON-D2 and ICON-D2 RUC products were performed and systematically compared. The analysis includes both event-based case studies and an overall statistical evaluation.

How to cite: Kiseleva, O., Deutschländer, T., Rauthe-Schöch, A., Bondy, J., Bremicker, M., Badde, U., and Elfgang, D.: Towards Improved Flood Forecasting: Evaluation of Discharge Forecasts Driven by New Rapid-Update Precipitation Forecast Products of DWD, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-406, https://doi.org/10.5194/ems2026-406, 2026.

Accurate flood forecasting in small and medium-sized catchments remains a major
challenge due to rainfall prediction uncertainties, limited hydrological data at that
scale as well as short warning times related to rapid response times of such systems.
The joint German research project KI-HopE-DE (KI-gestützte Hochwasserprognose
für kleine Einzugsgebiete in Deutschland) aims to improve flood prediction in
Germany by testing new machine learning-based approaches and bringing together
meteorologists and hydrologists, both from academia and from operational services.
Within this framework, the German Weather Service (DWD) contributes by providing
high-resolution meteorological datasets to support data-driven modelling, as well as
by testing training strategies that stronger account for weather model particularities.
KI-HopE-De develops a regionally-trained Long Short-Term Memory (LSTM) using
data from 1,626 catchments across Germany. Besides the classical training based on
meteorological observations, in our study we explore the use of the novel ICON-
FORCE (Fine-scale Observation-based Reanalysis for Central Europe) reanalysis
dataset for optimizing the LSTM. This approach attempts to leverage the proximity of
the ICON-FORCE reanalysis data and the ICON-D2 forecast model later used for
inference and making forecasts. Meteorological variables derived from ICON-FORCE
reanalysis are used as input features to capture spatiotemporal dependencies and
interactions relevant for runoff generation. The study is designed as a large-sample
experiment to systematically assess the added value of high-resolution
meteorological reanalysis for data-driven flood prediction, with a particular focus on
model robustness and applicability across divers catchments.
By focusing on the interface between meteorology and hydrology, this work
contributes to ongoing efforts to better integrate atmospheric and hydrological
information in flood forecasting models.

How to cite: Khosravi, S., Valmassoi, A., Bondy, J., Dolich, A., Ehret, U., Loritz, R., and Keller, J.: Integrating meteorological and hydrological modeling in KI-HopE-DE: AI-basedflood prediction in small river basin in Germany using ICON-FORCE reanalysis, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-667, https://doi.org/10.5194/ems2026-667, 2026.

Traditionally, to simulate future river discharge on a global scale, a global hydrological model would be forced with atmospheric input data from a climate model. However, this approach does not consider any coupling effects between earth system model components such as the ocean, sea-ice, and most importantly in this case: the land-surface. Nowadays, these components are often integrated in earth system models (ESMs), allowing to directly study the interaction between precipitation, evaporation and surface and subsurface runoff. Since very recent, ESMs are capable of performing multi-decadal simulations globally at kilometer-scale  (9 by 9 km spatial resolution), allowing to (partly) resolve convection, improve orographic precipitation, and include the impact of land surface heterogeneity, while also capturing all large-scale drivers. Given these accomplishments, we are asking the questions: 1) Can we assess future discharge changes by linking a kilometer-scale ESM directly to a river routing scheme? And 2) Do we see the impact of kilometer-scale resolved processes in the atmosphere and on land reflected in the discharge representation?

Here, we use kilometer-scale earth system simulations with the Integrated Forecasting System (IFS) model for past (1990-2020) and future climate (2020-2050; SSP3-7) as input for the river routing Catchment-Based Macro-scale Floodplain (CaMa-Flood) model. This provides global high-resolution historical and future river discharge simulations. Our modelling set-up allows to study how the water balance components react and interact; from atmospheric precipitation, to land-surface feedbacks including evaporation and runoff, to river routing generating streamflow. For the analysis, we first focus on the well-studied and well-observed Rhine basin. Thereafter, we extend our analysis to a diverse set of catchments of varying size globally. With the main goal to assess the impact of kilometer-scale resolved weather and land-atmosphere interactions on future discharge signals, and specifically during floods and droughts. 

How to cite: Benedict, I., Spierenburg, A., Pedruzo Bagazgoitia, X., and M.C. Denissen, J.: Projecting future global discharge signals with kilometer-scale earth system simulations combined with river routing, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-660, https://doi.org/10.5194/ems2026-660, 2026.

In recent years, climate change has increased the spatiotemporal variability of precipitation, leading to a rise in both the frequency and severity of drought events. Consequently, the importance of water resource management and drought response has been steadily growing. Drought is not merely a result of precipitation deficiency; rather, it is a complex phenomenon that propagates from meteorological drought to agricultural, hydrological, and eventually socioeconomic drought. However, previous studies on drought propagation have largely focused on individual stages, which limits the comprehensive understanding of compound drought propagation under climate crisis conditions. In this study, regions that have experienced severe historical drought damage were selected to analyze the characteristics of drought propagation. By integrating meteorological and climate data with regional characteristic data, this study aims to develop a methodology for evaluating compound drought propagation. First, drought indices were calculated for each stage: the Standardized Precipitation Index (SPI) for meteorological drought, the Standardized Precipitation Evapotranspiration Index (SPEI) for agricultural drought, and the Streamflow Drought Index (SDI) for hydrological drought. Next, various spatiotemporal correlation analyses were conducted to investigate both spatial and temporal propagation characteristics of drought indices. Transition matrices for each drought stage were then constructed and analyzed using network analysis techniques. Furthermore, based on the identified propagation characteristics, transition probability analysis between drought stages was performed. This enabled the estimation of when meteorological drought conditions are likely to propagate into agricultural and hydrological drought stages. The proposed drought propagation-based analytical methodology quantitatively reflects both time lags and transition characteristics of drought events. It is expected to serve as a foundational tool for the development of drought early warning systems and to support decision-making in water resource management.

How to cite: Park, J. H., Bae, S. Y., and Kim, B. S.: Development of a Drought Propagation Methodology Using Meteorological and Climate Data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-102, https://doi.org/10.5194/ems2026-102, 2026.

Southern Portugal is one of Europe’s climate change hotspots. Given its arid climate, droughts in this region are already long and frequent; however, recent studies estimate that their duration and frequency may increase, putting the region’s water availability at stake. Along its western coast lies the Sado River Basin (SRB), a large, entirely Portuguese catchment that is home to some of Portugal's most important agricultural hubs. Hence, considering how important water is in the SRB, and that precipitation events over the SRB may become scarcer, projecting the basin’s future water availability is a must. This study showcases and explores the estimated flow rates (FRs) of the Sado River in southern Portugal, focusing on the projected conditions expected in a near-term future period, between 2041 and 2060. These FRs were projected under the Shared Socioeconomic Pathways: 1–2.6 W/m² (SSP1–2.6), 3–7.0 W/m² (SSP3–7.0), and 5–8.5 W/m² (SSP5–8.5), using downscaled and bias-adjusted General Circulation Model (GCM) ensemble projections from the Inter-Sectoral Impact Model Intercomparison Project (CHELSA-ISIMIP3b-Sado) as input for hydrological simulations with the Hydrological Simulation Program—FORTRAN (HSPF). To our knowledge, this research marks the first time that future climate projections from the 6th phase of the Coupled Model Intercomparison Project (CMIP6) were used as input to hydrological simulations of the SRB. CHELSA-ISIMIP3b-Sado projections have shown increases in temperature, and decreases in precipitation during autumn and spring months. Moreover, the HSPF simulation outputs have projected reductions in the Sado FRs, which could cause decreases in yearly accumulated riverine water volume (29% under SSP3–7.0 and 33% under SSP5–8.5), as well as increases in summer riverine water deficit (31% under SSP3-7.0). In addition, there is a projected increase in the variability of precipitation during the winter months, and as a consequence, meeting surface water demands of the Sado region may be delayed by up to 22 days. Therefore, in the coming decades, winter precipitation could become even more important for the recharge of reservoirs and the fulfilment of summer surface water needs in the SRB. Considering the predominance of the agricultural sector in the region, the results of this research should prompt local agricultural stakeholders and administrative institutions to enhance winter surface water storage and management in order to meet summer crop irrigation demands.

How to cite: Claro, A., Fonseca, A., Fernandes, A., Menz, C., Almeida, C., Fraga, H., and Santos, J.: How will climate change impact Southern Portugal flow rates? A CMIP6-HSPF modelling approach, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-381, https://doi.org/10.5194/ems2026-381, 2026.

Downscaled key statistics for 24-hr daily rainfall, the wet-day frequency and wet-day mean precipitation, can provide a basis for input to hydrological models with the help of a simple weather generator. A demonstration is provided by the SPRINGS project (https://www.springsproject.eu/) that models the link between climate and public health. It focuses on some specific pilot regions, which include Ghana, Romania, Tanzania and Italy, and the lessons learned may also benefit other parts of the world. For example, water quality and quantity is modelled for the study of the dispersion of waterborne pathogens that lead to diarrhoea in the Volta catchment of Ghana and provide challenges for the water supply in Timisoara, Romania. A model chain employed by the SPRINGS project is presented together with preliminary results, starting from global climate models and ending with specific regional policy advice. The work includes empirical-statistical downscaling (ESD) of large multi-model CMIP5/6 ensembles of global climate models and the estimation of the number of days with heavy 24-hr rainfall. It is important to produce robust and reliable information that can provide a basis for decision-making. While robust and reliable downscaled results imply large multi-model ensembles, various downscaling strategies, and downscaling statistical parameters, hydrological models typically need time series of hourly or daily weather. Hence, an optimal chain of models, involving climate, downscaling, hydrological, epidemiological and economical models, needs to make some compromises. Here, a strategy for cross-disciplinary coproduction of knowledge is presented, with a particular emphasis on climate and hydrology while keeping in mind the needs for practical policy-making.

How to cite: Benestad, R.: A model chain for studying the link between climate and the dispersion of waterborne pathogens involving downscaling and hydrological models, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-472, https://doi.org/10.5194/ems2026-472, 2026.

Rain-on-snow (ROS) events occur when liquid precipitation falls onto snowpack, leading to accelerated snowmelt and the formation of ice layers. These events decrease albedo, intensifying the snow-albedo feedback, and can trigger avalanches and increase flood risk due to the combined effects of rainfall and snowmelt. As climate change shifts precipitation patterns from snow to rain, ROS events are becoming more frequent and intense, making their accurate detection increasingly important. Passive microwave satellite data offer promising potential for detecting ROS events. However, most previous studies have focused on a limited number of frequency channels, typically 19 and 37 GHz. Recently, L-band (1.4 GHz) has gained attention, but a comprehensive evaluation across the full microwave spectrum is still lacking. This study aims to address this gap by assessing the suitability of multiple microwave channels for ROS detection. We analyze the feasibility of identifying ROS events using brightness temperature (Tb) data from the Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR-2) satellites (1.4-89.0 GHz), evaluated against in situ observations. Additionally, we use the Snow Microwave Radiative Transfer (SMRT) model to simulate the impact of ROS events on Tb across a wide range of microwave frequencies. The results reveal distinct Tb changes during ROS, confirming microwave sensitivity to liquid water in snow. Using the Normalized Polarization Ratio (NPR), we find that L‑band NPR is particularly effective for identifying ROS events, consistent with SMRT simulations. Our analysis shows that a high NPR indicates a ROS event. We further apply this method across the Arctic and compare the results with ERA5 reanalysis, showing promising agreement. Overall, our findings advance the development of microwave-based approaches for detecting and monitoring ROS events under changing Arctic climate conditions.

How to cite: Kouki, K., Colliander, A., and Riihelä, A.: Detecting rain-on-snow events in the Arctic using passive microwave satellite data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-162, https://doi.org/10.5194/ems2026-162, 2026.

Rainfall interception loss (E_i) by vegetation plays a crucial role in the regional and global water cycle, as intercepted rainfall does not enter the terrestrial hydrological cycle. Moreover, E_i can account for up to 10–50% of gross precipitation in forested ecosystems. Accurate estimation of E_i is therefore essential for applications such as drought monitoring and prediction, flood prevention, and river discharge prediction.

Several approaches exist to estimate E_i, including empirical methods, numerical models such as the Rutter model, and analytical models such as the sparse Gash and van Dijk–Bruijnzeel models. The latter have proven particularly useful due to their simplicity and their assumption of one storm per day, which enables application at the global scale with minimal input data. When properly calibrated, these models provide reliable long-term estimates of E_i in forested environments. However, when applied at the scale of individual rainfall events without recalibration, they tend to overestimate (respectively underestimate) E_i for low (respectively high) observed values. These discrepancies may arise from uncertainties in parameter estimation, input data errors, conceptual model errors, or observational uncertainties.

To investigate the potential conceptual model errors in the sparse Gash and van Dijk–Bruijnzeel models, new analytical model formulations are introduced, which explicitly represent canopy drainage, based on the exponential drainage function of the Rutter model. Additionally, to systematically investigate the sources of error, a modular modelling framework is employed. This framework allows for multiple configurations of input data, variable implementations (e.g., canopy cover and storage, precipitation intensity and evaporation rate) and the aforementioned model structures. The objective is to develop a robust interception model capable of providing accurate daily estimates at the global scale using readily available data, while remaining applicable to both long-term observational datasets and individual rainfall events.

All combinations of input data, parameter estimation approaches, and model structures are evaluated using both literature-based parameter values and optimised parameter sets. Model calibration and validation are first conducted on a large subset of long-term observational water balance studies reported in literature. The optimised parameter sets are subsequently validated against an independent subset of long-term datasets, as well as event-based water balance observations. In addition, the modular framework enables a systematic assessment of the epistemic uncertainty associated with each modelling choice.

Finally, a global daily dataset of E_i (1980–present) at 0.1° spatial resolution is presented, derived from the best-performing model configuration. This dataset provides new opportunities to investigate the interactions between E_i and climate- and weather-related processes. Furthermore, this work is framed within the activities of the new ESA CCI Land Evaporation project and is a first example of the usage of the modular framework, which will be expanded towards a multiphysics land evaporation model.

How to cite: Delbare, K., Baez-Villanueva, O. M., Bonte, O., Schellekens, J., Zhong, F., and Miralles, D. G.: Improving event-scale and long-term estimates of rainfall interception using a modular analytical modelling framework, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-597, https://doi.org/10.5194/ems2026-597, 2026.

How to cite: Roque, J., Siebert, S., and Valmassoi, A.: Soil matters: Evaluating soil texture and irrigation impacts in Earth system simulations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-589, https://doi.org/10.5194/ems2026-589, 2026.

The probable maximum flood (PMF) is a key factor in dam safety and other hydrologic risk assessment contexts. It is often calculated using the probable maximum precipitation (PMP) in conjunction with streamflow prediction models. However, many such models are available, and structural differences between models and their differing data requirements could lead to significant differences in the PMF estimates they generate. We tested this hypothesis by estimating the PMF for the Alouette River in southwest British Columbia, Canada, using three models selected in part for their diversity: WRF-Hydro (complex spatially distributed process-based model), UBCWM in Raven (intermediate-complexity semi-distributed process-based model) and a machine learning model (lumped empirical model). WRF-Hydro provides the most physically complete representation and is also the most resource-intensive and data-demanding. UBCWM offers an effective balance between realism and efficiency, while the ML approach, though less physically interpretable, is computationally inexpensive. The models were developed while holding the meteorological inputs constant, although all three models handle the meteorological input very differently and have different input requirements. In addition to temperature and PMP sequences, WRF-Hydro required wind speed, radiation and specific humidity inputs which provided an additional challenge of how these inputs might change under a probable maximum storm. All three model approaches produced plausible PMF magnitudes, yet these PMF estimates varied by more than 400 m³/s or 28%, in terms of maximum peak flow and by about 8,000 m³ in terms of event volume. We also utilised IES-PEST++ to quantify parameter estimation uncertainty for the process-based models, which fell in the range of 12% to 16%. Since the PMF estimates produced by all three models appear reasonable for practical adoption—and the true value cannot be empirically verified — these results underscore the importance of explicitly accounting for uncertainty in PMF estimation. Additionally, machine learning models are not commonly applied in PMF studies due to well-known concerns around extrapolation to unseen data, we show that even a relatively simple ML model can extrapolate usefully to values more than 40% greater than the data it was trained on. The outcomes of this study provide a quantitative measure of the implications of hydrologic model and parameter selection uncertainty on PMF estimation and imply that a multi-model ensemble could be an effective pathway toward capturing that uncertainty in flood risk assessment processes.

How to cite: Legarth, E., Stull, R., and Fleming, S.:  The role of hydrologic model choice in probable maximum flood (PMF) estimation uncertainty , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-40, https://doi.org/10.5194/ems2026-40, 2026.