Flash Flood Modeling Research Articles

Toward Flash Flood Modeling Using Gradient Resolving Representative Hillslopes

AbstractIt is increasingly acknowledged that the acceleration of the global water cycle, largely driven by anthropogenic climate change, has a disproportionate impact on sub‐daily and small‐scale hydrological extreme events such as flash floods. These events occur thereby at local scales within minutes to hours, typically in response to high‐intensity rainfall events associated with convective storms. In the present work, we show that by employing physically based representative hillslope models that resolve the main gradients controlling overland flow hydrology and hydraulics, we can get reliable simulations of flash flood response in small data‐scarce catchments. To this end, we use climate reanalysis products and transfer soil parameters previously obtained for hydrological predictions in an experimental catchment in the same landscape. The inverted mass balance of flood reservoirs downstream is employed for model evaluation in these nearly ungauged basins. We show that our approach using representative hillslopes and climate data sets can provide reasonable uncalibrated estimates of the overland runoff response (flood magnitude, storm volume, and event runoff coefficients) in three of the four catchments considered. Given that flash floods typically occur at scales of a few km2 and in ungauged places, our results have implications for operational flash flood forecasting and open new avenues for using gradient resolving physically based models for the design of small and medium flood retention basins around the world.

Compilation method of a catalogue of reasonable worst-case rainfall series for flash flood simulations of short, convective rainstorms

In this study, a categorisation method was developed and applied to identify, select and rank reasonable worst-case rainfall storm events with time-series resolution of one minute and duration of up to one hour. The method yields a catalogue of 24 reasonable worst-case rainfall series, i.e. potentially catastrophic events that could be used for safe-to-fail flash flood model scenarios, thus encouraging the testing of critical infrastructure and pedestrian safety for people trapped in floods. The identified 24 events were shown to be considerably more extreme than corresponding design storms, with a return interval of 100 years. In the selection of the extremes, special care was taken to consider different hazard attributes, which are associated with intrinsically different observed hyetograph shapes, including rectangular, short and long triangle, and double peak shape categories. Hydrodynamic simulations were carried out using the 2D robust shallow water model hms++ to quantify the flooding impact of the 24 rainfall scenarios in a small urbanised catchment, which enabled the ranking of the scenarios based on different hazard attributes. Overall, the rectangular and long-triangle events showed the highest total discharge, and the rectangular events comprise both the longest duration for hazard velocity and deep flooding. The long-triangle events presented the highest peak discharge and peak water depths. The short-triangle ones showed the highest flashiness (shortest time to peak), whereas the double peak events yielded the longest time to peak. The ranking emphasised the importance of the hyetograph shape characteristics of extreme storms and which needs to be taken into account in flash flood management. The novel categorisation method is easily transferable to other regions, if high-resolution rain series are available.

Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions.

Downscaling the Z-R relationship and bias correction solution for flash flood assessment in a data-scarce basin, Thailand.

Weather radar is a form of alternative indirect rainfall measurement for use in mitigating flash flood hazards. It is a challenging task to obtain accurate radar rainfall data without integration with automatic rain gauge networks. This paper investigated transformation equations to convert the calibrated daily Z-R relationship to the sub-hourly scale and proposed optional schemes for downscaling the daily bias adjustment factor into 15 min resolution scale to produce a high-resolution radar rainfall product for flash flood modelling. Radar reflectivity data from three radar stations in Thailand and their corresponding daily gauge rainfall data were used in the analysis. Two bias adjustment schemes (DMFB and DS_DMFB), accounting for the temporal variation, and one spatiotemporal scheme (SPTB_IDS) were used to generate three corresponding rainfall datasets for the unified river basin simulator (URBS) model to simulate flood hydrographs in the Tubma basin, Thailand. The results showed that combining the proposed 15-min Z-R scaling equation and the SPTB_IDS produced the most reliable radar rainfall amount leading to an increase in the accuracy of flood modelling with the lowest uncertainty. This indicated that the temporal downscaling solution together with spatial interpolation technique for sub-hourly radar rainfall assessment could benefit flash flood simulation in a data-scarce basin.

An efficient flash flood forecasting system for the un-gaged Meghna basin using open source platform Delft-FEWS

The northeast region of Bangladesh is highly vulnerable to flash floods which often damage the only crop of that region, the boro rice. Developing an efficient and real-time flash flood forecasting system for that region is found essential. The traditional model-centric approach requires plenty of time and manpower to effectively model flash floods. This paper discusses an open-source data-centric integrated flash flood modelling approach using a platform named Delft-FEWS. It is based on three inter-dependent models, e.g., meteorological, hydrologic, and hydrodynamic. The model was calibrated and validated with the flash flood data of 2010-12 and 2013-15, respectively. The performance of the model in real-time was assessed in the context of the 2021 flash flood season. In both cases, it performed satisfactorily. The entire system helps the practitioners to work in a flexible environment to generate flash flood forecasting and early warnings.

Regional-scale prediction of pluvial and flash flood susceptible areas using tree-based classifiers

• Literature review on explanatory features used in flash flood modeling. • Comparison of three tree-based classifiers for a large investigation area. • Use of 11 spatially distributed and 6 catchment-related explanatory features. • Model interpretation using model-specific and model-agnostic methods. Flood events triggered by heavy rain, such as pluvial and flash floods, are a common threat worldwide. However, it is usually not known which areas and cities are particularly vulnerable to flooding caused by heavy rain. To enable regional-scale susceptibility assessment, we developed a novel methodology based on tree-based classifiers that considers both spatially distributed and catchment-related influencing factors. The performance of the developed methodology was evaluated using the region of Bavaria (Germany). For the case study area (70,500 km 2 ), we trained a Random Forest (RF), a Gradient Boosting Decision Tree (GBDT), and a CatBoost model (CB) using 1,864 flood and non-flood locations and 11 spatially distributed and six catchment-related influencing factors. Regarding performance metrics, all three models performed equally well (CB: AUC = 0.819, RF: AUC = 0.816, GBDT: AUC = 0.813), with the CatBoost model performing best. When modeling large areas, it proved critical to account for low sample density by ensuring (i) a homogeneous spatial coverage of the study area and (ii) the representation of the major landscapes in the training and test set. In addition, we propose an overall susceptibility score for cities based on the susceptibility map generated, which can be used to prioritize cities for detailed investigations. Those responsible for spatial planning and flood risk management can apply the proposed methodology to obtain a pluvial and flash flood susceptibility assessment for large territories.

Does Flash Flood Model Performance Increase with Complexity? Signature and Sensitivity-Based Comparison of Conceptual and Process-Oriented Models on French Mediterranean Cases

We compare three hydrological models of different complexities, GR4H (lumped, continuous), SMASH (distributed, continuous), and MARINE (distributed, event-based), for Mediterranean flash flood modeling. The objective was to understand how differently they simulate the catchment’s behavior, in terms of outlet discharge and internal dynamics, and how these can help to improve the relevance of the models. The methodology involved global sensitivity analysis, calibration/validation, and signature comparison at the event scale with good performances. For all models, we found transfer parameters to be sensitive in the case of Gardon and production parameters in the case of Ardeche. The non-conservative flow component of GR4H was found to be sensitive and could benefit the distributed models. At the event scale, the process-based MARINE model at finer resolution outperformed the two continuous hourly models at flood peak and its timing. SMASH, followed by GR4H, performed better in the volume of water exported. Using the operational surface model SIM2 to benchmark the soil moisture simulated by the three models, MARINE (initialized with SIM1) emerged as the most accurate. GR4H followed closely, while SMASH was the least accurate. Flexible modeling and regionalization should be developed based on multi-source signatures and worldwide physiographic databases.

Scale-dependence of observational and modelling uncertainties in forensic flash flood analysis

The mismatch between the space–time scales of flash flood occurrence and those of the typical hydro-meteorological monitoring networks has stimulated the development of forensic flash flood analysis, which involves post-flood indirect peak discharge estimation in ungauged channels and flood response modelling driven by weather-radar rainfall estimates. However, both approaches are potentially affected by significant uncertainties. Assessment of scale dependence of such uncertainties is important to identify how uncertainty affecting forensic flash flood analysis increases with decreasing basin size. In this work, we apply the forensic methodology to the flash flood of November 18, 2013 in Sardinia (Italy). We introduce the ‘flash flood forensic consistency index’ as a tool to compare the probability distribution of peak discharge uncertainties from observational and model estimates, and to determine the scale effects of the forensic analysis concept. Uncertainties in field-based peak flow estimates are evaluated through a first-order error analysis of the Taylor-series approximation of the slope-conveyance method, whereas uncertainties in flash flood modelling are based on the Generalized Likelihood Uncertainty Estimation methodology using a distributed hydrologic model. Results show no significant relationship between observational and modelling uncertainties, considered independently, and basin area and channel bed slope. Conversely, when considering the interaction between the two uncertainty distributions, a relationship arises between their degree of overlap and basin size. In particular, with decreasing basin area or increasing channel bed slope, the absolute relative bias between the estimated peak flow values increases more than their relative uncertainties, decreasing the consistency index. This calls for more robust approaches for the analysis of flash flood response in small-sized rugged-relief mountain basins, which are of high interest for flood risk management.

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

Flash Flood Modeling Using HEC-RAS (2D) model on Wadi Reem in the western region, Kingdom of Saudi Arabia

Various infrastructures in Saudi Arabia are affecting by floods and rainfall that may damage constructions at urban areas due to water flows in the main valleys and streams. Asser Region in western Saudi Arabia is characterized by its high rainfall intensity that leads to flash floods. The present study aims to evaluate and identify potential floods in coastal areas of Wadi Reem basin and to show the urban areas exposed to flooding with flood water in the coastal city. Runoff calculation is an important part of regional runoff forecast and water resources evaluation model. The Soil Conservation Service (SCS) curve number (CN) model developed by the U.S. Department of Agriculture National Resources Conversion Service (NRCS) is the most popular and widely applied model for direct runoff estimation. The current work used HEC-RAS (2D) in order to simulate Flood map depth and velocity in Wadi Reem coastal areas. Watershed and its stream was delineated using digital elevation model (DEM) and morphological parameters calculated using Watershed Modeling System (WMS). Rainfall frequency analysis was performed for selected three rainfall gages using annual maximums of 24-h rainfall. This study deals with geographic information systems (GIS), hydrologic modeling (water modeling system, WMS), and hydraulic modeling (Hydrologic Engineering Center River Analysis System, HEC-RAS) to evaluate the impact of flash flood hazards on Wadi Reem coastal area.

Investigation of the drainage loss effects with a street view based drainage calculation method in hydrodynamic modelling of pluvial floods in urbanized area

Drainage loss effects are crucial for flash flood modelling induced by torrential rains in urbanized environment. This paper proposes a novel approach for incorporating drainage loss effects into two-dimensional hydrodynamic overland flow simulations with the open source databases offered by geographical information providers. Street inlet locations within the road network are determined using street view images based on feature points extracted throughout automatic navigation along the roads in the panoramas, where the runoff of the surface flood flow is determined by the drainage network's capacity. By implementing the proposed data acquisition and drainage loss calculation method into the 2D overflow model HiPIMS as an alternative to the often-unrealizable physically based method or to the overly simplistic rainfall reduction method, the flood modelling results are validated against field observations during severe flood events in an intensely urbanized area to demonstrate the feasibility and effectiveness of the newly proposed method. In comparison to other simulation scenarios without drainage loss effects or with those considered by the rainfall reduction method, the drainage loss effects induced by the newly proposed method are analyzed for both the spatially lumped and distributed urban flood patterns. While the drainage effects are more essential after the rainfall peak hours for the flood extent as spatially lumped results, the low-lying area with dense building complexes are found to benefit more from the drainage effects in a spatially distributed way. Variations of the speed and direction of flood flows caused by the drainage effects at inlets are observed to depend on the surrounding environment. Competitions between the drainage effects and the gravity effects induced by topographical features are revealed with street-level evaluations of the flood modeling results using high-resolution hydrodynamic modeling in conjunction with the proposed drainage calculation approach.

Improving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data

Accurate estimation of spatiotemporal precipitation dynamics is crucial for flash flood forecasting; however, it is still a challenge in Andean-Amazon sub-basins due to the lack of suitable rain gauge networks. This study proposes a framework to improve hourly precipitation estimates by integrating multiple satellite-based precipitation and soil-moisture products using random forest modeling and bias correction techniques. The proposed framework is also used to force the GR4H model in three Andean-Amazon sub-basins that suffer frequent flash flood events: upper Napo River Basin (NRB), Jatunyacu River Basin (JRB), and Tena River Basin (TRB). Overall, precipitation estimates derived from the framework (BC-RFP) showed a high ability to reproduce the intensity, distribution, and occurrence of hourly events. In fact, the BC-RFP model improved the detection ability between 43% and 88%, reducing the estimation error between 72% and 93%, compared to the original satellite-based precipitation products (i.e., IMERG-E/L, GSMAP, and PERSIANN). Likewise, simulations of flash flood events by coupling the GR4H model with BC-RFP presented satisfactory performances (KGE* between 0.56 and 0.94). The BC-RFP model not only contributes to the implementation of future flood forecast systems but also provides relevant insights to several water-related research fields and hence to integrated water resources management of the Andean-Amazon region.

Flash flood modeling and mitigation of Al-Hussainiyah area, Makkah, Saudi Arabia

Flash flood modeling and mitigation of Al-Hussainiyah area, Makkah, Saudi Arabia

Flash flood modeling in the data-poor basin: A case study in Matina River Basin

Forecasting flooding hazards is a very effective non-engineering measure for flood control. Presently, the commonly used forecasting method in simulating flash flood events is through a watershed hydrological model. Many Asia-Pacific countries like the Philippines are prone to frequent hydrometeorological hazards such as tropical cyclones, resulting in frequent heavy rainfall events. However, most rivers in the many basins lack water flow observation data, which makes it challenging to use lumped and data-driven models for flash flood forecasting. With the continuous progress of remote sensing (RS) and geographic information system (GIS) techniques, the physically-based distributed hydrological model (PBDHMs) has rapidly advanced. PBDHMs can directly determine the model parameters according to the underlying surface characteristics from remotely-sensed data, which makes it possible for flood forecasting in areas with little to virtually no data. In this study, the Matina River basin in Davao City was selected as a case study in simulating a small data-poor basin in the region. The Liuxihe model was used to formulate a forecasting scheme and simulated the past flash flood events. The results show that there is a good correspondence between the past heavy rainfall events and their corresponding simulated river discharges. The results conform to the hydrological regularities, which can be used for flood forecasting and can serve as a baseline for the development of a flood forecasting system in the rivers of Davao City and elsewhere.

Environmental sensitivity of flash flood hazard based on surface water model and GIS techniques in Wadi El Azariq, Sinai, Egypt

Wadi El Azariq basin constitutes the administrative division of the province in the northeastern part of the Sinai Peninsula, Egypt. This wadi represents one of the most promising areas for different activities due to its hydrological and location importance. This study aims to implement a new methodology for flash flood assessment and modeling in this strategic region through the integration between physiographic features, surface water models (WMS and HEC-HMS), as well as Geographic Information System (GIS). Thirty-eight morphometric elements were analyzed qualitatively and quantitatively using topographic maps and recent satellite images. These parameters were inter-linked to yield nine important factors for assessment of the flash flood hazardous, which were determined and grouped in both high and low degrees. Also, hydrographs for Wadi El Azariq basin were constructed with different recurrence stages of extremely (maximum) daily rainfall using the last phase carried out in the modeling procedure. The obtained result proved that the total surface runoff ranges from 4.1×106 m3 to 13.8×106 m3 at recurrence stages of 5 years and 100 years respectively based on the maximum daily rainfall events. This study draws specifics about the flash flood-prone areas, planning rainwater harvesting and flood management approaches of Wadi El Azariq basin. Besides, areas of inundation and groundwater potentiality were also determined as the surface runoff potentiality.

A multi-sourced assessment of the spatiotemporal dynamics of soil moisture in the MARINE flash flood model

Abstract. The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.

FLASH FLOOD MANAGEMENT IN ARID REGION: A CASE STUDY URBAN BANI WASIL VILLAGE, SOHAG GOVERNORATE

Flash floods are serious phenomenon that attacks and troubles people in many areas’ under-mountain region, so the society should deal with these extreme natural events. This article is showing an example of flash flood modeling to protect the urban bani wasil village from the flash flood risk, so obstacle dam with channel have been used to flash flood mitigation purpose. A design flood wave that used, is for recommence period of 100 years, which estimate by HEC-HMS to be 456,000 m3 total runoff volume. EPA’s SWMM software was used for numerical modeling, which includes flood mitigation calculation. Additionally, the cost for dam with channel construction was estimated as early stage for the project.

Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt

AbstractDigital surface models, land use and rainfall data were used to simulate water areas using Hydrologic Engineering Centres River Analysis System (HEC‐RAS) software. Multi‐temporal synthetic aperture radar (SAR) was used for the detection of flood prone area to calibrate HEC‐RAS, due to the lack of data validation in the New Cairo City, Egypt. The thresholding water detection method was applied to two Sentinel‐1 images, one pre‐ and one post‐flash flood event from April 24 to 27, 2018. The threshold method was used to detect water areas from SAR Sentinel‐1 images. Feature statistical agreement F1 and F2 values ranged from 73.4 to 77.7% between water areas extracted based on backscattering values between 19.97 and 16.53 in decibels (dB) and reference water areas obtained using an optical image of the Sentinel‐2 satellite. The similarity between simulated HEC‐RAS two‐dimensional (2D) of water areas and reference water areas based on SAR data ranged between 74.2 and 89.7% using feature statistical agreement values F1 and F2. It provides a clear suggestion that, in the absence of field observations, SAR data can be used to calibrate the model. Two flood hazard maps created based on water velocity and depth were obtained from HEC‐RAS 2D simulation. The obtained maps indicated that 11% of the roads and 50% of the buildings in New Cairo City are exposed to high hazard areas. Furthermore, 28% of the bare land is situated in a very high vulnerability area. We recommend the use of obtained hazard map in supporting emergency response, and designing effective emergency plans.

Flash Flood Modeling on Wadi Reem in the western region, Kingdom of Saudi Arabia

Most of Saudi Arabia can be considered a typically arid region where the annual average amount of rainfall varies between 50 and 100 mm. Asser Region in western Saudi Arabia is characterized by high rainfall intensity that leads to flash floods. Using digital elevation model (DEM) Watershed and its stream were delineated and morphological parameters calculated using Watershed Modeling System (WMS). Rainfall frequency analysis was performed for selected three rainfall gages using annual maximums of 24-h rainfall. As a result of the sparse hydrologic information, the relation between rainfall and runoff was calculated depending on the morphometric parameters, GIS techniques, Watershed Modeling System (WMS) and Hydrologic Engineering Center- Hydrologic Modeling System software (HEC HMS). In this work, the flood risks were studied and the results are considered the baseline data required to designate the required protection system against these risks. This study aims to assess the impact of flash floods in the Wadi Reem basin, which located in Assir Region, Saudi Arabia

On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment

Abstract. Calibration of a conceptual distributed model is challenging due to a number of reasons, which include fundamental (model adequacy and identifiability) and algorithmic (e.g., local search vs. global search) issues. The aim of the presented study is to investigate the potential of the variational approach for calibrating a simple continuous hydrological model (GRD; Génie Rural distributed involved in several flash flood modeling applications. This model is defined on a rectangular 1 km2 resolution grid, with three parameters being associated with each cell. The Gardon d'Anduze watershed (543 km2) is chosen as the study benchmark. For this watershed, the discharge observations at five gauging stations, gridded rainfall and potential-evapotranspiration estimates are continuously available for the 2007–2018 period at an hourly time step. In the variational approach one looks for the optimal solution by minimizing the standard quadratic cost function, which penalizes the misfit between the observed and predicted values, under some additional a priori constraints. The cost function gradient is efficiently computed using the adjoint model. In numerical experiments, the benefits of using the distributed against the uniform calibration are measured in terms of the model predictive performance, in temporal, spatial and spatiotemporal validation, both globally and for particular flood events. Overall, distributed calibration shows encouraging results, providing better model predictions and relevant spatial distribution of some parameters. The numerical stability analysis has been performed to understand the impact of different factors on the calibration quality. This analysis indicates the possible directions for future developments, which may include considering a non-Gaussian likelihood and upgrading the model structure.