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HS4.7

EDI
Advances in the hydrologic and hydraulic modelling and design for extreme floods

The occurrences of extreme flood events have increased globally in the last two decades as noted by recent rare and catastrophic flooding events in Germany, Belgium, China, the USA and in the monsoon season of India. Advanced innovative methods and conceptual improvements in existing approaches are required to address the modelling and management of the spatial and temporal complexity of extreme floods. The observed increase in frequency and severity of events can be predicted by joint probabilistic analyses of precipitation and river flow extremes. Evidence from the rare extreme events indicates that assumptions of Holocene climate stationarity is not applicable anymore for hydrologic analysis and design. The observed significant changes in weather patterns and characteristics that lead to extreme precipitation in different parts of the world far exceeded the design capacities of local protection infrastructures and systems – resulting in massive flooding, casualties, and economic losses. The watershed response to the extreme precipitation is the worst when combined with saturated steep catchments combined with antecedent moisture conditions. Prediction of region-scale and localized extreme events well ahead of time is a real challenge. New design protocols have required that account for uncertainties in future meteorological events and provide flexibility in the design and operation of infrastructure to minimize the consequences of extreme events. Understanding the mechanisms of extreme precipitation and its hydro meteorological connection with flooding, especially under the circumstances of global climate change, is critical for flood prevention and mitigation. This session invites research papers that focus on scientific and technological developments in extreme precipitation estimation, flood monitoring, and flood modelling, with the end goal of improving flood prevention and mitigation. The research studies discussing advancements in situ measurement and remote sensing of extreme precipitation, rainfall-runoff modelling, statistical and hydrological analysis of extreme precipitation and flood, flood forecasting and warning, and impact assessment of climate change and land use/cover change on flood are also invited. Research works that emphasize and discuss case studies on modelling extreme events are also expected to gain and learn from insights gained from flood disaster modelling and management.

Convener: Sanjaykumar Yadav | Co-conveners: Ramesh Teegavarapu, Biswa Bhattacharya, Rashmi YadavECSECS, Ayushi PanchalECSECS
Presentations
| Wed, 25 May, 08:30–09:36 (CEST)
 
Room 2.17

Wed, 25 May, 08:30–10:00

Chairpersons: Ilias Pechlivanidis, Ramesh Teegavarapu, Sanjaykumar Yadav

08:30–08:40
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EGU22-1457
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solicited
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Highlight
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Virtual presentation
Luisa-Bianca Thiele et al.

For derived flood frequency analyses, stochastic rainfall models can be linked with rainfall-runoff models to improve the accuracy of design flood estimations when the length of observed rainfall and runoff data is not sufficient. Previous studies have shown here that for an optimal calibration of rainfall-runoff models, flood statistics should be considered and the same input data should be used for the calibration as for the application of the model. In general, however, the observed runoff data as annual maximum values are too short to follow the classical split-sampling approach and divide the sample into a calibration and validation period. To ensure an independent validation of the calibrated rainfall-runoff models with an increased sample size to enable split-sampling, this work will investigate a calibration framework using monthly maximum values of the observed runoff. The objective function takes into account flood statistics of monthly maximum flows, e.g. l-moments of the independent peaks and the ratios between peak and volume. The conceptual rainfall-runoff model HBV-IWW is driven by stochastically generated rainfall data on an hourly time step for 140 meso- and macroscale (30km² - 1500km²) catchments in Germany. The results of this calibration framework could be used as benchmarks for future studies.

How to cite: Thiele, L.-B., Pidoto, R., and Haberlandt, U.: Calibration framework for derived flood frequency analysis driving a rainfall-runoff model with stochastically generated rainfall data , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1457, https://doi.org/10.5194/egusphere-egu22-1457, 2022.

08:40–08:47
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EGU22-12560
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ECS
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Virtual presentation
Estimating critical rainfall for flash flood warning using integrated hydrologic-hydrodynamic modelling
(withdrawn)
Konstantinos Papoulakos et al.
08:47–08:54
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EGU22-3389
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ECS
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Virtual presentation
Lea Dasallas et al.

The integrated multiscale urban flood model (IMUFlood Model) is developed to incorporate the hydrologic influence of rainfall-runoff, and surface and sewer pipe interaction in a grid-size varying scheme for urban flooding. The aim of the research is to solve the calculation of the multiscale integrated relationship between the watershed-scale flood routing to urban domain-scale inundation, and the flow interaction between the surface and drainage pipe system.  The integration was performed by applying kinematic equation on the coarser-resolution watershed grid and 2D shallow water equation on the higher-resolution urban inundation domain. Likewise, the surface and subsurface interaction are calculated in the storm drain inlets using weir and orifice equations and the flow within the pipe system was estimated using Priessmann slot model discretized in finite volume and Euler Method. The flood extent and depth are validated for an extreme rainfall event in Marikina basin, Philippines.

Results show the possibility to simulate urban inundation without the need to require observed boundary conditions which opens the possibility of the use of rainfall forecast data for real-time flood prediction. The developed model can provide flood information such as the concentration of flood, estimated peak time, flood source point and flow velocity. The computation of spatial variations of pipe flow, wetted area and water depth inside the pipe can be used to identify the flood susceptible regions. This information can be used as supplementary tools to aid for early warning and flood prevention, as well as to be used for the improvement of sewer construction in decreasing urban flood risk.

How to cite: Dasallas, L., An, H., and Lee, S.: Integrated Multiscale Urban Flood Modeling with Drainage pipe system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3389, https://doi.org/10.5194/egusphere-egu22-3389, 2022.

08:54–09:01
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EGU22-10864
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ECS
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Virtual presentation
Zimeena Rasheed et al.

Global hydrologic climate assessments posit increasing flood risk. Hydrologic forecasting is critical in both gauged and ungauged basins having implications not only for hazard assessments and the development of mitigation strategies but also for informing the design and operation of critical infrastructure. The hydrology community grapples with the need to predict floods particularly in ungauged basins where the absence of continuous and spatially representative precipitation and streamflow data are enunciated. 

Global precipitation observations from satellite constellations combined with recent advancements of hydrologic forecasting with machine-learning (ML) models, offer an attractive solution for addressing flood prediction in ungauged regions. Towards that end, in this work, we investigate a) the performance of ML flood prediction models integrated with satellite precipitation estimates and b) the transferability/applicability of ML models trained in data rich regions for flood prediction in ungauged regions. We use NASA IMERG precipitation dataset for ML-based predictions and we train the ML models for ~600 catchments from different hydroclimatic zones in Contiguous US. The performance of the ML-IMERG predictions are then evaluated for a large number of catchments (~1000) in the UK, Brazil, Chile and Australia. Predictive performance is evaluated with respect to climate and catchment characteristics in each region. Results suggest that despite the variability in the performance across regions, there is great promise on the integration of global satellite precipitation estimates with ML models for flood prediction in ungauged basins.  

How to cite: Rasheed, Z., Aravamudan, A., Anagnostopoulos, G., and Nikolopoulos, E.: Flood prediction in ungauged basins with machine learning and satellite precipitation data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10864, https://doi.org/10.5194/egusphere-egu22-10864, 2022.

09:01–09:08
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EGU22-8051
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ECS
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Highlight
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Virtual presentation
Ayushi Panchal et al.

Water is the most essential resource, which is naturally available. Poor management of water can cause drought in some areas and also floods in some areas. Flood is one of the most disastrous events which can cause tremendous losses. In India, the techniques which are developed for real time flood forecasting are based on deterministic as well as statistical approach. The quantification of uncertainties is having the primary importance in flood modelling systems. The forecasts are simulated multiple times with slight changes in initial conditions as well as model parameters. This is known as ‘Ensemble forecasting’. The Ensemble Techniques approach minimizes the uncertainties in the forecasting. This approach is used in Numerical Weather Prediction (NWP). The advance techniques like remote sensing, data acquisition and monitoring system, hydrologic modelling have led to progress in the flood forecasting techniques and skills. The ensemble approach has potential for creating and disseminating the probabilistic predictions, extending lead-time as well as quantification of predictability. Due to ensemble techniques, the capability to issue the flood warnings and alerts can be increased. In addition to forecast the flood, the ensemble techniques can be used for the reservoir operation, drought estimation, hydropower as well as water management. Hence, moving towards probabilistic approach from deterministic approach would be much helpful to develop reliable flood forecasting systems.  Ensemble forecasting is the probabilistic approach and has the ability for giving information of probability of occurrence for the extreme events. The ensemble forecasting approach is used successfully in various countries of the world. India also needs the reliable approach for producing the operational forecasts.

How to cite: Panchal, A., Yadav, Dr. S. M., Yadav, R., and Patel, A.: A Review on Need of Application of Ensemble Techniques for Streamflow Forecasting in India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8051, https://doi.org/10.5194/egusphere-egu22-8051, 2022.

09:08–09:15
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EGU22-7790
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ECS
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Highlight
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On-site presentation
Mohamed Saadi et al.

Implementing an effective nationwide, extreme-flood forecasting system requires improving both the estimation of quantitative precipitation and the hydrological modelling tools. We investigated the ability of state-of-the-art radar-based precipitation products and two contrasting hydrological models in providing reliable flood hindcasts and nowcasts for the July 2021 catastrophic floods. Among others, rainfall retrievals based on specific attenuation, a polarimetric radar variable, were used to improve the accuracy of the rain rates, and different radar-based nowcasting methods (deterministic and stochastic) were tested to quantify their added value in improving the forecast lead time. Hydrological models consisted of a lumped, conceptual model (GR4H) and a distributed physically-based model (ParFlow-CLM) that couples 3D surface and sub-surface flows. The parameters of the lumped model were calibrated on historical data, whereas the parameters of the distributed model were estimated based on landscape and soil properties. Preliminary results indicate that differences in simulated peakflows were predominantly due to differences in the rainfall retrievals rather than hydrological models. Warm rain processes near the surface led to underestimated precipitation sums compared to ground-based estimations. The precipitation estimates largely impacted the ability of models in detecting the exceedance of the 100-year flood, which highlights the need for reliable precipitation estimates to forecast such extreme events.

How to cite: Saadi, M., Furusho-Percot, C., Belleflamme, A., Chen, J.-Y., Reinoso-Rondinel, R., Troemel, S., and Kollet, S.: Evaluating quantitative precipitation estimates and hydrological models for simulating the 2021 extreme events in Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7790, https://doi.org/10.5194/egusphere-egu22-7790, 2022.

09:15–09:22
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EGU22-7851
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ECS
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On-site presentation
Laura Ramsamy et al.

The severity and frequency of extreme flood events has intensified both globally, and across the UK. Climate change will influence weather patterns across the UK, making it increasingly important to understand the impacts this may have on future flood events.

We developed 90m hydraulic models to simulate extreme pluvial and fluvial flood events across the UK based on observed events. The models have been climate conditioned, allowing the potential impacts of climate change on extreme pluvial and fluvial flood events to be understood. Using different climate scenarios, we examine the variation in outcome depending on what efforts are taken to reduce emissions. Modelling the impacts climate change could have on flooding at a national scale, enables us to understand the spatial-temporal distribution of flood risk. This information can be used in the real world for decision making and providing a way to mitigate against the impacts of climate change.

How to cite: Ramsamy, L., Brennan, J., Mitchell, H., Zeneli, M., Burke, C., and Kluza, K.: Establishing the potential impacts of climate change on extreme flood events across the UK., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7851, https://doi.org/10.5194/egusphere-egu22-7851, 2022.

09:22–09:29
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EGU22-5409
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ECS
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On-site presentation
Sepehr Farhoodi et al.

Incorporation of observed streamflow into different hydrological models has resulted in improved streamflow forecasting in many studies. This approach is currently used by different hydropower companies to maximize hydroelectric production and also to predict and mitigate flood damages. In addition, snow-related model states, such as SWE, snow depth, and snow wetness, also carry important information regarding both timing and volume of spring flow in snow-dominated regions. Consequently, the main objective of this study is to combine assimilation of observed streamflow and reanalyzed SWE to enhance spring flow forecasting. The reanalyzed SWE product investigated is SNODAS.

SNODAS is a snow data assimilation system that improves outputs of a snow model by assimilating observed snow data provided by airborne platforms, satellites, and ground stations and generates snow-related data, such as SWE and snow depth at 1 km resolution.  SNODAS is run each day so that the data product is available in near real-time.

In this study, SNODAS SWE data has been assimilated into HYDROTEL, a physically-based distributed hydrological model equipped with a snow module based on a mixed energy budget – degree-day approach, along with observed streamflow during the spring flow season in order to enhance spring flow forecasts. The study site is Au Saumon watershed, located in southern Quebec, Canada.  The Au Saumon watershed has an area of 1022  and is predominantly forested. The simulation period is the 2014-2015 water year. Preliminary results show that combined assimilation of SNODAS SWE and observed streamflow improve spring flow predictions, while SWE assimilation has a more delayed impact than streamflow assimilation.

Keywords: Data Assimilation, SNODAS, SWE, Spring Flow Prediction

How to cite: Farhoodi, S., Leconte, R., and Trudel, M.: Combined assimilation of reanalyzed SWE and observed streamflow to enhance spring flow forecasts in southern Quebec, Canada, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5409, https://doi.org/10.5194/egusphere-egu22-5409, 2022.

09:29–09:36
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EGU22-10998
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Virtual presentation
Steven Weijs and Daniel Kovacek

In this presentation, some insights into the various contributing factors of the floods in southwestern British Columbia, Canada, will be shared. These floods followed a large atmospheric river event, combined with high antecedent soil moisture and rain on snow mechanisms. In some locations, including some that were affected by extensive wildfires in the preceding summer, estimated return periods of river flows during this event exceeded 2000 years. Various alternative estimations of this return period will be presented, conditional on various assumptions and side information  Due to the large scale disruption of infrastructure, this event is expected to be (one of) the costliest natural disaster in history for Canada. This presentation is informed both by probabilistic analysis of the various factors and anecdotal evidence based on an aerial reconnaissance of the flood affected area. 

How to cite: Weijs, S. and Kovacek, D.: The November 2021 floods in British Columbia, Canada: observations, mechanisms and probabilities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10998, https://doi.org/10.5194/egusphere-egu22-10998, 2022.