HS4.7

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.

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.

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.

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.

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.