A near real-time procedure for flood hazard mapping and risk assessment in Europe

The availability of a real-time operational system for mapping flood hazard and assessing potential consequences might be extremely useful to help emergency response and management and to mitigate the impact of large flood events. This work describes the development of an experimental procedure for rapid flood risk assessment within the European Flood Awareness System (EFAS), which since 2012 provides operational flood predictions for the major European rivers as part of the Copernicus Emergency Management Services. The hydro-meteorological data set available in EFAS is used to derive long-term streamflow simulations and design flood hydrographs in a wide number of locations, covering all the major European river network. Flood hydrographs are then used as input to a hydrodynamic 2D model to create a high resolution dataset of areas at risk of flooding for different return periods. Whenever a flood event is forecasted in EFAS, the flood maps of the river network sections potentially involved are merged together, based on the estimated magnitude of the event. In order to take into account the different flood forecasts available in EFAS, different combinations of flood hazard maps may be produced, to highlight the possible range of uncertainty in predictions. The merged flood maps can be combined with the available spatial information about land use, population, urban areas and infrastructures, to assess the potential impact of the forecasted flood event in terms of economic damage, affected population, major infrastructures and cities. A preliminary version of the procedure has been successfully tested in reproducing flooded areas and impacts in the recent floods in Bosnia-Herzegovina, Croatia and Serbia. Moreover, the reduced computational times are compatible with near real-time applications, even in case of multiple flood events affecting several countries. Currently, the integration of the procedure within EFAS for operational use is being tested.JRC.H.7-Climate Risk Managemen

Quantifying co-benefits and disbenefits of Nature-based Solutions targeting Disaster Risk Reduction

Nature-based Solutions function as an umbrella concept for ecosystem-based approaches that are an alternative to traditional engineering solutions for Disaster Risk Reduction. Their rising popularity is explained partly by their entailing additional benefits (so called co-benefits) for the environment, society, and economy. The few existing frameworks for assessing co-benefits are lacking guidance on co-benefit pre-assessment that is required for the NBS selection and permission process. Going beyond these, this paper develops a comprehensive guidance on quantitative pre-assessment of potential co-benefits and disbenefits of NBS tackling Disaster Risk Reduction. It is based on methods and frameworks from existing literature around NBS and related disciplines. Furthermore, this paper discusses the evaluation of the quantified results of the pre-assessment. In particular, the evaluation focuses on the significance of change of the estimated co-benefits and disbenefits as well as the sustainability of the NBS. This paper will support decision-making in planning processes on suitability and sustainability of Nature-based Solutions and assist in the preparation of Environmental Impact Assessments of projects

EFAS upgrade for the extended model domain

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication.JRC.E.1-Disaster Risk Managemen

EFAS upgrade for the extended model domain

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication.JRC.E.1-Disaster Risk Managemen

Semi-Distributed Parameter Optimization and Uncertainty Assessment for Large-Scale Streamflow Simulation Using Global Optimization

In catchments characterized by spatially varying hydrological processes and responses, the optimal parameter values or regions of attraction in parameter space may differ with location specific characteristics and dominating processes. This paper evaluates the value of semi-distributed calibration parameters for large-scale streamflow simulation using the LISFLOOD model. The model is driven by meteorological input data and simulates river discharges in large drainage basins as a function of spatial information on topography, soils and land cover. Even though LISFLOOD is physically based to a certain extent, some processes are only represented in a lumped conceptual way. As a result, some parameters lack physical basis and cannot be directly inferred from quantities that can be measured. We employ the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm to infer the calibration parameters using daily discharge observations. The resulting posterior parameter distribution reflects the uncertainty about the model parameters and forms the basis for making probabilistic flow predictions. We assess the value of semi-distributing the calibration parameters by comparing three different calibration strategies. In the first calibration strategy uniform values over the entire area of interest are adopted for the unknown parameters, which are calibrated against discharge observations at the downstream outlet of the catchment. In the second calibration strategy the parameters are also uniformly distributed, but they are calibrated against the discharge at the catchment outlet and at internal discharge stations. In the third strategy a semi-distributed approach is adopted. Starting from upstream, parameters in each subcatchment are calibrated against the observed discharges at the outlet of the subcatchment. In order not to propagate upstream errors in the calibration process, observed discharges at upstream catchment outlets are used as inflow when calibrating downstream subcatchments. As an illustrative example, we demonstrate the methodology for a part of the Morava catchment, covering an area of approximately 10,000 km2. The calibration results reveal that the additional value of the internal discharge stations is limited when applying a lumped parameter approach. Moving from a lumped to a semi-distributed parameter approach (i) improves the accuracy of the flow predictions, especially in the upstream subcatchments; and (ii) reduces flow prediction uncertainty. The results show the clear need to distribute the calibration parameters, especially in large catchments characterized by spatially varying hydrological processes and responses.JRC.H.7-Land management and natural hazard

Semi-Distributed Calibration of a Rainfall-Runoff Model for the Morava Catchment Using Global Optimization

In this paper we address the problem of parameter identification and parameter uncertainty estimation for the rainfall-runoff model LISFLOOD. The model is driven by meteorological input data and simulates river discharges in large drainage basins as a function of spatial information on topography, soils and land cover. Even though LISFLOOD is physically-based to a certain extent, some processes are only represented in a lumped conceptual way. As a result, some parameters lack physical basis and cannot be directly inferred from quantities that can be measured. In the current LISFLOOD version five parameters need to be determined by calibration. We employ the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm [Vrugt et al., 2003] to automatically calibrate the model against discharge observations. The resulting posterior parameter distribution reflects the residual uncertainty about the model parameters and forms the basis for making probabilistic flow predictions. Typically, uniform values are identified for the unknown parameters by calibration against discharge observations at the catchment outlet. We assess the value of semi-distributing the calibration parameters by comparing three different calibration strategies. In the first calibration strategy uniform values over the entire area of interest are adopted for the unknown parameters, which are calibrated against discharge observations at the downstream outlet of the catchment. In the second calibration strategy the parameters are also uniformly distributed, but they are calibrated against the discharge at the catchment outlet and at internal discharge stations. In the third strategy a semi-distributed approach is adopted. Starting from upstream, parameters in each subcatchment are calibrated against the observed discharges at the outlet of the subcatchment. In order not to propagate upstream errors in the calibration process, observed discharges at upstream catchment outlets are used as inflow when calibrating downstream subcatchments. As an illustrative example, we demonstrate the methodology for a part of the Morava catchment, covering an area of approximately 10.000 km2. The calibration results reveal that the additional value of the internal discharge stations is limited when applying a lumped parameter approach. Moving from a lumped to a semi-distributed parameter approach (i) improves the flow predictions, especially in the upstream subcatchments; and (ii) reduces parameter uncertainty, and consequently flow prediction uncertainty. The results show the clear need to spatially vary the calibration parameters, especially in large catchments characterized by spatially varying hydrological processes and responses.JRC.H.7-Land management and natural hazard

Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter

Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS) pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases

Forecasting Medium-range Flood Hazard on European Scale

The European Flood Alert System (EFAS) prototype has been running pre-operationally for all of Europe since 2005. EFAS is now providing 3-10 day probabilistic hydrological forecasts for 22 national hydrological services. Recently, the forecasts were also made accessible in real time to the EFAS partners via an interactive password-protected web-based information system (EFAS-IS). This article gives a short overview of EFAS and illustrates the communication tools that are used in this early warning system, and that were developed in close collaboration with the hydrological national services forming part of the EFAS network. Furthermore, some results of a long-term skill assessment are presented, which underline the positive aspects of using probabilistic forecasts for mid-range flood forecasts.JRC.A.1-Work programme E

Assimilation of MODIS snow cover area data in a distributed hydrological model

Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS) pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases.JRC.H.7-Climate Risk Managemen