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From precipitation to damage
(2018)
Flood risk assessments for large river basins often involve piecing together smaller-scale assessments leading to erroneous risk statements. We describe a coupled model chain for quantifying flood risk at the scale of 100,000 km(2). It consists of a catchment model, a 1D-2D river network model, and a loss model. We introduce the model chain and present two applications. The first application for the Elbe River basin with an area of 66,000 km(2) demonstrates that it is feasible to simulate the complete risk chain for large river basins in a continuous simulation mode with high temporal and spatial resolution. In the second application, RFM is coupled to a multisite weather generator and applied to the Mulde catchment with an area of 6,000 km(2). This approach is able to provide a very long time series of spatially heterogeneous patterns of precipitation, discharge, inundation, and damage. These patterns respect the spatial correlation of the different processes and are suitable to derive large-scale risk estimates. We discuss how the RFM approach can be transferred to the continental scale.
Flooding is an imminent natural hazard threatening most river deltas, e.g. the Mekong Delta. An appropriate flood management is thus required for a sustainable development of the often densely populated regions. Recently, the traditional event-based hazard control shifted towards a risk management approach in many regions, driven by intensive research leading to new legal regulation on flood management. However, a large-scale flood risk assessment does not exist for the Mekong Delta. Particularly, flood risk to paddy rice cultivation, the most important economic activity in the delta, has not been performed yet. Therefore, the present study was developed to provide the very first insight into delta-scale flood damages and risks to rice cultivation. The flood hazard was quantified by probabilistic flood hazard maps of the whole delta using a bivariate extreme value statistics, synthetic flood hydrographs, and a large-scale hydraulic model. The flood risk to paddy rice was then quantified considering cropping calendars, rice phenology, and harvest times based on a time series of enhanced vegetation index (EVI) derived from MODIS satellite data, and a published rice flood damage function. The proposed concept provided flood risk maps to paddy rice for the Mekong Delta in terms of expected annual damage. The presented concept can be used as a blueprint for regions facing similar problems due to its generic approach. Furthermore, the changes in flood risk to paddy rice caused by changes in land use currently under discussion in the Mekong Delta were estimated. Two land-use scenarios either intensifying or reducing rice cropping were considered, and the changes in risk were presented in spatially explicit flood risk maps. The basic risk maps could serve as guidance for the authorities to develop spatially explicit flood management and mitigation plans for the delta. The land-use change risk maps could further be used for adaptive risk management plans and as a basis for a cost-benefit of the discussed land-use change scenarios. Additionally, the damage and risks maps may support the recently initiated agricultural insurance programme in Vietnam.
Flooding is an imminent natural hazard threatening most river deltas, e.g. the Mekong Delta. An appropriate flood management is thus required for a sustainable development of the often densely populated regions. Recently, the traditional event-based hazard control shifted towards a risk management approach in many regions, driven by intensive research leading to new legal regulation on flood management. However, a large-scale flood risk assessment does not exist for the Mekong Delta. Particularly, flood risk to paddy rice cultivation, the most important economic activity in the delta, has not been performed yet. Therefore, the present study was developed to provide the very first insight into delta-scale flood damages and risks to rice cultivation. The flood hazard was quantified by probabilistic flood hazard maps of the whole delta using a bivariate extreme value statistics, synthetic flood hydrographs, and a large-scale hydraulic model. The flood risk to paddy rice was then quantified considering cropping calendars, rice phenology, and harvest times based on a time series of enhanced vegetation index (EVI) derived from MODIS satellite data, and a published rice flood damage function. The proposed concept provided flood risk maps to paddy rice for the Mekong Delta in terms of expected annual damage. The presented concept can be used as a blueprint for regions facing similar problems due to its generic approach. Furthermore, the changes in flood risk to paddy rice caused by changes in land use currently under discussion in the Mekong Delta were estimated. Two land-use scenarios either intensifying or reducing rice cropping were considered, and the changes in risk were presented in spatially explicit flood risk maps. The basic risk maps could serve as guidance for the authorities to develop spatially explicit flood management and mitigation plans for the delta. The land-use change risk maps could further be used for adaptive risk management plans and as a basis for a cost-benefit of the discussed land-use change scenarios. Additionally, the damage and risks maps may support the recently initiated agricultural insurance programme in Vietnam.
Floods frequently cause substantial economic and human losses, particularly in developing countries. For the development of sound flood risk management schemes that reduce flood consequences, detailed insights into the different components of the flood risk management cycle, such as preparedness, response, flood impact analyses and recovery, are needed. However, such detailed insights are often lacking: commonly, only (aggregated) data on direct flood damage are available. Other damage categories such as losses owing to the disruption of production processes are usually not considered, resulting in incomplete risk assessments and possibly inappropriate recommendations for risk management. In this paper, data from 858 face-to-face interviews among flood-prone households and small businesses in Can Tho city in the Vietnamese Mekong Delta are presented to gain better insights into the damage caused by the 2011 flood event and its management by households and businesses.
Large-scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process-based Regional Flood Model (RFM) to simulate a 5000-year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D-2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector-wise exposure data and empirical multi-variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5% confidence for Germany to be euro0.529 bn and euro8.865 bn, respectively. The commercial sector dominates by making about 60% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3% to the total risk. The overall EAD is comparable to other large-scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large-scale assessments with homogeneous return periods. Thus, the process-based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German-wide flood risk assessments.
Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk.
Despite its societal relevance, the question whether fluctuations in flood occurrence or magnitude are coherent in space has hardly been addressed in quantitative terms. We investigate this question for Germany by analysing fluctuations in annual maximum series (AMS) values at 68 discharge gauges for the common time period 1932-2005. We find remarkable spatial coherence across Germany given its different flood regimes. For example, there is a tendency that flood-rich/-poor years in sub-catchments of the Rhine basin, which are dominated by winter floods, coincide with flood-rich/-poor years in the southern sub-catchments of the Danube basin, which have their dominant flood season in summer. Our findings indicate that coherence is caused rather by persistence in catchment wetness than by persistent periods of higher/lower event precipitation. Further, we propose to differentiate between event-type and non-event-type coherence. There are quite a number of hydrological years with considerable nonevent-type coherence, i.e. AMS values of the 68 gauges are spread out through the year but in the same magnitude range. Years with extreme flooding tend to be of event-type and non-coherent, i.e. there is at least one precipitation event that affects many catchments to various degree. Although spatial coherence is a remarkable phenomenon, and large-scale flooding across Germany can lead to severe situations, extreme magnitudes across the whole country within one event or within one year were not observed in the investigated period. (C) 2018 Elsevier B.V. All rights reserved.
Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk.
Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100% for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.
Flood events can be expressed by a variety of characteristics such as flood magnitude and extent, event duration or incurred loss. Flood estimation and management may benefit from understanding how the different flood characteristics relate to the hydrological catchment conditions preceding the event and to the meteorological conditions throughout the event. In this study, we therefore propose a methodology to investigate the hydro-meteorological controls on different flood characteristics, based on the simulation of the complete flood risk chain from the flood triggering precipitation event, through runoff generation in the catchment, flood routing and possible inundation in the river system and floodplains to flood loss. Conditional cumulative distribution functions and regression tree analysis delineate the seasonal varying flood processes and indicate that the effect of the hydrological pre-conditions, i.e. soil moisture patterns, and of the meteorological conditions, i.e. weather patterns, depends on the considered flood characteristic. The methodology is exemplified for the Elbe catchment. In this catchment, the length of the build-up period, the event duration and the number of gauges undergoing at least a 10-year flood are governed by weather patterns. The affected length and the number of gauges undergoing at least a 2-year flood are however governed by soil moisture patterns. In case of flood severity and loss, the controlling factor is less pronounced. Severity is slightly governed by soil moisture patterns whereas loss is slightly governed by weather patterns. The study highlights that flood magnitude and extent arise from different flood generation processes and concludes that soil moisture patterns as well as weather patterns are not only beneficial to inform on possible flood occurrence but also on the involved flood processes and resulting flood characteristics.