Assessing flood risk at the global scale: model setup, results, and sensitivity

Globally, economic losses from flooding exceeded $19 billion in 2012, and are rising rapidly. Hence, there is an increasing need for global-scale flood risk assessments, also within the context of integrated global assessments. We have developed and validated a model cascade for producing global flood risk maps, based on numerous flood return-periods. Validation results indicate that the model simulates interannual fluctuations in flood impacts well. The cascade involves: hydrological and hydraulic modelling; extreme value statistics; inundation modelling; flood impact modelling; and estimating annual expected impacts. The initial results estimate global impacts for several indicators, for example annual expected exposed population (169 million); and annual expected exposed GDP ($1383 billion). These results are relatively insensitive to the extreme value distribution employed to estimate low frequency flood volumes. However, they are extremely sensitive to the assumed flood protection standard; developing a database of such standards should be a research priority. Also, results are sensitive to the use of two different climate forcing datasets. The impact model can easily accommodate new, user-defined, impact indicators. We envisage several applications, for example: identifying risk hotspots; calculating macro-scale risk for the insurance industry and large companies; and assessing potential benefits (and costs) of adaptation measures

Klimaat In Ruimtelijke Keuzes: Het Dialoogondersteunend Afwegingskader (DAK)

Het doel van het HSHL01 project ‘Klimaat in ruimtelijke keuzes’ is om een dialoog ondersteunend afwegingskader (DAK) te ontwikkelen en toe te passen. Met dit afwegingskader kunnen de betrokken partijen gezamenlijk de lange termijn effecten van klimaatverandering op het waterbeheer in een vroegtijdig stadium en op inzichtelijke wijze meewegen in het proces van ruimtelijke planvorming. Het gaat hierbij zowel om de locatiekeuze van ruimtelijke ontwikkelingen als de inrichting van eenmaal gekozen woningbouwlocaties, bedrijventerreinen en glastuinbouwlocaties

The credibility challenge for global fluvial flood risk analysis

Quantifying flood hazard is an essential component of resilience planning, emergency response, and mitigation, including insurance. Traditionally undertaken at catchment and national scales, recently, efforts have intensified to estimate flood risk globally to better allow consistent and equitable decision making. Global flood hazard models are now a practical reality, thanks to improvements in numerical algorithms, global datasets, computing power, and coupled modelling frameworks. Outputs of these models are vital for consistent quantification of global flood risk and in projecting the impacts of climate change. However, the urgency of these tasks means that outputs are being used as soon as they are made available and before such methods have been adequately tested. To address this, we compare multi-probability flood hazard maps for Africa from six global models and show wide variation in their flood hazard, economic loss and exposed population estimates, which has serious implications for model credibility. While there is around 30-40% agreement in flood extent, our results show that even at continental scales, there are significant differences in hazard magnitude and spatial pattern between models, notably in deltas, arid/semi-arid zones and wetlands. This study is an important step towards a better understanding of modelling global flood hazard, which is urgently required for both current risk and climate change projections

Satellite data as complementary information for hydrological modelling

Hydrological models at river basin scale are needed to predict floods, droughts, available water, and effects of future changes, caused by climate and land cover change. However, in many river basins, there is not enough data available to construct these models. Fortunately, it becomes more and more attractive to use earth observations from satellites as complementary data to construct hydrological models in these river basins. This thesis describes the development and application of methods that allow one to combine the scarce ground data, available in such poorly gauged catchments, with expert knowledge and modern satellite data, with the purpose to conceptualise, calibrate and validate hydrological models. Case studies show that satellite-based rainfall, space-based gravity observations and remotely sensed evaporation estimates are of great value in the improvement of our models.Water ResourcesCivil Engineering and Geoscience

Propagation of weather forecast uncertainties in flood forecasting. A case study on Rhine discharges at Lobith

Water ResourcesWater ManagementCivil Engineering and Geoscience

Estimation of predictive hydrological uncertainty using quantile regression: Examples from the National Flood Forecasting System (England and Wales)

In this paper, a technique is presented for assessing the predictive uncertainty of rainfall-runoff and hydraulic forecasts. The technique conditions forecast uncertainty on the forecasted value itself, based on retrospective Quantile Regression of hindcasted water level forecasts and forecast errors. To test the robustness of the method, a number of retrospective forecasts for different catchments across England and Wales having different size and hydrological characteristics have been used to derive in a probabilistic sense the relation between simulated values of water levels and matching errors. From this study, we can conclude that using Quantile Regression for estimating forecast errors conditional on the forecasted water levels provides a relatively simple, efficient and robust means for estimation of predictive uncertainty.Civil Engineering and Geoscience

Constraining model parameters on remotely sensed evaporation: Justification for distribution in ungauged basins?

OA-fund In this study, land surface related parameter distributions of a conceptual semi-distributed hydrological model are constrained by employing time series of satellite-based evaporation estimates during the dry season as explanatory information. The approach has been applied to the ungauged Luangwa river basin (150 000 (km)2) in Zambia. The information contained in these evaporation estimates imposes compliance of the model with the largest outgoing water balance term, evaporation, and a spatially and temporally realistic depletion of soil moisture within the dry season. The model results in turn provide a better understanding of the information density of remotely sensed evaporation. Model parameters to which evaporation is sensitive, have been spatially distributed on the basis of dominant land cover characteristics. Consequently, their values were conditioned by means of Monte-Carlo sampling and evaluation on satellite evaporation estimates. The results show that behavioural parameter sets for model units with similar land cover are indeed clustered. The clustering reveals hydrologically meaningful signatures in the parameter response surface: wetland-dominated areas (also called dambos) show optimal parameter ranges that reflect vegetation with a relatively small unsaturated zone (due to the shallow rooting depth of the vegetation) which is easily moisture stressed. The forested areas and highlands show parameter ranges that indicate a much deeper root zone which is more drought resistent. Clustering was consequently used to formulate fuzzy membership functions that can be used to constrain parameter realizations in further calibration. Unrealistic parameter ranges, found for instance in the high unsaturated soil zone values in the highlands may indicate either overestimation of satellite-based evaporation or model structural deficiencies. We believe that in these areas, groundwater uptake into the root zone and lateral movement of groundwater should be included in the model structure. Furthermore, a less distinct parameter clustering was found for forested model units. We hypothesize that this is due to the presence of two dominant forest types that differ substantially in their moisture regime. This could indicate that the spatial discretization used in this study is oversimplified.WatermanagementCivil Engineering and Geoscience

A global reanalysis of storm surges and extreme sea levels

Extreme sea levels, caused by storm surges and high tides, can have devastating societal impacts. To effectively protect our coasts, global information on coastal flooding is needed. Here we present the first global reanalysis of storm surges and extreme sea levels (GTSR data set) based on hydrodynamic modelling. GTSR covers the entire world's coastline and consists of time series of tides and surges, and estimates of extreme sea levels. Validation shows that there is good agreement between modelled and observed sea levels, and that the performance of GTSR is similar to that of many regional hydrodynamic models. Due to the limited resolution of the meteorological forcing, extremes are slightly underestimated. This particularly affects tropical cyclones, which requires further research. We foresee applications in assessing flood risk and impacts of climate change. As a first application of GTSR, we estimate that 1.3% of the global population is exposed to a 1 in 100-year flood

L'Oppiano del Marc. gr. 479. Note paleografiche e filologiche, in Miscellanea 3 (Studi in onore di Elpidio Mioni), Padova 1982, pp. 19-29.

Root zone storage capacity ( S r ) is an important variable for hydrology and climate studies, as it strongly influences the hydrological functioning of a catchment and, via evaporation, the local climate. Despite its importance, it remains difficult to obtain a well-founded catchment representative estimate. This study tests the hypothesis that vegetation adapts its S r to create a buffer large enough to sustain the plant during drought conditions of a certain critical strength (with a certain probability of exceedance). Following this method, S r can be estimated from precipitation and evaporative demand data. The results of this ‘‘climate-based method’’ are compared with traditional estimates from soil data for 32 catchments in New Zealand. The results show that the differences between catchments in climate-derived catchment represen- tative S r values are larger than for soil-derived S r values. Using a model experiment, we show that the climate-derived S r can better reproduce hydrological regime signatures for humid catchments; for more arid catchments, the soil and climate methods perform similarly. This makes the climate-based S r a valuable addition for increasing hydrological understanding and reducing hydrological model uncertainty