Hessel C. Winsemius

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

Globally, economic losses from flooding exceeded

Declining vulnerability to river floods and the global benefits of adaptation

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 (

Strong influence of El Niño Southern Oscillation on flood risk around the world

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.

A global reanalysis of storm surges and extreme sea levels.

Significance Understanding the vulnerability of societies around the world is crucial for understanding historical trends in flood risk and for producing accurate projections of fatalities and losses. We reproduced historical river flood occurrence using daily climate data for the period 1980–2010 and quantified the natural and socioeconomic contributions to flood risk trends. We show that the fatalities and losses as a share of the exposed population and gross domestic product are decreasing with rising income. We also show that there is a tendency of convergence in vulnerability levels between low- and high-income countries. Projections based on a wide range of climate change and socioeconomic development scenarios demonstrate that amplified adaptation efforts have the potential to largely contain losses from future floods. The global impacts of river floods are substantial and rising. Effective adaptation to the increasing risks requires an in-depth understanding of the physical and socioeconomic drivers of risk. Whereas the modeling of flood hazard and exposure has improved greatly, compelling evidence on spatiotemporal patterns in vulnerability of societies around the world is still lacking. Due to this knowledge gap, the effects of vulnerability on global flood risk are not fully understood, and future projections of fatalities and losses available today are based on simplistic assumptions or do not include vulnerability. We show for the first time (to our knowledge) that trends and fluctuations in vulnerability to river floods around the world can be estimated by dynamic high-resolution modeling of flood hazard and exposure. We find that rising per-capita income coincided with a global decline in vulnerability between 1980 and 2010, which is reflected in decreasing mortality and losses as a share of the people and gross domestic product exposed to inundation. The results also demonstrate that vulnerability levels in low- and high-income countries have been converging, due to a relatively strong trend of vulnerability reduction in developing countries. Finally, we present projections of flood losses and fatalities under 100 individual scenario and model combinations, and three possible global vulnerability scenarios. The projections emphasize that materialized flood risk largely results from human behavior and that future risk increases can be largely contained using effective disaster risk reduction strategies.

The credibility challenge for global fluvial flood risk analysis

Significance El Niño Southern Oscillation (ENSO) affects hydrological processes around the globe. However, little is known about its influence on the socioeconomic impacts of flooding (i.e., flood risk). We present, to our knowledge, the first global assessment of ENSO’s influence on flood risk in terms of economic damage and exposed population and gross domestic product. We show that reliable flood risk anomalies exist during ENSO years in basins spanning almost half of Earth’s surface. These results are significant for flood-risk management. Because ENSO can be predicted with lead times of several seasons with some skill, the findings pave the way for developing probabilistic flood-risk projections. These could be used for improved disaster planning, such as temporarily increasing food and medicine stocks by relief agencies. El Niño Southern Oscillation (ENSO) is the most dominant interannual signal of climate variability and has a strong influence on climate over large parts of the world. In turn, it strongly influences many natural hazards (such as hurricanes and droughts) and their resulting socioeconomic impacts, including economic damage and loss of life. However, although ENSO is known to influence hydrology in many regions of the world, little is known about its influence on the socioeconomic impacts of floods (i.e., flood risk). To address this, we developed a modeling framework to assess ENSO’s influence on flood risk at the global scale, expressed in terms of affected population and gross domestic product and economic damages. We show that ENSO exerts strong and widespread influences on both flood hazard and risk. Reliable anomalies of flood risk exist during El Niño or La Niña years, or both, in basins spanning almost half (44%) of Earth’s land surface. Our results show that climate variability, especially from ENSO, should be incorporated into disaster-risk analyses and policies. Because ENSO has some predictive skill with lead times of several seasons, the findings suggest the possibility to develop probabilistic flood-risk projections, which could be used for improved disaster planning. The findings are also relevant in the context of climate change. If the frequency and/or magnitude of ENSO events were to change in the future, this finding could imply changes in flood-risk variations across almost half of the world’s terrestrial regions.

Influence of soil and climate on root zone storage capacity

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 worlds 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.

A comparison of two global datasets of extreme sea levels and resulting flood exposure

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.

A 30 m Resolution Surface Water Mask Including Estimation of Positional and Thematic Differences Using Landsat 8, SRTM and OpenStreetMap: A Case Study in the Murray-Darling Basin, Australia

The root zone water storage capacity (Sr) of a catchment is an important variable for the hydrological behaviour of a catchment; it strongly influences the storage, transpiration and runoff generation in an area. However, the root zone storage capacity is largely heterogeneous and not measurable. There are different theories about the variables affecting the root zone storage capacity; among the most debated are soil, vegetation and climate. The effect of vegetation and soil is often accounted for by detailed soil and land use maps. To investigate the effect of climate on the root zone storage capacity, an analogue can be made between the root zone storage capacity of a catchment and the human habit to design and construct reservoirs: both storage capacities help to overcome a dry period of a certain length. Humans often use the mass curve technique to determine the required storage needed to design the reservoir capacity. This mass curve technique can also be used to derive the root zone storage capacity created by vegetation in a certain ecosystem and climate (Gao et al., 2014). Only precipitation and discharge or evaporation data are required for this method. This study tests whether Sr values derived by both the mass curve technique and from soil maps are comparable for a range of catchments in New Zealand. Catchments are selected over a gradient of climates and land use. Special focus lies on how Sr values derived for a larger catchment are representative for smaller nested catchments. The spatial differences are examined between values derived from soil data and from climate and flow data.

Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model

Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of −0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39–59% higher estimate of population exposure.

Compound simulation of fluvial floods and storm surges in a global coupled river-coast flood model : Model development and its application to 2007 Cyclone Sidr in Bangladesh

Accurate maps of surface water are essential for many environmental applications. Surface water maps can be generated by combining measurements from multiple sources. Precise estimation of surface water using satellite imagery remains a challenging task due to the sensor limitations, complex land cover, topography, and atmospheric conditions. As a complementary dataset, in the case of hilly landscapes, a drainage network can be extracted from high-resolution digital elevation models. Additionally, Volunteered Geographic Information (VGI) initiatives, such as OpenStreetMap, can also be used to produce high-resolution surface water masks. In this study, we derive a high-resolution water mask using Landsat 8 imagery and OpenStreetMap as well as (potential) a drainage network using 30 m SRTM. Our approach to derive a surface water mask from Landsat 8 imagery comprises the use of a lower 15% percentile of Landsat 8 Top of Atmosphere (TOA) reflectance from 2013 to 2015. We introduce a new non-parametric unsupervised method based on the Canny edge filter and Otsu thresholding to detect water in flat areas. For hilly areas, the method is extended with an additional supervised classification step used to refine the water mask. We applied the method across the Murray-Darling basin, Australia. Differences between our new Landsat-based water mask and the OpenStreetMap water mask regarding positional differences along the rivers and overall coverage were analyzed. Our results show that about 50% of the OpenStreetMap linear water features can be confirmed using the water mask extracted from Landsat 8 imagery and the drainage network derived from SRTM. We also show that the observed distances between river features derived from OpenStreetMap and Landsat 8 are mostly smaller than 60 m. The differences between the new water mask and SRTM-based linear features and hilly areas are slightly larger (110 m). The overall agreement between OpenStreetMap and Landsat 8 water masks is about 30%.