Sanne Muis

Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data

An accurate understanding of flood risk and its drivers is crucial for effective risk management. Detailed risk projections, including uncertainties, are however rarely available, particularly in developing countries. This paper presents a method that integrates recent advances in global-scale modeling of flood hazard and land change, which enables the probabilistic analysis of future trends in national-scale flood risk. We demonstrate its application to Indonesia. We develop 1000 spatially-explicit projections of urban expansion from 2000 to 2030 that account for uncertainty associated with population and economic growth projections, as well as uncertainty in where urban land change may occur. The projections show that the urban extent increases by 215%-357% (5th and 95th percentiles). Urban expansion is particularly rapid on Java, which accounts for 79% of the national increase. From 2000 to 2030, increases in exposure will elevate flood risk by, on average, 76% and 120% for river and coastal floods. While sea level rise will further increase the exposure-induced trend by 19%-37%, the response of river floods to climate change is highly uncertain. However, as urban expansion is the main driver of future risk, the implementation of adaptation measures is increasingly urgent, regardless of the wide uncertainty in climate projections. Using probabilistic urban projections, we show that spatial planning can be a very effective adaptation strategy. Our study emphasizes that global data can be used successfully for probabilistic risk assessment in data-scarce countries.

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 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

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

Water-related disasters, such as fluvial floods and cyclonic storm surges, are a major concern in the worlds mega-delta regions. Furthermore, the simultaneous occurrence of extreme discharges from rivers and storm surges could exacerbate flood risk, compared to when they occur separately. Hence, it is of great importance to assess the compound risks of fluvial and coastal floods at a large scale, including mega-deltas. However, most studies on compound fluvial and coastal flooding have been limited to relatively small scales, and global-scale or large-scale studies have not yet addressed both of them. The objectives of this study are twofold: to develop a global coupled river-coast flood model; and to conduct a simulation of compound fluvial flooding and storm surges in Asian mega-delta regions. A state-of-the-art global river routing model was modified to represent the influence of dynamic sea surface levels on river discharges and water levels. We conducted the experiments by coupling a river model with a global tide and surge reanalysis data set. Results show that water levels in deltas and estuaries are greatly affected by the interaction between river discharge, ocean tides and storm surges. The effects of storm surges on fluvial flooding are further examined from a regional perspective, focusing on the case of Cyclone Sidr in the Ganges-Brahmaputra-Meghna Delta in 2007. Modeled results demonstrate that a >3 m storm surge propagated more than 200 km inland along rivers. We show that the performance of global river routing models can be improved by including sea level dynamics.

Corrigendum: A global reanalysis of storm surges and extreme sea levels

Nature Communications 7: Article number: 11969 (2016); Published: 27 June 2016; Updated: 8 September 2016 In Fig. 4 of this Article, the y axes in panels ‘b–e’ are incorrect. The correct version of Fig. 4 appears below as Fig. 1.

A Mediterranean coastal database for assessing the impacts of sea-level rise and associated hazards

We have developed a new coastal database for the Mediterranean basin that is intended for coastal impact and adaptation assessment to sea-level rise and associated hazards on a regional scale. The data structure of the database relies on a linear representation of the coast with associated spatial assessment units. Using information on coastal morphology, human settlements and administrative boundaries, we have divided the Mediterranean coast into 13 900 coastal assessment units. To these units we have spatially attributed 160 parameters on the characteristics of the natural and socio-economic subsystems, such as extreme sea levels, vertical land movement and number of people exposed to sea-level rise and extreme sea levels. The database contains information on current conditions and on plausible future changes that are essential drivers for future impacts, such as sea-level rise rates and socio-economic development. Besides its intended use in risk and impact assessment, we anticipate that the Mediterranean Coastal Database (MCD) constitutes a useful source of information for a wide range of coastal applications.

Storm Surge Reconstruction and Return Water Level Estimation in Southeast Asia for the 20th Century

We present a methodology to reconstruct the daily maximum storm surge levels, obtained from tide gauges, based on the surrounding atmospheric conditions from an atmospheric reanalysis (20th Century Reanalysis-20CR). Tide gauge records in Southeast Asia are relatively short, so this area is often underrepresented in studies based on long observational records, and there are just a few studies that have analyzed storm surge trends, variability or return water levels (RWLs) from numerical models in this area. Here we develop, calibrate, and validate a multivariate linear regression model that relates the storm surge with the principal components of the local atmospheric conditions. This allows us to reconstruct storm surges for the 147 year 20CR period (1866-2012) and therefore to calculate more robust RWLs from the entire simulated data set and subsets thereof. RWLs are obtained by fitting the monthly maxima values to the Generalize Extreme Value (GEV) distribution. We find an increase in the 50 year RWL from the second half of the 19th century to the present unrelated to mean sea level; this increase is less noticeable when comparing only recent periods. Therefore, further research is needed since there is evidence that atmospheric reanalyses can include spurious trends in the late 19th and early 20th. RWLs obtained from the statistical reconstruction are validated against the ones obtained from observations and from a numerical model. Agreements are generally higher when using surge levels from the statistical model, even before its calibration.