• Energy Research

<p>Current global riverine flood risk studies assume a constant mean sea level boundary. In reality, high sea levels can propagate up a river leading to elevated water levels, and/or the drainage of high river discharge can be impeded by elevated sea levels. Riverine flood risk in deltas might therefore be underestimated if dynamic sea levels are ignored. This contribution presents the first global scale assessment of drivers of riverine flooding in deltas and underlines the importance of including dynamic downstream sea level boundaries in global riverine flood risk studies.</p><p>The assessment is based on extreme water levels at 3433 river mouth locations as modeled by the state-of-the-art global river routing model CaMa-Flood, forced with a multi-model runoff ensemble from the EartH2Observe project and bounded by dynamic sea level conditions from the global tide and surge model GTSM. Using this framework, we classified the drivers of riverine flooding at each location into four classes: surge dominant, discharge dominant, compound or insignificant. The classification is based on rank correlations between annual maximum riverine water levels and surge levels, and annual maximum riverine water levels and discharge. We developed a model experiment to quantify the effect of surge on flood levels and impacts.</p><p>We find that drivers of riverine flooding are compound at 19.7 % of the locations analyzed, discharge dominant at 69.2 % and surge dominant at 7.8 %. Compared to locations with either surge or discharge dominant flood drivers, locations with compound flood drivers generally have larger surge extremes, are located in basins with faster discharge response and/or flat topography. Globally, surge exacerbates 1-in-10 years flood levels at 64.0 % of the locations analyzed, with a mean increase of 13.5 cm. While this increase is the largest at locations with compound or surge dominant flood drivers, surge also affects flood levels at locations with discharge dominant flood drivers. A small decrease in 1-in-10 years flood levels is observed at 12.2 % of locations analyzed due to negative seasonal component of surge associated with dominant seasonal gyre circulations. Finally, we show that if surge is ignored, flood depths are underestimated for 38.2 million out of a total of 332.0 million (11.6 %) expected annual mean people exposed to riverine flooding.</p>

Droughts and changing rainfall patterns due to natural climate variability and climate change, threaten the livelihoods of Malawi’s smallholder farmers, who constitute 80% of the population. Provision of seasonal climate forecasts (SCFs) is one means to potentially increase the resilience of rainfed farming to drought by informing farmers in their agricultural decisions. Local knowledge can play an important role in improving the value of SCFs, by making the forecast better-suited to the local environment and decision-making. This study explores whether the contextual relevance of the information provided in SCFs can be improved through the integration of farmers’ local knowledge in three districts in central and southern Malawi. A forecast threshold model is established that uses meteorological indicators before the rainy season as predictors of dry conditions during that season. Local knowledge informs our selection of the meteorological indicators as potential predictors. Verification of forecasts made with this model shows that meteorological indicators based on local knowledge have a predictive value for forecasting dry conditions in the rainy season. The forecast skill differs per location, with increased skill in the Southern Highlands climate zone. In addition, the local knowledge indicators show increased predictive value in forecasting locally relevant dry conditions, in comparison to the currently-used El Niño-Southern Oscillation (ENSO) indicators. We argue that the inclusion of local knowledge in the current drought information system of Malawi may improve the SCFs for farmers. We show that it is possible to capture local knowledge using observed station and climate reanalysis data. Our approach could benefit National Meteorological and Hydrological Services in the development of relevant climate services and support drought-risk reduction by humanitarian actors.

Global flood models (GFMs) are becoming increasingly important for disaster risk management internationally. However, these models have had little validation against observed flood events, making it difficult to compare model performance. In this paper, we introduce the first collective validation of multiple GFMs against the same events and we analyse how different model structures influence performance. We identify three hydraulically diverse regions in Africa with recent large scale flood events: Lokoja, Nigeria; Idah, Nigeria; and Chemba, Mozambique. We then evaluate the flood extent output provided by six GFMs against satellite observations of historical flood extents in these regions. The critical success index of individual models across the three regions ranges from 0.45 to 0.7 and the percentage of flood captured ranges from 52% to 97%. Site specific conditions influence performance as the models score better in the confined floodplain of Lokoja but score poorly in Idah's flat extensive floodplain. 2D hydrodynamic models are shown to perform favourably. The models forced by gauged flow data show a greater level of return period accuracy compared to those forced by climate reanalysis data. Using the results of our analysis, we create and validate a three-model ensemble to investigate the usefulness of ensemble modelling in a flood hazard context. We find the ensemble model performs similarly to the best individual and aggregated models. In the three study regions, we found no correlation between performance and the spatial resolution of the models. The best individual models show an acceptable level of performance for these large rivers.

The flooding of rivers and coastlines is the most frequent and damaging of all natural hazards. Between 1980 and 2016, total direct damages exceeded $1.6 trillion, and at least 225,000 people lost their lives. Recent events causing major economic losses include the 2011 river flooding in Thailand ($40 billion) and the 2013 coastal floods in the United States caused by Hurricane Sandy (over $50 billion). Flooding also triggers great humanitarian challenges. The 2015 Malawi floods were the worst in the country’s history and were followed by food shortage across large parts of the country. Flood losses are increasing rapidly in some world regions, driven by economic development in floodplains and increases in the frequency of extreme precipitation events and global sea level due to climate change. The largest increase in flood losses is seen in low-income countries, where population growth is rapid and many cities are expanding quickly. At the same time, evidence shows that adaptation to flood risk is already happening, and a large proportion of losses can be contained successfully by effective risk management strategies. Such risk management strategies may include floodplain zoning, construction and maintenance of flood defenses, reforestation of land draining into rivers, and use of early warning systems. To reduce risk effectively, it is important to know the location and impact of potential floods under current and future social and environmental conditions. In a risk assessment, models can be used to map the flow of water over land after an intense rainfall event or storm surge (the hazard). Modeled for many different potential events, this provides estimates of potential inundation depth in flood-prone areas. Such maps can be constructed for various scenarios of climate change based on specific changes in rainfall, temperature, and sea level. To assess the impact of the modeled hazard (e.g., cost of damage or lives lost), the potential exposure (including buildings, population, and infrastructure) must be mapped using land-use and population density data and construction information. Population growth and urban expansion can be simulated by increasing the density or extent of the urban area in the model. The effects of floods on people and different types of buildings and infrastructure are determined using a vulnerability function. This indicates the damage expected to occur to a structure or group of people as a function of flood intensity (e.g., inundation depth and flow velocity). Potential adaptation measures such as land-use change or new flood defenses can be included in the model in order to understand how effective they may be in reducing flood risk. This way, risk assessments can demonstrate the possible approaches available to policymakers to build a less risky future.

When river and coastal floods coincide, their impacts are often worse than when they occur in isolation; such floods are examples of ‘compound events’. To better understand the impacts of these compound events, we require an improved understanding of the dependence between coastal and river flooding on a global scale. Therefore, in this letter, we: provide the first assessment and mapping of the dependence between observed high sea-levels and high river discharge for deltas and estuaries around the globe; and demonstrate how this dependence may influence the joint probability of floods exceeding both the design discharge and design sea-level. The research was carried out by analysing the statistical dependence between observed sea-levels (and skew surge) from the GESLA-2 dataset, and river discharge using gauged data from the Global Runoff Data Centre, for 187 combinations of stations across the globe. Dependence was assessed using Kendall’s rank correlation coefficient () and copula models. We find significant dependence for skew surge conditional on annual maximum discharge at 22% of the stations studied, and for discharge conditional on annual maximum skew surge at 36% of the stations studied. Allowing a time-lag between the two variables up to 5 days, we find significant dependence for skew surge conditional on annual maximum discharge at 56% of stations, and for discharge conditional on annual maximum skew surge at 54% of stations. Using copula models, we show that the joint exceedance probability of events in which both the design discharge and design sea-level are exceeded can be several magnitudes higher when the dependence is considered, compared to when independence is assumed. We discuss several implications, showing that flood risk assessments in these regions should correctly account for these joint exceedance probabilities.

AbstractPeople living in poverty are particularly vulnerable to shocks, including those caused by natural disasters such as floods and droughts. This paper analyses household survey data and hydrological riverine flood and drought data for 52 countries to find out whether poor people are disproportionally exposed to floods and droughts, and how this exposure may change in a future climate. We find that poor people are often disproportionally exposed to droughts and floods, particularly in urban areas. This pattern does not change significantly under future climate scenarios, although the absolute number of people potentially exposed to floods or droughts can increase or decrease significantly, depending on the scenario and region. In particular, many countries in Africa show a disproportionally high exposure of poor people to floods and droughts. For these hotspots, implementing risk-sensitive land-use and development policies that protect poor people should be a priority.