Development and evaluation of a framework for global flood hazard mapping

AbstractNowadays, the development of high-resolution flood hazard models have become feasible at continental and global scale, and their application in developing countries and data-scarce regions can be extremely helpful to increase preparedness of population and reduce catastrophic impacts.The present work describes the development of a novel procedure for global flood hazard mapping, based on the most recent advances in large scale flood modelling. We derive a long-term dataset of daily river discharges from the hydrological simulations of the Global Flood Awareness System (GloFAS). Streamflow data is downscaled on a high resolution river network and processed to provide the input for local flood inundation simulations, performed with a two-dimensional hydrodynamic model. All flood-prone areas identified along the river network are then merged to create continental flood hazard maps for different return periods at 30′′ resolution. We evaluate the performance of our methodology in several river basins across the globe by comparing simulated flood maps with both official hazard maps and a mosaic of flooded areas detected from satellite images. The evaluation procedure also includes comparisons with the results of other large scale flood models. We further investigate the sensitivity of the flood modelling framework to several parameters and modelling approaches and identify strengths, limitations and possible improvements of the methodology

A global streamflow reanalysis for 1980–2018

Global and continental scale hydrological reanalysis datasets receive growing attention due to their increasing number of applications, ranging from water resources management, climate change studies, water related hazards and policy support. Until recently, their use was mostly limited to qualitative assessments, due to their coarse spatial and temporal resolution, large uncertainty and bias in the model output, and limited extent of the dataset in space and time. This research reports on the setup of a gridded hydrological model with quasi-global coverage, able to reproduce a seamless 39-year streamflow simulation in all world’s medium to large river basins. The model was calibrated at 1226 river sections with a total drainage area of 51 million km2 within 66 countries, using ECMWF’s latest atmospheric reanalysis ERA5. A performance assessment revealed large improvements in reproducing past discharge observations, in comparison to the calibration used in the current operational setup of the hydrological model as part of the Copernicus – Global Flood Awareness System (GloFAS, www.globalfloods.eu), with median scores of Kling-Gupta Efficiency KGE = 0.67 and correlation r = 0.8. The simulation bias was also dramatically reduced and narrowed around zero, with more than 60% of stations showing percent bias within ±20%. Pronounced regional differences in the simulation results remain, pointing out the need for detailed investigation of the hydrological processes in specific regions, including parts of Africa and South Asia. In addition, observed discharges with high data quality is key to achieving skillful model output. The new calibrated model will become part of the operational runs of GloFAS in the next system release foreseen for Spring 2020, together with a near real time extension of the streamflow reanalysis

The benefit of continental flood early warning systems to reduce the impact of flood disasters

Flooding is a natural phenomenon, an intrinsic part of the natural cycle that serves important ecological functions. However, in highly anthropogenic-developed landscapes they cause serious consequences for human lives, societies in general, and their economy. Therefore comprehensive disaster risk reduction policies have been promoted in the last decade including actions on the development of early warning systems at local as well as regional scale. This report provides a brief global overview on the occurrences and damages resulting from riverine floods over the past decades. The first part of the report then summarises European policies put in place to deal with flooding in the different phases of the disaster management cycle addressing the prevention, preparedness, response, and recovery phase. This is followed by a description of the development of flood early warning capabilities at European scale, how such a system fits into the responsibility chain between national services and EU civil protection and what the potential financial benefit of flood early warning systems in Europe amounts to. The second part of the report addresses the gaps in flood early warning systems in Africa and presents a description of the African Flood Forecasting System (AFFS), which has been built in analogy to EFAS, but which is still in experimental stageJRC.H.7-Climate Risk Managemen

A pan-African high-resolution drought index dataset

Droughts in Africa cause severe problems, such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security on Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectional perspective that includes crops, hydrological systems, rangeland and environmental systems. Such assessments are essential for policymakers, their advisors and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high-resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole of Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time Series (TS) datasets, Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project and root zone soil moisture modelled by GLEAM. Agreement found between coarse-resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with an average correlation coefficient (R) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information for the study of drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) via the following link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a)

On the use of global flood forecasts and satellite-derived inundation maps for flood monitoring in data-sparse regions

Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012-2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: 1) general agreement was found between the GFDS and MODIS flood detection systems, 2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and 3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, the satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large scale flood monitoring tools.JRC.H.7-Climate Risk Managemen

Streamflow response to climate change in the Greater Horn of Africa

The Greater Horn of Africa region increasingly experiences high risk of water scarcity. A combination of frequent droughts, rapid population growth and rising urbanisation has reduced streamflow and intensified water abstraction, causing water and food shortages. Estimates of future streamflow changes in the region have so far been highly uncertain and evaluations using ground-based measurements are still limited. Here, future streamflow changes are estimated using a distributed hydrological model forced with an ensemble of high-resolution climate simulations produced using the European community Earth-System Model v3.1. The simulated streamflow is evaluated using observed data from 29 stations from river basins across different climate zones in the region. Evaluation results show large sub-regional variations in the performance of simulated streamflow. The sign and magnitude of future streamflow changes vary between climate simulations and river basins, highlighting the uncertainties in the hydrologic projections. Overall, the streamflow projections indicate large (seasonal, long-term mean and extreme) streamflow decreases for all major rivers in Ethiopia and increases in the equatorial parts of the region at the end of the century. The ensemble mean shows a 10 to 25% decrease in the long-term mean flow in Ethiopia and a 10% increase in the equatorial part of the region in 2080s. Similarly, there is a substantial change in high flows in 2080s, with up to − 50% reduction in the northern and 50% increase in the equatorial parts of the region. These findings are critical because the rivers provide water supply to a rapidly changing socio-economy of the region

Hydrologic Data Assimilation for Operational Streamflow Forecasting

Data assimilation (DA) is a method that optimally combines imperfect models and uncertain observations to correct model states using new information acquired from the incoming observations. In recent years, DA has been extensively used for improving the uncertainty of hydrologic prediction, largely due to the emergence of advanced remote sensing tools for observations of soil moisture, river discharge and precipitation. Several DA methods have been explored in hydrology; however the choice and the effectiveness of a specific DA method may vary depending on the model and the observation. The goal of this dissertation study was reducing streamflow forecast uncertainty, and was carried out in three parts. First, the effectiveness of four different DA methods (ensemble Kalman filter (EnKF), particle filter (PF), Maximum Likelihood Ensemble Filter (MLEF) and variational method (VAR)) for improving streamflow forecasting were evaluated. In-situ discharge was assimilated into The United States National Weather Service (NWS) river forecasting model (Sacramento Soil Moisture Accounting model (SAC-SMA)) for Greens Bayou basin (with area of 178km2), in eastern Texas. The results indicate that all the four DA methods enhanced the short lead time forecast when compared to the model without the data assimilation; however the performances of each method vary with flow magnitude and longer lead time forecasts. Overall, the PF and MLEF performed superior to other DA algorithms across all flow regimes. In the second part of this thesis, the value of satellite-based soil moisture retrievals for enhancing river discharge was assessed. Surface and root zone satellite-based soil moisture retrievals from AMSR-E (passive microwave) and ASCAT (active microwave) sensors were separately assimilated into the SAC-SMA model in Greens bayou using ensemble Kalman filter. Two different data assimilation experiments were carried out over a period of four years (2007 to 2010): updating the soil moisture state of the SAC-SMA model and combined correcting of soil moisture and total channel inflow (TCI) of the model. It was found that the remotely-sensed soil moisture assimilation reduced the discharge RMSE compared to the open loop for both assimilation schemes, and there was no appreciable difference between surface and root zone soil moisture results, as well as between the AMSR-E and ASCAT results. Furthermore, the dual correcting of soil moisture and TCI produced lower river discharge RMSE. In the third part, the utility of passive microwave-based river width estimates for river discharge nowcasting and forecasting were assessed for two major rivers, the Ganges and Brahmaputra, in south Asia. Multiple upstream satellite observations of river and flood plains were used to track downstream flood wave propagation, and using a cross-validation regression model, the downstream river discharge was forecasted for lead times up to 15 days. The results showed that satellite derived flow signals were able to detect the propagation of a river flow wave along both river channels. And the approach also provided better discharge forecasts at downstream location compared to a purely persistence forecast, especially for high flows when the water spills out of the river bank. Overall, it was concluded that satellitebased flow estimates are a useful source of dynamical surface water information in regions where there is a lack of ground discharge data

Climate Change Impact on Water Resources in the Awash Basin, Ethiopia

Rapid growth of agriculture, industries and urbanization within the Awash basin, Ethiopia, as well as population growth is placing increasing demands on the basin’s water resources. In a basin known for high climate variability involving droughts and floods, climate change will likely intensify the existing challenges. To quantify the potential impact of climate change on water availability of the Awash basin in different seasons we have used three climate models from Coupled Models Inter-comparison Project phase 5 (CMIP5) and for three future periods (2006⁻2030, 2031⁻2055, and 2056⁻2080). The models were selected based on their performance in capturing historical precipitation characteristics. The baseline period used for comparison is 1981⁻2005. The future water availability was estimated as the difference between precipitation and potential evapotranspiration projections using the representative concentration pathway (RCP8.5) emission scenarios after the climate change signals from the climate models are transferred to the observed data. The projections for the future three periods show an increase in water deficiency in all seasons and for parts of the basin, due to a projected increase in temperature and decrease in precipitation. This decrease in water availability will increase water stress in the basin, further threatening water security for different sectors, which are currently increasing their investments in the basin such as irrigation. This calls for an enhanced water management strategy that is inclusive of all sectors that considers the equity for different users

Saving lives: Ensemble-based early warnings in developing nations

Natural disasters disproportionately affect the developing nations due to the lack of effective early warning systems. In this chapter we present the need, challenges and opportunities of early warning systems in developing nations for decision making in disaster risk management and demonstrate the added value of ensemble forecasting in particular in data and infrastructure scarce regions. First, we review the global extent of flood and drought disaster damages in the last few decades on human lives and the economy, and demonstrate that a disproportionately high rate of death (per event) occurred in developing regions, where there is no (or ineffective) operational early disaster warning systems. Next, we present the everyday needs and challenges of preparing for and responding to natural disasters in Nigeria, a typical developing country with fragmented data infrastructure and limited national early warning system capability. Particularly, we share experiences from the most recent major flood disaster and demonstrate a potential value of ensemble-based flood early warnings, using streamflow forecasts from the Global Flood Awareness System. However, forecasting of disasters alone is not sufficient if the information is not translated into actionable advice at local community level. This is particularly important for ensemble forecasting which requires training for the forecasters as well as the receiving authorities. In order to achieve this, technical knowledge and communication infrastructure are needed to deliver the early warning information to the relevant communities and concerned authorities. Multi-stakeholder partnerships bringing together scientific community, policy and decision makers and end-users from international to local level could facilitate humanitarian aid organisations and decision makers understand and use the ensemble predictions on timely basis before, during and after disaster strikes. The chapter concludes with highlighting the multi-stakeholder partnership initiatives on floods (Global Flood Partnership) and droughts (Integrated Drought Management Programme), established with the common goal of reducing flood and drought risk across the globe.JRC.E.1-Disaster Risk Managemen

Development and evaluation of a framework for global flood hazard mapping

Nowadays, the development of high-resolution flood hazard models have become feasible at continental and global scale, and their application in developing countries and data-scarce regions can be extremely helpful to increase preparedness of population and reduce catastrophic impacts. The present work describes the development of a novel procedure for global flood hazard mapping, based on the most recent advances in large scale flood modelling. We derive a long-term dataset of daily river discharges from the global hydrological simulations of the Global Flood Awareness System (GloFAS). Streamflow data is downscaled on a high resolution river network and processed to provide the input for local flood inundation simulations, performed with a two-dimensional hydrodynamic model. All flood-prone areas identified along the river network are then merged to create continental flood hazard maps for different return periods at 30’’ resolution. We evaluate the performance of our methodology in several large river basins by comparing simulated flood maps with a mosaic of flooded areas detected from satellite images for the same reference period. We further investigate the sensitivity of the flood modelling framework to different parameters and modelling approaches and identify strengths, limitations and possible improvements of the methodology.JRC.H.7-Climate Risk Managemen