Rapid Climate Risk Assessment for the Southern Africa Development Community (SADC) Region

This rapid climate risk assessment for the Southern Africa Development Community (SADC) uses the Intergovernmental Panel on Climate Change (IPCC) 2014 risk analysis framework to assess the distribution of climate hazards and social and biophysical vulnerability to those hazards in order to identify climate risk hotspots. The assessment uses regional climate models from CORDEX-Africa to map rainfall extremes and drought hazards to 2031–2059. Ten social and biophysical vulnerability indicators are identified from across the capital assets (human, physical, social, financial, natural), using data from the Global Multidimensional Poverty Index (MPI), to develop a vulnerability index. The vulnerability index and distribution of climate hazards are mapped to identify hotspots. Hotspots of vulnerability to and risk of extreme rainfall are shown in northern Madagascar and in south west Tanzania, under both the RCP4.5 and 8.5 scenarios. These hotspots also correspond to the hotspots for drought risk under RCP4.5 and 8.5. However, it is clear that medium-high climate risk (high vulnerability, medium-high climate hazard) is widespread across Angola, Democratic Republic of the Congo (DRC), Tanzania, Mozambique, and Madagascar

Rapid Climate Risk Assessment for the Southern Africa Development Community (SADC) Region

This rapid climate risk assessment for the Southern Africa Development Community (SADC) uses the Intergovernmental Panel on Climate Change (IPCC) 2014 risk analysis framework to assess the distribution of climate hazards and social and biophysical vulnerability to those hazards in order to identify climate risk hotspots. The assessment uses regional climate models from CORDEX-Africa to map rainfall extremes and drought hazards to 2031–2059. Ten social and biophysical vulnerability indicators are identified from across the capital assets (human, physical, social, financial, natural), using data from the Global Multidimensional Poverty Index (MPI), to develop a vulnerability index. The vulnerability index and distribution of climate hazards are mapped to identify hotspots. Hotspots of vulnerability to and risk of extreme rainfall are shown in northern Madagascar and in south west Tanzania, under both the RCP4.5 and 8.5 scenarios. These hotspots also correspond to the hotspots for drought risk under RCP4.5 and 8.5. However, it is clear that medium-high climate risk (high vulnerability, medium-high climate hazard) is widespread across Angola, Democratic Republic of the Congo (DRC), Tanzania, Mozambique, and Madagascar

Broad-scale flood modelling in the cloud : validation and sensitivities from hazard to impact

Broad-scale flood modelling is a growing research area with applications in insurance, adaption and response. This has been fuelled by increasing availability of continental-global datasets providing inputs to a mounting array of models. However, outputs vary greatly and validation is challenging. This research developed a novel, consistent methodology for assigning performance scores to models using a range of gridded datasets and an accurate numerical 2D hydrodynamic modelling system. Validation using both extent and discharge was conducted for Storm Desmond in Northern England and the global applicability of the methodology demonstrated across Europe and in Indonesia. To meet computational demands, a cloud computing framework was implemented using a PostgreSQL database. Visualisation of results was achieved using a newly designed web interface. Finally OpenStreetMap data was overlaid to demonstrate the sensitivity of impacts to flood model inputs. The main findings are that relative importance of precipitation and topographic data changes depending on the metrics used for validation. More variability in peak discharge error was found between models using different rainfall inputs (22-70%) than different DEMs (9-37%). Conversely, flood extent critical success index (CSI) was more sensitive to the choice of topography (25-32%) than rainfall (27-30%), though overall variability in CSI was low. This was echoed in the impacts analysis with higher sensitivity of feature inundation to topography than rainfall. Importantly, there was far more overall variability in discharge accuracy than extent which indicates that reproduction of peak discharge is a more powerful measure for assessing model performance. Models driven by globalcontinental precipitation products underestimated peaks more than those using Met Office rain gauge data, though better performance was demonstrated by replacing ERA-Interim with the updated ERA5 dataset. The research highlights a growing need for more robust validation of broad scale flood simulations, and the difficulties this presents. Strong influence of dataset choice on infrastructure inundation has consequences for insurance premiums, development planning and adaptation to climate change risks which should not be ignored.NERC for funding the research through the Data, Risk and Environmental Analytical Methods (DREAM) training centre

Comparison of sea-ice freeboard distributions from aircraft data and cryosat-2

The only remote sensing technique capable of obtain- ing sea-ice thickness on basin-scale are satellite altime- ter missions, such as the 2010 launched CryoSat-2. It is equipped with a Ku-Band radar altimeter, which mea- sures the height of the ice surface above the sea level. This method requires highly accurate range measure- ments. During the CryoSat Validation Experiment (Cry- oVEx) 2011 in the Lincoln Sea, Cryosat-2 underpasses were accomplished with two aircraft, which carried an airborne laser-scanner, a radar altimeter and an electro- magnetic induction device for direct sea-ice thickness re- trieval. Both aircraft flew in close formation at the same time of a CryoSat-2 overpass. This is a study about the comparison of the sea-ice freeboard and thickness dis- tribution of airborne validation and CryoSat-2 measure- ments within the multi-year sea-ice region of the Lincoln Sea in spring, with respect to the penetration of the Ku- Band signal into the snow