Perspectives on Digital Elevation Model (DEM) Simulation for Flood Modeling in the Absence of a High-Accuracy Open Access Global DEM

Open-access global Digital Elevation Models (DEM) have been crucial in enabling flood studies in data-sparse areas. Poor resolution (>30 m), significant vertical errors and the fact that these DEMs are over a decade old continue to hamper our ability to accurately estimate flood hazard. The limited availability of high-accuracy DEMs dictate that dated open-access global DEMs are still used extensively in flood models, particularly in data-sparse areas. Nevertheless, high-accuracy DEMs have been found to give better flood estimations, and thus can be considered a ‘must-have’ for any flood model. A high-accuracy open-access global DEM is not imminent, meaning that editing or stochastic simulation of existing DEM data will remain the primary means of improving flood simulation. This article provides an overview of errors in some of the most widely used DEM data sets, along with the current advances in reducing them via the creation of new DEMs, editing DEMs and stochastic simulation of DEMs. We focus on a geostatistical approach to stochastically simulate floodplain DEMs from several open-access global DEMs based on the spatial error structure. This DEM simulation approach enables an ensemble of plausible DEMs to be created, thus avoiding the spurious precision of using a single DEM and enabling the generation of probabilistic flood maps. Despite this encouraging step, an imprecise and outdated global DEM is still being used to simulate elevation. To fundamentally improve flood estimations, particularly in rapidly changing developing regions, a high-accuracy open-access global DEM is urgently needed, which in turn can be used in DEM simulation

Increased population exposure to Amphan‐scale cyclones under future climates

International audienceAbstract Southern Asia experiences some of the most damaging climate events in the world, with loss of life from some cyclones in the hundreds of thousands. Despite this, research on climate extremes in the region is substantially lacking compared to other parts of the world. To understand the narrative of how an extreme event in the region may change in the future, we consider Super Cyclone Amphan, which made landfall in May 2020, bringing storm surges of 2–4 m to coastlines of India and Bangladesh. Using the latest CMIP6 climate model projections, coupled with storm surge, hydrological, and socio‐economic models, we consider how the population exposure to a storm surge of Amphan's scale changes in the future. We vary future sea level rise and population changes consistent with projections out to 2100, but keep other factors constant. Both India and Bangladesh will be negatively impacted, with India showing >200% increased exposure to extreme storm surge flooding (>3 m) under a high emissions scenario and Bangladesh showing an increase in exposure of >80% for low‐level flooding (>0.1 m). It is only when we follow a low‐emission scenario, consistent with the 2°C Paris Agreement Goal, that we see no real change in Bangladesh's storm surge exposure, mainly due to the population and climate signals cancelling each other out. For India, even with this low‐emission scenario, increases in flood exposure are still substantial (>50%). While here we attribute only the storm surge flooding component of the event to climate change, we highlight that tropical cyclones are multifaceted, and damages are often an integration of physical and social components. We recommend that future climate risk assessments explicitly account for potential compounding factors

Digital Elevation Models: Terminology and Definitions

Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background

Analysis of necessary evolution of the regulatory framework to enable the Web-of-Cells development : [ELECTRA]

Deliverable D3.3 discusses how the solutions proposed within ELECTRA can be tailored to the typical rules that will be imposed by national/EU regulators, and/or how the regulations can be extended or adapted to cover the new concepts developed in ELECTRA (Web-of-Cells architecture, associated control mechanisms, Cell System Operator role, etc.)

Advancing operational flood forecasting, early warning and risk management with new emerging science: Gaps, opportunities and barriers in Kenya

Kenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end-to-end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a ‘sweetspot’ for rainfall predictability at sub-seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

AbstractA large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.</jats:p