Enhancing Operational Flood Detection Solutions through an Integrated Use of Satellite Earth Observations and Numerical Models

Among natural disasters floods are the most common and widespread hazards worldwide (CRED and UNISDR, 2018). Thus, making communities more resilient to flood is a priority, particularly in large flood-prone areas located in emerging countries, because the effects of extreme events severely setback the development process (Wright, 2013). In this context, operational flood preparedness requires novel modeling approaches for a fast delineation of flooding in riverine environments. Starting from a review of advances in the flood modeling domain and a selection of the more suitable open toolsets available in the literature, a new method for the Rapid Estimation of FLood EXtent (REFLEX) at multiple scales (Arcorace et al., 2019) is proposed. The simplified hydraulic modeling adopted in this method consists of a hydro-geomorphological approach based on the Height Above the Nearest Drainage (HAND) model (Nobre et al., 2015). The hydraulic component of this method employs a simplified version of fluid mechanic equations for natural river channels. The input runoff volume is distributed from channel to hillslope cells of the DEM by using an iterative flood volume optimization based on Manning\u2019s equation. The model also includes a GIS-based method to expand HAND contours across neighbor watersheds in flat areas, particularly useful in flood modeling expansion over coastal zones. REFLEX\u2019s flood modeling has been applied in multiple case studies in both surveyed and ungauged river basins. The development and the implementation of the whole modeling chain have enabled a rapid estimation of flood extent over multiple basins at different scales. When possible, flood modeling results are compared with reference flood hazard maps or with detailed flood simulations. Despite the limitations of the method due to the employed simplified hydraulic modeling approach, obtained results are promising in terms of flood extent and water depth. Given the geomorphological nature of the method, it does not require initial and boundary conditions as it is in traditional 1D/2D hydraulic modeling. Therefore, its usage fits better in data-poor environments or large-scale flood modeling. An extensive employment of this slim method has been adopted by CIMA Research Foundation researchers for flood hazard mapping purposes over multiple African countries. As collateral research, multiple types of Earth observation (EO) data have been employed in the REFLEX modeling chain. Remotely sensed data from the satellites, in fact, are not only a source to obtain input digital terrain models but also to map flooded areas. Thus, in this work, different EO data exploitation methods are used for estimating water extent and surface height. Preliminary results by using Copernicus\u2019s Sentinel-1 SAR and Sentinel-3 radar altimetry data highlighted their potential mainly for model calibration and validation. In conclusion, REFLEX combines the advantages of geomorphological models with the ones of traditional hydraulic modeling to ensure a simplified steady flow computation of flooding in open channels. This work highlights the pros and cons of the method and indicates the way forward for future research in the hydro-geomorphological domain

Near Real Time Fire Detection Service via the PROBA-V Mission Exploitation Platform

In the framework of the PROBA -V Mission Exploitation Platform (PROBA-V MEP), the “Detection of fires and burned areas” research activity is part of the PROBA-V MEP Third Party Services aimed at better facilitating the exploitation of PROBA-V data across the EO open science community. Progressive Systems carried out this research activity dedicated to support the Centre de Suivi Ecologique (CSE, Senegal) participation in the Monitoring for Environment and Security in Africa (MESA) project through the development of a fire detection and burned areas characterization service over the “Economic Community of Western Africa States” (ECOWAS). The Fire Detection algorithm is based on a modified implementation of a temporal Kalman filter which is capable to detect hotspots in near real time from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) geostationary multispectral data. The first component of the algorithm consists of a clear-air anomaly detection making use of multispectral Kalman features. After that, the second component is capable to classify identified anomalies between clouds and hotspots. The service takes in input SEVIRI multispectral data provisioned in near real time by EumetCast every 15 minutes. A direct access to PROBA-V archive of vegetation index and burned area products is then provisioned via the PROBA-V MEP infrastructure. In order to initialize the algorithm over the entire domain, a background model has been retrieved for each pixel and considered channel to depict the daily radiance trend in time of nominal clear air observations. Average values have been calculated, for each channel used by the Kalman filter, by exploiting the EUMETSAT’s Cloud Mask products to filter out anomalies from SEVIRI measurements and consider only clear-sky conditions. Main outputs of the algorithm are fire detections given in tabular and vector formats containing information such as fire ID, geolocation and confidence level together with PROBA-V derived NDVI and NDWI index estimations. Moreover the system is capable to compute a “Fire Occurrence” product over a defined composite period that complements the available PROBA-V Burnt Area product. The main code has been developed in Python while wrapper scripts have been written in BASH. The service prototype has been deployed within a Virtual Machine equipped with 4vCPUs and 8GB RAM within the PROBA-V MEP. Such resources are sufficient to guarantee the near real-time processing over the Western Africa area according to the input product delivery every 15 minutes. First investigations on clear sky classification of MSG scenes over ECOWAS region have shown a strong correlation with respect to EUMETSAT’s Cloud Mask products. Furthermore preliminary Fire Detection comparisons with respect to EUMETSAT’s FRP products has shown a fairly good agreement within hotspots having similar confidence level. Fine tuning of clear-sky and anomaly thresholds is required together with a validation of fire detections with respect to other products (e.g. MODIS FIRMS). Finally further activities, such as field validation campaign and CSE staff training, are planned to validate results and gather feedback from local stakeholders

Exposure of Santos Harbor Metropolitan Area (Brazil) toWave and Storm Surge Climate Changes

Santos Harbor Metropolitan Area (SHMA) in São Paulo Coastline (Brazil) is the most important marine cargo transfer terminal in the Southern Hemisphere. In previous studies, the authors showed how this area is subject to climate changes determining, in the long run, a sea level rise and, as a consequence, a consistent impact on the global sea level rise and subsidence. In this research, a further and innovative analysis of a long-term-wave database (1957–2002) generated from a comparison between wave data modeled on a “deepwatermodel” (ERA-40Wavemodel—ECMWF) and wave data measured by a coastal buoy, over the years 1982– 1984, in SHMA littoral (São Paulo State, Brazil) was carried out. The calibration coefficients, according to angular sectors of the wave direction, were obtained by comparing the measured data with the modeled data and applying them to the original scenarios using a near-shorewavemodel (MIKE21). The analysis of the wave climate changes on the extreme storm surge wave conditions, selecting cases of Hs > 3.0 m and using that virtual database, has shown an increase in the wave significant height (Hs ) and in the wave peak perio