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

A Karst Feature Prediction Model For Prince Of Wales Island, Alaska Based On High Resolution Lidar Imagery

Investigation into surface karst formation is significant to hazard prediction, hydrogeologic drainage, and land management. Southeast Alaska contains over 600,000 acres of mapped carbonate bedrock, and some of the fastest recorded karst dissolution in the world. The objectives of this study are to develop and compare multiple semi-automated models to map and delineate karst features from bare-earth LiDAR imagery using ArcGIS Desktop 10.7, and to apply a preliminary geostatistical analysis of sinkhole morphometric parameters to highlight potential spatial patterns of karst evolution on Prince of Wales Island, Alaska. A semi-automated approach of mapping karst features provides a dataset that minimizes error from noise while maintaining accurate depression location and catchment boundaries. Several semi-automated models with different size parameters were compared against field-validated data using vulnerability as a proxy to determine the most accurate size threshold model. The model with the most overlap agreement was used to determine the morphometrics of karst features identified. This study conducted preliminary analysis of morphometric properties derived from the semi-automated karst feature prediction model to provide context for the geologic controls that allow for such large, rapid karstification observed in the region. Although beyond the scope of this study, morphometric analysis utilizing this semi-automated approach should be the focus of future studies to determine formation mechanisms and factors of karst landscape evolution through time

IPH-Hydro Tools : uma ferramenta open source para determinação de informações topológicas em bacias hidrográficas integrada a um ambiente SIG

Watershed delineation, drainage network generation and determination of river hydraulic characteristics are important issues in hydrological sciences. In gene- ral, this information can be obtained from Digital Elevation Models (DEM) processing within GIS commercial softwares, such as ArcGIS and IDRISI. On the other hand, the use of open source GIS tools has increased significantly, and their advantages include free distribution, continuous development by user communities and full customization for specific requirements. Herein, we present the IPH-Hydro Tools, an open source tool coupled to MapWindow GIS software designed for watershed topology acquisition, including preprocessing steps in hydrological models such as MGB-IPH. In addition, several tests were carried out assessing the performance and applicability of the developed tool, given by a comparison with available GIS packages (ArcGIS, IDRISI, WhiteBox) for similar purposes. The IPH-Hydro Tools provided satisfactory results on tested applications, allowing for better drainage network and less processing time for catchment delineation. Regarding its limitations, the developed tool was incompatible with huge terrain data and showed some difficulties to represent drainage networks in extensive flat areas, which can occur in reservoirs and large riversA delimitação de bacias hidrográficas, geração da rede de drenagem e determinação de características hidráulicas de um rio de interesse são partes importantes de estudos na área de hidrologia. Atualmente muitas dessas informações são obtidas com o processamento de modelos digitais de elevação (MDEs) em sof- twares comerciais de SIG, como o ArcGIS e o IDRISI. Por outro lado, pacotes de SIG para uso livre, ou seja, gratuitos e de código aberto, têm aumentado significativamente nos últimos anos, e as vantagens desses pacotes incluem ampla distribuição e customização, desenvolvimento continuado pela comunidade de usuários e atendimento a necessidades específicas. Este trabalho apresenta o pacote livre (open-source) denominado IPH-Hydro Tools, um conjunto de ferramentas acoplado ao software livre MapWindow GIS criado para facilitar a aquisição de informações topológicas em bacias hidrográficas, bem como realização de etapas de pré-processamento em modelos hidrológicos a exemplo do MGB-IPH. Para avaliar a aplicabilidade e o desempenho da ferramenta desenvolvida foram realizados testes específicos, através da comparação dos resultados do IPH-Hydro Tools em relação a outros pacotes de SIG (ArcGIS, IDRISI, WhiteBox) disponíveis para esta finalidade. O IPH-Hydro Tools apresentou qualidade de rede de drenagem geralmente superior aos demais pacotes e menor tempo de processamento necessário para delimitação de bacias, apesar de algumas limitações como incompatibilidade em relação a matrizes muito grandes e dificuldade na representação da rede de drenagem em áreas extensas de mesma cota, a exemplo de reservatórios e rios muito largos

Leveraging Crowdsourced Navigation Data In Roadway Pluvial Flash Flood Prediction

This dissertation develops and tests a new data-driven framework for short-term roadway pluvial flash flood (PFF) risk estimation at the scale of road segments using crowdsourced navigation data and a simplified physics-based PFF model. Pluvial flash flooding (PFF) is defined as localized floods caused by an overwhelmed natural or engineered drainage system. This study develops a data curation and computational framework for data collection, preprocessing, and modeling to estimate the risk of PFF at road-segment scales. A hybrid approach is also developed that couples a statistical model and a simplified physics-based simulation model in a machine learning (ML) model to rapidly predict the risk of roadway PFF using Waze alerts in real-time

A Virtual Tile Approach to Rastel-based Calculations of Large Digital Elevation Models in a Shared-Memory System

Grid digital elevation models (DEMs) are commonly used in hydrology to derive information related to topographically driven flow. Advances in technology for creating DEMs have increased their resolution and data size with the result that algorithms for processing them are frequently memory limited. This paper presents a new approach to the management of memory in the parallel solution of hydrologic terrain processing using a user-level virtual memory system for shared-memory multithreaded systems. The method includes tailored virtual memory management of raster-based calculations for datasets that are larger than available memory and a novel order-of-calculations approach to parallel hydrologic terrain analysis applications. The method is illustrated for the pit filling algorithm used first in most hydrologic terrain analysis workflows

An Hierarchical Labeling Technique for Interactive Computation of Watersheds

International audience—The watershed computation is a prevalent task in the geographical information systems. It is used, among other purposes, to forecast the pollutant concentration and its impact on the water quality. The algorithm to compute the watershed can be hard to parallelize and with the increasingly data growth, the need for parallel computation increases. In this paper we propose a new method to parallelize the watershed computation. Our algorithm is decomposed into two tasks, the parallel watershed segmentation into a hierarchy that allows in a second task to retrieve randomly large watersheds at run-time in interactive time