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

Watershed Delineation in the Field: A New Approach for Mobile Applications Using LiDAR Elevation Data

With the advancement of mobile devices, opportunities to take watershed management tasks out of the office and into the field can be realized. In turn, field workers can utilize these technologies to expedite the decision-making process so that they may focus on meeting with clients and addressing agricultural watershed management issues. High-resolution (∼1.5 m postspacing) elevation data gathered by light detection and ranging (LiDAR) provides the topographic detail necessary to model hydrology at the field-scale (∼1 km2). Non-artifactual surface depressions lead to erroneous surface flow patterns when using existing algorithms. So a sequential depression-filling algorithm (SDFA) has been developed to address topographies that contain these types of features. Given a rainfall amount, water distributed across the landscape accumulates and fills only those depressions as necessary, halting the filling process when the only depressions that remain require additional rainfall. After the filling process is completed, the watershed contributing area draining to any particular point of interest may be identified and in the future this may be used as input to hydrologic models. Methods have also been developed to implement subsurface drainage features such as culverts and tile-inlets as well as soil infiltration such that the dynamics of how water is shed from a given landscape can be better represented. Tile inlets and drainage features may be identified via user input and assigned a drainage rate while infiltration may be implemented by assigning a drainage rate to each grid cell in the DEM based on their soil-type. The combination of the sequential depression-filling algorithm and this drainage feature implementation provides the tools to model localized drainage patterns that will match user\u27s field observations at the scale of hundreds of hectares. The flow routing, depression identification, and filling procedures of the SDFA were compared to similar functions in the ArcGIS Hydrology Toolset under conditions where all depressions were filled in order to validate that those components of the algorithm are identical as intended. Furthermore, several digital elevation models (DEMs) were analyzed to determine the variability in hydrologic connectivity across these landscapes as a function of rainfall and as a function of DEM size. In addition to depression storage, the impacts of infiltration on hydrologic connectivity over these landscapes were also analyzed using the SCS Curve Number Method. The assumptions made by existing algorithms that require complete hydrologic connectivity do not hold up in all landscapes, even more so when considering the effects of infiltration. In these landscapes, surface hydrologic connectivity varies noticeably with rainfall excess and it is inaccurate to assume that the watershed should be modeled as a monotonically descending 14 surface. In an applicability study of DEM size, depression features began to be captured around the 1 km 2 scale while it is recommended to use DEMs larger than 2 km 2 to ensure that the depressional features and their contributing areas are completely captured within the DEM extent so that the SDFA may account for those features correctly. The SDFA algorithm was ported from Matlab to an Android application for mobile phones and tablets. The Watershed Delineation app is free and publicly available through the Google Play Store. Users may view DEMs on a Google Map, use the sequential depression-filling algorithm to fill depressions, and delineate watersheds. It was found that the performance of this algorithm is a function of the number of depressions in the DEM which increases with DEM resolution (due to signal-noise effects). At a 3-meter resolution, the ideal DEM dimensions suitable for use of the SDFA on a Google Nexus 4 phone are about 500 x 500 (225 hectares), which took 68 seconds to run. At DEM sizes much greater than this, performance is drastically reduced. As DEM resolution increases, noise effects in the data (which vary based on the raw LiDAR data) result in a high amount of depression features causing an excessive number of iterations of the filling procedure within the algorithm

Geo-Spatial Analysis in Hydrology

Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale

Estimating flood characteristics using geomorphologic flood index with regards to rainfall intensity-duration-frequency-area curves and CADDIES-2D model in three Iranian basins

This is the final version. Available on open access from MDPI via the DOI in this recordThere is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estimating flood extent and depth using only DEM SRTM 30 m and the Geomorphic Flood Index (GFI). The Geomorphologic Flood Assessment (GFA) tool which is the corresponding application of the GFI in QGIS is implemented to achieved the results in three basins in Iran. Moreover, the novel concept of Intensity-Duration-Frequency-Area (IDFA) curves is introduced to modify the GFI model by imposing a constraint on the maximum hydrologically contributing area of a basin. The GFA model implements the linear binary classification algorithm to classify a watershed into flooded and non-flooded areas using an optimized GFI threshold that minimizes the errors with a standard flood map of a small region in the study area. The standard hydraulic model envisaged for this study is the Cellular Automata Dual-DraInagE Simulation (CADDIES) 2D model which employs simple transition rules and a weight-based system rather than complex shallow water equations allowing fast flood modelling for large-scale problems. The results revealed that the floodplains generated by the GFI has a good agreement with the standard maps, especially in the fluvial rivers. However, the performance of the GFI decreases in the less steep and alluvial rivers. With some overestimation, the GFI model is also able to capture the general trend of water depth variations in comparison with the CADDIES-2D flood depth map. The modifications made in the GFI model, to confine the maximum precipitable area through implementing the IDFAs, improved the classification of flooded area and estimation of water depth in all study areas. Finally, the calibrated GFI thresholds were used to achieve the complete 100-year floodplain maps of the study areas.University of BasilicataCNR-IMAAOpenet TechnologiesRoyal Academy of Engineering (RAE

Flood Modeling and the Influence of Digital Terrain Models: A Case Study of the Swannanoa River in North Carolina

An increase in flood disasters nationally and internationally has highlighted the need for accurate flood modeling regarding flood insurance and emergency response. Topographic data is the most important variable in determining flood modeling accuracy according to the National Research Council. Increasing availability of airborne light detection and ranging (LiDAR) data warrants the investigation of the optimal resolution or range of resolutions needed to represent digital terrain models for accurate operational flood modeling. Few studies have focused on flood modeling in mountain environments. The Swannanoa River, located within the Appalachian Mountains of western North Carolina, was selected for this study based on unique physical characteristics, a substantial built environment within the 100 year floodplain, and significant recorded levels of historical flooding.Flood modeling accuracy was evaluated using LiDAR elevation data represented at a series of equivalent resolutions (3.77m, 6m, 8m, 10m, 12m, 15m, 20m, 25m, and 30m) and United Stated Geological Survey Level 2 digital elevation model data represented at 10m and 30m resolutions combined with a series of flood recurrence intervals (10yr, 25yr, 50yr, 100yr, and 500yr). A variety of descriptive and inferential statistics were used to evaluate generated water surface profiles and depth grids

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

Continental-scale high-resolution river geometry and real-time inundation mapping

Flooding is the most threatening natural disaster worldwide considering the fatalities and property damage it causes. Recent flood disasters have raised concerns for accurate and responsive inundation forecast due to the rapid spread and astonishing destructive power of these events. Although recent development in large scale hydrologic simulation has enabled the real-time streamflow simulation operating on millions of river reaches, a framework for converting the forecast discharge into corresponding water surface elevation and inundation maps at a continental-scale is absent to better support local flood response. To accurately map flood inundation extent, a comprehensive description of the geometry of the channel is indispensable. As such, this dissertation presents an innovative approach for estimating river geometry and conducting inundation mapping at a continental-scale with a high spatial resolution. This approach is based on the concept of Height Above Nearest Drainage (HAND). Advanced hydrologic terrain analysis workflows have been designed to derive channel hydraulic properties, stage-discharge rating curves, and inundation extents using HAND. After the mechanism being presented, the implementation of this approach across the contiguous United States has been demonstrated using the 10-meter National Elevation Dataset. The integrity of the outputs has been validated through the comparison with best available references at multiple test sites. Considering the increasingly availability of high-resolution topographic data derived from lidar technology, the dissertation further presents how advanced geomorphic feature extraction tools are integrated into the proposed approach to overcome the challenges associated with the enrichment of terrain details. At last, this dissertation presents how banklines, an essential piece of river geometry characteristic as the boundary differentiates channel zone from floodplain, is detected with enhanced geomorphic feature extraction tools for improving large-scale hydrologic simulation and inundation mapping accuracy.Civil, Architectural, and Environmental Engineerin

Spatial decision support system for coastal flood management in Victoria, Australia

Coastal climate impact can affect coastal areas in a variety of ways, such as flooding, storm surges, reduction in beach sands and increased beach erosion. While each of these can have major impacts on the operation of coastal drainage systems, this thesis focuses on coastal and riverine flooding in coastal areas. Coastal flood risk varies within Australia, with the northern parts in the cyclone belt most affected and high levels of risk similar to other Asian countries. However, in Australia, the responsibility for managing coastal areas is shared between the Commonwealth government, Australian states and territories, and local governments. Strategies for floodplain management to reduce and control flooding are best implemented at the land use planning stage. Local governments make local decisions about coastal flood risk management through the assessment and approval of planning permit applications. Statutory planning by local government is informed by policies related to coastal flooding and coastal erosion, advice from government departments, agencies, experts and local community experts. The West Gippsland Catchment Management Authority (WGCMA) works with local communities, Victorian State Emergency Services (VCSES), local government authorities (LGAs), and other local organizations to prepare the West Gippsland Flood Management Strategy (WGFMS). The strategy aims at identifying significant flood risks, mitigating those risks, and establishing a set of priorities for implementation of the strategy over a ten-year period. The Bass Coast Shire Council (BCSC) region has experienced significant flooding over the last few decades, causing the closure of roads, landslides and erosion. Wonthaggi was particularly affected during this period with roads were flooded causing the northern part of the city of Wonthaggi to be closed in the worst cases. Climate change and increased exposure through the growth of urban population have dramatically increased the frequency and the severity of flood events on human populations. Traditionally, while GIS has provided spatial data management, it has had limitations in modelling capability to solve complex hydrology problems such as flood events. Therefore, it has not been relied upon by decision-makers in the coastal management sector. Functionality improvements are therefore required to improve the processing or analytical capabilities of GIS in hydrology to provide more certainty for decision-makers. This research shows how the spatial data (LiDAR, Road, building, aerial photo) can be primarily processed by GIS and how by adopting the spatial analysis routines associated with hydrology these problems can be overcome. The aim of this research is to refine GIS-embedded hydrological modelling so they can be used to help communities better understand their exposure to flood risk and give them more control about how to adapt and respond. The research develops a new Spatial Decision Support System (SDSS) to improve the implementation of coastal flooding risk assessment and management in Victoria, Australia. It is a solution integrating a range of approaches including, Light Detection and Ranging (Rata et al., 2014), GIS (Petroselli and sensing, 2012), hydrological models, numerical models, flood risk modelling, and multi-criteria techniques. Bass Coast Shire Council is an interesting study region for coastal flooding as it involves (i) a high rainfall area, (ii) and a major river meeting coastal area affected by storm surges, with frequent flooding of urban areas. Also, very high-quality Digital Elevation Model (DEM) data is available from the Victorian Government to support first-pass screening of coastal risks from flooding. The methods include using advanced GIS hydrology modelling and LiDAR digital elevation data to determine surface runoff to evaluate the flood risk for BCSC. This methodology addresses the limitations in flood hazard modelling mentioned above and gives a logical basis to estimate tidal impacts on flooding, and the impact and changes in atmospheric conditions, including precipitation and sea levels. This study examines how GIS hydrological modelling and LiDAR digital elevation data can be used to map and visualise flood risk in coastal built-up areas in BCSC. While this kind of visualisation is often used for the assessment of flood impacts to infrastructure risk, it has not been utilized in the BCSC. Previous research identified terrestrial areas at risk of flooding using a conceptual hydrological model (Pourali et al., 2014b) that models the flood-risk regions and provides flooding extent maps for the BCSC. It examined the consequences of various components influencing flooding for use in creating a framework to manage flood risk. The BCSC has recognised the benefits of combining these techniques that allow them to analyse data, deal with the problems, create intuitive visualization methods, and make decisions about addressing flood risk. The SDSS involves a GIS-embedded hydrological model that interlinks data integration and processing systems that interact through a linear cascade. Each stage of the cascade produces results which are input into the next model in a modelling chain hierarchy. The output involves GIS-based hydrological modelling to improve the implementation of coastal flood risk management plans developed by local governments. The SDSS also derives a set of Coastal Climate Change (CCC) flood risk assessment parameters (performance indicators), such as land use, settlement, infrastructure and other relevant indicators for coastal and bayside ecosystems. By adopting the SDSS, coastal managers will be able to systematically compare alternative coastal flood-risk management plans and make decisions about the most appropriate option. By integrating relevant models within a structured framework, the system will promote transparency of policy development and flood risk management. This thesis focuses on extending the spatial data handling capability of GIS to integrate climatic and other spatial data to help local governments with coastal exposure develop programs to adapt to climate change. The SDSS will assist planners to prepare for changing climate conditions. BCSC is a municipal government body with a coastal boundary and has assisted in the development and testing of the SDSS and derived many benefits from using the SDSS developed as a result of this research. Local governments at risk of coastal flooding that use the SDSS can use the Google Earth data sharing tool to determine appropriate land use controls to manage long-term flood risk to human settlement. The present research describes an attempt to develop a Spatial Decision Support System (SDSS) to aid decision makers to identify the proper location of new settlements where additional land development could be located based on decision rules. Also presented is an online decision-support tool that all stakeholders can use to share the results

Flood Early Warning and Risk Modelling

Extreme hydrological phenomena are one of the most common causes of human life loss and material damage as a result of the manifestation of natural hazards around human communities. Climatic changes have directly impacted the temporal distribution of previously known flood events, inducing significantly increased frequency rates as well as manifestation intensities. Understanding the occurrence and manifestation behavior of flood risk as well as identifying the most common time intervals during which there is a greater probability of flood occurrence should be a subject of social priority, given the potential casualties and damage involved. However, considering the numerous flood analysis models that have been currently developed, this phenomenon has not yet been fully comprehended due to the numerous technical challenges that have arisen. These challenges can range from lack of measured field data to difficulties in integrating spatial layers of different scales as well as other potential digital restrictions.The aim of the current book is to promote publications that address flood analysis and apply some of the most novel inundation prediction models, as well as various hydrological risk simulations related to floods, that will enhance the current state of knowledge in the field as well as lead toward a better understanding of flood risk modeling. Furthermore, in the current book, the temporal aspect of flood propagation, including alert times, warning systems, flood time distribution cartographic material, and the numerous parameters involved in flood risk modeling, are discussed