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The measurement of streamflow in the world’s rivers is critical to the management of water as a resource and to predicting and managing the impacts of potentially damaging hydrological events such as major floods. The European Space Agency sponsored FluViSat (Fluvial Video from Satellite) project has successfully demonstrated the potential of very high resolution satellite imagery for the determination of water flow speeds, and hence streamflow rates, using established surface velocimetry techniques.Video imagery kindly provided by Planet Labs PBC from the 21 satellites in their SkySat constellation was pre-processed to stabilise and georectify it, and then analysed using Space Time Imaging Velocimetry (STIV) techniques to provide water speed vectors across the river’s surface. The method was successfully demonstrated on rivers in Australia, the UK and Africa, with field based validation undertaken where possible. Additionally, a series of six videos was obtained and analysed to provide near a sequence of observations of flood flows on the Indus River in Pakistan during the devastating flooding of 2022.Benefits of the FluViSat innovation include the ability to observe water flow rates almost anywhere on the planet, the potential for multiple daily repeat observations and largely eliminating the need for locally based people, equipment and infrastructure.This presentation presents results from the research, explains the methods employed to derive and validate flow speeds, and explores opportunities to further enhance the FluViSat methodology.

At the global level, several flood related tools are available for free, ranging from observations to modeling and forecasting, using field data, remotely sensed observations as well as hydrologic and hydrodynamic models (for more details of available tools, see EOTEC DevNet’s tool tracking capacity building resources for flooding at https://eotec-dev.ceos.org/tools/). In this context, the Global Flood Awareness System (GloFAS) managed by Copernicus, for instance, aims to facilitate response to flooding, particularly in countries that cannot forecast these events on their own.However, having an EWS available to all globally, with consistent accuracy and reliability, for alerting at different severity levels, will not only aid with reduction of flood impacts, but also assist with improving resilience of these counties. In this paper, we present the model of models (MoM), which is an ensembled model that forecasts flood severity daily, globally at sub-watershed level. MoM integrates the outputs of GloFAS, GFMS, and HWRF models to forecast severity and uses MODIS and VIIRS outputs for calibration and validation of severity scores.The flood severity risk score is used to obtain and process high-resolution Earth observation data to assess flood depth and extent at granular level and estimate flood impact on critical infrastructure.The flood severity score is used to trigger dissemination of alerts using PDC’s DisasterAWARE® platform.We present a number of real event cases where MoM has been activated to alert and assist with event response activities, including performance validation with high-resolution satellite flood maps.

Are your properties located far enough from rivers, sea shorelines or water bodies? If the answer is yes, this does not mean that they are fully safe from flooding.In an era governed by continuous climate instability and unstoppable expansion of cities, the exacerbation of hydrometeorological events is increasing the occurrence of pluvial floods. Pluvial flooding is induced by the combination of two factors: extreme precipitations and the incapability of the ground/drainage systems to effectively handle excessive rainwater.In an urban environment, the runoff generated by localized and intense rainstorms may quickly inundate streets and buildings undermining the safety of people and assets. The characteristic of being hardly predictable has inspired the definition of pluvial flood as an ‘invisible hazard’ and the related damages and losses are increasingly weighing on the budget of municipalities and private citizens.Looking at the upsetting climate projections, experts are resolute in developing comprehensive methodologies and strategies for flood risk assessment and management.In this work, we present the attempt of accomplishing a high-resolution pluvial flood risk assessment at the city scale. The city of Differdange (Luxembourg's third largest city) is used as case study in which the extreme rainfall-related impacts and hazards are analyzed through the implementation of a fully coupled 1D/2D dual drainage model. This type of hydrodynamic model closely mimics the complexity of an urban landscape allowing to simulate all hydraulic phenomena occurring both on the surface and through the sewer network. Despite the digital accuracy of these models, they are rarely implemented due to the vast amount of detailed information required; which are often unavailable.The implementation of the hydraulic model follows two main steps: the bi-dimensional discretization of the surface and the 1D modelling of the whole drainage network.Nowadays, many countries provide open-access high-resolution digital elevation models of their territories (50 cm for Luxembourg) and up-to-date cadastral planimetries from which essential information for the 2D component are extrapolated. Ground data is enriched by land use/cover and soil maps for the estimation of roughness and infiltration parameters.The drainage network contemplates all pipes carrying rainwater, meaning the newer storm-water system and the old combined sewer network. The geometric specifications required are size, shape, elevation, material of pipes, manholes and tanks. Important infrastructures, such as flooding barriers, have been systematically added to the model. The fully-distributed hydrological engine allows operating the rainfall-runoff transformation on each cell of the domain and the exchange of water between the surface and drainage network occurs through the nodes of the network (storm drains and manholes).The model’s outcomes allow for assessing the level of hazard to which each building is exposed, identifying the critical nodes within the drainage network, and proposing mitigation strategies.Furthermore, these insights may help authorities to improve their warning systems and emergency plans.

PurposeAs stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of earth observation (EO), specifically ongoing efforts within the Group on Earth Observations (GEO) Work Programme and the Committee on Earth Observation Satellites (CEOS) Work Plan, to support risk-informed decision-making, based on documented national and subnational needs and requirements.Design/methodology/approachPromotion of open data sharing and geospatial technology solutions at national and subnational scales encourages the accelerated implementation of successful EO applications. These solutions may also be linked to specific Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 Global Targets that provide trusted answers to risk-oriented decision frameworks, as well as critical synergies between the Sendai Framework and the 2030 Agenda for Sustainable Development. This paper provides examples of these efforts in the form of platforms and knowledge hubs that leverage latest developments in analysis ready data and support evidence-based DRR measures.FindingsThe climate crisis is forcing countries to face unprecedented frequency and severity of disasters. At the same time, there are growing demands to respond to policy at the national and international level. EOs offer insights and intelligence for evidence-based policy development and decision-making to support key aspects of the Sendai Framework. The GEO DRR Working Group and CEOS Working Group Disasters are ideally placed to help national government agencies, particularly national Sendai focal points to learn more about EOs and understand their role in supporting DRR.Originality/valueThe unique perspective of EOs provide unrealized value to decision-makers addressing DRR. This paper highlights tangible methods and practices that leverage free and open source EO insights that can benefit all DRR practitioners.

Abstract The ability to map floods from satellites has been known for over 40 years. Early images of floods were rather difficult to obtain, and flood mapping from satellites was thus rather opportunistic and limited to only a few case studies. However, over the last decade, with a proliferation of open-access EO data, there has been much progress in the development of Earth Observation products and services tailored to various end-user needs, as well as its integration with flood modeling and prediction efforts. This article provides an overview of the use of satellite remote sensing of floods and outlines recent advances in its application for flood mapping, monitoring and its integration with flood models. Strengths and limitations are discussed throughput, and the article concludes by looking at new developments.

Every year riverine flooding affects millions of people in developing countries, due to the large population exposure in the floodplains and the lack of adequate flood protection. Preparedness and monitoring are effective ways to reduce flood risk. State-of-the-art technologies relying on satellite remote sensing as well as numerical hydrological and meteorological predictions can detect and monitor severe flood events at the global scale. This chapter describes the emerging role of the Global Flood Partnership (GFP), a global network of scientists, users, and private and public organizations active in global flood risk management. Currently, a number of GFP partner institutes regularly share results from their experimental products, developed to predict and monitor where and when flooding is taking place in near real time. Products of the GFP have already been used on several occasions by national environmental agencies and humanitarian organizations to support emergency operations and to reduce the overall socioeconomic impacts of disasters. The chapter includes a discussion on existing challenges and ways forward to improve rapid access to flood information and increase resilience to flooding.

A reliable flood event inventory that reflects the occurrence and evolution of past floods is important for studies of flood hazards and risks, hydroclimatic extremes, and future flood projections. However, currently available flood inventories are based on single-sourced data and often neglect underreported or less impactful flood events. Furthermore, traditional archives store flood events only at sparse geographic points, which significantly limits their further applicability. Also, few publicly available archives contain all-inclusive records of potential natural flooded area over time. To tackle these challenges, we construct two types of multisourced flood event inventories (MFI) for all river basins across the contiguous United States covering the period 1998–2013 on daily and subcatchment scales, which is publicly available at http://flood.umd.edu/download/CONUS/. These archives integrate flood information from in situ observations, remote sensing observations, hydrological model simulations, and five high-quality precipitation products. The first inventory (MFI-Actual) includes all actual floods that occurred in the presence of flood protection infrastructures, while the second, “natural (undefended)” inventory (MFI-Natural) reconstructs the possible “historical” floods without flood protection, which could be more directly influenced by climate variation. In the proposed two inventories, 2,755 and 4,661 flood events were estimated, respectively. MFI-Natural reconstructed 1,597 floods in ungauged basins, and recovered 608 extreme streamflow events in gauged subcatchments where floods would have happened if there were no flood protection. There is an average of four upstream dams located in these flood-recovered subcatchments, which indicates that modern flood defenses efficiently prevent significant flooding from extreme precipitation in many catchments over the country.

Flooding is a frequent disaster that has a wide-spread footprint globally with significant financial and societal impacts. With availability of Earth observation data from private and public entities at varying spatial, temporal, and spectral resolution as well as data from crowdsourcing, there is no shortage of models. In fact, models and algorithms are abundant and proliferating. However, the question remains where is a global flood model when we need one? Just because models are available does not mean they are usable or accessible and adequate for emergency managers, first responders and other stakeholders who use the model outputs for preparedness, response and resource planning. Often the issue of usability stems from the fact that the models are not always reproducible or replicable. The accuracy and uncertainty associated with the models and how they change based on the scale of analysis and the resolution of input and output datasets are often not communicated properly to stakeholders so they can be part of their decision-making process. The proliferation of machine learning and data driven models that rely on historical data also adds to this problem. This paper discusses several important issues associated with global flood models and provides recommendations that could be used to increase the usability of these models.

The prolonged mei-yu/baiu system with anomalous precipitation in the year 2020 has swollen many rivers and lakes, caused flash flooding, urban flooding and landslides, and consistently wreaked havoc across large swathes of China, particularly in the Yangtze River basin. Significant precipitation and flooding anomalies have already been seen in magnitude and extension so far this year, which have been exerting much higher pressure on emergency responses in flood control and mitigation than in other years, even though a rainy season with multiple ongoing serious flood events in different provinces is not that uncommon in China. Instead of delving into the causes of the uniqueness of this year’s extreme precipitation-flooding situation, which certainly warrants in-depth exploration, in this article we provide a short view toward a more general hydrometeorological solution to this annual nationwide problem. A "glocal" (global to local) hydrometeorological solution for floods (GHS-F) is considered to be critical for better preparedness, mitigation, and management of different types of significant precipitation-caused flooding, which happen extensively almost every year in many countries such as China, India and the United States. Such a GHS-F model is necessary from both scientific and operational perspectives, with the strength in providing spatially consistent flood definitions and spatially distributed flood risk classification considering the heterogeneity in vulnerability and resilience across the entire domain. Priorities in the development of such a GHS-F are suggested, emphasizing the user’s requirements and needs according to practical experiences with various flood response agencies.

The Niger River and floodplain landscape is experiencing a constant change as a result of natural and human processes thereby contributing to the yearly occurrence of flooding. The increasing flood frequency and intensity causes loss of life, destruction of assets and disrupts the livelihood of a large proportion of the population. Due to the current data challenges and lack of hydrological information we are developing a 2-D flood inundation model showing the spatially distributed dynamics of water surface elevation and future flood extent of Niger river and its surroundings. We considered the following parameters such as floodplain topography, river channel widths, banks heights, model parameters, and hydrology information to develop our final output which is an interactive web visualization map showing the inundated extent. Our developed 2D flood prediction model can be extended to other parts of the Niger River Basin which will contribute to a positive regional economic and environmental impact. It will also help the relevant ministries, emergency institutions, local partners and national government of Niger to build safe and resilient communities through effective risk communication and contribute to the achievement of the Sustainable Development Goal (SDG) 11 and 13.

The rising number of flooding events combined with increased urbanization is contributing to significant economic losses due to damages to structures and infrastructures. From a risk reduction and resilience perspective, it is not only essential to forecast flood risk and potential impacts, but also to disseminate the information to stakeholders on the ground for rapid implementation of mitigation and response measures. This paper provides (i) an introduction to DisasterAWARE®, a global alerting system, that is used to disseminate flood risk information to stakeholders across the globe, and (ii) a discussion of the models implemented using earth observation data (Synthetic Aperture Radar and optical imagery) for near real-time assessment of flood severity and potential flood impacts to infrastructures. While the models are still in their nascent stage, a case study implementation of the models for the 2020 flooding event in Africa is presented to showcase the model integration with DisasterAWARE®.

This review provides a broad overview of the current state of flood research, current challenges, and future directions. Beginning with a discussion of flood-generating mechanisms, the review synthesizes the literature on flood forecasting, multivariate and nonstationary flood frequency analysis, urban flooding, and the remote sensing of floods. Challenges and future flood research directions are outlined and highlight emerging topics where more work is needed to help mitigate flood risks. It is anticipated that the future urban systems will likely have more significant flood risk due to the compounding effects of continued climate change and land-use intensification. The timely prediction of urban floods, quantification of the socioeconomic impacts of flooding, and developing mitigation strategies will continue to be challenging. There is a need to bridge the scales between model capabilities and end-user needs by integrating multiscale models, stakeholder input, and social and citizen science input for flood monitoring, mapping, and dissemination. Although much progress has been made in using remote sensing for flood applications, recent and upcoming Earth Observations provide excellent potential to unlock additional benefits for flood applications. The flood community can benefit from more downscaled, as well as ensemble scenarios that consider climate and land-use changes. Efforts are also needed for data assimilation approaches, especially to ingest local, citizen, and social media data. Also needed are enhanced capabilities to model compound hazards and assess as well as help reduce social vulnerability and impacts. The dynamic and complex interactions between climate, societal change, watershed processes, and human factors often confronted with deep uncertainty highlights the need for transdisciplinary research between science, policymakers, and stakeholders to reduce flood risk and social vulnerability

In order to optimize the management of groundwater resources, accurate estimates of groundwater level (GWL) fluctuations are required. In recent years, the use of artificial intelligence methods based on data mining theory has increasingly attracted attention. The goal of this research is to evaluate and compare the performance of adaptive network-based fuzzy inference system (ANFIS) and Wavelet-ANFIS models based on FCM for simulation/prediction of monthly GWL in the Maragheh plain in northwestern Iran. A 22-year dataset (1996&ndash, 2018) including hydrological parameters such as monthly precipitation (P) and GWL from 25 observation wells was used as models input data. To improve the prediction accuracy of hybrid Wavelet-ANFIS model, different mother wavelets and different numbers of clusters and decomposition levels were investigated. The new hybrid model with Sym4-mother wavelet, two clusters and a decomposition level equal to 3 showed the best performance. The maximum values of R2 in the training and testing phases were 0.997 and 0.994, respectively, and the best RMSE values were 0.05 and 0.08 m, respectively. By comparing the results of the ANFIS and hybrid Wavelet-ANFIS models, it can be deduced that a hybrid model is an acceptable method in modeling of GWL because it employs both the wavelet transform and FCM clustering technique.

In this study, a new approach for rainfall spatial interpolation in the Luxembourgian case study is introduced. The method used here is based on a Fuzzy C-Means (FCM) clustering method. In a typical FCM procedure, there are a lot of available data and each data point belongs to a cluster, with a membership degree [0 1]. On the other hand, in our methodology, the center of clusters is determined first and then random data are generated around cluster centers. Therefore, this approach is called inverse FCM (i-FCM). In order to calibrate and validate the new spatial interpolation method, seven rain gauges in Luxembourg, Germany and France (three for calibration and four for validation) with more than 10 years of measured data were used and consequently, the rainfall for ungauged locations was estimated. The results show that the i-FCM method can be applied with acceptable accuracy in validation rain gauges with values for R2 and RMSE of (0.94–0.98) and (9–14 mm), respectively, on a monthly time scale and (0.86–0.89) and (1.67–2 mm) on a daily time scale. In the following, the maximum daily rainfall return periods (10, 25, 50 and 100 years) were calculated using a two-parameter Weibull distribution. Finally, the LISFLOOD FP flood model was used to generate flood hazard maps in Dudelange, Luxembourg with the aim to demonstrate a practical application of the estimated local rainfall return periods in an urban area.

With the ongoing proliferation of satellite data, in particular open-access satellite imagery, from both optical and synthetic aperture radar (SAR) sensors, the number of downstream applications is rapidly growing. Developers of Earth Observation (EO)-based products and services, as well as expert and non-expert users of such tools, thus need access to a cloud computing infrastructure offering interoperable analysis functionality. Here, we present the versatility of such a cloud-based infrastructure called WASDI. WASDI, a web-advanced space development interface, is an online EO analytics platform where EO experts can develop and deploy applications (apps) and users can use them to processes satellite images on demand to generate value-added content.

Achieving an efficient management of agricultural fields is crucial for farmers, considering the challenges posed by water resources sustainability. Monitoring tools allow winemakers to keep their vineyards under control and improve the plants' health with targeted actions, such as irrigations scheduling or specific treatments. Knowing the actual number of plants may not be evident, particularly in old plantation, where vines might have been removed or added to the initial planting scheme, or even they have never been counted. However, the knowledge of the number and position of the plants is important to be able to adapt the irrigation network and properly adjust the irrigation schedule. The aim of this project is to build a drone service for precision monitoring of vineyards. Here, an algorithm to detect the position and number of plants in vineyards using drone RGB imagery is presented. First results show a plant detection accuracy of 87%.

Flooding is one of the most prevalent and costliest global disasters. Disaster managers face significant challenges in managing essential information for preparedness, response, and recovery efforts. The development of an open access global flood alerting system for effective classification of potential impacts and the formulation of effective emergency response measures requires the incorporation of a wide variety of flood outputs derived from hydrologic and hydraulic models as well as from remote sensing derived data sets from multiple satellite/sensor platforms. We seek to rapidly classify flood severity using a model of models (MoM) approach that leverages products of existing flood models and incorporates Synthetic Aperture Radar (SAR) derived outputs for ground-truthing of model results and delineation of flood impact areas. The flood severity classification along with potential impacts estimated by using optical imagery will be disseminated as alerts using the Pacific Disaster Center's DisasterAWARE® decision support platform to users globally.

We evaluated NASA soil moisture active–passive (SMAP) soil moisture (SM) and freeze-thaw (FT) datasets for the utility to identify FT and SM conditions as precursors to a 2017 spring-melt flood event in the Red River of the North Basin. SMAP FT and SM datasets were analyzed at basin-scale and at specific North Dakota Agricultural Weather Network (NDAWN), National Oceanic and Atmospheric Administration (NOAA), and Red River of the North U.S. Geological Survey (USGS) stations prior to and during the observed flood. Results indicate that SMAP FT dataset had better agreement with NDAWN and NOAA air temperature measurements than with soil temperature. SMAP FT and SM were able to observe FT states and saturated soil conditions at basin-level and significant increases in SM content up to five days before USGS gauge height increase and the manifestation of the flood event. A Spearman's rank ( $R_{s}$ ) cross-correlation coefficient lag function was applied to SMAP SM and USGS river gauge heights and the strength of the relationship varied by location and lead time. Downstream locations near and in the flood area (North Grand Forks, Oslo, North Drayton) displayed moderate to strong relationships at 1-, 3-, and 4-day lead times ( R s = 0.67, 0.84, 0.71; p $R_{s}= 0.88; p ), well during the flood event recorded. This study suggests that SMAP SM and FT datasets can potentially provide useful information on surface state conditions to spring-melt floods in the Red River of the North Basin.

In our changing world, floods are a threat of increasing concern. Within this context, flood mapping is important for both damage assessment and forecast improvement. Due to the suitability of synthetic aperture radar (SAR) for flood mapping, a broad range of SAR-based flood mapping algorithms has been developed during the past years. However, most of these algorithms were presented based on a single test case only and comparisons between methods are rare. This paper presents an in-depth assessment and comparison of the established pixel-based flood mapping approaches, including global and enhanced thresholding, active contour modeling and change detection. The methods were tested on medium-resolution SAR images of different flood events and lakes across the U.K. and Ireland and were evaluated on both accuracy and robustness. Results indicate that the most suited method depends on the area of interest and its characteristics as well as the intended use of the observation product. Due to its high robustness and good performance, tiled thresholding is suited for automated, near-real time flood detection and monitoring. Active contour models can provide higher accuracies but require long computation times that strongly increase with increasing image sizes, making them more appropriate for accurate flood mapping in smaller areas of interest.

The development of precise and simple spatial interpolation methods to estimate rainfall data in ungauged locations provides not only better understating and new insights into the predictive hydrological models but also improves the accuracy of these models. In this Scientific Briefing a new approach for rainfall spatial interpolation in Luxembourgian case study has been introduced. The method used here is based on a Fuzzy C-Means (FCM) clustering method. In the normal FCM procedure, there are a lot of available data and each data point belongs to a cluster, with a membership degree [0 1], i.e. the data points clustered in an iterative process, whereas in our methodology the center of clusters has been determined first and then random data will be generated around cluster centers. Therefore, this approach is called inverse FCM (i-FCM) from here on. In order to calibrate and validate the new spatial interpolation method four rain gauges in Luxembourg (3 for calibration and one for validation) with 10 years of measured data were used and consequently the rainfall for ungauged locations were estimated. The results show that the i-FCM method can be applied with acceptable accuracy in validation rain gauge with values for R2 and RMSE of 0.92 and 12 mm, respectively, on monthly time scale and 0.84 and 1.8 mm on daily time scale.

There is no doubt that the devastating socio-economic impacts associated with floods has been increasing. According to the International Disaster Database (EM-DAT), floods represent the most frequent and most impacting, in terms of the number of people affected, among the weather-related disasters: nearly 1 billion people were affected by inundations in the last decade (2006–2015), while the overall economic damage is estimated to be more than $300 billion. Despite this evidence, and the awareness of the environmental role of rivers and their inundation, our capability to respond to and forecast floods remains relatively poor.In this context, satellite sensors represent a highly valuable source of observation data that could fill many of the gaps, especially in remote areas and developing countries. In the last decade, with the proliferation of more satellite data and the advent of ESA’s operational Sentinel missions under the EC Copernicus open data programme, satellite images, in particular SAR, have been assisting flood disaster mitigation, response and recovery operations globally. Although the number of state-of-the-art and innovative research studies in those areas is increasing, the full potential of remotely sensed data to enhance flood mapping has yet to be unlocked, especially the latency issue is not being sufficiently well addressed. Latency, i.e. the time between image acquisition to the flood map delivery to the person that actually needs it, is not at all in line with disaster response requirements and is, to a large extent, responsible for the slow uptake of EO-based products, such as flood maps, into an operational timeline or disaster response protocols of various potential user organizations, such as the UN World Food Programme for instance.We call to develop a prototype or concept of a product. Specifically, a digital twin experiment should be developed first to generate a prototype AI-based algorithm that could be deployed onboard a SAR satellite to produce flood maps in real time. The mapping result, which consists of simple column/row (x/y) vector indices of flood edges in the form of a short “text message”, will be delivered to the field response teams via satellite communication technology for use within minutes, rather than many hours to days as is currently the case.In this paper, we illustrate the concept of the proposed innovation, including the future possibility of in-orbit processing. This is in part a synthetic, proof-of-concept study, and, although the societal impact and value of the service prototype developed is clear, once successfully demonstrated, the economic value of this service as well as its market share and value can be established. At this point in time, there is no service of this type in existence. For optical/hyperspectral sensors on CubeSats, onboard AI-based processing has been trialed but not for SAR and not including a rapid flood map delivery service using AI. For instance, based on the results of an ESA-supported FDL Europe challenge, Mateo-Garcia et al. (2019) demonstrated the application of a fully convolutional neural network to prototype a GPU-based onboard flood segmentation system using degraded Sentinel-2 imagery (to mimic CubeSat capability).

This study reports a new and significantly enhanced analysis of US flood hazard at 30 m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level, and inclusion of the impact of local interventions in the flooding system. For the first time, we consider pluvial, fluvial, and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centered on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high-quality local models and the entire catalog of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69–0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high-frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national-scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near-future time horizon, we project clear patterns of changing flood hazard (3σ changes in 100 years inundated area of −3.8 to +16% at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defense protection may become compromised by this time.

Climate-related natural disasters have increased significantly in the last few decades. However, conventional water monitoring systems such as discharge guaging stations, even if data are widely shared, have limitations for monitoring, understanding, and predicting these disasters. This chapter provides an overview of water-related products that the DFO (Dartmouth Flood Observatory)—Flood Observatory, in collaboration with other agencies and initiatives, has developed and shares. Most of these products are experimental and semi-operational and are based on freely-available satellite measurements and images. DFO preserves for public use a digital map record of the Earth’s changing surface water, conducts remote sensing-based water measurement, and mapping in “near real time” for humanitarian purposes, and supports and encourages operational uses of remote sensing-based surface water information. It is our vision that these products can support the larger hydrological community in an effort to reduce the impact of flooding and related natural disasters.

This short editorial summarizes the main messages of the chapters in this edited book volume, which is a collection of chapters describing the latest progress and perspectives on the use of Earth Observation for flood applications.

Earth observation satellite systems play an important role in the provision of a wide range of information, especially in data-scarce regions. This role becomes extremely relevant during a disaster and when direct access to the affected area is difficult. This chapter provides an overview of the state of the art remote sensing techniques and tools for the detection and monitoring of hydrometeorological disasters, focusing on tropical cyclones Idai and Kenneth. In a perspective of disaster preparedness, rainfall measurements provided by satellites can enable decision-makers to take urgent measures in the pre-event phase and can be used as input for early warning systems. Data acquired from satellite missions are used for a set of different tools developed to map flood extent, while several satellite-based emergency mapping mechanisms provide timely post-event information by taking advantage of observations provided by satellites (including commercial platforms providing images at very high geometric resolution). Analysis of vegetation dynamics derived from time-series of multispectral imagery is used to assess changes, such as the detection of cyclone-damaged areas, identification of critical conditions in vegetation health/productivity, and land cover change mapping.

Decision making and planning for the prediction of flood events and conditions requires the use of adequate models and methods. In recent years, appropriate models and algorithms such as machine learning (ML) and deep learning (DL) have been developed and used in many research projects dealing with flood mapping. In this regard, the main purpose of this chapter is to present a review of applying ML/DL methods to process satellite images for generating flood maps. Moreover, the basic concepts of some ML data driven methods are presented. Three case studies with different ML algorithms have been selected and are illustrated to provide a better understanding of their application in flood studies. The findings of these case studies show that ML models are mostly applied to identify and predict flooded versus non-flooded pixels in images but important challenges remain. These challenges need to be solved if ML is to be valuable for decision making processes related to flood management.

The ability to map floods from satellites has been known for over 40 years. Early images of floods were rather difficult to obtain and flood mapping from satellites was thus rather opportunistic and limited to only a few case studies. However, over the last decade, with a proliferation of open-access Earth Observation (EO) data, interoperability, and the Internet of Things, there has been much progress in the development of EO products and services tailored to various end-user needs. EO has clearly entered a new era now, where, with respect to flood mapping, various services are offered but many challenges still remain, particularly in relation to satellite signal interactions within vegetated and urban environments. Also, major limitations in measuring critical flood variables directly from remote sensing, such as flow depth and velocity, and the lack of open-access high-accuracy auxiliary global datasets, such as topography, seriously hamper the development of reliable flood mapping algorithms that work well anytime and anywhere and can be deployed locally as well as globally across cloud compute services. All these non-trivial challenges present pitfalls but also opportunities and in order to unlock the full potential of EO for flood applications, scientists, product developers, and end-users alike need to manage expectations and form partnerships.

This paper reports a new and significantly enhanced analysis of US flood hazard at 30m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel d...

Flooding is the most common natural hazard worldwide, affecting over a billion people and costing $100 billion every year. This will likely increase in the future due to increasing population and assets in the flood-prone zones and climate change. Earth observation data have been utilized to map flooding on a global scale. This was mostly done by utilizing optical bands, as for these satellites the return period is relatively short; often daily. However, the usage of optical data can be restrictive due to e.g. cloud cover and nighttime. With synthetic aperture radar data most of these limitations can be overcome. As such, many entities now provide remotely sensed flood products, such that it becomes difficult for users to determine the best available flood data source. Here we describe the long-time development and implementation of a semi operational ‘one-stop-shop’ portal that contains globally scoped, flood prediction, monitoring capabilities and risk evaluations by leveraging on efforts of the entire flood community.

Floods and their impacts are highly local in nature and vulnerable population and exposed assets are most at risk in coastal monsoonal regions. This is aggravated if the region is also exposed to tropical cyclones, such as Mozambique and the Licungo basin along the eastern coastline of the country.In order to be better prepared against future high-impact flood events, Mozambique’s National Institute for Disaster Management (INGC) has mapped the watershed of the country’s central Licungo River with drones to reduce flood risks and improve emergency response planning. The mapping is intended to “minimize risks” and promote timely preparation of actions when cyclones and floods are expected in the area.In the proposed project, the acquired drone terrain model and collected field data (water levels) will be used to drive a bespoke localized 2-D flood model to accurately reproduce flood hazard and risk in the central Licungo basin for the 2013 and 2015 flood disasters. In addition, high-resolution population and exposure layers have been used to define bespoke local flood risk maps.Accurate flood risk assessment of past events at the local scale can better inform decision support systems and facilitate the decision-making process and preparedness for future high-impact events. Knowing who is at risk where and when is vital information that is missing in many vulnerable regions and is most of the time not available at the required local level.Moreover, global or large-scale flood prediction models do not contain the necessary detail to infer meaningful flood risk at the local level and such models are known to be inaccurate, albeit they represent best efforts at the scales they are simulating. However, to what degree these models are wrong at the local scale of impact and what is needed to improve them is not known, largely because local flood data and bespoke predictions of flood risk are missing at the local scale for many vulnerable regions. The collected high-resolution data and the local flood risk assessment this project proposes would allow the validation of large-scale modeling efforts thereby advancing our understanding of model limitations and would create opportunities to improve them at large scales.