Flood Detection and Monitoring with EO Data Tools and Systems

A timely identification and monitoring of flood events by means of Earth Observation (EO) data is, nowadays, increasingly feasible thanks to recent advances achieved in remote sensing and hydrological process simulations. Despite the notable progress in these fields, a considerable effort will still be required to reduce the intrinsic inaccuracies of these types of approaches. The coarse spatial and temporal resolution of satellite measurements (compared to the one that characterizes in-situ instruments), in fact, continues to require a local-scale validation. Taking into account pros and cons of the approaches based on remotely-sensed data, this chapter reviews some of the most relevant open-access techniques, products, and services that research and academic institutes are currently providing for the detection and the near real-time monitoring of extreme hydrometeorological events

Unraveling Long-Term Flood Risk Dynamics Across the Murray-Darling Basin Using a Large-Scale Hydraulic Model and Satellite Data

River floods are one of the most devastating extreme hydrological events, with oftentimes remarkably negative effects for human society and the environment. Economic losses and social consequences, in terms of affected people and human fatalities, are increasing worldwide due to climate change and urbanization processes. Long-term dynamics of flood risk are intimately driven by the temporal evolution of hazard, exposure and vulnerability. Although needed for effective flood risk management, a comprehensive long-term analysis of all these components is not straightforward, mostly due to a lack of hydrological data, exposure information, and large computational resources required for 2-D flood model simulations at adequately high resolution over large spatial scales. This study tries to overcome these limitations and attempts to investigate the dynamics of different flood risk components in the Murray-Darling basin (MDB, Australia) in the period 1973–2014. To this aim, the LISFLOOD-FP model, i.e., a large-scale 2-D hydrodynamic model, and satellite-derived built-up data are employed. Results show that the maximum extension of flooded areas decreases in time, without revealing any significant geographical transfer of inundated areas across the study period. Despite this, a remarkable increment of built-up areas characterizes MDB, with larger annual increments across not-flooded locations compared to flooded areas. When combining flood hazard and exposure, we find that the overall extension of areas exposed to high flood risk more than doubled within the study period, thus highlighting the need for improving flood risk awareness and flood mitigation strategies in the near future

Flow Duration Curve from Satellite:Potential of a Lifetime SWOT Mission

A flow duration curve (FDC) provides a comprehensive description of the hydrological regime of a catchment and its knowledge is fundamental for many water-related applications (e.g., water management and supply, human and irrigation purposes, etc.). However, relying on historical streamflow records, FDCs are constrained to gauged stations and, thus, typically available for a small portion of the world’s rivers. The upcoming Surface Water and Ocean Topography satellite (SWOT; in orbit from 2021) will monitor, worldwide, all rivers larger than 100 m in width (with a goal to observe rivers as small as 50 m) for a period of at least three years, representing a potential groundbreaking source of hydrological data, especially in remote areas. This study refers to the 130 km stretch of the Po River (Northern Italy) to investigate SWOT potential in providing discharge estimation for the construction of FDCs. In particular, this work considers the mission lifetime (three years) and the three satellite orbits (i.e., 211, 489, 560) that will monitor the Po River. The aim is to test the ability to observe the river hydrological regime, which is, for this test case, synthetically reproduced by means of a quasi-2D hydraulic model. We consider different river segmentation lengths for discharge estimation and we build the FDCs at four gauging stations placed along the study area referring to available satellite overpasses (nearly 52 revisits within the mission lifetime). Discharge assessment is performed using the Manning equation, under the assumption of a trapezoidal section, known bathymetry, and roughness coefficient. SWOT observables (i.e., water level, water extent, etc.) are estimated by corrupting the values simulated with the quasi-2D model according to the mission requirements. Remotely-sensed FDCs are compared with those obtained with extended (e.g., 20–70 years) gauge datasets. Results highlight the potential of the mission to provide a realistic reconstruction of the flow regimes at different locations. Higher errors are obtained at the FDC tails, where very low or high flows have lower likelihood of being observed, or might not occur during the mission lifetime period. Among the tested discretizations, 20 km stretches provided the best performances, with root mean absolute errors, on average, lower than 13.3%

Bayesian Data-Driven approach enhances synthetic flood loss models

Flood loss estimation models are developed using synthetic or empirical approaches. The synthetic approach consists of what-if scenarios developed by experts. The empirical models are based on statistical analysis of empirical loss data. In this study, we propose a novel Bayesian Data-Driven approach to enhance established synthetic models using available empirical data from recorded events. For five case studies in Western Europe, the resulting Bayesian Data-Driven Synthetic (BDDS) model enhances synthetic model predictions by reducing the prediction errors and quantifying the uncertainty and reliability of loss predictions for post-event scenarios and future events. The performance of the BDDS model for a potential future event is improved by integration of empirical data once a new flood event affects the region. The BDDS model, therefore, has high potential for combining established synthetic models with local empirical loss data to provide accurate and reliable flood loss predictions for quantifying future risk

A Conceptual Framework for Vulnerability and Risk Assessment in the Context of Nature-Based Solutions to Hydro-Meteorological Risks

Various frameworks for vulnerability and risk assessment of social-ecological systems (SES) to natural hazards have been developed addressing different contexts. However, none were specifically developed in the context of implementing nature-based solutions (NBS) to hydro-meteorological risks. Since the basic concepts and principles of NBS are mainly focused on ensuring balance between ecological and social benefits, the entire vulnerability and risk assessment process should focus equally on various social and ecological components of a location where an NBS would be implemented. As a part of the OPEn-air laboRAtories for Nature baseD solUtions to Manage hydro-meteo risks (OPERANDUM) project, this research proposes a conceptual framework for vulnerability and risk assessment in the context of NBS to hydro-meteorological risks. This conceptual framework is developed mainly considering the major components of the existing Delta-SES risk assessment framework (Sebesvari et al. 2016) and other similar frameworks proposed in recent studies, as well as the proposed principles for NBS endorsed by International Union for Conservation of Nature (IUCN). The major components of the framework include: (i) the exposure of SES to multiple hydro-meteorological hazards (e.g., flood, drought); (ii) vulnerability of SES that consists of ecosystem susceptibility, social susceptibility, ecosystem robustness, and coping and adaptive capacity of the social system; (iii) risks in the NBS project site determined by the combination of hazard exposure and vulnerability; and (iv) the impacts of hydro-meteorological hazards on the SES surrounding or within the NBS project site. While the basic space of risk assessment would be the NBS project site (usually at the local level within sub-catchments) with specific SES characteristics, this framework also reflects the interrelationships between ecosystem and social system as well as the effects of multiple hazards and risks at local up to the global scales. The framework also considers the changes over time that would capture the maturation time lag of the ecological components of an NBS, as well as the sustainability of the system with the intervention of NBS and other risk reduction measures. An indicator-based risk assessment approach can be used to operationalize the framework. To facilitate that, an indicator library has been developed comprising of indicators for different exposure and vulnerability components of the framework. The proposed framework can be applicable to any geographical conditions where an NBS project is to be implemented to reduce hydro-meteorological risks. The framework can also be tailored for other natural hazards (e.g. geological hazards like earthquake) and anthropogenic hazards (e.g. pollution). We will explain the conceptualisation process of the framework and of the indicator library and how these will be tested within the OPERANDUM project in the context of NBS implementation

Anticipated Improvements to River Surface Elevation Profiles From the Surface Water and Ocean Topography Mission

Existing publicly available digital elevation models (DEMs) provide global-scale data but are often not precise enough for studying processes that depend on small-scale topographic features in rivers. For example, slope breaks and knickpoints in rivers can be important in understanding tectonic processes, and riffle-pool structures are important drivers of riverine ecology. More precise data (e.g., lidar) are available in some areas, but their spatial extent limits large-scale research. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission is planned to launch in 2021 and will provide measurements of elevation and inundation extent of surface waters between 78° north and south latitude on average twice every 21 days. We present a novel noise reduction method for multitemporal river water surface elevation (WSE) profiles from SWOT that combines a truncated singular value decomposition and a slope-constrained least-squares estimator. We use simulated SWOT data of 85–145 km sections of the Po, Sacramento, and Tanana Rivers to show that 3–12 months of simulated SWOT data can produce elevation profiles with mean absolute errors (MAEs) of 5.38–12.55 cm at 100–200 m along-stream resolution. MAEs can be reduced further to 4–11 cm by averaging all observations. The average profiles have errors much lower than existing DEMs, allowing new advances in riverine research globally. We consider two case studies in geomorphology and ecology that highlight the scientific value of the more accurate in-river DEMs expected from SWOT. Simulated SWOT elevation profiles for the Po reveal convexities in the river longitudinal profile that are spatially coincident with the upward projection of blind thrust faults that are buried beneath the Po Plain at the northern termination of the Apennine Mountains. Meanwhile, simulated SWOT data for the Sacramento River reveals locally steep sections of the river profile that represent important habitat for benthic invertebrates at a spatial scale previously unrecognizable in large-scale DEMs presently available for this river

On the management of nature-based solutions in open-air laboratories: new insights and future perspectives

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