Guy Schumann

Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management

Abstract Since several space-borne synthetic aperture radar (SAR) instruments providing high spatial resolutions and multi-polarisation capabilities will be mounted on satellites to be launched from 2006 onwards, radar imagery promises to become an indispensable asset for many environmental monitoring applications. Due to its all weather, day and night capabilities, SAR imagery presents obvious advantages over optical instruments, especially in flood management applications. To date, however, the coarse spatial resolution of available SAR datasets restricts the information that can be reliably extracted and processing techniques tend to be limited to binary floodplain segmentation into ‘flooded’ and ‘non flooded’ areas. It is the purpose of this paper to further improve the exploitation of SAR images in hydraulic modelling and near real-time crisis management by means of developing image processing methodologies that allow for the extraction of water levels at any point of the floodplain. As high-precision digital elevation models (DEM) produced, for instance, from airborne laser scanning become more readily available, methods can be exploited that combine SAR-derived flood extent maps and precise topographic data for retrieving water depth maps. In a case study of a well-documented flood event in January 2003 on the River Alzette, Grand Duchy of Luxembourg, a root mean squared error (R.M.S.E.) of 41xa0cm was obtained by comparing the SAR-derived water heights with surveyed high water marks that were collected during image acquisition. Water levels that were computed by a previously calibrated hydraulic model also suggest that the water surface profiles provided by the combined use of topographic data and SAR accurately reflect the true water line. The extraction of flooded areas within vegetated areas further demonstrates the usefulness of the proposed methodology.

Observing Global Surface Water Flood Dynamics

Flood waves moving along river systems are both a key determinant of globally important biogeochemical and ecological processes and, at particular times and particular places, a major environmental hazard. In developed countries, sophisticated observing networks and ancillary data, such as channel bathymetry and floodplain terrain, exist with which to understand and model floods. However, at global scales, satellite data currently provide the only means of undertaking such studies. At present, there is no satellite mission dedicated to observing surface water dynamics and, therefore, surface water scientists make use of a range of sensors developed for other purposes that are distinctly sub-optimal for the task in hand. Nevertheless, by careful combination of the data available from topographic mapping, oceanographic, cryospheric and geodetic satellites, progress in understanding some of the world’s major river, floodplain and wetland systems can be made. This paper reviews the surface water data sets available to hydrologists on a global scale and the recent progress made in the field. Further, the paper looks forward to the proposed NASA/CNES Surface Water Ocean Topography satellite mission that may for the first time provide an instrument that meets the needs of the hydrology community.

Automated River Reach Definition Strategies: Applications for the Surface Water and Ocean Topography Mission

The upcoming Surface Water and Ocean Topography (SWOT) mission will measure water surface heights and widths for rivers wider than 100 m. At its native resolution, SWOT height errors are expected to be on the order of meters, which prevent the calculation of water surface slopes and the use of slope-dependent discharge equations. To mitigate height and width errors, the high-resolution measurements will be grouped into reaches (∼5 to 15 km), where slope and discharge are estimated. We describe three automated river segmentation strategies for defining optimum reaches for discharge estimation: (1) arbitrary lengths, (2) identification of hydraulic controls, and (3) sinuosity. We test our methodologies on 9 and 14 simulated SWOT overpasses over the Sacramento and the Po Rivers, respectively, which we compare against hydraulic models of each river. Our results show that generally, height, width, and slope errors decrease with increasing reach length. However, the hydraulic controls and the sinuosity methods led to better slopes and often height errors that were either smaller or comparable to those of arbitrary reaches of compatible sizes. Estimated discharge errors caused by the propagation of height, width, and slope errors through the discharge equation were often smaller for sinuosity (on average 8.5% for the Sacramento and 6.9% for the Po) and hydraulic control (Sacramento: 7.3% and Po: 5.9%) reaches than for arbitrary reaches of comparable lengths (Sacramento: 8.6% and Po: 7.8%). This analysis suggests that reach definition methods that preserve the hydraulic properties of the river network may lead to better discharge estimates.

Chapter 2 - Measuring and Mapping Flood Processes

Floods are no doubt a major hazard and the risks they pose are increasing due to shifts in meteorological forcings, population pressures, as well as anthropogenic change to riverine landscapes. Flood waves and related processes are observed globally, through either river gauging networks or remote sensing acquisitions. River gauging stations are declining globally and although historical and current gauges are providing useful and frequent data in the developed world, the number of gauges in developing and emerging economies is very small and measurement stations are often very far apart and in remote locations thus making inference of processes and data collection difficult. Remote sensing, space-borne, and airborne can alleviate some of these limitations but has its own shortcomings. Hydrodynamic models can complement observations but the accuracy and complexity of the flood flow models used vary with both spatial scale at which they are applied and complexity of the topographic landscape. Furthermore, models are only as good as the data used to drive and calibrate them. In recent years, substantial efforts are being made to improve this complex situation of observing, mapping, and modeling flood processes, both in terms of flood model development and remote sensing, particularly satellite platforms. This chapter provides a detailed account of this complex interplay between models and data to observe, simulate, and understand flood processes on various scales and in different landscape settings.

Active and Passive Microwave Sensors as a Tool to Monitor Soil Moisture Over Winter

The present case study focuses on monitoring the wetness state of the experimental Bibeschbach catchment (10.8 km2), located within the Alzette river basin in the Grand-Duchy of Luxemburg over the last three winters (2005-2008). The objectives of this study are (1) to retrieve soil moisture from spaceborne active and passive microwave sensors, namely AMSR-E and ERS-2 SAR, (2) to compare the remote sensing-derived estimates of basin-averaged soil moisture with ground measurements that are performed throughout the catchment.

Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment

Floods are among the top-ranking natural disasters in terms of annual cost in insured and uninsured losses. Since high-impact events often cover spatial scales that are beyond traditional regional monitoring operations, remote sensing, in particular from satellites, presents an attractive approach. Since the 1970s, there have been many studies in the scientific literature about mapping and monitoring of floods using data from various sensors onboard different satellites. The field has now matured and hence there is a general consensus among space agencies, numerous organizations, scientists, and end-users to strengthen the support that satellite missions can offer, particularly in assisting flood disaster response activities. This has stimulated more research in this area, and significant progress has been achieved in recent years in fostering our understanding of the ways in which remote sensing can support flood monitoring and assist emergency response activities. This paper reviews the products and services that currently exist to deliver actionable information about an ongoing flood disaster to emergency response operations. It also critically discusses requirements, challenges and perspectives for improving operational assistance during flood disaster using satellite remote sensing products.

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%.

Improving flood resilience through effective integration of earth observation data and modeling over large scales

We demonstrate the complementarity of a multitude of satellite flood maps and large-scale flood inundation modeling. We employ a unique set of maps, from both optical and radar imagery, that were delivered to emergency responders during the Texas flood disaster of late May, early June 2015. Specifically, for this study, a two-dimensional hydrodynamic model was built to simulate the best possible inundation re-analysis of the flood event in locations along the major rivers, including urban and coastal settings. Subsequently, integrating the model event re-analysis and the satellite flood data demonstrated the unique complementarity of these available multi-temporal and multi-resolution imagery and the large-scale inundation model. This allowed a thorough assessment of the uncertainty and value of “big” Earth Observation data for flood disaster response and for integration with flood modeling for effective event re-analysis, which we anticipate can help guide better flood resilience planning.

A Method to Assess Localized Impact of Better Floodplain Topography on Flood Risk Prediction

Many studies have highlighted the need for a higher accuracy global digital elevation model (DEM), mainly in river floodplains and deltas and along coastlines. In this paper, we present a method to infer the impact of a better DEM on applications and science using the Lower Zambezi basin as a use case. We propose an analysis based on a targeted observation algorithm to evaluate potential data acquisition subregions in terms of their impact on the prediction of flood risk over the entire study area. Consequently, it becomes trivial to rank these subregions in terms of their contribution to the overall accuracy of flood prediction. The improvement from better topography data may be expressed in terms of economic output and population affected, providing a multifaceted assessment of the value of acquiring better elevation data. Our results highlight the notion that having higher resolution measurements would improve our current large-scale flood inundation prediction capabilities in the Lower Zambezi by at least 30% and significantly reduce the number of people affected as well as the economic loss associated with high magnitude flooding. We believe this procedure to be simple enough to be applied to other regions where high quality topographic and hydrodynamic data are currently unavailable.