Integrating remotely sensed surface water extent into continental scale hydrology

Revilla-Romero, Beatriz; Wanders, Niko; Burek, Peter; Salamon, Peter; Roo, Ad de

doi:10.1016/j.jhydrol.2016.10.041

Cited by 58 publications

(46 citation statements)

References 64 publications

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“…Many studies on data assimilation into hydraulic models or forecasting systems integrate synthetic, in situ or remote sensing-derived observations of water levels. For example, see Table 1 in Revilla-Romero et al (2016), and also Neal et al (2007), Matgen et al (2010), Hostache et al (2010), Giustarini et al (2011), Yoon et al (2012), Andreadis and Schumann (2014), García-Pintado et al (2015), Hostache et al (2015), and Xu et al (2017). Indeed, water level is a diagnostic variable of any hydraulic model and hence is more straightforward to assimilate than flood extent (Lai et al, 2014), which is a prognostic variable (diagnostic variables are defined as variables that are required to solve the model, that is, state variables, whereas prognostic variables are derived quantities).…”

Section: 1029/2017wr022205mentioning

confidence: 99%

“…Revilla-Romero et al (2016) used the ensemble Kalman filter to assimilate low resolution (0.1 ∘ × 0.1 ∘ ) satellite-derived flood extents based on the Global Flood Detection System (http://www.gdacs.org/flooddetection/) into a global forecasting system composed of a hydrological model and a routing function, with an objective toward real-time forecasting; their study over 101 stations in Africa and South America shows that flood extent assimilation improves simulated streamflow at the majority of stream gauges, especially at the gauges with poorest skill scores on open-loop runs. One limitation of the study by Revilla-Romero et al (2016) is the relatively coarse spatial resolution of the model results (i.e., 0.1 ∘ ), which may not meet operational needs in a crisis management context. This study develops an efficient framework for the assimilation of high-resolution flood extent information derived from SAR images, for the purposes of improving near real-time flood forecasts.…”

Section: 1029/2017wr022205mentioning

confidence: 99%

“…Flood forecasts can help meet many of the foreseeable needs identified by the World Health Organization, including coordinating search and rescue, providing medical assistance, managing displacement of people and goods, and avoiding the interruption of critical supply chains that are known to cause extreme financial losses. Hydrological (rainfall‐runoff) and hydraulic models are powerful tools in flood forecasting systems (Revilla‐Romero et al, ). Yet, optimal decision making in emergency situations is hampered by forecast uncertainties (Hostache et al, ; Pappenberger et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts

Hostache

Chini

Giustarini

et al. 2018

Water Resources Research

109

108

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Short‐ to medium‐range flood forecasts are central to predicting and mitigating the impact of flooding across the world. However, producing reliable forecasts and reducing forecast uncertainties remains challenging, especially in poorly gauged river basins. The growing availability of synthetic aperture radar (SAR)‐derived flood image databases (e.g., generated from SAR sensors such as Envisat advanced synthetic aperture radar) provides opportunities to improve flood forecast quality. This study contributes to the development of more accurate global and near real‐time remote sensing‐based flood forecasting services to support flood management. We take advantage of recent algorithms for efficient and automatic delineation of flood extent using SAR images and demonstrate that near real‐time sequential assimilation of SAR‐derived flood extents can substantially improve flood forecasts. A case study based on four flood events of the River Severn (United Kingdom) is presented. The forecasting system comprises the SUPERFLEX hydrological model and the Lisflood‐FP hydraulic model. SAR images are assimilated using a particle filter. To quantify observation uncertainty as part of data assimilation, we use an image processing approach that assigns each pixel a probability of being flooded based on its backscatter values. Empirical results show that the sequential assimilation of SAR‐derived flood extent maps leads to a substantial improvement in water level forecasts. Forecast errors are reduced by as much as 50% at the assimilation time step, and improvements persist over subsequent time steps for 24 to 48 hr. The proposed approach holds promise for improving flood forecasts at locations where observed data availability is limited but satellite coverage exists.

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Section: 1029/2017wr022205mentioning

confidence: 99%

Section: 1029/2017wr022205mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts

Hostache

Chini

Giustarini

et al. 2018

Water Resources Research

109

108

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“…[1][2][3][4]. In hydro sciences, continuous water level heights are required as ground truth for calibrating and validating rainfall-runoff models [5] and multidimensional hydrodynamic-numerical models, also referred to as computational fluid dynamic (CFD) models. Multidimensional CFD models require a spatially continuous description of the water bottom geometry as basic input and extensive water surface levels for calibration.…”

Section: Introductionmentioning

confidence: 99%

On the Feasibility of Water Surface Mapping with Single Photon LiDAR

Mandlburger

Jutzi

2019

IJGI

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Single photon sensitive airborne Light Detection And Ranging (LiDAR) enables a higher area performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light potentially capable of penetrating clear shallow water. The technology is designed for large-area topographic mapping, which also includes the water surface. While the penetration capabilities of green lasers generally lead to underestimation of the water level heights, we specifically focus on the questions of whether Single Photon LiDAR (i) is less affected in this respect due to the high receiver sensitivity, and (ii) consequently delivers sufficient water surface echoes for precise high-resolution water surface reconstruction. After a review of the underlying sensor technology and the interaction of green laser light with water, we address the topic by comparing the surface responses of actual Single Photon LiDAR and Multi-Photon Topo-Bathymetric LiDAR datasets for selected horizontal water surfaces. The anticipated superiority of Single Photon LiDAR could not be verified in this study. While the mean deviations from a reference water level are less than 5 cm for surface models with a cell size of 10 m, systematic water level underestimation of 5–20 cm was observed for high-resolution Single Photon LiDAR based water surface models with cell sizes of 1–5 m. Theoretical photon counts obtained from simulations based on the laser-radar equation support the experimental data evaluation results and furthermore confirm the feasibility of Single Photon LiDAR based high-resolution water surface mapping when adopting specifically tailored flight mission parameters.

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“…In particular, satellite precipitation products are very useful because of their larger area coverage during rainstorms. Several rainfall-runoff model were integrated with satellite precipitation products to forecast floods and their severity [25,26]. The Tropical Rainfall Measuring Mission (TRMM)-based Multi-satellite Precipitation Analysis (TMPA) products [27] have been effectively used in many hydrological models including extreme flood event disaster studies [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Rainfall Variability and Flood Prognosis Analysis Using Satellite Data over North Bihar during the August 2017 Flood Event

2019

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Flooding is one of the most common natural disasters in India. Typically, the Kosi and Gandak river basins are well-known for lingering flood affected basins in North Bihar every year, which lies in the eastern part of India. There were no such comprehensive studies available in North Bihar that discussed flood progression and regression at shorter time-scales like two day intervals. So in this study, we employed high temporal resolution data to capture inundation extent and further, the flood extent has been validated with high spatial resolution data. The specific objective of this study was to analyze the satellite-derived Near Real Time (NRT) MODIS flood product for spatiotemporal mapping of flood progression and regression over the North Bihar. The synthetic aperture RADAR (SAR) data were also used to validate the MODIS NRT Flood data. As a case study, we selected a recent flood event of August–September 2017 and captured the flood inundation spatial extent at two day intervals using the 2 day composite NRT flood data. The flood prognosis analysis has revealed that during the peak flooding period, 12% to 17% of the area was inundated and the most adversely affected districts were Darbhanga and Katihar in North Bihar. We estimated that in total nearly 6.5% area of the North Bihar was submerged. The method applied was simple, but it can still be suitable to be applied by the community involved in flood hazard management, not necessarily experts in hydrological modeling. It can be concluded that the NRT MODIS flood product was beneficial to monitor flood prognosis over a larger geographical area where observational data are limited. Nevertheless, it was noticed that the flood extent area derived from MODIS NRT data has overestimated areal extent, but preserved the spatial pattern of flood. Apparently, the present flood prognosis analysis can be improved by integrating microwave remote sensing data (SAR) and hydrological models.

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Integrating remotely sensed surface water extent into continental scale hydrology

Abstract: HighlightsFirst continent-scale assimilation of surface water extent into hydrological model.Improvements in flood peaks timing and volume for 60% of validated gauges.Daily surface water extent provide promising opportunities for ungauged regions.

Cited by 58 publications

References 64 publications

Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts

Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts

On the Feasibility of Water Surface Mapping with Single Photon LiDAR

Spatio-Temporal Rainfall Variability and Flood Prognosis Analysis Using Satellite Data over North Bihar during the August 2017 Flood Event

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