Integrating remote sensing observations of flood hydrology and hydraulic modelling

Bates, Paul; Horritt, M. S.; Smith, Cassia; Mason, David C.

doi:10.1002/(sici)1099-1085(199711)11:14<1777::aid-hyp543>3.0.co;2-e

Cited by 151 publications

(57 citation statements)

References 42 publications

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“…As a result, researchers may seek to produce adequate models for vulnerable areas without extensive in situ data on soils, channel morphology, and/or streamflow records, not to mention the regular measurements of pathogen or sediment concentrations needed to calibrate fate and transport models (Coffey, et al 2010). Remotely sensed Synthetic Aperture Radar (SAR) and LIDAR images of flood extent and topographical features have proven useful for parameterizing and validating a number of flood inundation models at high spatial resolutions (Bates 2004, Horritt and Bates 2002, Bates, et al 2003, Straatsma and Baptist 2007, Smith 1997, Patro, et al 2009, Bates, et al 1997). However, the use of such image-based techniques to describe the dynamics of inundation over the duration of a flood event, and thus establish credible estimates of flow velocities and other transport-relevant parameters, requires high resolution imagery, both spatially and temporally (Bates 2012, Di Baldassarre and Uhlenbrook 2012).…”

Section: Discussionmentioning

confidence: 99%

Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport

Collender

Cooke

Bryant

et al. 2016

Critical Reviews in Environmental Science and Technology

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Flooding is known to facilitate infectious disease transmission, yet quantitative research on microbiological risks associated with floods has been limited. Pathogen fate and transport models provide a framework to examine interactions between landscape characteristics, hydrology, and waterborne disease risks, but have not been widely developed for flood conditions. We critically examine capabilities of current hydrological models to represent unusual flow paths, non-uniform flow depths, and unsteady flow velocities that accompany flooding. We investigate the theoretical linkages between hydrodynamic processes and spatio-temporally variable suspension and deposition of pathogens from soils and sediments; pathogen dispersion in flow; and concentrations of constituents influencing pathogen transport and persistence. Identifying gaps in knowledge and modeling practice, we propose a research agenda to strengthen microbial fate and transport modeling applied to inland floods: 1) development of models incorporating pathogen discharges from flooded sources (e.g., latrines), effects of transported constituents on pathogen persistence, and supply-limited pathogen transport; 2) studies assessing parameter identifiability and comparing model performance under varying degrees of process representation, in a range of settings; 3) development of remotely sensed datasets to support modeling of vulnerable, data-poor regions; and 4) collaboration between modelers and field-based researchers to expand the collection of useful data in situ.

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Section: Discussionmentioning

confidence: 99%

Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport

Collender

Cooke

Bryant

et al. 2016

Critical Reviews in Environmental Science and Technology

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“…Topography Experiment (TOPEX)/Poseidon radar altimeter can be used to estimate flood inundation, water level, and river discharge. Applications of satellite-based surface water measurements can be found in Hossain et al (2014a,b), Khan et al (2014), Alsdorf et al (2007), Bjerklie et al (2005), Brakenridge et al (2007), Bates et al (1997), and Behrangi et al (2011). The near-realtime (NRT) Global Flood Mapping system recently developed by NASA for near-real-time global flood monitoring and its archived data will be a great resource for validating the proposed system (http://oas.gsfc.nasa.…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting and Inundation Mapping Using HiResFlood-UCI and Near-Real-Time Satellite Precipitation Data: The 2008 Iowa Flood

Nguyen

Thorstensen

Sorooshian

et al. 2015

Journal of Hydrometeorology

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Floods are among the most devastating natural hazards in society. Flood forecasting is crucially important in order to provide warnings in time to protect people and properties from such disasters. This research applied the high-resolution coupled hydrologic-hydraulic model from the University of California, Irvine, named HiResFlood-UCI, to simulate the historical 2008 Iowa flood. HiResFlood-UCI was forced with the near-realtime Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) and NEXRAD Stage 2 precipitation data. The model was run using the a priori hydrologic parameters and hydraulic Manning n values from lookup tables. The model results were evaluated in two aspects: point comparison using USGS streamflow and areal validation of inundation maps using USDA's flood extent maps derived from Advanced Wide Field Sensor (AWiFS) 56-m resolution imagery. The results show that the PERSIANN-CCS simulation tends to capture the observed hydrograph shape better than Stage 2 (minimum correlation of 0.86 for PERSIANN-CCS and 0.72 for Stage 2); however, at most of the stream gauges, Stage 2 simulation provides more accurate estimates of flood peaks compared to PERSIANN-CCS (49%-90% bias reduction from PERSIANN-CCS to Stage 2). The simulation in both cases shows a good agreement (0.67 and 0.73 critical success index for Stage 2 and PERSIANN-CCS simulations, respectively) with the AWiFS flood extent. Since the PERSIANN-CCS simulation slightly underestimated the discharge, the probability of detection (0.93) is slightly lower than that of the Stage 2 simulation (0.97). As a trade-off, the false alarm rate for the PERSIANN-CCS simulation (0.23) is better than that of the Stage 2 simulation (0.31).

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“…Within the more recent scientific literature, dating back ten years, it is recognised that remote sensing can support flood monitoring, modelling and management (Smith, 1997;Bates et al, 1997). In particular, satellites and aircraft carrying Synthetic Aperture Radar (SAR) sensors are valuable as radar wavelengths can penetrate cloud cover and obtain land cover information both night and day (Woodhouse, 2006).…”

Section: Introductionmentioning

confidence: 99%

The Use of Radar Imagery in Riverine Flood Inundation Studies

Schumann¹,

Bates

Baldassarre

et al. 2012

Fluvial Remote Sensing for Science and Management

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Integrating remote sensing observations of flood hydrology and hydraulic modelling

Cited by 151 publications

References 42 publications

Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport

Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport

Flood Forecasting and Inundation Mapping Using HiResFlood-UCI and Near-Real-Time Satellite Precipitation Data: The 2008 Iowa Flood

The Use of Radar Imagery in Riverine Flood Inundation Studies

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