Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model

Afshari, Shahab; Tavakoly, Ahmad A.; Rajib, Adnan; Zheng, Xiong; Follum, Michael L.; Omranian, Ehsan; Fekete, B M

doi:10.1016/j.jhydrol.2017.11.036

Cited by 152 publications

(118 citation statements)

References 36 publications

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“…Performances and complexity may vary across models. Recent studies show that low-complexity new-generation models for flood mapping may perform as well, as presented for example in the comparative work of Afshari et al [3] between two hydrological models of low complexity (i.e., AutoRoute [54] and Height Above the Nearest Drainage (HAND) [55]) and a 2D hydrodynamic model (Hydrologic Engineering Center-River Analysis System (HEC-RAS 2D)).…”

Section: Discussionmentioning

confidence: 99%

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

et al. 2018

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Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics.

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Section: Discussionmentioning

confidence: 99%

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

et al. 2018

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“…The two dimensional (2D) HEC-RAS model was developed by the Hydrologic Engineering Center (HEC) of United States Army Corps of Engineers [50] and has been applied in many studies for flood inundation modelling (e.g., [9,10,[25][26][27][51][52][53][54]). Moreover, a benchmark analysis based on the two dimensional modelling capabilities, conducted by the U.S. Army Corps of Engineers, proved that HEC-RAS performed extremely well compared to the leading 2D models [55].…”

Section: Hydraulic-hydrodynamic Modellingmentioning

confidence: 99%

An Operational Method for Flood Directive Implementation in Ungauged Urban Areas

et al. 2018

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An operational framework for flood risk assessment in ungauged urban areas is developed within the implementation of the EU Floods Directive in Greece, and demonstrated for Volos metropolitan area, central Greece, which is frequently affected by intense storms causing fluvial flash floods. A scenario-based approach is applied, accounting for uncertainties of key modeling aspects. This comprises extreme rainfall analysis, resulting in spatially-distributed Intensity-Duration-Frequency (IDF) relationships and their confidence intervals, and flood simulations, through the SCS-CN method and the unit hydrograph theory, producing design hydrographs at the sub-watershed scale, for several soil moisture conditions. The propagation of flood hydrographs and the mapping of inundated areas are employed by the HEC-RAS 2D model, with flexible mesh size, by representing the resistance caused by buildings through the local elevation rise method. For all hydrographs, upper and lower estimates on water depths, flow velocities and inundation areas are estimated, for varying roughness coefficient values. The methodology is validated against the flood event of the 9th October 2006, using observed flood inundation data. Our analyses indicate that although typical engineering practices for ungauged basins are subject to major uncertainties, the hydrological experience may counterbalance the missing information, thus ensuring quite realistic outcomes.

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“…However, different inputs to a hydrologic model can highly affect the model outputs because they have biases compared with the unknown true values and bring uncertainty for hydrological models [45]. In this study, we calibrated the sensitive parameters with individual satellite precipitation products as model inputs, considering the potential effects of input uncertainty on calibration and the streamflow simulations [30,46] Note: v_, the parameter value is replaced by a given value; r_, the parameter value is multiplied by a given value.…”

Section: Model Calibration Validation and Uncertainty Analysismentioning

confidence: 99%

Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling over the Luanhe River Basin, China

Ren

Feng

et al. 2018

Water

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Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. The 3B42 product shows the best hydrological performance, while PERSIANN shows unsatisfactory hydrological performance. Therefore, 3B42 performs better than the other two satellite precipitation products over the study area.

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Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model

Cited by 152 publications

References 36 publications

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

An Operational Method for Flood Directive Implementation in Ungauged Urban Areas

Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling over the Luanhe River Basin, China

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