Utilizing Flood Inundation Observations to Obtain Floodplain Topography in Data-Scarce Regions

Abstract

Flood models predict inundation extents, and can be an important source of information for flood risk studies. Accurate flood models require high resolution and high accuracy digital elevation models (DEM); current global DEMs do not capture the topographic details in floodplains, and this often leads to inaccurate prediction of flood extents by flood models. Flood extents obtained from remotely sensed data provide indirect information about topography. Here, we attempt to use this information along with model predictions to produce better floodplain topography. The algorithm we describe is a two-step process: first, we reduce the noise along the observed flood boundaries for all particles. Then, the model predictions from these modified DEMs are assimilated with observations using a particle batch smoother. We implemented the algorithm for a synthetic test case. For the nominal case, we observed a significant improvement in accuracy in terms of RMSE (35% reduction), bias (20%), and standard deviation (40%). We conducted sensitivity analysis by using priors of varying bias (0.5, 1, and 2 m) and standard deviation (1, 2, and 4 m). The bias reduced to ∼0.5 m or below in all the cases: the reduction in bias varied from 11 to 76%. The standard deviation of errors in the final estimate was almost half of the prior: the reduction varied from 40 to 49%. The reduction in RMSE ranged between 35 and 67%. For the case with 2 m bias and 4 m standard deviation (SRTM-like error levels), bias went down to 0.48 m (76% reduction), and standard deviation reduced to 2.24 m (44% reduction). Flood inundation maps produced from the final estimate DEMs also improved on its prior. For the 2 m bias cases, true positive rate (TPR) for peak inundation went from ∼30% to more than 57% in all three cases. The algorithm produces promising results, and this type of analysis can be performed in data-poor floodplains where high resolution DEMs do not exist.

Global Relationships Between River Width, Slope, Catchment Area, Meander Wavelength, Sinuosity, and Discharge

“…However, the choice of mean annual flow may have prevented us from finding direct relationships between discharge and meander wavelength and discharge sinuosity. Additionally, exploring relationships between our centerline locations and basin and floodplain characteristics such as those cataloged by Shen et al (2017) and Nardi et al (2019) may lead to further insight on meander formation, flood wave propagation (e.g., Allen & Pavelsky, 2018), and refinement of local digital elevation models (Shastry & Durand, 2019). Furthermore, our data set may be improved by using techniques such as hydrography-driven coarsening applied to high-resolution digital elevation models as described by Moretti and Orlandini (2018), which may lead to improved estimates of catchment areas and slopes, particularly in mountainous areas and in areas where high-resolution topographic data sets exist, for example, local LiDAR or 1 arc-second SRTM data.…”

“…Furthermore, our data set may be improved by using techniques such as hydrography-driven coarsening applied to high-resolution digital elevation models as described by Moretti and Orlandini (2018), which may lead to improved estimates of catchment areas and slopes, particularly in mountainous areas and in areas where high-resolution topographic data sets exist, for example, local LiDAR or 1 arc-second SRTM data. Additionally, exploring relationships between our centerline locations and basin and floodplain characteristics such as those cataloged by Shen et al (2017) and Nardi et al (2019) may lead to further insight on meander formation, flood wave propagation (e.g., Allen & Pavelsky, 2018), and refinement of local digital elevation models (Shastry & Durand, 2019).…”

Will the Surface Water and Ocean Topography (SWOT) Satellite Mission Observe Floods?

“…If SWOT information reaches the end users quickly, it can be valuable for disaster management (Allen et al, ). However, even if flood data are only available sometime after the fact, they can still be of value for, namely, diagnostic and improvement of forecast inundation models, identification of poorly modeled areas, and possibly contribute to refining floodplain DEMs (Shastry & Durand, ). Furthermore, repeated SWOT passes over an area will allow the creation of relationships between water surface elevation and inundation extent, which can be used in conjunction with inundation maps generated from other platforms, such as Sentinel 1 to provide denser time series.…”

Leveraging machine learning for predicting flash flood damage in the Southeast US

“…The flash flood events that caused property damage were randomly divided into two parts: training (85% of dataset) and testing (15% dataset). The result of the developed model are evaluated using two performance measures: correlation coefficient (R) and bias, both of which have been commonly used to measure the accuracy and performance of the ML models (Gavahi et al 2019, Neri et al 2019, Shastry and Durand 2019, Abbaszadeh et al 2019a. Here, the regression (i.e.…”