ORCID Profile
0000-0002-8758-2554
Current Organisation
CSIRO
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Publisher: MDPI AG
Date: 27-02-2023
DOI: 10.3390/GEOSCIENCES13030067
Abstract: Rainfall runoff and topography are among the major factors controlling the accuracy of modelled riverine inundation extents. We have evaluated the sensitivity of both these variables on a novel 1-D conceptual flood inundation model employing Height Above Nearest Drainage (HAND) thresholds within sub-catchment units called Reach Contributing Area (RCA). We examined the March 2021 flood extent over the Hawkesbury–Nepean Valley (HNV) with 0.05′ gridded runoff derived from the Australian Water Resources Assessment (AWRA) modelling framework. HAND thresholds were enforced within each RCA using rating curve relationships generated by a modelled river geometry dataset obtained from Jet Propulsion Laboratory (JPL) and by modelling Manning’s roughness coefficient as a function of channel slope. We found that the step-like topographic nature of HNV significantly influences the back-water effect within the floodplain. At the same time, the improved accuracy of the GeoFabric Digital Elevation Model (DEM) outperforms SRTM DEM-derived flood output. The precision of HAND thresholds does not add significant value to the analysis. With enhanced access to river bathymetry and an ensemble point-based runoff modelling approach, we can generate an ensemble runoff-based probabilistic extent of inundation.
Publisher: Springer Singapore
Date: 20-06-2019
Publisher: Copernicus GmbH
Date: 26-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-450
Abstract: & & India is one of the world's most flood-prone countries, with 113 million people exposed to floods. Large-scale hydrological models integrated with complicated Navier& #8211 Stokes based hydraulic, and inundation models traditionally address flood preparedness, control, and mitigation. In addition to being highly data-intensive at the fine spatial and temporal resolution, this approach has a considerable computational cost that limits real-time applications. We employ the parameter-free Dynamic Budyko (DB) hydrological model to map observed precipitation with gridded runoff to overcome data scarcity. We propose a time-efficient Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model (SCI-FRIM) framework that can be used with any hydrological model to generate a probability map of inundation. To model the catastrophic flood extents that the state of Kerala in India experienced during August 2018, we use gridded 0.25 deg & #215 0.25 deg IMD precipitation data. We use a parameter-free iterative approach to update flood velocity by assuming that river velocity does not fluctuate geographically across a particular river network at a given time instant. We pre-compute the iterative velocity and model the relationship between flood velocity-discharge and discharge-inundation height for each reach by combining the globally available SRTM/ASTER DEMs with empirically obtained river-reach geometry data (JPL). We compute the reach slope from the absolute vertical error-prone DEM by segmenting the river network into a series of independent channels and extracting the relationship between the channel pixel's elevation and the pixel's distance to the pour point. We use the Height Above Nearest Drainage (HAND) to map the probabilistic spatial extent corresponding to an ensemble of derived reach inundation heights. We then compare the proposed model with observed flood data points provided by the Kerala State Disaster Management Authority (KSDMA). The model captures up to 52% of 370,000 flood data points in a single run for the peak flood day within 15 minutes on a desktop computer. With reliable estimates of empirical bankfull discharge, the proposed model can achieve higher accuracy in lesser time.& &
Publisher: MDPI AG
Date: 17-09-2020
DOI: 10.3390/RS12183026
Abstract: The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating frequency at L-band. However, because of the pseudo-random nature of these points, it is not possible to obtain continuous flood inundation maps at adequately high resolution. By considering topological indicators, such as height above nearest drainage (HAND) and slope of nearest drainage (SND), which indicate the probability of a certain area being prone to flooding, we hypothesize that combining static topographic information with the dynamic GNSS-R signals can result in large-scale, high-resolution flood inundation maps. Flood mapping was performed and validated with flood extent derived using available Sentinel-1A synthetic aperture radar (SAR) data for flooding in Kerala during August 2018, and North India during August 2017. The results obtained after thresholding indicate that the model exhibits a flooding accuracy ranging from 60% to 80% for lower threshold values. We observed significant overestimation error in mapping inundation across the flooding period, resulting in an optimal critical success index of 0.22 for threshold values between 17–19.
Publisher: Informa UK Limited
Date: 19-01-2020
No related grants have been discovered for Kesav Unnithan.