ORCID Profile
0000-0001-8567-1388
Current Organisations
CSIRO
,
CSIRO Black Mountain Laboratories
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Publisher: Copernicus GmbH
Date: 03-04-2014
DOI: 10.5194/HESS-18-1273-2014
Abstract: Abstract. Accounting for groundwater recharge from overbank flooding is required to reduce uncertainty and error in river-loss terms and groundwater sustainable-yield calculations. However, continental- and global-scale models of surface water–groundwater interactions rarely include an explicit process to account for overbank flood recharge (OFR). This paper upscales previously derived analytical equations to a continental scale using national soil atlas data and satellite imagery of flood inundation, resulting in recharge maps for seven hydrologically distinct Australian catchments. Recharge for three of the catchments was validated against independent recharge estimates from bore hydrograph responses and one catchment was additionally validated against point-scale recharge modelling and catchment-scale change in groundwater storage. Flood recharge was predicted for four of the seven catchments modelled, but there was also unexplained recharge present from the satellite's flood inundation mapping data. At a catchment scale, recharge from overbank flooding was somewhat under-predicted using the analytical equations, but there was good confidence in the spatial patterns of flood recharge produced. Due to the scale of the input data, there were no significant relationships found when compared at a point scale. Satellite-derived flood inundation data and uncertainty in soil maps were the key limitations to the accuracy of the modelled recharge. Use of this method to model OFR was found to be appropriate at a catchment to continental scale, given appropriate data sources. The proportion of OFR was found to be at least 4% of total change in groundwater storage in one of the catchments for the period modelled, and at least 15% of the riparian recharge. Accounting for OFR is an important, but often overlooked, requirement for closing water balances in both the surface water and groundwater domains.
Publisher: IEEE
Date: 07-2018
Publisher: CSIRO
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2005
Publisher: IEEE
Date: 07-2019
Publisher: Wiley
Date: 02-12-2016
DOI: 10.1002/HYP.10714
Publisher: CSIRO
Date: 2018
Publisher: IEEE
Date: 07-2018
Publisher: CSIRO
Date: 2013
Publisher: CSIRO
Date: 2021
DOI: 10.25919/PAZT-J052
Publisher: Springer Science and Business Media LLC
Date: 23-09-2021
Publisher: Springer Science and Business Media LLC
Date: 24-06-2013
Publisher: Wiley
Date: 05-2007
DOI: 10.1002/AQC.833
Publisher: MDPI AG
Date: 15-07-2019
DOI: 10.3390/DATA4030100
Abstract: A research alliance between the Commonwealth Scientific and Industrial Research Organization and Geoscience Australia was established in relation to Digital Earth Australia, to develop a Synthetic Aperture Radar (SAR)-enabled Data Cube capability for Australia. This project has been developing SAR analysis ready data (ARD) products, including normalized radar backscatter (gamma nought, γ0), eigenvector-based dual-polarization decomposition and interferometric coherence, all generated from the European Space Agency (ESA) Sentinel-1 interferometric wide swath mode data available on the Copernicus Australasia Regional Data Hub. These are produced using the open source ESA SNAP toolbox. The processing workflows are described, along with a comparison of the γ0 backscatter and interferometric coherence ARD produced using SNAP and the proprietary software GAMMA. This comparison also evaluates the effects on γ0 backscatter due to variations related to: Near- and far-range look angles SNAP’s default Shuttle Radar Topography Mission (SRTM) DEM and a refined Australia-wide DEM as well as terrain. The agreement between SNAP and GAMMA is generally good, but also presents some systematic geometric and radiometric differences. The difference between SNAP’s default SRTM DEM and the refined DEM showed a small geometric shift along the radar view direction. The systematic geometric and radiometric issues detected can however be expected to have negligible effects on analysis, provided products from the two processors and two DEMs are used separately and not mixed within the same analysis. The results lead to the conclusion that the SNAP toolbox is suitable for producing the Sentinel-1 ARD products.
Publisher: Elsevier BV
Date: 08-1997
Publisher: CSIRO
Date: 2013
Publisher: Wiley
Date: 02-2007
Publisher: CSIRO
Date: 2020
DOI: 10.25919/TG90-3X77
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 11-2004
Publisher: Elsevier BV
Date: 03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-1998
DOI: 10.1109/36.673684
Publisher: American Geophysical Union (AGU)
Date: 11-2022
DOI: 10.1029/2022WR032031
Abstract: Simple models continue to be important for continental‐scale floodwater depth mapping due to the prohibitively expensive cost of calibrating and applying hydrodynamic models. This paper investigates the accuracy of three simple models for floodwater depth estimation from remote sensing derived water extent and/or Digital Elevation Models (DEMs) in semiarid regions. The three models are Height Above Nearest Drainage (HAND Nobre et al., 2011, 0.1016/j.jhydrol.2011.03.051 ), Teng Vaze Dutta (TVD Teng et al., 2013, 02.100.100/97033?index=1 ), and Floodwater Depth Estimation Tool (FwDET Cohen, Brakenridge, et al., 2018, 0.1111/1752-1688.12609 ). The model accuracy and nature of errors are established using industry's best practice hydrodynamic models as benchmarks in three regions in eastern Australia. The overall results show that FwDET tends to underestimate (by 0.32 m at 50th percentile) while HAND and TVD overestimate floodwater depth for almost all floods (by 0.97 and 0.98 m, respectively). We quantify how switching DEM from 5 m LiDAR to national or global data sets DEM‐H (Gallant et al., 2011, ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/72759 ), MERIT (Yamazaki et al., 2019, 0.1029/2019WR024873 ), or FABDEM (Hawker et al., 2022, 0.1088/1748-9326/ac4d4f ) can affect different models differently and we evaluate model performance against reach geomorphology and magnitude of flood events. The findings emphasize the importance of choosing a model that is fit for the intended application. By describing the applicability, advantages, and limitations of these models, this paper assists practitioners to choose the most appropriate model based on characteristics of their study area, type of problems they try to solve, and data availability.
Publisher: CSIRO Land and Water
Date: 2018
Publisher: MDPI AG
Date: 26-02-2022
DOI: 10.3390/RS14051158
Abstract: Mapping surface water extent is important for managing water supply for agriculture and the environment. Remote sensing technologies, such as Landsat, provide an affordable means of capturing surface water extent with reasonable spatial and temporal coverage suited to this purpose. Many methods are available for mapping surface water including the modified Normalised Difference Water Index (mNDWI), Fisher’s water index (FWI), Water Observations from Space (WOfS), and the Tasseled Cap Wetness index (TCW). While these methods can discriminate water, they have their strengths and weaknesses, and perform at their best in different environments, and with different threshold values. This study combines the strengths of these indices by developing rules that applies an index to the environment where they perform best. It compares these indices across the Murray-Darling Basin (MDB) in southeast Australia, to assess performance and compile a heuristic rule set for accurate application across the MDB. The results found that all single indices perform well with the Kappa statistic showing strong agreement, ranging from 0.78 for WOfS to 0.84 for TCW (with threshold −0.035), with improvement in the overall output when the index best suited for an environment was selected. mNDWI (using a threshold of −0.3) works well within river channels, while TCW (with threshold −0.035) is best for wetlands and flooded vegetation. FWI and mNDWI (with threshold 0.63 and 0, respectively) work well for remaining areas. Selecting the appropriate index for an environment increases the overall Kappa statistic to 0.88 with a water pixel accuracy of 90.5% and a dry pixel accuracy of 94.8%. An independent assessment illustrates the benefit of using the multi-index approach, making it suitable for regional-scale multi-temporal analysis.
Publisher: Wiley
Date: 20-04-2016
DOI: 10.1002/ECE3.2140
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 03-2015
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 16-12-2021
Publisher: Informa UK Limited
Date: 2003
Publisher: Springer Science and Business Media LLC
Date: 17-04-2015
Publisher: CSIRO
Date: 2016
Publisher: IEEE
Date: 26-09-2020
Publisher: CSIRO
Date: 2017
Publisher: CSIRO
Date: 2018
Publisher: IEEE
Date: 11-2019
Publisher: CSIRO
Date: 2016
Publisher: Wiley
Date: 23-04-2008
Publisher: CSIRO
Date: 2013
Publisher: Elsevier BV
Date: 02-2020
Publisher: Informa UK Limited
Date: 03-05-2023
Publisher: CSIRO
Date: 2018
Publisher: Elsevier BV
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 23-09-2023
Publisher: CSIRO
Date: 2018
Publisher: Informa UK Limited
Date: 04-1997
Publisher: MDPI AG
Date: 27-11-2014
DOI: 10.3390/RS61211791
Location: Australia
No related grants have been discovered for Catherine Ticehurst.