Developing Remotely-Sensed Data Approaches to Studying Hydrological Processes in Data-Poor Dryland Landscapes Abdollah Asadzadeh Jarihani B

Developing Remotely-Sensed Data Approaches to Studying Hydrological Processes in Data-Poor Dryland Landscapes Abdollah Asadzadeh Jarihani B. Eng. M. Eng.

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2015 School of Geography, Planning and Environmental Management

Abstract

Drylands occupy one third of the Earth’s surface and are home to around 400 million people, yet the water resources of these regions are often poorly understood because of a lack of fundamental hydrological data. Rivers are often low-gradient with multiple channels across large floodplains, adapted to transmitting episodic and slow-moving flood pulses with flows diminishing downstream due to high transmission losses. At a coarse-scale, it is known that these losses sustain the biologically rich and diverse floodplain-waterhole “boom-and-bust” ecosystems, yet a detailed understanding of the way these losses are partitioned and other fundamental questions of (eco)hydrological function of these river systems cannot be understood at a detailed scale in dryland environments.

This thesis aims to develop remotely-sensed data approaches to support hydrodynamic modelling in order to improve our understanding of hydrological processes in data-sparse dryland landscapes. Four objectives, which constitute the main chapters of this thesis and which are presented as 4 peer- reviewed papers, were investigated, they are:

(i) to evaluate the accuracy and effectiveness of satellite derived altimetry data for estimating flood water depths in low-gradient, multi-channel rivers in central Australia;

(ii) to detect and map flood extents and optimise the trade-off between image frequency and spatial resolution using Landsat and MODIS imagery;

(iii) to determine the optimum Digital Elevation Models (DEMs) for hydrodynamic modelling in low gradient dryland environments in relation to accuracy, DEM preparation and trade- offs in model grid size; and

(iv) to use a hydrodynamic model supported by altimetry, optical and optimal DEM data to investigate the partitioning of transmission loss within a multi-channel river in central Australia.

Regarding objective (i), based on an assessment of six altimetry satellites at two lakes (in Victoria and Western Australia) and six sites along the Diamantina River and Cooper Creek, ICESat (mean = 0.00m, RMSE = 0.04m) was found to have the highest accuracy, while Jason-2 (mean = -0.04m, RMSE = 0.28m) offered potential for ongoing applications where water elevation time series are of use.

Regarding objective (ii), two advanced blending algorithms were applied to MODIS and Landsat imagery; STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) and ESTARFM (Enhanced STARFM) and evaluated for nine common indices used in vegetation studies, environmental moisture assessment and standing water identification across three sites with different flood extents and spatio-temporal variability. The investigation of these factors, including the order that index calculation and blending should occur (i.e. (i) ‘Index-then-Blend’ (IB); and (ii) ‘Blend-then-Index’ (BI)), found that the IB approach was more accurate for both blending algorithms as it was; computationally less intensive due to blending single indices rather than multiple bands; more accurate due to reduced error propagation; and less sensitive to the choice of algorithm.

Near-global coverage Digital Elevation Models (DEMs) such as Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) are required for hydrological–hydrodynamic modelling in remote areas. For objective (iii), point accuracy and geometric co-registration error; effects of DEM preparation (vegetation smoothed and hydrologically corrected); and effects of grid size on hydrodynamic model performance were investigated. SRTM outperformed ASTER after applying geometric correction followed by vegetation smoothing and hydrological correction and a grid size of around 120m offered the optimal balance between hydrodynamic model and computational performance.

Combining modelling, remotely-sensed data and limited field measurements for objective (iv), a calibrated hydrodynamic model (TUFLOW) was used to investigate the overall water balance , with a focus on transmission losses, across seven flood pulses along a 180 km reach of the Diamantina River. Results showed that transmission losses were up to 68% (mean = 46%) of the total inflow to the system, with evaporation the most significant component (21.6%), then infiltration (13.2%) and terminal water storage (11.2%).

This research concluded that it is now possible to realistically constrain water balances in data- sparse dryland rivers using hydrodynamic models in combination with remote sensing and simple field measurements to address limitations in the availability of conventional hydrological datasets. This research has implications for the opportunities, limitations, and future directions of using remotely-sensed data to better understand water balance and hydrodynamics of data-sparse regions. This knowledge is imperative for improved management of the limited water resources in dryland, low-gradient, and multi-channel river systems both in Australia and around the world.

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Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

Publications during candidature

Peer-reviewed papers:

Asadzadeh Jarihani, A., Callow, J.N., Johansen, K., & Gouweleeuw, B. (2013). Evaluation of multiple satellite altimetry data for studying inland water bodies and river floods. Journal of Hydrology, 505, 78-90, doi: 10.1016/j.jhydrol.2013.09.010.

Jarihani, A.A., McVicar, T.R., Van Niel, T.G., Emelyanova, I.V., Callow, J.N. and Johansen, K. (2014) Blending Landsat and MODIS data to generate multispectral indices: A comparison of ‘Index-then-Blend’ and ‘Blend-then-Index’ approaches. Remote Sensing. 6(10), 9213-9238, doi: 10.3390/rs6109213.

Jarihani, A.A., Callow, J.N., McVicar, T.R., Van Niel, T.G., and Larsen, J. R. (2015) Satellite- derived digital elevation model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low gradient and data-sparse catchments. Journal of Hydrology. 524, 489-506, doi: 10.1016/j.jhydrol.2015.02.049.

Jarihani, A.A., Larsen, J. R., Callow, J.N. and McVicar, T.R. Johansen K. (2015) Where does all the water go? Partitioning water transmission loss in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing. Journal of Hydrology. In Press,doi:10.1016/j.jhydrol.2015.08.030.

Conference abstracts:

Jarihani A. A. Callow J.N., Larsen J., 2015, How is water transmitted in large, low gradient dryland river systems? Geophysical Research Abstract, Vol. 17, EGU2015-8235, 2015 European Geoscience Union General Assembly 12-18 April 2015, Vienna Austria. http://meetingorganizer.copernicus.org/EGU2015/EGU2015-8235.pdf

Jarihani A. A., Callow J.N., Larsen J., 2014, Studying hydrology and water-dependent processes in large data-scarce dryland river basins with no hydrological data. 16th biennial Australian and New Zealand Geomorphology Group conference, December 2014 Gold Coast, Australia.

Jarihani A.A., Callow J.N., Johansen K. and Thew P., 2013, Satellite Remote Sensing of Hydrology of Large Rivers: Case Study of Lake Eyre Basin, Australia [abs. HS01-D2-PM2-P-015 (HS01-A007)]. Asia Oceania Geoscience Society Annual Meeting, 24-28 June 2013, Brisbane, Australia.

(http://www.asiaoceania.org/aogs2013/mars2/pubViewAbs.asp?sMode=poster§ionIdP=3&sub mit=Browse+Abstracts).

Jarihani A.A., Callow J.N., 2013, Studying large river hydrology with no gauging data: Can altimetry satellites help in data-poor regions? [abs. EGU2013-3814]. European Geoscience Union General Assembly 7-12 April 2013, Vienna Austria.

(http://meetingorganizer.copernicus.org/EGU2013/EGU2013-3814.pdf).

Jarihani A.A., Callow J.N., McVicar T. R., Van Niel T.G., Johansen K. and Emelyanova I., 2013, Blending and Downscaling of Landsat and MODIS Surface Reflectance for Water Body Delineation: A Comparison of Index-Simulate and Simulate-Index Methods [abs. H43G-1568]. American Geophysical Union fall Meeting 9-13 December 2013, San Francisco, USA.

(http://abstractsearch.agu.org/meetings/2013/FM/sections/H/sessions/H43G/abstracts/H43G- 1568.html).

Asadzadeh Jarihani, A., Callow J.N. and Daylami A.A., 2012, Evaluation of Multiple Satellite Altimetry Platforms for Studying Inland Water Bodies and River Floods [abs. 2545658]. 20 Years of Progress in. Radar Altimetry Symposium. 24-29 September 2012, Venice, Italy.

(http://www.congrexprojects.com/docs/12c01_docs/20ypra_abstracts_12_08_27_v9.pdf?sfvrsn=2).

Jarihani A.A. and Callow J.N., 2011, Development and calibration of hydraulic models using GIS and remotely sensed data [abs. EGU2011-572]. European Geoscience Union General Assembly 3-8 April 2011, Vienna Austria.

(http://meetingorganizer.copernicus.org/EGU2011/EGU2011-572.pdf)

Jarihani A. A., Callow J.N. and Daylami A. A., 2011 Calibration of Hydraulic Models Using Satellite Based Observation of Flood Extent. International Conference on the Status and Future of the World‘s Large Rivers, 11-14 April 2011 Vienna, Austria. (http://worldslargerivers.boku.ac.at/wlr/images/stories/downloads/programme_v1.5.pdf)

Callow J.N. and Jarihani A. A., 2012 Studying Large River Hydrology and Geomorphology With No Or Limited Gauging Data. 15th Biennial Australian and New Zealand Geomorphology Group conference, 2-7 December 2012 NSW, Australia.

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Publications included in this thesis

Contributor Statement of contribution

Abdollah A. Jarihani (Candidate) Designed experiments (90%)

Processing and manuscript (90%)

Nik Callow Designed experiments (10%)

Manuscript review and editing (6%)

Kasper Johansen Manuscript review and editing (2%)

Ben Gouweleeuw Manuscript review and editing (2%)

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Contributor Statement of contribution

Abdollah A. Jarihani (Candidate) Designed experiments (85%)

Processing and manuscript (85%)

Tim McVicar Designed experiments (5%)

Manuscript review and editing (5%)

Tom Van Niel Designed experiments (3%)

Manuscript review and editing (3%)

Irina Emelyanova Designed experiments (3%)

Manuscript review and editing (3%)

Nik Callow Designed experiments (2%)

Manuscript review and editing (2%)

Kasper Johansen Designed experiments (2%)

Manuscript review and editing (2%)

Jarihani, A.A., Callow, J.N., McVicar, T.R., Van Niel, T.G., and Larsen, J. (2015) Satellite- derived digital elevation model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low gradient and data-sparse catchments. Journal of Hydrology. 524, 489-506, doi: 10.1016/j.jhydrol.2015.02.049 – incorporated as Chapter 4.

Contributor Statement of contribution

Abdollah A. Jarihani (Candidate) Designed experiments (85%)

Processing and manuscript (85%)

Nik Callow Designed experiments (10%)

Manuscript review and editing (6%)

Tim McVicar Designed experiments (5%)

Manuscript review and editing (5%)

Tom Van Niel Manuscript review and editing (2%)

Joshua Larsen Manuscript review and editing (2%)

Contributor Statement of contribution

Abdollah A. Jarihani (Candidate) Designed experiments (85%)

Processing and manuscript (85%)

Joshua Larsen Designed experiments (7.5%)

Manuscript review and editing (5%)

Nik Callow Designed experiments (7.5%)

Manuscript review and editing (5%)

Tim McVicar Manuscript review and editing (3%)

Kasper Johansen Manuscript review and editing (2%)

Contributions by others to the thesis

Supervision team Nik Callow, Tim McVicar, Joshua Larsen and Kasper Johansen made standard contributions to this thesis. This includes shared experimental design, revision and editing of the work. Ben Gouweleeuw served as an initial supervisor (2012-13) and contributed during this period.

Statement of parts of the thesis submitted to qualify for the award of another degree

None.

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Acknowledgements

First of all, thank you to my wife, Neda Mardani and my son Aidin Jarihani, for their support throughout my PhD. This journey would not have been possible without their support. I am especially grateful to my parents, who have supported me emotionally all my life.

Special thanks to my principal advisor, Dr Nik Callow for his support and encouragement throughout my PhD. Thanks Nik for listening to me, understanding me and giving valuable advice in no time when I needed. Also a big thank you to my advisory team: Dr Kasper Johansen, Dr Joshua Larsen, Dr Tim McVicar and Dr Ben Gouweleeuw for always being helpful and service- minded. Your guidance and support have brought me to a new place in my professional life. I would like to also thank Tom Van Niel and Irina Emelyanova from CSIRO for their assistance on the remote sensing section of this PhD work.

This work would not have been possible without the University of Queensland Research Scholarship (UQRS) and Integrated Natural Resource Management (INRM) scholarship from the Commonwealth Scientific and Industrial Research Organisation (CSIRO).

A special thank you to the School of Geography, Planning and Environmental Management’s very friendly and supportive academic and professional staff. Especially thanks to Jamie Schulmeister, Lara Atzeni, Christina Jack, Judy Nankiville, Claire Lam, Jurgen Overheu, Michael Tobe, Alan Victor, Clwedd Burns, Joel Almond, Lorraine Liang, Suhan Chin, Genna Apted and Lia Gardiner for making this journey much easier for me.

I am also grateful to the social team of GPEM, Matt Rice, Lincoln Steinberger, Zoe Stone, Alvaro Salazar, Anthony Kimpton, Kiran K.C. and Ralph Trancoso for organizing social events. Special thanks to my office mates, David Alexander, Jie Chang, Amirul Zolkafli and Roja Gholamhosseini for their support and friendship.

I would like to thank all data providers of this PhD project, including the National Snow and Ice Data Center (NSIDC) for providing ICESat data, European Space Agency for providing ERS and Envisat altimetry data, CNES AVISO for TOPEX/Poseidon and Jason-1, 2 altimetry data, Water Corporation of Western Australia for Lake Argyle datasets, and Victorian Water Resources Data Warehouse for Lake Eildon gauge data.

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I would like to thank CSIRO Marine and Atmospheric Research, Time Series Remote Sensing Team for sharing MODIS time series and Peter Scarth from Queensland Department of Science, Information Technology and Innovation for providing Landsat-5 TM time series I also thank the developers of STARFM and ESTARFM for providing their code for this study. Special thanks to Bill Syme, TUFLOW manager and author, for providing the TUFLOW license and technical support for this project.

I kindly thank the generous and friendly assistance from present landholders and managers of Isis Downs, Tulmur, Verdun Valley, Brighton Downs, Davenport Downs, Monkira, Roseberth and Keeroongooloo Stations for access to the field sites.

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Keywords surface water hydrology, dryland rivers, remote sensing, hydrodynamic modelling, ecohydrology, transmission losses, digital elevation models (DEMs), altimetry, satellite-derived flood extent, blending algorithms

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 040608, Surfacewater Hydrology, 70%

ANZSRC code: 050209, Natural Resources Management, 10%

ANZSRC code: 090905, Photogrammetry and Remote Sensing, 20%

Fields of Research (FoR) Classification

FoR code: 0406, Physical Geography and Environmental Geoscience, 70%

FoR code: 0502, Environmental Science and Management, 5%

FoR code: 0909, Geomatic Engineering, 25%

TABLE OF CONTENTS

Section Page*

Chapter 1 Introduction 1-1

Chapter 2 Evaluation of multiple satellite altimetry data for 2-1 (78-90) studying inland water bodies and river floods

Chapter 3 Blending Landsat and MODIS data to generate 3-1 (9213-9238) multispectral indices: A comparison of ‘Index-then- Blend’ and ‘Blend-then-Index’ approaches

Chapter 4 Satellite-derived digital elevation model (DEM) 4-1 (489-506) selection, preparation and correction for hydrodynamic modelling in large, low gradient and data-sparse catchments

Chapter 5 Where does all the water go? Partitioning water 5-1 transmission loss in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Chapter 6 Discussion 6-1

Chapter 7 Conclusion and Recommendations 7-1

* Note that the page numbering within Chapters 2 to 4 (inclusively) is as assigned by the international peer-reviewed journals in which each chapter’s research has been published. Such numbering is presented in italics. The numbering of the remaining chapters, as well as the title pages of the afore-mentioned chapters, follow the ‘chapter - page’ format outlined in the Table of Contents.

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LIST OF FIGURES

Chapter 1 / Introduction

Figure # Description Page

Figure 1 The conceptual and structural organisation of the thesis. 1-13

Chapter 2 / Paper 1

Figure # Description Page

Figure 1 Details of locations for the study of satellite altimetry 2-3 (80) accuracy for river flooding, including; A) location of selected study sites in Australia, B) installed loggers located at selected reaches of Diamantina River and cooper Creek, C) Lake Eildon, D) Lake Argyle, E) Logger and Jason-2 track at Windorah, F) Logger and Jason-2 track at Longreach, G) Logger and Jason-2 track at Isis Downs, H) Logger and Jason- 2 track at Brighton Downs, I) Logger and Jason-2 track at Roseberth, J) Logger and Jason-2 track at Tulmur. The background of C, D, E, F, G, H, I and J represents a false colour composite of Landsat-5/7 images of the area at 06 Feb 2011, 28 Sep 2011, 01 Dec 2010, 02 Jan 2011, 07 Feb 2012, 05 Feb 2012, 16 Apr 2012 and 05 Feb 2012 respectively.

Figure 2 TOPEX/Poseidon (track #62) derived water elevation over 2-5 (82) Lake Argyle and associated water level records from gauging station.

Figure 3 GFO (track #221) derived water elevation over Lake Argyle 2-5 (82) and associated water level records from gauging station.

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Figure 4 Water level calculated from Jason-1 1 Hz and 20 Hz altimetry 2-6 (83) ranges in ”C” and “Ku” bands.

Figure 5 Water level time series calculated from Jason-2 different 2-6 (83) retracking algorithms against Lake Eildon gauge measurements.

Figure 6 Water level time series calculated from Envisat different 2-7 (84) retracking algorithms against Lake Argyle gauge measurements.

Figure 7 Water level time series calculated from ICESat against Lake 2-8 (85) Argyle gauge measurements.

Figure 8 Correlation between water elevation time series derived from 2-8 (85) Jason-2 and field based logger data records for six selected river stations.

Figure 9 Water level time series calculated from Jason-2 data using the 2-9 (86) “Ice3” retracking algorithms compared to the field based Longreach gauge.

Figure 10 Top: Water elevation changes across Cooper Creek extracted 2-10 (87) from four passes of Jason-2 satellite during major floods. Bottom: Location of Jason-2 tracks and elevation points from four dates during the 2011-2012 floods (background: Landsat image captured on 1/12/2010).

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Chapter 3 / Paper 2

Figure # Description Page

Figure 1 The study sites: (a) location of three study sites in Australia; 3-5 (9217) (b–d) are Landsat images of the study sites with Bands 5, 4 and 3 shown as RGB composite on dates 2 January 2011, 8 October 2001 and 12 December 2004 for Thomson, Coleambally and Gwydir, respectively. A standard deviation of 1.5 was used to stretch the RGB Landsat images. All the extreme values of the histograms, falling outside the 1.5 standard deviation, were trimmed out, and remaining values were redistributed between 0–255 to enhance the images.

Figure 2 Mean bias, R2 and RMSD statistics for eight simulated indices 3-13 (9225) by IB and BI approaches and STARFM and ESTARFM for Thomson, Coleambally and Gwydir. SR results are not shown here as their extreme magnitude, especially in bias and RMSD, dampens the information content from other indices.

Figure 3 Bias, R2 and RMSD statistics between Landsat-observed 3-14 (9226) Normalized Difference Water Index (NDWI24) and simulated NDWI24 by IB and BI approaches and STARFM and ESTARFM at the flood date 12 December 2004 at Gwydir. Parts (a,b,c,d) show the spatial bias produced by IB- ESTARFM, IB-STARFM, BI-ESTARFM and BI-STARFM, respectively. Parts (e,f,g,h) show the corresponding crossplots (and the associated bias, RMSD and R2 statistics) between the observed and simulated values by IB-ESTARFM, IB- STARFM, BI-ESTARFM and BI-STARFM, respectively. In all cases there are 8,544,889 pixels.

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Figure 4 Thomson temporal to spatial variance ratio time series plots 3-17 (9229) for bands and indices from Landsat and MODIS. Parts (a,b) are temporal variance to spatial variance ratio of all Landsat reflective bands and their corresponding MODIS bands; the legend in (a) applies to (b). Parts (c,d) are temporal variance to spatial variance ratio of the nine indices from Landsat and MODIS; the legend in (c) applies to (d). The spatial and temporal variances are calculated using all possible (18) 3- sequential date images and the results are plotted at the date of the central image. Vertical grid lines indicate dates of the central images. The horizontal dashed line in (c,d) is where T/S = 1.0.

Figure 5 Coleambally temporal to spatial variance ratio time series 3-18 (9230) plots for the nine indices from Landsat and MODIS; the legend in (a) applies to (b). The spatial and temporal variances are calculated using all possible (15) 3-sequential date indices and the results plotted at the date of the central image. Vertical grid lines indicate dates of the central images. The horizontal dashed line is where T/S =1.0.

Figure 6 Gwydir temporal to spatial variance time series plots for the 3-19 (9231) nine indices from Landsat and MODIS; the legend in (a) applies to (b). The spatial and temporal variances are calculated using all possible (12) 3-sequential date indices and the results plotted at the date of the central image. Vertical grid lines indicate dates of the central images. The horizontal dashed line is where T/S = 1.0.

Figure 7 Bias, R2 and RMSD of all 12 simulations of NDWI25 at 3-20 (9232) Gwydir using STARFM and ESTARFM for the two approaches IB and BI. Part (a), represents bias statistics and T/S ratio, parts (b,c) represent R2 and RMSD statistics, respectively. Vertical grid lines indicate dates of the central images, and the color components of the legend in (a) apply to parts (b,c). The horizontal dashed line in (a) is where T/S = 1.0.

Chapter 4 / Paper 3

Figure # Description Page

Figure 1 Study site. (a) Location of Diamantina (western) and 4-5 (493) Cooper (eastern) catchments shown in black line and Lake Eyre Basin shaded gray in Australia. (b) Hydrodynamic model domain shown by the dashed line and ICESat ground tracks #1, #2 and #3 shown in pink lines in Cooper Creek. Background is Landsat image of major flood event in 29 April 1990 (bands 5, 4 and 3 as RGB). (c) Location of GCPs and ICESat points in Diamantina/Cooper catchments.

Figure 2 Conceptual arrangement of the study is shown. In 4-7 (495) objectives 1 and 2, the four existing DEMs are shown in blue and the two created DEMs are shown in red.

Figure 3 Cross-platform DEM-to-DEM comparison. In (a) to (d) 4-9 (497) the SRTM DEM is compared with ASTER GDEM respectively, in original, co-registered, vegetation smoothed and hydrologically corrected. There are 5.28 million 30 m grid contributing to each histogram.

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Figure 4 Comparison of ICESat-derived elevation transect with 4-11 (499) SRTM DEM and ASTER GDEM transects. The elevation transects under ground track #3 of ICESat (“A” as located on Figure 1b, Sec #3, is at 0 km on this figure and “B” at 50 km) on 4 June 2005 were extracted from (a) original, (b) co-registered, (c) vegetation smoothed and (d) hydrologically corrected versions of digital elevation models. The legend for part (a) applies to all other parts.

Figure 5 Comparison of five evaluated flow metrics by using 4-12 (500) SRTM DEM and ASTER GDEM in original, vegetation smoothed (those in the legend proceeding S-) and channel-enforced versions (those abbreviated H- in the legend) being: (a-c) peak discharge (m3/s); (d-f) peak height (m); (g-i) travel time (hrs); (j-l) terminal water storage (M m3); and (m-o) flood extent (% using F- statistic). For each metric the minor, moderate and major floods were compared by using six DEMs at multiple grid sizes (30-2000 m).

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Figure 6 Observed and simulated moderate 1990 flood extent 4-13 (501) comparison. Part (a) shows the Landsat image captured on 29 April 1990 during the flood peak (bands 5, 4 and 3 as RGB) and (b) shows Landsat-derived flood extent on the same date for the area zoomed by the red square in part (a). Parts (c-e) show simulated peak flood extent by using SRTM DEM, S-DEM and H-DEM. Parts (f-h) show simulated peak flood extent by using ASTER GDEM, S-GDEM and H-GDEM. Landsat-derived flood extent (b) has 30 m resolution and (c-h) have a 90 m grid size. Depth units are in (m) and are the difference at the maximum water level associated with the flood and the underlying elevation from the DEMs. Parts (f-h) have different depth range compared to (c-e).

Figure 7 Grid size effect on flood characteristics. The changes in 4-14 (502) (a) peak discharge, (b) peak height, (c) travel time, (d) terminal water storage and (e) flood extent were calculated for each change in grid size for two H-DEM and H-GDEM. Results are the average of the minor, moderate and major flood events and relative to the value for 30 m grid.

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Supplementary Observed and simulated minor 2008 flood extent 4-19 Figure 1 comparison. Part (a) shows the Landsat image captured on 26 April 2008 during the flood peak (bands 5, 4 and 3 as RGB) and (b) shows Landsat-derived flood extent on the same date for the area zoomed by the red square in part (a). Parts (c-e) show simulated peak flood extent by using DEM, S-DEM and H-DEM. Parts (f-h) show

simulated peak flood extent by using DEM3sec, S-DEM3sec

and H-DEM3sec. Parts (i-k) show simulated peak flood extent by using GDEM, S-GDEM and H-GDEM. Landsat-derived flood extent (b) has 30 m resolution and (c-k) have a 90 m grid size. Depth units are in (m) and are the difference at the maximum water level associated with the flood and the underlying elevation from the DEMs. Parts (i-k) have different depth range compared to (c-h).

Supplementary Observed and simulated major 2000 flood extent 4-20 Figure 2 comparison. Part (a) shows the Landsat image captured on 29 February 2000 during the flood peak (bands 5, 4 and 3 as RGB) and (b) shows Landsat-derived flood extent on the same date for the area zoomed by the red square in part (a). Parts (c-e) show simulated peak flood extent by using DEM, S-DEM and H-DEM. Parts (f-h)

show simulated peak flood extent by using DEM3sec, S-

DEM3sec and H-DEM3sec. Parts (i-k) show simulated peak flood extent by using GDEM, S-GDEM and H-GDEM. Landsat-derived flood extent (b) has 30 m resolution and (c-k) have a 90 m grid size. Depth units are in (m) and are the difference at the maximum water level associated with the flood and the underlying elevation from the DEMs. Parts (i-k) have different depth range compared to (c-h).

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Supplementary Grid size effect on simulated minor 2008 flood extent. 4-21 Figure 3 Part (a) shows Landsat image captured during flood peak and (b) shows Landsat-derived flood extent on 26 April 2008 (bands 5, 4 and 3 as RGB). Parts (c-j) show simulated peak flood extent by using H-DEM in multiple 30, 60, 90, 120, 250, 500, 1000 and 2000 m grid sizes.

Supplementary Terminal water storage after minor 2008 flood pulse. Part 4-22 Figure 4 (a) shows Landsat image captured during flood peak and (b) shows Landsat-derived flood extent on 26 April 2008 (bands 5, 4 and 3 as RGB). Parts (c-e) show terminal water storage when using DEM, S-DEM and H-DEM.

Parts (f-h) show terminal water storage by using DEM3sec,

S-DEM3sec and H-DEM3sec. Parts (i-k) show terminal water storage by using GDEM, S-GDEM and H-GDEM. Landsat-derived flood extent has 30 m resolution. Parts (c-k) have a 90 m grid size. Parts (i-k) have different depth range compared to (c-h).

Supplementary Terminal water storage after moderate 1990 flood pulse. 4-23 Figure 5 Part (a) shows Landsat image captured during flood peak and (b) shows Landsat-derived flood extent on 29 April 1990 (bands 5, 4 and 3 as RGB). Parts (c-e) show terminal water storage when using DEM, S-DEM and H- DEM. Parts (f-h) show terminal water storage by using

DEM3sec, S-DEM3sec and H-DEM3sec. Parts (i-k) show terminal water storage by using GDEM, S-GDEM and H- GDEM. Landsat-derived flood extent has 30 m resolution. Parts (c-k) have a 90 m grid size. Parts (i-k) have different depth range compared to (c-h).

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Supplementary Terminal water storage after major 2000 flood pulse. Part 4-24 Figure 6 (a) shows Landsat image captured during flood peak and (b) shows Landsat-derived flood extent on 29 February 2000 (bands 5, 4 and 3 as RGB). Parts (c-e) show terminal water storage when using DEM, S-DEM and H- DEM. Parts (f-h) show terminal water storage by using DEM3sec, S-DEM3sec and H-DEM3sec. Parts (i-k) show terminal water storage by using GDEM, S-GDEM and H-GDEM. Landsat-derived flood extent has 30 m resolution Parts (c-k) have a 90 m grid size. Parts (i-k) have different depth range compared to (c-h).

Chapter 5 / Paper 4

Figure # Description Page

Figure 1 Study site map. (a) Lake Eyre Basin, Australia, (b) 5-10 Diamantina catchment in Lake Eyre Basin. The darker grey shows the Diamantina catchment area upstream of the Diamantina Lakes gauging station. Part (c) location of Diamantina Lakes gauging station, four virtual gauge stations, location of two ground tracks of the Jason-2 altimeter satellite and 11 sub-catchments, which are used for rainfall-runoff modelling.

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Figure 2 Monthly discharge and rainfall from July 2005 to November 5-12 2013. Monthly discharge at Diamantina Lakes gauging station is shown for seven flood events (Flood 1 to 7) that were all modelled herein. Total rainfall (GL) of the sub- catchments between the four virtual stations and the Diamantina Lake gauge station is also presented. Water elevations were also recorded from 01 November 2011 to 30 November 2013 at the four virtual gauges capturing all of Flood 7. The temporal extent of the 7 floods are defined by the following dates pairs: (i) 07 Feb 2006 to 05 May 2006; (ii) 08 Jan 2007 to 11 Aug 2007; (iii) 14 Nov 2007 to 16 Mar 2008; (iv) 25 Dec 2008 to 21 Mar 2009; (v) 25 Dec 2009 to 12 May 2010; (vi) 14 Oct 2010 to 19 Apr 2011; and (vii) 22 Nov 2011 to 13 April 2012.

Figure 3 Schematic view of reaches and flood water balance equation 5-16 components.

Figure 4 Relative magnitude of the water balance equation’s 5-25 components to inflow for each individual river reach, Reach 1, Reach 2, Reach 3, Reach 4 and combined (1 to 4) reach for seven flood events between February 2006 to April 2012.

Qlat = lateral inflow, Ea = actual evapotranspiration, IIL = initial infiltration loss, CIL = continuous infiltration loss,

TWS = terminal water storage, Qout = outflow and TTL = total transmission losses. Parts a-d present components values of Reaches 1 to4, respectively, and part (e) shows these components for a combined 180 km river reach. All these components are normalized to inflow of seven flood events from February 2006 to April 2012. Total transmission losses (TTL) = Ea + IIL + CIL + TWS. All values were normalized to the reach upstream inflow. The legend in part (a) applies to all other parts.

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Figure 5 Water budget diagram of four flood events along 180 km 5-26 reach of Diamantina River between four virtual stations (Tulmur, Tulmur2, Verdun Valley and Brighton Downs) and Diamantina gauging station. Parts (a) to (d) show the water budget of four flood events (i.e., Floods #2, #4, #5 and #6 respectively; see Fig.2 for more details of each flood event). The “Gain” in the total lateral inflow, Loss is total transmission losses (i.e., actual evaporation, infiltration to the soil moisture store and terminal water storage) and Q is the total discharge in location of five stations; all have units GL per flood event. Error bars are shown for each component-reach combination and represent 1 standard deviation. Legend of (a) applies for all other parts.

Figure 6 Total transmission losses against inflow for the seven flood 5-27 events February 2006 to April 2012. Parts a-d show reaches 1-4, respectively, and part (e) is the combined 180 km reach. The horizontal axis is total inflow to the system (upstream inflow + lateral inflow). The vertical axis (TTL/Q) is total transmission losses (GL/km) relative to the total inflow (these are partitioned into the component parts in Fig. 4). The seven flood events are shown by numbers 1-7 on graphs, with more details for each flood presented in Fig. 2.

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Figure 7 Spatial distribution of total transmission losses for two flood 5-29 events. (a) and (b) show the spatial distribution of TUFLOW modelled total transmission losses (i.e., evapotranspiration, infiltration to groundwater and terminal water storage) of flood #1(February 2006 to May 2006) and flood #7 (November 2011 to April 2012), respectively. (c) and (d) are the flood water residence time, calculated from daily MODIS OWL flood maps for the duration of the two floods. Legend of (a) applies to (b) and legend in (c) applies to (d). Scale in part (d) applies to all parts (a-d).

Supplementary Comparison of altimetry derived water elevation time series 5-55 Figure 1 at the Tulmur and Brighton Downs virtual stations with water elevation recorded by loggers. Altimetry satellite data were extracted from Jason-2 satellite’s Coastal and Hydrological products (PISTACH, (Mercier et al., 2008)) which is available from July 2008 to present at a 10-day temporal frequency. Loggers were installed under the ground-track of Jason-2 satellite and recorded water elevation in 15-minute temporal frequency from Nov 2011 to Nov 2013.

Supplementary Water balance components of seven flood events between 5-56 Figure 2 February 2006 to April 2012 for four river reaches 1-4. Inflow, lateral inflow, outflow and total transmission losses are presented in parts a-d. Parts e-f show the individual components of the total transmission losses (i.e., evapotranspiration, initial infiltration loss, continious infiltration loss and terminal water storage) for each reach (1-4). Fig. 4 shows these data as normalised to flood inflow, here the raw data (i.e., non-normalised) are provided.

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Supplementary Gauge stations cross sections and rating curves. (a) 5-57 Figure 3 discharge-elevation (rating curve) charts of four virtual stations (Tulmur, Tulmur2, Verdun Valley and Brighton Downs) and Diamantina gauging station. Part (b) presents the cross sections of the river extracted from SRTM DEM by overlaying Landsat 5 image during a major flood event on 20/ 02/2009. Part (c) rating curve at the location of Diamantina Lakes gauging station for a range of Manning’s n values from 0.01 to 0.1. These rating curves were extracted using SRTM topographic data in the 2-D TUFLOW model with a 60 m grid size. The surveyed rating curve of the Diamantina Lakes station is also presented (black line) for comparison.

Supplementary Total transmission losses against inflow for the seven flood 5-58 Figure 4 events February 2006 to April 2012. Parts a-d show reaches 1-4, respectively, and part (e) is the combined 180 km reach. The horizontal axis is total inflow to the system (upstream inflow + lateral inflow). Vertical axis show total transmission losses per kilometer (these are partitioned into the component parts in Fig. 4). The seven flood events are shown by numbers 1-7 on graphs, with more details for each flood presented in Fig. 2. This figure shows absolute changes in transmission losses against total inflow which increases with discharge. This is distinctive from Fig. 6 that shows transmission losses relative to total inflow and decreases with discharge.

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Supplementary Comparison of simulated and observed flood hydrograph. 5-59 Figure 5 Part (a) compares the 2011 Flood #7 hydrograph simulated by TUFLOW with recorded data by logger in Brighton Downs station. Part (b) shows the TUFLOW simulated 2011 Flood #7 with data recorded in Diamantina Lakes gauging station (see Fig. 2 for full details).

Supplementary Comparison of TUFLOW simulated flood hydrograph with 5-60 Figure 6 altimetry-derived water elevation time series. Parts (a) and (b) show the major 2008 Flood #4 and 2010 Flood #6 in Tulmur station, respectively (see Fig. 2 for full details).

Supplementary Reach-based water balance components averaged across the 5-61 Figure 7 seven floods of four individual Reaches 1 to 4 and all combined as a 180 km reach.

Supplementary Comparison of simulated and observed inundation area. The 5-62 Figure 8 hydrodynamic model, TUFLOW, simulated inundation area and MODIS OWL-derived maximum inundation area of seven flood events are compared in 180 km reach of the Diamantina River. Parts (a) to (g) represent seven flood events from February 2006 to April 2012 (see Fig. 2 for full details).

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LIST OF TABLES

Chapter 1 / Introduction

Table # Description Page

Table 1 Summary of remotely-sensed (RS) and ground-based (GB) 1-14 datasets used in this research. In the “Application” column, (FD) = Forcing data; (P) = Parameters; (C) = Calibration; (E) = Evaluation; and (R) = Regionalisation, as defined by McVicar et al. (2009).

Chapter 2 / Paper 1

Table # Description Page

Table 1 Details of altimetry missions and their product characteristics. 2-2 (79)

Table 2 Altimetry products information for selected sites. 2-4 (81)

Table 3 Results of comparing different retracking algorithm on lake 2-6 (83) water bodies.

Table 4 Lakes Argyle water elevation extracted from ICESat and 2-7 (84) gauging stations.

Table 5 Statistical characteristics of the regression between altimetry- 2-9 (86) extracted and ground-based water levels at six selected stations along the Jason-2 satellite tracks.

Table 6 Summary of the performance of the various altimetry 2-11 (88) platforms.

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Chapter 3 / Paper 2

Table # Description Page

Table 1 List of nine indices and their equations used here. NIR, 3-3 (9215) SWIR1 and SWIR2 are abbreviations for Near-Infrared, Shortwave-Infrared1 and Shortwave-Infrared2 bands, respectively. Except for Enhanced Vegetation Index (EVI),

Simple Ratio (SR) and Depth of 1650 nm (D1650), all indices use a normalized difference formulation generically given as (band x − band y)/(band x + band y), where x and y represent bands.

Table 2 Dates of cloud-free Landsat-5 and Terra Moderate Resolution 3-6 (9218) Imaging Spectroradiometer (MODIS) images from 2008– 2011 for Thomson. The bold row indicates the image captured during major flood event (Image # 11, see Figure 1b).

Table 3 Bands and band-widths of Landsat TM and corresponding 3-7 (9219) MODIS bands.

Table 4 Mean bias, R2 and root mean square deviation (RMSD) 3-11 (9223) statistics between the observed and simulated index values from Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) for the both the Index-then-Blend (IB) and Blend-then-Index (BI) approaches for Thomson, Coleambally and Gwydir, which are abbreviated as T, C and G respectively in the column headings. Bias and RMSD are presented in index units.

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Table 5 t-test results to assess approaches and algorithms are 3-12 (9224) statistically different. Probability of <0.05 is shown in the italic and bold font, and probability <0.1 are provided as italic, >0.1 is normal font. Last two rows show number of cases with <0.05 and <0.1 probabilities and their percentage (count/18 × 100) in brackets. Under the ‘Approach’ headings, testing differences due to IB or BI, statistics for the first grouping of the nine indices use STARFM and the second grouping of nine use ESTARFM. Under the ‘Algorithm’ headings, testing differences due to blending algorithms, statistics for the first grouping use the IB approach and the second grouping use the BI approach. Thomson, Coleambally and Gwydir are abbreviated as T, C and G, respectively.

Chapter 4 / Paper 3

Table # Description Page

Table 1 Summary of relevant modelling studies using remotely- 4-3 (491) sensed DEMs (the current paper is added for completeness). Not all papers performed DEM accuracy assessment and modelling studies and these are denoted with a N/A representing ‘not applicable’ in the relevant part of the ‘Data / Model used’ column. In the ‘Key results’ column the abovementioned three components are identified by the code: (1) assess absolute accuracy of DEMs and general corrections; (2) assess the relative accuracy of DEM selection and preparation methods on hydrodynamic flood modelling; and (3) quantify the impact of grid size on hydrodynamic modelling accuracy and computational expense. Many studies do not assess all three components and N/A directly follows the code in such cases.

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Table 2 DEM-to-control point elevation differences statistics in (i) 4-8 (496) original DEMs and after applying: (ii) 3D co-registration corrections; (iii) vegetation smoothing algorithm; and (iv) hydrological corrections. Units of bias, SD and RMSD are meters. 373066 GCPs used. ICESat data have been used as truth point to co-register all DEMs.

Table 3 DEM-to-DEM elevation difference between original DEM 4-10 (498) and after sequentially applying the (i) 3D co-registration; (ii) smoothing; and (iii) hydrological corrections. Mean bias and SD show the average and standard deviation of elevation changes after each step of corrections. Topographic roughness (TR) shows the averaged standard deviation of elevation changes in 3x3 and 5x5 moving window across the entire DEMs. Numbers in brackets are the percentage of roughness decrease due to vegetation smoothing and hydrological corrections relative to the roughness of original (co-registered) DEM. All units are in meters.

Table 4 Elevation difference between ICESat and three cross sections 4-11 (499) after sequentially applying the: (i) 3D co-registration; (ii) vegetation smoothing; and (iii) hydrologically corrected. The locations of the three sections are shown on Figure 1b. Mean bias and SD show the average and standard deviation of elevation changes after each step of corrections. All units are in meters.

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Table 5 TUFLOW GPU computational time of 1990 flood costs in 4-14 (502) multiple grid sizes. The TUFLOW models have been run on a moderate specification desktop computer with 64-bit operating system, 8 GB RAM, processor Intel® core™2 Quad CPU, [email protected] GHz and NVIDIA Quadro 4000 GPU. These run times are for 1050 hour flood hydrograph with adaptive time-stepping (starting time step of 15 second with Courant Number = 1.0).

Supplementary dataset information of this study. Horizontal and vertical 4-25 Table 1 accuracy of SRTM quoted at 90% confidence level circular accuracy.

Supplementary DEM-to-control point elevation differences statistics of 4-26 Table 2 SRTM X-band DEM (XDEM), SRTM 3-arc-sec C-band

DEM (DEM 3sec) and ASTER GDEM version 1 (GDEM1) in (i) original and after applying: (ii) 3D co-registration corrections; (iii) vegetation smoothing algorithm; and (iv) hydrological corrections. Units of bias, SD and RMSD are meters. Total of 180583 GCPs used for XDEM and DEM

3sec, 373066 GCPs used in GDEM1.

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Supplementary DEM-to-DEM elevation difference between original and 4-27 Table 3 after sequentially applying the (i) 3D co-registration; (ii) smoothing; and (iii) hydrological corrections for SRTM 90 m DEM and ASTER GDEM version 1. Mean bias and SD show the average and standard deviation of elevation changes after each step of corrections. Topographic roughness (TR) shows the averaged standard deviation of elevation changes in 3x3 and 5x5 moving window across the entire DEMs. Numbers in brackets are the percentage of roughness decrease due to vegetation smoothing and hydrological corrections relative to the roughness of original (co-registered) DEM. All units are in meters.

Chapter 5 / Paper 4

Table # Description Page

Table 1 Summary of relevant transmission loss estimation research 5-6 conducted in dryland environments (the current paper is added for completeness). Not all papers performed transmission losses estimation and flood routing modelling studies and these are denoted with a N/A representing ‘not applicable’ in the relevant part of the ‘Key results’ column. In the ‘Key results’ column the abovementioned three components are identified by the code: (1) estimating transmission losses; (2) flood routing; and (3) uncertainty analysis and use of remote sensing data.

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Table 2 Information of river reaches and gauge stations. Total reach 5-14 channel system area is calculated taking into account the meandering nature of the river. See Fig. 1 for upstream and downstream of reaches.

Table 3 Rainfall-runoff and hydrodynamic models calibration 5-19

locations and parameters. In this table Qobs is observed

discharge, Qsim is simulated discharge, IIL is initial infiltration loss for each flood event, CIL is average continuous loss during flood event and ETa is averaged

actual evapotranspiration during flood event. Qerr was

calculated by using (Qobs – Qsim) / Qobs)*100. See caption of Fig.2 for duration of seven flood events.

Table 4 Water balance equation components (rainfall, inflow, lateral 5-23 inflow, outflow, actual evapotranspiration, infiltration and terminal water storage) calculated for each individual river reach, Reach 1, Reach 2, Reach 3, Reach 4 and all reaches (1 to 4) together for seven flood events between February 2006 to April 2012. In the heading, T = number of hours that each

reach was in flood each year, Pup=upstream precipitation,

Psub=sub-catchment precipitation, Qin= inflow, Qout= outflow,

Qlat= lateral inflow, Ea= actual evapotranspiration, IIL=initial infiltration loss, CIL=continuous infiltration loss, TWS=terminal water storage and TTL=total transmission losses.

Table 5 Sensitivity of water elevation to discharge changes at four 5-30 virtual gauging stations and Diamantina Lakes gauge station.

Table 6 Annual rainfall, runoff and runoff coefficients for the seven 5-31 water years from July 2005 to June 2012. Total rainfall integrated over space (the catchment area upstream from the Diamantina Lakes gauging station) and for each water year.

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The runoff coefficient is the ratio of runoff to rainfall.

Table 7 Status of remotely-sensed datasets for hydrodynamic 5-37 modelling of data-sparse low-gradient dryland river systems. In the “Application” column, (FD) = Forcing data; (P) = Parameters; (C) = Calibration; (E) = Evaluation; and (R) = Regionalisation, as defined by McVicar et al. (2009).

Supplementary Percentage of rainfall in each sub-catchment expressed as a 5-54 Table 1 percentage of total water-year annual rainfall. For each sub- catchment its area (km2) and percentage of total area (55,721 km2) are also provided.

Supplementary Long-term (1981-2010) monthly averaged Penman potential 5-54 Table 2 evapotranspiration (mm/month) for the 55,721 km2 area upstream from the Diamantina Lakes gauging station. The first three letter of each month are provided.

Chapter 6

Table # Description Page

Table 1 Accuracy, potential and limitations of satellite-derived 6-3 altimetry data to study hydrological processes. Table 2 Accuracy, potential and limitations of satellite images to 6-6 study hydrological processes. Table 3 Accuracy, potential and limitations of remotely-sensed 6-9 topographic data to study hydrological processes.

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LIST OF ABBREVIATIONS

Abbreviation Definition

RS Remote sensing

GIS Geographic Information System

DEM Digital Elevation Model

CSIRO Commonwealth Scientific and Industrial Research Organisation

MODIS MODerate-resolution Imaging Spectroradiometer

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

ETM+ Enhanced Thematic Mapper Plus

EVI Enhanced Vegetation Index

GPS Global Positioning System

IDL Interactive Data Language ® (Research Systems, ITT)

LAI Leaf Area Index

NDVI Normalized Difference Vegetation Index

NDWI Normalized Difference Water Index

NIR Near InfraRed

RMSE Root Mean Square Error

R2 Correlation of Determination

INRM Integrated Natural Resource Management

GCP Ground Control Point

DERM Department of Environment and Resource Management (Queensland Government)

BoM Bureau of Meteorology (Australian Government)

UTM Universal Transverse Mercator

WGS84 World Geodetic System 1984

OSTM Ocean Surface Topography Mission

ICESat Ice, Cloud, and land Elevation Satellite

ENVISAT Environmental Satellite

RA-2 Radar Altimeter-2

ERS European Remote-Sensing Satellite

GFO GeoSat Follow-On

PISTACH Prototype Innovant de Système de Traitement pour les Applications Côtières et l’Hydrologie, or Innovative Processing System Prototype for Coastal and Hydrology Applications)

GDR Geophysical Data Record

NSIDC United States National Snow and Ice Data Center

BRAT Basic Radar Altimetry Tool

ESA European Space Agency

AHD Australian Height Datum

BI Blend-then-Index’

IB Index-then-Blend

STARFM Spatial and Temporal Adaptive Reflectance Fusion Model

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ESTARFM Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model

AVHRR Advanced Very High Resolution Radiometer

EVI Enhanced Vegetation Index

GVMI Global Vegetation Moisture Index

MNDWI Modified Normalized Difference Water Index

SWIR1 Shortwave-Infrared1

SWIR2 Shortwave-Infrared2

D1650 Depth of 1650 nm relative to a reference continuum line determined at 835 nm and 2208 nm

LPDAAC Land Processes Distributed Active Archive Center

USGS United States Geological Survey

NASA National Aeronautics and Space Administration

EROS Earth Resources Observation and Science Center

TM Thematic Mapper

RMSD Root Mean Square Deviation

SR Simple Ratio

T/S Temporal/Spatial

TUFLOW Two-dimensional Unsteady FLOW n Manning’s roughness coefficient

0D 0-dimensional

1D 1-dimensional

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2D 2-dimensional

GA Geoscience Australia

OWL Open Water Likelihood index

Qin upstream inflow

Qout outflow from downstream

Qobs Observed discharge

Qsim Simulated discharge

Qlat lateral inflow

Qerr Error of simulated error

ETa Actual evapotranspiration

ETp Potential evapotranspiration

TTL Total transmission losses

IIL Initial infiltration loss

CIL Continuous infiltration loss

TWS Terminal Water Storage

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Chapter 1

Introduction

Chapter 1. Introduction

1.1 Background

Defined as areas with annual rainfall less than 500 mm, drylands cover one-third of the world’s land area and accommodate almost 400 million people (Williams, 1999). These semi-arid and arid areas represent some of the most socio-economically disadvantaged regions and also contain some of the highest contemporary development pressures (Tooth, 2000; Walker et al., 1995). A defining feature of the hydrometeorology and streamflow regime of rivers in these regions is the high inter- and intra-annual variability in rainfall and streamflow, and the resultant extreme longitudinal variability in flood magnitude and frequency due to high transmission losses during occasional flood pulses (Knighton and Nanson, 1994). These characteristics have led the unique ecology of these regions to adapt to “boom-and-bust” cycles (Bunn et al., 2006).

Drylands can also be characterised by a distinctive geomorphological form—anabranching—which are low-gradient multi-channel river systems with large floodplains adapted to transmitting occasional and slow-moving flood pulses (Costelloe et al., 2006; Jarihani et al., 2013; Kingsford et al., 1999; Knighton and Nanson, 1994). A key feature of anabranching rivers in central Australia is that rainfall occurs predominantly in the headwaters and as the flood pulse then moves downstream, through a dry mid-to-lower catchment, there are high transmission losses, which diminish discharge downstream (Knighton and Nanson, 1994). The ecological significance of these flood pulses is widely recognised as performing a range of critical ecosystem services including: (i) recharging groundwater aquifers (Cendón et al., 2010; Karim et al., 2011); (ii) filling “terminal water” refuge waterholes (Knighton and Nanson, 2000); (iii) supporting nutrient cycling and breeding cycles underpinning aquatic ecological communities (Arthington and Balcombe, 2011); and (iv) underpinning the riparian ecohydrology (D'Odorico et al., 2009). In dryland rivers, transmission losses have major consequences for the ecological response with regards to vegetation growth and water resources for arid zone flora and fauna (Costelloe et al., 2003).

Accurately determining water balance and transmission losses is difficult in these systems because of their remoteness, highly episodic flow regime and lack of gauging or climatic infrastructure and hence this remains a key knowledge gap. Arid and semi-arid catchments of central Australia are highly variable in flow pattern and create a wide array of ecological conditions which support a rich and abundant aquatic fauna and riparian flora of this unique area. These important ecosystem values are highly dependent on the frequency, magnitude and duration of flow events in these river systems, which are naturally highly variable. Tooth (2000) has identified the common features of

1-1

Chapter 1. Introduction

many dryland fluvial environments worldwide as: (i) extreme temporal and spatial variability of rainfall, runoff and sediment transport; (ii) poor integration between tributary and trunk channels; (iii) importance of large floods as a control on ecohydrological processes and channel morphology; and (iv) lack of equilibrium between process and form.

The coefficient of variation of annual flows in dryland streams in Australia (e.g., Lake Eyre Basin) are approximately double the average variability found worldwide (McMahon et al., 2008). Natural resource management in these data-poor drylands requires a comprehensive understanding of hydrological processes of these important environmental systems. Dryland areas of Australia (and the World) remain poorly understood due to the paucity of fundamental hydrological datasets. Hydrological studies and research on water-dependent processes are constrained by the lack of fundamental data and information in water-limited (dryland) landscapes such as inland Australia. Indeed, the Northern Australia Land and Water Science Review (page 10 of CSIRO, 2009) concluded that:

“Northern Australia comprises >1.2 million km2 of highly variable landscape. Understanding its form and function at the scale required to identify the impacts of specific uses of land or water (i.e. certain types of development) is beyond the scope of this report. It is, as we show, also beyond the current capability of any report. Northern Australia cannot currently be understood at a detailed, site‐specific scale – the raw observational data required for such understanding simply doesn’t exist.”

Conventional modelling approaches to estimate these hydrological variables and parameters are limited by the data paucity. Therefore, novel approaches to augment the limited conventional hydrological data are required to better understand the hydrological processes and functioning of these dryland systems. Understandably, these are major challenges in dryland river systems since each component of the water balance is usually difficult to constrain given the high spatial and temporal variability and the high likelihood that significant proportions of the catchment are ungauged (Sivapalan et al., 2003).

The potential for remote sensing to constrain some of the uncertainty associated with individual components of hydrological processes is extremely high and now well recognised, in data-sparse river systems (Alsdorf et al., 2007; Callow and Boggs, 2013; Karim et al., 2011; Schumann et al., 2009). Remote sensing techniques have the potential to bridge these knowledge and application gaps and provide multi-temporal and multi-spatial information relevant for hydrological studies and

1-2

Chapter 1. Introduction

modelling approaches that are not possible at present due to the paucity of observational data. In data-sparse regions, remote sensing data have been used to provide:

(i) quantification of flood levels (Baghdadi et al., 2011; Birkett and Beckley, 2010; Hall et al., 2012; Jarihani et al., 2013; Troitskaya et al., 2012; Zhang et al., 2011); (ii) flood inundation maps (Brivio et al., 2002; Cruz et al., 2010; Frazier and Page, 2000; Schumann et al., 2007a; Sheng et al., 2001); (iii) basic topographic forcing data (Callow et al., 2007; Hancock et al., 2006; Hirt et al., 2010; Rexer and Hirt, 2014); (iv) hydrometeorological data (Khan et al., 2012; Khan et al., 2011; Milewski et al., 2009; Xue et al., 2013; Yao et al., 2014); (v) discharge estimation (Callow and Boggs, 2013; Frappart et al., 2005b; Kouraev et al., 2004; Smith et al., 1996; Zakharova et al., 2006; Zhang et al., 2004); (vi) soil moisture estimation (Brocca et al., 2010; Milewski et al., 2009; Mohamed et al., 2004; Scipal et al., 2005); (vii) evapotranspiration estimation (Donohue et al., 2010; Glenn et al., 2011; Mohamed et al., 2004; Smettem et al., 2013; Zhang et al., 2009); (viii) infiltration estimation (Frappart et al., 2008); and (ix) estimation of terminal water storage (Frappart et al., 2005a).

While some of these remote sensing capabilities have been independently developed, no single published piece of research has been able to integrate data across these multi-disciplinary fields to allow the study of hydrology and water-dependent processes at multiple spatial and temporal scales in data-poor drylands. Combining these remote sensing capabilities with a hydrodynamic modelling approach to understand water balance and transmission losses in dryland river systems may therefore fully or at least partially address this significant gap.

There remain some significant challenges when coupling field and remote sensing based information in dryland environments with hydrodynamic models to understand the water balance. This is a rapidly evolving area of research, but the integrated application of these emerging methods for constructing and calibrating hydrodynamic models with remote sensing in otherwise data-poor dryland environments has received little attention as yet. This presents a significant opportunity to current research and analytical capabilities that then underpin sound water resource, biodiversity and ecological management related directly to water resources and water-dependent processes in these landscapes. While the inherent challenges and uncertainty may never be completely

1-3

Chapter 1. Introduction

addressed, and there is significant ongoing effort related to estimating individual water balance components, there is a need for integrated approaches to test the application of remote sensing to improve hydrological prediction in data-poor drylands.

This PhD thesis contributes to filling part of this this knowledge gap by first assessing accuracy, potential and limitations of remotely-sensed data to study hydrological processes of dryland river systems and then assessing feasibility of a modelling approach based on the remote sensing data and limited ground-based observations. These different methods are outlined, evaluated and discussed in the following sections.

1.2 Status of remotely-sensed data in hydrology

The availability of hydrological datasets derived from remotely-sensed data has increased substantially in the past decade, and variables such as topographic data, precipitation, actual evapotranspiration, soil moisture, water elevation, flood inundation extent and terrestrial water storage variations can now be measured or predicted at different spatial and temporal scales (Albergel et al., 2012; Anderson et al., 2011; Schumann et al., 2009; Stephens and Kummerow, 2007). There is a growing body of research assessing remotely-sensed data for terrestrial water balance applications. This work has not been specifically applied with respect to multiple remotely- sensed datasets used to study anabranching river systems, which present specific challenges due to the low-gradient, slow moving flood wave, wide floodplain and large number of (up to 100) of varying size channels. Herein we focus on three key types of remotely-sensed datasets, which are critical in hydrological processes of dryland river systems; (i) water elevation from satellite altimetry; (ii) flood extent from optical and microwave satellite imagery; and (iii) remotely-sensed topographic data. These three datasets are individually discussed in the following sections.

1.2.1 Satellite-derived altimetry data

River stage in ungauged catchments can be estimated indirectly at the land-water interface by using high spatial resolution images (< 100 m pixels) and highly accurate and high spatial resolution topographic data (Callow and Boggs, 2013; Schumann et al., 2009; Smith, 1997) or directly from space by altimeter satellites (Alsdorf et al., 2007). In absence of highly accurate and high spatial resolution topographic data, altimetry satellite data are the only available options to estimate water stage in dryland river systems. The earliest hydrological applications of altimetry satellite data were 1-4

Chapter 1. Introduction

to study oceans and ice (Chu et al., 2008). Since then, progress made in data acquisition techniques, e.g. global repeat observations, cloud penetration and night-time observations, has enabled the study of lakes (Crétaux and Birkett, 2006), wetlands (Lee et al., 2009) and, more recently, large rivers (Santos da Silva et al., 2010). Retracking algorithms have been used to reprocess the altimetry satellites’ waveform for inland water studies. Depending on the altimeter satellite orbit, the surface water level has been measured at temporal resolutions varying from 10 to 35 days by individual altimetry satellite sensors, starting from the early 1990s. Altimetry data from the TOPEX/Poseidon (CASH project, (CASH, 2013)), ERS (OSCAR, project (OSCAR, 2013)) and Jason-2 (PISTACH, (Mercier et al., 2010)) satellites were reprocessed for coastal and hydrology applications. These data have been used for studying large terrestrial inland water bodies (Andreoli et al., 2008; Calmant and Seyler, 2006; Crétaux and Birkett, 2006; Troitskaya et al., 2012; Zhang, 2009).

However, the accurate measurement of stream flow in multi-channel systems with extensive anabranching river systems, such as the Diamantina River and Cooper Creek in central Australia, can be challenging due to high variability of flows and the complex hydrodynamic behaviour of the systems. Yet, to date, there have not been any studies assessing the accuracy of satellite-derived altimetry data for mapping large floods of dryland river systems such as these. One focus of this thesis will be to address this key knowledge gap by evaluation of multiple satellite altimetry datasets for studying dryland river floods in complex multi-channel (anabranching) systems.

1.2.2 Remotely-sensed flood extent

Aerial photography has the highest spatial resolution as a source of flood inundation data (Yu and Lane, 2006). However, high airborne acquisition cost and associated challenges in collecting a complete event dataset limits its application for flood monitoring and management (Schumann et al., 2009). The value of microwave and/or optical satellite based flood information acquisition has been utilized since the launch of the first Landsat sensor in 1972, and these datasets represent the highest temporal frequency and longest (multi-decadal) archive of flood inundation data. Passive microwave remote sensing data have mostly been used for studying large floods (Schumann et al., 2009; Ticehurst et al., 2010) and also for improving flood studies in conjunction with other remote sensing data (Bindlish et al., 2009; Guerschman et al., 2011; Zhang et al., 2013) due to their low 20- 100 km spatial resolution. The benefit of most microwave data is that it can be acquired during day and night and also it is not contaminated by clouds and rain the same way that optical data are, and most floods are usually associated high amounts of cloud coverage causing the high precipitation 1-5

Chapter 1. Introduction

amounts (Kumar and Reshmidevi, 2013; Schumann et al., 2009). Active microwave data have proved useful to study smaller (< 1 km) water bodies (Schumann et al., 2009) and have been used widely for flood monitoring (Alsdorf et al., 2007; Cruz et al., 2010; Matgen et al., 2005; O'Grady et al., 2014; Ticehurst et al., 2010). However, availability and temporal frequency (11-46 day, see Table 1 of Schumann et al., 2009) limit their application in flood studies. Optical imagery also has been used extensively in flood studies (Alsdorf et al., 2007; Bai et al., 2009; Frazier and Page, 2000; Habib et al., 2009; Marcus and Fonstad, 2010; McFeeters, 1996b; Ouma and Tateishi, 2006; Schumann et al., 2009; Sheng et al., 2001; Tang et al., 2009; Ticehurst et al., 2010).

Optical image data will in many cases be useful for mapping dryland river systems with lower proportion of cloud cover than temperate coastal and tropical locations. In dryland rivers such as central Australia, the majority of rain falls in the headwaters. The slow-moving (up to 40 days) flood pulses, moving through a low-gradient and dry landscape with sparse vegetation canopies, often combined with cloud-free conditions, provide a great opportunity for optical remote sensing of flood events. However, trade-offs between acquisition frequency and spatial resolutions of satellite image data are inherent in all single-sensor satellites (Emelyanova et al., 2013).

Several advanced blending algorithms (see Table 3, Emelyanova et al., 2013) have been developed to combine data observed from multiple sensors with various spatial resolutions and temporal densities to simulate reflectance data (e.g., Landsat-like imagery at the frequency of MODIS acquisition). Gao et al. (2006) developed the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to blend surface reflectance from two sensors to simulate more frequent higher spatial resolution surface reflectance output. Zhu et al. (2010) enhanced the STARFM algorithm (denoted ESTARFM) improving the algorithm to better deal with high spatial variability associated with heterogeneous study sites. Many recent studies using STARFM first blend reflectance from Landsat and MODIS data and then use these outputs to calculate water or vegetation indices (e.g., Bhandari et al., 2012; Chang and Vannah, 2013; Hilker et al., 2009; Walker et al., 2014; Walker et al., 2012; Watts et al., 2011).

While these approaches to blending have been widely applied, the effect of the order that blending and index calculations are performed has not previously been investigated and is likely an important factor in the accuracy of the end product. Herein, we refer to these as ‘Blend-then-Index’ (BI), with the alternative approach being ‘Index-then-Blend’ (IB). A previous study only focused on vegetation indices and compared different blending algorithm or BI vs. IB approaches (Tian et al., 2013). However, to date, no study has comprehensively assessed accuracy of multiple blending

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algorithms and BI and IB approaches to simulate commonly used standing water, environmental moisture and vegetation indices that can all be useful for flood studies. Water indices, such as the Normalized Difference Water Index (NDWI; McFeeters, 1996a) are widely used in its original and modified forms (Ji et al., 2009; Xu, 2006) to delineate open water features and enhance their presence in satellite images (Bai et al., 2009; Gao, 1996; McFeeters, 1996b). There are also several environmental moisture indices that are commonly used, including: (i) Global Vegetation Moisture Index (GVMI; Ceccato et al., 2002); and (ii) Depth of 1650 nm relative to a reference continuum line determined at 835 nm and 2208 nm (D1650; Van Niel et al., 2003). Vegetation indices are also widely used to effectively characterize particular biophysical or biochemical properties and processes for vegetated surfaces (Huete et al., 2002). The Normalized Difference Vegetation Index (NDVI; Rouse et al., 1973) and the Enhanced Vegetation Index (EVI; Huete et al., 2002) are the most common satellite-derived indices used by the remote sensing community for monitoring vegetation for numerous applications (Chen et al., 2014; Jamali et al., 2014; Senf et al., 2013; Sesnie et al., 2011). The Simple Ratio (SR) is best used for estimating Leaf Area Index (LAI) (Lu et al., 2003).

A key focus with respect to understanding the application of blending algorithms to produce higher- spatial and more-frequent temporal simulated data to support remotely-sensed based flood monitoring and modelling is to systematically evaluate the BI and IB approaches using the abovementioned remotely-sensed indices from the: (i) water domain; (ii) environmental moisture domain; and (iii ) vegetation domain. Specific objectives are to:

(i) Comprehensively examine if one of the approaches (i.e., IB versus BI) or algorithms (i.e., STARFM versus ESTARFM) consistently outperforms the other for the abovementioned 9 indices; and

(ii) Explore the impact that the approach and algorithm has on blending accuracy (i.e., approach versus algorithm).

1.2.3 Digital elevation models

Digital Elevation Models (DEMs) are the fundamental topographic input data and have been shown to be perhaps the most important input when modelling hydrological and other environmental processes (Bates et al., 1998; Bates et al., 2005; Baugh et al., 2013; Callow et al., 2007; Sanders, 2007). DEM selection typically contains trade-offs between cost, accuracy, spatial coverage and

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grid size (Robinson et al., 2014), as well as the way they are prepared and/or corrected (Callow et al., 2007). Airborne-based Light Detection And Ranging (LiDAR) systems can produce highly accurate and high spatial resolution DEMs. Their limiting application to large dryland river basins is due to the spatial coverage of available data and high costs of data acquisition and processing (Baugh et al., 2013; Sampson et al., 2012). Modelling studies in such basins therefore relies on broader-scale satellite-derived DEMs such as: (i) the Shuttle Radar Topography Mission DEM (SRTM DEM) (Hensley et al., 2001); and (ii) the Advanced Spaceborne Thermal Emission and Reflection Radiometer - Global Digital Elevation Model (ASTER GDEM) (Nikolakopoulos et al., 2006; Wang et al., 2012; Zandbergen, 2008).

A growing body of research (Baugh et al., 2013; Brown et al., 2010; Callow et al., 2007; Coe et al., 2008; Wilson et al., 2007) identifies three key issues related to quality and preparation of DEMs that affect hydrodynamic model performance. They are: (i) source and absolute accuracy of DEMs and corrections to address errors; (ii) DEM preparation methods to be more physically realistic (i.e., vegetation smoothing and hydrological correction algorithms); and (iii) impacts of grid size.

Point accuracy of DEMs also has been shown to be important in hydrodynamic modelling and is typically assessed against permanent reference registered ground control points (GCPs), or project- specific survey data (Hengl et al., 2008; Li et al., 2012; Ludwig and Schneider, 2006). In data- sparse regions this can be a challenging undertaking with respect to scarcity of registered survey marks and the limitations in accessing locations to collect additional GCP data. More recently, remotely-sensed altimetry data such as the Geoscience Laser Altimeter System aboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) have been used for DEM accuracy assessment and calibration (Carabajal and Harding, 2005; Carabajal and Harding, 2006; Leon and Cohen, 2012; Rodriguez et al., 2006; Zhao et al., 2010).

There is also growing recognition that DEMs should be subjected to horizontal mis-registration assessment and possibly corrected when seeking to calibrate model outputs such as flood extent against satellite data (e.g., Callow and Boggs, 2013; Jarihani et al., 2013; Jarihani et al., 2014; Nuth and Kääb, 2011). This approach is of particular interest within remote dryland areas across the world where lower numbers of registered survey marks are usually available. Therefore novel approaches are required to combine the limited registered survey marks and ICESat data to provide an extensive control point dataset to validate and calibrate DEMs, suitable for application in hydrological applications in data-sparse, dryland, and large anabranching river landscapes. This

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becomes particularly critical when multiple data sources from multiple sensors are combined to assist hydrological prediction in remote areas.

Vegetation smoothing or filtering is often necessary for SRTM and GDEM as the raw surface is typically higher than the actual land surface due to vegetation artifacts (ASTER GDEM Validation Team, 2011; Yamazaki et al., 2012). This vegetation artifact impact is non-uniform through the landscape, influenced by the vegetation structural characteristics (i.e., tree height, canopy density, wood moisture, branch angle and soil moisture) (Baugh et al., 2013; Brown et al., 2010; Coe et al., 2008; Paiva et al., 2013b; Wilson et al., 2007). This effect is particularly significant in drylands where the vegetation characteristics of riparian zone compared to surrounding woodlands and grasslands (the rangeland) are pronounced. Vegetation artefacts can also add artificial “roughness”, impacting the ability of models to accurately route a flood wave through a landscape (Schumann et al., 2007b; Straatsma and Middelkoop, 2006). There are examples of vegetation affecting DEMs that have been corrected (Gallant et al., 2011). However, their subsequent impact within hydrodynamic models of drylands basins, which are often associated with low-gradient and complex multi-channel (e.g. anabranching) river systems, has previously not been examined.

DEM flow-path replication is also important for hydrodynamic model accuracy, particularly in anabranching rivers, which are synonymous with dryland regions. Due to these characteristics, they are particularly sensitive to DEM quality when modelling flows since much of the flow is distributed across the unchannelised or poorly channelised floodplain. This is important since flow- path enforcement of DEMs is typically based on using digitised river networks and satellite inundation (Costelloe et al., 2006; Horritt et al., 2006). There are examples of corrected and uncorrected SRTM and GDEM products used in modelling single channel river systems, but subsequent effect of these DEM preparation methods and associated accuracies within hydrodynamic models have yet to be examined.

Due to the computational intensity of 2D hydrodynamic approaches, understanding the tradeoffs in DEM spatial resolution (i.e., grid size) and the associated impact on model performance is critical (Horritt and Bates, 2001; Horritt et al., 2006; Li and Wong, 2010; Vaze et al., 2010). It is known that both DEM accuracy and model grid size impact on the calculation of spatial indices such as water inundation extent, which can be problematic when assessing model performance against satellite-derived inundation maps (Li and Wong, 2010; Vaze et al., 2010). However there is a need to investigate potential effects of the spatial resolution of DEMs on model output accuracy to allow

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for optimal selection of model grid size and more efficient simulation performance in dryland river systems.

An important limitation of past work is its independent focus on either SRTM or GDEM (V1 and V2) and only occasionally on both. While DEM preparation and grid sizes have also been assessed, this has usually been for only one type of base DEM and mostly in single channel river systems. While the need to assess these through integrated and systematic analyses has been recognised (e.g. Baugh et al., 2013; Callow et al., 2007; Carabajal and Harding, 2006; Gallant et al., 2011; Li and Wong, 2010; Van Niel et al., 2008; Vaze et al., 2010; Wang et al., 2012), this has not yet been investigated simultaneously for all three issues. Therefore an assessment on multiple commonly used DEMs using a range of techniques and approaches to comprehensively address all three abovementioned issues simultaneously, is needed. This is particularly critical for this type of river system that is under-represented in the literature, making it a challenge to develop hydrodynamic models. These developments might offer some of the greatest opportunities to overcome this limitation in-part through the use of satellite data to accurately map the flood footprint. This is a third key focus on this thesis.

1.3 Modelling framework

1.3.1 Water balance and transmission losses

The scarcity of water measurement infrastructure and the information on water sustaining environmental and economic activities within dryland floodplain environments, is a key limitation to understanding the capacity of the dryland environments to support sustainable livelihoods and ecosystems. Our inability to use conventional approach to understand these questions requires novel approaches to augment the limited conventional hydrological data. There is a potential to addresses this data scarcity through the use of hydrodynamic models supported by remote sensing data (discussed above) to interrogate the question of where does the water go and how/where are transmission losses partitioned. These losses include actual evaporation, infiltration to the soil and groundwater and residual (terminal) water remaining after flood events. These critical components of the water balance of dryland river systems remain largely unknown due to the scarcity of observational data and the difficulty in accurately accounting for the flow distribution in such large multi-channel floodplain systems.

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1.3.2 Modelling transmission losses in data-sparse catchments

Estimating total transmission losses and/or individual components in dryland river systems has previously been undertaken using three main approaches (Cataldo et al., 2004; Cataldo et al., 2010). They are: (i) small-scale field experiments relying on direct field observations during flood events at specific locations (Dahan et al., 2008; Dunkerley and Brown, 1999; Dunkerley, 2008; Maurer, 2002; Parsons et al., 1999); (ii) interpolation of sparse streamflow networks (Arnott et al., 2009; Costelloe et al., 2006; Knighton and Nanson, 1994; Knighton and Nanson, 2001; McCallum et al., 2012; Schmadel et al., 2010); and (iii) hydrodynamic models to complete the water balance and allow estimation of total and component transmission losses (Morin et al., 2009). All three approaches rely on field measurements during flood events and are applicable for small scale point- or reach-based estimation of transmission losses. However in large dryland river systems with highly variable flow regimes and consequently high spatial and temporal variability of water and transmission losses, field measurements are very challenging (or even impossible). The potential for remote sensing to overcome this gap and constrain individual components of hydrological processes is extremely high and now well recognised (see Section 1.1 for a complete list of remote sensing applications). The ability of integrating limited ground-based point data with remote sensing to parameterise, calibrate and validate hydrodynamic models in data-sparse dryland river systems to investigate transmission loss partitioning through assessment of the flood dynamics and water balance dynamics is not established.

Although the scarcity of flow measurements and high uncertainties of the required parameters constrain model accuracy, the availability of other remotely-sensed data sources provides an ability to augment these limited traditional datasets to then interrogate the transmission loss partitioning for infrequently flooding anabranching systems. While still likely to be an imperfect solution, in this part of this PhD, we advance our understanding of floods in these dryland landscapes and their implications for the water balance and ecology, and to evaluate the status of modelling of remote and data-sparse hydrological systems.

1.4 Research aim and objectives

This thesis aims to develop, assimilate and evaluate remotely-sensed data approaches for better modelling hydrological processes in data-poor dryland landscapes. The specific applied aim is to evaluate whether this approach can assist in better understanding the partitioning of transmission losses from anabranching rivers. To achieve this aim, the accuracy of available remote sensing 1-11

Chapter 1. Introduction

dataset was first evaluated and then a modelling approach was used to employ these remote sensing data to understand the water balance and major flood dynamics in data scarce dryland river systems. This thesis has been structured into four main research objectives, addressing the individual areas of knowledge deficiency discussed above. Each of the four objectives is presented in a separate chapter (Chapters 2-5). These works have been published (Chapters 2-5) in peer-reviewed international journals. This thesis focusses on four key areas, each aligned with these papers:

Chapter 2, Jarihani et al (2013), Journal of Hydrology:

Evaluation of multiple satellite altimetry data for studying inland water bodies and river floods;

Chapter 3, Jarihani et al (2014), Remote Sensing:

Blending Landsat and MODIS data to generate multispectral indices: A comparison of ‘Index-then- Blend’ and ‘Blend-then-Index’ approaches;

Chapter 4, Jarihani et al (2015), Journal of Hydrology:

Satellite-derived digital elevation model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low gradient and data-sparse catchments; and

Chapter 5, Jarihani et al (2015), Journal of Hydrology:

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing.

This conceptual and structural organisation of these papers is summarised in Figure 1 below, identifying the relations between the four papers and the chapters in which they are presented with regard to the overall aim of the thesis. In addition, Table 1 summarizes remotely-sensed and ground-based dataset used in this thesis.

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1 Introduction

Remotely Assessing satellite-derived 2

altimetry data

- Sensed DataAssessment

Assessing optical satellite 3 imagery blending

Assessing remote 4

topographic data

Hydrodynamic model performance & calibration odellingF  Flood water balance 5  Transmission losses ramework  Flood wave movement  Important ecohydrological parameters

6 Discussion

7 Conclusion and Recommendations

Figure 1: The conceptual and structural organisation of the thesis.

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Table 1. Summary of remotely-sensed (RS) and ground-based (GB) datasets used in this research. In the “Application” column, (FD) = Forcing data; (P) = Parameters; (C) = Calibration; (E) = Evaluation; and (R) = Regionalisation, as defined by McVicar et al. (2009).

Dataset Data source Processing applied Application Chapter (paper) used RS - water elevation Altimetry satellites; (Jason-2,  elevation datum correction C / E Chapter 2 (Paper 1) ENVISAT, ICESat)  wet and dry tropospheric corrections Chapter 4 (Paper 3)  ionospheric correction Chapter 5 (Paper 4)  solid earth tide correction RS - Digital SRTM  3D-coregistration corrections P Chapter 4 (Paper 3) Elevation Models GDEM  vegetation smoothing Chapter 5 (Paper 4)  hydrological correction RS - inundation Optical images; (Landsat,  flood extent mapping By OWL MODIS C / E Chapter 3 (Paper 2) extent MODIS),  flood extent mapping By NDWI Chapter 4 (Paper 3)  STARFM and ESTARFM blending algorithms Chapter 5 (Paper 4) GB - discharge Gauging stations N/A FD / C / E Chapter 4 (Paper 3) Chapter 5 (Paper 4) GB - water levels Pressure transducers (loggers),  elevation datum correction FD / C / E Chapter 2 (Paper 1) gauging stations  level to discharge by hydrodynamic model Chapter 4 (Paper 3) produced rating curves Chapter 5 (Paper 4) GB - precipitation Bureau of Meteorology, Daily N/A FD Chapter 4 (Paper 3) gridded 0.05° by 0.05°, (Jones Chapter 5 (Paper 4) et al., 2009) RS - actual CSIRO, (Donohue et al., 2010) N/A FD Chapter 4 (Paper 3) evaporation Chapter 5 (Paper 4) GB - survey marks Department of Natural  elevation datum correction E Chapter 4 (Paper 3) Resources and Mines (DERM)

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Chapter 1: Introduction

This chapter has presented an introduction of the significance of this PhD thesis and provides the literature review and background to the research. Additionally, the thesis structure was presented.

Chapter 2 (paper 1): Evaluation of multiple satellite altimetry data for studying inland water bodies and river floods, published in Journal of Hydrology.

In this paper first we begin by comparing all available satellite altimetry data derived from Envisat RA2, TOPEX/Poseidon, Jason-1&2 and GeoSat Follow-On against gauge water elevation data from Lake Argyle in Western Australia and Lake Eildon in Victoria. Having cross-validated the performance of the multiple altimetry satellite data for terrestrial water bodies, we then evaluate the application of satellite altimetry data to measure flood pulses in river systems. We assess the satellite elevation measurement accuracy based on the preferred retracking algorithms (determined from the analysis of the longer time series data for terrestrial water bodies), at six locations of the Cooper Creek and Diamantina River. We validate against water level loggers installed under selected tracks of the Jason-2 satellite for the 2011-12 wet season. Results of this paper have improved our understanding of uncertainty related to estimation of direct water elevation time series by altimeter satellites and have been used in chapter 5 (i.e., paper 4) to calibrate and validate hydrodynamic models in data-sparse dryland river systems to investigate transmission loss partitioning through assessment of the flood dynamics and water balance.

Chapter 3 (paper 2): Blending Landsat and MODIS data to generate multispectral indices: A Comparison of ‘Index-then-Blend’ and ‘Blend-then-Index’ Approaches, published in Remote Sensing.

This chapter evaluates the applicability of the satellite images for monitoring major flood events. The accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat image data was evaluated. Two approaches are tested: (i) “Index- then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Our results have direct impact on operational considerations when blending Landsat and MODIS data for the purposes of generating multispectral indices for vegetation, environmental moisture and/or water applications. Moreover, the downscaled water indices can be used to produce more

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detailed inundation information of floods for calibration/validation of hydrodynamic models. However, in this study (chapter 5 / paper 4) we used MODIS OWL images (without blending with Landsat) as a 500 m spatial resolution was adequate for our application and model set up, and as the simulated images produced by the blending algorithms in highly dynamic flooding environments had residual levels of uncertainty that was too high for use in subsequent hydrodynamic modelling.

Chapter 4 (paper 3): Satellite-derived digital elevation model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments, published in Journal of Hydrology.

The aim of this chapter is to assess the accuracy of freely-available remote sensing topographic datasets and their utility in modelling floodplains of large, low-gradient and multi-channel river systems. First, the accuracy of Digital Elevation Models (DEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) and Shuttle Radar Topography Mission DEMs are assessed against surveyed Ground Control Points (GCP) and global land surface altimetry data of the Geoscience Laser Altimeter System (GLAS) instrument onboard Ice, Cloud, and land Elevation (ICESat) satellite. The effects of DEM preparation methods (vegetation smoothed and hydrologically-corrected) on hydrodynamic modelling relative accuracy are investigated. Moreover, the effect of the hydrodynamic model grid size (30 m to 2000 m) and the associated relative computational costs (run time) on relative accuracy in model outputs are quantified. The results of this paper are used in paper 4 by selecting the most accurate DEM and proper model grid size.

Chapter 5 (paper 4): Where does all the water go? Partitioning water transmission loss in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing, in Press in Journal of Hydrology.

This paper is the culmination of three previous enabling papers and evaluates the application of three remote sensing products ((i) altimeter satellite-derived water elevation time series; (ii) satellite image-derived flood footprint maps; and (iii) satellite-derived digital elevation models) for studying hydrology of low-gradient and data-sparse dryland catchments.

This chapter trials a novel approach to employ modelling and remotely-sensed data from papers 1, 2 and 3 to understand critical questions relating to the water balance and water movement of major floods in dryland river systems. Water elevation time series from altimetry satellites (Objective 1), satellite-derived flood extent maps (Objective 2) and a remotely-sensed digital elevation model

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(Objective 3) are used to build and calibrate a hydrodynamic model. The hydrodynamic is employed to investigate the water balance within the constraints of uncertainty, and to establish the partitioning of transmission losses (direct evaporation, plant transpiration, infiltration to groundwater and terminal water storage) and the related implications for the ecology and function of these river systems. The opportunities, limitations, potential and future directions of using remotely-sensed data in data-sparse regions to better understand the water balance and hydrodynamics of ungauged or poorly-gauged regions are also investigated.

Chapter 6: Discussion

This chapter considers the individual implications of each of the four main objectives discussed in chapters 2-5 and then integrates them to address the key points and knowledge gap that were identified in chapter 1.

Chapter 7: Conclusion and Recommendations

The last chapter highlights on the most significant findings of this PhD thesis and how this thesis can contribute to effective management of water resources in ungauged and rural catchments. finally, the future research and remote sensing approaches to better understand hydrological process in dryland environments are also discussed.

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Chapter 2 Evaluation of multiple satellite altimetry data for studying inland water bodies and river floods

Journal of Hydrology 505 (2013) 78–90

Contents lists available at ScienceDirect

Journal of Hydrology

journal homepage: www.elsevier.com/locate/jhydrol

Evaluation of multiple satellite altimetry data for studying inland water bodies and river ﬂoods ⇑ Abdollah Asadzadeh Jarihani a,c, , John Nikolaus Callow b, Kasper Johansen a, Ben Gouweleeuw c a School of Geography, Planning and Environmental Management, The University of Queensland, St Lucia, QLD 4172, Australia b School of Earth and Environment, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia c CSIRO, Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia article info summary

Article history: Satellite altimeters have been launched with the objective to monitor changes in sea level and glacial ice Received 24 December 2012 sheet topography. More recently, their potential to monitor inland water bodies such as lakes, rivers and Received in revised form 3 September 2013 wetlands has been recognised. The objective of this study was to assess the accuracy of measuring surface Accepted 10 September 2013 water elevation changes of large multi-channel inland river systems using data from multiple altimetry Available online 19 September 2013 satellite sensors and different retracking methods. We initially validated satellite altimetry data with This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, in situ gauge data on Lake Argyle and Lake Eildon (Australia), and then investigated data of the only pres- with the assistance of Venkat Lakshmi, ently operational altimeter, Jason-2/Ocean Surface Topography Mission (OSTM), data at six locations on Associate Editor large inland rivers where temporary gauges were installed during 2011–2012 floods. Our analysis on Lake Argyle showed that the application of an alternative retracking algorithm signif- Keywords: icantly improved the agreement of altimeter and gauge data. We also found that the altimeter with the Satellite altimetry smallest footprint (50–90 m) and the highest along-track resolution (40 Hz, 170 m), ICESat, provided Ungauged basin more accurate lake water surface elevation measurements (mean = 0.00 m, RMSE = 0.04 m) than other Hydrology altimeters, Jason-2 (mean = À0.04 m, RMSE = 0.28 m), Envisat (mean = 0.25 m, RMSE = 0.42 m), Jason-1 River level (mean = À0.04 m, RMSE = 1.07 m), GFO (mean = 0.5 m, RMSE = 0.89 m) and T/P (mean = 0.77 m, River discharge RMSE = 1.5 m). Flood This study also investigated altimetry satellite data accuracy at six Jason-2/OSTM ground track sites crossing the Cooper/Diamantina Rivers where water level loggers were installed to collect data during the 2011–2012 floods (N.B. ICESat ceased operations in 2009). It is shown that satellite altimetry data is able to simulate moderate to major flood events in these large multi-channel inland river systems (R2 = 0.90–0.98). Altimetry data further revealed a variation of water height in the channels across the river system. The general usefulness of satellite altimetry in hydrological applications in remote data sparse regions is confirmed, more specifically to study large multi-channel anabranching river systems such as the Cooper/Diamantina Rivers of the Lake Eyre Basin in Australia and similar systems worldwide where conventional gauging methods are difficult to use. This study also highlights the potential operational applications for monitoring inland flood wave characteristics and hydrodynamic behaviour of remote and multi-channel floodplain systems particularly using the still-operational Jason-2 platform. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction height to volume per unit time using a stage-discharge rating curve. There are several factors that prevent deployment of spa- Measuring and understanding the quantity of surface water tially representative gauge stations in a catchment such as cost moving through landscapes is critical for water resources manage- of installation and maintenance (Hannah et al., 2011) as well as ment, flood management, agricultural irrigation and aquatic accessibility and political issues (Pan and Nichols, 2012). The accu- ecological applications (Coe and Birkett, 2004). Traditionally, water rate measurement of stream flow in multi-channel systems can be volume has been quantified by measuring discharge at gauging challenging due to high variability of flows and the complex hydro- stations by recording the elevation of water and converting water dynamic behaviour of the systems. In extensive anastomosing river systems such as the Diamantina River and Cooper Creek in central Australia, inundated sections can be 50–60 km wide and have ⇑ Corresponding author at: School of Geography, Planning and Environmental more than 20 individual channels. The conditions to set up a Management, The University of Queensland, St Lucia, QLD 4072, Australia. Tel.: +61 (7)33657027. high-density river gauge network in these locations to measure E-mail address: [email protected] (A. Asadzadeh Jarihani). in- and outflow can be challenging. Remote sensing techniques

0022-1694/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jhydrol.2013.09.010 2-1 A. Asadzadeh Jarihani et al. / Journal of Hydrology 505 (2013) 78–90 79 have the ability to bridge this gap with multi-temporal and multi- Jason-1 products include 20 Hz and 1 Hz altimeter ranges with spatial information relevant to hydrological studies and modelling the ‘‘Ocean’’ retracking algorithm in ’’C’’ and ‘‘Ku’’ bands (CNES, approaches. Satellite altimetry data offer the possibility to 2004) and hence these data have mainly been used for studying construct time series of stage, discharge, river altitude profile and coastal and large water bodies such as seas and lakes (Hwang levelling of in situ stations (Calmant and Seyler, 2006). et al., 2011). However, inland water applications using Jason-1 data The earliest applications of altimetry satellite data were to often report issues with small size water bodies related to unreal- study oceans and ice (Chu et al., 2008). Since then, progress made istic waveform ranges. Consequently, only a few larger targets (i.e., in data acquisition techniques, e.g., global repeat observations, the Aral Sea) have been studied successfully (Berry et al., 2007; cloud penetration and night-time observations has enabled the Troitskaya et al., 2012). As part of the Jason-2 Project, the French study of lakes (Crétaux and Birkett, 2006), wetlands (Lee et al., government space agency (CNES) funded the PISTACH project (Pro- 2009) and, more recently, large rivers. Altimetry satellite sensors totype Innovant de Système de Traitement pour les Applications effectively measure the distance between the satellite and earth’s Côtières et l’Hydrologie, or Innovative Processing System Prototype surface based on the time of travel between signal transmission for Coastal and Hydrology Applications) to improve Jason-2 satel- and sensor reception. Shape and magnitude of waveforms reflected lite radar altimetry products over coastal areas and continental by different surface cover types also contain information about the waters. For PISTACH data, new dedicated algorithms have been characteristics of the land surface, i.e., ocean, land, ice, river and developed for waveforms retracking (Mercier et al., 2008), includ- vegetation. Radar altimetry uses several different frequencies ing wet and dry tropospheric corrections, high resolution global depending on mission objectives. For example, the Envisat Radar models for topography and land and water mask data for coastal Altimeter system (RA-2) is a nadir pointing radar operating at and hydrology applications (Mercier et al., 2010). As such, PISTACH two frequencies of 13.575 GHz (‘‘Ku’’ band) and 3.2 GHz (‘‘S’’ band) data are optimised for hydrological applications, similar to how (Resti et al., 1999). Altimeters on TOPEX/Poseidon (T/P), Jason- ‘‘Ocean’’ and ‘‘Ice’’ retracking algorithms are optimised for sea level 1and Jason-2 operate at 13.575 GHz (‘‘Ku’’ band) and 5.3 GHz and glacial monitoring. (C-band) ((AVISO/Altimetry, 1996), also see Table 1). Depending The Envisat radar altimetry mission launched in 2002 is a on satellite orbit, the surface water level has been measured at follow-on mission to the European Space Agency’s ERS-1 temporal resolutions varying by 10–35 days by individual altime- (1991–2000) and ERS-2 (1995–2011) satellites. Altimetry data try satellite sensors, starting from the early 1990s (Table 1). The time series from the ERS-1, ERS-2 and Envisat satellites have been spatial resolution (along track sampling and between track used to study large inland water bodies such as lakes, wetlands and distances) also varies between different altimetry satellites. rivers with some success. For water bodies in Amazon Santos da Retracking is a post-processing procedure which is applied to the Silva et al. (2010) achieved a 0.4 m accuracy compared to gauge waveforms of satellite altimetry data to produce accurate surface data using Envisat standard GDR products and ERS2 OSCAR data. elevation measurements. Retracking algorithms use waveform A study by Benveniste et al. (2007) found a high correlation coeffi- characteristics of returned echoes to measure the distance between cient between river gauge network data for the Amazon River and the sensor and the land surface, and a more detailed description of Envisat-derived water level data (R2 = 0.927–0.987 and root mean different retracking algorithms is available from the Envisat mission square error (RMSE) = 0.3–0.36 m). ‘‘RA-2/MWR Level 2 Products And Algorithms’’ handbook (ESA GLAS/ICESat was launched by NASA in January 2003 and 2012). Products from earlier altimetry missions (TOPEX/Poseidon operated until February 2010. ICESat has been used to study the and ERS) have been retracked for terrestrial water applications with hydrology of lake environments such as the hyper-arid Toshka various retracking algorithms which are different to publically dis- Depression of southern Egypt (Chipman and Lillesand, 2007) and tributed Geophysical Data Record (GDR) standard products. Altime- lake elevations of the Tibetan Plateau (Zhang et al., 2011). try data from primary tracks (cycles 10–359) of the TOPEX/Poseidon Baghdadi et al. (2011) evaluated ICESat for rivers in France and (T/P) satellite were reprocessed with ‘‘Ice1’’ and ‘‘Ice2’’ algorithms found that the RMSE of water elevation measurements was for water resources applications in the CASH project (Contribution 1.14 m for narrow rivers. This was reduced to <0.15 m for rivers de l’Altimetrie Spatiale pour l’Hydrologie (CASH, 2013)). ERS data wider than 1.5 km, due to the higher number of sample points. Hall were retracked in the OSCAR (Observation des Surfaces Continen- et al. (2012) investigated the accuracy of ICESat data of the Missis- tales par Altimétrie Radar) project to produce improved altimetry sippi and Danube rivers. The mean difference between ICESat data time series from ERS raw waveforms for continental hydrological and gauge records for 85 observations was reported as monitoring (OSCAR, 2013). The standard T/P products have mostly 0.16 m ± 0.73 m with a mean absolute error of 0.54 m. Excluding been used to study large water bodies (Frappart et al., 2006; Mercier observations which required correction for saturation improved and Zanife, 2006; Papa et al., 2006, 2010; Zhang et al., 2005). An the results to À0.10 m ± 0.27 m with a mean absolute error of example of large area mapping is provided by Frappart et al. 0.19 m (Hall et al., 2012). (2005) who used T/P data to calculate seasonal water elevation in The overall objective of this research was to assess the water the Negro River (a tributary of the Amazon River) and found that surface elevation mapping accuracy of data derived from multiple the accuracy of the T/P derived time series was within 27 cm. satellite altimetry platforms and their suitability to measure water

Table 1 Details of altimetry missions and their product characteristics.

Satellite Operational Altimeter Temporal resolution (days) Cross-track separation (km) Along track sampling Frequencies (bands)

T/P 1992–2006 Poseidon 10 315 600 m (10 Hz)–7 km (1 Hz) C (5.3 GHz), Ku (13.6 GHz) Jason-1 2001–2013 Poseidon-2 10 315 300 m (20 Hz)–7 km (1 Hz) C (5.3 GHz), Ku (13.6 GHz) Jason-2 2008-Current Poseidon-3 10 315 300 m (20 Hz)–7 km (1 Hz) C (5.3 GHz), Ku (13.6 GHz) ERS-1 1991–1996 RA 3/35/168 80 (35 days) – Ku (13.8 GHz) ERS-2 1995–2011 RA 10 80 – Ku (13.8 GHz) Envisat 2002–2012 RA2 30/35 80 390 m(18 Hz)–7 km (1 Hz) S (3.2 GHz) Ku (13.6 GHz) GFO 1998–2008 GFO-RA 17 165 7 km (1 Hz) Ku 13.5 GHz ICESat 2003–2009 GLAS 8/91 30 170 m 1064 and 532 nm

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Fig. 1. Details of locations for the study of satellite altimetry accuracy for river flooding, including; (A) location of selected study sites in Australia, (B) installed loggers located at selected reaches of Diamantina River and cooper Creek, (C) Lake Eildon, (D) Lake Argyle, (E) Logger and Jason-2 track at Windorah, (F) Logger and Jason-2 track at Longreach, (G) Logger and Jason-2 track at Isis Downs, (H) Logger and Jason-2 track at Brighton Downs, (I) Logger and Jason-2 track at Roseberth, (J) Logger and Jason-2 track at Tulmur. The background of C–J represents a false colour composite of Landsat-5/7 images of the area at 06 February 2011, 28 September 2011, 01 December 2010, 02 January 2011, 07 February 2012, 05 February 2012, 16 April 2012 and 05 February 2012 respectively. surface levels during floods events for large and wide anastomo- of the Cooper Creek and Diamantina River, where inundation ex- sing rivers. Firstly, we conducted a cross-platform evaluation of tent may vary between 5 and 65 km in width. As such, we address altimetry-derived water elevation time series for inland water the usefulness of satellite altimetry data for hydrological applica- bodies in Australia. While the performance of altimetry platforms tions in remote and data sparse inland regions, and more specifi- individually (and occasionally in cross comparison) has been ad- cally in extensive multi-channel river systems. dressed in previous studies, we compare all satellite altimeters at one specific location. We compare satellite altimetry data derived from Envisat RA2, T/P, Jason-1&2 and GFO with gauge water eleva- 2. Study area tion data from Lake Argyle in Western Australia and Lake Eildon in Victoria. We tested a number of retracking algorithms of the stan- Lake Argyle in Western Australia and Lake Eildon in Victoria dard GDR products and compared these to gauge data. were selected as study sites. Lake Argyle was selected as the main Secondly, we assessed the feasibility of satellite altimetry data focus area because it is the largest inland water body in Australia, to measure flood pulses in multi-channel inland river systems. To with ground orbit coverage of the ICESat, ERS-1&2, Envisat, T/P, Ja- this end we evaluated optimal altimetry accuracy at six locations son-1 (new track) and Geosat Follow-on (GFO) satellites, and daily 2-3 A. Asadzadeh Jarihani et al. / Journal of Hydrology 505 (2013) 78–90 81

Table 2 Altimetry products information for selected sites.

Site Satellite Repeat cycle (days) Pass number Product Data provider Time period Lake Argyle Envisat 35 #576, #808 RA2-GDR-2P ESA September 2002–November 2010 T/P 10 #12 GDR CNES-AVISO August 2002–May 2005 Jason-1 10 #12 GDR-M CNES-AVISO February 2009–February 2012 GFO 17 #221 GDR NOAA March 2000–February 2008 ICESat – – GLA14 NSIDC October 2003–November 2009 Lake Eildon Jason-2 10 #149,#169 PISTACH-IGDR CNES-AVISO July 2008–July 2012 Thomson River at Windorah (Site E) Jason-2 10 #240 PISTACH-IGDR CNES-AVISO July 2011–July 2012 Thomson River at Longreach (Site F) Jason-2 10 #062 PISTACH-IGDR CNES-AVISO July 2011–July 2012 Thomson River at Isis Downs (Site G) Jason-2 10 #062 PISTACH-IGDR CNES-AVISO July 2011–July 2012 Diamantina River at Brighton Downs (Site H) Jason-2 10 #240 PISTACH-IGDR CNES-AVISO July 2011–July 2012 Diamantina River at Roseberth (Site I) Jason-2 10 #164 PISTACH-IGDR CNES-AVISO July 2011–July 2012 Diamantina River at Tulmur (Site J) Jason-2 10 #099 PISTACH-IGDR CNES-AVISO July 2011–July 2012

surface water elevation records date back to 1973. Daily surface tions were collected at 15 min time intervals to validate Jason-2 water elevation records for Lake Eildon in Victoria date back to PISTACH products (also see Fig. 1 for location of the loggers). In- 1992. The ground tracks of Jason-1 (primary track) and Jason-2 situ Ruggedtroll 100 loggers were bolted to immovable objects cross the lake (see Fig. 1). (bridges and large trees) with Ruggedtroll 100 Baro loggers in- The Diamantina River and Cooper Creek are large waterways stalled nearby above the surface. The barometric loggers were used that flow into Lake Eyre. Lake Eyre Basin is the largest internally to correct the pressure measured by the water level loggers to ac- draining river systems in the world and covers almost one-sixth count for barometric influence on measured pressure of the water of Australia (about 1.2 million km2) in South Australia, the North- body. Ruggedtroll 100 loggers have a 9 mm accuracy under labora- ern Territory, Queensland and western New South Wales. The tory conditions and a 27 mm accuracy or better under the opera- Diamantina/Cooper system has one of the most variable flow pat- tional temperature and pressure range in field (In-Situ Inc, 2012). terns in the world, including long dry periods interspersed with significant floods (Knighton and Nanson, 2001). Flood events result 3.3. Extraction of lake water elevation from satellite altimetry data from intense monsoon lows and ex-tropical cyclones in the headwaters that cause a large and slow-moving flood pulse that moves The Basic Radar Altimetry Tool (BRAT, CNES/ESA 2012) was through the system, with high transmission losses (Knighton and used to extract surface water elevation time series. BRAT is able Nanson, 2001). Due to very low elevation gradients and the vari- to handle most radar altimetry data derived from the ERS-1 & 2 able flow regime, the rivers feature multiple channels that convey (ESA), TOPEX/Poseidon (NASA/CNES), Geosat Follow-On (US Navy), water across a wide floodplain with inundation occurring to Jason-1 (CNES/NASA), Envisat (ESA), Cryosat (ESA) and Jason-2 widths in excess of 50 km. There are just 12 gauging stations in (CNES/NASA/EUMETSAT/NOAA) satellites (Benveniste et al., 2008). the Lake Eyre Basin, with two in the Georgina Basin, two on the GDR from altimetry satellites T/P, Jason-1, GFO and Envisat over Diamantina River and eight stations along the Cooper Creek. Six Lake Argyle and Jason-2 over Lake Eildon were processed in BRAT reaches were selected where water loggers were installed along to calculate water elevation time series. The BRAT software was the Jason-2 satellite track prior to the 2011–2012 wet season used to visually check the quality of altimetry points over lakes (see Fig. 1). and exclude points from non-water or mixed water/land objects (based on the radar pulse backscattering coefficients). Surface backward-scattering coefficients of the radar pulse (sig0) were 3. Material and methods plotted geographically and a half diameter footprint of each satellite was used as a threshold to eliminate all possible land-contam- 3.1. Satellite altimetry data inated points close to shorelines. For selected points, water elevations were calculated by subtracting range (elevation of satel- Geophysical Data Record (GDR) altimetry products of Jason-1 lite above land surface) from satellite altitude. To eliminate atmo- GFO and Envisat and Merged GDR (GDR-M) products of T/P for spheric and other geophysical influences on elevation calculations, available tracks over Lake Argyle were downloaded from altimetry a series of environmental and geophysical corrections such as ion- data provider websites (NOAA, ESA and CNES-AVISO, see Table 2 ospheric correction, and dry and wet tropospheric correction and for more details). ICESat/GLAS14 Release-33 elevation data for 14 solid earth tide was applied to correct ranges (Rosmorduc, 2006). passes of ICESat from 2003 to 2009 over Lake Argyle were obtained To ensure compatibility with the in situ data, altimetry elevation from the United States National Snow and Ice Data Center (NSIDC). data were converted to the Australian Height Datum (AHD) by For Lake Eildon and the six selected river reaches, Jason-2 applying ellipsoid differences between ellipsoids used by AHD PISTACH-IGDR data were also obtained from the CNES-AVISO data and altimetry satellites and Australian geoid (AUSgeoid09, Geosci- centre. Table 2 summarises all altimetry data used in this study. ences Australia 2013). Range measurements from all available retracking algorithms 3.2. Field hydrological data were used to compute time series for each satellite and subsequently compared to in situ measurements. For example, Envisat Gauge data for Lake Argyle were provided by Water Corporation water elevation time series were produced for the ‘‘Ku’’ band using of Western Australia (Water Corporation of Western Australia, both the ‘‘Ice1’’ and ‘‘Ice2’’ retracking algorithms. 2012) and data for Lake Eildon were downloaded from Victorian To extract elevation time series from the ICESat Glas14 altime- Water Resources Data Warehouse (Victorian Government, 2011). try data, the NGAT NSIDC GLAS Altimetry elevation extractor Tool In addition, six water loggers were installed along the tracks of (NGAT) was used. NGAT was used to extract water surface eleva- the Jason-2 satellite in the Diamantina and Cooper catchments tion and geoid from 14 ICESat overpasses of Lake Argyle from GLAS for the 2011–2012 wet season. Water elevations at these six sta- altimetry products (GLA06, 12, 13, 14 and 15). These elevation data 2-4 82 A. Asadzadeh Jarihani et al. / Journal of Hydrology 505 (2013) 78–90

Fig. 2. TOPEX/Poseidon (track #62) derived water elevation over Lake Argyle and associated water level records from gauging station. were referenced to the TOPEX/Poseidon ellipsoid and EGM96 geoid tracks and includes all passes of the selected satellite track above and then converted to the AHD. The 0.7 m elevation offset between the wet part of the river. All elevation points from GDR datasets TOPEX/Poseidon and the WGS84 ellipsoid was subtracted from the inside the selected windows were selected for analysis. Quality GLAS data the EGM96 geoid was subsequently replaced with control checks were carried out to eliminate non-water targets AUSgeoid09 to convert elevation to AHD. The saturation elevation (same approach as extraction of lake water elevation time series correction (i_satElevCorr) was applied to all available points for (see Section 3.3)). Finally, all elevation points of a given pass (date) each ICESat pass over Lake Argyle. Then all selected points of each were visually checked for outliers and averaged geographically pass (date) were averaged and compared with gauge data of the within the selected polygon to construct water elevation time same date to determine the effect of saturation corrections. Gauge series. data were compared to ICESat data with and without applying the saturation correction. 3.5. Validation

3.4. Extraction of river flood water elevation from satellite altimetry Altimetry satellite water elevation time series were com- data pared with field gauge data. For each altimetry satellite, the error (mean error and RMSE) was calculated, as well as the Virtual stations were defined along the satellite altimeter correlation coefficient (R2). The advantage of RMSE is its assess- ground tracks to extract water elevation time series. A virtual sta- ment of the error magnitude and mean error is an indicator of tion is a polygon which intersects the river channel and satellite error direction (over- or under estimation of water surface

Fig. 3. GFO (track #221) derived water elevation over Lake Argyle and associated water level records from gauging station. 2-5 A. Asadzadeh Jarihani et al. / Journal of Hydrology 505 (2013) 78–90 83

Fig. 4. Water level calculated from Jason-1 1 Hz and 20 Hz altimetry ranges in ‘‘C’’ and ‘‘Ku’’ bands.

Fig. 5. Water level time series calculated from Jason-2 different retracking algorithms against Lake Eildon gauge measurements.

Table 3 Results of comparing different retracking algorithm on lake water bodies.

Target Satellite Band Retracker Mean RMSE nR2

Lake Eildon Jason2 Ku (13.6 GHz) Ice1 À0.04 0.28 m 100 0.99 Ice3 +0.01 0.32 m 100 0.99 Lake Argyle Envisat Ku (13.6 GHz) Ice1 À0.25 0.42 m 73 0.93 Ice2 +0.92 1.38 m 74 0.66 T/P Ku (13.6 GHz) Ocean 0.77 1.5 m 61 0.68 GFO Ku (13.6 GHz) Ocean À0.5 0.89 19 0.8 Jason 1 Ku (13.6 GHz) Ocean À0.06 1.12 25 0.94

elevation time series comparing with gauge data). These param- Due to the unknown zero-elevation (in AHD) of the installed eters were selected as the most appropriate statistic parameters loggers and the variation in satellite track location (±1 km), a direct which have been used in similar studies (Crétaux and Birkett, comparison of surface water height was not possible for the six se- 2006; Zhao et al., 2010). lected river reaches. Here, only the correlation coefﬁcient between

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Fig. 6. Water level time series calculated from Envisat different retracking algorithms against Lake Argyle gauge measurements.

Table 4 Lakes Argyle water elevation extracted from ICESat and gauging stations.

Date Gauge level ICESat ICESat data before ICESat data after Elevation difference before Elevation difference after (m) phase correction (m) correction (m) correction correction 17/10/2003 89.88 L2A 89.88 89.91 0.03 0.03 18/02/2004 94.11 L2B 94.11 94.03 À0.08 À0.08 5/10/2004 90.30 L3B 91.56 91.59 1.29 0.03 20/02/2005 90.50 L3C 90.71 90.68 0.18 À0.03 22/05/2005 91.51 L3D 91.51 91.48 À0.03 À0.03 22/10/2005 89.70 L3E 89.70 89.67 À0.03 À0.03 23/02/2006 93.59 L3F 93.62 93.62 0.03 0.00 25/05/2006 93.01 L3G 93.13 93.10 0.09 À0.03 26/10/2006 91.26 L3H 91.26 91.27 0.01 0.01 13/03/2007 91.11 L3I 91.14 91.12 0.01 À0.02 4/10/2007 90.57 L3K 90.97 91.00 0.43 0.03 5/10/2008 90.49 L2A 90.49 90.56 0.07 0.07 10/03/2009 93.45 L2B 93.88 93.89 0.44 0.01 2/10/2009 90.98 L2F 90.98 90.94 À0.04 À0.04

recorded water depths by loggers and altimetry-extracted data 4.1.2. GFO were calculated. The GFO satellite had 63 passes between 2000 and 2008 (with up to 17-day temporal resolution), though seven passes were found to contain inaccurate water elevation estimates as part of 4. Results our primary quality control process and were eliminated from further use. The remaining 56 altimetry-based water elevation points 4.1. Comparison of satellite altimetry derived elevation of water bodies varied between À4.5 m and 6.15 m in relation to the gauge records from the same date (mean = À0.63 m, RMSE = 2.2 m, R2 = 0.46, 4.1.1. TOPEX/Poseidon n = 56, see Fig. 3). Applying further data selection criteria (i.e., The 1 Hz and 10 Hz ranges of the T/P’s GDR-M products (cycles excluding points close to shorelines, applying threshold to back- 365–481) were calculated for Lake Argyle (Fig. 2). GDR-M 1 Hz data scattering coefficient to eliminate points with low Sigma0 from have a 6 km along track resolution so there are 2–3 points for the non-water bodies) eliminated more cycles and the 19 remaining 20 km intersection of track number 12. This coarse resolution data cycles showed an improved comparison with the lake levels produced water elevation time series with relatively low accuracy (mean = +0.5 m, RMSE = +0.89 m, R2 = 0.8, n = 19, see Fig. 3). compared to the 10 Hz time series records because of the relatively small water body size, the presence of small islands and the highly variable topography of the surrounding land (Birkett et al., 2002). 4.1.3. Jason-1 The exclusion of affected data improved the accuracy for both 1 Analysing 1 Hz ranges over Lake Argyle revealed that from cycle and 10 Hz ranges. Despite this, the remaining time series from 61 263 on 20 February 2009 to cycle 371 on 28 December 2011 only cycles of 1 Hz T/P products remained noisy (mean = 0.96 m, 14 dates (passes) in the ‘‘Ku’’ band were available, i.e., 87% of the RMSE = +3.2 m, R2 = 0.68, n = 61) compared to 10 Hz time series time altimetry points were filtered out because of the onboard (mean = 0.77 m, RMSE = +1.5 m, R2 = 0.73, n = 54, see Fig. 2). land/water filtering algorithm. Comparing the results of the 2-7 A. Asadzadeh Jarihani et al. / Journal of Hydrology 505 (2013) 78–90 85

Fig. 7. Water level time series calculated from ICESat against Lake Argyle gauge measurements.

Fig. 8. Correlation between water elevation time series derived from Jason-2 and ﬁeld based logger data records for six selected river stations. 2-8 86 A. Asadzadeh Jarihani et al. / Journal of Hydrology 505 (2013) 78–90

Table 5 (mean = 0.01 m, RMSE = 0.32 m, R2 = 0.99, n = 100, see Fig. 5 and Statistical characteristics of the regression between altimetry-extracted and ground- Table 3). Fig. 5 shows the general good agreement of Jason-2 based water levels at six selected stations along the Jason-2 satellite tracks. ‘‘Ice1’’ data and gauged water levels at Lake Eildon. Station Jason-2 No. of passes Distance to ground- R2 track no. during floods based station (km) 4.1.5. Envisat Longreach 62 9 1.5 0.90 The evaluation of Envisat RA-2 using the ‘‘Ice1’’ and ‘‘Ice2’’ Roseberth 164 15 8.3 0.98 retracking algorithms from 2002 to 2010 revealed some inconsis- Isis 62 3 0.15 0.98 tency. Once again, this required the filtering of inaccurate wave- Downs Brighton 240 11 0.13 0.96 forms from mixed land/water targets at the margins of the lake. Downs Although it has been stated by other studies (Birkett et al., 2002; Windorah 240 7 0.45 0.93 Santos da Silva et al., 2010), water bodies as small as 1 km in width Tulmur 99 4 1.2 0.98 (to have at least one nadir point on water body) can be monitored by Envisat, relatively high error budget related to these small water bodies. In this study, we found that optimal accuracy of water ele- remaining 14 cycles of the 1 Hz data with the gauge data showed vation time series (RMSE < 0.5 m) can be achieved by selecting the two cycles with distinctively high error (+47 m), which were ex- sample points 1.5 km from the lake margin. Overall, data from the cluded from further analysis. The 12 remaining cycles produced ‘‘Ice1’’ retracker produced the best statistics (mean = À0.25 m, the following statistics: mean = À0.04 m, RMSE = +1.07 m, RMSE = 0.42 m, R2 = 0.93, n = 73) than the ‘‘Ice2’’ algorithm R2 = 0.91, n = 12 (also see Fig. 4). (mean = +0.92 m, RMSE = 1.38 m, R2 = 0.66, n = 74, see Fig. 6 and A comparison of 1 Hz data and 20 Hz data showed that the Table 3). 20 Hz waveforms produced more data for Lake Argyle (68 cycles) than the 1 Hz waveforms (14 cycles). Applying restricted data 4.1.6. ICESat selection criteria as described above, eliminated 43 of the 68 cy- Water elevation time series were extracted from ICESat data for cles. The remaining higher quality data (25 cycles) produced higher Lake Argyle for 14 passes of various laser operational periods be- statistics (mean = À0.06 m, RMSE = 1.12 m, R2 = 0.94, n = 25) com- tween 2003 and 2009 (see Table 4). For all 14 passes (dates) eleva- paring to statistics of the 68 original cycles (mean = 1.0 m, tion extracted from the ICESat/GLAS products showed good RMSE = +2.08 m, R2 = 0.81, n = 68, also see Fig. 4). Fig. 4 shows most agreement with ground-based elevation records. The difference of the 1 Hz points and some of the 20 Hz points were eliminated. between gauge elevation and ICESat data varied from À0.08 m to 1.29 m (mean = 0.17 m, RMSE = 0.4 m, R2 = 0.99, n = 14) without applying saturation corrections. With saturation corrections the 4.1.4. Jason-2 elevation differences were reduced to a range from À0.08 to A comparison between the Lake Eildon gauge datum and altim- 0.07 m (mean = 0.00 m, RMSE = 0.04 m, R2 = 0.99, n = 14). Table 4 etry data (adjusted to GDA) revealed a systematic discrepancy and Fig. 7 show the ICESat altimetry satellite data and the gauge (À2.05 m), which resulted in an adjustment of the gauge data records. (R2 = 99, n = 100). ‘‘Oce3’’ and ‘‘Red3’’ retracking algorithms showed a significant data gap and large dispersion in the relatively 4.2. Satellite altimetry derived elevation of river floods small water body of Lake Eildon (<2 km wide). ‘‘Ice1’’ and ‘‘Ice3’’ retracking algorithms showed better results than ‘‘Oce3’’ and Our analysis shows ICESat, Jason-2 and Envisat data provide the ‘‘Red3’’, so, these retracking algorithms were compared with gauge most accurate measures of surface water elevation. Jason-2 data records (Fig. 5). The statistics of the two retracked ranges are have the advantage of a relatively high temporal resolution relatively similar, the ‘Ice1’’ algorithm being slightly superior (10-day return, see Table 1) and is operational. We therefore (mean = À0.04 m, RMSE = +0.28 m, R2 = 0.99, n = 100) than ‘‘Ice3’’ selected Jason-2 altimetry derived elevation data to validate Water level Jason2 (m) Water elevation Longreach station (m)

Date

Fig. 9. Water level time series calculated from Jason-2 data using the ‘‘Ice3’’ retracking algorithms compared to the ﬁeld based Longreach gauge measurements. 2-9 A. Asadzadeh Jarihani et al. / Journal of Hydrology 505 (2013) 78–90 87

Fig. 10. Top: water elevation changes across Cooper Creek extracted from four passes of Jason-2 satellite during major floods. Bottom: location of Jason-2 tracks and elevation points from four dates during the 2011–2012 floods (background: Landsat image captured on 1/12/2010). against our field dataset collected for river floods between Novem- part of the southern end of Lake Argyle (8 km by 18 km) so most of ber 2011 and May 2012. We installed water loggers using 4WD the elevation points from this small area were ‘‘contaminated’’ by vehicle and helicopter as close as possible to ground tracks of Ja- surrounding land and small islands. As a result 1 Hz (6 km along son-2. In addition, a permanent gauge site at Longreach (Station track) T/P data were not able to produce high accuracy water ele- number 003202A) was included due to its proximity to the Ja- vation time series of Lake Argyle. With a higher 580 m along track son-2 ground track. resolution, 10 Hz data yield more sample points. T/P 1 Hz products Due to the remoteness of these sites it was not possible to sur- are known to be noisy for small water bodies (Crétaux and Birkett, vey the gauge height, relative to the Australian Height Datum. As a 2006) and requiring a robust data selection process. This study result, data are reported as a plot relative to gauge height. The confirms that T/P 1 Hz data from the ‘‘Ocean’’ retracker are suitable objective of this experiment was measuring the surface water dur- for studying large terrestrial water bodies and flood processes only. ing flood events. As such, a compromise was sought between a se- The 10 Hz data with smaller footprint (10 km) can be used for cure place (attached to large, immovable objects, i.e., trees, and studying smaller water bodies where the water body is larger than bridge pylons) and the best place (lowest point in the channel) to 1km(Birkett, 1998). Using retracking algorithms, i.e., ‘‘Ice1’’ and locate the loggers. As a result some of the lower (sub-bankfull) ‘‘Ice2’’, can improve the accuracy of T/P data for inland water body flows that were evident from the altimetry data were missed by applications (Mercier and Zanife, 2006). the gauges. Correlation between recorded flows was generally strong 5.1.2. GFO 2 (R = 0.9–0.99, Fig. 8 and Table 5), particularly at high flow where GFO data showed relatively large errors in water elevation esti- water extent exceeded 1 km width and surface roughness effects mations compared to Envisat and Jason-2 derived elevation data. are less significant. Fig. 9 presents a long-term analysis at the Long- This is likely caused by elevation biases and the use of the onboard reach gauge (Station number 003202A), 15 km downstream of the waveform tracker unsuitable for continental water bodies Jason-2 satellite track. Despite the distance between the gauge (Lillibridge et al., 2006). Therefore, datum validation and retracking locations (15 km) and the effect of the local morphology, the must be applied before any comparison with other altimeters for long-term correlation of flow is strong. inland water studies. Based on the additional data processing Altimetry points from each satellite pass produced a continuous requirements and the expected low accuracy, GFO data have lim- data series across the river channel and floodplain (Fig. 10). As ited application value for monitoring of river flood elevation. such, water body elevation derived from data of a single satellite altimetry track can provide information on flow height of individ- 5.1.3. Jason-1 ual channels and depict how these vary spatially (Fig. 10) assisting Time series from the 20 Hz and 1 Hz ocean-oriented ranges of a better understanding of flow hydrodynamics in multi-channel Jason-1 data were found to be noisy for Lake Argyle. This confirms systems during floods. For example Fig. 10 contains information results for coastal and inland water applications reported in the lit- on the water surface slope, and changes in relative water elevation erature (Berry et al., 2007; Yang et al., 2008), which found the auto- and localised variability in flow characteristics at different flood mated-on-board filtering algorithms significantly reduce the stages. number of data points available at 1 Hz frequency, thus compro- mising the utility of Jason-1 data application in terrestrial hydrol- 5. Discussion ogy. The 20 Hz data are the averaged raw sample in a smaller time window (1/20 s) with approximately 300 m ground coverage com- 5.1. Comparison of satellite altimetry derived lakes water body pared to the 6 km for the 1 Hz data. As a result, more sample points elevation are available for the 20 Hz data. However, accurate inland water body elevation time series can be obtained from Jason-1 with fil- 5.1.1. TOPEX/Poseidon tering off-ranging waveforms and applying retracking algorithms T/P datasets have been used to study large water bodies such as (Berry et al., 2007). With comparing the performance of 1 Hz data the Caspian Sea (Cazenave et al., 1997), generally yielding good re- (mean = À0.04 m, RMSE = 1.07 m, R2 = 0.91, n = 12) and 20 Hz data sults for water bodies greater than 100 km2 in size (Mercier et al., (mean = À0.06 m, RMSE = 1.12 m, R2 = 0.94, n = 25) of Jason-1 with 2002; Morris and Gill, 1994; Zhang et al., 2005). While surface Envisat data (mean = 0.25 m, RMSE = 0.42 m) on Lake Argyle, con- water extent of Lake Argyle is 1500 km2 at full supply (Water firms that Jason-1 can provide moderate mapping accuracies of in- Corporation of Western Australia, 2012), T/P passes over a narrow land water bodies, though with relatively low spatial resolution 2-10 88 A. Asadzadeh Jarihani et al. / Journal of Hydrology 505 (2013) 78–90 due to the reliance on the 1 Hz sampling frequency of the Jason- (mean difference with gauged data of À0.04 m and a RMSE of 1data archive (2001–2013). 0.28 m). There is a range of legacy platforms that operated through the 5.1.4. Jason-2 ‘‘naughties’’ (2000–2010, see Table 6). These datasets offer a poten- A broader range of available retracking algorithms are available tial to map inland water elevation in data poor and ungauged for Jason-2 data, including the PISTACH products that are opti- (large) basins. This is relevant to many water resources applica- mised for hydrological applications. The ‘‘Oce3’’ and ‘‘Red3’’ tions such as digital elevation model accuracy assessment and val- retracking algorithms showed a significant data gap and large dis- idation and calibration of hydrodynamic models (Bhang et al., persion in the relatively small water body of Lake Eildon. Our eval- 2005; Braun and Fotopoulos, 2007; Carabajal and Harding, 2005, uation of the altimetry water elevation time series produced from 2006; Kon Joon et al., 2007; Yuzhuo et al., 2010). The ICESat/GLASS 20 Hz ‘‘Ice’’ and ‘‘Ice3’’ retracking algorithms showed that Jason-2 mission provides altimetric surface water elevation with the high- PISTACH products are able to produce accurate water elevation est accuracy, though with a limited coverage and for a restricted data of small water bodies such as Lake Eildon (1.8 km wide). period (2003–2009). The standard GDR products of other platforms This agrees with results reported by Birkett and Beckley (2010) (T/P, Jason-1 and GFO) were found unsuitable for terrestrial water who found that lakes and reservoirs of >100 km2 in size, or body mapping, particularly for extensive multi-channel systems. 800 m wide can be monitored by Jason-2 data. Jason-2 data of Lake Eildon outperformed GFO, Envisat, T/P and Jason-1 data of 5.3. Evaluating river floods and stage height with satellite altimetry Lake Argyle because of the hydrology-specific retracking algo- data rithms used for Jason-2 products (Mercier et al., 2008). Further, we found a comparable accuracy for the Jason-2 ‘‘Ice’’ and ‘‘Ice3’’ Field validation of satellite altimetry-derived river gauging re- retracking algorithms when applied to small terrestrial water vealed significant noise for low in-channel flows where channel bodies. width is narrow. The evaluation of Jason-2 data for multiple flood events between 2008 and 2012 further confirms its ability to pro- 5.1.5. Envisat vide elevation data for water surfaces >1 km in width. Therefore, Envisat 20 Hz data provided accurate water elevation time ser- we suggest a minimum of 1 km water extent as a guide to use Ja- ies for inland water bodies (RMSE of 0.42 m for the’’Ice1’’ and RMSE son-2 altimetry data with higher levels of certainty for hydrologi- of 1.38 m for the ‘‘Ice2’’ algorithm). This is in agreement with re- cal applications. This corresponds with results reported by other sults reported by Santos da Silva et al. (2010) who found that the studies such as (Birkett and Beckley, 2010), who indicated that quality of the water elevation time series from Envisat varies from 800 m is the minimum river reach which can be monitored with 12 cm to several meters, with values around 40 cm found in most data from the Jason-2 satellite. The observed variation in water ele- cases. The 35 days frequency of Envisat means the temporal reso- vation between the sites and gauge data partly reflects the varia- lution is relatively low, which makes it suitable for monitoring tion in channel morphology between the location of satellite on a monthly to seasonal time scale (Benveniste et al., 2007). The tracks and the gauging station. This study also illustrates the capa- width between ground tracks, however, is comparatively small bility of satellite altimeters to monitor complex two-dimensional (80 km at the equator), thus providing relatively high spatial cov- hydrodynamic behaviour (i.e., water elevation difference in multi- erage. Envisat data are available from 2002 until April 2012 when ple channels as well as water surface slope across the river) in mul- the satellite ceased operations. ti-channel systems (see Fig. 10). This presents a new and as yet unutilised application and capability of data from altimetry satel- 5.1.6. ICESat lites to help understanding of the hydrodynamic behaviour in ICESat derived data produced the highest accuracy of water data-poor large rivers. body elevation for Lake Argyle (mean = 0.0 m, RMSE = 0.04 m, n = 14). The high accuracy of the ICESat data is most likely due to 6. Conclusions the relatively small footprint (50–90 m) and high along-track resolution (40 Hz, 170 m) in relation to the size of the lake. Baghdadi The capability of satellite altimeters, past and present, to study et al. (2011) applied ICESat data to French rivers and found that the inland water surface elevation was evaluated. We show a number RMSE of water elevation measurements was 1.14 m for small riv- of satellite altimeters in combination with specific retracking algo- ers and better than 0.15 m for rivers wider than 1.5 km. This high- rithms are able to monitor surface water level changes, where pro- lights the potential for ICESat to provide elevation data for lakes, vided water bodies are sufficiently large and wide and floods are wetlands and large rivers during its operational period from slow moving. As such, satellite altimetry data has great potential 2003 to 2009. for hydrology studies of remote, ungauged, poorly-gauged catchments. 5.2. Evaluating lake water elevation with satellite altimetry data This research confirms that the more recent altimetry missions such as Envisat and Jason-2 provide retracking algorithms, other Altimetry data using the ocean-oriented retrackering algo- than the classical ocean-oriented trackers (i.e., ‘‘Ocean’’), which rithms from T/P and Jason-1 were not able to produce accurate water elevation time series. In addition, there was a significant loss of information from T/P and Jason-1 data due to the non-ocean Table 6 waveform filtering by GDR. Envisat and Jason-2 PISTACH products Summary of the performance of the various altimetry platforms. revealed that the ‘‘Ice’’ retracking of their ‘‘Ku’’ band produced Satellite Operational Best performing band/ Mean RMSE R2 accurate water elevation time series. Jason-2 is the only presently period algorithm combination operational altimetry satellite to provide inland water elevation, T/P 1992–2006 Ocean 0.77 1.5 0.68 potentially near-real time. Overall, the results for the Envisat plat- Jason-1 2001–2013 Ku À0.06 1.12 0.94 form suggest that the ‘‘Ku’’ band with the ‘‘Ice1’’ algorithm outper- Jason-2 2008-Current Ku/Ice1 À0.04 0.28 0.99 formed the ‘‘Ice2’’ retracking algorithm, (mean absolute error of Envisat 2002–2012 Ku/Ice1 À0.25 0.42 0.93 GFO 1998–2008 – 0.5 0.89 0.80 À0.25 m and a RMSE of 0.42 m). Jason-2 results showed that the À ICESat 2003–2009 GLA14 0.01 0.04 0.99 ‘‘Ice1’’ algorithm marginally outperformed the other retrackers 2-11 A. Asadzadeh Jarihani et al. / Journal of Hydrology 505 (2013) 78–90 89 are better suited for terrestrial water bodies. For example, PISTACH Chipman, J.W., Lillesand, T.M., 2007. Satellite-based assessment of the dynamics of products as part of the Jason-2 project, offers a superior data to new lakes in southern Egypt. International Journal of Remote Sensing 28 (GEOBASE), 4365–4379. study terrestrial hydrology. It further offers the potential to study Chu, Y.H. et al., 2008. Monitoring level fluctuations of the lakes in the Yangtze River wetland and flood pulses in near-real time at so-called ‘‘virtual basin from radar altimetry. Terrestrial Atmospheric and Oceanic Sciences 19 (1– gauging stations’’ in areas where telemetry is lacking. PISTACH 2), 63–70. CNES, 2004. Ssalto products specifications, vol. 1. Jason-1 User Products. product also include several improvements (i.e., correction param- Coe, M.T., Birkett, C.M., 2004. Calculation of river discharge and prediction of lake eters) to account for environmental and geophysical factors which height from satellite radar altimetry: example for the Lake Chad basin. Water result in more accurate data (Mercier et al., 2008). Resources Research 40 (10), W102051–W1020511. Crétaux, J.-F., Birkett, C., 2006. Lake studies from satellite radar altimetry. Comptes This study forms part of a growing body of research that high- Rendus Geosciences 338 (14–15), 1098–1112. lights the added value of remote sensing to conventional hydrol- Frappart, F., Seyler, F., Martinez, J.M., Leon, J.G., Cazenave, A., 2005. Floodplain water ogy, particularly in data poor, remote regions. As such, satellite storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels. Remote Sensing of Environment 99 (4), 387– altimetry may help improve our understanding of inland water 399. and river system processes, and related issues of ecosystem health Frappart, F. et al., 2006. Water volume change in the lower Mekong from satellite and water availability, in many remote regions around the world. altimetry and imagery data. Geophysical Journal International 167, 570–584. Hall, A.C., Schumann, G.J.P., Bamber, J.L., Bates, P.D., Trigg, M.A., 2012. Geodetic corrections to Amazon River water level gauges using ICESat altimetry. Water Resources Research 48 (6), n/a–n/a. Acknowledgments Hannah, D.M. et al., 2011. Large-scale river flow archives: importance, current status and future needs. Hydrological Processes 25 (7), 1191–1200. We kindly thank the generous and friendly assistance from Hwang, C., Kao, Y.-C., Tangdamrongsub, N., 2011. A preliminary analysis of lake level and water storage changes over lakes Baikal and Balkhash from satellite present landholders and managers of Isis Downs, Tulmur, Verdun altimetry and gravimetry. Terrestrial, Atmospheric and Oceanic Sciences 22 Valley, Brighton Downs, Davenport Downs, Monkira, Roseberth (GEOBASE), 97–108. and Keeroongooloo Stations for access to field sites. In-Situ Inc, 2012. Rugged TROLLÒ 100 and 200 Instruments. Available online: , (accessed 01.13). The lead author was supported by an Integrated Natural Re- Knighton, A.D., Nanson, G.C., 2001. An event-based approach to the hydrology of source Management (INRM) scholarship from The University of arid zone rivers in the Channel Country of Australia. Journal of Hydrology 254 Queensland (UQ) and the Commonwealth Scientific and Industrial (1–4), 102–123. Research Organisation (CSIRO), an Australian Postgraduate Award Kon Joon, B., Schwartz, F.W., Braun, A., 2007. Verification of the vertical error in C- band SRTM DEM using ICESat and Landsat-7, Otter Tail County, MN. IEEE and funds from the School of Geography, Planning and Environ- Transactions on Geoscience and Remote Sensing 45, 36–44. mental Management (UQ). Lee, H. et al., 2009. Louisiana wetland water level monitoring using retracked Data were supplied to this project by NSIDC for ICESat, ERS and TOPEX/Poseidon altimetry. Marine Geodesy 32 (3), 284–302. Lillibridge, J., Calmant, S., Lellouch, G., 2006. Geosat follow-on waveforms: Envisat by ESA, TOPEX/Poseidon and Jason-1, 2 by CNES AVISO and retracking for hydrology applications. In: 15 Years of Progress in Radar gauge data, by DERM, BoM, Water Corporation of Western Austra- Altimetry, Venice, Italy. lia (Lake Argyle) and Victorian Water Resources Data Warehouse Mercier, F., Zanife, O.-Z., 2006. Improvement of the Topex/Poseidon altimetric data processing for hydrological purposes (cash project). In: Symposium on 15 years (Lake Eildon). of Progress in Radar Altimetry, March 13, 2006–March 18, 2006. European Space Agency, (Special Publication). European Space Agency, Center National d’Etudes Spatiales, Venice, Italy. References Mercier, F., Cazenave, A., Maheu, C., 2002. Interannual lake level fluctuations (1993– 1999) in Africa from TOPEX/POSEIDON; connections with ocean-atmosphere interactions over the Indian Ocean. Global and Planetary Change 32 (2–3), 141– AVISO/Altimetry, 1996. AVISO User Handbook for Merged TOPEX/Poseidon 163. Products, third ed. AVI-NT-02-101. Mercier, F., Rosmorduc, V., Carrere, L., Thibaut, P., 2008. Recent Improvements in the Baghdadi, N., Lemarquand, N., Abdallah, H., Bailly, J.S., 2011. The relevance of GLAS/ processing of Jason-2 altimetry products for Continental Waters (PISTACH ICESat elevation data for the monitoring of river networks. Remote Sensing 3 Project), OSTST Nice, November 2008. (4), 708–720. Mercier, F., Rosmorduc, V., Carrere, L., Thibaut, P., 2010. Coastal and Hydrology Benveniste, J., et al., 2007. River and lake level data from Radar Altimetry in support Altimetry Product (PISTACH) Handbook, CLS-DOS-NT-10-246. of the Tiger initiative. In: Hydrospace07 Workshop, 12–14th November, 2007, Morris, C.S., Gill, S.K., 1994. Evaluation of the TOPEX/POSEIDON altimeter system Geneva, Switzerland. over the Great Lakes. Journal of Geophysical Research 99, 24527–24539. Benveniste, J., Rosmorduc, V., Niemeijer, S., Picot, N., 2008. In: Basic Radar Altimetry OSCAR, 2013. Observation des Surfaces Continentales par Altimétrie Radar. Toolbox, Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE Pan, F., Nichols, J., 2012. Remote sensing of river stage using the cross sectional International, pp. III – 895–III – 898. inundation area – river stage relationship (IARSR) constructed from digital Berry, P.A.M., Freeman, J., Smith, R.G., 2007. Global analysis of Jason-1 over Inland elevation model data. Hydrological Processes, n/a–n/a. water. In: 2nd Space for Hydrology Workshop, Switzerland. Papa, F., Prigent, C., Rossow, W.B., Legresy, B., Remy, F., 2006. Inundated wetland Bhang, K.J., Schwartz, F.W., Allen, G.R., 2005. Assessment and reduction of the dynamics over boreal regions from remote sensing: the use of Topex–Poseidon vertical error in SRTM DEMs using ICESat. Abstracts with Programs – Geological dual frequency radar altimeter observations. International Journal of Remote Society of America 37 (7), 108. Sensing 27 (GEOBASE), 4847–4866. Birkett, C.M., 1998. Contribution of the TOPEX NASA Radar Altimeter to the global Papa, F., Durand, F., Rossow, W.B., Rahman, A., Bala, S.K., 2010. Satellite altimeter- monitoring of large rivers and wetlands. Water Resources Research 34 (5), derived monthly discharge of the Ganga–Brahmaputra River and its seasonal to 1223–1239. interannual variations from 1993 to 2008. 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Remote Sensing of Environment monument elevations in Canada. Photogrammetric Engineering and Remote 114 (10), 2160–2181. Sensing 73 (Compendex), 1333–1342. Troitskaya, Y.I. et al., 2012. Satellite altimetry of inland water bodies. Water Calmant, S., Seyler, F., 2006. Continental surface waters from satellite altimetry. Resources 39 (2), 184–199. Comptes Rendus – Geoscience 338 (GEOBASE), 1113–1122. Victorian Government, 2011. Victorian Water Resources Data Warehouse. , (accessed 07.12). elevation models. Geophysical Research Letters 32, 5. Water Corporation of Western Australia, 2012. Water Corporation of Western Carabajal, C.C., Harding, D.J., 2006. SRTM C-band and ICESat laser altimetry Australia webpage. . elevation comparisons as a function of tree cover and relief. Photogrammetric Yang, L., Lin, M., Bai, Y., Pan, D., 2008. Retracking Jason1 altimeter waveform over Engineering and Remote Sensing 72 (Compendex), 287–298. China coastal zone, 71540K–71540K. CASH, 2013. 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2-13 Chapter 3 Blending Landsat and MODIS data to generate multispectral indices: A comparison of ‘Index-then-Blend’ and ‘Blend-then-Index’ approaches

Remote Sens. 2014, 6, 9213-9238; doi:10.3390/rs6109213 OPEN ACCESS remote sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Article Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches

Abdollah A. Jarihani 1,*, Tim R. McVicar 2, Thomas G. Van Niel 3, Irina V. Emelyanova 3, John N. Callow 4 and Kasper Johansen 1

1 School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia; E-Mail: [email protected] 2 CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia; E-Mail: [email protected] 3 CSIRO Land and Water, Private Bag No. 5, Wembley, WA 6913, Australia; E-Mails: [email protected] (T.G.V.N.); [email protected] (I.V.E.) 4 School of Earth and Environment, University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia; E-Mail: [email protected]

* Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +61-07-3365-7027; Fax: +61-07-3365-6899.

External Editors: Nicolas Baghdadi, Prasad S. Thenkabail

Received: 12 August 2014; in revised form: 11 September 2014 / Accepted: 11 September 2014 / Published: 26 September 2014

Abstract: The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) “Index-then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R2; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for

3-1 Remote Sens. 2014, 6 9214

all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm.

Keywords: data fusion; blending; STARFM; ESTARFM; multispectral indices

1. Introduction

Trade-offs between acquisition frequency and spatial resolutions of satellite image data are inherent in all single-sensor satellites [1]. In the last decade, several advanced blending algorithms have been developed to combine data observed from multiple sensors with various spatial resolutions and temporal densities (e.g., Landsat, Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR)). Gao et al. [2] developed the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to blend surface reflectance from two sensors to simulate more frequent higher spatial resolution surface reflectance output (e.g., Landsat-like imagery at the frequency of MODIS acquisition). Zhu et al. [3] enhanced the STARFM algorithm (denoted ESTARFM) to improve the model spatial variability of heterogeneous study sites. Both algorithms are widely used by the remote sensing community ([1] their Table 3). The objective of much blending research is to simulate reflectance data from which multispectral indices can be calculated, such as vegetation and water indices at a high spatial resolution and temporal density [4–8]. Vegetation indices are widely used to effectively characterize particular biophysical or biochemical properties and processes for vegetated surfaces [9]. The Normalized Difference Vegetation Index (NDVI; [10]) and the Enhanced Vegetation Index (EVI; [9]), are the most common satellite-derived indices used by the remote sensing community for monitoring vegetation at regional to global scale for numerous applications [11–14]. The Simple Ratio (SR) is best used for estimating Leaf Area Index (LAI; [15]). There are also several environmental moisture indices that are commonly used, including: (i) Global Vegetation Moisture Index (GVMI; [16]); and (ii) Depth of 1650 nm relative to a reference continuum line determined at 835 nm and 2208 nm (D1650; [17]). Water indices, such as the Normalized Difference Water Index (NDWI) are also widely used to delineate open water features and enhance their presence in satellite images. The NDWI [18] has been used by researchers in its original and modified forms (Table 1). An example includes the modified version of NDWI (MNDWI of [19]), which uses the Shortwave-Infrared (SWIR1) band (i.e., Landsat TM band 5) in place of the Near-Infrared (NIR) band (i.e., Landsat TM band 4). The selected nine indices are the most widely used subset of remotely sensed indices from the: (i) vegetation domain; (ii) environmental moisture domain; and (iii) water domain.

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Table 1. List of nine indices and their equations used here. NIR, SWIR1 and SWIR2 are abbreviations for Near-Infrared, Shortwave-Infrared1 and Shortwave-Infrared2 bands, respectively. Except for Enhanced Vegetation Index (EVI), Simple Ratio (SR) and Depth

of 1650 nm (D1650), all indices use a normalized difference formulation generically given as (band x − band y)/(band x + band y), where x and y represent bands.

Index # Bands Theoretical Bands Used Equation Name Used Range NDVI Red, NIR 2 [−1.0, +1.0] (NIR – Red)/(NIR + Red) EVI Blue, Red, NIR 3 [0.0, +1.0] 2.5 × (NIR – Red)/(NIR + 6 × Red – 7.5 × Blue + 1) SR Red, NIR 2 [0.0,→∞] NIR/Red GVMI NIR, SWIR1 2 [−0.82, 0.96] ((NIR + 0.1) – (SWIR1 + 0.02))/((NIR + 0.1) + (SWIR1 + 0.02)) NIR, SWIR1, SWIR1 1 – ( ) D1650 3 [→−∞, +1.0] NIR × (1 – 0.59359) + 0.59359 × SWIR2 SWIR2

NDWI24 Green, NIR 2 [−1.0, +1.0] (Green – NIR)/(Green + NIR)

NDWI25 Green, SWIR1 2 [−1.0, +1.0] (Green – SWIR1)/(Green + SWIR1)

NDWI27 Green, SWIR2 2 [−1.0, +1.0] (Green – SWIR2)/(Green + SWIR2)

NDWI45 NIR, SWIR1 2 [−1.0, +1.0] (NIR – SWIR1)/(NIR + SWIR1)

Many recent studies using STARFM first blend reflectance from Landsat and MODIS data and then use these outputs to calculate vegetation or water indices (e.g., [4,6–8,20,21]). Herein, we refer to this process as Blend-then-Index (BI), with the alternative approach being Index-then-Blend (IB). For the IB approach, the indices were calculated first and these indices were input into the blending algorithms to simulate indices at the date of simulation. For the BI approach, reflectance bands were input into the blending algorithms to simulate reflectance, which was used as input to calculate multispectral indices. In the IB approach we assume that a linear mixture model is applicable to indices (i.e., the mixed index for each MODIS pixel is the sum of the index weighted by the class area proportions), as it is for the reflectance bands. According to Kerdiles and Grondona [22] this assumption introduces very small errors to statistics when using indices directly into a linear mixture model (i.e., IB) instead of using individual band reflectance data in the model (i.e., BI). In the only previous study to compare IB with BI, Tian et al. [23] evaluated the accuracy of STARFM for simulating a time series of 12 NDVI images over a single study site. Our paper extends that study by: (i) using both the STARFM and ESTARFM algorithms; (ii) using three sites with contrasting spatial and temporal dynamics; (iii) calculating nine commonly used indices in vegetation, environmental moisture and standing water applications; and (iv) partitioning the spatial and temporal variances to explain the results. The aims of our paper are to: (i) comprehensively examine if one of the approaches (i.e., IB versus BI) consistently outperforms the other for a range of vegetation, environmental moisture and standing water indices; (ii) explore whether spatial and temporal variances are related to the blending accuracy of indices by the two algorithms (i.e., STARFM versus ESTARFM); and (iii) isolate the impact that the approach or algorithm has on blending accuracy (i.e., approach versus algorithm). These three aims provide the structure of our paper and are used as subheadings in the Methods, Results and Discussion sections.

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2. Materials

2.1. Study Site and Data Sets

Three study sites with different relative spatial and temporal variances and different land cover patch sizes were selected in this study (Figure 1); they are introduced in turn. The Thomson River floodplain study site (Thomson herein) is an extensive anabranching river system located in central Queensland, Australia (143.20°E, 24.50°S, see Figure 1). The Thomson study site covers 3850 km2 (55 km E–W × 70 km N–S) within a Landsat-5 TM scene (path 96, row 77). The Thomson site is located in the Lake Eyre Basin in a region called the “Channel Country”, which is characterized by extensive floodplains and a complex anabranching river system with ephemeral flows following precipitation [24,25]. It has a low topographic gradient and dynamic land cover in watercourses and floodplains [25,26]. Its Köppen-Geiger climate is in the arid (B), steppe (S) and hot (h) zone, with a mean annual temperature greater than 18 °C [27]. The land use in the Lake Eyre Basin is dominated by grazing. Mitchell Grass plains, sand dunes, spinifex grasslands, gibber deserts, stony plains and acacia woodlands are landscapes of the Cooper Creek catchment [28]. The flooding is the only water source for flora and fauna of the area, and there is a distinct greening up following the passage of floodwaters. The Coleambally Irrigation Area study site (Coleambally from herein) is a rice based irrigation system located in southern New South Wales (NSW, Australia; 34.0034°E, 145.0675°S). Standing water associated with flood irrigation of summer rice fields is present in October and November [17,29,30]. Summer crop development (i.e., rice, soybeans, corn and sorghum—the last three crops being furrow irrigated) occurs from December to April, with many crops harvested by May. The surrounding dryland agricultural areas mainly have a winter growing season (cereals and pasture), and several small residual woodland patches in the northern part of the images are fairly constant throughout the time series. The Lower Gwydir Catchment study site (Gwydir from herein) is located in northern NSW (149.2815°E, 29.0855°S). The temporal extent of data over the Gwydir was greater than one year, and included a winter and a summer crop-growing season. The Gwydir, which covers the typical dual growing season crop phenology and surrounding dryland agricultural area, experienced a large flood in mid-December 2004. This flooding, and subsequent inundation, occupied a large spatial extent of the Gwydir imagery and was temporally very dynamic (Figure 1). The Gwydir site is spatially more homogenous than the Coleambally and Thomson sites, because of the larger agricultural fields, coupled with the large (and quick) flooding event. The flooding/inundation at Gwydir was a significant test of the blending algorithms in conditions with extremely high spatio-temporal variability. For Thomson, 20 pairs of cloud-free Landsat-MODIS (L–M) images were used from April 2008–October 2011 (Table 2). This period was characterized by an intense La Niña, and major flooding occurred over much of eastern and central Australia in 2009 and 2010, during which large areas were covered with standing water. The most likely time to observe standing water at this site is during the wet season, from November–April. Between 2008 and 2011 most of the Landsat and/or MODIS images were cloudy during January and February. After selecting all cloud-free inundated images during the wet season, the other images were selected to be as close as possible to when the inundated images were acquired (Table 2). The Coleambally site images were Landsat 7. The Thomson and Gwydir sites images were Landsat 5 data and were corrected for Bidirectional Reflectance

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Distribution Function (BRDF) effects. The Gwydir images were corrected using the Li et al. [31] BRDF algorithm. The Thomson images were corrected to at-surface reflectance using the Flood et al. [32] BRDF algorithm (also taking into account atmospheric conditions, topography, sensor location and sun elevation) using the parameterized bi-directional reflectance model for eastern Australia [32]. All MODIS data were BRDF corrected Terra MODIS Collection 5, daily reflectance (MOD09GA) images with 500 m pixels for all bands [33]. MODIS data, which were originally processed by the Land Processes Distributed Active Archive Center (LPDAAC) at the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS), were obtained from The Commonwealth Scientific and Industrial Research Organization (CSIRO) Marine and Atmospheric Research Division.

Figure 1. The study sites: (a) location of three study sites in Australia; (b–d) are Landsat images of the study sites with Bands 5, 4 and 3 shown as RGB composite on dates 2 January 2011, 8 October 2001 and 12 December 2004 for Thomson, Coleambally and Gwydir, respectively. A standard deviation of 1.5 was used to stretch the RGB Landsat images. All the extreme values of the histograms, falling outside the 1.5 standard deviation, were trimmed out, and remaining values were redistributed between 0–255 to enhance the images.

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Table 2. Dates of cloud-free Landsat-5 and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2008–2011 for Thomson. The bold row indicates the image captured during major flood event (Image # 11, see Figure 1b).

Image # Date Day since Start of Dataset (15 April 2008) MODIS Sensor Zenith (Degree) 1 15 April 2008 0 13.26 2 22 September 2008 160 13.29 3 24 October 2008 192 13.51 4 17 March 2009 336 13.62 5 2 April 2009 352 13.42 6 24 August 2009 496 13.37 7 9 September 2009 512 13.28 8 25 September 2009 528 13.39 9 11 October 2009 544 13.57 10 12 September 2010 880 13.27 11 2 January 2011 992 12.85 12 19 February 2011 1040 12.86 13 8 April 2011 1088 12.86 14 10 May 2011 1120 13.43 15 13 July 2011 1184 13.65 16 29 July 2011 1200 13.74 17 14 August 2011 1216 13.86 18 30 August 2011 1232 13.90 19 15 September 2011 1248 13.19 20 1 October 2011 1264 13.25

For detailed information about the other two study sites, Coleambally and Gwydir, see [1]. Briefly, 17 L–M image pairs acquired over eight months for Coleambally and 14 L–M image pairs acquired over 12 months at Gwydir were used. Partitioning the variance into its spatial and temporal components [1] showed that Coleambally reflectance data had higher spatial variance (than temporal variance) and more accurate results were obtained with ESTARFM due to its design. In contrast, at Gwydir temporal variance dominated spatial variance and due to algorithmic assumptions STARFM worked best. Finally, Coleambally has a smaller effective patch size than Gwydir [1]. The three study sites have different relative spatial and temporal variances and different patch sizes, governing the area-to-perimeter ratio within the different resolution imagery used in the blending algorithms. These three sites are purposefully selected to form a continuum between solely man-made standing water and entirely natural standing water: (i) at Coleambally all standing water is man-made (due to irrigation); (ii) at Gwydir both irrigated fields and standing water associated with flooding are present, and (iii) at Thomson standing water is only associated with flooding. The dynamics and area-to-perimeter ratios of standing water (and associated responses) varied across the three sites, and these three sites are therefore a robust selection from which to evaluate performance within and between both blending algorithms across the IB and BI approaches.

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2.2. Data Pre-Processing

A Landsat–like image was generated on a given simulation date by using a total of five input images, being two L–M pairs (one before and one after the simulation date) and the MODIS image on the simulation date as input to either STARFM or ESTARFM [1]. The observed Landsat image on the date of simulation was preserved for validation and was not used as input to the blending algorithms.

Herein, the date of simulation is denoted t2, the first L–M pair date will be referred to as t1 and the date of the second L–M pair is indicated as t3. For example, at Thomson, L–M pairs from 15 April 2008 (t1) and 24 October 2008 (t3) and the MODIS image on 22 September 2008 (t2) were used to create a

Landsat-like image on 22 September 2008 (t2), see Table 2. At Thomson, a total of 18 Landsat-like images were simulated in this manner using the nearest temporal neighboring L–M pairs to the central dates listed in Table 2 as image #’s 2–19, herein referred to as “3-sequential date images”. Observed MODIS images were resampled to Landsat resolution using the nearest neighbor approach and the five Landsat or MODIS images involved in any given blending operation were then co-registered based on a correlation test [1]. The co-registration results of Terra MODIS images in this study confirm the along-track and along-scan band-to-band co-registration error in Terra MODIS bands [34]. The optimal spatial offset to maximize the correlation between corresponding bands of L–M images was calculated by using the IDL (Exelis Visual Information Solutions, Boulder, Colorado) code developed by NASA [35] and applied to MODIS images. We used the six reflective Landsat bands and the corresponding MODIS bands for the blending algorithms (Table 3). All nine indices (i.e., NDVI, EVI, SR, GVMI, D1650, NDWI24, NDWI25, NDWI27 and NDWI45, see Table 1) were calculated for all Landsat and MODIS images at each site. All simulated indices (from both approaches) were compared with the observed Landsat indices at date t2. Then all nine indices were calculated and compared with indices calculated from the observed Landsat images at date t2.

Table 3. Bands and band-widths of Landsat TM and corresponding MODIS bands.

Landsat TM a Band/Band Landsat TM Band-Width MODIS Band/Band MODIS Band-Width Name (nm) Name (nm) Band 1/Blue 450–520 Band 3/Blue 459–479 Band 2/Green 520–600 Band 4/Green 545–565 Band 3/Red 630–690 Band 1/Red 620–670 Band 4/NIR 760–900 Band 2/NIR 841–876 Band 5/SWIR1 1550–1750 Band 6/SWIR1 1628–1652 Band 7/SWIR2 2080–2350 Band 7/SWIR2 2105–2155 a The ETM+ band-widths are slightly different from TM band-widths. The ETM+ characteristic are reported in Chandler et al. ([36], Table 4).

2.3. Blending Algorithms

STARFM and ESTARFM assume that images from different sensors are acquired under similar land surface conditions and that surface reflectance is comparable after pre-processing [3]. The

STARFM algorithm can use either one pair or two pairs of L–M images (i.e., dates t1 and/or t3) and

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one low resolution image (date t2) to simulate a high resolution image at date t2, while the ESTARFM algorithm uses two pairs of L–M images (i.e., dates t1 and t3) and one low resolution image (date t2) to simulate date t2. For STARFM we use the two L–M image pair option to have consistent input with ESTARFM. The algorithms identify spatial changes of reflectance from the high spatial resolution images by finding spectrally similar neighbor pixels and temporal changes from the low-resolution images to simulate the high spatial resolution and high temporal density images at selected dates. A moving search window (w) is used to select similar neighboring pixels, and heterogeneity of the landscape is considered by the number of land cover classes in each pixel of the low resolution image [3]. The algorithms use weight factors for each spectrally similar pixel to blend temporal and spatial information. Proximal to the central pixel and spectrally similar fine resolution pixels have higher weights [3]. To make the result comparable with former studies, here the size of w was 50 by 50 Landsat resolution pixels and the assumed number of spectrally-different classes was four. STARFM is able to model non-linear changes between two Landsat images and would be expected to model temporal variability better than ESTARFM. In contrast, ESTARFM has been designed to work better in more spatially heterogeneous areas [1,2].

3. Methods

3.1. IB versus BI

The accuracy of the STARFM and ESTARFM algorithms for simulating all nine Landsat-like indices was assessed by comparing the bias (calculated as observed minus simulated), correlation coefficient of determination (R2) and Root Mean Square Deviation (RMSD) between the simulated and observed Landsat indices, using both the IB and BI approaches. Additionally, the results from the IB and BI approaches for both blending algorithms were examined by comparing temporally mean bias, R2 and RMSD across the entire blended dataset for each site (18 dates for Thomson, 15 dates for Coleambally and 12 dates for Gwydir; there are two less instances than the number of images available at each site due to using the blending algorithms with L–M pairs before and after each simulation date). The paired t-test was used to assess if the difference of the mean error between the IB and BI approaches was statistically significant. Mean error between IB and BI for each “3-sequential date image” was paired. The assessment was performed for both the STARFM and ESTARFM algorithms, for each of the three sites, and for each of the three above-mentioned error statistics at the 90% (i.e., p < 0.1) and 95% (i.e., p < 0.05) confidence levels. For example using STARFM at Thomson, the mean bias of each of the 18 simulated images generated using IB were paired to the corresponding 18 mean biases generated using BI to test whether the biases between IB and BI were statistically significant. This example was extended to all combinations of error statistics, sites and algorithms.

3.2. STARFM versus ESTARFM

Quantification of spatial and temporal variances of image and index time series, given the strengths and weaknesses of each algorithm, is an important step toward selecting blending algorithms [1]. Here we used the same method ([37], their Equation 10) to partition the grand (or spatio-temporal) variance

3-8 Remote Sens. 2014, 6 9221 into the spatial and temporal variance components and assessed the suitability of STARFM and ESTARFM. Following [1], we calculated spatial and temporal variances of each possible combination of 3-sequential dates of the high and low resolution images. The temporal to spatial variances ratio (T/S), as an indicator of algorithm selection, was also calculated for each 3-sequential dates of L–M bands and indices. This was performed for all six reflective bands and nine indices of L–M images at Thomson. Since the temporal and spatial variances were already reported for Coleambally and Gwydir for the bands [1], here we only report the indices’ variances for these two sites. The paired t-test was used to assess if the difference of the mean error between the STARFM and ESTARFM algorithms was statistically significant, using the general technique as previously explained.

3.3. Approach versus Algorithm

To compare and quantify the impact of the IB versus BI approaches on the accuracy of STARFM versus ESTARFM, R2 and RMSD statistics were calculated for four parameters (i.e.,

(STARFM-ESTARFM)IB, (STARFM-ESTARFM)BI, (IB-BI)STARFM and (IB-BI)ESTARFM). To quantify STARFM versus ESTARFM for the IB approach, differences between STARFM and ESTARFM statistics; (STARFM–ESTARFM)IB; were calculated and averaged for all 405 simulations (nine indices by 45 dates—the total from the three sites). For the BI approach, a similar parameter; 2 (STARFM-ESTARFM)BI; was also calculated. To quantify IB versus BI, averaged difference R and RMSD statistics were calculated across all 405 simulations (as above) using the STARFM algorithm;

(IB-BI)STARFM; and ESTARFM algorithm; (IB-BI)ESTARFM.

4. Results

4.1. IB versus BI

The statistics (Table 4) showed that for all nine indices examined, the IB approach outperformed the BI approach at all three sites. The paired t-test analysis showed that the means of the three abovementioned error statistics produced for the three sites of “3-sequential date images” for the two approaches were statistically different at the 95% confidence interval in 65% of the STARFM and 53% of the ESTARFM cases (Table 5). The higher accuracy of the IB approach is most likely explained by error propagation, as the IB approach only incurs one instance of blending so there is only one process where blending-induced error can be introduced. In contrast, the BI approach incurs multiple blending instances and therefore multiple instances of error that subsequently propagates to the resultant indices.

Moreover, for those indices having a normalized difference formulation (i.e., NDVI, GVMI, NDWI24,

NDWI25, NDWI27 and NDWI45, see Table 1) their algebra reduces error. At the Thomson site, comparing the R2 of the blended indices with those calculated from observed Landsat data revealed that the IB approach resulted in higher accuracy than the BI approach in 89% of 162 (9 indices by 18 dates) STARFM simulations and 75% of ESTARFM simulations (Figure 2). The three averaged error statistics for the 18 STARFM and ESTARFM indices were statistically different at the 90% confidence level (bias; 11% of the cases, R2; 61% of the cases and RMSD; 61% of the cases, Table 5). The sign of the average bias produced by STARFM overestimated (negatively-biased) 32% of 162 simulations and ESTARFM overestimated 51% of all simulations by the IB approach.

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Comparing the nine indices showed that site-averaged simulated NDVI and EVI produced lowest bias in all four options compared with other indices due to use of red and infrared bands. ESTARFM results overestimated NDVI, EVI and GVMI and underestimated SR, D1650, NDWI24, NDWI25, NDWI27 and

NDWI45 (Table 4). Statistics derived using the BI approach have higher spatial variances during the wet season (December–March) of each year; especially during the major flood event on date 2 January 2011 (date 992). As was found for the IB approach, most of the 162 BI simulations were underestimated by STARFM (62%) and ESTARFM (53%). The NDVI and EVI values were overestimated by STARFM for the BI approach, while the NDVI, EVI and GVMI indices were overestimated by ESTARFM at Thomson (Table 4). At Coleambally, from all 135 (nine indices by 15 dates) simulations, 90% produced higher accuracy by the IB approach when using STARFM and 98% when using ESTARFM (Figure 2). Using the paired t-test the IB and BI approaches were statistically different when comparing means of statistics (bias; for 56% of the cases, R2; for 83% of the cases and RMSD for 78% of the cases) at the 90% confidence level (Table 5). When using IB the site-averaged bias in ESTARFM was lower compared with STARFM as shown for BI approach (Figure 2). The STARFM algorithm overestimated NDWI24 and underestimated all other indices when using IB, while NDVI and EVI indices were overestimated by both STARFM and ESTARFM algorithms approach and other seven indices were underestimated by using BI. From all 135 simulations, 27% and 50% are overestimated when using STARFM and ESTARFM, respectively, using the IB approach. By using BI, STARFM overestimated 36% and ESTARFM overestimated 48% of the simulations in Coleambally. At Gwydir, the IB approach outperformed the BI by producing higher R2 in 90% of all 108 (nine indices by 12 dates) simulations when using STARFM, and 100% of all simulations when using ESTARFM (Figure 2). The IB and BI approaches were statistically different at the 90% confidence level when comparing means of statistics (bias; 18%, R2; 89%, RMSD; 78%; see Table 5). STARFM underestimated 71% of 108 simulations and ESTARFM underestimated 57% of all 108 simulations when using the IB approach. When using the BI approach 41% were overestimated by STARFM and 40% were overestimated by ESTARFM. Site-averaged NDVI was overestimated and the other eight indices were underestimated by using either STARFM or ESTARFM when using IB and BI approach (Table 4). STARFM produced higher mean bias compared with ESTARFM for all nine indices when using IB and BI approach at Gwydir. The IB approach also produced a lower RMSD (higher accuracy) at all three sites for both STARFM (Thomson; 89%, Coleambally; 81% and Gwydir; 82%) and ESTARFM (Thomson; 70%, Coleambally; 94% and Gwydir; 98%). The mean bias, R2 and RMSD statistics for each site are presented in Figure 2. As shown in Figure 2a,c, Thomson had lower mean bias and RMSD statistics for all nine indices compared with Coleambally and Gwydir, because of its lower spatio-temporal variances (Section 4.2). The results for each index at each site presented in Figure 2 and Table 4 are the site-averaged statistics from all 3-sequential date simulations. The performance of STARFM and ESTARFM in each individual simulation is likely to be different from these averaged results.

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Table 4. Mean bias, R2 and root mean square deviation (RMSD) statistics between the observed and simulated index values from Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) for the both the Index-then-Blend (IB) and Blend-then-Index (BI) approaches for Thomson, Coleambally and Gwydir, which are abbreviated as T, C and G respectively in the column headings. Bias and RMSD are presented in index units.

Bias R2 RMSD Indices STARFM ESTARFM STARFM ESTARFM STARFM ESTARFM T C G T C G T C G T C G T C G T C G IB-NDVI −0.0003 0.0057 −0.0013 −0.0008 0.0005 −0.0008 0.92 0.90 0.91 0.91 0.94 0.93 0.0278 0.0709 0.0747 0.0284 0.0576 0.0667 BI-NDVI −0.0006 −0.0065 −0.0076 −0.0005 −0.0021 −0.0012 0.89 0.88 0.88 0.90 0.91 0.87 0.0321 0.0778 0.0835 0.0310 0.0691 0.0862 IB–EVI 0.0000 0.0071 0.0104 0.0000 0.0011 0.0018 0.89 0.89 0.90 0.89 0.94 0.93 0.0160 0.0531 0.0602 0.0160 0.0418 0.0475 BI-EVI −0.0001 −0.0024 0.0028 −0.0001 −0.0007 0.0032 0.84 0.88 0.88 0.86 0.92 0.86 0.0193 0.0562 0.0605 0.0185 0.0475 0.0671 IB-SR 0.0086 0.1838 0.2658 0.0024 0.0250 0.0943 0.90 0.87 0.88 0.91 0.90 0.90 0.1245 1.3270 1.4802 0.1179 1.2051 1.6800 BI-SR 0.0028 0.1781 0.1392 0.0011 0.0796 0.0813 0.88 0.85 0.87 0.90 0.87 0.80 0.1346 1.3799 1.4005 0.1291 1.2874 1.6800 IB-GVMI 0.0048 0.0081 0.0023 −0.0013 0.0003 0.0038 0.93 0.93 0.90 0.94 0.94 0.90 0.0311 0.0749 0.0797 0.0299 0.0682 0.0764 BI-GVMI 0.0030 0.0044 0.0009 −0.0005 0.0001 0.0091 0.92 0.93 0.88 0.94 0.94 0.86 0.0333 0.0769 0.0833 0.0305 0.0734 0.0913

IB-D1650 0.0114 0.0050 0.0018 0.0034 −0.0003 0.0027 0.87 0.91 0.88 0.88 0.93 0.89 0.0507 0.0303 0.0272 0.0492 0.0269 0.0271

BI-D1650 0.0092 0.0043 0.0013 0.0074 0.0001 0.0041 0.81 0.90 0.85 0.85 0.92 0.82 0.0626 0.0315 0.0301 0.0559 0.0284 0.0339

IB-NDWI24 0.0048 −0.0034 0.0035 0.0010 0.0004 0.0020 0.90 0.88 0.90 0.90 0.91 0.92 0.0281 0.0569 0.0666 0.0283 0.0485 0.0630

BI-NDWI24 0.0044 0.0027 0.0069 0.0011 0.0025 0.0018 0.87 0.86 0.87 0.89 0.88 0.85 0.0317 0.0625 0.0763 0.0305 0.0584 0.0826

IB-NDWI25 0.0076 0.0161 0.0144 0.0023 0.0018 0.0097 0.92 0.78 0.88 0.91 0.83 0.89 0.0339 0.0936 0.0879 0.0347 0.0773 0.0902

BI-NDWI25 0.0082 0.0095 0.0119 0.0029 0.0042 0.0135 0.90 0.76 0.85 0.91 0.80 0.84 0.0377 0.0925 0.0927 0.0348 0.0864 0.1049

IB-NDWI27 0.0088 0.0148 0.0099 0.0007 0.0018 0.0075 0.92 0.85 0.86 0.91 0.89 0.87 0.0407 0.1032 0.1008 0.0414 0.0891 0.1034

BI-NDWI27 0.0075 0.0086 0.0094 0.0007 0.0045 0.0158 0.90 0.83 0.82 0.91 0.85 0.79 0.0445 0.1057 0.1103 0.0411 0.1026 0.1270

IB-NDWI45 0.0072 0.0147 0.0141 0.0003 0.0003 0.0083 0.92 0.92 0.89 0.92 0.94 0.89 0.0314 0.0869 0.0906 0.0306 0.0770 0.0845

BI-NDWI45 0.0053 0.0102 0.0094 0.0025 0.0015 0.0136 0.90 0.91 0.86 0.91 0.92 0.83 0.0347 0.0922 0.0979 0.0322 0.0861 0.1069

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Table 5. t-test results to assess approaches and algorithms are statistically different. Probability of <0.05 is shown in the italic and bold font, and probability <0.1 are provided as italic, >0.1 is normal font. Last two rows show number of cases with <0.05 and <0.1 probabilities and their percentage (count/18 × 100) in brackets. Under the ‘Approach’ headings, testing differences due to IB or BI, statistics for the first grouping of the nine indices use STARFM and the second grouping of nine use ESTARFM. Under the ‘Algorithm’ headings, testing differences due to blending algorithms, statistics for the first grouping use the IB approach and the second grouping use the BI approach. Thomson, Coleambally and Gwydir are abbreviated as T, C and G, respectively.

Bias R2 RMSD Indices Approach Algorithm Approach Algorithm Approach Algorithm T C G T C G T C G T C G T C G T C G NDVI 0.835 0.000 0.000 0.882 0.000 0.001 0.001 0.251 0.204 0.835 0.003 0.011 0.000 0.353 0.935 0.987 0.003 0.026 EVI 0.773 0.000 0.009 0.714 0.001 0.710 0.002 0.044 0.037 0.669 0.001 0.006 0.000 0.050 0.015 0.736 0.000 0.020 SR 0.130 0.860 0.337 0.015 0.001 0.280 0.024 0.000 0.000 0.172 0.000 0.000 0.042 0.378 0.293 0.195 0.077 0.603 GVMI 0.120 0.001 0.208 0.002 0.000 0.744 0.030 0.003 0.101 0.033 0.000 0.829 0.043 0.015 0.104 0.511 0.000 0.501

D1650 0.239 0.842 0.743 0.000 0.000 0.003 0.000 0.000 0.000 0.327 0.010 0.265 0.000 0.000 0.000 0.166 0.002 0.155

NDWI24 0.561 0.002 0.086 0.000 0.018 0.552 0.000 0.008 0.009 0.649 0.002 0.037 0.000 0.011 0.001 0.916 0.001 0.069

NDWI25 0.450 0.002 0.620 0.000 0.001 0.279 0.002 0.193 0.000 0.308 0.008 0.181 0.002 0.772 0.020 0.675 0.035 0.375

NDWI27 0.413 0.010 0.887 0.000 0.000 0.742 0.005 0.105 0.000 0.428 0.013 0.283 0.016 0.532 0.000 0.666 0.029 0.522

NDWI45 0.076 0.003 0.113 0.000 0.000 0.040 0.010 0.004 0.004 0.494 0.005 0.489 0.006 0.010 0.003 0.539 0.002 0.224 NDVI 0.939 0.188 0.579 0.975 0.056 0.873 0.093 0.004 0.004 0.116 0.001 0.211 0.069 0.000 0.009 0.354 0.000 0.161 EVI 0.821 0.057 0.849 0.837 0.000 0.104 0.357 0.000 0.000 0.253 0.001 0.352 0.248 0.000 0.000 0.453 0.001 0.495 SR 0.784 0.032 0.798 0.571 0.001 0.311 0.169 0.000 0.002 0.099 0.092 0.016 0.140 0.047 1.000 0.296 0.050 0.067 GVMI 0.585 0.832 0.347 0.001 0.000 0.411 0.226 0.000 0.010 0.000 0.004 0.285 0.673 0.000 0.005 0.003 0.006 0.354

D1650 0.076 0.321 0.454 0.019 0.000 0.238 0.012 0.000 0.000 0.000 0.000 0.177 0.004 0.000 0.000 0.001 0.000 0.825

NDWI24 0.935 0.057 0.936 0.000 0.787 0.187 0.521 0.000 0.002 0.098 0.001 0.249 0.393 0.000 0.000 0.381 0.009 0.135

NDWI25 0.610 0.244 0.236 0.000 0.001 0.881 0.818 0.000 0.000 0.000 0.002 0.753 0.981 0.003 0.000 0.000 0.011 0.049

NDWI27 0.981 0.181 0.242 0.000 0.025 0.676 0.951 0.000 0.000 0.000 0.018 0.202 0.892 0.000 0.000 0.000 0.096 0.071

NDWI45 0.180 0.536 0.216 0.000 0.000 0.611 0.279 0.001 0.004 0.016 0.004 0.216 0.347 0.000 0.003 0.002 0.006 0.358 Count<0.05 0(0) 8(44) 2(11) 13(72) 16(89) 3(17) 10(56) 15(83) 16(89) 6(33) 17(94) 5(28) 10(56) 13(72) 14(78) 5(28) 15(83) 3(17) Count<0.1 2(11) 10(56) 3(17) 13(72) 17(94) 3(17) 11(61) 15(83) 16(89) 8(44) 18(100) 5(28) 11(61) 14(78) 14(78) 5(28) 18(100) 6(33)

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Figure 2. Mean bias, R2 and RMSD statistics for eight simulated indices by IB and BI approaches and STARFM and ESTARFM for Thomson, Coleambally and Gwydir. SR results are not shown here as their extreme magnitude, especially in bias and RMSD, dampens the information content from other indices.

0.024 0.95 0.15 (a) Thomson bias (b) Thomson R2 (c) Thomson RMSD 0.016 0.90 0.10 0.85 0.008 2

R 0.80 Bias 0.05 0.000 RMSD 0.75 -0.008 0.00 0.70

0.024 0.95 0.15 (d) Coleambally bias (e) Coleambally R2 (f) Coleambally RMSD 0.016 0.90 0.10 0.85 0.008 2

0.80R 0.05 0.000Bias 0.75 RMSD

-0.008 0.70 0.00

0.024 0.95 0.15 (g) Gwydir bias (h) Gwydir R2 (i) Gwydir RMSD 0.90 0.016 0.10 0.85 0.008 2 0.80 R Bias 0.05

0.000 RMSD 0.75 BI_STARFM BI_ESTARFM -0.008 0.70 0.00 IB_STARFM IB_ESTARFM

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Results (Table 4) showed that both STARFM and ESTARFM algorithms simulated indices with higher bias and RMSD and lower R2 for dates with higher spatial and temporal variances (Section 4.2). For example, high temporal and spatial variances at date 992 for Thomson, due to inundation of the river channel, and at date 192, most likely due to changes in soil surface moisture and seasonal vegetation changes, resulted in lower accuracies when simulating all nine indices. 2 As an example, Figure 3 compares bias and R statistics for NDWI24 of Gwydir simulated by the IB and BI approaches by STARFM and ESTARFM at the flood date on 12 December 2004. As shown in Figure 3, IB outperformed BI when using either STARFM or ESTARFM. On this date STARFM produced higher accuracy (higher R2 and lower RMSD and bias) compared with ESTARFM due to higher T/S variances ratio by a flood event, which is in agreement with the algorithm selection criteria proposed by [1]. Higher biases were shown in highly variable inundated areas of Gwydir by both STARFM and ESTARFM (Figure 3a,b,c,d).

Figure 3. Bias, R2 and RMSD statistics between Landsat-observed Normalized Difference

Water Index (NDWI24) and simulated NDWI24 by IB and BI approaches and STARFM and ESTARFM at the flood date 12 December 2004 at Gwydir. Parts (a,b,c,d) show the spatial bias produced by IB-ESTARFM, IB-STARFM, BI-ESTARFM and BI-STARFM, respectively. Parts (e,f,g,h) show the corresponding crossplots (and the associated bias, RMSD and R2 statistics) between the observed and simulated values by IB-ESTARFM, IB-STARFM, BI-ESTARFM and BI-STARFM, respectively. In all cases there are 8,544,889 pixels.

(a) (b)

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Figure 3. Cont.

(e) (f)

Mean = -0.010 Mean = -0.010 RMSD = 0.125 RMSD = 0.119 2 R2 = 0.75 R = 0.77

(g) (h)

Mean = 0.0 Mean = -0.022 RMSD = 0.151 RMSD = 0.126 R2 = 0.68 R2 = 0.75

4.2. STARFM versus ESTARFM

Performance of STARFM and ESTARFM algorithms in simulating IB and BI indices was related to the T/S variances ratio. STARFM showed higher accuracy in simulating all nine indices at dates with higher T/S ratio of Thomson, Coleambally and Gwydir. In contrast, ESTARFM produced better simulations at dates with lower T/S ratio (Table 4). Comparing the statistics also showed that all nine indices produced different T/S ratio by using similar inputs, therefore, even on a certain date, there is no single optimum algorithm (STARFM or ESTARFM) for all nine indices. For example, on date 880 at Thomson, the T/S ratio of NDVI and EVI were higher than the other indices (Figure 4), which resulted in higher R2 and lower RMSD in simulating these indices by STARFM. Alternately, the other indices, which had lower T/S ratios, produced higher accuracies by ESTARFM. Higher temporal variance resulted when any 3-sequential date image set contained highly dynamic land-cover change (e.g., associated with flood events). For example the Thomson flood event (date 992) resulted in higher T/S variances ratio at dates 880, 992 and 1040 (Figure 4). The site-averaged spatio-temporal variance results were smaller for Thomson (0.015) than Coleambally (0.784) and Gwydir (1.610). The highly variable (river channel and floodplain) portion of Thomson imagery is relatively small compared to the surrounding low variance portion of that imagery (Figure 1), whereas in both Coleambally and Gwydir the highly variable portions of the imagery were relatively larger, being relatively largest for Gwydir (Figure 1). At Thomson, ESTARFM produced slightly higher accuracies than those yielded from STARFM for both IB and BI approaches. Using the paired t-test the ESTARFM and STARFM approaches were statistically different when comparing means of statistics (bias; for 72% of the cases, R2; for 44% of

3-15 Remote Sens. 2014, 6 9228 the cases and RMSD for 28% of the cases) at the 90% confidence level (Table 5). Similar to findings for Coleambally and Gwydir reported by [1], at Thomson the T/S variances ratio of Bands 7 and 5 were the dominant variances in both Landsat and MODIS resolutions followed by Band 4, Band 3, Band 2 and Band 1 (Figure 4a,b). This is due to selecting hydrologically active sites (in all cases). For all six bands through the entire time series results showed that spatial variance was greater than temporal variances (T/S < 1, Figure 4a,b), which means the area is more variable in space than in time. Temporal variances of all six Landsat bands were lower than corresponding MODIS bands. In contrast, spatial variances of Landsat bands were higher than spatial variances of the corresponding MODIS bands; most likely because of the lower spatial resolution. Landsat bands showed higher spatio-temporal variances compared with MODIS bands. Comparison of the spatial and temporal variances of indices through the dataset showed that the magnitude of spatial and temporal variances depends on the magnitude of the indices (Figure 4). For example, SR had highest averaged spatio-temporal variance followed by NDWI27, D1650, GVMI, NDWI25, NDWI45, NDVI, NDWI24 and EVI (Figure 4c,d). Distinct changes in the spatial and temporal variances occurred during the flood (date 992) and at the point of transition between the dry and wet seasons (dates 1088 and 1120) due to increased precipitation and water flow in the multi-channel river system and consequent vegetation growth. Normalized indices calculated from bands with diverse spectral regions showed higher spatial and temporal variances compared with other indices calculated from bands with similar spectral regions. For example, NDWI27 and NDWI25 water indices, which use SWIR1 and NIR bands, had higher spatio-temporal variances when compared with other water indices, i.e., NDWI24 and NDWI45

(Figure 4). EVI showed the highest T/S ratio followed by SR, NDVI, NDWI24, NDWI45, GVMI, D1650,

NDWI25 and NDWI27 (Figure 4c,d). Vegetation indices showed a higher T/S ratio due to higher changes in greenness of the study site after precipitation and corresponding rapid vegetation growth. Comparing Landsat and MODIS variances revealed that all nine Landsat indices had higher temporal, spatial and spatio-temporal variances than the MODIS indices in Thomson. At Coleambally, with moderate spatio-temporal changes and lower T/S ratio for all dates (Figure 5), ESTARFM produced better results than STARFM by using both IB and BI approaches. IB-ESTARFM produced the most accurate results (0.82 < R2 < 0.95) followed by BI-ESTARFM (0.80 < R2 < 0.94), IB-STARFM (0.78 < R2 < 0.93) and BI-STARFM (0.76 < R2 < 0.93). The three averaged error statistics for the 18 STARFM and ESTARFM indices were statistically different at the 90% confidence level (bias; 94% of the cases, R2; 100% of the cases and RMSD; 100% of the cases, Table 5). All nine indices showed lower temporal variances compared with spatial variances in both Landsat and MODIS resolutions (Figure 5). Date 97 was a transition date at Coleambally: for all dates before date 97, higher temporal and lower spatial variances of NDWI24, NDVI and EVI resulted in a higher T/S ratio compared with the other indices. In contrast for dates after 97, these indices showed lower T/S ratios (Figure 5). Rice was the dominant crop at Coleambally. Dates 0 through 97 were when rice fields were flooded, rice was planted and the crop grew to full canopy closure. During this time of active plant growth, the T/S ratio was higher for the three indices that make use of the visible and NIR bands when compared to the indices that make use of the longer wave bands. SR had the highest averaged spatio-temporal variance followed by D1650, NDWI45, GVMI, NDWI27, NDVI,

NDWI25, EVI and NDWI24 (Figure 5). SR showed the highest T/S ratio followed by EVI, NDWI24,

NDVI, NDWI45, NDWI27, D1650, NDWI25 and GVMI (Figure 5). Comparing Landsat and MODIS

3-16 Remote Sens. 2014, 6 9229 variances revealed that, all nine Landsat indices had temporal, spatial and spatio-temporal variances that were higher than the MODIS indices in Coleambally.

Figure 4. Thomson temporal to spatial variance ratio time series plots for bands and indices from Landsat and MODIS. Parts (a,b) are temporal variance to spatial variance ratio of all Landsat reflective bands and their corresponding MODIS bands; the legend in (a) applies to (b). Parts (c,d) are temporal variance to spatial variance ratio of the nine indices from Landsat and MODIS; the legend in (c) applies to (d). The spatial and temporal variances are calculated using all possible (18) 3-sequential date images and the results are plotted at the date of the central image. Vertical grid lines indicate dates of the central images. The horizontal dashed line in (c,d) is where T/S = 1.0.

1.0 (a) Landsat bands T/S ratio 0.8

0.6 Band 1 Band 2 Band 3 Band 4 0.4 Band 5 Band 7 0.2 Temporal to spatial ratio 0.0 0 200 400 600 800 1000 1200

1.0 (b) MODIS bands T/S ratio 0.8

0.6

0.4

0.2 Temporal to spatial ratio 0.0 0 200 400 600 800 1000 1200

4 (c) Landsat indices T/S ratio NDVI EVI SR GVMI D1650 NDWI24 3 NDWI25 NDWI27 NDWI45

1 Temporal to spatial ratio 0 0 200 400 600 800 1000 1200

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Figure 4. Cont.

4 (d) MODIS indices T/S ratio 3

1 Temporal to spatial ratio 0 0 200 400 600 800 1000 1200 Day since start of dataset (15 April 2008)

Figure 5. Coleambally temporal to spatial variance ratio time series plots for the nine indices from Landsat and MODIS; the legend in (a) applies to (b). The spatial and temporal variances are calculated using all possible (15) 3-sequential date indices and the results plotted at the date of the central image. Vertical grid lines indicate dates of the central images. The horizontal dashed line is where T/S =1.0.

1.5 (a) Landsat indices T/S ratio NDVI EVI SR GVMI D1650 NDWI24 NDWI25 NDWI27 NDWI45 1

0.5

Temporal Temporal to ratio spatial 0 0 50 100 150 200

1.5 (b) MODIS indices T/S ratio

0.5

0 Temporal Temporal to ratio spatial 0 50 100 150 200 Day since start of dataset (8 October 2001)

At the highly temporally dynamic Gwydir with higher T/S ratio at few selected dates, averaged statistics of all simulations showed that ESTARFM produced better results than STARFM by using the IB approach (Figure 2). By using the BI approach, STARFM produced better results than ESTARFM (Figure 2h,i). The STARFM and ESTARFM approaches were statistically different at the 90% confidence level when comparing means of statistics (bias; 17%, R2; 28%, RMSD; 33%; see Table 5).

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Gwydir had higher temporal variances overall than the other two sites while non-normalized SR and normalized NDWI25 and NDWI27 produced higher temporal variances than the other six indices. At dates 208, 240 and 256, temporal variance was higher than spatial variance resulting in higher T/S ratios at these dates compared with other dates (Figure 6). The T/S ratios of dates 128, 288, 304, 320 and 336 were lower than the other dates due to lower temporal variances and higher spatial variances

(Figure 6). SR had the highest averaged spatio-temporal variance followed by D1650. SR and D1650 are not normalized difference indices (Table 1), so they do not benefit from error reduction due to their formulation like the normalized difference indices do (as discussed in Section 4.1). SR produced the least accurate results as it is unbounded, so when the dominator → 0 then SR →∞, hence possibly producing large relative errors. Furthermore, D1650 uses three bands (NIR, SWIR1 and SWIR2), while other moisture indices (i.e., GVMI and NDWI45) only use two of these three bands. This increases the 2 likelihood of higher error propagation in D1650. For these reasons, the R statistic for both SR and D1650 improved most when using IB compared to BI at Gwydir where the variance was highest due to extreme flood-related moisture dynamics. This suggests that using the IB approach might be even more important when the index is not a normalized difference index. The EVI is also not a normalized difference index using three bands (like D1650) but two of these are visible bands which have much lower variances than the NIR and SWIR bands [1], so the EVI produces lower errors.

SR showed the highest T/S ratio followed by EVI, D1650, NDWI45, NDVI, GVMI, NDWI24,

NDWI27, and NDWI25 (Figure 6). Comparing Landsat and MODIS variances revealed that all nine Landsat indices had higher temporal, spatial and spatio-temporal variances than the MODIS indices due to lower spatial resolution of MODIS compared with Landsat.

Figure 6. Gwydir temporal to spatial variance time series plots for the nine indices from Landsat and MODIS; the legend in (a) applies to (b). The spatial and temporal variances are calculated using all possible (12) 3-sequential date indices and the results plotted at the date of the central image. Vertical grid lines indicate dates of the central images. The horizontal dashed line is where T/S = 1.0.

5 (a) Landsat indices T/S ratio NDVI EVI SR 4 GVMI D1650 NDWI24 NDWI25 NDWI27 NDWI45 3

ratio 2

1 Temporal to spatial 0 0 50 100 150 200 250 300 350 5 (b) MODIS indices T/S ratio 4 3

ratio 2 1 Temporal to spatial 0 0 50 100 150 200 250 300 350 Day since start of dataset (16 April 2004)

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All presented results in Figure 2 and Table 4 are site-averaged and do not show results of individual simulations. As an example here in Figure 7, we presented individual statistics for all 12 simulations of

NDWI25 of Gwydir by using STARFM and ESTARFM and two IB and BI approaches. As shown on Figure 7, on dates with a higher T/S ratio (i.e., date 256), STARFM outperformed ESTARFM by producing higher R2 and lower RMSD and bias compared with other dates.

2 Figure 7. Bias, R and RMSD of all 12 simulations of NDWI25 at Gwydir using STARFM and ESTARFM for the two approaches IB and BI. Part (a), represents bias statistics and T/S ratio, parts (b,c) represent R2 and RMSD statistics, respectively. Vertical grid lines indicate dates of the central images, and the color components of the legend in (a) apply to parts (b,c). The horizontal dashed line in (a) is where T/S = 1.0.

0.15 5 (a) Bias and T/S ratio IB-ES IB-S BI-ES BI-S 0.10 4 T/S 0.05 3 0.00 Bias

2 T/S ratio -0.05

-0.10 1

-0.15 0 0 50 100 150 200 250 300 350

1.00 (b) R2 0.90

0.80 2 R 0.70

0.60

0.50 0 50 100 150 200 250 300 350

0.4 (c) RMSD

0.3

0.2 RMSD

0.1

0 0 50 100 150 200 250 300 350 Day since start of dataset (16 April 2004)

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4.3. Approach versus Algorithm

2 Comparing the R and RMSD of four parameters, that is (STARFM-ESTARFM)IB,

(STARFM-ESTARFM)BI, (IB-BI)STARFM and (IB-BI)ESTARFM, revealed that the differences between statistics of the IB and BI approaches in both algorithms were greater than STARFM and ESTARFM statistics by using both approaches. This means improvement in accuracy of simulations by selecting the right approach (IB) is more important and produces higher simulation accuracies than selecting the right algorithm. It was shown that using the IB approach improved the average R2 by 0.4% when using STARFM and 3.7% when using ESTARFM. ESTARFM improved the average R2 by 4.6% when using the IB approach and 1.2% when using the BI approach. Using the IB approach lowered the RMSD by 12.8% when using STARFM and by 16% when using ESTARFM compared to using the BI approach. STARFM versus ESTARFM lowered the RMSD by 6.1% using the IB approach and by 3.95% for the BI approach. This reduction in RMSD also emphasizes that the selection of the right approach (IB) is more important than choosing either the STARFM or ESTARFM algorithms. According to the t-test, the difference between the means of error statistic values (average of three sites) were also more significant when comparing the IB and BI approaches (bias; 28%, R2; 78% and RMSD; 72%) than comparing STARFM and ESTARFM algorithms (bias; 61%, R2; 57% and RMSD; 54%).

5. Discussion

5.1. IB versus BI

This study found that the IB approach consistently outperformed the BI approach for all indices at all three study sites. The IB approach was less computationally expensive than the BI approach due to blending single indices rather than blending multiple bands. For example, the computational time of EVI using the IB approach is one-third the time required when using the BI approach due to blending three single bands (Blue, Red and NIR) rather than blending the single EVI when using IB. Brown et al. [38] showed that the long-term NVDI time series derived from multiple sensors, are comparable because of the similarity between them, (i.e., Landsat-and MODIS-derived NDVI are comparable with ±1 standard error, and R2 = 0.7). Tian et al. [23] compared the IB and BI approaches in simulating an NDVI time series by only using STARFM and found that the IB approach consistently generated better results (0.70 < R2 < 0.76) than the BI approach (0.56 < R2 < 0.70) in their study area. In this study we demonstrated how blending algorithms can be used for simulating nine multispectral indices directly at higher spatial resolution and temporal frequency. This paper introduced new insights into the downscaling approaches by blending indices directly from surface reflectance data. This approach (IB) demonstrated two major advantages over the three sites studied: (i) higher accuracy; and (ii) less computational time.

5.2. STARFM versus ESTARFM

This study confirms that performance of STARFM and ESTARFM in simulating Landsat-like indices from Landsat and MODIS indices depends on temporal and spatial variances of the input L–M indices into the algorithm; which is in agreement with [1,3]. Emelyanova et al. [1] proposed a

3-21 Remote Sens. 2014, 6 9234 conceptual model (Figure 9g of their paper) for advanced algorithm selection by using spatio-temporal variances of the study site. In this study we used a similar method to assess the blending algorithms performance by calculating the T/S variance ratio of input indices (3-sequential dates of MODIS with two Landsat indices). Results of this study confirmed their advanced algorithm selection conceptual model, which suggested using STARFM in sites with higher temporal and lower spatial (higher T/S ratio) variance and using ESTARFM for sites with lower T/S ratio (Figure 2c,d, and Figures 3,4a,b in each). Our study also found that the T/S variances ratio of each index was not similar to the T/S values of individual reflectance bands, which were used to calculate that index. This meant that the performance of STARFM and ESTARFM in IB and BI approaches was different. For example, for NDVI calculated with the IB approach, the performance depended on the T/S ratio of NDVI, whereas for the BI approach, where we blended individual reflectance data, performance depended on the T/S ratio of the individual reflectance Bands 3 and 4. At all three sites, temporal, spatial and spatio-temporal variances of MODIS were lower than Landsat variances due to the lower spatial resolution of MODIS (i.e., each 500 m MODIS pixel covers approximately 277 Landsat 30 m pixels). Rapid changes in land surface conditions (i.e., flood events) resulted in higher changes in temporal variances compared with spatial variance and produced higher T/S ratios. When comparing STARFM and ESTARFM and using three study sites with different spatial and temporal variances, we showed that no blending algorithm was optimal in all conditions. Emelyanova et al. [1] showed that the performance of these algorithms depended on the spatial and temporal characteristics of the study site. Their analysis was performed using reflectance data, and here we show that the framework for algorithm selection can be extended to indices. We extended their framework by using the T/S ratio of 3-sequential indices, as opposed to using reflectance data over the entire dataset as the algorithm selection criteria. As ESTARFM was developed to blend Landsat and MODIS data in spatially complex heterogeneous regions [3], it outperformed STARFM when the 3-sequential date spatial variance dominated 3-sequential date temporal variance (i.e., a low T/S ratio). In contrast, STARFM outperformed ESTARFM, when the 3-sequential date temporal variance dominated the 3-sequential date spatial variance (i.e., a high T/S ratio).

5.3. Approach versus Algorithm

In this study, it was shown that the choice of the IB or BI approaches had a greater impact on the accuracy of simulations compared with choice of algorithm (STARFM or ESTARFM). It means choice of approach is more important than choice of algorithm in blending L–M indices. Comparing STARFM and ESTARFM in both IB and BI approaches showed that improvement in the accuracy of simulations by ESTARFM was higher than accuracy of simulations by STARFM.

6. Conclusion

The six main conclusions of this research were: (i) the IB approach consistently outperformed the BI approach for all nine indices at all three study sites;

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(ii) the choice of approach (IB versus BI) had a larger impact on accuracy of blending indices than did the choice of algorithm (STARFM versus ESTARFM); (iii) the IB approach was less sensitive than the BI approach to choice of algorithm; (iv) STARFM was less sensitive to the choice of approach (IB versus BI) than was ESTARFM (which does not mean that STARFM was always the most accurate); (v) using the IB approach was even more important for non-normalized difference indices because they did not benefit from the inherent cancelling of blending-induced errors in their algebraic implementation; and (vi) we confirmed previous findings [1] that STARFM had higher accuracy than ESTARFM when temporal variance was higher than spatial variance (T/S > 1) and ESTARFM had higher accuracy than STARFM when spatial variance was higher than temporal variance (T/S < 1). To simulate Landsat-like indices from Landsat-MODIS images, the Index-then-Blend approach (IB) consistently produced better results in our study than when blending individual image bands, then calculating indices: the blend-then-index (BI) approach. We conclude the reason for this is that the IB approach only incurs one instance of blending and therefore only one instance of error due to blending, whereas the BI approach incurs multiple blending instances and therefore multiple instances of error. While [1] showed that algorithm selection between STARFM and ESTARFM was important to achieve a more accurately blended output of reflectance bands, we showed here that for blending indices, the choice of approach (IB versus BI) was more important than blending algorithm selection (STARFM versus ESTARFM). Our results have direct impact on operational considerations when blending Landsat and MODIS data for the purposes of generating multispectral indices for vegetation, environmental moisture and/or water applications. For this purpose, our study suggests that the IB approach should be implemented as it is: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to choice of blending algorithm.

Acknowledgments

The authors would like to thank CSIRO Marine and Atmospheric Research, Time Series Remote Sensing Team for sharing MODIS time series and Queensland Department of Science, Information Technology, Innovation and the Arts for providing Landsat-5 TM time series. We also thank the developers of STARFM and ESTARFM for providing their code for this study. The lead author was supported by an Integrated Natural Resource Management (INRM) scholarship from The University of Queensland (UQ) and the CSIRO, an Australian Postgraduate Award and funds from the School of Geography, Planning and Environmental Management (UQ).

Author Contributions

Abdollah A. Jarihani performed the experiments and wrote the first draft of the paper, all authors conceived and designed the experiments and edited the paper.

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Conflicts of Interest

The authors declare no conflict of interest.

References and Notes

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3-26 Chapter 4 Satellite-derived digital elevation model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low gradient and data-sparse catchments

Journal of Hydrology 524 (2015) 489–506

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Journal of Hydrology

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Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments ⇑ Abdollah A. Jarihani a, , John N. Callow b, Tim R. McVicar c, Thomas G. Van Niel d, Joshua R. Larsen a a School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia b Environmental Dynamics and Ecohydrology, School of Earth and Environment, University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia c CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia d CSIRO Land and Water, Private Bag No. 5, Wembley, WA 6913, Australia article info summary

Article history: Digital Elevation Models (DEMs) that accurately replicate both landscape form and processes are critical Received 6 December 2014 to support modelling of environmental processes. Topographic accuracy, methods of preparation and grid Received in revised form 25 February 2015 size are all important for hydrodynamic models to efficiently replicate flow processes. In remote and Accepted 26 February 2015 data-scarce regions, high resolution DEMs are often not available and therefore it is necessary to evaluate Available online 7 March 2015 lower resolution data such as the Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne This manuscript was handled by Geoff Syme, Editor-in-Chief Thermal Emission and Reflection Radiometer (ASTER) for use within hydrodynamic models. This paper does this in three ways: (i) assessing point accuracy and geometric co-registration error of the original DEMs; (ii) quantifying the effects of DEM preparation methods (vegetation smoothed and hydrologi- Keywords: Digital elevation models cally-corrected) on hydrodynamic modelling relative accuracy; and (iii) quantifying the effect of the Hydrodynamic modelling hydrodynamic model grid size (30–2000 m) and the associated relative computational costs (run time) DEM preparation on relative accuracy in model outputs. We initially evaluated the accuracy of the original SRTM Spatial resolution (30 m) seamless C-band DEM (SRTM DEM) and second generation products from the ASTER (ASTER Low-gradient river systems GDEM) against registered survey marks and altimetry data points from the Ice, Cloud, and land Elevation Satellite (ICESat). SRTM DEM (RMSE = 3.25 m,) had higher accuracy than ASTER GDEM (RMSE = 7.43 m). Based on these results, the original version of SRTM DEM, the ASTER GDEM along with vegetation smoothed and hydrologically corrected versions were prepared and used to simulate three flood events along a 200 km stretch of the low-gradient Thompson River, in arid Australia (using five metrics: peak discharge, peak height, travel time, terminal water storage and flood extent). The hydrologically corrected DEMs performed best across these metrics in simulating floods compared with vegetation smoothed DEMs and original DEMs. The response of model performance to grid size was non-linear and while the smaller grid sizes (6120 m) improved the hydrodynamic model results, these offered only slight improvements at very significant computational costs compared to grid size of 120 m, with grid sizes 250 m and greater decreasing in model accuracy. This study highlights the important impact that the quality of the underlying DEM has, and in particular how sensitive hydrodynamic models are to preparation methods and how important vegetation smoothing and hydrological correction of the base topographic data for modelling floods in low-gradient and multi-channel environments. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction processes within a landscape (Callow et al., 2007). Digital Elevation Models (DEMs) are the base topographic input data Modelling hydrological and other environmental processes and have been shown to be perhaps the most important input gov- depend on topographic data to replicate characteristics and erning hydrologic and hydrodynamic model accuracy (Bates et al., 1998, 2005; Baugh et al., 2013; Sanders, 2007). DEM production and choice typically contains trade-offs between cost, accuracy, ⇑ Corresponding author. Tel.: +61 07 3365 7027; fax: +61 07 3365 6899. E-mail addresses: [email protected] (A.A. Jarihani), [email protected] spatial coverage and grid size (Robinson et al., 2014), as well as (J.N. Callow), [email protected] (T.R. McVicar), [email protected] (T.G. Van the way they are prepared and/or corrected (Callow et al., 2007). Niel), [email protected] (J.R. Larsen). http://dx.doi.org/10.1016/j.jhydrol.2015.02.049 0022-1694/Ó 2015 Elsevier B.V. All rights reserved.

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Airborne-based Light Detection And Ranging (LiDAR) systems see Table 1 (Baugh et al., 2013; Coe et al., 2008; Paiva et al., 2013; can produce highly accurate and high resolution DEMs, but often Trigg et al., 2009; Wilson et al., 2007). Anabranching rivers are a have a limited spatial coverage with data acquisition and process- common river morphology, and especially synonymous with drying incurring high costs (Baugh et al., 2013; Sampson et al., 2012). land regions and are found across the globe in South America In remote, dryland river basins, there are usually no local- or regio- (Smith, 1986), China (Wang et al., 2005), Australia (Knighton and nal-scale highly accurate DEMs available. Modelling studies in Nanson, 2001; Nanson et al., 1986; Schumm et al., 1996), North such basins therefore rely on broader-scale satellite-derived America (Schumann, 1989; Smith, 2009; Smith and Smith, 1980), DEMs. In this case the two most widely used are: (i) the Shuttle Africa (Makaske, 2001) and Europe (Gurnell et al., 2009). They Radar Topography Mission DEM (SRTM DEM) (Hensley et al., are characterised by shallow water flows, low-gradients, and wide 2001); and (ii) the Advanced Spaceborne Thermal Emission and floodplains with multiple channels (Carling et al., 2014). Due to Reflection Radiometer – Global Digital Elevation Model (ASTER these characteristics, they are particularly sensitive to DEM quality GDEM, hereafter GDEM) (Nikolakopoulos et al., 2006; Wang when modelling flows since much of the flow is distributed across et al., 2012; Zandbergen, 2008). the unchannelised or poorly channelised floodplain. This is impor- Dryland basins are often associated with low-gradient and com- tant since flow pathway enforcement of DEMs are typically based plex multi-channel (e.g. anabranching) river systems, and are land- on using digitised river networks and satellite inundation scapes that have been shown to be particularly sensitive to DEM (Costelloe et al., 2006; Horritt et al., 2006), but their subsequent accuracy (Callow and Smettem, 2009; Callow et al., 2007). DEM accuracy within hydrodynamic models has yet to be examined. (and ultimately hydrologic and hydrodynamic model) accuracy is The poor-performance of 0-dimensional (0D) (Mohammadi determined by underlying dataset-specific factors such as topo- et al., 2013) and 1-dimensional (1D) models (Merwade et al., graphic accuracy. When different DEM datasets are compared or 2008) in routing water between the complex cross sections that model calibration and validation is informed by assimilating other characterise anabranching rivers has resulted in the suggestion geospatial data (e.g. optical or altimetry satellite data), geometric that 2-dimentional (2D) hydrodynamic models (e.g., Mike 21 and co-registration to align these datasets is a critical step (Karkee TUFLOW) are required to accurately model floods in these rivers et al., 2008; Nuth and Kääb, 2011; Van Niel et al., 2008; Wenjian (Mohammadi et al., 2013). Single-channel river systems can be et al., 2014). DEMs and model performance are also sensitive to adequately modelled by 1-D hydraulic models and simple topo- preparation methods including vegetation smoothing, hydrological graphic correction methods can be applied to correct single correction algorithms and selection of grid resolution, particularly cross-sections of the river (Gichamo et al., 2012; Honnorat et al., in remote and low-gradient landscapes (Baugh et al., 2013; Brown 2009; Merwade et al., 2008; Roux and Dartus, 2008). This is not et al., 2010; Callow et al., 2007; Coe et al., 2008; Wilson et al., a case in complex anabranching river systems which have complex 2007). multiple (up to 100) channels, hence requiring advanced DEM cor- Point accuracy is typically assessed against permanent refer- rection and preparation methods. Due to the computational inten- ence registered survey marks (GCPs), or project-specific survey sity of 2D hydrodynamic approaches, understanding the tradeoffs data (Hengl et al., 2008; Li et al., 2012; Ludwig and Schneider, in DEM spatial resolution (i.e., grid size) and the associated impact 2006). More recently, remotely-sensed altimetry data such as the on model performance is critical (Horritt and Bates, 2001; Horritt Geoscience Laser Altimeter System aboard the Ice, Cloud, and land et al., 2006; Li and Wong, 2010; Vaze et al., 2010b). It is known that Elevation Satellite (ICESat/GLAS) has been used for DEM accuracy both DEM accuracy and model grid size impact the calculation of assessment and calibration (Carabajal and Harding, 2005, 2006; spatial indices such as water inundation extent, which can be prob- Leon and Cohen, 2012; Rodriguez et al., 2006; Zhao et al., 2010). lematic when assessing model performance against satellite- This approach is of particular interest within more remote areas derived inundation maps (Li and Wong, 2010; Vaze et al., 2010b). across the globe where lower numbers of registered survey marks Understanding potential non-linear scaling of the DEM resolution are usually available, let alone measured hydrological data. There relative to model output accuracy allows for optimal selection of is also growing recognition that DEMs should be subjected to hori- model grid size and more efficient simulation performance (Bates zontal mis-registration assessment and possibly corrected when et al., 1998; Vaze et al., 2010b). seeking to calibrate model outputs such as flood extent against This growing body of research identifies three key issues related satellite data (e.g. (Callow and Boggs, 2013; Jarihani et al., 2013, to quality and preparation of DEMs that affect model performance, 2014; Nuth and Kääb, 2011)). they are: (1) source and absolute accuracy of DEMs and corrections Vegetation smoothing filtering is often necessary for SRTM and to address errors; (2) DEM preparation methods to be more physi- GDEM as the raw surface is typically higher than the actual land cally realistic (i.e., vegetation smoothing and hydrological correc- surface due to vegetation artifacts (ASTER GDEM Validation tion algorithms); and (3) impacts of grid size (key papers are Team, 2011; Yamazaki et al., 2012). This vegetation artifact impact summarised in Table 1 in relation to these factors). The limitation is non-uniform through the landscape, influenced by the veg- of past works is that they have generally focused on SRTM or etation structural characteristics (i.e., tree height, canopy density, GDEM (V1 and V2) and occasionally both. While DEM preparation wood moisture, branch angle and soil moisture) (Baugh et al., and grid sizes have also been assessed, this has usually been for 2013; Brown et al., 2010; Coe et al., 2008; Paiva et al., 2013; only one type of base DEM. While the need to assess these through Wilson et al., 2007). This effect is particularly significant in dry- integrated and systematic analyses has been recognised (e.g. Baugh lands where the vegetation characteristics of riparian zone com- et al., 2013; Callow et al., 2007; Carabajal and Harding, 2006; pared to surrounding woodlands and rangelands are pronounced. Gallant et al., 2011a; Li and Wong, 2010; Van Niel et al., 2008; Vegetation artefacts can also add artificial ‘‘roughness’’, impacting Vaze et al., 2010b; Wang et al., 2012), this has not yet been inves- the ability of models to accurately route a flood wave through a tigated simultaneously for all three issues, and doing so provides landscape (Schumann et al., 2007; Straatsma and Middelkoop, the motivation for this paper. The overarching aim of this paper 2006). is to assess and assimilate a range of techniques and approaches DEM flow pathway replication is important for model accuracy, into a single and integrated analysis that is able to comprehen- however, DEM preparation methods can impact other components sively address all three abovementioned issues simultaneously of model performance (Callow et al., 2007). While there are exam- (compare this to previous studies that only addressed one, or, at ples of corrected and uncorrected SRTM and GDEM products used best two, of the abovementioned three issues). We focus on a type in modelling, most examples relate to single channel river systems, of river system that is under-represented in the literature due to 4-2 Table 1 Summary of relevant modelling studies using remotely-sensed DEMs (the current paper is added for completeness). Not all papers performed DEM accuracy assessment and modelling studies and these are denoted with a N/A representing ‘not applicable’ in the relevant part of the ‘Data/Model used’ column. In the ‘Key results’ column the abovementioned three components are identified by the code: (1) assess absolute accuracy of DEMs and general corrections; (2) assess the relative accuracy of DEM selection and preparation methods on hydrodynamic flood modelling; and (3) quantify the impact of grid size on hydrodynamic modelling accuracy and computational expense. Many studies do not assess all three components and N/A directly follows the code in such cases.

Study Data / Model used Location / landscape / study size Examined aspect of Key results DEM 1–Rexer and SRTM 90 m, GDEM V2 / N/A Australian continent / mixed from tropical rainforest DEM source, co- (1) GDEM to SRTM: RMSE = 9.5 m Mean bias = 5 m, GDEM to GCPs Hirt (2014) to desert landscapes / 7.6 106 km2 registration and RMSE = 8.5 m, SRTM to GCPs RMSE = 4.5 m elevation correction (2) N/A (3) N/A 6 2 2–Hirt et al. GDEM, SRTM 90 GEODATA 270 m / Western Australia / arid / 2.5 10 km DEM source, co- (1) RMSE: GDEM; 15 m, SRTM; 6 m and GEODATA; 9 m. (2010) N/A registration and (2) N/A elevation correction (3) N/A 3–Wang et al. SRTM DEM 90 m, GDEM, 1:50,000 Mountain area, Southeast Tibet / single channel river / DEM source, co- (1) SRTM overestimated, ASTER underestimated valley ﬂoor. Inundation (2012) maps / 1D HEC-RAS 45 km registration and extent by SRTM 6.8% larger, by GDEM 2.2%. mean depth SRTM 2.4 m elevation correction shallower, GDEM 2.3 m deeper (2) N/A

(3) N/A 489–506 (2015) 524 Hydrology of Journal / al. et Jarihani A.A. 2 4–Vaze et al. LiDAR and contour map-derived DEM NSW Australia, Koondrook–Perricoota / forest / 320 km Grid sizes 1 m, 2 m, (1) LiDAR accuracy was <0.4 m. NSW DEM had 4 m mean accuracy (2010b) 25 m / N/A 5 m, 10 m and 25 m (2) The quality of DEM-derived hydrological features is sensitive to both DEM accuracy and DEM grid size (3) N/A 5–Horritt and 10 m LiDAR / LISFLOOD-FP River Severn, UK / single channel / 60 km Grid sizes 10–100 m (1) N/A Bates (2) N/A (2001) (3) The model reaching maximum performance at 100 m however 500 m grid size proves adequate for predicting water levels 6–Li and SRTM 30 m, LiDAR 2 m and National Kansas River (Missouri River), US / flat area DEM source and grid (1) DEM source is more important than DEM grid size in flood modelling. Wong Hydrography Dataset (NED) 30 m / size SRTM was 3 m higher than LiDAR (2010) MICRODEM (2) N/A (3) DEM spatial resolution had minor impacts on flood simulations but higher impact on accuracy of extracted networks 7–Baugh SRTM90 m / LISFLOOD-FP Amazon River / forest floodplain with lakes / 280 km Vegetation error (1) N/A et al. removal (2) Greatest improvements to hydrodynamic model accuracy were obtained (2013) by subtracting 50–60% of the vegetation height from the SRTM (3) N/A 8–Sanders Airborne LiDAR, SRTM, NED, IfSAR / Santa Clara River, California, US / single channel / 8.7 km DEM source (1) IfSAR and SRTM may include non-physical pools due to error. LiDAR (2007) 2D hydrodynamic flood had 0.1 m vertical accuracy (2) N/A (3) N/A DEM source (1) The overall quality of the SRTM X-SAR DEMs is sufficient for hydrologic 9–Ludwig SRTM X-SAR / TOPMODEL Ilm River (Munich), and Traun River (Bavarian Alps), 2 and 145 km 2 model applications. Mountainous area had lower accuracy and And 1:25000 DEM / TOPMODEL Southern Germany / 199 km Schneider (RMSD = 36.21 m) (2006) (2) Landcover correction improved the accuracy from RMSD = 4.52 m to RMSD = 3.63 m (3) N/A 10 – Czubski SRTM-X and ASTER GDEM / N/A Pienin Mts, Poland / single channel / 20 km 2 DEM source (1) SRTM X-band DEM (RMSD = 12.05 m) was better than ASTER GDEM et al. (SRTM = 17.43 m). They need additional corrections for hydrological (2013) modelling (2) N/A (3) N/A 11 – Hancock SRTM 90 m DEM and 20 m DEM / Tin Camp Creek, Swift Creek and Jemmy Creek, DEM source (1) For broad-scale qualitative assessment of large catchments the SRTM 2 2 2 et al. SIBERIA Australia / 2.5 km , 6.5 km and 106 km data are of benefit (2006) (2) Coarse grid and SRTM data have a higher network convergence than the high resolution 10 m DEM (3) N/A 12 – Gichamo ASTER GDEM / HEC-RAS Tisza River, Hungary / single channel / 30 km DEM corrections (1) ASTER GDEM had mean bias = 8.2 m and SD = 8 m. Pre-processing is et al. required for GDEM (2012) (2) N/A 491

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data scarcity making it a challenge to develop hydrodynamic models and overcome this limitation in-part through the use of satellite data to assess accurate representation of the ﬂood footprint. These are systems that, due to the low-gradient, slow moving ﬂood wave and large number of channels, are likely to be highly dependent upon high-quality and physically-realistic input topographic data. Accordingly, the three objectives of this paper are:

(1) Assess point accuracy and geometric co-registration error of the original SRTM and ASTER DEMs, and vegetation smoothed and hydrologically corrected version of each (and correct these for subsequent steps); (2) Quantify the effects of DEM preparation methods (original, vegetation smoothed and hydrologically corrected) on the relative accuracy of hydrodynamic modelling (using five metrics being: (i) peak discharge; (ii) flood height; (iii) travel time; (iv) terminal water storage; and (v) flood extent); and (3) Quantify the effect of grid size and the associated relative computational costs on hydrodynamic modelling accuracy.

These objectives provide the structure for our following Methods, hydrological predictions. Results and Discussion sections. (3) N/A (1) SRTM (RMSD =(2) 3.25 m) Vegetation outperformed ASTER (RMSD smoothing = 7.43 m) and(3) hydrological 90–120 m correction is optimum grid improved size the

Key results 2. Study site and materials

2.1. Study site

The DEM accuracy component of the study (Objectives 1 and 2) was performed across the Diamantina River and the Cooper Creek catchments within the Lake Eyre Basin (LEB) in Australia, an area of approximately 455,000 km2 (Fig. 1c). The hydrodynamic modelling Examined aspect of DEM DEM source and correction Vegetation smoothing Hydrological correction and grid sizes 30–2000 m component of the study (Objective 3) focused on a 200 km reach of the Thomson River (major tributary of the Cooper Creek catchment) between Longreach, Darr and Stonehenge gauging stations (Fig. 1b). This location was selected to provide a robust upstream and downstream boundary condition and calibration/validation data. The Diamantina/Cooper catchments are part of LEB, the largest endorheic basin in the world covering almost one-sixth (1.1 million km2) of the Australian continent (Croke et al., 1996). This area is called the ‘‘Channel country’’ due to the complex anastomosing river system with extremely variable flow regimes and wide floodplain inundation during major flood events (up to 60 km) (Jarihani et al., 2013; Knighton and Nanson, 2001). The arid zone rivers are , 200 km reach

2 morphologically similar, but widely scattered with multiple channels (up to 100) and with varying width (up to 200 m) and large wide-to-depth ratio (Knighton and Nanson, 2001). These arid zone rivers are characterised by distinct periods of runoff during the Location / landscape / study size Diamantina and Cooper catchments, LakeAustralia Eyre / Basin, low gradient and455,000 km multi-channel / north Australian monsoon (November–March) separated by no flow period (April–October), (Knighton and Nanson, 2001). The LEB is a complex hydrological system, with higher rainfall in the headwater catchments generating the majority of runoff, with significant transmission losses downstream (Costelloe et al., 2006; Knighton and Nanson, 2001). River flow in the LEB is unregulated and contains high ecological value rivers (Costelloe et al., 2006) supporting migratory and non-migratory fish (Puckridge et al., 2000) and water-bird (Kingsford et al., 1999) populations. The composition of plant communities in the Cooper Creek catchment are related to soil type and moisture availability and restricted to Data / Model used SRTM 30 m, SRTM 90 m ASTER (GDEM, GDEM) / TUFLOW the margins of major drainage and water bodies ranging from open

) forest to low open woodland (Boyland et al., 1984; Capon, 2003). Narrow bands of Eucalyptus woodlands comprise riparian areas and decrease in structural and complexity with distance from continued ( water bodies and river channels (Boyland et al., 1984; Capon, study 13 – This Study 2003). The majority of the ﬂoodplain consists of short grass and

Table 1 sparse scattered trees. The predominant land use of the region is 4-4 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506 493

Fig. 1. Study site. (a) Location of Diamantina (western) and Cooper (eastern) catchments shown in black line and Lake Eyre Basin shaded grey in Australia. (b) Hydrodynamic model domain is shown by the dashed line and ICESat ground tracks #1, #2 and #3 are shown in pink lines in Cooper Creek. The background is a Landsat image of the moderate ﬂood event on 29 April 1990 (bands 5, 4 and 3 as RGB). (c) Location of GCPs and ICESat points in Diamantina/Cooper catchments.

4-5 494 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506 cattle and sheep grazing. Therefore flood events are also critical in most are located along roads and away from the river floodplain supporting vegetation for rangeland cattle grazing (Costelloe et al., (see Fig. 1). These points were augmented with data from the laser 2006). altimeter ICESat, launched by NASA and operational from January 2003 to October 2009. ICESat was to measure ice sheet and land 2.2. Materials elevation, sea ice thickness and cloud and aerosol height transects, with high along track frequency (every 172 m) and high vertical The two most widely applied satellite-derived DEM datasets accuracy (<35 cm) on flat surfaces and inland water bodies (SRTM and GDEM) were used in this study. SRTM provides near- (Abdallah et al., 2011; Bhang et al., 2007; Chipman and Lillesand, global topographic coverage of the Earth’s surface with a horizontal 2007; Schutz et al., 2005; Urban et al., 2008). This means that grid size of 3-arc-seconds (90 m), recently, 1-s (30 m) data also ICESat ground track points are ideally suited to measure elevation has been released for all globe, except for the Middle East region. on large flat surfaces including complex floodplains, where ground SRTM has a reported absolute vertical height accuracy of better than control points are largely absent (Borsa et al., 2008; Jarihani et al., 16 m, a relative vertical height accuracy of better than 10 m, and an 2013; Leon and Cohen, 2012). The level-2 ICESat/GLAS Global Land absolute horizontal circular accuracy of better than 20 m (Bamler, Surface Altimetry Data (GLA14) release-33 data were downloaded 1999; Bhang et al., 2007; Hancock et al., 2006; Hirt et al., 2010; from US National Snow and Ice Data Centre (NSIDC) archives Huggel et al., 2008; Jarvis et al., 2008; Reuter et al., 2007; Rexer and (http://nsidc.org/data/icesat), with 370,500 ICESat/GLAS altimetry Hirt, 2014; Rodriguez et al., 2006; Schumann et al., 2008; Van Niel points used across the study area. et al., 2008). During the SRTM mission in February 2000, two radars: Water level and discharge data from three gauging stations on (i) National Aeronautics and Space Administration (NASA) C-band the Thomson River (Longreach, Darr and Stonehenge) were SIR-C (5.6 cm); and (ii) the joint German Aerospace Agency (DLR) acquired from Queensland Government water monitoring portal and Italian Space Agency (ASI) X-band (3.1 cm) Synthetic Aperture (http://www.derm.qld.gov.au/). These data were used for hydrody- Radar (X-SAR)) collected global surface models using the single pass namic model calibration, establishing input boundary conditions technique (Farr et al., 2007). While the X-SAR instrument has higher and validation. accuracy, the 50 km wide swath (Adam et al., 2002; Ludwig and Schneider, 2006) limits its application to large-scale modelling (our 3. Methods focus). Only SRTM C-band products are assessed in this paper, yet for completeness, an assessment of the X-band accuracy is presented The overall approach to assess the three research objectives is in the supplementary materials. shown in Fig. 2. To address our first objective, we corrected datums SRTM C-band data of 3-arc-second grid size (90 m) have been to a similar baseline, evaluated point accuracy and then assessed made available since 2001 (http://www2.jpl.nasa.gov/srtm/), with DEM co-registration error (and correction) against registered sur- global (except Middle East) 1-arc-second (30 m) released in 2014. vey marks and ICESat/GLAS points. This was done for the two most This study used SRTM 1-arc-sec (30 m) DEM (herein termed commonly used uncorrected and unmodified satellite-derived ‘‘SRTM DEM’’). Two processed 1-arc-second (30 m) DEMs DEMs, being: (produced by GA) were also assessed: (i) the vegetation artifact removed, ‘‘smoothed’’ DEM based on the adaptive smoothing (i) SRTM-derived 1-arc-second (30 m) C-band DEM (herein technique of Gallant (2011) termed ‘‘S-DEM’’ herein which also SRTM DEM); had tree-offset correction applied (Gallant, 2011); and (ii) the (ii) Second generation ASTER-GDEM (30 m) product (herein hydrologically corrected ‘‘H-DEM’’ that used streamlines from the ASTER GDEM). 1:250,000 national topographic maps to hydrologically correct the S-DEM (Gallant et al., 2011a,b). For our second objective, the impact of vegetation smoothing NASA and The Ministry of Economy, Trade and Industry of Japan and hydrological correction were investigated. The SRTM DEM is (METI) produced a global 1-arc-second (30 m) grid size ASTER available in both a smoothed (S-DEM) and hydrologically corrected GDEM product. The original GDEM was released in June 2009 (H-DEM) version, however, ASTER GDEM was not. The methods (GDEM1) has an absolute vertical accuracy of 18.3 m (ASTER used to produce the S-DEM and H-DEM from the base SRTM GDEM Validation Team, 2009), with GDEM2 released in October DEM (Gallant, 2011; Gallant et al., 2011a), were then applied to 2011 with an improved absolute vertical accuracy of 17 m ASTER GDEM. This created a total of six DEMs that were evaluated (ASTER GDEM Validation Team, 2011) and an absolute horizontal in Objectives 2 and 3 (see Fig. 2, which identified the two created accuracy of ±30 m (ASTER GDEM Validation Team, 2011). GDEM DEMs in the red and the four existing DEMs in blue). Assessment of data is available from NASA Reverb, LP DAAC Global Data all base (already completed for Objective 1), vegetation smoothed Explorer (https://lpdaac.usgs.gov/data_access), or the Japan-space and hydrologically corrected DEMs was conducted against the systems ASTER GDEM Page (http://www.jspacesystems.or.jp/ registered survey marks and ICESat/GLAS dataset. Floodplain ersdac/GDEM/E/index.html). cross-section replication was also investigated for transects where In this study SRTM DEM (30 m) and ASTER GDEM version 2 ICESat/GLAS ground tracks crossed the river. For the third objec- were used. however for completeness the Version 4.1 of the seam- ’’ tive, these six DEMs were resampled at up to eight different grid less SRTM (90 m) DEM, herein termed ‘‘DEM3sec (Jarvis et al., resolution of 30, 60, 90, 120, 250, 500, 1000, 2000 m. 2008), downloaded from: http://srtm.csi.cgiar.org., SRTM X band and ASTER GDEM version 1 also have been assessed and the results are presented in the supplementary materials. 3.1. DEMs point accuracy and co-registration error correction Registered survey marks represent the highest vertical accuracy ground control points to a known height datum (i.e., Australian Registered survey marks, DEMs and ICESat/GLAS data were all Height Datum, (Geoscience Australia, 2014a)) in the Cooper and referenced to different horizontal and vertical datums Diamantina catchments. A total of 2700 points from the (Table Supp 1), so all datasets were transformed to the same hori- Queensland Government Department of Natural Resources and zontal (WGS84) and vertical (EGM96) datum. ICESat data was Mines (DERM) database were available. These are non-randomly extracted using the NGAT NSIDC GLAS Altimetry elevation extrac- distributed through the landscape, as they are the basis for cadas- tor Tool (https://nsidc.org/data/icesat/tools.html), and a 0.7 m ele- tral parcel surveying and infrastructure position control. As such, vation offset between the TOPEX/Poseidon and WGS84 ellipsoids 4-6 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506 495

Fig. 2. Conceptual arrangement of the study is shown. In Objectives 1 and 2, the four existing DEMs are shown in blue and the two created DEMs are shown in red. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) was added to ICESat/GLAS data. Registered survey marks and the developed by Nuth and Kääb (2011), using slope, direction of terrain river gauges were referenced to the Geocentric Datum of (aspect) and elevation differences between two elevation datasets to Australia (GDA94) which uses the GRS80 ellipsoid which is directly calculate optimum dx, dy and dz errors. The best sinusoidal curve to comparable with the WGS84 horizontal datum (Geoscience minimise the errors was fitted to the scatterplot of terrain aspects Australia, 2014a). The vertical datum was converted from and bias/tan (slope) parameters of elevation data (Nuth and Kääb, Australian Height Datum (AHD) to the EGM96 geoid using the 2011). Then each DEM has been corrected by applying these calcu- AUSGeoid09 model from Geoscience Australia (http://www. lated dx, dy and dz parameters. As elevation bias has been shown ga.gov.au/geodesy/ausgeoid/). to be unevenly spatially distributed and elevation-dependent DEMs error is typically assessed at pre-existing locations or at (Berthier et al., 2007, 2010; Nuth and Kääb, 2011; Paul, 2008), we project-specific control points (Hengl et al., 2008). This study used also examined DEMs for the elevation-dependent bias by polynomial 2700 registered survey marks and an additional 370,500 ICESat regression method developed by Nuth and Kääb (2011). points (total 373,200 points, as the GCP reference dataset) to assess We used both error statistics and also visually inspected the the accuracy of the two original DEMs (Objective 1) and then the spatial patterns of elevation transects and DEM-to-DEM error to additional four prepared DEMs (part of Objective 2). The elevation assess DEM accuracy (Ludwig and Schneider, 2006). Transformed at control point locations was extracted from each DEM using bi- DEMs were compared to each other by subtracting one from the linear interpolation (Nuth and Kääb, 2011), with mean bias, root other and calculating mean bias, RMSD, R2 and SD statistics and mean square deviation (RMSD), coefficient of determination (R2) also the SD in 33 and 55 moving window as a measure of rela- and standard deviation (SD) calculated as the error statistics. tive differences in hydrodynamic roughness. The replication of the RMSD is not a proper estimate of the statistical error distribution floodplain morphology at three cross sections was assessed from if averaged bias does not equal zero (Fisher, 1998; Nuth and the DEMs at the position of ICESat tracks, comprised of over Kääb, 2011), so values for mean bias and SD are also presented. 70 km of floodplain of Cooper Creek (see Fig. 1b for location). While vertical error correction is often undertaken, the horizon- Visual inspection of the elevation transects allowed evaluation of tal and vertical mis-registration of features such as river channels the relative differences of how well the DEMs replicated floodplain that can affect vertical accuracy assessments is less frequently con- and channel morphology, and also the ‘‘roughness’’ of the cross sidered (Berthier et al., 2007; Howat et al., 2008; Nuth and Kääb, sections was also assessed qualitatively, both around the defined 2011; Rodriguez et al., 2006). Van Niel et al. (2008) found that channel, across the floodplain and at more distal locations. sub-pixel mis-registration between two DEMs caused equal or This process described above for assessing DEM accuracy for greater difference than the true difference between two DEMs. Objective 1, was also repeated and applied to the vegetation The co-registration of all DEMs was therefore required to allow smoothed and hydrologically corrected ASTER GDEM that were for DEM-to-DEM comparison to ensure pixels of each DEM repre- specifically prepared (S-GDEM, H-GDEM) and those already avail- sent the same geometric location (Karkee et al., 2008; Nuth and able (S-DEM and H-DEM)) for Objective 2 (using the preparation Kääb, 2011; Wenjian et al., 2014). ICESat data have been used as methods described below). For the sake of brevity, some results truth point to co-register all DEMs. relating to these analyses on the two original DEMs (Objective 1) Several approaches have been used to undertake co- and the four prepared DEMs (Objective 2) are presented together registration. Karkee et al. (2008) used hydrological features like within the results section. stream networks and watershed boundaries as control points to determine the level of mis-registration between SRTM DEM and 3.2. DEM preparation methods for smoothing and hydrological ASTER GDEM. Linear regressions between elevation and other ter- correction rain parameters (i.e., slope and aspect) have also been used to co- register DEMs (Bolch et al., 2008; Gorokhovich and Voustianiouk, DEMs often require pre-processing to better replicate hydro- 2006; Peduzzi et al., 2010). We applied the co-registration method logical reality (Santini et al., 2009), particularly where flow 4-7 496 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506 connectivity along river networks (especially in flat river basins) is around 5 (Syme, 1991). When simulations produced unstable lost due to DEM errors, other processing steps (i.e., sink filling and results, the time-step was reduced until simulations stabilised. DEM reconditioning) and/or large elevation changes (Hagen et al., Discharge data at Longreach (main channel) and Darr (major 2010; Jenson and Domingue, 1988; Yamazaki et al., 2012). While tributary) stations were used as the upper boundary conditions numerous algorithms have been developed and used (Callow and (inflow to the system). Flows in this region are primarily generated Smettem, 2009; Callow et al., 2007; Hellweger, 1997; Jones, by upper catchment rainfall (Costelloe et al., 2006), and most 2002; Li, 2014), to be consistent with the SRTM DEM products of inflow to the model domain was from the upstream Longreach Geoscience Australia (i.e., S-DEM and H-DEM), the same vegetation gauge. Direct rainfall on the model domain was excluded to reduce smoothing and hydrological correction methods were applied to model complexity, with the discharge of each sub-basin contribut- ASTER GDEM to produce two new prepared GDEMs (S-GDEM, ing flow into the simulation reach estimated from the Darr gauge H-GDEM, see Fig. 2). The vegetation smoothed DEMs were created records and scaled to sub-basins size (Oudin et al., 2008; Vaze using a multi-scale adaptive smoothing approach (Gallant, 2011) et al., 2010a). which considers both the noise level and local relief in the DEM, Due to the arid climate (Jarihani et al., 2013) and the ground smoothing the DEM more where the noise is larger than the local water table being deeper than the major channels (Knighton and relief and less where noise is less than local relief. Hydrological Nanson, 2001) and that modelling was undertaken for flood events, correction was applied by channel enforcement using the groundwater inflow to the flooding river was excluded from the ANUDEM (Hutchinson, 2011) program, based on the 1:250,000 model structure. Transmission loses in this system are due to a Australian national topographic mapping river network combination of actual evapotranspiration and infiltration. (Geoscience Australia, 2014b) which captures the individual chan- Terminal water storage, model configuration for these simulations, nels of the anabranching rivers of the Channel country to create the and an absence of any infiltration information in the region means hydrologically corrected DEMs. transmission losses from the flooding river due to aquifer recharge are difficult to realistically account for. Therefore, a constant infil- 3.3. DEM grid size and hydrodynamic modelling tration rate over the entire model domain is used. To eliminate the downstream boundary condition effect on Stonehenge (model val- Objective 2 (DEM preparation) and Objective 3 (grid size) were idation point) an open downstream boundary condition at 8 km tested within the Two-dimensional Unsteady FLOW (TUFLOW) downstream of Stonehenge was selected. The Initial Loss/ model (Syme, 1991). TUFLOW 2D uses the complex 2-dimensional Contentious Loss (IL/CL) method (El-Kafagee and Rahman, 2011; (2D) solution based on Stelling’s (Stelling, 1983) computational Hill, 1996) was used to include infiltration into the model domain. methods for shallow water flow problems, and has been tested Due to mostly clay structure of the river channel/floodplain (Barton, 2001) and validated (Barton, 2001; Huxley, 2004; Nelson (Maroulis, 2000) the IL = 10 mm with CL = 2 mm/h were selected. and Rusty, 2012). TUFLOW solves the full 2D, depth averaged, Friction in-channel and floodplain was parameterized using a sin- momentum and continuity equations for free-surface flow. The gle Manning’s n coefficient for the channel and floodplain due to scheme includes the viscosity or sub-grid-scale turbulence term homogeneity in land cover types of the model domain and the that other mainstream hydrodynamic models omit (Syme, 1991). insensitivity of hydrodynamic models to the number of land use The 2D simulation is particularly suited to free surface flows in classes and application of complex formulae to establish roughness coastal water, estuaries, rivers, floodplains, urban areas and low- values (Werner et al., 2005). Manning n varied from 0.03 to 0.1 in gradient multi-channel rivers where the flow behaviour is 2D the calibration phase and modelled discharge peak time was com- and cannot be adequately represented by 0D or 1D models pared with that observed by the recorded hydrograph. The (Syme, 1991). n = 0.075 produced better results when comparing model outputs A 200 km by 80 km model domain along the Thomson River with the observed 2000 flood hydrograph and Landsat (introduced floodplain between upstream stations at Longreach and Darr and below) derived inundation extent. the downstream station at Stonehenge was selected. To reduce Model calibration was performed on a major flood event from 1 computational cost, the maximum flood extent over the simulation February to 31 March 2000 using the SRTM DEM. Calibration was period was extracted from a Landsat image which was acquired assessed using performance metrics at the downstream location during the peak time of the major flood on 29 February 2000. (Stonehenge) for the difference between modelled and observa- 3 This maximum flood extent plus a 2 km buffer was used to define tions of: (i) peak discharge (m /s); (ii) peak height (m); (iii) travel hydraulically-active cells in the model (Fig. 1), with external cells time (hrs); and (iv) terminal water storage (i.e., volume of water 3 excluded from calculations. The final model domain consisted of captured in depressions after passing flood pulse, in M m ) for 8.71 million 30 m grid cells with up to 3.34 million active cells. the three flood events. Simulation of the 2D instantaneous inunda- The TUFLOW GPU module, which is 10–100 times faster than tion (flood footprint) on 29 February and 16 March 2000 were the classic module (that runs on a CPU), was used for all sim- compared with same-day Landsat images of flood extent based ulations presented in this paper. The adaptive time-stepping func- on Normalized Difference Water Index (NDWI, Eq. (1)) tionality of TUFLOW GPU was employed to set a dynamic Courant (McFeeters, 1996). Two Landsat images, one acquired by Landsat stability criterion (Courant et al., 1967) for more hydrodynamic 7 ETM + at peak flood (29 February 2000) and the other by stability control based on recommended values less than 10 and Landsat 5 TM on the falling limb (16 March 2000), and the

Table 2 DEM-to-control point elevation differences statistics in (i) original DEMs and after applying: (ii) 3D co-registration corrections; (iii) vegetation smoothing algorithm; and (iv) hydrological corrections. Units of bias, SD and RMSD are metres. 373,066 GCPs used. ICESat data have been used as truth point to co-register all DEMs.

DEM Original 3D Co-registration Vegetation smoothed Hydrologically corrected (S-DEMs) (H-DEMs) Bias SD RMSD R2 dx dy dz Bias SD RMSD R2 Bias SD RMSD R2 Bias SD RMSD R2

SRTM DEM +2.68 1.84 3.25 0.999 0.0 0.6 2.5 0.00 1.79 1.79 0.999 0.00 1.55 1.55 0.999 0.00 1.8 1.8 0.999 ASTER GDEM 1.95 7.17 7.43 0.994 0.5 2.6 1.8 0.00 7.16 7.16 0.994 0.00 6.29 6.29 0.995 0.00 6.29 6.29 0.995

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Fig. 3. Cross-platform DEM-to-DEM comparison. In (a) to (d) the SRTM DEM is compared with ASTER GDEM respectively, in original, co-registered, vegetation smoothed and hydrologically corrected. There are 5.28 million 30 m grid contributing to each histogram. observed flood hydrograph at Stonehenge were used to calibrate images were cloud free except the 29 February 2000 image, in the model. The flood extent was calculated from band 2 and band which some cloud patches were nulled from inundation extent 4 of Landsat 5 TM and Landsat 7 ETM + images by using zero as calculations. threshold, with NDWI classified into water (NDWI 6 0) and non- band2 band4 water (NDWI > 0) classes. All Landsat images were atmospherically NDWI ¼ ð1Þ band2 þ band4 corrected by using the FLAASH package (Cooley et al., 2002). All 4-9 498 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506

We used the same F statistic (Eq. (2)) as other studies (i.e. Bates Applying the co-registration method of Nuth and Kääb (2011) to and De Roo, 2000; Cook and Merwade, 2009; Horritt and Bates, all DEMs found that all were horizontally co-registered to less than 2002; Tayefi et al., 2007) to assess model performance in simulat- 30 m (i.e., 1 pixel), based on the dx and dy values. ASTER GDEM ing inundated area. Although recent literature (Stephens et al., (dx = 0.5 m and dy = 2.6 m) showed slightly higher mis-registra- 2014) has highlighted some limitations when using this statistic tion values compared with SRTM DEM (dx = 0.0 m and (i.e., biased in favour of over-predicting flood extent), however it dy = 0.6 m). By applying 3D co-registration, all DEMs co-registered is well suited to this study as we only relatively compare flood horizontally and vertically to GCPs using these values. The aver- extents. aged bias for all DEMs were equal to zero and the RMSD also improved by 1.46 m and 0.27 m, respectively, for SRTM DEM and Aos F ¼ 100 ð2Þ ASTER GDEM (Table 2). The co-registration error varied across Ao þ As Aos these products, RMSD values for ASTER GDEM was higher (7.16 m) than SRTM DEM (1.79 m), (Table 2). Elevation-dependent where Ao refers to observed inundated area from Landsat imagery error values were generally very low, most likely due to the low- (km2), As refers to Simulated area from the model (km2) and Aos gradient topography of the study site, and no elevation-dependent refers to the area of overlap where both Landsat and the model correction was applied to DEMs. (km2) describe the surface as inundated. The F statistic varies Two vegetation smoothed DEMs showed better agreement between 100 for perfect match and 0 for no match conditions. (lower RMSD, see Table 2) with GCPs when compared with original Once calibrated, the same parameters for Manning’s n coeffi- DEMs. The vegetation smoothed SRTM DEM had lower RMSD cient and transmission losses/infiltration rate were used across (1.55 m) than ASTER GDEM (6.29 m). The hydrologically corrected the six input DEMs. In calibrating the model in this way we DEMs showed a similar pattern of results as for the vegetation acknowledge the potential for bias in calibrating using the SRTM smoothed DEMs, except for H-DEM which was slightly lower than DEM input data. To overcome this, and to align with our objectives, S-DEM. Herein all DEMs used in further analysis have undergone we place an emphasis on the relative accuracy or departure in the 3D-coregistration corrections using the method of Nuth and model performance from other results (rather than absolute accu- Kääb (2011) to the correction specification as outlined in Table 2. racy) in assessing the impact of these different DEM sources and Cross platform DEM-to-DEM comparison showed that original preparation methods, and we discuss this further within the dis- ASTER GDEM have north to south strip elevation errors of approxi- cussion. The functionality of TUFLOW was used to automatically mately ±30 m (Fig. 3). After co-registration this error reduced to resample the DEMs to grid sizes of 60, 90, 120, 250, 500, 1000 ±15 m. Table 3 presents statistics error analysis between the and 2000 m. TUFLOW uses a triangulation technique to read eleva- original DEMs and after applying (i) 3D co-registration; (ii) 3D tion of model grids from DEM. First it makes a triangular network co-registration and then vegetation smoothing; and (iii) previous from DEM cells centre and corner points (elevation of the point in corrections plus hydrological correction. ASTER GDEM showed the centre of cell is equal to cell elevation and elevation of the cor- improvement in the accuracy and variability in elevation values ner points are average of for surrounding central points). Then ele- relative to the raw version (Table 3). The hydrological correction vation of each model grid is the average of 3-corners of covering further improved the accuracy of DEMs by enforcing channel net- (overlapping) triangle (WBM, 2008). work and replicating channel morphology on top of vegetation artifact removal and also correcting the elevation errors in other 4. Results part of DEM outside of river channel/floodplain system. This explains the general performance in elevation error being lower 4.1. DEMs point accuracy and co-registration error correction for H-DEMs than S-DEMs and original DEMs due to the influence in reducing error around the river channel (see Table 3 and Fig. 4). The accuracy of ICESat altimetry points was first investigated by Comparing the topographic roughness of DEMs in 33 and 55 comparing elevation against registered survey marks at 110 loca- cells moving windows across the DEMs showed that both ASTER tions. Results showed high correlation (R2 = 0.99, RMSD = 0.23 m and SRTM S-DEMs had significantly lower roughness compared and SD = 0.08 m), with a minor mean bias (ICESat data on average, with their original DEMs, with around a 60% reduction in topo- were 0.11 m lower than the registered survey marks); within the graphic roughness from vegetation smoothing. Hydrological cor- inherent error of each dataset. Therefore, the 370,500 ICESat points rection had a further minor effect of lowering topographic and 2700 registered survey marks were combined into a reference roughness for the SRTM DEM due to minor changes in elevation dataset of 373,200 GCPs and used to assess DEM elevation accuracy at the location of channels, but increased for ASTER GDEM which of the two original DEMs, this combined dataset contained a large explain higher elevation changes at the location of river channels number of points (i.e., 373,200) across the floodplain. (Table 3). Analysis found that ASTER GDEM product had elevation that The ICESat ground tracks on the floodplain allowed for compar- was on average lower than GCPs (mean bias = 1.95 m, ison of DEM elevation at a cross-sectional transect (see Fig. 4). SD = 7.17 m). In contrast, SRTM DEM was higher than GCPs (mean Overall, SRTM DEM (RMSD = 1.00 m) cross sections were more bias = +2.68 m, SD = 1.84 m, see Table 2). This might be related to accurate with both lower bias and RMSD compared with ASTER the different data acquisition (optical or InSAR) and/or data pro- GDEM (RMSD = 2.57 m) (see Table 4). When co-registration was cessing methods of SRTM and ASTER. applied, the elevation shift is clearly shown for all DEMs, with

Table 3 DEM-to-DEM elevation difference between original DEM and after sequentially applying the (i) 3D co-registration; (ii) smoothing; and (iii) hydrological corrections. Mean bias and SD show the average and standard deviation of elevation changes after each step of corrections. Topographic roughness (TR) shows the averaged standard deviation of elevation changes in 33 and 55 moving window across the entire DEMs. Numbers in brackets are the percentage of roughness decrease due to vegetation smoothing and hydrological corrections relative to the roughness of original (co-registered) DEM. All units are in metres.

DEM 3D Co-registration Vegetation smoothed (SDEMs) Hydrologically corrected (HDEMs)

Mean bias SD TR 33TR55 Mean bias SD TR 33TR55 Mean bias SD TR 33TR55 SRTM DEM 3.00 0.45 0.99 1.35 2.99 1.35 0.32 (68%) 0.52 (61%) 3.06 1.71 0.27 (73%) 0.45 (67%) ASTER GDEM 1.63 1.93 2.43 3.14 2.35 2.73 0.71 (71%) 1.14 (64%) 2.26 3.25 0.73 (70%) 1.14 (64%)

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Fig. 4. Comparison of ICESat-derived elevation transect with SRTM DEM and ASTER GDEM transects. The elevation transects under ground track #3 of ICESat (‘‘A’’ as located on Fig. 1b, Sec #3, is at 0 km on this ﬁgure and ‘‘B’’ at 50 km) on 4 June 2005 were extracted from (a) original, (b) co-registered, (c) vegetation smoothed and (d) hydrologically corrected versions of digital elevation models. The legend for part (a) applies to all other parts.

Table 4 Elevation difference between ICESat and three cross sections after sequentially applying the: (i) 3D co-registration; (ii) vegetation smoothing; and (iii) hydrologically corrected. The locations of the three sections are shown on Fig. 1b. Mean bias and SD show the average and standard deviation of elevation changes after each step of corrections. All units are in metres.

DEM Section # Original 3D Co-registration Vegetation smoothed (SDEMs) Hydrologically corrected (HDEMs) Bias SD RMSD R2 Bias SD RMSD R2 Bias SD RMSD R2 Bias SD RMSD R2

SRTM DEM #1 +2.97 1.39 3.28 0.995 0.17 1.37 1.38 0.995 0.10 0.81 0.82 0.998 0.36 1.07 1.13 0.997 #2 +2.69 1.53 3.09 0.996 0.46 1.51 1.58 0.996 0.40 1.06 1.14 0.998 0.47 1.07 1.16 0.998 #3 +2.49 1.26 2.79 0.996 0.55 1.23 1.35 0.996 0.48 0.87 1.00 0.998 0.66 0.92 1.13 0.998 ASTER GDEM #1 7.94 3.46 8.66 0.964 6.31 3.46 7.19 0.964 5.45 2.27 5.91 0.985 5.84 2.29 6.27 0.985 #2 7.70 3.13 8.31 0.980 6.05 3.13 6.81 0.980 5.04 1.80 5.35 0.993 5.22 2.06 5.61 0.991 #3 3.60 3.69 5.15 0.963 1.98 3.69 4.18 0.964 1.09 2.33 2.57 0.985 1.42 2.33 2.73 0.986

lower bias and RMSD for all products (see Fig. 4b and Table 4). The averaged-elevation) nature of ICESat data (Fig. 4c and d). While smoothing algorithm (Fig. 4c) further reduced the bias and RMSD the overall performance of the SRTM DEM showed a general repli- between DEM elevation profile and the ICESat profile (Table 4). cation of the ICESat ground tracks across the 50 km floodplain Applying the hydrological correction increased the overall bias cross section (Fig. 4), this DEM still contain a degree of smoothing and RMSD, potentially explained by numerous small channels that and simplification of the surface when compared to the ICESat were hydrologically-enforced but are beyond the detection cross sections. capabilities of ICESat with 170 m along-track resolution or are no Overall, the results of the point analysis, co-registration and longer in the landscape. impacts of vegetation smoothing and hydrological correction The vegetation smoothed DEMs show the strongest agreement showed some relatively consistent trends. SRTM DEM generally with ICESat, with the hydrologically corrected DEMs showing outperformed the ASTER GDEM in terms of point accuracy, slightly higher error partly due to the smoothed (170 m co-registration error, and topographic roughness on the floodplain. 4-11 500 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506

4.2. Model simulation precision products (also see results of terminal water storage later). Vegetation smoothing the GDEM improved flood peak discharge Assessing absolute model accuracy (based on the five metrics: simulation relative to the original GDEM (28% and 27% reduction (i) peak discharge; (ii) flood height; (iii) travel time; (iv) terminal in error for the 1990 moderate and 2000 major floods, respec- water storage; and (v) flood extent), would require calibration of tively), with a greater relative improvement than for the SRTM model input parameters across the various DEMs and grid sizes. DEMs (Fig. 5b and c). Applying vegetation smoothing to the DEM This would compromise the control of these DEM related variables, (5%, 4% and 9% peak discharge error for minor, moderate and major as such, we calibrated the model to the 2000 flood event using the floods, respectively) also improved simulation results relative to SRTM DEM, and kept all other input parameters constant. By pur- original versions (Fig. 5). Hydrological correction further improved posefully focusing on the relative accuracy (as opposed to absolute flood peak simulations in all flood events, with results slightly bet- accuracy) means we emphasise the comparison of model accuracy ter than for vegetation smoothed DEMs, with an average improve- (or error) based on the relative grouping of results from various ment of 11.6% compared to 10.5% for vegetation smoothing alone DEMs preparation methods and the departure of this performance (Fig. 5). with grid size variation (as opposed to focus on model calibration/ Vegetation smoothing the SRTM DEM caused a slight decrease performance). This focus on relative accuracy is in line with our in the flood peak height relative to the original DEM, for the minor stated objectives. (0.00 m), moderate (0.17 m) and major (0.04 m) floods. The vegetation smoothed GDEM increased the flood peak height when 4.2.1. DEM preparation method impacts moderate (0.60 m) and major (0.37 m) floods were simulated (note Model simulation results for the peak discharge were consistent no results for the minor flood due to the previously explained strip with some errors identified above. For example, all of the ASTER error). SRTM DEM had a further decrease in flood height (relative GDEM products (original, vegetation smoothed and hydrologically to the original), with the H-DEM being lower for the minor corrected) were unable to route the 2008 minor flood (Fig. 5a), giv- (0.42 m), moderate (0.60 m) and major (0.44 m) flood events. ing a peak discharge of 0 m3/s for all results. This was due to the The H-GDEM was not able to simulate minor flood events, how- presence of strip errors in ASTER GDEM (identified in Section 4.1 ever, the flood peak height was lower for the moderate also see Fig. 3) that were higher than the water level elevation of (0.74 m) and major (0.92 m) flood events. These effects are the minor 2008 flow event. The moderate (Fig. 5b) and major likely to be related to changes in roughness that routes water more (Fig. 5c) flood events were routed by ASTER GDEM (i.e., the flood rapidly through the landscape reducing the peak flood height much was larger than the striping error), though the absolute peak dis- in the same way that real vegetation roughness does, and is consis- charge values were significantly lower than for the SRTM DEM tent with the influences of vegetation smoothing and hydrological

Fig. 5. Comparison of five evaluated flow metrics by using SRTM DEM and ASTER GDEM in original, vegetation smoothed (those in the legend proceeding S-) and channel- enforced versions (those abbreviated H- in the legend) being: (a–c) peak discharge (m3/s); (d–f) peak height (m); (g–i) travel time (h); (j–l) terminal water storage (M m3); and (m–o) flood extent (% using F-statistic). For each metric the minor, moderate and major floods were compared by using six DEMs at multiple grid sizes (30–2000 m). 4-12 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506 501 correction identified above (see Table 3). The decrease in flood flood where the roughness factor becomes more significant for peak height and inundation extent could also be due to increased simulation accuracy. The travel time of all three flood hydrographs channel volume and hence carrying same amount of water with when using H-DEMs was slightly shorter than S-DEMs with an lower peak height. average reduction of 34.5 h compared to 33.9 h for vegetation The results for the flood wave travel time are consistent with smoothing alone (Fig. 5g–i). this above interpretation in relation to more efficient routing Terminal water storage is the volume of water that is left in reducing the flood peak for the prepared DEMs (see Fig. 5g–i). the landscape when the flood wave routing is complete. This is The travel time (in hours) of the flood hydrograph (i.e., the error effectively the pits and sinks within the DEMs that are filled with in the model calculating the time it takes for the peak discharge water, mainly on the floodplain in this study. This is in of itself to travel from Longreach to Stonehenge) was reduced by applying another estimate of topographic roughness and the effect that the vegetation smoothing algorithm for all three DEMs when com- sinks have in accurately simulating total event discharge at the pared to original DEMs (Fig. 5). The S-DEM shortened the travel lower gauging station. It should be noted that in these large time of the flood hydrograph 63 h, 11 h and 11 h when simulating dryland river systems terminal water storage of floodplain and minor, moderate and major flood events, respectively. The S-GDEM channel (billabongs or waterholes) can be an important compo- reduced the moderate and major floods more than S-DEM by 111 h nent of the overall water balance (Bunn and Arthington, 2002), and 60 h. These results are consistent with the results for the rela- and therefore not an error that needs complete correction tive topographic roughness and the greater error for the minor (Knighton and Nanson, 2001).

Fig. 6. Observed and simulated moderate 1990 flood extent comparison. Part (a) shows the Landsat image captured on 29 April 1990 during the flood peak (bands 5, 4 and 3 as RGB) and (b) shows Landsat-derived flood extent on the same date for the area enclosed by the red square in part (a). Parts (c–e) show simulated peak flood extent by using SRTM DEM, S-DEM and H-DEM. Parts (f–h) show simulated peak flood extent by using ASTER GDEM, S-GDEM and H-GDEM. Landsat-derived flood extent (b) has 30 m resolution and (c–h) have a 90 m grid size. Depth units are in (m) and are the difference at the maximum water level associated with the flood and the underlying elevation from the DEMs. Parts (f–h) have different depth range compared to (c–e). 4-13 502 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506

The vegetation smoothing algorithm applied to both DEMs reduced the terminal water storage when simulating the minor, moderate and major flood events. S-DEM lowered the terminal water storage from 18% to 6% of total inflow of the minor flood (2545 M m3), from 4.7% to 1.4% of total inflow (10,746 M m3)in the moderate flood and from 5.8% to 1.8% of total inflow (9321 M m3) in the major flood event. The original version of GDEM showed the highest terminal water storage when compared with SRTM DEM. The vegetation smoothed GDEM reduced the volume of terminal water storage from 65% to 58% of total inflow when simulating the minor flood, from 39.9% to 22.2% in the moderate flood and from 47.4% to 26.4% in the major flood event. These results further highlight the impact of the striping effect in creating artificial flow barriers and causing large quantities of water to be stored in the landscape after the flood event simulation. Hydrological correction for DEM lowered the volume of terminal water storage to 5%, 1.1% and 1.3% for the minor, moderate and major flood events, respectively. The H-GDEM also showed slightly higher terminal water storage than S-GDEM for all three flood events (minor, 62%, moderate, 24.4% and major, 29%). Vegetation smoothing also improved performance when simulating flood extent for all flood events when compared to the original DEMs. Examples of the observed and simulated moderate 1990 flood extent are compared in Fig. 6 for original, vegetation smoothed and hydrologically corrected DEMs. S-DEM improved simulation of the flood extent (higher F statistic) by 0.4% in minor, 0.7% in moderate and 0.8% in major flood event compared to DEM. The improvement in the S-GDEM F statistic simulation was proportionally greater than for the SRTM DEMs (minor (6.2%), moderate Fig. 7. Grid size effect on flood characteristics. The changes in (a) peak discharge, (1.4%) and major flood (2.1%) events), though overall accuracy (b) peak height, (c) travel time, (d) terminal water storage and (e) flood extent were was lower (see Fig. 5e). Channel-enforcing correction also calculated for each change in grid size for two H-DEM and H-GDEM. Results are the improved the flood extent simulation in all flood events when average of the minor, moderate and major flood events and relative to the value for 30 m grid. compared to vegetation smoothed DEMs (Fig. 5). H-DEM improved the inundation modelling by 7.3% in minor, 2.1% in moderate and 1.9% in major flood event when compared to S-DEM. H-GDEM Table 5 improved the F statistic by 3.8%, 3.3% and 3.3% in the flood events TUFLOW GPU computational time of 1990 flood costs in multiple grid sizes. The TUFLOW models have been run on a moderate specification desktop computer with when compared to the S-GDEM. The relative performance of the 64-bit operating system, 8 GB RAM, processor IntelÒ core™ 2 Quad CPU, various DEMs in terms of flow routing and the replication of flood [email protected] GHz and NVIDIA Quadro 4000 GPU. These run times are for 1050 h behaviour are also apparent from Fig. 6. flood hydrograph with adaptive time-stepping (starting time step of 15 s with Courant Number = 1.0).

4.3. DEM grid size and hydrodynamic modelling Grid size (m) Number of active cells TUFLOW GPU run time (h) 30 3,342,151 121 The results of DEM hydrological preparation (Section 4.2.1), 60 835,517 24 reported that across the evaluated metrics the hydrologically cor- 90 371,414 16 rected SRTM and ASTER products had higher accuracy than either 120 208,938 2 250 48,133 0.7 the original, and for the vegetation smoothed versions across all 500 12,038 0.4 metrics evaluated. In this section, therefore, we focus on the 1000 3014 0.17 comparison of grid size effect for the two hydrologically corrected 2000 760 0.05 H-DEM and H-GDEM (Fig. 5). General trends in the response of model precision to altered grid size are visible from Fig. 7 across the five metrics: (7a) peak Simulation results for peak height using the H-GDEM had high discharge; (7b) peak height; (7c) travel time; (7d) terminal water sensitivity (0.6–36.5%), particularly for grid size larger than 500 m storage; and (7e) flood extent. These changes are more clearly (Fig. 7b) and H-DEM had less sensitivity (0.5–15.6%) across the grid apparent when the performance relative to the smallest grid size sizes. Similar simulation performance was found for grid sizes from (presumed to be the most accurate) was evaluated when changing 30 m to 120 m with significant deterioration once grids were each grid size (Fig. 7). For both DEMs, the smaller grid sizes showed coarsened beyond 500 m. Travel time had similar trend for both consistent performance in simulation accuracy across all of the five the SRTM and ASTER products with very small changes <5% across metrics, with limited departure noted until grid size was coarsened grid sizes 30–90 m and higher changes for greater grid sizes 120– from around 120 m to 250 m, though this threshold was not abso- 2000 m (Fig. 7c). Terminal water storage showed up to 17% change lutely consistent. in H-GDEM and 10% in H-DEM across tested grid sizes (Fig. 7d), Peak discharge (Fig. 7a), showed a similar pattern of conver- with similar performance for grid sizes 120 m and smaller. Flood gence across the grid sizes from 30 m to 250 m for all SRTM and extent showed only minor sensitivity (5%) when grid sizes were ASTER products, with performance deteriorating for grid sizes less than 250 m. The larger grid size showed lower accuracy (lower greater than 500 m (Fig. 7a). By varying grid size from 30 m to F statistic) in simulating flood extent (Fig. 7e). Overall, these results 2000 m, the H-GDEM showed higher changes in the peak discharge showed that the model reached optimum performance at grid (0.0–57%) than H-DEM (0.6–36.5%). size of 60–120 m (Fig. 7). Performance generally exponentially 4-14 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506 503 deteriorated from there for coarser grids and very limited accuracy 5.2. DEM preparation methods for smoothing and hydrological gains achieved for having higher resolution grid sizes. As a guide, correction Table 5 presents the computational time for 1990 moderate flood simulations using TUFLOW GPU for a moderate specification desk- Absolute DEM elevation accuracy results showed that the veg- top computer. etation smoothing algorithm produced DEMs that were more accurate (when assessed using ground control points) than the 5. Discussion hydrologically-enforced DEMs. This result may be due to the along-track resolution of the ICESat points with respect to the res- 5.1. DEMs point accuracy and co-registration error correction olution of the river channels used in the hydrological enforcement algorithm (Fig. 4), rather than an introduction of additional DEM This study has presented a novel approach to overcome the error from hydrological correction. Hydrological correction was issue of a lack of elevation GCPs on the floodplain of a remote shown to improve the hydrological conditions of the DEMs by region, by combining registered survey marks and ICESat ground channel-enforcing the original DEM and improving stream posi- control points to co-register DEMs. The highly accurate ICESat data tions (Callow et al., 2007). are ideal for assessing the elevation of large flat surfaces, including Both vegetation smoothing and hydrological correction had complex floodplains such as in the study region (Borsa et al., 2008; more impact relative to the original DEMs, in decreasing hydrody- Jarihani et al., 2013; Leon and Cohen, 2012). Results of our study namic roughness of the DEM. This caused changes in the flood peak correspond with other works suggesting that ICESat data can be and reduced flood travel times as water was simulated to be more useful as ground elevation control (Gorokhovich and efficiently routed through the catchment due to the reduction of Voustianiouk, 2006; Li et al., 2012; Rexer and Hirt, 2014; artificial hydrodynamic roughness caused by the methods used Rodriguez et al., 2006; Zhao et al., 2010), with this paper the first to produce the original DEMs (both SRTM and GDEM products). to show the value of this application for low-gradient complex Our result extends that reported by Horritt and Bates (2001),as multi-channel systems. The work then extends previous studies this is the first time this analysis was conducted in a large dryland on DEM co-registration and primary corrections such as datum anabranching river system, which is representative of many data changes (Karkee et al., 2008; Nuth and Kääb, 2011; Wenjian poor dryland river systems globally. Relative to the vegetation et al., 2014) in using the registered survey marks and ICESat data- smoothed (S-DEMs) surfaces, all hydrologically corrected DEMs set to undertake these DEM error assessments and co-registration across all three floods generally caused a further decrease in the corrections in a remote and data-sparse catchment. flood peak height due to increased channel capacity. It has been shown that the elevation (McVicar and Körner, The flood travel time in S-DEMs and H-DEMs was shorter than 2013) bias is related to elevation of the area varying 1–40 m per the original DEMs for all three flood events by using both ASTER each 1000 m elevation changes (Berthier et al., 2007, 2010; Nuth and SRTM data. S-DEMs were slightly quicker in transferring floods and Kääb, 2011). In low-gradient floodplains such as anastomosing along the floodplain and channel most likely due to smoother DEM river system, with minor elevation changes, our analysis showed surface and less tortious channels enforced by hydrological correc- that there is no apparent correlation between elevation bias and tion, proportionally impacting lower flows. ASTER GDEM showed elevation of the DEM products. longer travel time than SRTM DEM due to higher surface rough- In low relief landscapes, the DEMs often display striping error ness; similar results were reported by Horritt and Bates (2001) in patterns that can make them unsuitable for hydrological and other a single channel river in a landscape with higher relative relief. studies (Albani and Klinkenberg, 2003; Gallant et al., 2011a; The smoothing and hydrological correction can produce more Garbrecht and Martz, 2000; Hirt et al., 2010). DEM-to-DEM com- efficient DEMs and possibly route water through the landscape. parison revealed that this error was higher in ASTER products than These results are significant for hydrodynamic modelling in the SRTM products. types of low-gradient and multi-channel landscapes that were Cross section comparison also has been shown to be useful in the focus of this paper. Due to the way we controlled model input DEM comparison. Cross section comparison with ICESat transects parameters, vegetation smoothing and hydrological correction showed that applying pre-processing and general corrections algorithms resulted in lower than expected flood heights and faster improves the cross-sectional accuracy of river channel and flood- travel times result. These results do not suggest higher error, but plains (Arefi and Reinartz, 2011; Gichamo et al., 2012; rather that the model roughness parameters become more sensi- Suwandana et al., 2012). tive in being able to constrain and validate model performance Overall the SRTM products outperformed the ASTER products under a nominal model parameter calibration simulation as the (lowest SD and RMSD), when compared with control points. base DEM becomes hydraulically smoother and flow paths more SRTM products showed higher elevation than control points while realistic. These impacts are particularly evident in Fig. 6, which also ASTER products had lower elevations than control point, a result highlights the ways in which these different base topographic that corresponds to the finding of Hirt et al. (2010) which found datasets route water through this landscape. 7.7 m RMSE between GDEM and SRTM (SRTM higher than The vegetation smoothed DEMs and hydrologically corrected GDEM) over the Australian continent. The results of our study DEMs both improved the flood extent simulations. Vegetation revealed that, although ASTER products have same spatial res- smoothed DEMs also reduced the total amount of terminal water olution when compared with freely available 30 m SRTM products, storage across all three simulated flood events. While the absolute they suffer from higher elevation errors which limits their applica- volume of terminal water storage that would be retained from this tion in hydrological studies (Hirt et al., 2010; Huggel et al., 2008; system during a flow is hard to explicitly quantify, the rates for Rexer and Hirt, 2014). When linked to the impact of grid size on GDEM (20–60%) were much higher than DEM. Hydrological correc- hydrodynamic model precision (i.e., 120 m resolution is adequate tion had a differential effect for the various base DEMs, with the for a hydrodynamic model in a low-gradient dryland region), this DEM having lower terminal water storage and H-GDEM slightly suggests that where 30 m SRTM products are not available higher compared with smoothed versions. This may be due to (Middle East region), the 90 m SRTM products represent a better the differential and sometimes random effects of hydrological option than the 30 m resolution ASTER products. enforcement creating abandoned channels that existed in the base

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DEM that store water as part of the hydrological correction pro- landscapes. A comparison of SRTM and ASTER DEMs against cess. Terminal water storage may also increase due to the increase ICESat data, indicated that these DEMs are more accurate than in channel volume offered by burning in the channel geometry. their nominal vertical errors, even in this low-gradient large river Initial hydrodynamic model simulations showed that, uncorrected system. Elevation values for the SRTM DEM were on average higher and original DEMs produced unstable hydraulic conditions due to while the ASTER data had a lower elevation than control data. This poor hydrological replication of the channel system and flow direc- accuracy was still improved by adjusting for localised bias and is tion presentation (Mohammadi et al. (2013). The presence of artifi- an important step when approaches such as calibration of inun- cial sinks in DEMs also increased the terminal water storage in the dated area against satellite imagery requires datasets to be in the river channel system and floodplain, such that the volume of water same datum. left in these sinks after flood wave recession resulting in poor and We showed that DEM hydrological corrections are necessary to unrealistically low overall mass-balance. While transmission alter elevation values of cells to better represent the known losses of these rivers are high (Knighton and Nanson, 2001), com- hydrology (i.e., river position and number of channels) and hydrau- parison of flood input and output hydrographs revealed that our lic smoothness of the river channel and floodplain. DEM correc- results were due to DEM sink artifacts, rather than real terminal tions methods such as vegetation smoothing and hydrological water storage transmission losses. correction reduced the impacts of artifacts from DEM’s creation in the original elevation data. These resulted in important improvements to the hydrodynamic model simulations, and were 5.3. DEM grid size and hydrodynamic modelling able to produce more physically-realistic results in a challenging low-gradient floodplain environment. These advanced DEM pre- Our results were relatively consistent in terms of the impact paration methods are not comparable with the simple methods that grid size had on the flow metrics, across preparation methods that have been used to correct river cross-sections for hydraulic and for both the SRTM and ASTER products. The general trends modelling of single channel river systems. Our evaluation of grid across the five model performance metrics examined (i.e., peak dis- size influence revealed while DEM sources was important, the sim- charge, peak height, travel time, terminal water storage and flood ulation runs generally produced similar accuracies for grid sizes of extent), reported that limited change from 30 m to about 120 m, 120 m, or less. Overall this study provides the hydrological com- with a gradual rise to 250 m which then exponentially increased munity robust and integrated investigation to assess the implica- with larger grid sizes, was found. Results become unstable and tions of DEM source, calibration and validation methods (and unreliable for simulation run with grid sizes of 1000 m and base DEM error), DEM preparation and grid size considerations 2000 m irrespective of the underlying DEM for the kinds of hydro- for modelling floods in low-gradient and multi-channel environ- dynamic modelling undertaken in this study. As reported for small ments, or to adapt the workflow to test these variables to other catchments (Horritt and Bates, 2001; Li and Wong, 2010), our landscapes. results also indicated less sensitivity of predicted water elevation and flood extent to grid size in hydrodynamic modelling in large, low-gradient and data-sparse catchments. This means for a quick Acknowledgments and more accurate flood extent predictions, projecting the water elevation produced by a coarse resolution model onto a high res- Special thanks to Bill Syme TUFLOW manager and author for olution DEM, is a reasonable solution (Horritt and Bates, 2001). providing TUFLOW license and technical support for this project. Running the hydrodynamic model with grid sizes smaller than The authors would like to thank the editor and two reviewers 120 m showed very minimal (<5%) improvements in all measured (Dr. Calum Baugh and an anonymous reviewer) for their construc- metrics, however, the run time of hydrodynamic model dramati- tive suggestions that improved the manuscript. The authors thank cally increased by five times (Table 5). This result of low-gradient the National Snow and Ice Data Center for providing ICESat data large river systems agrees with Horritt and Bates’s (2001) finding and USGS EROS data center for providing SRTM DEM datasets. which examined grid sizes of 10–1000 m and concluded that run- We thank Dr. Kasper Johansen for his comments on first draft. ning the model with grid sizes smaller than 100 m does not We also thank Dr. John Gallant from CSIRO Australia for providing improve the results, but results in longer computational time. adaptive smoothing algorithm code and advice on DEM hydrologi- While individual applications will vary, results presented in cal preparation methods. Abdollah Jarihani was supported by an Table 5 provide some guidance to selecting optimum grid size Integrated Natural Resource Management (INRM) scholarship from and the relative trade-offs in computation time for large scale flood The University of Queensland (UQ) and CSIRO, an Australian applications. As emphasised by Vaze et al. (2010b), this is not only Postgraduate Award and funds from the School of Geography, allows quick model calibrations and sensitivity analysis, it also Planning and Environmental Management (UQ). allows for real time solutions in operational flood forecasting and flood warning systems. Appendix A. Supplementary material

Supplementary data associated with this article can be found, in 6. Conclusion the online version, at http://dx.doi.org/10.1016/j.jhydrol.2015.02. 049. This paper assessed point accuracy and geometric co- registration error of the DEMs, quantified the effects of DEM References preparation methods (vegetation smoothed and hydrologically- corrected) on hydrodynamic modelling relative accuracy and Abdallah, H., Bailly, J.-S., Baghdadi, N., Lemarquand, N., 2011. Improving the quantified the effect of the hydrodynamic model grid size and assessment of ICESat water altimetry accuracy accounting for autocorrelation. the associated relative computational costs (run time) on relative ISPRS J. Photogramm. Remote Sens. 66 (6), 833–844. Adam, N., Breit, H., Knöpfle, W., Eineder, M., 2002. The Global SRTM X-SAR DEM – accuracy in model outputs. This paper novelly combined the regis- Calibration, Validation, Production Status and Results. European Space Agency, tered survey marks and ICESat data to provide an extensive control (Special Publication) ESA SP(526), pp. 36–41. point dataset to validate and calibrate SRTM DEM and ASTER Albani, M., Klinkenberg, B., 2003. A spatial filter for the removal of striping artifacts in digital elevation models. Photogramm. Eng. Remote Sens. 69 (7), 755–765. GDEM, suitable for application in hydrological and other applica- Arefi, H., Reinartz, P., 2011. Accuracy enhancement of ASTER global digital elevation tions in data poor, dryland, and large anabranching river models Using ICESat data. Remote Sens. 3 (7), 1323–1343. 4-16 A.A. Jarihani et al. / Journal of Hydrology 524 (2015) 489–506 505

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4-18 Appendix A. Supplementary material

Supplementary Fig. 1. Observed and simulated minor 2008 flood extent comparison. Part (a) shows the Landsat image captured on 26 April 2008 during the flood peak (bands 5, 4 and 3 as RGB) and (b) shows Landsat-derived flood extent on the same date for the area zoomed by the red square in part (a). Parts (c-e) show simulated peak flood extent by using DEM, S-DEM and H-DEM. Parts (f-h) show simulated peak flood extent by using DEM3sec, S-DEM3sec and H-DEM3sec. Parts (i-k) show simulated peak flood extent by using GDEM, S-GDEM and H-GDEM. Landsat-derived flood extent (b) has 30 m resolution and (c-k) have a 90 m grid size. Depth units are in (m) and are the difference at the maximum water level associated with the flood and the underlying elevation from the DEMs. Parts (i-k) have different depth range compared to (c-h).

4-19

Supplementary Fig. 2. Observed and simulated major 2000 flood extent comparison. Part (a) shows the Landsat image captured on 29 February 2000 during the flood peak (bands 5, 4 and 3 as RGB) and (b) shows Landsat-derived flood extent on the same date for the area zoomed by the red square in part (a). Parts (c-e) show simulated peak flood extent by using DEM, S-DEM and H-DEM. Parts (f-h) show simulated peak flood extent by using DEM3sec, S-DEM3sec and H-DEM3sec. Parts (i-k) show simulated peak flood extent by using GDEM, S-GDEM and H-GDEM. Landsat-derived flood extent (b) has 30 m resolution and (c-k) have a 90 m grid size. Depth units are in (m) and are the difference at the maximum water level associated with the flood and the underlying elevation from the DEMs. Parts (i-k) have different depth range compared to (c-h).

4-20

Supplementary Fig. 3. Grid size effect on simulated minor 2008 flood extent. Part (a) shows Landsat image captured during flood peak and (b) shows Landsat- derived flood extent on 26 April 2008 (bands 5, 4 and 3 as RGB). Parts (c-j) show simulated peak flood extent by using H-DEM in multiple 30, 60, 90, 120, 250, 500, 1000 and 2000 m grid sizes.

4-21

Supplementary Fig. 4. Terminal water storage after minor 2008 flood pulse. Part (a) shows Landsat image captured during flood peak and (b) shows Landsat- derived flood extent on 26 April 2008 (bands 5, 4 and 3 as RGB). Parts (c-e) show terminal water storage when using DEM, S-DEM and H-DEM. Parts (f-h) show terminal water storage by using DEM3sec, S-DEM3sec and H-DEM3sec. Parts (i-k) show terminal water storage by using GDEM, S-GDEM and H-GDEM. Landsat- derived flood extent has 30 m resolution. Parts (c-k) have a 90 m grid size. Parts (i-k) have different depth range compared to (c-h).

4-22

Supplementary Fig. 5. Terminal water storage after moderate 1990 flood pulse. Part (a) shows Landsat image captured during flood peak and (b) shows Landsat- derived flood extent on 29 April 1990 (bands 5, 4 and 3 as RGB). Parts (c-e) show terminal water storage when using DEM, S-DEM and H-DEM. Parts (f-h) show terminal water storage by using DEM3sec, S-DEM3sec and H-DEM3sec. Parts (i-k) show terminal water storage by using GDEM, S-GDEM and H-GDEM. Landsat- derived flood extent has 30 m resolution. Parts (c-k) have a 90 m grid size. Parts (i-k) have different depth range compared to (c-h).

4-23

Supplementary Fig. 6. Terminal water storage after major 2000 flood pulse. Part (a) shows Landsat image captured during flood peak and (b) shows Landsat- derived flood extent on 29 February 2000 (bands 5, 4 and 3 as RGB). Parts (c-e) show terminal water storage when using DEM, S-DEM and H-DEM. Parts (f-h) show terminal water storage by using DEM3sec, S-DEM3sec and H-DEM3sec. Parts (i-k) show terminal water storage by using GDEM, S-GDEM and H-GDEM. Landsat- derived flood extent has 30 m resolution Parts (c-k) have a 90 m grid size. Parts (i-k) have different depth range compared to (c-h).

4-24 Supplementary Tables:

Supplementary Table 1: dataset information of this study. Horizontal and vertical accuracy of SRTM quoted at 90% confidence level circular accuracy.

SRTM SRTM ASTER Landsat Registered survey ICESat/GLAS C-Band X-Band GDEM marks

60 m spots with172 points/concrete Grid size 30 m / 90 m 25 m 30 m 30 m m along-track monuments

Horizontal <=20 m ±20 3.7 m ±30 m 50 m < 1 m accuracy(abs.)

Horizontal <=20 m ±15 m _ _ _ < 1 m accuracy(rel.)

Vertical <=16 m ±16 m < 1 m ±20 m _ < 1 m accuracy(abs.)

Vertical <= 10 m ±6 m < 1 m _ _ < 1 m accuracy(rel.)

Horizontal datum WGS-84 WGS-84 TOPEX/Poseidon WGS-84 WGS-84 GRS80

EGM96 Vertical Datum Ellipsoid WGS84 EGM96 Geoid EGM96 Geoid _ AHD Geoid

4-25 Supplementary Table 2: DEM-to-control point elevation differences statistics of SRTM X-band DEM (XDEM), SRTM 3-arc-sec C-band DEM (DEM

3sec) and ASTER GDEM version 1 (GDEM1) in (i) original and after applying: (ii) 3D co-registration corrections; (iii) vegetation smoothing algorithm;

and (iv) hydrological corrections. Units of bias, SD and RMSD are meters. Total of 180583 GCPs used for XDEM and DEM 3sec, 373066 GCPs used in GDEM1.

Hydrologically Corrected (H- Original 3D Co-registration Vegetation Smoothed (S-DEMs) DEMs) DEM Bias SD RMSD R2 dx dy dz Bias SD RMSD R2 Bias SD RMSD R2 Bias SD RMSD R2

SRTM DEM 3sec +2.73 2.11 3.45 0.999 0.7 -5.7 -2.5 0 2.04 2.05 0.999 0 1.82 1.82 0.999 0 1.82 1.82 0.999

SRTM XDEM +0.78 2.90 3.01 0.999 0 -0.6 -2.5 0 2.90 2.90 0.999 0 3.22 3.22 0.998 0 3.21 3.21 0.999

ASTER GDEM -5.72 6.97 9.01 0.994 0.4 -5.4 5.4 -0.01 6.97 6.97 0.994 0 6.5 6.5 0.995 0 6.5 6.5 0.995

4-26 Supplementary Table 3: DEM-to-DEM elevation difference between original and after sequentially applying the (i) 3D co-registration; (ii) smoothing; and (iii) hydrological corrections for SRTM 90 m DEM and ASTER GDEM version 1. Mean bias and SD show the average and standard deviation of elevation changes after each step of corrections. Topographic roughness (TR) shows the averaged standard deviation of elevation changes in 3x3 and 5x5 moving window across the entire DEMs. Numbers in brackets are the percentage of roughness decrease due to vegetation smoothing and hydrological corrections relative to the roughness of original (co-registered) DEM. All units are in meters.

3D Co-Registration Vegetation Smoothed (SDEMs) Hydrologically Corrected (HDEMs) DEM Mean Mean Mean SD TR 3x3 TR 5x5 SD TR3x3 TR5x5 SD TR3x3 TR5x5 Bias Bias Bias

SRTM DEM3sec 3.18 0.64 1.4 1.85 3.14 1.04 0.84 (40%) 1.26 (32%) 3.22 1.88 1.02 (27%) 1.56 (16%)

ASTER GDEM V1 -5.66 0.07 0.97 1.53 -5.72 1.85 0.39 (60%) 0.66 (57%) -5.64 2.24 0.47 (52%) 0.78 (49%)

4-27

Chapter 5

Where does all the water go? Partitioning

water transmission loss in a data-sparse,

multi-channel and low-gradient dryland

river system using modelling and remote

sensing

Chapter 5

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing.

Abdollah A. Jarihani a,*, Joshua R. Larsen a, John N. Callow b, Tim R. McVicar c, Kasper

Johansen a (Family names or surnames are underlined) a School of Geography, Planning and Environmental Management, University of Queensland,

Brisbane, QLD 4072, Australia; Email: [email protected], J.L: [email protected], K.J:

[email protected] b Environmental Dynamics and Ecohydrology, School of Earth and Environment, University of

Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia; E-Mail:

[email protected] c CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia;

E-Mail: [email protected]

* Corresponding author:

E-Mail: [email protected]

Tel.: +61-07-3365-7027

Fax: +61-07-3365-6899

Submitted to: Journal of Hydrology

Submission date: 01 May 2015

Keywords: Transmission losses; Hydrodynamic modelling; Remote sensing; Water balance; Low gradient river systems.

5-1

Chapter 5

Abstract

Drylands cover approximately one-third of the Earth’s surface, are home to nearly 40 percent of the Earth’s population and are characterized by limited water resources and ephemeral river systems with an extremely variable flow regime and high transmission losses. These losses include actual evaporation, infiltration to the soil and groundwater and residual (terminal) water remaining after flood events. These critical components of the water balance of dryland river systems remain largely unknown due to the scarcity of observational data and the difficulty in accurately accounting for the flow distribution in such large multi-channel floodplain systems. While hydrodynamic models can test hypotheses concerning the water balance of infrequent flood events, the scarcity of flow measurement data inhibits model calibration, constrains model accuracy and therefore utility. This paper provides a novel approach to this problem by combining modelling, remotely-sensed data, and limited field measurements, to investigate the partitioning of flood transmissions losses based on seven flood events between February 2006 and April 2012 along a 180 km reach of the Diamantina River in the Lake Eyre Basin, Australia. Transmission losses were found to be high, on average 46% of total inflow within 180 km reach segment or 7 GL/km (range: 4 to 10 GL/km). However, in 180 km reach, transmission losses vary non-linearly with flood discharge, with smaller flows resulting in higher losses (up to 68%), which diminish in higher flows (down to 24%) and in general there is a minor increase in losses with distance downstream. Partitioning these total losses into the major components shows that actual evaporation was the most significant component (21.6% of total inflow), followed by infiltration (13.2%) and terminal water storage (11.2%). Lateral inflow can be up to 200% of upstream inflow (mean = 86%) and is therefore a critical parameter in the water balance and transmission loss calculations. This study shows that it is possible to constrain the water balance using hydrodynamic models in dryland river systems using remote sensing and simple field measurements to address the otherwise scarce availability of data. The results of this study also enable a better understanding of the water resources available for ecosystems in these unique multi-channel and large floodplain rivers. The combined modelling / remote sensing approach of this study can be applied elsewhere in the world to better understand the water balances and water transmission losses, important drivers of ecohydrological processes in dryland environments.

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1. Introduction

Drylands cover approximately one-third of the Earth’s surface and their limited water resources are under increasing pressure (Williams, 1999). Dryland regions are characterised by highly variable rainfall and episodic river flows (Tooth, 2000), often resulting in rivers with a unique geomorphological form—anabranching—which is characterised by low-gradient and multi-channel river systems with large floodplains that are graded to transmit large yet infrequent and slow-moving flood pulses. Anabranching rivers with sizeable floodplain are found in dryland regions across the world, including South America (Smith, 1986), China (Wang et al., 2005), Australia (Knighton and Nanson, 2001; Nanson et al., 1986; Schumm et al., 1996), North America (Schumann, 1989; Smith, 2009; Smith and Smith, 1980), Africa (Makaske, 2001) and Europe (Gurnell et al., 2009).

The wide floodplains of many of these rivers are often only inundated during major flood events at sub- decadal to multi-decadal frequencies (Costelloe et al., 2006; Jarihani et al., 2013; Kingsford et al., 1999; Knighton and Nanson, 1994). These large yet infrequent flood events fill enlarged channel segments (or waterholes), recharge aquifers (Cendón et al., 2010) and support the flora and fauna of these systems, which are often adapted to ‘boom-and-bust’ ecological cycles (Costelloe et al., 2006; Kingsford et al., 1999; Puckridge et al., 2000). These flood pulses are also known to have large transmission losses which result in diminishing discharge downstream and therefore exert considerable control on the water resource availability that is important to the ecohydrology of such systems (Knighton and Nanson, 1994).

The large transmission losses are broadly related to the majority of rainfall falling in the headwaters generating a relatively slow moving flood pulse travelling through a low-gradient and dry landscape with highly variable infiltration capacity and significant evaporative demands. Transmission losses are then a combination of actual evaporation (here direct evaporation from the flood water surface), infiltration to soils and groundwater and terminal water storage in waterholes and local floodplain depressions. Because of: (i) the remoteness of dryland rivers; (ii) their episodic flow regime; and (iii) low levels of economic development, there is often a lack of water gauging infrastructure (often gauges are tens-to-hundreds of kilometres apart, Costelloe et al., 2003) with which to directly estimate transmission losses. This restricts our ability to accurately understand flood dynamics, the partitioning of total transmission losses into its separate components, and ecohydrological processes of such systems. 5-3

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While dryland anabranching river systems are known to have some of the highest spatial and temporal variability in streamflow worldwide (Puckridge et al., 1998), how losses are partitioned as flow is transmitted downstream remains a key knowledge gap (Costelloe et al., 2003; Knighton and Nanson, 1994). Conventional modelling approaches to estimate transmission loss partitioning are also limited by the lack of adequate water gauging and climate observations. From the sparse gauging network in the ‘Channel Country’ rivers of the Lake Eyre Basin, Australia, current transmission loss estimates range between (70-98%) within ~350 km of the mid to lower catchment reaches (Costelloe et al., 2003; Knighton and Nanson, 1994; Thomas, 2011), and most years 100% of the flow is eventually lost in the lowest reaches of the catchment, or else enters the terminal Lake Eyre.

Estimating total transmission losses and/or individual components in dryland river systems has previously been undertaken using three main approaches (Cataldo et al., 2004; Cataldo et al., 2010): (i) small-scale field experiments (Dahan et al., 2008; Dunkerley and Brown, 1999; Dunkerley, 2008; Maurer, 2002; Parsons et al., 1999); (ii) interpolation of sparse streamflow networks using simple regression and/or differential equations (Arnott et al., 2009; Costelloe et al., 2006; Knighton and Nanson, 1994; Knighton and Nanson, 2001; McCallum et al., 2012; Schmadel et al., 2010); and (iii) water balance modelling to allow estimation of total and component transmission losses (Morin et al., 2009). Key papers for these approaches are summarised in Table 1, and includes examples where hydrodynamic modelling has incorporated remotely sensed data in order to: (i) provide input data; (ii) calibrate and validate such models; and (iii) estimate various components of transmission losses (Karim et al., 2011; Milewski et al., 2009; Sharma and Murthy, 1994).

In data-sparse dryland regions, remote sensing data have been used to provide:

(i) basic topographic forcing data (Callow et al., 2007; DeVogel et al., 2004; Hancock et al., 2006; Hirt et al., 2010; Jarihani et al., 2015; Leon and Cohen, 2012; Rexer and Hirt, 2014);

(ii) hydrometeorological data (Khan et al., 2012; Khan et al., 2011; Milewski et al., 2009; Xue et al., 2013; Yao et al., 2014);

(iii) flood inundation maps (Brivio et al., 2002; Cruz et al., 2010; Frazier and Page, 2000; Jarihani et al., 2014; Schumann et al., 2007; Sheng et al., 2001);

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(iv) quantification of flood levels (Baghdadi et al., 2011; Birkett and Beckley, 2010; Jarihani et al., 2013; Troitskaya et al., 2012; Zhang et al., 2011);

(v) discharge estimation (Callow and Boggs, 2013; Frappart et al., 2005; Kouraev et al., 2004; Smith et al., 1996; Zhang et al., 2004);

(vi) evapotranspiration estimation (Donohue et al., 2010; Glenn et al., 2011; Mohamed et al., 2004; Smettem et al., 2013; Zhang et al., 2009);

(vii) infiltration estimation (Frappart et al., 2008); and

(viii) estimation of terminal water storage (Frappart et al., 2005).

This demonstrates the potential for remote sensing to constrain individual components of hydrological processes is extremely high and now well recognised. However, the ability to integrate many of these individual components in order to parameterise, calibrate and validate hydrodynamic models in data- sparse dryland river systems for the investigation of the water balance dynamics is not well established.

To better understand the partitioning of transmission losses in dryland flood events, we used a novel combination of minimal field data, remote sensing, and hydrodynamic modelling. By using this modelling approach, we can advance our understanding of floods in these dryland landscapes and their implications for the water balance and ecology, and improve the status of modelling of remote and data-sparse hydrological systems. This paper has three objectives:

1) to build a hydrodynamic model using remotely-sensed inputs to augment traditional hydrological data for a series of linked reaches along a dryland anabranching river system; 2) to use a hydrodynamic model to investigate the water balance and associated uncertainties, and to establish the partitioning of transmission losses (actual evaporation, infiltration to the soil moisture store and terminal water storage); and 3) to evaluate the opportunities, limitations, potential and future directions of using remotely- sensed data to better understand water balance and hydrodynamics of dryland and data-sparse regions.

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Table 1. Summary of relevant transmission loss estimation research conducted in dryland environments (the current paper is added for completeness). Not all papers performed transmission losses estimation and flood routing modelling studies and these are denoted with a N/A representing ‘not applicable’ in the relevant part of the ‘Key results’ column. In the ‘Key results’ column the abovementioned three components are identified by the code: (1) estimating transmission losses; (2) flood routing; and (3) uncertainty analysis and use of remote sensing data. Study Data / Model used Location / Landscape / Study size Key results

1. Knighton Gauge data of Currareva and Cooper Creek, Lake Eyre Basin, 1. Transmission losses vary non-linearly with stage. Above a threshold and Nanson Nappa Marrie / No model Australia / dryland / 420 km reach flow of about 25% duration, Transmission Losses exceed 75%. (1994) used 2. N/A 3. N/A

2. Costelloe Gauge data from Diamantina Diamantina River, Lake Eyre Basin, 1. Transmission losses are 70-98% for floods with total discharge <2300 et al. (2003) Lakes to Birdsville / Australia / dryland / 330 km reach Mm3. conceptual grid-based (0.05° 2. Flood travel time was non-linear, increases with increasing discharge. x 0.05°) model 3. Satellite images are used to identify the flow-paths

3. Dunkerly Direct infiltration Small desert stream in western 1. Transmission losses in ephemeral streams may be minimized in near and Brown observations / No model used NSW, Australia / 7.6 km bank-full flow stage, and be higher in both sub-bank-full and (1999) overbank flows. Transmission losses in sub-bank flow were (13.2% per km) more than twice the bank-full rate. 2. N/A 3. N/A

4. McKenzi Gauge data / ISIS hydraulic Orange River, South Africa / 1. Actual evaporation rates from a flowing river were found to be in the e and Craig model extremely hot and arid area / same order of magnitude as Class A pan evaporation data. (2001) 1400 km. 2. The accuracy of flow recording gauges at low flows is insufficient to enable the losses to be estimated accurately. 3. N/A

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Table 1. (continued)

Study Data / Model used Location / Landscape / Study size Key results 5. Lange Gauge data / mathematical Kuiseb River, Namib Desert, 1. Transmission losses are minor during small to medium flows but (2005) flow routing scheme Namibia/ /arid/ 150 km. concentrate during high discharge peaks. (MVPMC4-method) 2. Water losses are mostly in flooded overbank areas. 3. N/A

6. Milewski Gauge data and remotely- Sinai Peninsula (61,000 km2) and the 1. Annual runoff in sub-catchments was 9% and annual groundwater et al. (2009) sensed TRMM, AVHRR and Eastern Desert (220,000 km2) of recharge was 19.6% of total precipitation. AMSR-E data / SWAT Egypt / arid area 2. Initial losses (e.g., infiltration and actual evaporation) were 71.4% of total precipitation. 3. Remote sensing data were used for hydrodynamic modelling set up.

7. Dahan et Field measurements of Flood Kuiseb River, Namibia/ hyper-arid 1. Average downward fluxes in the vadose zone was (∼10 mm/h) in al. (2008) and groundwater levels, and desert ephemeral river / 33 m cross relatively well-sorted sandy sediments channel bed. Large floods river bed Infiltration rate / No section of river. showed larger transmission losses. model used 2. N/A 3. N/A

8. Sharma Landsat images, gauge data / Luni Basin, India / arid region / 1. Transmission losses reduced the runoff by 8-56%. and Murthy linear regression and multiple reaches 23-186 km 2. Infiltration rate positively correlated (r2 = 0.56) with weighed mean (1994) differential equation. diameter of bed material. 3. Landsat images were used to extract channel geometry.

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Table 1. (continued)

Study Data / Model used Location / Landscape / Study size Key results 9. Walters Hydrographs / regression Arid zone wadi systems Saudi 1. Different regression equations were needed for smaller or larger floods. (1990) equations. Arabia / arid region/ 170-4930 km2 Upstream flow volume and channel length are important parameters especially for smaller flows. 2. N/A 3. N/A

10. Cataldo Gauge data / regression Western USA / arid ephemeral 1. The results indicate that discharge (either as total event volume or peak et al. (2010) equation streams / 1.4-252 km flow) and hydraulic conductivity are important factors affecting transmission losses. 2. N/A 3. N/A

11. Karim et MODIS daily images and Fitzroy River, Western Australia/ 1. Gauge records over predicted discharge at some location al. (2011) gauge data/ Mike 21 model large tropical catchment / 32,000 2. Hydrodynamic model can be used to investigate ground water recharge km2 in a topographically complex catchment. 3. Remote sensing can be used in calibrating hydrodynamic model in ungauged catchments and improves flood discharge estimation.

12. This Remotely-sensed altimeter Diamantina catchments, Lake Eyre 1. Lateral inflow is the most important source of uncertainty in water study satellites (Jason-2), optical Basin, Australia / arid, low-gradient balance calculation. Total transmission losses are high. Actual images (MODIS-derived and anabranching system / 55,721 evapotranspiration is the most important component of the inundation extent), ground- km2, 180 km reach transmission losses followed by infiltration and terminal water based logger (water storage. elevation) and gauge (water 2. Hydrodynamic model can be used to estimate water balance. elevation and discharge) data 3. Remote sensing can be used in hydrodynamic model in data-sparse, low- / 2D TUFLOW model gradient dryland catchments.

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2. Study site

The Diamantina River above the Diamantina Lakes gauging station (catchment area = 55,721 km2) was selected for this study (Fig. 1). The Diamantina River is a major tributary of Lake Eyre Basin (LEB), one of the world largest endorheic basins (spreading over 1.14 million km2), of arid central Australia. The Diamantina river rises in Swords Range and flows north-east before turning clockwise and flowing southwest where it is joined by two major tributaries (Mayne and Western Rivers) above the Diamantina Lakes gauging station (Fig. 1).

The river has a very low-gradient with a large anastomosing channel system which features up to hundreds of individual channels spread across a floodplain that can inundate to a width of up to 60 km (Bullard et al., 2007; Costelloe et al., 2003; Jarihani et al., 2015; Knighton and Nanson, 2001). Floods normally develop in the headwaters of the catchment and are more frequent during La Niña phases of the El Niño–Southern Oscillation (ENSO) phenomenon (Kotwicki and Allan, 1998). The annual rainfall of the area varies between 200 to 400 mm/yr and decreases downstream (Costelloe et al., 2003). Class A type pan evaporation ranges from 2400 to 3600 mm/yr with a pan coefficient of 0.5 to 0.6 and a reported potential evaporation of around 1800 to 2000 mm/yr for the LEB (Kotwicki and Allan, 1998). Given the strong rainfall seasonality, these arid zone rivers are also characterised by some of the most variable flow regimes in the world, with distinct runoff periods during the November – March wet season, separated by a distinct dry season of generally no flow, (April – October, Knighton and Nanson, 2001). The system is also notable for the water that can be retained year-round within enlarged channel segments, or waterholes, that become disconnected at low flow and which are critical ecological refuge sites (Bunn et al., 2006). River flow in the LEB is unregulated and sustains (albeit in ‘boom-and-bust’ cycles) high ecological value habitat (Costelloe et al., 2006) for migratory and non- migratory fish (Puckridge et al., 2000) and water-bird (Kingsford et al., 1999) populations. The composition of plant communities are related to soil type and moisture availability, both of which decrease in structure and complexity with distance from water bodies and river channels (Boyland et al., 1984; Capon, 2003). The majority of the floodplain sustains short grass and sparse scattered trees, with tree-lined channels (Boyland et al., 1984; Capon, 2003; Jarihani et al., 2015).

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Fig. 1. Study site map. (a) Location of study site in Diamantina Catchment, Lake Eyre Basin, Australia. The darker grey shows the Diamantina catchment area upstream of the Diamantina Lakes gauging station. Part (b) location of Diamantina Lakes gauging station, four virtual gauge stations, location of two ground tracks of the Jason-2 altimeter satellite and 11 sub-catchments, which are used for rainfall-runoff modelling.

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3. Materials

Water elevation loggers were installed within the main river channel and determine the extent of the four study reaches: Tulmur, Tulmur2, Verdun Valley and Brighton Downs (Fig. 1), with water elevation time series recorded every 15-minute from November 2011 to November 2013, and captured a multi-month flood event between January-April 2012 (Fig. 2). Diamantina Lakes gauging station’s (commenced 1965 and is operational, 002104A, see Fig. 1) 15-minute water elevation and discharge data for this study period, 2005-2013, were acquired from the Queensland Department of Natural Resources and Mines.

Jarihani et al. (2015) demonstrated that the SRTM-derived Digital Elevation Models “H-DEM” hydrologically enforced to the known channel network (Gallant et al., 2011) of Geoscience Australia (GA) is the most accurate available topographic data (mean bias = 0.00 m, RMSD = 1.8 m) for hydrodynamic modelling in low-gradient anabranching river systems. Therefore the H-DEM (30 m) was used in this study for topographic forcing of the hydrodynamic model. TUFLOW was used to automatically resample the DEM to a 120 m grid size using the triangulation technique (Jarihani et al., 2015).

Water elevations with a temporal frequency of 10-days were also available from Jason-2 altimeter satellite data at the location of two virtual stations, Tulmur and Brighton Downs (see Fig. 1). These data were acquired from Coastal and Hydrological products (PISTACH, (Mercier et al., 2008)) which is available from July 2008 onwards, and has been previously shown by Jarihani et.al. (2013) to have a mean accuracy of −0.04 m, (RMSD = 0.28 m) for this type of application.

Daily water areal extents extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) and classified using the Open Water Likelihood index (OWL) (Guerschman et al., 2011) were used to map and monitor the flood footprint. MODIS OWL utilises the strong relationship between the Normalized Difference Vegetation Index (NDVI; (Rouse et al., 1973)), Normalized Difference Water Index (NDWI; (McFeeters, 1996)) the shortwave infrared (SWIR) bands and surface water to produce fractional area water coverage (Ticehurst et al., 2014). The MODIS OWL daily low spatial resolution (500 m) water maps have been shown to be efficient for mapping medium to large water features; however, it lacks fine spatial detail and underestimates narrow water bodies around the edge of the flood. It has been shown that removing pixels containing less than 6% water from MODIS

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OWL, can produce highly accurate surface water maps (Ticehurst et al., 2014). The MODIS OWL product has been used to: (i) calibrate hydrodynamic models (Karim et al., 2011); (ii) map wetlands (Chen et al., 2013); and (iii) estimate floodplain recharge (Doble et al., 2014).

Seven flood events from July 2005 to November 2013 were used in this study (Fig. 2). Daily gridded 0.05° by 0.05° (~ 5 km) rainfall data (Jones et al., 2009) were used as input to the simple rainfall-runoff model within TUFLOW. Fig. 2 shows amount of rainfall over sub-catchments between four virtual stations and the Diamantina Lake gauge station.

5000 0

4000 5000

3000 10000 Rain above Tulmur Flood 4 Rain Tulmur to Diamantina Discharge Diamantina Lakes

2000 15000 Discharge (GL/month)

Flood 1 Flood 2 Flood 3 Food 5 Flood 6 Flood 7 TotalRainfall (GL/month) 1000 20000

0 25000

Jul-06 Jul-09 Jul-12 Jul-05 Jul-07 Jul-08 Jul-10 Jul-11 Jul-13

Nov-05 Nov-06 Nov-07 Nov-08 Nov-09 Nov-10 Nov-11 Nov-12 Nov-13

Mar-06 Mar-07 Mar-08 Mar-09 Mar-10 Mar-11 Mar-12 Mar-13 Date (month)

Fig. 2. Monthly discharge and rainfall from July 2005 to November 2013. Monthly discharge at Diamantina Lakes gauging station is shown for seven flood events (Flood 1 to 7) that were all modelled herein. Total rainfall (GL) of the sub-catchments between the four virtual stations and the Diamantina Lake gauge station is also presented. Water elevations were also recorded from 01 November 2011 to 30 November 2013 at the four virtual gauges capturing all of Flood 7. The temporal extent of the 7 floods are defined by the following dates pairs: (i) 07 Feb 2006 to 05 May 2006; (ii) 08 Jan 2007 to 11 Aug 2007; (iii) 14 Nov 2007 to 16 Mar 2008; (iv) 25 Dec 2008 to 21 Mar 2009; (v) 25 Dec 2009 to 12 May 2010; (vi) 14 Oct 2010 to 19 Apr 2011; and (vii) 22 Nov 2011 to 13 April 2012. 5-12

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4. Methods

4.1. Initial model calibration

A ubiquitous feature of anabranching rivers is that they have a large number of individual channels of various sizes within wide floodplains (Tooth and Nanson, 1999). Because of the multiple channels and inconsistent geometry, 0-dimensional (0D) (Mohammadi et al., 2013) and 1-dimensional (1D) hydrodynamic models (Merwade et al., 2008) have limitations in routing flow accurately. More computationally-intensive 2D hydrodynamic models are therefore required when routing water between the complex and discontinuous channels that exist between cross sections which are a characteristic feature of anabranching rivers. The Two-dimensional Unsteady FLOW (TUFLOW) model (Syme, 1991), which utilises Stelling’s (1983) 2D numerical solutions for the shallow water wave equations was used. TUFLOW is designed for shallow water flow applications, and has previously been used in such systems (Jarihani et al., 2015).

Four virtual gauging stations (two with additional validations from satellite altimetry) were created, above the only available gauge station at Diamantina Lakes (Fig. 1). Diamantina Lakes gauge provides the downstream boundary condition for hydrodynamic model. Water elevation data was recorded using pressure transducers at each virtual gauging station and later converted to discharge using a TUFLOW constructed rating curve (further details below in Section 4.2). Based on water elevation and discharge at these five locations, the river was divided into four Reaches; each bounded with a station at either end (Fig. 3). Table 2 summarises the hydrodynamic characteristics of these four river Reaches.

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Table 2. Information of river reaches and gauge stations. Total reach channel system area is calculated taking into account the meandering nature of the river. See Fig. 1 for upstream and downstream of reaches. Gauge Reach # Reach length straight (km) Average reach Average reach channel

elevation (m) / lon, lat / Reach length meander slope (m/km) system width (km) /

(km) Total reach channel

system area (km2)

Tulmur

(140) / 142.3° E, 22.59° S 1 56.5 / 59 0.25 2.37 / 139.6

Tulmur2

(125) / 141.88° E, 22.92° S 2 19.5 / 21 0.27 4.05 / 81.1

Verdun Valley

(119.5) / 141.74° E, 23.04° S 3 42.5 / 45 0.23 6.65 / 299.2

Brighton Downs

(109.5) / 141.50° E,23.36° S 4 52.5 / 55 0.23 6.79 / 373.6

Diamantina Lakes Total

(93.5) / 141.12° E, 23.69° S (1 to 4) 172 / 180 0.245 4.96 / 893

A rainfall-runoff model was needed to estimate the lateral inflow (or tributary inputs) for each of four Reaches during the 7 flood events, and the TUFLOW ‘direct rainfall’ module was used. Sub-catchment polygons were used to apply the daily rainfall directly onto the 2D hydrodynamic model domain. Initial and continuous losses into the ground (differentiated by soil type, see below) and actual evaporation losses were applied to each model grid (120 x 120 m) to calculate excess rainfall at each model time step. The rainfall-runoff model outputs were calibrated to the Jason-2 altimetry data at the two virtual gauging stations (Fig. 1) and the Diamantina Lakes gauge data. The initial and continuous infiltration losses were calibrated in this study to obtain the total water volume as close as possible to observed discharge. For all seven flood events (#1 to #7) the control point (validation point) is the Diamantina gauge station.

Flow resistance (roughness - Manning’s n in TUFLOW) was estimated by iteratively adjusting the n value to match the timing of the hydrograph peak (i.e., flood celerity) between each virtual station and 5-14

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Diamantina Lakes gauging station. Classified daily 500 m MODIS OWL data were also used to calibrate Manning’s n. Movement of the flood pulse front (i.e., the location of downstream wet/dry boundary) was used to calibrate the model rather than total inundation area as the later has higher uncertainty and was affected by factors other than floodplain roughness.

The adaptive time-stepping functionality of TUFLOW, incorporating the Courant stability criterion (Courant et al., 1967), was employed to satisfy numerical stability criteria. To avoid the effects of initial instability, the model simulation period was selected to be larger than the actual analysis period and the first five days of each model run were used as model ‘spin-up’ and were not included in data analysis (Beven, 2008; Van Der Knijff et al., 2008).

4.2. Water balance and transmission loss partitioning

The water balance and from the final calibrated model was first assessed in relation to: (i) transmission loss partitioning; and (ii) spatial and temporal variability. We applied a reach-based water balance approach (Fig. 3 / Eq. 1a) to quantify the total losses and /or gains within each reach. The hydrodynamic model provides estimations of the inflow, outflow and transmission loss components (i.e., actual evaporation, infiltration to the soil moisture store and terminal water storage) by using the continuity of the flow mass balance (Eq. 1a). The logger records of the 2011-2012 flood hydrograph were used as inflow/outflow to the water balance equation. For the seven flood events between February 2006 and April 2012 (Fig. 2), in the absence of logger data, the rainfall-runoff model produced discharge at the location of the four virtual stations that were used as inflow/outflow.

푄푖푛 + 푄푙푎푡 = 푄표푢푡 + 푇푇퐿 + 퐸푟푟표푟 (Eq.1a)

TTL = Ea + (IIL + CIL) + TWS (Eq.1b)

In Eq.1a, Qin is upstream inflow to the reach, Qout is the downstream outflow, Qlat is lateral inflow to the system (tributary inputs) and TTL is total transmission losses. In Eq.1b, TTL is a combination of actual evaporation (Ea), initial infiltration loss (IIL), continuous infiltration rate (CIL) and terminal water storage (TWS). IIL and CIL are added to define the infiltration loss (IL). Due to: (i) the arid climate (Jarihani et al., 2013; Jarihani et al., 2014); (ii) the groundwater table being considerably deeper than the major channels (Cendón et al., 2010; Knighton and Nanson, 2001); and (iii) that modelling was undertaken exclusively for the flood events, groundwater inflow to the reaches is 5-15

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negligible and was excluded from the model structure. The absolute error term in Eq.1.a was calculated by comparing the modelled outflow with: (i) logger recorded flows for the 2011 flood (i.e., Flood 7 in Fig. 2) at the four virtual stations; and (ii) the Diamantina Lakes gauge observations.

Fig. 3. Schematic view of reaches and flood water balance equation components.

To consider lateral inflow into the hydrodynamic model, the ArcHydro tool of ArcGIS was used to delineate 10 sub-catchments between Tulmur and Diamantina Lakes gauging stations from the H-DEM (there are 11 sub-catchments shown on Fig. 1 and one is above Tulmur). The discharge of each sub- catchment was linked to the hydrodynamic model domain at the junction of the sub-catchments and the main river system. Daily rainfall data of all 11 sub-catchments above Diamantina Lakes (i.e., one above Tulmur and 10 between Tulmur and Diamantina Lakes) were used as input to the rainfall-runoff model.

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A wide variety of pan coefficients (0.6 to 1.0) have been used to convert pan evaporation rates into actual evaporation rates from large water surfaces (Kotwicki, 2000; McKenzie and Craig, 2001). As the rainfall events causing these flood pulses are significantly above-average, and the vegetation cover is relatively sparse, it can be assumed that interception of rainfall from the surface of vegetation is negligible (Dunkerley, 2000).

In the absence of ground-based actual evaporation observations, remote sensing and gridded meteorological based estimates of Penman potential evaporation (Donohue et al., 2010) was used to estimate actual evaporation rate within the water balance calculations. In drylands the actual evaporation from an open waterbody can be considered energy-limited (not water-limited), and thus, we assumed the actual evaporation from the flood footprint and terminal stored water to be equal to the Penman potential evaporation rate.

The Initial Infiltration Loss/Continuous Infiltration Loss (IIL/CIL) method (El-Kafagee and Rahman, 2011; Hill, 1996) was used to estimate infiltration in the model domain. The IIL and CIL components of infiltration were estimated by considering the study sites’ dominant soil type (clay); using soil data available from the Soil and Landscape Grid of Australia website (http://www.clw.csiro.au/aclep/soilandlandscapegrid/) for different depth ranges (0 to 200 cm).

The final component of transmission losses considered here is terminal water storage, which is the volume of water captured in topographic depressions and channels following the cessation of the flood. This was estimated using the hydrodynamic model water elevation output extents to extract the total captured water volume in every reach of the river at the end of the event (defined as zero outflow).

4.3. Uncertainty and limitations

Previous studies have shown that infiltration losses for the Diamantina River floodplain are higher during the initial stages of flood events, due to the presence of ‘cracking clays’ with large fissures allowing for significant initial infiltration that may then seal following the swelling of the clay (Costelloe et al., 2003; Knighton and Nanson, 1994; McMahon et al., 2008). To investigate the sensitivity of the rainfall-runoff and hydrodynamic model to the infiltration rate, varying rates for IIL (0, 10, 20, 30, 40 and 50 mm) and CIL (0, 0.1, 0.2, 0.3, 0.4 and 0.5 mm/hr) were considered and their effect on the water balance of each river reach was investigated by comparing the modelled and recorded total outflow at the Diamantina gauge station. 5-17

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The initial infiltration transmission losses are also known to be highly sensitive to the initial moisture and antecedent conditions. Multiple IIL values were used depending on whether flow events were on a dry or wet floodplain to account for the possibility of wet antecedent conditions, for example when one flow event happens soon after a previous one.

Sensitivity analysis on the Manning’s n roughness parameter was performed by using a range of n values (0.01 to 0.1 with 0.01 consistent increment) in the model and comparing the model outputs to discharge data at Diamantina Lakes gauging station. Uncertainty in calculating the water balance components related to satellite altimetry was also investigated by comparing the impact of the absolute water elevation error on discharge estimates at each of the virtual gauging stations. The uncertainty related to the DEM data was also investigated by comparing the DEM-derived and survey-based rating curves at Diamantina Lakes gauging station.

5. Results

5.1. Initial model calibration

The results showed that calibrating to both peak flow height and timing of peak flow was difficult due to the low spatial (0.05° by 0.05°) resolution and temporal (daily) frequency of the rainfall data. As our main aim focuses on the water balance, and not the flow centre of mass, we calibrated the rainfall- runoff model to total water mass only. The rainfall-runoff model and IIL/CIL parameters were calibrated by minimising the difference between observations and simulations of flood total water mass during the Nov 2011 to Apr 2012 flood event (Flood #7; see Fig 2) at the virtual stations (Table 3). The rainfall-runoff model for the three flood events (Floods #4 to #6; see Fig. 2) between December 2008 to May 2011 was calibrated by comparing modelled water elevation with satellite altimetry derived water elevation time series at the two virtual gauging stations (see Fig. 1) with altimetry tracks crossings the river. The rainfall-runoff model of the Flood #1 to Flood #3 were calibrated to discharge at Diamantina Lakes gauging station. The reach-based hydrodynamic model of flood inundation footprint was also calibrated to MODIS OWL daily flood maps from February 2006 to April 2012.

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Table 3. Rainfall-runoff and hydrodynamic models calibration locations and parameters. In this table Qobs is observed discharge, Qsim is simulated discharge, IIL is initial infiltration loss for each flood event, CIL is average continuous loss during flood event and Ea is averaged actual evaporation during flood event over flood inundation area. Qerr was calculated by using (Qobs – Qsim) / Qobs)*100. See caption of Fig.2 for duration of seven flood events.

Qobs IIL CIL Ea Qsim Q err Flood # Calibration station (GL) Manning’s n (mm) (mm/hr) (mm/hr) (GL) (%) 1 Diamantina Lakes 581 0.04 10 0.4 0.29 544 -6 2 Diamantina Lakes 370 0.04 10 0.3 0.26 330 -11 3 Diamantina Lakes 473 0.04 10 0.25 0.35 513 8 4 Diamantina Lakes 4925 0.04 10 0.5 0.32 4967 1 5 Diamantina Lakes 1855 0.04 20 0.4 0.29 2095 13 6 Diamantina Lakes 1869 0.04 20 0.2 0.29 2164 16

7 Tulmur 993 0.04 40 0.3 0.3 1045 5 Tulmur2 1522 0.04 20 0.3 0.3 1524 0 Verdun Valley 1524 0.04 20 0.3 0.3 1482 -3 Brighton Downs 1569 0.04 20 0.3 0.3 1377 -12 Diamantina Lakes 2045 0.04 40 0.2 0.3 2098 3

Sensitivity of the hydrodynamic model to the roughness parameter (Manning’s n) was also investigated. Absolute changes in the water elevation in response to varying Manning’s n were lower for the wide floodplain reaches, particularly those lower in the catchment proximal to Brighton Downs, relative to more confined reaches that were typical of the upper parts of the catchments (i.e., proximal to Tulmur) but also at the Diamantina Lake gauging station (and the reason for the selection of this site as a gauging station). For example, when the Manning’s n value varied between 0.01 and 0.1, a flow rate of 2000 m3/s produced 3 m of change in water elevation at Tulmur, 2.4 m for Diamantina Lakes and Tulmur2, 1.7 m for Verdun Valley, and 0.3 m for Brighton Downs. For flow ranges between 0 and 10000 m3/s, Tulmur2 showed the highest depth change (8.73 m), followed by Tulmur (7.06 m), Diamantina Lakes (5.63 m), Verdun Valley (3.99 m) and Brighton Downs (1.7 m). For discharges < 1000 m3/s, both rising and falling limbs of hydrographs showed different discharge (higher discharge during the rising limb) for the same water elevation due to the higher water surface gradient during the rising limb. Altering the Manning’s n value varied between 0.01 and 0.1 also caused changes in the transmission time of the flood pulse from Tulmur station to Diamantina Lake station (180 km) from 21 to 111 hours corresponding to changes in the average celerity of the flood wave was between 0.45 to 2.4 m/s. Based on the calibration and sensitivity testing, a single Manning’s n of 0.04 was selected for all Reaches (Table 3).

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Comparing DEM-derived rating curves with the surveyed rating curve showed that there are larger errors for small discharge events < 2000 m3/s due to higher differences between the SRTM-derived river cross section and the surveyed cross section. This is due to inherent error in the SRTM DEM relative to surveying in capturing multiple small floodplain channels plus additional error introduced by resampling the DEM from its original 30 m spatial resolution to a lower spatial resolution (120 m) to allow for computational efficiency (Jarihani et al., 2015). In addition, there was a 2 m difference between the ‘zero-point’ (i.e., the gauge reading corresponding to no discharge) of the DEM-derived (94 m) and ground based (92 m) rating curves, which was caused by overall uncertainty in the SRTM DEM. Hydrodynamic modelling is very challenging in multi-channel systems with wide floodplains and low gradients even in catchments with adequate gauging data. Previous work by Jarihani et al. (2015) showed that DEM precision in replicating individual channel features and spatial resolution become less important for larger flows (such as the seven flood evens considered in this study) as the proportion of in-channel flow in relation to total flow decreases.

5.2. Water balance and transmission loss partitioning

The different components of Equation 1, including: (i) inflow to and outflow from the reach; (ii) infiltration; (iii) actual evaporation; (iv) lateral inflow; and (vi) terminal water storage are estimated in this section. Table 4 presents water balance and transmission loss components calculated for each of four reaches (Reaches 1 to 4, see Fig. 3) individually, and also as a combined 180 km reach. Water balance components are presented for each seven flood events from February 2006 to April 2012 (Fig. 2).

The total upstream inflow at Tulmur ranged between 494 GL and 4926 GL for two minor (Flood #1, February 2006 to May 2006) and major (Flood #4, December 2008 to March 2009) flood events, respectively. The inflow values were 4% to 30% (mean = 14%) of the upstream total volume of rainfall. During the seven floods, the outflow downstream of four Reaches (at the Tulmur2, Verdun Valley, Brighton Downs and Diamantina Lakes gauging station) ranged between 330 GL and 4967 GL. Depending on the magnitude of the total transmission losses relative to lateral inflow, outflow from the system could be larger than inflow to the system (total transmission losses < lateral inflow), however this was for two flood events.

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The calculated lateral inflow of the four river Reaches was highly variable and a significant contributor to the water balance in some reaches, ranging from 4 GL to 1353 GL (0.5 % – 78% of upstream inflow, mean = 19%). The lateral inflow decreased from upstream to downstream due to the decrease in rainfall in southern parts of the Diamantina catchments (Fig. 4). For example, during the largest recorded flood event (#4), the smaller (9079 km2) sub-catchment of Reach 1 produced higher (1214 GL) lateral inflow than the sub-catchment of Reach 4, covering 13,299 km2, which produced an estimated 582 GL of lateral inflow, hence our results quantify an important process that has previously only been qualitatively described in terms of downstream water balance behaviour in these systems.

The reach-level terminal water storage between all seven floods was on average was 1.30 GL/km varying between 0.55 to 2.26 GL/km. Upstream reaches generally had lower terminal water storage (Fig. 4), with the loss increasing down-system as the floodplain width increased and along with the number of channels. For example, Reach 1 had 1.03 GL/km, Reach 2 1.79 GL/km, followed by Reach 3 (1.09 GL/km) and Reach 4 (1.27 GL/km). Smaller flood events (i.e., Flood #1) showed a higher proportion of terminal water storage relative to total inflow compared with larger flood events (i.e., Flood #4). The average terminal water storage for all Reaches accounted for around 4 % of the total inflow (upstream inflow + lateral inflow generated within the reach) to the river system, varying between 0.5% and 16% (Table 4).

The reach scale actual evaporation across the seven floods had a range from 0.91 to 7.25 GL/km with an average of 3.39 GL/km. The proportion of actual evaporation compared to the total inflow for each river reach was on average 7.7%, ranging from 1.5% to 19.1% of the upstream and lateral inflow to the river reach. Larger flood events showed higher evaporation losses than minor flood events (see Table 4 and Fig. 4). Actual evaporation generally increased from upstream to downstream, also due to the increase in channel/floodplain width (Table 2). Evaporation was on average (for the seven flood events) 2.34 GL/km for Reach 1, 3.31 GL/km for Reach 2, 4.09 GL/km for Reach 3 and 3.81 GL/km for Reach 4. Flood events with longer duration and multiple flood pulses had higher total evaporation losses than flood events with shorter duration. For example, flood # 5, with six months duration (Fig. 2), had a total evaporation of 910 GL compared with Flood #4, with four months duration (and similar magnitude of Flood #5), which had a total actual evaporation loss of 636 GL.

Across the seven floods, reach-level total infiltration (IIL+CIL) ranged from 0.68 to 5.35 GL/km with an average value of 2.11 GL/km. Infiltration was on average 4.6 % of the total inflow to the river reach 5-21

Chapter 5

(range; 1.4 % to10.6%). Downstream reaches showed that a higher infiltration rate (Table 4) is more likely due to greater channel/floodplain width and longer water inundation period (wetting period). The average infiltration for all floods was 1.46 GL/km for Reach 1, 2.06 GL/km for Reach 2, 2.55 GL/km for Reach 3 and 2.34 GL/km for Reach 4. The IIL was approximately 10% of the total infiltration (on average 0.23 GL/km) compared with CIL, which represented 90% of total infiltration (average of 1.88 GL/km).

For the seven floods, reach-level total transmission losses varied between 3.02 and 12.04 GL/km with an average of 6.79 GL/km (Fig. 5 / Table 4). The upper reach, with lower channel/floodplain width and steeper slopes (Table 2) had lower total transmission losses on average (Reach 1 = 4.84 GL/km) compared with the downstream reaches that have lower gradients and a wider channel/floodplain system width (Reach 2 = 7.16 GL/km; Reach 3 = 7.74 GL/km and Reach 4 = 7.42 GL/km). The total transmission losses at the reach level ranged from 3.9 % to 39.2 % (mean = 16.3 %) of the total inflow. Actual evaporation counted for the highest proportion (26 % to 67 %; mean = 48 %) of the total transmission losses followed by infiltration (15 % to 58 %; mean = 29 %) and terminal water storage (7 % to 47 %; mean = 23 %).

Combining the whole 180 km reach, for seven floods, total transmission losses varied between 3.85 to 9.91 GL/km with an average of 6.62 GL/km, (or 23% to 68% of total inflow, mean = 46%). All-reach (180 km) actual evaporation had the highest proportion (9.8 % to 35 %; mean = 21.6 %) of the total transmission losses followed by infiltration (8.4 % to 18.6 %; mean = 13.2 %) and terminal water storage (2.2 % to 27.4 %; mean = 11.2 %). In general, total transmission losses increases from upstream to downstream due to increase in channel/floodplain width and water residence time (Fig. 5). Minor flood events had higher relative total transmission losses (~0.006 GL/km, Fig. 6) than major flood events (~0.002 GL/km, see Fig. 6), however in absolute terms the loss rate (GL/km) increases with the size of the event until a threshold rate (~8 GL/km).

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Table 4. Water balance equation components (rainfall, inflow, lateral inflow, outflow, actual evaporation, infiltration and terminal water storage) calculated for each individual river reach, Reach 1, Reach 2, Reach 3, Reach 4 and all reaches (1 to 4) together for seven flood events between February 2006 to April 2012. In the heading,

T = number of hours that each Reach was in flood each year, Pup=upstream precipitation, Psub=sub-catchment precipitation, Qin= inflow, Qout= outflow, Qlat= lateral inflow, Ea= actual evaporation, IIL=initial infiltration loss, CIL=continuous infiltration loss, TWS=terminal water storage and TTL=total transmission losses.

Flood T Pup Psub Qin Qlat Qin+Qlat Qout Ea IIL CIL IIL+CIL TWS TTL Ea IIL CIL IIL+CIL TWS TTL Reach # (hr) (GL) (GL) (GL) Qin/Pup (GL) (GL) (GL) (GL) (GL) (GL) (GL) (GL) (GL) (GL/km) (GL/km) (GL/km) (GL/km) (GL/km) GL/km) Reach 1: 1 3000 6406 1315 1023 0.16 94 1117 939 54 6 53 59 66 178 0.91 0.10 0.89 0.99 1.11 3.02 Tulmur - 2 5100 8273 1705 747 0.09 147 894 702 60 6 45 51 80 192 1.02 0.10 0.77 0.87 1.36 3.25 Tulmur2 3 4000 7683 1742 963 0.13 152 1114 921 109 6 34 40 44 193 1.84 0.10 0.58 0.68 0.75 3.28 59 km 4 2700 12785 4017 3831 0.30 1214 5045 4706 136 6 158 164 39 339 2.30 0.10 2.67 2.77 0.67 5.74 5 4500 11656 4446 2145 0.18 1353 3499 3083 202 12 128 140 74 416 3.42 0.20 2.17 2.38 1.25 7.05 6 7000 13228 4829 1269 0.10 987 2257 1853 255 12 74 86 64 404 4.32 0.20 1.25 1.45 1.08 6.85 7 4500 10583 3595 1580 0.15 775 2356 2080 151 24 40 64 60 275 2.56 0.41 0.68 1.09 1.02 4.67

Reach 2: 1 3000 7721 100 939 0.12 4 944 852 23 3 23 26 42 92 1.12 0.13 1.09 1.22 2.02 4.36 Tulmur2 - 2 5100 9978 193 702 0.07 22 724 626 27 3 21 23 47 98 1.31 0.13 0.99 1.12 2.26 4.68 Verdun 3 4000 9426 243 921 0.10 21 942 842 55 3 17 20 25 100 2.61 0.13 0.82 0.95 1.21 4.77 21 km 4 2700 16802 488 4706 0.28 159 4865 4678 73 3 85 88 26 188 3.49 0.13 4.05 4.18 1.26 8.93 5 4500 16102 611 3083 0.19 249 3332 3109 107 5 68 74 42 223 5.11 0.26 3.24 3.50 2.02 10.63 6 7000 18057 636 1853 0.10 198 2051 1849 122 5 35 41 40 202 5.79 0.26 1.68 1.94 1.89 9.61 7 4500 14178 539 2080 0.15 142 2223 2073 79 11 21 32 39 150 3.76 0.52 1.00 1.53 1.86 7.15

Reach 3: 1 3000 7821 692 852 0.11 43 895 706 59 7 58 65 65 189 1.32 0.16 1.29 1.45 1.44 4.20 Verdun - 2 5100 10171 1028 626 0.06 58 684 494 67 7 51 58 64 190 1.50 0.16 1.13 1.29 1.43 4.22 Brighton 3 4000 9669 970 842 0.09 52 894 697 126 7 40 47 25 198 2.80 0.16 0.88 1.04 0.55 4.39 45 km 4 2700 17289 2653 4678 0.27 721 5399 4926 201 7 234 241 31 473 4.47 0.16 5.19 5.35 0.69 10.51 5 4500 16712 2947 3109 0.19 811 3920 3378 289 14 183 198 56 542 6.42 0.32 4.07 4.39 1.23 12.04 6 7000 18694 3064 1849 0.10 594 2443 1967 321 14 93 107 48 476 7.13 0.32 2.07 2.38 1.07 10.59 7 4500 14717 2287 2073 0.14 349 2421 2053 225 29 60 88 56 369 4.99 0.64 1.33 1.97 1.24 8.19

1 3000 8513 1594 706 0.08 96 802 544 79 7 77 84 95 258 1.43 0.14 1.40 1.54 1.72 4.69 Reach 4: 2 5100 11200 2100 494 0.04 49 543 330 67 7 50 58 88 213 1.22 0.14 0.92 1.05 1.60 3.87 Brighton - 3 4000 10638 1974 697 0.07 45 741 513 141 7 44 52 35 229 2.57 0.14 0.81 0.94 0.64 4.16 Diamantina 4 2700 19942 3723 4926 0.25 582 5508 4967 226 7 263 270 44 541 4.11 0.14 4.78 4.91 0.80 9.83 Lakes 5 4500 19660 4411 3378 0.17 431 3809 3206 313 15 198 213 77 603 5.68 0.27 3.61 3.88 1.40 10.96 55 km 6 7000 21758 6214 1967 0.09 796 2762 2164 399 15 116 130 69 599 7.25 0.27 2.10 2.37 1.26 10.88 7 4500 17005 4502 2053 0.12 459 2511 2098 241 30 64 94 79 414 4.38 0.54 1.17 1.71 1.43 7.52

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Chapter 5

Table 4. (continued)

Flood T Pup Psub Qin Qlat Qin+Qlat Qout Ea IIL CIL IIL+CIL TWS TTL Ea IIL CIL IIL+CIL TWS TTL Reach # (hr) (GL) (GL) (GL) Qin/Pup (GL) (GL) (GL) (GL) (GL) (GL) (GL) (GL) (GL) (GL/km) (GL/km) (GL/km) (GL/km) (GL/km) GL/km) Reaches 1-4: 1 3000 10107 3702 1023 0.10 237 1261 544 215 23 211 234 268 717 1.19 0.13 1.17 1.30 1.49 3.98 Tulmur - 2 5100 13300 5027 747 0.06 276 1023 330 222 23 167 191 280 693 1.23 0.13 0.93 1.06 1.55 3.85 Diamantina 3 4000 12613 4929 963 0.08 270 1232 513 431 23 135 159 130 720 2.39 0.13 0.75 0.88 0.72 4.00 Lakes 180 km 4 2700 23666 10881 3831 0.16 2676 6507 4967 636 23 739 763 141 1540 3.53 0.13 4.11 4.24 0.78 8.55 5 4500 24071 12415 2145 0.09 2845 4990 3206 911 47 578 624 249 1784 5.06 0.26 3.21 3.47 1.38 9.91 6 7000 27971 14744 1269 0.05 2576 3845 2164 1096 47 317 364 221 1681 6.09 0.26 1.76 2.02 1.23 9.34 7 4500 21506 10924 1580 0.07 1725 3306 2098 695 93 185 279 233 1208 3.86 0.52 1.03 1.55 1.30 6.71

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Chapter 5

1.5 (a) Reach 1 1 Flood #1 Flood #2 Flood #3 Flood #4 Flood #5 Flood #6 Flood #7 0.5

-0.5 1.5 (b) Reach 2 1

0.5

-0.5 1.5 (c) Reach 3 1

0.5

-0.5 1.5 (d) Reach 4 1

0.5

-0.5 1.5

1 (e) Reaches 1-4

0.5 0

-0.5

Normalised Normalised component -1 -1.5 Qlat Ea IIL CIL TWS TTL Qout Fig. 4. Relative magnitude of the water balance equation’s components to inflow for each individual river reach, Reach 1, Reach 2, Reach 3, Reach 4 and combined (1 to 4) reach for seven flood events between February 2006 to April 2012. Qlat = lateral inflow, Ea = actual evaporation, IIL = initial infiltration loss, CIL = continuous infiltration loss, TWS = terminal water storage, Qout = outflow and TTL = total transmission losses. Parts a-d present components values of Reaches 1 to 4, respectively, and part (e) shows these components for a combined 180 km river reach. All these components are normalized to inflow of seven flood events from February 2006 to April 2012. Total transmission losses (TTL) = Ea + IIL + CIL + TWS. All values were normalized to the reach upstream inflow. The legend in part (a) applies to all other parts. 5-25

Chapter 5

6000 (a) minor flood (Flood #2) Gain

4000 Loss Q 2000

0 Total volume Totalvolume (GL) -2000

6000 (b) minor to moderate flood (Flood #6)

4000

2000

0 Total volume Totalvolume (GL) -2000

6000 (c) moderate flood (Flood #5)

4000

2000

0 Total volume Totalvolume (GL) -2000

6000 (d) major flood (Flood #4)

4000

2000

0 Total volume Totalvolume (GL)

-2000 Tulmur Tulmur2 Verdun Brighton Diamantina

Fig. 5. Water budget diagram of four flood events along 180 km reach of Diamantina River between four virtual stations (Tulmur, Tulmur2, Verdun Valley and Brighton Downs) and Diamantina gauging station. Parts (a) to (d) show the water budget of four flood events (i.e., Floods #2, #4, #5 and #6 respectively; see Fig.2 for more details of each flood event). The “Gain” in the total lateral inflow, Loss is total transmission losses (i.e., actual evaporation, infiltration to the soil moisture store and terminal water storage) and Q is the total discharge in location of five stations; all have units GL per flood event. Error bars are shown for each component-reach combination and represent 1 standard deviation. Legend of (a) applies for all other parts.

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0.008 0.008 2 (a) Reach 1 (b) Reach 2 y = 0.0041e-2E-04x y = 0.0068e-3E-04x

0.006 0.006 3

R² = 0.844 1 6 R² = 0.8929 2 0.004 6 0.004 7 5

3 1 TTL / Q TTL 7 5 / Q TTL 4 0.002 4 0.002

0.000 0.000 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Q(inflow+lateral) Q(inflow+lateral) 0.008 0.008 2 (d) Reach 4 2 3 1 -3E-04x (c) Reach 3 y = 0.0063e-2E-04x 0.006 y = 0.0072e 0.006 3 R² = 0.9374 R² = 0.9146 1 6 6

0.004 7 5 0.004 7 5 TTL / Q TTL

TTL / Q TTL 4 4 0.002 0.002

0.000 0.000 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Q(inflow+lateral) Q(inflow+lateral) 0.008 (e) Reachs 1-4 y = 0.0041e-2E-04x

0.006 R² = 0.9225

2 0.004 3 1 6 TTL / Q 7 5 0.002 4

0.000 0 1000 2000 3000 4000 5000 6000 7000 Q(inflow+lateral)

Fig. 6. Total transmission losses against inflow for the seven flood events from February 2006 to April 2012. Parts a-d show Reaches 1-4, respectively, and part (e) is the combined 180 km reach. The horizontal axis is total inflow to the system (upstream inflow + lateral inflow). The vertical axis (TTL/Q) is total transmission losses (GL/km) relative to the total inflow (these are partitioned into the component parts in Fig. 4). The seven Flood events are shown by numbers 1-7 on each graph, with more details for each flood presented in Fig. 2. 5-27

Chapter 5

The spatial distribution of the transmission losses was also investigated (Fig. 7). The total amount of actual evaporation and infiltration depends on the residence time of flood water in the system. For each of the seven flood events (Fig. 2), flood water residence time was calculated from the hydrodynamic model and is shown as the number of hours each pixel was inundated during each flood event. The flood water residence map is therefore a direct indicator of the spatial influence of the two major components (actual evaporation and infiltration) of the total transmission losses (Fig. 7). Flood water residence time was higher in the main channels of the system and lower along the boundaries of the floodplain, which are inundated only during the higher flood peaks. In addition, the upstream reaches had lower water residence time than downstream reaches. Flood water residence time was also calculated independently from MODIS OWL daily maps and showed good agreement with simulated residence time from the TUFLOW hydrodynamic model (Fig. 7 c-d).

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Fig. 7. Spatial distribution of total transmission losses for two flood events. Parts (a) and (b) show the spatial distribution of TUFLOW modelled total transmission losses (i.e., actual evaporation, infiltration to groundwater and terminal water storage) of Flood #1(February 2006 to May 2006) and Flood #7 (November 2011 to April 2012), respectively. Parts (c) and (d) are the flood water residence time, calculated from daily MODIS OWL flood maps for the duration of the same two floods. Legend of (a) applies to (b) and legend in (c) applies to (d). Scale in part (d) applies to all parts (a-d).

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5.3. Uncertainty and limitations

Discharge estimates from the water elevation time series recorded by loggers proved to be very sensitive to the accuracy of the elevation datasets. The rating curve of the virtual stations at wider sections of river channel/floodplain showed very low changes in water elevation for very high changes in discharge. This is an inherent feature with these anabranching river systems and indeed a possible reason why these locations are not currently gauged. Translating water elevation into discharge is therefore challenging and there is high variability in any discharge estimation whether through conventional gauging or modelling in a dryland anabranching floodplain. Table 5 shows the changes in water elevation for every 1000 m3/s changes in discharge. As an example, in order to increase the discharge from 1000 m3/s to 2000 m3/s at Brighton Downs station, an increase of only 0.05 m in water elevation was needed. However, the water elevation required for the same increase in discharge was much larger at other stations, i.e., Verdun Valley (0.64 m), Diamantina Lakes (0.83 m), Tulmur (0.90 m) and Tulmur2 (1.01 m) (Table 5). This is due to the relatively large channel/floodplain width at Brighton Downs (Table 2) compared to the other stations, which has channel/floodplain widths varying from 2.5-6 km. Therefore any minor error of > 0.05 m in water elevation at Brighton Downs will cause an error of 1000 m3/s in discharge estimates.

Table 5. Sensitivity of water elevation to discharge changes at four virtual gauging stations and Diamantina Lakes gauge station.

Discharge Water elevation change (m) 3 (m /s) Tulmur Tulmur2 Verdun Valley Brighton Downs Diamantina Lakes 0-500 2.44 4.16 1.33 1.11 1.87 500-1000 0.75 0.96 0.55 0.13 0.68 1000-2000 0.90 1.01 0.64 0.05 0.83 2000-3000 0.65 0.66 0.38 0.04 0.55 3000-4000 0.53 0.51 0.26 0.03 0.41 4000-5000 0.45 0.41 0.20 0.03 0.33 5000-6000 0.39 0.33 0.18 0.04 0.29 6000-7000 0.35 0.27 0.16 0.07 0.25 7000-8000 0.31 0.23 0.15 0.10 0.22 8000-9000 0.29 0.20 0.13 0.10 0.20

Analysis of the spatial distribution of rainfall data of seven water years (water year starts 1 July and ends 30 June the following year) from July 2005 to June 2012 were performed to understand the 5-30

Chapter 5

proportion of the rainfall in each sub-catchment which adds lateral inflow to the river system between gauging stations. On average 61% of rainfall is falling in sub-catchments above Tulmur (24,598 km2), 15% between Tulmur and Tulmur2 (Reach 1; area = 9,079 km2), 2% between Tulmur2 and Verdun Valley (Reach 2; area = 1,395 km2), 8% between Verdun Valley and Brighton Downs (Reach 3; area = 7,344 km2) and 14% between Brighton Downs and Diamantina Lakes gauging station (Reach 1; area = 13,299 km2). Total rainfall data (GL) of each sub-catchment were in agreement (R2 = 0.83) with discharge estimated at each of the virtual gauging stations for Flood #7 from November 2011 to April 2012 (Fig. 2).

Comparing average daily rainfall for the entire upstream catchments contributing to the Diamantina Lakes gauging station and corresponding runoff observed at the Diamantina Lakes gauging station from July 2005 until June 2013 revealed that for < 20 mm of daily rainfall, there is no recorded runoff by the gauge. Rainfall events up to 50 mm relate to approximately 1000 m3/s discharge events (runoff coefficient = 0.04). A rainfall event of 100 mm is needed to provide a discharge of 3000 m3/s or greater at Diamantina Lakes gauging station (runoff coefficient = 0.08). The annual runoff coefficient (total annual runoff / total rainfall) was higher in wet years (> 20000 GL total rainfall) than drier year (Table 6). The correlation between annual rainfall and runoff from July 2005 to June 2013 was high (R2 = 0.9) when small rainfalls (< 20 mm) were excluded from total rainfall calculations (R2 = 0.51 when included); Table 6 reports the annual rainfall data for both options.

Table 6. Annual rainfall, runoff and runoff coefficients for the seven water years from July 2005 to June 2012. Total rainfall integrated over space (the catchment area upstream from the Diamantina Lakes gauging station) and for each water year. The runoff coefficient is the ratio of runoff to rainfall. Year Total Runoff Total Rainfall (GL) Runoff Coefficient (GL) All data < 20 mm (per event) All data < 20 mm (per event) 2005-2006 579 10107 4119 0.06 0.14

2006-2007 370 13300 3561 0.03 0.10 2007-2008 473 12613 4243 0.04 0.11 2008-2009 4928 23666 15152 0.21 0.33 2009-2010 1855 24071 11302 0.08 0.16 2010-2011 1869 27971 7945 0.07 0.24 2011-2012 2046 21506 7486 0.10 0.27

The runoff coefficients varied between 0.1 and 0.33 and depended on the spatial and temporal distribution of rainfall intensity of rainfall and antecedent soil moisture. Intense rainfall events

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produced a higher peak discharge and runoff coefficient. For example, the flood during the 2008-2009 water year had a total rainfall of 23,666 GL over the sub-catchment upstream of the Diamantina Lakes gauging station, whereas the December 2008 to March 2009 flood produced higher (4,928 GL) runoff than the 2009-2010 water year with similar (24,071 GL) total rainfall data, but lower total water mass (1,855 GL), due to higher rainfall intensity in 2008-2009 (Table 6, also see Fig. 2 for duration of different rainfall events). The runoff coefficient varied from 0.03 for the 2006-2007 water year to 0.21 for the 2008-2009 water year. High intensity rainfall events were related to high peak discharge and quicker flood pulses, which had lower transmission losses and consequently higher runoff coefficients.

Actual evaporation was the second most important source of variability in the water balance calculation. For the seven flood events from February 2006 to April 2012, evaporation varied from 215 GL to 1096 GL in the 180 km reach of river (1.19 to 6.09 GL/km) and was sensitive to both the duration and peak discharge of the flood event.

The sensitivity of total transmission losses to different infiltration losses (IIL/CIL) was examined. The results showed that the IIL is independent of the flood duration and depends mainly on the antecedent soil moisture prior to the flood event. However, when calibrating the model, relatively low initial infiltration losses were required (10-40 mm) due to the low permeability of the floodplain soil, clay (Table 3). Although the CIL rate was small due to the low permeability of the soil type, higher total CIL was experienced (5 to10 times higher than IIL) due to the longer (~3 month) durations of flood water presence in the system.

Terminal water storage is determined based on the topographic structure of the floodplain, but in practice is dominated by the DEM noise (Jarihani et al., 2015). Nonetheless, we found that terminal water storage is not likely to be as significant component of transmission losses, representing only 16% of the total inflow. Terminal water storage was the parameter that was potentially most sensitive due to the influence of the DEM quality (Jarihani et al., 2015), but with relatively small values ranging from 130 GL to 280 GL for the 180 km reach of the Diamantina River (0.72 to 1.55 GL/km) across the seven flood events.

In summary, our reach-scale water balance analysis of dryland anastomosing river systems with limited gauging stations found that, over the 180 km reach, the lateral inflow can produce the highest variability in the water budget (86% of upstream inflow), followed by evaporation (21.6% of the

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upstream and lateral inflow), infiltration (13.2% of the upstream and lateral inflow) and terminal water storage (11.2% of the upstream and lateral inflow) for the seven evaluated flood events.

6. Discussion

6.1. Initial model calibration

Calibration challenges are well known in hydrodynamic models (Hall et al., 2005; Horritt and Bates, 2001; Horritt et al., 2006; Jarihani et al., 2015). This study, as well as previous research, has identified that in dryland, low-gradient river systems this challenge becomes even more pronounced due to a limited number of events for calibration and flows across large floodplains with multiple-channels over highly variable soil moisture conditions and a floodplain that is shallower and wider than the more confined coastal river settings where much hydrodynamic modelling is performed (Hall et al., 2005; Horritt and Bates, 2001; Jarihani et al., 2015; Karim et al., 2011; Khan et al., 2012; Mohammadi et al., 2013). Given their extensive inundation area and large changes in surface properties, remote sensing derived information (or any other data source) is unlikely to ever fully overcome such uncertainty in hydrological prediction in these dryland settings (Hall et al., 2005; Jarihani et al., 2015; Karim et al., 2011; Mohammadi et al., 2013), but it may be possible to help constrain some of the model parameters. That being said, we acknowledge that differing input datasets and model setups will produce different results compared to this study.

In line with previous research (Bates et al., 1998; Bates et al., 2005; Baugh et al., 2013; Callow et al., 2007; Jarihani et al., 2015; Sanders, 2007), our study found that the quality of DEMs govern the success of hydrodynamic models of low-gradient river systems, especially for small flood (< 2000 GL) events. As found by Jarihani et al., (2015), Manning’s n roughness is typically low and dominated by the H-DEM surface accuracy and roughness that also impact the physical realism of values in such large systems due to the DEMs hydraulically-rough floodplain surfaces (Jarihani et al., 2015). The low- gradient, wide and shallow flow depth therefore introduces a higher sensitivity to terminal water storage (real or DEM derived) relative to the overall water balance.

We found that calibrating the hydrodynamic model to both flood extent and flood travel time was very challenging when modelling our seven specific flood events in such low-gradient anabranching river systems. Choosing whether to target timing or flood extent depends on the objective of the modelling, 5-33

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so users can decide to calibrate the hydrodynamic model to have more accurate total outflow or more correct flood pulse travel times. This calibration challenge has also been reported by Horritt and Bates (2001) who found that the LISFLOOD-FP 2D hydrodynamic model could not be calibrated to give both acceptable travel time and inundation results. In this study we settled for more accurate outflow (flood extent) given the focus on the water balance. Errors in the discharge time series mainly occur through uncertainty in stage-discharge relationship determination (Tomkins, 2014). The source of uncertainty can be related to measurements of flow height, width and river cross section. McKenzie and Craig (2001) found that the high uncertainty of flow observations at low-flows means it is difficult to estimate losses accurately. Here we found that the error budget in discharge estimation is high for our virtual gauge stations with flat and wide cross sections of the river.

Within large dryland catchments, the potential for large rainfall events to occur in downstream parts of the catchment potentially introduces a higher sensitivity to lateral tributary inflows relative to the overall water balance. Any uncertainty in rainfall-runoff models input data (i.e., rainfall) and calibration/evaluation process will also introduce uncertainty to lateral inflow estimation. However, we found that the error from caused by the rainfall-runoff model was comparatively low when only the total water mass (and not the timing) is considered which is more important for the overall water balance, than matching the complete hydrograph (see Table 3).

As stated by Karim et al., (2011), using remote sensing data can help to address the problem of calibrating hydrodynamic models in data-scarce regions and provides improved estimates of discharge. However, any error within remote sensing data is also a possible source of uncertainty in the hydrodynamic model results. For example, errors present in satellite derived flood inundation maps (i.e., MODIS OWL; (Guerschman et al., 2011; Ticehurst et al., 2014)) can produce bias in the hydrodynamic model calibration and consequently simulated flood hydrograph parameters. This error was higher for smaller flood events (< 2000 m3/s) that were mainly confined to the main channels due to higher uncertainty in inundation area when only extracting from multiple small channels obscured by vegetation canopy and islands. Model calibration and consequent discharge estimation was also very sensitive to water elevation time series accuracy from altimeter satellite data (i.e., Jason-2, RMSE = 0.28 m; Jarihani et al., 2013). The uncertainty in discharge estimation related to, H-DEM, and the derived rating curve was higher (mean = 88%) for small (< 2000 m3/s) flood events due to higher uncertainty in replicating channel morphology from the H-DEM, and therefore impacts the water

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balance calculations. In contrast, for larger floods (> 2000 m3/s) the uncertainty and differences between rating curves was small (mean = 5.5%). To conclude, we found that remote sensing can provide valuable information for calibration and validation of the hydrodynamic models, especially for the large inundation areas experienced by drylands anabranching rivers. However, the uncertainty was higher in remotely-sensed flood stage and inundation data of small flood events due to the spatial resolution of the remote sensing data.

6.2. Water balance and transmission loss partitioning

In an ungauged reach (180 km) of a large dryland river system, we found that the transmission losses are on average high (46%), vary non-linearly with flood discharge and increases downstream (Fig. 6). For lower parts of the Diamantina River (further downstream from our study site) and nearby Cooper Creek, high transmission losses have also been reported (75-90%), which are higher than our results most likely due to wider floodplains, higher actual evaporation losses (from wider flow extents), longer water residence times and a lower gradient with increasing distance downstream (Costelloe et al., 2006; Knighton and Nanson, 1994). Water balance calculations based on gauge data are often blurred by unknown lateral inflows (Lange, 2005). We found (see Table 4) that the lateral inflow between available sparse gauging stations provided the highest uncertainty in water balance estimation. However, high uncertainty of daily gridded rainfall data, especially in areas with sparse rainfall gauges (Asadullah et al., 2008; Chappell et al., 2013), is in turn a major driver of this uncertainty in lateral inflows during flood events.

Our results for infiltration losses were in agreement with results of previous studies in dryland environments which found that initial infiltration losses were independent of flood duration and were lower than the continuous losses during the main flood phase (Lange et al., 1998), which is controlled by flood duration (Morin et al., 2009). Schwartz (2001) found that the transmission losses were significantly reduced due to initial soil moisture when the time interval between two floods was less than one week. Our study also found that lower initial infiltration losses depend mainly on the initial soil moisture conditions. Smaller initial infiltration losses were experienced for flood events when the soil was already wetter, and higher initial infiltration losses for flood events occurring after a long dry period.

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When transmission losses are normalised to total inflow (see Fig. 4 and Fig. 6), the losses per km are high for small floods and these decreases non-linearly for larger flood events. This means per unit discharge, transmission loss rates (GL/km) are higher for smaller flows, and therefore experience comparatively greater losses. This is mostly a result of hydrograph shape, with events delivering more water in a shorter period of time experiencing lower losses per km, and events delivering similar or smaller volumes of water over a longer period of time experiencing increasing losses per km. However, it is important to note that in absolute terms, the transmission losses per km actually increase non- linearly with flood size, but only until ~8 GL/km, after which the loss rate no longer appears to increase with flood size. The changes in transmission losses with total water mass is most likely due to the non- linear flood extent/flood stage relationship on low-gradient floodplain, and once the floodplain of a given reach is completely inundated, the losses diminish as additional water increases flow depth rather than width (at which point discharge can contribute to increase with comparatively lower loss rates). Two major components of the total transmission losses (actual evaporation and infiltration) are directly related to flood inundation extent, and can explain this trend.

6.3. Uncertainty and limitations

We highlighted the importance of the remote sensing datasets in hydrological studies of data-sparse dryland river environments. McVicar et al. (2009) defined the application of remote sensing to catchment-scale modelling as falling into the following five categories: (i) forcing data; (ii) parameters; (iii) calibration; (iv) evaluation; and (v) regionalisation. We found that remotely-sensed topographic forcing data are necessary for hydrodynamic modelling in data-sparse regions, supporting earlier studies (Callow et al., 2007; Hancock et al., 2006; Hirt et al., 2010; Jarihani et al., 2015; Rexer and Hirt, 2014). We also found that remote sensing data provide valuable information for hydrodynamic model calibration and evaluation such as flood inundation maps and flood levels. The potential for remote sensing to constrain hydrodynamic models in dryland river systems is therefore extremely valuable and could greatly improve our understanding of the flood dynamics, water balance, and ecohydrology of these poorly-studied environments.

That being said, uncertainty related to remote sensing data may limit their applicability in some cases. For example, errors in topographic forcing data (Jarihani et al., 2015), flood extent (Jarihani et al., 2014; Ticehurst et al., 2014) and water elevation time series (Baghdadi et al., 2011; Birkett and Beckley, 2010; Hall et al., 2012; Jarihani et al., 2013) could possibly add low to medium uncertainty 5-36

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(related to the error budget) within hydrodynamic model results (Table 7). Spatial resolution and temporal frequency of remote sensing data also inherently constrain their application for hydrodynamic modelling. The results of this study, and the summary of developments occurring more broadly within this discipline area, are presented in Table 7 and we suggest that the greatest benefits to the hydrological community in relation to prioritising investment in new satellite technologies or methodological advances is to emphasise improving topographic data accuracy and resolution (subject to computational constraints) and identification of water inundation extent. Water surface elevation from altimetry and better spatially-distributed precipitation are also of significant interest, with soil moisture and groundwater products providing some value to hydrodynamic modelling in data-scarce regions but remaining a lower overall priority on the basis of uncertainty and value to constraining hydrodynamic model accuracy.

Finally, by utilising new blending algorithms (Emelyanova et al., 2013; Jarihani et al., 2014) and combining different data products from multiple sensors we may be able to reduce the uncertainty related to available remote sensing data. This will also be greatly aided by future space missions dedicated to hydrological studies (i.e., Surface Water and Ocean Topography) that will cover most of the freshwater bodies in the world and will provide fine temporal and spatial resolution information (i.e., elevation and area) for water body dynamics (Durand et al., 2010; Fu et al., 2009). Additional approaches that may reduce uncertainty from currently available remote sensing data is to use a probabilistic framework to propagate the error from remote sensing data into modelling results (Callaghan et al., 2008; de Bruin et al., 2008; Hengl et al., 2010; Leon et al., 2014).

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Table 7. Status of remotely-sensed datasets for hydrodynamic modelling of data-sparse low-gradient dryland river systems. In the “Application” column, (FD) = Forcing data; (P) = Parameters; (C) = Calibration; (E) = Evaluation; and (R) = Regionalisation, as defined by McVicar et al. (2009).

Prioritising investment based Remote sensing data Applicatio Level of uncertainty / Example data source on value for hydrodynamic derived variables n reason modelling

Water elevation (i) Direct: Altimetry satellites FD / C / E low to medium / temporal high frequency (ii) Indirect: satellite or airborne water extent + topographic data

Discharge Indirectly using inundation FD / C / E high / related to accuracy of high extent + DEM DEM

Digital Elevation SRTM P Medium to high / vegetation high Models effect, striping errors, mis- GDEM registration

Inundation extent Optical images; (Landsat, C / E medium / related to spatial medium to high MODIS), Passive microwave resolution, temporal (AMSR-E); Active microwave; frequency and water non- (ERS, RADARSAT, water classification methods ENVISAT)

Precipitations TRMM FD low to medium / based on medium to high spatial resolution, rainfall density and terrain

Actual AVHRR, MODIS, Landsat FD medium to high / based on medium to high evapotranspiration method and availability of parameters

Groundwater recharge GRACE FD high / due to very low spatial medium and depletion resolution

Soil moisture ASCAT FD medium to high / related to low to medium spatial resolution

Manning’s n Optical images (Landsat), P high / based on DEM low to medium accuracy and model grid size Airborne Lidar

7. Conclusion

This study investigated the feasibility of using a hydrodynamic modelling approach to quantify transmission losses in large, data-sparse, multi-channel and low-gradient dryland catchments. For our spatial-temporal study extents we found that:

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1) Transmission losses in a 180 km reach of the Diamantina River are high and can be up to 68% (mean = 46%) of the total inflow (upstream + lateral) to the system. This is on average 7 GL/km (range: 4-10 GL/km); 2) Lateral inflow is the largest source of uncertainty in water balance estimation and can be up to 200% of upstream inflow (mean = 86%); 3) Actual evaporation is the most influential component on transmission losses (mean = 21.6% of the total inflow); 4) Infiltration was the second most significant (mean = 13.2% of the total inflow) component resulting in transmission losses, with initial infiltration losses being lower (1.2%) than continuous infiltration losses (12%); 5) Terminal water storage was on average only 11.2% of the total inflow. Terminal water storage is related to DEM accuracy, which can affect terminal water storage estimation. The evaluation process of the terminal water storage estimates with remote sensing based optical images showed higher uncertainty due to misclassification of the wet soil than water after hydrograph recession; 6) The runoff coefficient is low in drylands river systems (average for the seven flood events was 0.14); 7) When partitioning the transmission losses to its major components, temporal variability in actual evaporation, heterogeneity in soil structure and soil depth are most likely to impact the infiltration rate; 8) The water balance calculation using ‘virtual’ gauging stations was sensitive to rating curve accuracy when converting water elevation data to discharge. The grid based rainfall data accuracy is also important for accurately simulating the hydrograph peak flow and timing; 9) When using the hydrodynamic model for estimation of total water mass and water balance, Manning’s n proved to be less important; 10) Hydrodynamic modelling is able to produce spatial and temporal variability maps of the transmission losses and flood water which are ecologically important in dryland river systems (i.e., water depth, inundation period, connectivity); and 11) Using limited ground-based measurements coupled with remotely-sensed data is important for quantifying uncertainties arising from scarcity of hydrological data.

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The results of this study are of significant interest for application to other dryland and data-sparse regions elsewhere in the world especially in better understanding the dynamics of dryland flood waters, which is important for driving ecohydrological processes. This research provides an approach to reduce the uncertainty in water balance estimation in data-sparse regions by using appropriate temporal and spatial resolution remote sensing data for model calibration, implementation and evaluation.

Acknowledgments

Special thanks to Bill Syme (TUFLOW manager and developer) for providing the TUFLOW license and technical support for this project. We also thank editor, two anonymous reviewers and Professor Paul Bates and Dr Marc LeBlanc, who examined this manuscript which was presented as a chapter as part of a PhD thesis recently completed by the lead author, for their very useful and constructive comments that improved the manuscript. Ben Jarihani was supported by an Integrated Natural Resource Management (INRM) scholarship from The University of Queensland (UQ) and CSIRO, an Australian Postgraduate Award and funds from the School of Geography, Planning and Environmental Management (UQ).

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Zhang, J. et al., 2004. Estimation of river discharge using TOPEX/Poseidon altimeter data. Acta Geographica Sinica, 59(1): 95-100.

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Supplementary materials:

Supplementary Table 1. Percentage of rainfall in each sub-catchment expressed as a percentage of total water-year annual rainfall. For each sub-catchment its area (km2) and percentage of total area (55,721 km2) are also provided. A water-year is defined as 1 July to 30 June the following year.

Tulmur Tulmur2 Verdun Valley Brighton Downs Diamantina Lakes

Year (24600 km2 / 44%) (9080 km2 / 16%) (1396 km2 / 3%) (7345 km2 / 13%) (13300 km2 / 24%)

2005-2006 81 6 1 4 8 2006-2007 80 7 1 0 12 2007-2008 79 12 2 0 7 2008-2009 56 16 2 11 15 2009-2010 50 23 3 16 8 2010-2011 33 20 3 15 29 2011-2012 48 23 4 10 15 Average 61 15 2 8 14

Supplementary Table 2. Long-term (1981-2010) monthly averaged Penman potential evapotranspiration (mm/month) for the 55,721 km2 area upstream from the Diamantina Lakes gauging station. The first three letter of each month are provided. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

min 201 198 210 146 126 98 112 153 187 233 231 239

max 323 280 260 207 164 135 143 184 235 289 301 331

mean 281 236 236 187 148 119 131 167 211 263 278 294

Std dev 30 22 14 16 12 9 7 8 11 13 19 21

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(a) Tulmur 149

Altimetry

147 Logger

145

Elevation (m) Elevation 143

141

139 Feb 2008 Sep 2008 Mar 2009 Oct 2009 May 2010 Nov 2010 Jun 2011 Dec 2011 Jul 2012 Date

113 (b) Brighton Downs 112.5 Altimetry 112 Logger

111.5

111

110.5

110 Elevation (m) Elevation 109.5

109

108.5

108 Feb 2008 Sep 2008 Mar 2009 Oct 2009 May 2010 Nov 2010 Jun 2011 Dec 2011 Jul 2012 Date

Supplementary Fig. 1. Comparison of altimetry derived water elevation time series at the Tulmur and Brighton Downs virtual stations with water elevation recorded by loggers. Altimetry satellite data were extracted from Jason-2 satellite’s Coastal and Hydrological products (PISTACH, (Mercier et al., 2008)) which is available from July 2008 to present at a 10- day temporal frequency. Loggers were installed under the ground-track of Jason-2 satellite and recorded water elevation in 15-minute temporal frequency from Nov 2011 to Nov 2013

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5000 5000 (a) Inflow (b) Lateral inflow 4000 4000 Flood 1 Flood 2 3000 3000 Flood 3 Flood 4 Flood 5 Flood 6 2000 2000

Flood 7 Inflow (GL)

1000 1000 Lateral inflow (GL) 0 0 1 2 3 4 1 2 3 4 5000 5000

4000 4000 3000 3000

2000 2000 Outflow (GL) 1000 Totalloss (GL) 1000 0 0 1 2 3 4 1 2 3 4

10 10

(e) Terminal water storage (f) Initial infiltration loss

8 8

6 6

4 4 Loss Loss (GL/km) 2 Loss (GL/km) 2 0 0 1 2 3 4 1 2 3 4 Reach # Reach # 10 10

(g) Continious infiltration loss (h) Actual evapotranspiration

8 8

6 6

4 4 Loss Loss (GL/km) 2 Loss (GL/km) 2 0 0 1 2 3 4 1 2 3 4 Reach # Reach #

Supplementary Fig. 2. Water balance components of seven flood events between February 2006 to April 2012 for four river Reaches 1-4. Inflow, lateral inflow, outflow and total transmission losses are presented in parts a-d. Parts e-f show the individual components of the total transmission losses (i.e., actual evaporation, initial infiltration loss, continious infiltration loss and terminal water storage) for each reach (1-4). Fig. 4 shows these data as normalised to flood inflow, here the raw data (i.e., non-normalised) are provided.

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10000 (a)

8000

/s) 3 6000 Tulmur 4000 Tulmur2 Verdun Valley Discharge (m 2000 Brighton Downs Diamantina Lakes 0 0 1 2 3 4 5 6 7 8 9 10 Water depth (m) 180 170 (b) Tulmur Tulmur2 160

Verdun Valley Brighton Downs 150 140 Diamantina Lakes 130 120

Elevation Elevation (m) 110 100 90 80 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Distance from left bank of the river (km)

10000 9000 (c) n=0.01 8000 n=0.02 n=0.03

/s) 7000 3 n=0.04 6000 n=0.05 5000 n=0.06 4000 n=0.07

3000 n=0.08 Discharge (m 2000 n=0.09 n=0.10 1000 0 90 92 94 96 98 100 102 104 Water elevation (m)

Supplementary Fig. 3. Gauge stations cross sections and rating curves. Part (a) discharge-elevation (rating curve) charts of four virtual stations (Tulmur, Tulmur2, Verdun Valley and Brighton Downs) and Diamantina gauging station. Part (b) presents the cross sections of the river extracted from SRTM H-DEM by overlaying Landsat 5 image during a major flood event on 20/ 02/2009. Part (c) is rating curve at the location of Diamantina Lakes gauging station for a range of Manning’s n values from 0.01 to 0.1. These rating curves were extracted using SRTM H-DEM topographic data in the 2-D TUFLOW model with a 60 m grid size. The surveyed rating curve of the Diamantina Lakes gauging station is also presented (black line) for comparison.

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15 15

(a) Reach 1 (b) Reach 2 5

6 4 10 10 6 5 7 4 7 2 3 1 5 2 5 0.4724

3 1 y = 0.121x0.4795 y = 0.1989x

TTL (GL/km)TTL (GL/km)TTL R² = 0.7266 R² = 0.801 0 0 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Q(GL) Q(GL) 15 15 5

6 5

2 3 2 3 1 5 1 5 y = 0.0994x0.5671 0.4861

TTL (GL/km)TTL y = 0.1809x TTL (GL/km)TTL R² = 0.9032 R² = 0.9083 0 0 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Q(GL) Q(GL) 15

(e) reach 1-4 6 5 10 4 7

5 2 3 1 y = 0.0846x0.5467 TTL(GL/km) R² = 0.919 0 0 1000 2000 3000 4000 5000 6000 7000 Q (GL) Supplementary Fig. 4. Total transmission losses against inflow for the seven flood events from February 2006 to April 2012. Parts a-d show Reaches 1-4, respectively, and part (e) is the combined 180 km reach. The horizontal axis is total inflow to the system (upstream inflow + lateral inflow). Vertical axis show total transmission losses per kilometer (these are partitioned into the component parts in Fig. 4). The seven flood events are shown by numbers 1-7 on graphs, with more details for each flood presented in Fig. 2. This figure shows absolute changes in transmission losses against total inflow which increases with discharge. This is distinctive from Fig. 6 that shows transmission losses relative to total inflow and decreases with discharge. 5-58

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2000 (a) 2011 Flood #7 - Brighton Downs observed (logger)

Simulated

1500

/s) 3

1000

Discharge Discharge (m 500

0 0 500 1000 1500 2000 2500 3000 3500 4000 Time in hr (starting 4 Nov 2011)

2000 (b) 2011 Flood #7 - Diamantina Lakes Observed Simulated

1500

/s) 3

1000

Discharge Discharge (m 500

0 0 500 1000 1500 2000 2500 3000 3500 4000 Time in hr (starting 4 Nov 2011)

Supplementary Fig. 5. Comparison of simulated and observed flood hydrograph. Part (a) compares the 2011 Flood #7 hydrograph simulated by TUFLOW with recorded data by logger in Brighton Downs station. Part (b) shows the TUFLOW simulated 2011 Flood #7 with data recorded in Diamantina Lakes gauging station (see Fig. 2 for full details).

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150 (a) 2008 Flood #4 - Tulmur Altimetry Simulated

148

146

144 Elevation(m)

142

140 Dec 2008 Feb 2009 Mar 2009 May 2009 Jul 2009 Aug 2009 Date 148 (b) 2010 Flood #6 - Tulmur Simulated

146 Altimetry

(m) 144

142 Elevation

140

138 Aug 2010 Nov 2010 Feb 2011 Jun 2011 Sep 2011 Date

Supplementary Fig. 6. Comparison of TUFLOW simulated flood hydrograph with altimetry-derived water elevation time series. Parts (a) and (b) show the major 2008 Flood #4 and 2010 Flood #6 in Tulmur station, respectively (see Fig. 2 for full details).

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9 Reach 1 (59 km)

8 Reach 2 (21 km)

7 Reach 3 (45 km)

6 Reach 4 (55 km) 5 All Reaches (180 km)

Loss Loss (GL/km) 4

0 Actual Initial Continious Terminal water Total evaporation infiltration loss infiltration loss storage transmission losses Supplementary Fig. 7. Reach-based water balance components averaged across the seven floods of four individual Reaches 1 to 4 and all combined as a 180 km reach.

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Supplementary Fig. 8. Comparison of simulated and observed inundation area. The hydrodynamic model, TUFLOW, simulated inundation area and MODIS OWL-derived maximum inundation area of seven flood events are compared in 180 km reach of the Diamantina River. Parts (a) to (g) represent seven flood events from February 2006 to April 2012 (see Fig. 2 for full details).

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Discussion

Chapter 6. Discussion

6. Discussion

This thesis aimed to evaluate how remotely-sensed data and hydrodynamic modelling approaches can be used to deepen our knowledge of hydrological processes in data-poor dryland landscapes. Chapters 2, 3, 4 focused on key aspects of assessing the accuracy of three types of remotely-sensed data (i.e., water elevation, flood inundation extent and fundamental topographic data, respectively). Chapter 5 sought to integrate these components to test whether it is possible to realistically constrain the water balance and specifically the transmission losses of large dryland river systems, using a combination of field data, remotely-sensed data, and hydrodynamic modelling.

This chapter begins by outlining the individual implications of each of the previous chapters (papers) through the first four main sections (6.1 to 6.4). This builds and extends the discussion within these individual papers to consider aspects beyond the scope of that work as a stand-alone paper. Within this chapter, some of the broader opportunities and limitations of the approaches within the discipline and possibilities for advancing this area of research are also assessed. This chapter concludes by integrating and synthesising these individual pieces of work to provide a broader discussion of the key points and knowledge gaps that were identified in Chapter 1, including assessing accuracy, potential and limitations of remotely-sensed altimetry, flood inundation extent and topography datasets and finally assessing the feasibility of using hydrodynamic models which utilise these remotely-sensed data for the applications in dryland, data- scarce landscapes that were the focus on this thesis.

6.1. The value of satellite-derived altimetry data in hydrology of dryland river systems

Since the launch of first altimeter satellite, GeoSat, in 1985, satellite-derived altimetry data have been used to study ocean and ice elevation changes and recently have been used for studying inland water bodies (Baghdadi et al., 2011; Cretaux et al., 2011; Hall et al., 2012; Troitskaya et al., 2012; Zhang et al., 2011). While altimetry satellites have been evaluated for large inland waterbodies such as lakes (Crétaux and Birkett, 2006; Cretaux et al., 2011), wetlands (Birkett, 1998; Cai and Ji, 2009; Lee et al., 2009), and large river channels (Birkett et al., 2002; de Oliveira Campos et al., 2001; Maillard et al., 2015), the accuracy and potential of these satellite-derived altimetry data to be applied to small water bodies such as streamflow in multi-channel river systems was largely unknown. Recent studies demonstrated some success in retrieval of water levels of small single channel rivers (Kuo and Kao, 2011; Michailovsky et al., 2012), however, they found reprocessing of RaDAR waveforms very challenging for small water bodies because of spatial and temporal

6-1

Chapter 6. Discussion

limitations (Sulistioadi et al., 2015). In this thesis the capability of six, past and present satellite altimeters were evaluated for their suitability to study inland water surface elevations. By comparing satellite-derived altimetry data on two lakes and six dryland anabranching river sections this section concluded that:

 Jason-2 data are able to provide accurate (mean = -0.04m, RMSE = 0.28m) elevation information for water surfaces > 1 km in width at a 10 day temporal frequency;

 ICESat produced the highest accuracy water body elevation data (mean = 0.00m, RMSE = 0.04m);

 Of the altimetry satellites available through the duration of this study and in an ongoing operational sense, Jason-2 offers the highest accuracy data for flood monitoring;

 Envisat 20 Hz data provided moderately accurate water elevation time series (mean = - 0.25m, RMSE = 0.42m) for inland water bodies with low (35 day intervals) temporal frequencies;

 Water elevation data from the 20 Hz and 1 Hz ocean-oriented ranges of Jason-1 were found to be noisy for inland water studies and of limited value for river water level studies (mean = -0.06m, RMSE = 1.12m);

 GFO data showed relatively large errors in water elevation estimations (mean = -0.5m, RMSE = 0.89m) compared to Envisat and Jason-2; and

 T/P data proved to be valuable only for water bodies greater than 100 km2 in size (consistent with findings of Birkett and Beckley, 2010; Crétaux and Birkett, 2006) and therefore of limited value for flood monitoring (mean = 0.77m, RMSE = 1.5m).

This research showed that the more recent altimetry missions such as Envisat and Jason-2 provide higher water elevation accuracy to monitor inland water elevation changes, where water bodies are sufficiently wide (>1 km) and floods are slow moving. This higher accuracy is due to new retracking algorithms (i.e., Ice1 and Ice3), other than the classical ocean-oriented trackers (i.e. “Ocean”) that are designed for very large and featureless ocean surface and glaciers. This study also highlighted the value of the hydrological-oriented altimetry data such as Jason-2 PISTACH, (Mercier et al., 2010) which was found to offer superior data to study inland water bodies. The

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Chapter 6. Discussion

strengths, weakness and opportunities for altimeter satellites in studying inland water bodies are summarised in Table 1.

Table 1: Accuracy, potential and limitations of satellite-derived altimetry data to study hydrological processes.

Strengths (i) all-weather capabilities (cloud, rainy, during night); (ii) length of historical data (since 1985); (iii) laser altimeter ICESat is able to produce high accuracy (~ < 10 cm) on small water bodies because of its small footprint (170 m); and (iv) moderate to high accuracy of RaDAR altimeters on small water bodies. Weaknesses (i) low temporal frequency of 10 to 35 days (Jarihani et al., 2013); (ii) very low spatial (along track and cross-track separation, 80 to 315 km) coverage (Table 1 of Jarihani et al., 2013); (iii) the RaDAR signal returned by water bodies smaller than the satellite large spatial footprint (2-10 km, Durand et al., 2010) is most likely contaminated by non-water surfaces; (iv) some altimetry missions are non-continuous and non-regular data acquisition during lifetime of satellites; and (v) previous missions generally require retracking the altimeter RaDAR signal to determine accurate elevation estimates over inland waters (Durand et al., 2010; Jarihani et al., 2013). Opportunities (i) further development and refinement of retracking can improve the accuracy of satellite-derived altimetry data for small inland water applications (Michailovsky et al., 2012; Sulistioadi et al., 2015); (ii) temporal downscaling by combining with other remotely-sensed data (e.g. high-temporal optical imagery) may improve flood estimation potential; (iii) future mission Surface Water and Ocean Topography (SWOT) will provide high spatial and temporal (~10 day) resolution measurements of surface water’s elevation, area and slope (Durand et al., 2010); (iv) Satellite-derived altimetry data can be combined with other remotely- sensed data for hydrological applications (Alsdorf et al., 2007; Smith, 1997; Tang et al., 2009); and (v) Opportunities to use satellite altimeter data in near-real time flood monitoring (Jarihani et al., 2013; Lee et al., 2011).

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Chapter 6. Discussion

To summarise, this work has highlighted the value of the satellite-derived altimetry data in estimating hydrological variables of data-poor landscapes. Moreover, this result should encourage and support the potential of assimilating satellite-derived altimetry data in a large scale hydrologic- hydrodynamic modelling of remote, ungauged or poorly-gauged catchments (Liu et al., 2012; Paiva et al., 2013a; Van Dijk and Renzullo, 2011). When comparing altimetry data with other remotely- sensed data or ground-based data, height datum adjustment is an important pre-processing task (Song et al., 2014). The standard geophysical data records (GDR) have been used in this study and have been shown to have acceptable accuracy for studying moderate to major flood events with > 1 km width. Recent studies on retracking altimetry RaDAR waveforms to study small rivers (<200 m) indicated high challenges in data processing and found accuracy of Envisat data to be RMSD = 0.685 m in a humid tropic environment in Indonesia (Sulistioadi et al., 2015). In another study (Kuo and Kao, 2011) improved accuracy of elevation data by retracking Jason-2 data from 50 cm to 31 cm in small rivers (100m – 200m) in mountainous area of Taiwan. However, there are opportunities for future work to investigate the accuracy of retracked altimeter data in dryland river systems (further discussed in Chapter 7 of this thesis).

6.2. Optical satellite imagery applications in dryland river hydrology

High spatial resolution and temporal frequency flood maps are vital for environmental modelling, water resources management and studying ecohydrological processes (Ticehurst et al., 2014). There is trade-off between spatial resolutions and temporal frequency and no single satellite can provide high frequent (i.e., daily) and high spatial (i.e., 30 m) inundation information of small water bodies and narrow channels such as multi-channel river systems. Our use of blending algorithms to produce high spatial resolution and more frequent water indices from Landsat and MODIS images proved to be very valuable in providing daily water inundation extents in data-poor multi-channel landscapes. By comparing two different approaches (i.e., ‘Index-then-Blend’ (IB) and ‘Blend-then- Index’ (BI)), and two blending algorithms (i.e., STARFM and ESTARFM) to simulate Landsat-like indices from Landsat-MODIS images, this thesis found that:

 The IB approach consistently outperformed the BI approach for all nine indices at all three study sites;

 The choice of approach (IB versus BI) had a larger impact on accuracy of blending indices than did the choice of algorithm (STARFM versus ESTARFM);

6-4

Chapter 6. Discussion

 The IB approach was less sensitive than the BI approach to choice of algorithm;

 STARFM was less sensitive to the choice of approach (IB versus BI) than ESTARFM was. However, STARFM was always the most accurate algorithm;

 Using the IB approach was more important for non-normalized difference indices because they did not benefit from the inherent cancelling of blending-induced errors in their algebraic implementation; and

 This study confirmed previous findings (Emelyanova et al., 2013) that STARFM had higher accuracy than ESTARFM when temporal variance was higher than spatial variance (T/S > 1) and ESTARFM had higher accuracy than STARFM when spatial variance was higher than temporal variance (T/S < 1).

Our results have direct impact on operational considerations when blending Landsat and MODIS data for the purposes of generating multispectral indices for vegetation, environmental moisture and/or water applications. For example, this result provides new opportunities for real time flood mapping at high spatial resolution and high temporal frequency, which are vital for operational flood management (FEMA, 2003; Ticehurst et al., 2014). For this purpose, our study suggests that the IB approach should be more widely implemented as it is: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to choice of blending algorithm. The result of this research paper can also be used to the direct discharge measurement of ungauged basins by providing daily high spatial resolution flood maps. For high accurate discharge estimation, high spatial resolution flood maps are required for large and braided river systems (Frappart et al., 2005b; Smith et al., 1996).

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Chapter 6. Discussion

Table 2: Accuracy, potential and limitations of satellite images to study hydrological processes. Strengths (i) historical data availability since 1972 launch of Landsat1 and future continuity (AVHRR since 1982 and MODIS since1999); (ii) moderate to high spatial resolution of optical images (25-1000 m); (iii) ability to in-fill gaps associated with cloud conditions when Landsat data are acquired every 16 days; and (iv) seeking to generate simulations with the positive characteristics of Landsat data (i.e., high spatial resolution / low acquisition frequency) and MODIS data (i.e., high acquisition frequency / low spatial resolution) to simulate Landsat-like data (i.e., high spatial resolution / high acquisition frequency). Weaknesses (i) cloud, rain and day-light limitation in optical images; (ii) trade-off between temporal resolution and spatial frequency of optical images; (iii) high error in water/non-water classification in shallow water and wet soil; and (iv) suboptimal of tested blending algorithms to adequately deal with highly dynamic floods which induce a large change in band-specific responses as flood-water inundates previous drylands Opportunities (i) to blend optical images from multiple sensors to produce high spatial and more frequent images; (ii) to investigate effect of number of pair input data in blending algorithm; (iii) to access the accuracy of using hydrologically similar input images from different years instead of image from closest date; (iv) opportunity to use blending approaches (i.e., IB) in near-real time operational applications; and (v) generate a level of uncertainty grid when simulation generations are performed.

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Chapter 6. Discussion

Recent researches on blending algorithm emphasise the importance of the combining multiple remotely-sensed data to reduce uncertainty related to individual datasets (Chen et al., 2015; Emelyanova et al., 2013). This research paper found that the blending approach can produce daily information of flood inundation with 30 m resolution which is critical for small scale flood modelling. These high spatial resolution and high temporal frequency blended images are very valuable in discharge estimation in presence of high accurate topographic maps (Bjerklie et al., 2003; Schumann et al., 2009). As an opportunity for future work it is suggested that the results of this section could be used in combination with other remotely sensed data for discharge estimation. However the use of high spatial resolution blended products may not be necessary in hydrodynamic models of large dryland river systems when adequate observed data is available. In Paper 3, Chapter 4 (which focuses on large scale hydrodynamic modelling of a single flood event), cloud-free Landsat images were available at the time of peak flood (29 February 2000) and on the falling limb (16 March 2000) and were used rather than using daily blended images. Paper 4 reports the flood water balance and transmission losses investigation, in which large scale hydrodynamic modelling was again performed, and MODIS-derived flood extent were used rather that blended high resolution images. This decision was influenced by later work, specifically the results of the third paper (Chapter 4) in relation to grid size. This demonstrated that within these river systems, a DEM with a grid resolution of around 120m which represents around one quarter a MODIS pixel is a sufficient grid resolution to accurately drive a simulation of a flood within a hydrodynamic model in a timely manner. In this situation, the decision was made that daily MODIS water inundation without the uncertainties and processing requirements associated with blending would be sufficient, and supported by Landsat imagery where available. For systems requiring higher grid resolution or for simulations at a finer spatial scale, blending approach will offer increasing opportunities to overcome the spatial-temporal trade-offs associated with much optical satellite imagery.

6.3. Remotely-sensed topographic data application for dryland river systems

While the value of systematic DEM selection, preparation and grid size optimization for hydrodynamic models have long been recognized (e.g. Baugh et al., 2013; Callow et al., 2007; Carabajal and Harding, 2006; Gallant et al., 2011; Li and Wong, 2010; Van Niel et al., 2008; Vaze et al., 2010; Wang et al., 2012), this had not yet been investigated simultaneously for all three issues. Therefore, an assessment of commonly used DEMs using a range of techniques and approaches to comprehensively address all three issues simultaneously is needed. This thesis 6-7

Chapter 6. Discussion

assessed the point-based accuracy and geometric co-registration error of the ASTER GDEM2 and SRTM DEM, quantified the effects of DEM preparation methods (vegetation smoothed and hydrologically-corrected) on relative hydrodynamic modelling accuracy and quantified the effect of the hydrodynamic model grid size and the associated relative computational costs (run time) on relative accuracy in hydrodynamic model outputs. These are the main findings of this research:

 The SRTM DEM had higher accuracy than ASTER GDEM;  The vegetation smoothed and hydrologically-corrected DEM improved the hydrodynamic model results; and  The hydrodynamic model reached optimum performance at a grid size of 120 m.

Results showed that combining the registered survey marks and ICESat data of data poor river systems provided an extensive control point dataset to validate and calibrate SRTM DEM and ASTER GDEM. These calibrated DEMs are suitable for hydrodynamic modeling and other applications in data-poor, dryland, and large anabranching river landscapes. A comparison of SRTM and ASTER DEMs against ICESat data indicated that these DEMs are more accurate than their nominal vertical errors in the studied low-gradient large river system. The SRTM DEM had on average higher elevation, while the ASTER data had a lower elevation than control data. The elevation accuracy was improved by adjusting for localised bias and is an important step when approaches such as calibration of flood inundated extent against satellite imagery requires datasets to be in the same datum. Table 3 summarises the critical key factors in implications, limitations and opportunities related to remotely-sensed topographic data applications in data-poor dryland anabranching river systems.

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Chapter 6. Discussion

Table 3: Accuracy, potential and limitations of remotely-sensed topographic data to study hydrological processes. Strengths (i) Freely available DEMs have near-global coverage; (ii) high (~ 30 m) spatial resolution; and (iii) remotely-sensed DEMs can be used in hydrodynamic modelling in anabranching river systems; Weaknesses (i) vegetation cover effect; (ii) striping error in SRTM and ASTER DEMs (which are negligible in mountainous zones) are significant in the flat areas where their magnitude is comparable with the real topographic gradient; and (iii) unable to show detailed channel morphology. Opportunities (i) vertical systematic error can be reduced by using ICESat and GCP data; (ii) applying smoothing algorithm can reduce random noise; (iii) vegetation effect can be removed with using satellite images derived landcover maps; and (iv) using flood inundation frequency to map flow pathways and impose structure on DEMs – rather than coarse hand-drawn linear hydrology networks.

This study found that 3D co-registration is required when combining multiple remotely-sensed data. However, in our study site horizontal co-registration was less important than vertical because of large scale modelling practices. Vegetation smoothing was found to be a very important DEM processing procedure for hydrodynamic modelling. However, despite the importance of the hydrological corrections for hydrodynamic modelling, the improvements in final hydrodynamic model results were most related to: (i) accuracy of the channel network that is used in stream burning or hydrological correction process; (ii) hydrodynamic model grid size; and (iii) hydrodynamic model spatial extent.

This study found that in large dryland river systems such as the Diamantina River and Cooper Creek in central Australia, during major flood events, the proportion of in-channel flow is very low compared with overbank floodplain flow, meaning the micro-scale DEM accuracy is less important (as is roughness). This study also highlighted that in hydrodynamic modelling of large rivers for flood inundation modelling, very high resolution grid sizes (<120 m) are not critical, although does

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Chapter 6. Discussion

smooth the role of floodplain micro-channels systems which are eliminated when resampling DEMs to lower (90-120 m) grids. For small-scale hydrodynamic modelling or modelling of minor flood events dominated by in-channel flow, a higher spatial resolution DEM with accurate channel morphology is required. There are also opportunities to improve accuracy of freely available remotely-sensed DEMs by using channel information extracted from other remotely-sensed data such as high resolution optical images and aerial photos (Alsdorf et al., 2007; Bjerklie et al., 2003; Frappart et al., 2005b). These opportunities are discussed in more detail in Table 3 of this chapter and Chapter 7 of this thesis.

6.4. Hydrodynamic modelling approach and partitioning of transmission losses

Chapter 5 utilised the results of previous chapters (Chapter 2-4) to construct a hydrodynamic model using remotely-sensed data and limited ground-based data in order to constrain the reach-level water balance, focusing on quantification of transmission losses in a large, low-gradient and data- poor dryland catchment. Accurate topographic data is critical, and in this regard H-DEM provided the best topographic forcing parameter for hydrodynamic model in drylands. Remotely-sensed flood inundation extent also provided valuable data for validation and evaluation of the hydrodynamic model. Satellite-derived altimetry data were able to overcome some data scarcity issues by providing water elevation data that were used to calibrate and evaluate the hydrodynamic model, and by using model outputs to generate stage-discharge relationships, to directly convert altimetry flood stage to discharge. The main results of hydrodynamic modelling and downstream changes in flood water balances are presented here:

 A combination of RS and field data allows hydrodynamic modelling in data-sparse basins;  Hydrodynamic model simulated complex flooding in anabranching dryland rivers;  Transmission losses in "channel country" are up to 68% of inflow (mean = 46%);  Losses are a combination of evaporation, infiltration and terminal water storage; and  Lateral inflow is the largest source of uncertainty in water balance estimation.

The results of this research can be used in dryland environments with similar morphologic and hydrologic characteristics elsewhere in the world to understand the dynamics of the regional scale hydrological processes, which are important for driving ecological processes in dryland environments. Anabranching rivers with sizeable floodplain are found in dryland regions across the world, including South America (Smith, 1986), China (Wang et al., 2005), Australia (Knighton and 6-10

Chapter 6. Discussion

Nanson, 2001; Nanson et al., 1986; Schumm et al., 1996), North America (Schumann, 1989; Smith, 2009; Smith and Smith, 1980), Africa (Makaske, 2001) and Europe (Gurnell et al., 2009).

The approaches presented in this work offer a significant opportunity and potential for new research into the water balance and ecohydrology of dryland rivers. However, given the extensive inundation area of the floods and the large changes in surface properties, remotely-sensed (and/or ground-based) data are unlikely to ever fully account for the full uncertainty in hydrological prediction in these dryland settings with complex multi-channel system, such wide floodplain and low gradients. Section 6.5 presents an overall summary of the value of remotely-sensed data in hydrological studies of data-poor landscapes.

6.5. The value of remotely-sensed data for modelling hydrology of data- poor regions

The value of remotely-sensed data to monitor floods has been known for decades since the first remotely sensed instruments where launched on satellites and availability of aerial photography (Collins, 1969; Lathram, 1968; McFeeters, 1996b; Ramey, 1970; Schumann et al., 2009). With the current extensive availability of earth observation satellites, there has been significant progress in advancing flood modelling by using remotely-sensed data. Remotely-sensed data provide unique capabilities to understand spatial and temporal variability in the inundation extent and movement of water in ways that are not possible where there are limitations from the coverage of traditional in situ gauge stations (Alsdorf et al., 2007). Dryland river systems (such as arid and semi-arid catchments of central Australia, in particular) illustrate the need of remotely-sensed data for hydrological studies due to the lack of gauging or climatic infrastructure. McVicar et al. (2009) identified five classes of how remotely-sensed data can be used for catchment-scale modelling, they are: (i) forcing data; (ii) parameters; (iii) calibration; (iv) evaluation; and (v) regionalisation. This thesis highlighted the value of the remotely-sensed data approaches to retrieve hydrological information and also assessed the uncertainty related to remotely-sensed data that are widely used in hydrodynamic models. However, before using these remotely-sensed data for hydrological studies, the error and uncertainty characteristics of these observations should be assessed, as this can add extra error which then is propagated through hydrodynamic model results in additional to the other imitations of hydrodynamic models due to model structure.

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Chapter 6. Discussion

Remotely-sensed data can be used for in combination with hydrologic-hydrodynamic models to estimate hydrological variables (Paiva et al., 2013a) or in combination with other remotely-sensed datasets to directly estimate hydrological information models. Water elevation can be estimated by using high spatial resolution aerial photography, satellite optical images and RaDAR images (i.e., SAR) in conjunction with topographic maps or digital elevation models (Callow and Boggs, 2013; Frappart et al., 2005b). Light detection and ranging (LiDAR) and radio detection and ranging (RaDAR) images, have been used to estimate water elevation with accuracies ranging from 20 cm (Puech and Raclot, 2002) to 58 cm (Hostache et al., 2009). Direct estimates of the discharge requires high spatial resolution and more frequent flood maps to be used in combination with highly accurate DEMs, which are not available in dryland river systems.

The extent of uncertainty in hydrodynamic model results due to uncertainty in remotely-sensed data inputs can be reduced by performing some pre-processing and data combining from multiple satellite sources. For example, uncertainty in the SRTM DEM can be reduced by using ICESat points to correct point accuracy and using Landsat-derived vegetation cover maps for vegetation smoothing. These corrections are essential to reduce the impacts of artifacts from a DEM to use them in hydrodynamic modelling simulations in a challenging low-gradient floodplain environment.

When using a hydrodynamic model with remotely-sensed water elevation as the boundary condition for water balance calculations, the accuracy of water elevation has significant impact on discharge estimations. Accuracy of the topographic forcing DEM is also very important when constructing elevation-discharge (i.e., rating curve) relationships to convert water elevation to discharge. This is due to high sensitivity of flood inundation extent to elevation changes in dryland river systems with low-gradient floodplain.

Current remotely-sensed data generally with low-to-moderate spatial and temporal resolutions are related with moderate-to-high uncertainty in water balance estimation. However in ungauged or data-poor dryland river systems they provide a valuable estimate of hydrological variables in large scale (i.e., course resolution soil moisture products and GRACE data). However, there is a need for future satellite missions to provide highly accurate information on hydrological processes that can be used directly or in combination with hydrodynamic models for meso-to-micro-scale hydrological studies.

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Chapter 7

Conclusion and Recommendations

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Chapter 7. Conclusion and Recommendation

7.1 Conclusion

The main objective of this PhD research was to establish remotely-sensed data approaches to study hydrological processes of dryland river systems. The main outcomes of this research are briefly summarised below. This final chapter also considers some specific recommendations that arise from this work and concludes in outlining the opportunities to continuing to apply and advance this field of research.

7.1.1 The value of satellite-derived altimetry data in hydrology of dryland river systems

This study forms part of a growing body of research that highlights the added value of satellite- derived altimetry data to conventional hydrology, extending this to specifically evaluate anabranching river systems. As such, satellite-derived altimetry data may help improve our understanding of inland water and river system processes, and related issues of ecosystem health and water availability, in many remote regions around the world. We highlighted the limitations of using ocean-oriented satellite-derived altimetry data in studying inland water bodies and the value of the recent retrackers for hydrological applications. Although low temporal frequency of the altimeter satellites limits their application in rapid and dynamic hydrological events, they can be used in combination with other remotely-sensed data or limited ground-based data in hydrological- hydrodynamic modelling. This research also illustrates the capability of some satellite altimeters to monitor water elevation differences in multiple channels as well as water surface slope across the multi-channel systems, which can improve our understanding of the hydrodynamic behaviour in data-poor large rivers.

7.1.2 Optical satellite imagery applications in dryland rivers hydrology

By comparing two different approaches, to simulate Landsat-like indices from Landsat-MODIS images we found that the Index-then-Blend approach (IB) consistently produced better results than when blending individual image bands, and then calculate indices: the blend-then-index (BI) approach. We concluded that the reason for this is that the IB approach only incurs one instance of blending and therefore only one instance of error due to blending, whereas the BI approach incurs multiple blending instances and therefore multiple instances of error. While (Emelyanova et al., 2013) showed that algorithm selection between STARFM and ESTARFM was important to achieve a more accurately blended output of reflectance bands, we showed here that for blending indices,

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Chapter 7. Conclusion and Recommendation

the choice of approach (IB versus BI) was more important than blending algorithm selection (STARFM versus ESTARFM). The downscaled water indices can be used to produce more detailed inundation information of floods for calibration/validation of hydrodynamic models. However, in this study (chapter 5 / paper 4) we used MODIS OWL images (without blending with Landsat) as a 500 m spatial resolution was adequate for our application and model setup, and as the simulated images produced by the blending algorithms in highly dynamic flooding environments had residual levels of uncertainty that was too high for use in subsequent hydrodynamic modelling.

7.1.3 Remotely-sensed topographic data application for dryland systems

We showed that DEM correction methods such as vegetation smoothing and hydrological correction reduced the impacts of artifacts from the DEM’s creation from the original source elevation data. DEM hydrological corrections are necessary to alter elevation values of cells to better represent the river position and number of channels and hydraulic smoothness of the river channel and floodplain. These resulted in important improvements to the hydrodynamic model simulations, and were able to produce more physically-realistic results in a challenging low- gradient floodplain environment. Our evaluation of grid size influence, revealed that DEM sources were important and that the simulation runs generally produced similar accuracies for grid sizes less than 120 m. Overall this research provides the hydrological community with a robust and integrated investigation to assess the implications of the DEM source, calibration and validation methods (and base DEM error), DEM preparation and grid size considerations for modelling floods in low- gradient and multi-channel environments, or to adapt the workflow to test these variables in other landscapes.

7.1.4 Hydrodynamic modelling approach and transmission loss partitioning

The final paper demonstrated how ground-based and remotely-sensed data can be used to quantify spatial and temporal availability of water and transmission losses in data-poor or ungauged dryland river systems. The approach also highlighted the importance of using limited ground-based measurements coupled with remotely-sensed data for quantifying uncertainties arising from scarcity of hydrological data and providing reliable estimation of surface water and transmission loss components in dryland regions. In ungauged parts of the dryland river systems, transmission losses are high (68%) and varies non-linearly with flood discharge and increases downstream. Water balance calculations based on gauging data are often blurred by unknown lateral inflows (Lange, 2005). We found that the lateral inflow between available sparse gauging stations had the highest

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Chapter 7. Conclusion and Recommendation

uncertainty in water balance estimation. The partitioning of transmission losses into the major components showed that actual evapotranspiration was the most significant component (21.6% of total inflow), followed by infiltration (13.2%) and terminal water storage (11.2%). Quantifying these components improves our understandings of availability of water resources which is important drivers of ecohydrological processes in dryland environments.

7.2. Recommendations for applying this research

Overall outcomes from this PhD research provides the hydrological community with a robust and integrated investigation to assess the implications of remotely-sensed data for modelling floods in low-gradient and multi-channel environments. The results of this study may be used in similar environments in the world or the workflow may be adapted to test these approaches in other landscapes. It is recommended that:

 Using Altimetry, Ku band, ‘Ice’ retrackers produce higher accuracy than classic ‘ocean’ retracker on inland water bodies. These data can be used to; (i) individually, study water elevation changes of lakes, wetlands and large river floods; (ii) in combination with topographic data and/or satellite-derived flood inundation extent to estimate discharge (iii) can be used as a forcing data and/or calibration/evaluation of hydrodynamic model, in data- poor environments.  When using blending approach to downscaled standing water, environmental moisture and vegetation indices: (i) STARFM or ESTARFM can be used depends on spatial and temporal variance of the study site (ii) we recommend to calculate index and then blend index (IB approach) rather than calculate indices from blended images (BI approach); (iii) the choice of approach (IB versus BI) has a larger impact on accuracy of blending indices than did the choice of algorithm (STARFM versus ESTARFM); and (iv) simulated Landsat-like indices are more valuable in producing accurate flood inundation extent of minor to moderate flood events of large rivers.  In using remotely-sensed topographic data for hydrodynamic modelling: (i) SRTM DEM has higher accuracy than ASTER GDEM data; (ii) vertical accuracy of topographic data is more important than spatial resolution; (iii) advanced preparations methods are critical and are not comparable with simple methods which have been used in single-channel river systems; and (iv) vegetation smoothing correction is more important than hydrological correction and co-registration when modelling major flood events.

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Chapter 7. Conclusion and Recommendation

 In combining hydrodynamic model and remotely-sensed data for water balance studies the main recommendations are: (i) topographic data is the most important parameter; (ii) model results are not very sensitive to surface roughness; (iii) elevation accuracy of topographic data is more important than model grid size; (iv) to calibrate the model to total discharge of flood instead of peak flood and travel time of flood; (v) converting water elevation to discharge by using rating curves will add more error to water balance calculations; (vi) the accuracy of rainfall data is important when using rainfall data as upstream boundary condition; (vii) it is critical to account for lateral flow when performing water balance calculations; and (viii) elevation datum correction is required when using multiple ground- based and remotely-sensed data from multiple data sources.

7.3. Opportunities for future work

Several opportunities exist for extending the research presented in this thesis. There is scope to further improve existing approaches and developing new remotely-sensed approaches to provide basic inputs for hydrological models. This may improve the calibration and evaluation of the models and estimation of various components of the water balance and transmission losses in data- poor dryland river systems.

Opportunities for future work include:

 Develop a multi-mission altimetry approach to improve the temporal frequency and spatial resolution of the water elevation time series of floods in ungauged dryland catchments and realise the opportunities that these offer for weather-independent near real-time flood forecasting and other applications in data-sparse regions;  Develop data-fusion methods to downscale temporal frequency of satellite-derived altimetry data by combining with other more frequent remotely-sensed data (i.e., MODIS products) to produce high temporal and high spatial/accuracy datasets.

We concluded that Landsat and MODIS images can be combined to produce Landsat-like (i.e., 30 m) daily water indices. However to use these data for daily water volume estimation requires high accurate topographic data. The opportunity for future work in this area is:

 To evaluate the improvement in discharge estimation by using downscaled water indices in combination with high accurate topographic data such as LiDAR.

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Chapter 7. Conclusion and Recommendation

Topographic data:

 Explore possibilities of using commercially-available high spatial resolution topographic data i.e., LiDAR data or TerraSAR-X / TanDEM-X to improve hydrodynamic modelling of complex anabranching river systems, however this is not critical for large scale modelling which requires moderate resolution/accuracy topographic data;  Using reflective remotely sensed indices that identify standing water to look at flood inundation frequency to map flow pathways and impose structure on DEMs – rather than coarse hand-drawn linear hydrology networks.

Integrated remotely-sensed data to support hydrodynamic modelling:

 Explore the effect of spatially and temporally varying parameters, such as evaporation and infiltration, on hydrodynamic model results;  Continue to evaluate developments in the Gravity Recovery and Climate Experiment (GRACE) satellites for estimating catchment-scale water balance changes. The opportunities for constraining water balance changes are significant, however the spatial resolution and the fact that large cells cross catchments or span the catchment and floodplain, means that this data is not useable at present for this scale of research;  Explore the possibilities of using other available remotely-sensed datasets from optical and RaDAR sensors to estimate soil moisture, precipitation and evapotranspiration to improve hydrodynamic modelling of dryland river systems.

The thesis began in quoting a significant scientific report from only six years ago, which declared that “Understanding is (Northern Australia’s) form and function at the scale required to identify the impacts of specific uses of land or water (i.e. certain types of development) is beyond the scope of this report. It is, as we show, also beyond the current capability of any report. Northern Australia cannot currently be understood at a detailed, site‐specific scale – the raw observational data required for such understanding simply doesn’t exist” (page 10 of CSIRO, 2009). Within the body of research presented in this thesis, there is the optimism that a substantial and significant contribution has been made, such that critical questions around the form and function of the land and water resources of data-poor regions might be actually understood in detailed and site‐specific ways. Through innovative approaches such as those presented within this thesis, we expect that the enhanced biophysical understanding that this can now facilitate will lead to better management of

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Chapter 7. Conclusion and Recommendation

the environmental, human and economic assets of data-poor dryland regions such as those in northern and central Australia and other dryland regions globally.

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