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An integrated remote-sensing mapping method for groundwater dependent ecosystems associated with diffuse discharge in the Great Artesian Basin, Australia

Une méthode de cartographie intégrée à partir de la télédétection appliquée aux écosystèmes tributaires des eaux souterraines associés à une décharge diffuse dans le Grand Bassin Artésien, Australie

Un método integrado de mapeo por teledetección para los ecosistemas dependientes de aguas subterráneas asociados con la descarga difusa en la Great Artesian Basin, Australia

澳大利亚大自流盆地与扩散排泄相关的地下水依赖型生态系统的集成遥感制图方法

Método integrado de mapeamento de ecossistemas dependentes de água subterrânea por sensoriamento remoto associado à descarga difusa na Grande Bacia Artesiana, Austrália

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Abstract

Vertical leakage (discharge to upper aquifers) is an important but poorly constrained component of water balance in the Great Artesian Basin (GAB), Australia. It ranges from negligible discharge where the GAB is overlain by aquitards, to high discharge where artesian water feeds the shallow unconfined aquifer (thereby raising the water table) causing elevated surface soil moisture and extensive surface salinisation. Adequately representing the temporal and spatial variability of vertical leakage is difficult due to the large scale over which the discharge occurs. An innovative method is presented that integrates a supervised classification of high-discharge zones using time-series Landsat data with landform mapping information to improve classification results. ‘Wetness persistence’ and ‘salt persistence’ classes, determined from the time series data, are related to groundwater discharge processes through a discharge framework that allows scaling up of field-based discharge estimates. The results show that using multi-image classification integrated with landform data will significantly reduce uncertainty by reducing false positives. No significant temporal trends were found in a time series assessment, with results featuring high variability, most likely due to image normalisation issues. The lack of a clear temporal signal suggests that an assumption of steady-state discharge is valid for estimating annual fluxes of vertical leakage. Supervised classification and landform outputs provide updated knowledge on GAB vertical leakage rates by providing useful lower and upper bounds of discharge rates respectively. Additionally, groundwater-dependent ecosystem classification, covering the full extent of the basin margins, is a new source of information resulting from the work.

Résumé

Le drainage vertical (la décharge vers les aquifères supérieurs) est une composante importante mais mal définie du bilan de l’eau dans le Grand Bassin Artésien (GBA), Australie. Cela va d’une décharge négligeable là où le GBA est recouvert par des aquitards, à une décharge importante là où l’eau artésienne alimente l’aquifère libre peu profond (donnant lieu à une augmentation du niveau piézométrique) causant une forte humidité et une salinisation étendue à la surface du sol. Représenter de manière adéquate la variabilité temporelle et spatiale du drainage vertical est difficile en raison du fait que la décharge se produit à grande échelle. Une méthode innovante est présentée, qui intègre une classification assistée des zones de forte décharge utilisant les données des séries chronologiques Landsat en même temps qu’une information cartographique sur le relief pour améliorer les résultats de la classification. Les classes d’“humidité persistante” et de “sel persistant”, déterminées à partir des données de séries chronologiques, sont reliées aux processus de décharge des eaux souterraines par le biais d’un canevas qui permet de reproduire à plus grande échelles les estimations effectuées sur le terrain. Les résultats montrent que l’utilisation d’une classification multi-images intégrée à des données sur le relief réduira significativement l’incertitude en réduisant les faux positifs. Aucune tendance temporelle significative n’a été décelée dans l’évaluation de l’une des séries chronologiques, avec des résultats présentant une grande variabilité, très probablement en raison des problèmes de normalisation de l’image. L’absence d’un signal temporel clair suggère qu’une hypothèse de décharge permanente est valable pour évaluer les flux annuels du drainage vertical. La classification assistée et la restitution du relief procurent une connaissance actualisée des taux de drainage vertical dans le GBA, en fournissant des limites supérieures et inférieures utiles des taux de décharge respectivement. De plus, la classification des écosystèmes tributaires des eaux souterraines, couvrant la totalité de l’extension du bassin, est une nouvelle source d’information qui résulte de ce travail.

Resumen

La filtración vertical (descarga a los acuíferos superiores) es una componente importante pero un poco limitada en el balance hídrico en la Great Artesian Basin (GAB), Australia. Va desde una descarga insignificante donde el GAB está cubierto por acuíferos, hasta una alta descarga donde el agua alimenta el acuífero no confinado poco profundo (elevando así la capa freática) causando una elevada humedad superficial del suelo y una extensa salinización de la superficie. La representación adecuada de la variabilidad temporal y espacial de las filtraciones verticales es difícil debido a la gran escala en la que se produce la descarga. Se presenta un método innovador que integra una clasificación supervisada de las zonas de alta descarga utilizando datos Landsat de series temporales con información de mapeo de formas del terreno para mejorar los resultados de la clasificación. Las clases de ‘permanencia en la humedad’ y ‘permanencia en la sal’, determinadas a partir de los datos de las series temporales, están relacionadas con los procesos de descarga de aguas subterráneas a través de un marco de descarga que permite ampliar las estimaciones de descarga basadas en el campo. Los resultados muestran que el uso de la clasificación multiimagen integrada con los datos del relieve reducirá significativamente la incertidumbre al reducir los falsos positivos. No se encontraron tendencias temporales significativas en una evaluación de series de tiempo, con resultados de alta variabilidad, probablemente debido a problemas de normalización de la imagen. La falta de una señal temporal clara sugiere que una suposición de descarga en estado estacionario es válida para estimar los flujos anuales de filtración vertical. La clasificación supervisada y los resultados de los relieves proporcionan conocimientos actualizados sobre los índices de filtraciones verticales de GAB al proporcionar límites inferiores y superiores útiles de los índices de descarga, respectivamente. Además, la clasificación de los ecosistemas dependientes de las aguas subterráneas, que cubre la totalidad de los márgenes de la cuenca, es una nueva fuente de información resultante del trabajo.

摘要

在澳大利亚大自流盆地(GAB),垂直渗漏(排泄到上部含水层)是水均衡中重要而且约束性很弱的要素。排泄量的变化小到GAB上覆于隔水层区域的可忽略不计,大到自流水补给潜水含水层(从而提高地下水位)引起表层土壤湿度升高和表层盐渍化加剧区域的高排泄量。由于排泄区尺度较大,难以充分表示垂直泄漏的时空变化。本研究提出了一种新方法,该方法将使用时间序列的Landsat数据的高排泄区的监督分类与地形图信息集成在一起,以改善分类结果。将时间序列数据确定的“湿度持久性”和“盐持久性”类别与通过基于实地排泄估计来进行比例放大的排泄模式的地下水排泄过程关联起来。结果表明,将多图像分类与地形数据集成在一起,通过减少误报率从而显著减少不确定性。在时间序列评估中未发现明显的时间变化趋势,其结果具有较高的可变性,这很可能是图像归一化问题所致。缺少清晰的时间信号表明,估算垂直泄漏的年通量的稳定排泄量假定是有效的。监督分类和地形输出通过分别提供有用的排泄率上下限来提供有关GAB垂直泄漏率的最新知识。此外,依赖于地下水的生态系统分类涵盖了流域边缘的全部范围,是本研究产生的新数据信息。

Resumo

A percolação vertical (descarga para aquíferos superiores) é um componente importante, embora pouco compreendido, para o balanço hídrico na Grande Bacia Artesiana (GBA), na Austrália. Ela pode ser desprezível, onde a GBA é coberta por aquitardos, ou elevada, em regiões onde a água artesiana alimenta o aquífero superficial não confinado (elevando o nível d’agua), elevando a umidade do solo e causando uma salinização superficial extensiva. Representar de maneira adequada a variabilidade temporal e espacial da percolação vertical é difícil, devido à larga escala que ocorre a descarga. Este trabalho apresenta um método inovador que integra uma classificação supervisionada de zonas de alta descarga usando dados de séries temporais Landsat com informações de mapeamento de relevo para melhorar os resultados da classificação. Classes de ‘persistência da umidade’ e ‘persistência de sal’, determinadas a partir das séries temporais, estão relacionadas aos processos de descarga de águas subterrâneas do sistema, e podem ser calibrados com dados de descarga medidos a campo para melhorar as estimativas. Os resultados mostram que o uso da classificação de imagens integradas aos dados de relevo diminui significativamente a incerteza por reduzir falsos positivos. Não foram encontradas tendências temporais significativas na avaliação das séries temporais, embora os resultados apresentassem alta variabilidade, provavelmente devido a problemas de normalização das imagens. A falta de um sinal temporal claro sugere que a premissa de fluxo estacionário é válida para estimar fluxos anuais de percolação vertical. A classificação supervisionada aliada aos dados de relevo melhora o entendimento da percolação vertical na GBA por fornecer limites inferiores e superiores das taxas de descarga. Além disso, a classificação da dependência por águas subterrâneas pelo ecossistema, cobrindo toda a área da bacia, é um resultado importante obtido neste trabalho.

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References

  • Allison GB, Barnes CJ (1985) Estimation of evaporation from the normally “dry” Lake Frome in South Australia. Journal of Hydrology 78 (3-4):229–242

  • Ambrose G, Flint RB (1980) BILLA KALINA map sheet. SA,1:250 000, Sheet SH 53–7, Department of Mines, South Australia, Adelaide, Australia

  • Angstrom A (1925) The albedo of various surfaces of ground. Geogr Ann 7:323

    Google Scholar 

  • Australian Bureau of Meteorology (2019) www.bom.gov.au/climate. Accessed 16 February 2019

  • Benbow MC (1981) COOBER PEDY map sheet. SA,1: 250 000, Sheet SH 53–6, Department of Mines, South Australia, Adelaide, Australia

  • Bowers SA, Hanks RJ (1965) Reflection of radiant energy from soil. Soil Sci 100:130–138

    Article  Google Scholar 

  • Bowers SA, Smith SJ (1972) Spectrophotometric determination of soil water content. Soil Sci Am Proc 36:978–980

    Article  Google Scholar 

  • Bredehoeft JD, Papadopulus SS, Cooper HH Jr (1982) Groundwater: the water budget myth, scientific basis of water resource management. National Research Council Geophysics Study Committee, National Academy Press, Washington, DC, pp 51–57

    Google Scholar 

  • Chan RA (1988) Regolith terrain mapping for mineral exploration in Western Australia. Zeitschrift Geomorphol Supp Bnd 68:205–221

    Google Scholar 

  • Craig JC, Shih SF, Bowman B J, Carter G A (1998) Detection of salinity stress in citrus trees using narrow-band multispectral imaging. Proceedings First International Conference on Geospatial Information in Agriculture and Forestry, Ann Arbor MI, June 1998

  • Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46

    Article  Google Scholar 

  • Costelloe JF, Irvine EC, Western AW, Matic V, Walker JP, Tyler M (2008) Quantifying near-surface, diffuse groundwater discharge along the south-west margin of the Great Artesian Basin. Water Down Under 2008:831–840

    Google Scholar 

  • Costelloe JF, Matic V, Western AW, Walker JP, Tyler M (2015) Determining vertical leakage from the Great Artesian Basin, Australia, through upscaling field estimates of phreatic evapotranspiration. J Hydrol 529:1079–1094

    Article  Google Scholar 

  • Crowley JK (1991) Visible and near-infrared (04–25 micro meter) reflectance spectra of playa evaporite minerals. J Geochem Res 96:16231–16240

    Google Scholar 

  • Curcio AP, Petty CC (1951) The near infrared absorption spectrum of liquid water. J Opt Soc Am 41:302–304

    Article  Google Scholar 

  • Danielopol DL, Griebler C, Gunatilaka A, Notenboom J (2003) Present state and future prospects for groundwater ecosystems. Environ Conserv 30:104–130

    Article  Google Scholar 

  • Dehaan R, Taylor GR (2002) Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induces soil salinization. Remote Sens Environ 80:406–417

    Article  Google Scholar 

  • Dehaan R, Taylor GR (2003) Image-derived spectral endmembers as indicators of salinization. Int J Remote Sens 24(4–20):775–794

    Article  Google Scholar 

  • Drake NA (1995) Reflectance spectra of evaporite minerals (400–2500 nm): applications of remote sensing. Int J Remote Sens 16:2555–2571

    Article  Google Scholar 

  • Dwivedi R S, Rao RM (1992) The selection of the best possible Landsat TM band combination for delineating salt-affected soils. Int J Remote Sens 13(11):2051–2058

  • Engman ET, Chauhan N (1995) Status of microwave soil moisture measurements with remote sensing. Remote Sens Environ 51(1):189–198

    Article  Google Scholar 

  • Farifteh J, Farshed A, George RJ (2006) Assessing salt-affected soils using remote sensing, solute modelling, and geophysics. Geoderma 130:191–206

    Article  Google Scholar 

  • Farifteh J, van der Meer F, Meijde M, Atzberger C (2008) Spectral characteristics of salt-affected soils: a laboratory experiment. Geoderma 145(3–4):196–206

    Article  Google Scholar 

  • Famiglietti JS, Lo M, Ho SL, Bethune J, Anderson KJ, Syed H, Swenso SC, de Linage CR, Rodell M (2011) Satellites measure recent rates of groundwater depletion in California’s Central Valley. Geophys Res Lett 38:L03403. https://doi.org/10.1029/2010GL046442

    Article  Google Scholar 

  • Forbes BG, Coats RP, Webb BP, Horwitz RC (1995) MAREE map sheet. Department ofMines and Energy, South Australia, South Australia, Adelaide, Australia

  • Freytag IB, Heath GR, Wopfner H (1967) ODNADATTA map sheet. SA,1:250 000, Sheet SG 53–15, Department of Mines, South Australia, Adelaide, Australia

  • Geoscience Australia (2011) Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM). Bioregional Assessment Source Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/9a9284b6-eb45-4a13-97d0-91bf25f1187b

  • Gotch TB (2013) Spatial survey of springs. Allocating Water and Maintaining Springs in the Great Artesian Basin, 4

  • Habermehl MA (1980) The Great Artesian Basin. Australia BMR J Aust Geol Geophys 5:9–38

    Google Scholar 

  • Haubrock S, Chabrillat S, Lemmnitz C, Kaufmann H (2008) Surface soil moisture quantification models from reflectance data under field conditions. Int J Remote Sens 29:3–29

    Article  Google Scholar 

  • Herczeg AL (2008) Background report on the Great Artesian Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. CSIRO, Canberra, Australia, 18 pp

  • Hick PT, WGR R (1990) Some spectral considerations for remote sensing of soil salinity. Aust J Soil Res 28:417–431

    Article  Google Scholar 

  • Hick PT, Davies JR, Steckis RA (1984) Mapping dryland salinity in Western Australia using remotely sensed data Proceedings of the 10th International Conference on Satellite Remote Sensing, Remote Sensing Society, Reading, UK, September 1984, pp 343–350

  • Hunt GR, Salisbury JW (1976) Visible and near infrared spectra of minerals and rocks: XII metamorphic rocks. Mod Geol 5:219–229

    Google Scholar 

  • Hunt GR, Salisbury JW, Lenoff CJ (1972) Visible and near-infrared spectra of minerals and rocks: V. halides, arsenates, vanadates, and borates. Mod Geol 3:121–132

    Google Scholar 

  • Hydroshare (No Date) https://www.hydroshare.org/resource/0db68579d3ab492ea94bcceefde900a6/. Accessed October 2019

  • Kampf SK, Tyler SW (2006) Spatial characterisation of land surface energy fluxes and uncertainty estimation at the Salar de Atacama, northern Chile. Adv Water Resour 29:336–354

    Article  Google Scholar 

  • Kalf FRP, Woolley DR (2005) Applicability and methodology of determining sustainable yield in groundwater systems. Hydrogeol J 13:295–312

    Article  Google Scholar 

  • Krapf CBE, Irvine JA, Cowley WM (2012) Compilation of the 1:2 000 000 state regolith map of South Australia: a summary. Report book 2012/00016, Department of Manufacturing, Innovation, Trade, Resources and Energy, South Australia, Adelaide, Australia

  • Krieg GW (1985) DALHOUSIE map sheet, 1:250 000, Sheet SG 53–11, Department of Mines, South Australia, Adelaide, Australia

  • Krieg GW, Rogers PA, Callen RA, Belperio AP, Forbes BG (1992) CURDIMURKA map sheet. Geological Atlas 1: 250 000 Series, Sheet SH 53–8, Geological Survey, South Australia, Adelaide, Australia

  • Lewis M, Lewis, White D, Gotch T (2013) Allocating water and maintaining springs in the Great Artesian Basin, vol IV: spatial survey and remote sensing of artesian springs of the Western Great Artesian Basin. National Water Commission, Canberra

  • Liu C, Frazier P, Kumar L (2007) Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ 107:606–616

    Article  Google Scholar 

  • Lui W, Baret F, Gu XF, Tong Q, Zheng L, Zhang B (2002) Relating soil surface moisture to reflectance. Remote Sens Environ 81:238–246

    Article  Google Scholar 

  • Lui W, Baret F, Gu XF, Zhang B, Tong Q, Zheng L (2003) Evaluation of methods for soil surface moisture estimation from reflectance data. Int J Remote Sens 24(10):2069–2083

    Article  Google Scholar 

  • Lobell DB, Asner GP (2002) Moisture effects on soil reflectance. Soil Sci Am J 66:722–727

    Article  Google Scholar 

  • Love AJ, Shand P, Crossey L, Harrington GA, Rousseau-Gueutin P (2013) Groundwater discharge of the western Great Artesian Basin. National Water Commission, Canberra

  • Matic V (2018) Assessing the utility of remote sensing in estimating groundwater discharge along the southwest margin of the Great Artesian Basin, South Australia. PhD Thesis, University of Melbourne. http://hdlhandlenet/11343/213416. Accessed October 2019

  • Matternicht GI, Zinck JA (2003) Remote sensing of soil salinity: potentials and constraints. Remote Sens Environ 85:1–20

    Article  Google Scholar 

  • Menking KM, Anderson RY, Brunsell NA, Allen BE, Amy LL, Thomas AH, Steven W (2000) Evaporation from groundwater discharge playas, Estancia Basin, Central New Mexico. Glob Planet Chang 25:133–147

    Article  Google Scholar 

  • Merlin O, Walker JP, Panciera R, Young R, Kalma JD, Klin EJ (2007) Calibration of a soil moisture sensor in heterogeneous terrain. MODSIM, Christchurch, New Zealand

  • Miles CR, White M, Scholz G (2012) Assessment of the impacts of future climate and groundwater development on Great Artesian Basin springs. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment, Canberra

  • Mougenot B (1993) Remote sensing of salt-affected soils. Remote Sens Rev 7:241–259

    Article  Google Scholar 

  • Mulders M (1987) Remote sensing in soil science. In: Development in soil science. Elsevier, Amsterdam, 379 pp

    Google Scholar 

  • Njoku EG, Entekhabi D (1996) Passive microwave remote sensing of soil moisture. J Hydrol 184:101–129

    Article  Google Scholar 

  • Ollier CD, Pain CF (1995) Reply: Landscape evolution and tectonic in south eastern Australia. AGSO J Austral Geol Geophys 16:325–331

    Google Scholar 

  • Patten DT, Rouse L, Stromberg JC (2008) Isolated spring wetlands in the Great Basin and Mojave Deserts, USA: potential responses of vegetation to groundwater withdrawal. Environ Manag 41:398–413

    Article  Google Scholar 

  • Ransley TR, Smerdon BD (2012) Hydrostratigraphy, hydrogeology and system conceptualisation of the Great Artesian Basin: a technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment CSIRO Water for a Healthy Country Flagship, Canberra, Australia

  • Rogers PA, Freeman PJ (1996) WARRINA map sheet. Geological atlas, 1: 250 000 Series, Sheet SH 53–3, Geological Survey, South Australia, Adelaide, Australia

  • Schmugge T, Jackson TJ, Kustas WP, Wang JR (1992) Passive microwave remote sensing of soil moisture: results from HAPEX, FIFE and MONSOON 90. J Photogramm Remote Sens 47:127–143

    Article  Google Scholar 

  • Sheard MJ, and Callen RA, 2000, CALLABONNA map sheet. Geological Atlas 1:250 000 Series, Sheet SH54-8, Geological Survey, South Australia, Adelaide, Australia

  • Siegal BS, Gillespie AR (1980) Remote sensing in geology. Wiley, Chichester, UK, 702 pp

  • Stoner ER, Baumgardner MF (1981) Characteristics variations in reflectance of surface soils. Soil Sci Soc Am J 45(6):1161–1165

  • Taylor G, Butt CRM (1998) The Australian regolith and mineral exploration. AGSO J Geol Geophys 17:55–67

    Google Scholar 

  • Taylor T, Eggleton RA (2001) Regolith geology and geomorphology. Wiley, Hoboken, NJ

  • Turner D, Clarke K, White D, Lewis M (2015) Mapping areas of Great Artesian Basin diffuse discharge. DEWNR Technical report 2015/55, Department of Environment, Water and Natural Resources, South Australia, Adelaide, Australia

  • USGS (2017) Product guide Landsat surface reflectance-derived spectral indices V36. https://landsat.usgs.gov/sites/default/files/documents/si_product_guide.pdf. Accessed December 2017

  • Ulaby FT, Dubios PC, van Zyl J (1996) Radar mapping of surface Soil moisture. J Hydrol 184: 57–84

  • Vermote EF, Kotchenova S (2008) Atmospheric correction for the monitoring of land surfaces. J Geophys Res: Atmos (1984–2012) 113(D23). https://doi.org/10.1029/2007JD009662

  • Waclawick 2006 Landscape evolution of the Umbum Creek Catchment, Western Lake Eyre, central Australia. PhD Thesis, The University of Adelaide, Adelaide, Australia

  • Welsh WD (2000) GABFLOW: a steady state groundwater flow model of the Great Artesian Basin. Bureau of Rural Sciences, Canberra

    Google Scholar 

  • Welsh WD (2006) Great Artesian Basin transient groundwater model. Bureau of Rural Sciences, Canberra

    Google Scholar 

  • Whiting ML, Li L, Ustin SL (2004) Predicting water content using Gaussian model on soil spectra. Remote Sens Environ 89:535–552

  • Wigneron JP, Calvert JC, Pellarin T, Van de Griend AA, Berger M, Ferrazzoli P (2003) Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans. Remote Sens Environ 85:489–506

    Article  Google Scholar 

  • Woods (1990) Evaporative discharge of groundwater from the margin of the Great Artesian Basin near Lake Eyre, South Australia. Flinders University, Adelaide, Australia

    Google Scholar 

  • Woods PH, Walker GR, Allison GB (1990) Estimating groundwater discharge at the southern margin of the Great Artesian Basin near Lake Eyre, South Australia, in Proceedings of the International Conference on Groundwater in Large Sedimentary Basins, Perth, Australian Water Resources Council Conference Series 20:298–309

  • Wu W, Sun X, Wang X, Fan J, Luo J, Shen Y, Yang Y (2018) A long time series radiometric normalisation method for Landsat. Sensors 18(2)

  • Yechieli Y, Wood WW (2002) Hydrogeologic processes in saline systems: playas, sabkhas, and saline lakes. Earth Sci Rev 58:343–365

    Article  Google Scholar 

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Acknowledgments

Thank you to the field volunteers, Graeme Tomlinson, Dien Phu Nguyen, Belis Matabire, Peter Richards, Maria Friderich and Susan Hayes. Geoscience Australia provided the ASTER imagery. Megan Lewis (University of Adelaide) provided airborne hyperspectral imagery. Alan Marks (Geoscience Australia) provided ASD equipment for data collection. Scott Tyler (University Nevada, Reno) is thanked for the 3-month hosted visit to develop eddy covariance modelling techniques. The permission of the following landholders to conduct fieldwork is gratefully acknowledged: Andrew Clarke (Allandale Station), Trevor Williams (Nilpinna Station), Robert Khan (Marree Station), George Morphett (Callanna station), David Brook and Frank Booth (Murnpeowie Station). Also thank you to all the volunteers and researchers on the Nilpina Field Campaign, November 2008. The author acknowledges the Arabunna people as the traditional owners and custodians of the land studied in this project. We are thankful to the reviewers and editor for comments on the manuscript that significantly improved it.

Funding

Funding for this research was provided by the Australian Research Council Linkage Grant LP0774814 and Discovery Grant DP0450334, in conjunction with industry partners BHP-Billiton, The Great Artesian Basin Coordinating Committee, Santos Limited, and the South Australian Arid Lands Natural Resource Management Board.

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Published in the special issue “Advances in hydrogeologic understanding of Australia’s Great Artesian Basin”

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Matic, V., Costelloe, J.F. & Western, A.W. An integrated remote-sensing mapping method for groundwater dependent ecosystems associated with diffuse discharge in the Great Artesian Basin, Australia. Hydrogeol J 28, 325–342 (2020). https://doi.org/10.1007/s10040-019-02062-4

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