Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-1-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-1-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Synthesizing the impacts of baseflow contribution on concentration–discharge (C–Q) relationships across Australia using a Bayesian hierarchical model
Department of Infrastructure Engineering, University of Melbourne,
Victoria, 3010, Australia
Camille Minaudo
EPFL, Physics of Aquatic Systems Laboratory, Margaretha Kamprad
Chair, Lausanne, Switzerland
Anna Lintern
Department of Civil Engineering, Monash University, Victoria, 3800,
Australia
Ulrike Bende-Michl
Department of Infrastructure Engineering, University of Melbourne,
Victoria, 3010, Australia
Bureau of Meteorology, Science and Innovations Group, Parkes ACT 2600, Australia
Shuci Liu
Department of Infrastructure Engineering, University of Melbourne,
Victoria, 3010, Australia
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Kefeng Zhang
Water Research Centre, School of Civil and Environmental
Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
Clément Duvert
Research Institute for the Environment and Livelihoods, Charles
Darwin University, Darwin, NT, Australia
National Centre for Groundwater Research and Training (NCGRT),
Adelaide, SA, Australia
Related authors
Olaleye Babatunde, Meenakshi Arora, Siva Naga Venkat Nara, Danlu Guo, Ian Cartwright, and Andrew W. Western
EGUsphere, https://doi.org/10.5194/egusphere-2025-2456, https://doi.org/10.5194/egusphere-2025-2456, 2025
Short summary
Short summary
Nitrogen inputs can pollute streams and degrade water quality. We estimated fertiliser nitrogen inputs across different land uses and assessed their relationship with stream nitrogen concentrations. Only a small fraction of the applied nitrogen was exported, with most retained within the landscape. Land use, rainfall, and flow patterns strongly influenced nitrogen dynamics and export. These findings support strategies to reduce stream pollution and protect water quality in agricultural areas.
Shuci Liu, Dongryeol Ryu, J. Angus Webb, Anna Lintern, Danlu Guo, David Waters, and Andrew W. Western
Hydrol. Earth Syst. Sci., 25, 2663–2683, https://doi.org/10.5194/hess-25-2663-2021, https://doi.org/10.5194/hess-25-2663-2021, 2021
Short summary
Short summary
Riverine water quality can change markedly at one particular location. This study developed predictive models to represent the temporal variation in stream water quality across the Great Barrier Reef catchments, Australia. The model structures were informed by a data-driven approach, which is useful for identifying important factors determining temporal changes in water quality and, in turn, providing critical information for developing management strategies.
Camille Minaudo, Andras Abonyi, Carles Alcaraz, Jacob Diamond, Nicholas J. K. Howden, Michael Rode, Estela Romero, Vincent Thieu, Fred Worrall, Qian Zhang, and Xavier Benito
Earth Syst. Sci. Data, 17, 3411–3430, https://doi.org/10.5194/essd-17-3411-2025, https://doi.org/10.5194/essd-17-3411-2025, 2025
Short summary
Short summary
Many waterbodies undergo nutrient decline, called oligotrophication, globally, but a comprehensive dataset to understand ecosystem responses is lacking. The OLIGOTREND database comprises multi-decadal chlorophyll a and nutrient time series from rivers, lakes, and estuaries with 4.3 million observations from 1894 unique measurement locations. The database provides empirical evidence for oligotrophication responses with a spatial and temporal coverage that exceeds previous efforts.
Olaleye Babatunde, Meenakshi Arora, Siva Naga Venkat Nara, Danlu Guo, Ian Cartwright, and Andrew W. Western
EGUsphere, https://doi.org/10.5194/egusphere-2025-2456, https://doi.org/10.5194/egusphere-2025-2456, 2025
Short summary
Short summary
Nitrogen inputs can pollute streams and degrade water quality. We estimated fertiliser nitrogen inputs across different land uses and assessed their relationship with stream nitrogen concentrations. Only a small fraction of the applied nitrogen was exported, with most retained within the landscape. Land use, rainfall, and flow patterns strongly influenced nitrogen dynamics and export. These findings support strategies to reduce stream pollution and protect water quality in agricultural areas.
Clément Duvert, Vanessa Solano, Dioni I. Cendón, Francesco Ulloa-Cedamanos, Liza K. McDonough, Robert G. M. Spencer, Niels C. Munksgaard, Lindsay B. Hutley, Jean-Sébastien Moquet, and David E. Butman
EGUsphere, https://doi.org/10.5194/egusphere-2025-1600, https://doi.org/10.5194/egusphere-2025-1600, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
This study examines the age and composition of carbon in tropical streams. We find that dissolved organic carbon (DOC) is centuries to millennia old, while dissolved inorganic carbon (DIC) is consistently younger, indicating a decoupling between the two. DOC age varies seasonally, with rainforest streams exporting younger DOC during high flow, while agricultural streams mobilise older DOC. Our results suggest land conversion alters carbon export, potentially worsening with climate change.
Jing Jia, Sisi Zlatanova, Kefeng Zhang, and Scott Hawken
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-2024, 255–262, https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-255-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-255-2024, 2024
Anna M. Ukkola, Steven Thomas, Elisabeth Vogel, Ulrike Bende-Michl, Steven Siems, Vjekoslav Matic, and Wendy Sharples
EGUsphere, https://doi.org/10.31223/X56110, https://doi.org/10.31223/X56110, 2024
Short summary
Short summary
Future drought changes in Australia –the driest inhabited continent on Earth– have remained stubbornly uncertain. We assess future drought changes in Australia using projections from climate and hydrological models. We show an increasing probability of drought over highly-populated and agricultural regions of Australia in coming decades, suggesting potential impacts on agricultural activities, ecosystems and urban water supply.
Stephen Lee, Dylan J. Irvine, Clément Duvert, Gabriel C. Rau, and Ian Cartwright
Hydrol. Earth Syst. Sci., 28, 1771–1790, https://doi.org/10.5194/hess-28-1771-2024, https://doi.org/10.5194/hess-28-1771-2024, 2024
Short summary
Short summary
Global groundwater recharge studies collate recharge values estimated using different methods that apply to different timescales. We develop a recharge prediction model, based solely on chloride, to produce a recharge map for Australia. We reveal that climate and vegetation have the most significant influence on recharge variability in Australia. Our recharge rates were lower than other models due to the long timescale of chloride in groundwater. Our method can similarly be applied globally.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Artur Safin, Damien Bouffard, Firat Ozdemir, Cintia L. Ramón, James Runnalls, Fotis Georgatos, Camille Minaudo, and Jonas Šukys
Geosci. Model Dev., 15, 7715–7730, https://doi.org/10.5194/gmd-15-7715-2022, https://doi.org/10.5194/gmd-15-7715-2022, 2022
Short summary
Short summary
Reconciling the differences between numerical model predictions and observational data is always a challenge. In this paper, we investigate the viability of a novel approach to the calibration of a three-dimensional hydrodynamic model of Lake Geneva, where the target parameters are inferred in terms of distributions. We employ a filtering technique that generates physically consistent model trajectories and implement a neural network to enable bulk-to-skin temperature conversion.
J. Jia, S. Zlatanova, K. Zhang, and H. Liu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W2-2022, 153–160, https://doi.org/10.5194/isprs-annals-X-4-W2-2022-153-2022, https://doi.org/10.5194/isprs-annals-X-4-W2-2022-153-2022, 2022
Shuci Liu, Dongryeol Ryu, J. Angus Webb, Anna Lintern, Danlu Guo, David Waters, and Andrew W. Western
Hydrol. Earth Syst. Sci., 25, 2663–2683, https://doi.org/10.5194/hess-25-2663-2021, https://doi.org/10.5194/hess-25-2663-2021, 2021
Short summary
Short summary
Riverine water quality can change markedly at one particular location. This study developed predictive models to represent the temporal variation in stream water quality across the Great Barrier Reef catchments, Australia. The model structures were informed by a data-driven approach, which is useful for identifying important factors determining temporal changes in water quality and, in turn, providing critical information for developing management strategies.
Stella Guillemot, Ophelie Fovet, Chantal Gascuel-Odoux, Gérard Gruau, Antoine Casquin, Florence Curie, Camille Minaudo, Laurent Strohmenger, and Florentina Moatar
Hydrol. Earth Syst. Sci., 25, 2491–2511, https://doi.org/10.5194/hess-25-2491-2021, https://doi.org/10.5194/hess-25-2491-2021, 2021
Short summary
Short summary
This study investigates the drivers of spatial variations in stream water quality in poorly studied headwater catchments and includes multiple elements involved in major water quality issues, such as eutrophication. We used a regional public dataset of monthly stream water concentrations monitored for 10 years over 185 agricultural catchments. We found a spatial and seasonal opposition between carbon and nitrogen concentrations, while phosphorus concentrations showed another spatial pattern.
J. Jia, S. Zlatanova, and K. Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-3-W1-2020, 73–80, https://doi.org/10.5194/isprs-archives-XLIV-3-W1-2020-73-2020, https://doi.org/10.5194/isprs-archives-XLIV-3-W1-2020-73-2020, 2020
J. Jia, S. Zlatanova, S. Hawken, and K. F. Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4-W2-2020, 79–86, https://doi.org/10.5194/isprs-annals-VI-4-W2-2020-79-2020, https://doi.org/10.5194/isprs-annals-VI-4-W2-2020-79-2020, 2020
Cited articles
Ator, S. W., Brakebill, J. W., and Blomquist, J. D.: Sources,
fate, and transport of nitrogen and phosphorus in the Chesapeake Bay
watershed: An empirical model, Vol. 5167, US Department of the Interior, US
Geological Survey, Baltimore, MD 21228, 2011.
Beck, H. E., van Dijk, A. I. J. M., Miralles, D. G., de Jeu, R. A. M., Bruijnzeel, L. A., McVicar, T. R., and Schellekens, J.: Global patterns in base flow index and recession based on streamflow observations from 3394 catchments, Water Resour. Res., 49, 7843–7863, https://doi.org/10.1002/2013WR013918, 2013.
Bende-Michl, U., Verburg, K., and Cresswell, H. P.: High-frequency nutrient
monitoring to infer seasonal patterns in catchment source availability,
mobilisation and delivery, Environ. Monit. Assess., 185, 9191–9219,
https://doi.org/10.1007/s10661-013-3246-8, 2013.
Cartwright, I.: Concentration vs. streamflow (C-Q) relationships of major ions in south-eastern Australian rivers: Sources and fluxes of inorganic ions and nutrients, Appl. Geochem., 120, 104680, https://doi.org/10.1016/j.apgeochem.2020.104680, 2020.
Dupas, R., Abbott, B. W., Minaudo, C., and Fovet, O.: Distribution of
Landscape Units Within Catchments Influences Nutrient Export Dynamics,
Front. Environ. Sci., 7, p. 43, https://doi.org/10.3389/fenvs.2019.00043,
2019.
Dupas, R., Causse, J., Jaffrezic, A., Aquilina, L., and Durand, P.: Flowpath controls on high-spatial-resolution water-chemistry profiles in headwater streams, Hydrol. Process., 35, e14247, https://doi.org/10.1002/hyp.14247, 2021.
Durand, P., Breuer, L., Johnes, P. J., Billen, G., Butturini, A., Pinay, G.,
Van Grinsven, H., Garnier, J., Rivett, M., and Reay, D. S.: Nitrogen processes in aquatic ecosystems, in: European Nitrogen Assessment, Cambridge University Press, Cambridge, 126–146, ISBN 9781107006126, available at: http://centaur.reading.ac.uk/20855/ (last access: 14 October 2021), 2011.
Drewry, J., Newham, L., Greene, R., Jakeman, A., and Croke, B.: A review of nitrogen and phosphorus export to waterways: context for catchment modelling, Mar. Freshwater Res., 57, 757–774. 2006.
Ebeling, P., Kumar, R., Weber, M., Knoll, L., Fleckenstein, J. H., and Musolff, A.: Archetypes and Controls of Riverine Nutrient Export Across German Catchments, Water Resour. Res., 57, e2020WR028134, https://doi.org/10.1029/2020WR028134, 2021.
Eckhardt, K.: A comparison of baseflow indices, which were calculated with seven different baseflow separation methods, J. Hydrol., 352, 168–173, https://doi.org/10.1016/j.jhydrol.2008.01.005, 2008.
Ehrhardt, S., Kumar, R., Fleckenstein, J. H., Attinger, S., and
Musolff, A.: Trajectories of nitrate input and output in three nested
catchments along a land use gradient, Hydrol. Earth Syst. Sci., 23,
3503–3524, https://doi.org/10.5194/hess-23-3503-2019, 2019.
Frost, A. J., Ramchurn, A., and Smith, A.: The bureau's operational AWRA landscape (AWRA-L) Model, retrieved from: http://www.bom.gov.au/water/landscape/assets/static/publications/Frost__Model_Description_Report.pdf (last access: 1 July 2021), 2016.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B.,
Vehtari, A., and Rubin, D. B.: Bayesian Data Analysis, 3rd edn., Taylor and
Francis, Boca Raton, FL 33487-2742, 2013.
Godsey, S. E., Kirchner, J. W., and Clow, D. W.: Concentration–discharge relationships reflect chemostatic characteristics of US catchments, Hydrol. Process., 23, 1844–1864, https://doi.org/10.1002/hyp.7315, 2009.
Godsey, S. E., Hartmann, J., and Kirchner, J. W.: Catchment chemostasis revisited: Water quality responds differently to variations in weather and climate, Hydrol. Process., 33, 3056–3069, https://doi.org/10.1002/hyp.13554, 2019.
Gorski, G. and Zimmer, M. A.: Hydrologic regimes drive nitrate export behavior in human-impacted watersheds, Hydrol. Earth Syst. Sci., 25, 1333–1345, https://doi.org/10.5194/hess-25-1333-2021, 2021.
Gu, S., Gruau, G., Dupas, R., Rumpel, C., Crème, A., Fovet, O.,
Gascuel-Odoux, C., Jeanneau, L., Humbert, G., and Petitjean, P.: Release of Dissolved Phosphorus from Riparian Wetlands: Evidence for Complex Interactions among Hydroclimate Variability, Topography and Soil Properties, Sci. Total Environ., 598, 421–431, https://doi.org/10.1016/j.scitotenv.2017.04.028, 2017.
Guo, D., Lintern, A., Webb, J. A., Ryu, D., Liu, S., Bende-Michl, U.,
Leahy, P., Wilson, P., and Western, A. W.: Key Factors Affecting Temporal Variability in Stream Water Quality, Water Resour. Res., 55, 112–129, https://doi.org/10.1029/2018wr023370, 2019.
Guo, D., Lintern, A., Webb, J. A., Ryu, D., Bende-Michl, U., Liu, S., and Western, A. W.: A data-based predictive model for spatiotemporal variability in stream water quality, Hydrol. Earth Syst. Sci., 24, 827–847, https://doi.org/10.5194/hess-24-827-2020, 2020.
Jensen, C. K., McGuire, K. J., McLaughlin, D. L., and Scott, D. T.: Quantifying spatiotemporal variation in headwater stream length using flow intermittency sensors, Environ. Monit. Assess., 191, 226, https://doi.org/10.1007/s10661-019-7373-8, 2019.
Kennard, M. J., Pusey, B. J., Olden, J. D., Mackay, S. J., Stein, J. L., and Marsh, N.: Classification of natural flow regimes in Australia to support environmental flow management, Freshwater Biol., 55, 171–193, https://doi.org/10.1111/j.1365-2427.2009.02307.x, 2010.
Kirchner, J. W., Feng, X., Neal, C., and Robson, A. J.: The fine structure of water-quality dynamics: the (high-frequency) wave of the future, Hydrol. Process., 18, 1353–1359, https://doi.org/10.1002/hyp.5537, 2004.
Knapp, J. L. A., von Freyberg, J., Studer, B., Kiewiet, L., and Kirchner, J. W.: Concentration–discharge relationships vary among hydrological events, reflecting differences in event characteristics, Hydrol. Earth Syst. Sci., 24, 2561–2576, https://doi.org/10.5194/hess-24-2561-2020, 2020.
Ladson, A. R., Brown, R., Neal, B., and Nathan, R.: A standard approach to baseflow separation using the Lyne and Hollick filter, Australian Journal of Water Resources, 17, 25–34. 2013.
Lintern, A., Liu, S., Minaudo, C., Dupas, R., Guo, D., Zhang, K.,
Bende-Michl, U., and Duvert, C.: The influence of climate on water chemistry
states and dynamics in rivers across Australia, Hydrol. Process., 35, e14423,
https://doi.org/10.1002/hyp.14423, 2021.
Liu, S., Ryu, D., Webb, J. A., Lintern, A., Guo, D., Waters, D., and Western, A. W.: A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments, Hydrol. Earth Syst. Sci., 25, 2663–2683, https://doi.org/10.5194/hess-25-2663-2021, 2021.
Lyne, V. and Hollick, M.: Stochastic time-variable rainfall-runoff modelling,
Paper presented at the Hydrology and Water Resources Symposium, 10–12 September 1979, Perth, Australia,
1979.
McGuire, K. J., Torgersen, C. E., Likens, G. E., Buso, D. C., Lowe, W. H., and Bailey, S. W.: Network analysis reveals multiscale controls on streamwater chemistry, P. Natl. Acad. Sci. USA, 111, 7030–7035, https://doi.org/10.1073/pnas.1404820111, 2014.
Minaudo, C., Dupas, R., Gascuel-Odoux, C., Roubeix, V., Danis, P.-A., and
Moatar, F.: Seasonal and event-based concentration-discharge relationships to
identify catchment controls on nutrient export regimes, Adv. Water Resour.,
131, 103379, https://doi.org/10.1016/j.advwatres.2019.103379, 2019.
Moatar, F., Abbott, B. W., Minaudo, C., Curie, F., and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment, and major ions, Water Resour. Res., 53, 1270–1287, https://doi.org/10.1002/2016wr019635, 2017.
Moatar, F., Floury, M., Gold, A. J., Meybeck, M., Renard, B., Ferréol, M.,
Chandesris, A., Minaudo, C., Addy, K., Piffady, J., and Pinay, G.: Stream
Solutes and Particulates Export Regimes: A New Framework to Optimize Their
Monitoring, Frontiers in Ecology and Evolution, 7, 516, https://doi.org/10.3389/fevo.2019.00516, 2020.
Musolff, A., Schmidt, C., Selle, B., and Fleckenstein, J. H.: Catchment controls on solute export, Adv. Water Resour., 86, 133–146, https://doi.org/10.1016/j.advwatres.2015.09.026, 2015.
Musolff, A., Zhan, Q., Dupas, R., Minaudo, C., Fleckenstein, J. H., Rode, K., Dehaspe, J., and Rinke, K.: Spatio-temporal Variability in Concentration-Discharge Relationships at the Event Scale, Water Resour. Res., 57, e2020WR029442, https://doi.org/10.1029/2020WR029442, 2021.
Nathan, R. J. and McMahon, T. A.: Evaluation of automated techniques for base flow and recession analyses, Water Resour. Res., 26, 1465–1473, https://doi.org/10.1029/WR026i007p01465, 1990.
Rode, M., Wade, A. J., Cohen, M. J., Hensley, R. T., Bowes, M. J., Kirchner, J. W., Arhonditsis, G. B., Jordan, P., Kronvang, B., Halliday, S. J., Skeffington, R. A., Rozemeijer, J. C., Aubert, A. H., Rinke, K., and Jomaa, S.: Sensors in the Stream: The High-Frequency Wave of the Present, Environ. Sci. Technol., 50, 10297–10307, https://doi.org/10.1021/acs.est.6b02155, 2016.
Rusjan, S., Brilly, M., and Mikoš, M.: Flushing of nitrate from a forested watershed: An insight into hydrological nitrate mobilization mechanisms through seasonal high-frequency stream nitrate dynamics. J. Hydrol., 354, 187–202, https://doi.org/10.1016/j.jhydrol.2008.03.009, 2008.
Stackpoole, S. M., Stets, E. G., and Sprague, L. A.: Variable Impacts of
Contemporary versus Legacy Agricultural Phosphorus on US River Water Quality,
P. Natl. Acad. Sci. USA, 116, 20562–20567, https://doi.org/10.1073/pnas.1903226116,
2019.
Stan Development Team: RStan: the R interface to Stan, R package version
2.18.1, version 2.21.3, aailable at: https://mc-stan.org/ (last access: 1 July 2021), 2018.
Sturtz, S., Ligges, U., and Gelman, A.: R2WinBUGS: A Package for Running WinBUGS from R, J. Stat. Softw., 12, 1–16, 2005.
Thompson, S. E., Basu, N. B., Lascurain Jr, J., Aubeneau, A., and
Rao, P. S. C.: Relative dominance of hydrologic versus biogeochemical factors
on solute export across impact gradients, Water Resour. Res., 47, W00J05,
https://doi.org/10.1029/2010WR009605, 2011.
Tunqui Neira, J. M., Andréassian, V., Tallec, G., and Mouchel, J.-M.: Technical note: A two-sided affine power scaling relationship to represent the concentration–discharge relationship, Hydrol. Earth Syst. Sci., 24, 1823–1830, https://doi.org/10.5194/hess-24-1823-2020, 2020a.
Tunqui Neira, J. M., Tallec, G., Andréassian, V., and Mouchel, J.-M.: A
combined mixing model for high-frequency concentration–discharge
relationships, J. Hydrol., 591, 125559, https://doi.org/10.1016/j.jhydrol.2020.125559,
2020b.
von Freyberg, J., Allen, S. T., Seeger, S., Weiler, M., and Kirchner, J. W.: Sensitivity of young water fractions to hydro-climatic forcing and landscape properties across 22 Swiss catchments, Hydrol. Earth Syst. Sci., 22, 3841–3861, https://doi.org/10.5194/hess-22-3841-2018, 2018.
Van Meter, K. J., Basu, N. B., and Van Cappellen, P.: Two Centuries of Nitrogen Dynamics: Legacy Sources and Sinks in the Mississippi and Susquehanna River Basins, Global Biogeochem. Cy., 31, 2–23, https://doi.org/10.1002/2016GB005498, 2017.
Webb, J. A. and King, L. E.: A Bayesian hierarchical trend analysis finds strong evidence for large-scale temporal declines in stream ecological condition around Melbourne, Australia, Ecography, 32, 215–225, https://doi.org/10.1111/j.1600-0587.2008.05686.x, 2009.
Zhang, J., Zhang, Y., Song, J., and Cheng, L.: Evaluating relative merits of four baseflow separation methods in Eastern Australia, J. Hydrol., 549, 252–263, https://doi.org/10.1016/j.jhydrol.2017.04.004, 2017.
Zhang, Q.: Synthesis of nutrient and sediment export patterns in the Chesapeake Bay watershed: Complex and non-stationary concentration-discharge relationships, Sci. Total Environ., 618, 1268–1283, https://doi.org/10.1016/j.scitotenv.2017.09.221, 2018.
Short summary
We investigate the impact of baseflow contribution on concentration–flow (C–Q) relationships across the Australian continent. We developed a novel Bayesian hierarchical model for six water quality variables across 157 catchments that span five climate zones. For sediments and nutrients, the C–Q slope is generally steeper for catchments with a higher median and a greater variability of baseflow contribution, highlighting the key role of variable flow pathways in particulate and solute export.
We investigate the impact of baseflow contribution on concentration–flow (C–Q) relationships...