Articles | Volume 27, issue 11
https://doi.org/10.5194/hess-27-2205-2023
© Author(s) 2023. 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-27-2205-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Developing a Bayesian network model for understanding river catchment resilience under future change scenarios
School of Geosciences, The University of Edinburgh, Edinburgh, Scotland
Environmental and Biochemical Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen, Scotland
Christopher A. J. Macleod
Information and Computational Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen, Scotland
Marc J. Metzger
School of Geosciences, The University of Edinburgh, Edinburgh, Scotland
Nicola Melville
Scottish Environment Protection Agency, Strathallan House, Stirling, Scotland
Rachel C. Helliwell
Environmental and Biochemical Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen, Scotland
Jim Pritchard
Scottish Environment Protection Agency, Strathallan House, Stirling, Scotland
Miriam Glendell
CORRESPONDING AUTHOR
Environmental and Biochemical Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen, Scotland
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Many peatlands around the world are eroding and causing carbon losses to the atmosphere and to freshwater systems. To accurately report emissions from peatlands we need to understand how much of the eroded peat is converted to CO2 once exposed to the atmosphere. We need more direct measurements of this process and a better understanding of the environmental conditions that peat is exposed to after it erodes. This information will help quantify the emissions savings from peatland restoration.
Mads Troldborg, Zisis Gagkas, Andy Vinten, Allan Lilly, and Miriam Glendell
Hydrol. Earth Syst. Sci., 26, 1261–1293, https://doi.org/10.5194/hess-26-1261-2022, https://doi.org/10.5194/hess-26-1261-2022, 2022
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Pesticides continue to pose a threat to surface water quality worldwide. Here, we present a spatial Bayesian belief network (BBN) for assessing inherent pesticide risk to water quality. The BBN was applied in a small catchment with limited data to simulate the risk of five pesticides and evaluate the likely effectiveness of mitigation measures. The probabilistic graphical model combines diverse data and explicitly accounts for uncertainties, which are often ignored in pesticide risk assessments.
Cited articles
Adams, K. J., Metzger, M. J., Macleod, C. J. A., Helliwell, R. C., and
Pohle, I.: Understanding knowledge needs for Scotland to become a resilient
Hydro Nation: Water stakeholder perspectives, Environ. Sci.
Policy, 136, 157–166, https://doi.org/10.1016/j.envsci.2022.06.006, 2022.
Adger, W. N.: Social and ecological resilience: are they related?, Prog. Hum. Geog., 24,
347–364, 2000.
Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., and
Salmerón, A.: Bayesian networks in environmental modelling,
Environ. Modell. Softw., 26, 1376–1388,
https://doi.org/10.1016/j.envsoft.2011.06.004, 2011.
Aguilera, P. A., Fernández, A., Ropero, R. F., and Molina, L.:
Groundwater quality assessment using data clustering based on hybrid
Bayesian networks, Stoch. Env. Res. Risk A.,
27, 435–447, https://doi.org/10.1007/s00477-012-0676-8, 2013.
Alcamo, J.: Chapter Six The SAS Approach: Combining Qualitative and
Quantitative Knowledge in Environmental Scenarios, in: Developments in
Integrated Environmental Assessment, edited by: Alcamo, J., Elsevier,
123–150, https://doi.org/10.1016/S1574-101X(08)00406-7, 2008.
Ames, D. P., Neilson, B. T., Stevens, D. K., and Lall, U.: Using Bayesian
networks to model watershed management decisions: an East Canyon Creek case
study, J. Hydroinform., 7, 267–282,
https://doi.org/10.2166/hydro.2005.0023, 2005.
Barton, D. N., Saloranta, T., Moe, S. J., Eggestad, H. O., and Kuikka, S.:
Bayesian belief networks as a meta-modelling tool in integrated river basin
management – Pros and cons in evaluating nutrient abatement decisions
under uncertainty in a Norwegian river basin, Ecol. Econ., 66,
91–104, https://doi.org/10.1016/j.ecolecon.2008.02.012, 2008.
Barton, D. N., Kuikka, S., Varis, O., Uusitalo, L., Henriksen, H. J., Borsuk, M., de la Hera, A., Farmani, R., Johnson, S., and Linnell, J. D.: Bayesian networks in environmental and resource management, Integr. Environ. Asses., 8, 418–429, https://doi.org/10.1002/ieam.1327, 2012.
Basco-Carrera, L., Warren, A., van Beek, E., Jonoski, A., and Giardino, A.:
Collaborative modelling or participatory modelling? A framework for water
resources management, Environ. Modell. Softw., 91, 95–110,
https://doi.org/10.1016/j.envsoft.2017.01.014, 2017.
BayesFusion, L. L. C.: GeNIe Modeler, User Manual,
https://support.bayesfusion.com/docs/ (last access: 28 July 2022), 2017.
Beuzen, T., Marshall, L., and Splinter, K. D.: A comparison of methods for
discretizing continuous variables in Bayesian Networks, Environ.
Modell. Softw., 108, 61–66,
https://doi.org/10.1016/j.envsoft.2018.07.007, 2018.
Boretti, A. and Rosa, L.: Reassessing the projections of the World Water
Development Report. npj Clean Water, 2, 15,
https://doi.org/10.1038/s41545-019-0039-9, 2019.
Borsuk, M. E., Stow, C. A., and Reckhow, K. H.: A Bayesian network of
eutrophication models for synthesis, prediction, and uncertainty analysis,
Ecol. Model., 173, 219–239,
https://doi.org/10.1016/j.ecolmodel.2003.08.020, 2004.
Borsuk, M. E., Schweizer, S. and Reichert, P.: A Bayesian network model for
integrative river rehabilitation planning and management, Integr. Environ. Asses., 8, 462–472,
https://doi.org/10.1002/ieam.233, 2012.
Brown, K.: Resilience, development and global change, Routledge, ISBN 9780415663472, 2015.
Callahan, B., Miles, E., and Fluharty, D. J. P. S.: Policy implications of
climate forecasts for water resources management in the Pacific Northwest,
Policy Sci., 32, 269–293, https://doi.org/10.1023/A:1004604805647, 1999.
Carvalho, L., Mackay, E. B., Cardoso, A. C., Baattrup-Pedersen, A., Birk,
S., Blackstock, K. L., Borics, G., Borja, A., Feld, C. K., Ferreira, M. T., Globevnik, L., Grizzetti, B., Hnedry, S., Hering, D., Kelly, M., Langaas, S., Meissner, K., Panagopoulos, Y., Penning, E., Rouillard, J., Sabater, S., Schmedtje, U., Spears, B. M., Venhor, M., van de Bund, W., and Solheim, A. L.: Protecting and restoring Europe's
waters: An analysis of the future development needs of the Water Framework
Directive, Sci. Total Environ., 658, 1228–1238,
https://doi.org/10.1016/j.scitotenv.2018.12.255 2019.
Castelletti, A. and Soncini-Sessa, R.: Bayesian Networks and participatory
modelling in water resource management, Environ. Modell.
Softw., 22, 1075–1088, https://doi.org/10.1016/j.envsoft.2006.06.003,
2007.
Chen, S. H. and Pollino, C. A.: Good practice in Bayesian network
modelling, Environ. Modell.
Softw., 37, 134–145,
https://doi.org/10.1016/j.envsoft.2012.03.012, 2012.
Cretney, R.: Resilience for Whom? Emerging Critical Geographies of
Socio-ecological Resilience, Geograpy compass, 8, 627–640, 2014.
Crossman, J., Whitehead, P. G., Futter, M. N., Jin, L., Shahgedanova, M.,
Castellazzi, M., and Wade, A. J.: The interactive responses of water quality
and hydrology to changes in multiple stressors, and implications for the
long-term effective management of phosphorus, Sci. Total
Environ., 454–455, 230–244,
https://doi.org/10.1016/j.scitotenv.2013.02.033, 2013.
Dodds, W. K., Perkin, J. S., and Gerken, J. E.: Human impact on freshwater
ecosystem services: a global perspective, Environ. Sci.
Technol., 47, 9061–9068, https://doi.org/10.1021/es4021052, 2013.
Düspohl, M.: A Review of Bayesian Networks as a Participatory Modeling
Approach in Support of Sustainable Environmental Management, Journal of
Sustainable Development, 5, 1–18, https://doi.org/10.5539/jsd.v5n12p1, 2012.
Esri Inc.: ArcGIS Pro (version 2.58.0),
https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview (last access: 28 July 2022), 2021.
Falconi, S. M. and Palmer, R. N.: An interdisciplinary framework for
participatory modeling design and evaluation – What makes models effective
participatory decision tools?, Water Resour. Res., 53, 1625–1645,
https://doi.org/10.1002/2016WR019373, 2017.
Falkenmark, M.: Freshwater as shared between society and ecosystems: from
divided approaches to integrated challenges, Philos. T.
R. Soc. B, 358, 2037–2049,
https://doi.org/10.1098/rstb.2003.1386, 2003.
Folke, C.: Resilience: The emergence of a perspective for social–ecological
systems analyses, Global Environ. Chang., 16, 253–267,
https://doi.org/10.1016/j.gloenvcha.2006.04.002, 2006.
Folke, C.: Resilience (Republished), Ecol. Soc., 21, 44, https://doi.org/10.5751/ES-09088-210444, 2016.
Gallaun, H., Dohr, K., Puhm, M., Stumpf, A., and Huge, J.: EU-Hydro – River
Net User Guide 1.3, Copernicus Land Monitoring Service, European Environment
Agency, https://land.copernicus.eu/user-corner/technical-library/eu-hydro_user_guide.pdf (last access: 30 April 2021), 2019.
Glendell, M., Gagkas, Z., Stutter, M., Richards, S., Lilly, A., Vinten, A., and
Coull, M.: A systems approach to modelling phosphorus pollution risk in
Scottish rivers using a spatial Bayesian Belief Network helps targeting
effective mitigation measures, Frontiers in Environmental Science, 10, 1825,
https://doi.org/10.3389/fenvs.2022.976933 2022.
Gray, S., Voinov, A., Paolisso, M., Jordan, R., Bendor, T., Bommel, P.,
Glynn, P., Hedelin, B., Hubacek, K., Introne, J., Kolagani, N., Laursen, B.,
Prell, C., Schmitt Olabisi, L., Singer, A., Sterling, E., and Zellner, M.:
Purpose, processes, partnerships, and products: four Ps to advance
participatory socio-environmental modeling, Ecol. Appl., 28,
46–61, https://doi.org/10.1002/eap.1627, 2018.
Hamby, D. M.:. A comparison of sensitivity analysis techniques, Health
Phys., 68, 195–204, 1995.
Hare, M.: Forms of Participatory Modelling and its Potential for Widespread
Adoption in the Water Sector, Environ. Policy Gov., 21,
386–402, https://doi.org/10.1002/eet.590, 2011.
Harrison, P. A., Dunford, R. W., Holman, I. P., and Rounsevell, M. D. A.:
Climate change impact modelling needs to include cross-sectoral
interactions, Nat. Clim. Change, 6, 885–890,
https://doi.org/10.1038/nclimate3039, 2016.
Heathwaite, A. L.: Multiple stressors on water availability at global to
catchment scales: understanding human impact on nutrient cycles to protect
water quality and water availability in the long term, Freshwater Biol.,
55, 241–257, https://doi.org/10.1111/j.1365-2427.2009.02368.x, 2010.
Hobbs, B. F.: Bayesian Methods for Analysing Climate Change and Water
Resource Uncertainties, J. Environ. Manage., 49, 53–72,
https://doi.org/10.1006/jema.1996.0116, 1997.
Holling, C. S.: Resilience and stability of ecological systems, Annu.
Rev. Ecol. Syst., 4, 1–23, 1973.
Holman, I. P., Harrison, P. A., and Metzger, M. J.: Cross-sectoral impacts of
climate and socio-economic change in Scotland: implications for adaptation
policy, Reg. Environ. Change, 16, 97–109,
https://doi.org/10.1007/s10113-014-0679-8, 2016.
Jakeman, A. J., Letcher, R. A., and Norton, J. P.: Ten iterative steps in
development and evaluation of environmental models, Environ. Modell.
Softw., 21, 602–614, https://doi.org/10.1016/j.envsoft.2006.01.004,
2006.
Kaikkonen, L., Parviainen, T., Rahikainen, M., Uusitalo, L., and Lehikoinen,
A.: Bayesian networks in environmental risk assessment: A review, Integr.
Environ. Asses., 17, 62–78,
https://doi.org/10.1002/ieam.4332, 2021.
Landis, W. G.: The origin, development, application, lessons learned, and
future regarding the Bayesian network relative risk model for ecological
risk assessment, Integr.
Environ. Asses., 17,
79–94, https://doi.org/10.1002/ieam.4351, 2021.
Liu, Y., Gupta, H., Springer, E., and Wagener, T.: Linking science with
environmental decision making: Experiences from an integrated modeling
approach to supporting sustainable water resources management, Environ.
Modell. Softw., 23, 846–858,
https://doi.org/10.1016/j.envsoft.2007.10.007, 2008.
Lowe, J. A., Bernie, D., Bett, P., Bricheno, L., Brown, S., Calvert,
D., Clark, R., Eagle, K., Edwards, T., Fosser, G, Fung, F., Gohrar, L., Good, P., Gregory, J., Harris, G., Howard, T., Kaye, N., Kendon, E., Krijnen, J., Maisey, P., McDonald, R., McInnes, R., McSweeney, C., Mitchell, J. F. B., Murphy, J., Palmer, M., Roberts, C., Roston, J., Sexton, D., Thornton, H., Tinker, J., Tucker, S., Yamazaki, K., and Belcher, S.: UKCP18 Science Overview Report, Met Office Hadley Centre, Exeter, UK, https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP18-Overview-report.pdf (last access: 18 October 2022), 2018.
Macgregor, C. J. and Warren, C. R.: Evaluating the impacts of nitrate
vulnerable zones on the environment and farmers' practices: a Scottish case
study, Scot. Geogr. J., 132, 1–20,
https://doi.org/10.1080/14702541.2015.1034760, 2016.
Marcot, B. G. and Penman, T. D.: Advances in Bayesian network modelling:
Integration of modelling technologies, Environ. Model.
Softw., 111, 386–393, https://doi.org/10.1016/j.envsoft.2018.09.016, 2019.
Marcot, B. G., Steventon, J. D., Sutherland, G. D., and McCann, R. K.: Guidelines for developing and updating Bayesian belief networks
applied to ecological modeling and conservation, Can. J. Forest. Res., 36, 3063–3074,
https://doi.org/10.1139/x06-135, 2006.
Mayfield, H. J., Bertone, E., Smith, C., and Sahin, O.: Use of a structure
aware discretisation algorithm for Bayesian networks applied to water
quality predictions, Math. Comput. Simul., 175, 192–201,
https://doi.org/10.1016/j.matcom.2019.07.005, 2020.
Moe, S. J., Couture, R.-M., Haande, S., Lyche Solheim, A., and
Jackson-Blake, L.: Predicting Lake Quality for the Next Generation: Impacts
of Catchment Management and Climatic Factors in a Probabilistic Model
Framework, Water, 11, 1767, https://doi.org/10.3390/w11091767, 2019.
Moe, S. J., Carriger, J. F., and Glendell, M.: Increased Use of Bayesian
Network Models Has Improved Environmental Risk Assessments, Integr.
Environ. Asses., 17, 53–61,
https://doi.org/10.1002/ieam.4369, 2021.
Molina, J.-L., Pulido-Velázquez, D., García-Aróstegui, J. L.,
and Pulido-Velázquez, M.: Dynamic Bayesian networks as a decision
support tool for assessing climate change impacts on highly stressed
groundwater systems, J. Hydrol., 479, 113–129,
https://doi.org/10.1016/j.jhydrol.2012.11.038, 2013.
Morton, R. D., Marston, C. G., O'Neil, A. W., and Rowland, C. S.: Land Cover
Map 2019 (land parcels, GB), NERC Environmental Information Data Centre [data set],
https://doi.org/10.5285/44c23778-4a73-4a8f-875f-89b23b91ecf8, 2020.
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K.,
Rothman, D. S., Van Ruijven, B. J., Van Vuuren, D. P., Birkmann, J., Kok,
K., Levy, M., and Solecki, W.: The roads ahead: Narratives for shared
socioeconomic pathways describing world futures in the 21st century, Global
Environ. Chang., 42, 169–180,
https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017.
Pahl-Wostl, C.: Transitions towards adaptive management of water facing
climate and global change, Water Resour. Manag., 21, 49–62,
https://doi.org/10.1007/s11269-006-9040-4, 2007.
Pahl-Wostl, C., Jeffrey, P., Isendahl, N. and Brugnach, M.: Maturing the New
Water Management Paradigm: Progressing from Aspiration to Practice, Water
Resour. Manag., 25, 837–856,
https://doi.org/10.1007/s11269-010-9729-2, 2011.
Pearl, J.: Fusion, propagation, and structuring in belief networks,
Artif. Intell., 29, 241–288,
https://doi.org/10.1016/0004-3702(86)90072-X, 1986.
Pedde, S., Harrison, P. A., Holman, I. P., Powney, G. D., Lofts, S.,
Schmucki, R., Gramberger, M., and Bullock, J. M.: Enriching the Shared
Socioeconomic Pathways to co-create consistent multi-sector scenarios for
the UK, Sci. Total Environ., 756, 143172,
https://doi.org/10.1016/j.scitotenv.2020.143172, 2021.
Plummer, R. and Baird, J.: The emergence of water resilience: An
introduction, Water Resilience, Springer,
https://doi.org/10.1007/978-3-030-48110-0_1, 2021.
Pham, H. V., Sperotto, A., Furlan, E., Torresan, S., Marcomini, A., and
Critto, A.: Integrating Bayesian Networks into ecosystem services assessment
to support water management at the river basin scale, Ecosyst. Serv.,
50, 101300, https://doi.org/10.1016/j.ecoser.2021.101300, 2021.
Phan, T. D., Smart, J. C. R., Capon, S. J., Hadwen, W. L., and Sahin, O.:
Applications of Bayesian belief networks in water resource management: A
systematic review, Environ. Modell. Softw., 85, 98–111,
https://doi.org/10.1016/j.envsoft.2016.08.006, 2016.
Phan, T. D., James, C. R. S., Ben, S.-K., Oz, S., Wade, L. H., Lien, T. D., Iman, T., and Samantha, J. C.: Applications of Bayesian networks as decision support tools for water resource management under climate change and socio-economic stressors: a critical appraisal, Water, 11, 2642, https://doi.org/10.3390/w11122642, 2019.
Pollino, C. A. and Henderson, C.: Bayesian networks: A guide for their
application in natural resource management and policy, Landscape Logic,
Technical Report, 14, Please include the following link: https://www.utas.edu.au/__data/assets/pdf_file/0009/588474/TR_14_BNs_a_resource_guide.pdf (last access: 22 September 2022), 2010.
Renaud, F. G., Birkmann, J., Damm, M., and Gallopín, G. C.:
Understanding multiple thresholds of coupled social–ecological systems
exposed to natural hazards as external shocks, Nat. Hazards, 55, 749–763,
2010.
Rodina, L.: Defining “water resilience”: Debates, concepts, approaches,
and gaps, WIREs Water, 6, e1334, https://doi.org/10.1002/wat2.1334, 2019.
Rounsevell, M. D. A., and Metzger, M. J.: Developing qualitative scenario
storylines for environmental change assessment, WIREs Climate Change, 1,
606–619, https://doi.org/10.1002/wcc.63, 2010.
SEPA: Sustainable Growth Agreement Scottish Water and Scottish Environment
Protection Agency Progress Update February,
https://www.sepa.org.uk/media/496202/scottish-water-sga-update.pdf (last access: 24 March 2023),
2020.
Sperotto, A., Molina, J.-L., Torresan, S., Critto, A., and Marcomini, A.:
Reviewing Bayesian Networks potentials for climate change impacts assessment
and management: A multi-risk perspective, J. Environ.
Manage., 202, 320–331, https://doi.org/10.1016/j.jenvman.2017.07.044,
2017.
Tang, C., Yi, Y., Yang, Z., and Sun, J.: Risk analysis of emergent water
pollution accidents based on a Bayesian Network, J. Environ.
Manage., 165, 199–205, https://doi.org/10.1016/j.jenvman.2015.09.024,
2016.
Environmental Change Network: The ECN Data Centre – Site Information: Eden (Fife),
http://data.ecn.ac.uk/sites/ecnsites.asp?site=R17 (last access: 6 September 2021), 2021.
Troldborg, M., Gagkas, Z., Vinten, A., Lilly, A., and Glendell, M.: Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks, Hydrol. Earth Syst. Sci., 26, 1261–1293, https://doi.org/10.5194/hess-26-1261-2022, 2022.
United Nations, U. W.: Wastewater Management – UN-Water Analytical Brief
Geneva, Switzerland: World Meteorogical Organisation, https://www.unwater.org/publications/wastewater-management-un-water-analytical-brief (last access: 20 August 2021), 2015.
Uusitalo, L.: Advantages and challenges of Bayesian networks in
environmental modelling, Ecol. Model., 203, 312–318,
https://doi.org/10.1016/j.ecolmodel.2006.11.033, 2007.
Van Vuuren, D. P., Kriegler, E., O'Neill, B. C., Ebi, K. L., Riahi, K.,
Carter, T. R., Edmonds, J., Hallegatte, S., Kram, T., and Mathur, R.: A new
scenario framework for climate change research: scenario matrix
architecture, Climatic Change, 122, 373–386, 2014.
Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D.,
Prusevich, A., Green, P., Glidden, S., Bunn, S. E., Sullivan, C. A.,
Liermann, C. R., and Davies, P. M: Global threats to human water security and
river biodiversity, Nature, 467, 555–561,
https://doi.org/10.1007/s10584-013-0906-1, 2010.
Voinov, A. and Bousquet, F.: Modelling with stakeholders, Environ.
Modell. Softw., 25, 1268–1281,
https://doi.org/10.1016/j.envsoft.2010.03.007, 2010.
Wada, Y., Flörke, M., Hanasaki, N., Eisner, S., Fischer, G., Tramberend, S., Satoh, Y., van Vliet, M. T. H., Yillia, P., Ringler, C., Burek, P., and Wiberg, D.: Modeling global water use for the 21st century: the Water Futures and Solutions (WFaS) initiative and its approaches, Geosci. Model Dev., 9, 175–222, https://doi.org/10.5194/gmd-9-175-2016, 2016.
Wade, M., O'Brien, G. C., Wepener, V., and Jewitt, G.: Risk Assessment of
Water Quantity and Quality Stressors to Balance the Use and Protection of
Vulnerable Water Resources, Integr. Environ. Asses., 17, 110–130, https://doi.org/10.1002/ieam.4356, 2021.
Walker, B., Carpenter, S., Anderies, J., Abel, N., Cumming, G., Janssen, M.,
Lebel, L., Norberg, J., Peterson, G. D., and Pritchard, R.: Resilience
Management in Social-ecological Systems a Working Hypothesis for a
Participatory Approach, Conserv. Ecol., 6, 1,
http://www.consecol.org/vol6/iss1/art14/ (last access: 3 August 2021), 2002.
Wöhler, L., Niebaum, G., Krol, M., and Hoekstra, A.Y.: The grey water
footprint of human and veterinary pharmaceuticals, Water Res. X, 7, 100044, https://doi.org/10.1016/j.wroa.2020.100044, 2020.
Xue, J., Gui, D., Lei, J., Sun, H., Zeng, F., and Feng, X.: A hybrid
Bayesian network approach for trade-offs between environmental flows and
agricultural water using dynamic discretization, Adv. Water
Resour., 110, 445–458, https://doi.org/10.1016/j.advwatres.2016.10.022,
2017.
Yüksel, I.: Developing a multi-criteria decision making model for PESTEL
analysis, International Journal of Business and Management, 7, 52,
https://doi.org/10.5539/ijbm.v7n24p52, 2012.
Short summary
We applied participatory methods to create a hybrid equation-based Bayesian network (BN) model to increase stakeholder understanding of catchment-scale resilience to the impacts of both climatic and socio-economic stressors to a 2050 time horizon. Our holistic systems-thinking approach enabled stakeholders to gain new perspectives on how future scenarios may influence their specific sectors and how their sector impacted other sectors and environmental conditions within the catchment system.
We applied participatory methods to create a hybrid equation-based Bayesian network (BN) model...