Abstract
Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.
Résumé
L’intégration de modèle économique et de modèle de nappe pour la gestion de l’eau souterraine peut aider à améliorer la compréhension des compromis imposés par les objectifs socioéconomiques et biophysiques. Cependant, l'incertitude est grande dans la plupart des situations de prise de décision stratégiques et dans les modèles construits pour les représenter. Si elle n’est pas abordée, cette incertitude peut être utilisée pour mettre en doute la légitimité du modèle et des décisions prises en l’utilisant. Cette analyse démontre comment dans un tel modèle des incertitudes variées peuvent être gérée. Plusieurs méthodes sont utilisées, y compris la propagation d'incertitude exprimée par scénarios, par limites sur les paramètres, et par modèles multiples, l’échantillonnage d’une séries temporelle (Cyrano ou Bootstrap) et régression linéaire Robuste pour la calibration d’un modèle. Ces méthodes sont décrites dans un cadre théorique de gestion de l’incertitude, en utilisant un ensemble de «mesures de gestion d’incertitude», ainsi qu’une typologie de l’incertitude.
Resumen
La integración de modelos de aguas subterráneas y económicos para el manejo de agua subterránea pueden ayudar a mejorar la comprensión de las ventajas y desventajas involucradas entre los conflictos socioeconómicos y los objetivos biofísicos. Sin embargo, existe una incertidumbre significativa en la mayoría de las situaciones para la toma de decisiones estratégicas, incluso en los modelos construidos para representarlas. Si no es tratada, esta incertidumbre puede ser usada para desafiar la legitimidad de los modelos y las decisiones tomadas a partir de ellos. En este contexto, se realizó un análisis de incertidumbre preliminar de un modelo dinámico acoplado agua subterránea – económico destinado a evaluar las normas de extracción de agua subterránea. El análisis demuestra como una variedad de incertidumbres de un modelo puede ser tratada. Una cantidad de métodos son usados incluyendo la reproducción de escenarios y los límites en los parámetros, modelos múltiples, bloques de remuestreo de series temporales y regresión lineal robusta para la calibración del modelo. Estos métodos son descriptos dentro del contexto de un esquema de manejo de la incertidumbre teórica, usando un conjunto de tareas fundamentales del manejo y de la tipología de la incertidumbre.
摘要
用于地下水管理的整合经济地下水模将有助于改善对社会经济学和生物物理学之间相冲突的目标之间的权衡理解. 然而, 显著的不确定性在模拟情景的大多数战略性决议中存在, 包括在已构建的模型中表征他们. 若没处理好, 这些不确定性将对模型的合理性以及由此做的决定产生质疑. 本文对用于评价地下水开采条例的经济-地下水双动力模型进行初步的不确定性分析. 分析演示模型中这些不确定性如何处理的. 包括参数情景及边界传递、多重模型, 批量引导程序时间序列取样以及模型识别的稳健线性回归在内的一些方法均被使用. 利用一系列基本的不确定性管理任务和不确定性象征论对模型的理论不确定性管理框架的内容进行描述.
Resumo
A integração de modelos económicos e modelos de águas subterrâneas para a gestão das águas subterrâneas pode ajudar a melhorar a compreensão dos valores comerciáveis envolvidos nos conflitos entre objetivos sócio-económicos e objetivos biofísicos. No entanto, há uma incerteza significativa na maior parte das situações estratégicas de tomada de decisões, inclusive nos modelos construídos para os representar. Se não for abordada, esta incerteza pode ser usada para questionar a legitimidade dos modelos e das decisões tomadas ao usá-los. Neste contexto, uma análise preliminar da incerteza foi conduzida para um modelo dinâmico acoplado económico-águas subterrâneas, tendo por objetivo avaliar as regras de extração de água subterrânea. A análise demonstra como uma variedade de incertezas, neste tipo de modelo, pode ser resolvido. São utilizados vários métodos, incluindo a propagação de cenários e ligações entre parâmetros, modelos múltiplos, amostragem de séries temporais com a técnica de bootstrap em blocos e regressão linear robusta para calibração do modelo. Estes métodos são descritos no contexto de um quadro de gestão de incerteza teórico, utilizando um conjunto de tarefas de gestão da incerteza fundamentais e uma tipologia de incerteza.
Similar content being viewed by others
References
Adelaide and Mount Lofty Ranges NRM Board (2006) The environmental water needs of the watercourses of the Willunga Basin: final report. Adelaide and Mount Lofty Ranges NRM Board, Eastwood, Australia
Adelaide and Mount Lofty Ranges NRM Board (2007) Water allocation plan for the McLaren Vale Prescribed Wells Area. http://www.amlrnrm.sa.gov.au/Plans/Waterallocationplans/McLarenValeWAP.aspx. Accessed 21 March 2011
Anderson MP, Woessner WW (1992) The role of the postaudit in model validation. Adv Water Resour 15:167–173. doi:10.1016/0309-1708(92)90021-S
Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36. doi:10.1016/j.jhydrol.2005.07.007
BOM (2011) Climate data online: Monthly rainfall Willunga. http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=139&p_display_type=dataFile&p_startYear=&p_stn_num=023753. Accessed 21 March 2011
Botes JHF, Bosch DJ, Oosthuizen LK (1996) A simulation and optimization approach for evaluating irrigation information. Agric Syst 51:165–183. doi:10.1016/0308-521X(95)00042-4
Brown JD (2004) Knowledge, uncertainty and physical geography: towards the development of methodologies for questioning belief. Trans Inst Br Geogr 29:367–381
Checkland P (1995) Model validation in soft systems practice. Syst Res 12:47–54. doi:10.1002/sres.3850120108
Cools J, Broekx S, Vandenberghe V, Sels H, Meynaerts E, Vercaemst P, Seuntjens P, Van Hulle S, Wustenberghs H, Bauwens W, Huygens M (2011) Coupling a hydrological water quality model and an economic optimization model to set up a cost-effective emission reduction scenario for nitrogen. Environ Model Softw 26:44–51. doi:10.1016/j.envsoft.2010.04.017
CSIRO (2010) Water for a healthy country flagship, Sustainable Yields Projects. http://www.csiro.au/partnerships/SYP.html. Accessed 21 March 2011
Diez E, McIntosh BS (2011) Organisational drivers for, constraints on and impacts of decision and information support tool use in desertification policy and management. Environ Model Softw 26:317–327. doi:10.1016/j.envsoft.2010.04.003
Dixon P, Rimmer M, Wittwer G (2009) Modelling the Australian Government’s buyback scheme with a dynamic multi-regional CGE model. Centre of Policy Studies/IMPACT Centre Working Papers, Centre of Policy Studies/IMPACT, Clayton, Australia
Dupačová J, Gaivoronski A, Kos Z, Szantai T (1991) Stochastic programming in water management: a case study and a comparison of solution techniques. Eur J Oper Res 52:28–44. doi:10.1016/0377-2217(91)90333-Q
Ferson S, Joslyn CA, Helton JC, Oberkampf WL, Sentz K (2004) Summary from the epistemic uncertainty workshop: consensus amid diversity. Reliab Eng Syst Saf 85:355–369. doi:10.1016/j.ress.2004.03.023
Government of South Australia (2004) South Australian Legislation: Natural Resources Management Act 2004. http://www.legislation.sa.gov.au/LZ/C/A/Natural%20Resources%20Management%20Act%202004.aspx. Accessed 21 March 2011
Griffith M, Codner G, Weinmann E, Schreider S (2009) Modelling hydroclimatic uncertainty and short-run irrigator decision making: the Goulburn system. Aust J Agric Resour Econ 53:565–584. doi:10.1111/j.1467-8489.2009.00465.x
Guillaume JHA (2011) A risk-based tool for documenting and auditing the modelling process. In Chan F, Marinova D, Anderssen RS (eds) MODSIM2011, 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Perth, Australia, December 2011, pp 3854–3860.http://www.mssanz.org.au/modsim2011/I9/guillaume.pdf. Accessed 2 May 2012
Guillaume JHA, Pierce SA, Jakeman AJ (2010) Managing uncertainty in determining sustainable aquifer yield. Paper presented at the Groundwater 2010, Canberra, Australia, 31 October–4 November 2010. http://www.groundwater2010.com/documents/GuillaumeJoseph_000.pdf. Accessed 2 May 2012
Guillaume JHA, Croke BFW, El Sawah S, Jakeman AJ (2011) Implementing a framework for managing uncertainty holistically. In: Watermatex 2011: Conference Proceedings. 8th IWA Symposium on Systems Analysis and Integrated Assessment (Watermatex 2011), San Sebastian, Spain, .20–22 June 2011, pp 240–247
Harou JJ, Pulido-Velazquez M, Rosenberg DE, Medellín-Azuara J, Lund JR, Howitt RE (2009) Hydro-economic models: concepts, design, applications, and future prospects. J Hydrol 375:627–643. doi:10.1016/j.jhydrol.2009.06.037
Helton JC, Oberkampf WL (2004) Alternative representations of epistemic uncertainty. Reliab Eng Syst Saf 85:1–10. doi:10.1016/j.ress.2004.03.001
Helton JC, Johnson JD, Oberkampf WL (2004) An exploration of alternative approaches to the representation of uncertainty in model predictions. Reliab Eng Syst Saf 85:39–71. doi:10.1016/j.ress.2004.03.025
Hodgkin T (2004) Aquifer storage capacities of the Adelaide region. Report 2004/47 DWLBC, Adelaide, Australia
Huang GH (1998) A hybrid inexact-stochastic water management model. Eur J Oper Res 107:137–158. doi:10.1016/S0377-2217(97)00144-6
Hunt RJ, Luchette J, Schreuder WA, Rumbaugh JO, Doherty J, Tonkin MJ, Rumbaugh DB (2010) Using a cloud to replenish parched groundwater modeling efforts. Ground Water 48:360–365. doi:10.1111/j.1745-6584.2010.00699.x
International Standards Organisation (2009) ISO 31000:2009, risk management: principles and guidelines. ISO, Geneva
Jakeman AJ, Letcher RA, Norton JP (2006) Ten iterative steps in development and evaluation of environmental models. Environ Model Softw 21:602–614. doi:10.1016/j.envsoft.2006.01.004
Jakeman T, Letcher R, Chen S (2007) Integrated assessment of impacts of policy and water allocation changes across social, economic and environmental dimensions. In: Hussey K, Dovers S (eds) Managing water for Australia: the social and institutional challenges. CSIRO, Collingwood, Victoria, Australia, pp 97–112
Jeuland M (2010) Economic implications of climate change for infrastructure planning in transboundary water systems: an example from the Blue Nile. Water Resour Res 46:W11556. doi:10.1029/2010wr009428
Kelleher C, Wagener T (2011) Ten guidelines for effective data visualization in scientific publications. Environ Model Softw 26:822–827. doi:10.1016/j.envsoft.2010.12.006
Knowles I, Teubner M, Yan A, Rasser P, Lee J (2007) Inverse groundwater modelling in the Willunga Basin, South Australia. Hydrogeol J 15:1107–1118. doi:10.1007/s10040-007-0189-6
Konikow LF, Bredehoeft JD (1992) Ground-water models cannot be validated. Adv Water Resour 15:75–83. doi:10.1016/0309-1708(92)90033-X
Kragt ME, Newham LTH, Bennett J, Jakeman AJ (2011) An integrated approach to linking economic valuation and catchment modelling. Environ Model Softw 26:92–102. doi:10.1016/j.envsoft.2010.04.002
Labadie JW (2004) Optimal operation of multireservoir systems: state-of-the-art review. J Water Resour Plan Manag 130:93–111. doi:10.1061/(ASCE)0733-9496(2004)130:2(93
Maimone M (2004) Defining and managing sustainable yield. Ground Water 42:809–814. doi:10.1111/j.1745-6584.2004.tb02739.x
Marazzi A (1993) Algorithms, routines, and S-functions for robust statistics. Wadsworth and Brooks/Cole, Belmont, CA
Matott LS, Babendreier JE, Purucker ST (2009) Evaluating uncertainty in integrated environmental models: a review of concepts and tools. Water Resour Res 45. doi:10.1029/2008wr007301
Molina JL, Bromley J, García-Aróstegui JL, Sullivan C, Benavente J (2010) Integrated water resources management of overexploited hydrogeological systems using object-oriented Bayesian networks. Environ Model Softw 25:383–397. doi:10.1016/j.envsoft.2009.10.007
Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33:161–174
Narasimhan TN (2005) Hydrogeology in North America: past and future. Hydrogeol J 13:7–24. doi:10.1007/s10040-004-0422-5
Norton JP (1996) Roles for deterministic bounding in environmental modelling. Ecol Model 86:157–161. doi:10.1016/0304-3800(95)00045-3
Norton JP (2008) Algebraic sensitivity analysis of environmental models. Environ Model Softw 23:963–972. doi:10.1016/j.envsoft.2007.11.007
Oliver DM, Page T, Hodgson CJ, Heathwaite AL, Chadwick DR, Fish RD, Winter M (2010) Development and testing of a risk indexing framework to determine field-scale critical source areas of faecal bacteria on grassland. Environ Model Softw 25:503–512. doi:10.1016/j.envsoft.2009.10.003
Phylloxerra and Grape Industry Board of South Australia (2011) South Australian Wine Grape Utilisation and Pricing Survey http://www.healthyvines.com.au/SAWinegrapeCrushSurvey.aspx. Accessed 2 May 2012
Qureshi ME, Qureshi SE, Goesch T, Hafi A (2006) Preliminary economic assessment of groundwater extraction rules. Econ Pap 25:41–67. doi:10.1111/j.1759-3441.2006.tb00383.x
R Development Core Team (2010) R: a language and environment for statistical computing R. Computing R Foundation for Statistical Computing, Vienna
Ravalico J, Maier H, Dandy G (2009) Sensitivity analysis for decision-making using the MORE method: a Pareto approach. Reliab Eng Syst Saf 94:1229–1237. doi:10.1016/j.ress.2009.01.009
Ravalico JK, Dandy GC, Maier HR (2010) Management Option Rank Equivalence (MORE): a new method of sensitivity analysis for decision-making. Environ Model Softw 25:171–181. doi:10.1016/j.envsoft.2009.06.012
Refsgaard JC, van der Sluijs JP, Højberg AL, Vanrolleghem PA (2007) Uncertainty in the environmental modelling process: a framework and guidance. Environ Model Softw 22:1543–1556. doi:10.1016/j.envsoft.2007.02.004
Refsgaard JC, Højberg AL, Møller I, Hansen M, Søndergaard V (2009) Groundwater modeling in integrated water resources management: visions for 2020. Ground Water 48:633–648
Reichert P, Borsuk ME (2005) Does high forecast uncertainty preclude effective decision support? Environ Model Softw 20:991–1001. doi:10.1016/j.envsoft.2004.10.005
Rittel HWJ, Webber MM (1973) Dilemmas in a general theory of planning. Pol Sci 4:155–169. doi:10.1007/bf01405730
Rizzoli AE, Young WJ (1997) Delivering environmental decision support systems: software tools and techniques. Environ Model Softw 12:237–249. doi:10.1016/S1364-8152(97)00016-9
Roach J, Tidwell V (2009) A compartmental spatial system dynamics approach to ground water modeling. Ground Water 47:686–698. doi:10.1111/j.1745-6584.2009.00580.x
Rousseeuw PJ, Yohai VJ (1984) Robust regression by means of S-estimators. In: Franke J, Härdle W, Martin RD (eds) Robust and nonlinear time series analysis. Lectures Notes in Statistics, vol 26. Springer, New York, pp 256–272
Rousseeuw P, Croux C, Todorov V, Ruckstuhl A, Salibian-Barrera M, Verbeke T, Koller M, Maechler M (2011) robustbase: Basic Robust Statistics. R package version 0.7-3., http://CRAN.R-project.org/package=robustbase. Accessed 2 May 2012
Sahinidis NV (2004) Optimization under uncertainty: state-of-the-art and opportunities. Comput Chem Eng 28:971–983. doi:10.1016/j.compchemeng.2003.09.017
Saltelli A, Annoni P (2010) How to avoid a perfunctory sensitivity analysis. Environ Model Softw 25:1508–1517. doi:10.1016/j.envsoft.2010.04.012
Saltelli A, Chan K, Scott E (2004) Sensitivity analysis. Wiley, New York
Stedinger JR, Vogel RM, Lee SU, Batchelder R (2008) Appraisal of the generalized likelihood uncertainty estimation (GLUE) method. Water Resour Res 44: W00B06 doi: 10.1029/2008wr006822
Thyer M, Renard B, Kavetski D, Kuczera G, Franks SW, Srikanthan S (2009) Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: a case study using Bayesian total error analysis. Water Resour Res 45. doi:10.1029/2008wr006825
van Delden H, Seppelt R, White R, Jakeman AJ (2011) A methodology for the design and development of integrated models for policy support. Environ Model Softw 26:266–279. doi:10.1016/j.envsoft.2010.03.021
Venables WN, Ripley BD (2002) Modern applied statistics with S. Springer, Heidelberg
Voinov A, Bousquet F (2010) Modelling with stakeholders. Environ Model Softw 25:1268–1281. doi:10.1016/j.envsoft.2010.03.007
Voinov A, Cerco C (2010) Model integration and the role of data. Environ Model Softw 25:965–969. doi:10.1016/j.envsoft.2010.02.005
Vrugt JA, ter Braak CJF, Clark MP, Hyman JM, Robinson BA (2008) Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour Res 44: W00B09 doi: 10.1029/2007wr006720
Vrugt J, ter Braak C, Gupta H, Robinson B (2009) Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stoch Environ Res Risk Assess 23:1011–1026. doi:10.1007/s00477-008-0274-y
Wagner BJ, Gorelick SM (1987) Optimal groundwater quality management under parameter uncertainty. Water Resour Res 23:1162–1174. doi:10.1029/WR023i007p01162
Walker WE, Harremoës P, Rotmans J, van der Sluijs JP, van Asselt MBA, Janssen P, von Krauss MPK (2003) Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integr Assess 4:5–17. doi:10.1076/iaij.4.1.5.16466
Walter E, Piet-Lahanier H (1990) Estimation of parameter bounds from bounded-error data: a survey. Math Comput Simul 32:449–468. doi:10.1016/0378-4754(90)90002-Z
Wang J, Zamar R, Marazzi A, Yohai V, Salibian-Barrera M, Maronna R, Zivot E, Rocke D, Martin D, Maechler M, Konis K (2010) robust: Insightful Robust Library. R package version 0.3-11. http://CRAN.R-project.org/package=robust. Accessed 2 May 2012
Warmink JJ, Janssen JAEB, Booij MJ, Krol MS (2010) Identification and classification of uncertainties in the application of environmental models. Environ Model Softw 25:1518–1527. doi:10.1016/j.envsoft.2010.04.011
Watkins NL, Telfer AL (1995) Willunga Basin review of hydrogeology and water budget. Department of Mines and Energy, Adelaide, South Australia
Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer, New York
Yang J (2011) Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis. Environ Model Softw 26:444–457. doi:10.1016/j.envsoft.2010.10.007
Yohai VJ, Stahel WA, Zamar RH (1991) A procedure for robust estimation and inference in linear regression. In: Stahel WA, Weisberg SW (eds) Directions in robust statistics and diagnostics, part II, Springer, Heidelberg
Acknowledgements
The authors are grateful to two anonymous reviewers and to the editor Nicholas Brozović for their helpful comments and suggestions. This work forms part of the research program on uncertainty and decision support of the National Centre for Groundwater Research & Training, which is an Australian Government initiative, supported by the Australian Research Council and the National Water Commission.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Guillaume, J.H.A., Qureshi, M.E. & Jakeman, A.J. A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules. Hydrogeol J 20, 915–932 (2012). https://doi.org/10.1007/s10040-012-0864-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10040-012-0864-0