Applications of sensitivity analysis to environmental sciences

From HandWiki

Sensitivity analysis studies the relationship between the output of a model and its input variables or assumptions. Historically, the need for a role of sensitivity analysis in modelling, and many applications of sensitivity analysis have originated from environmental science and ecology.[1]

Early works

Hydrology and water quality are two modelling fields where sensitivity analysis was applied quite early. Relevant examples are the work of Bruce Beck,[2] George M. Hornberger,[3] Keith Beven[4] and Robert C. Spear.[5]

Other applications

More recent applications encompass snow avalanche models,[6] land depletion,[7] marine biogeochemical modelling,[8] irrigation[9] and again hydrological modelling.[10]

Methods

Several methods related sensitivity analysis have been developed in the context of environmental applications, such as Data Based Mechanistic Model due to Peter Young[11] and VARS due to S. Razavi and H. V.Gupta.[12][13][14]

Prevalence across disciplines

In a 2019 work on the take-up of sensitivity analysis in different disciplines, among 19 different subject areas, environmental sciences were found to have the highest number of papers, which become even higher if the papers in earth sciences are included.[15]

Journals

Reference journals for applications of sensitivity analysis in environmental science are Environmental Modelling & Software, Water Resources Research, Water Research, Ecological indicators[16] and others.

Checklists

Sensitivity analysis is part of recent checklists or guidelines for environmental modelling.[17][18][19][20]

Forthcoming special issues

A Special Issue on Sensitivity analysis for environmental modelling in preparation.[21]

References

  1. F. Pianosi et al., “Sensitivity analysis of environmental models: A systematic review with practical workflow,” Environ. Model. Softw., vol. 79, pp. 214–232, May 2016.
  2. M. Bruce Beck, 1987, WATER QUALITY MODELING: A REVIEW OF THE ANALYSIS OF UNCERTAINTY, Water Resources Research, volume 23, No. 8, August 1987.
  3. George M.Hornberger and Bernard J.Cosby, Selection of parameter values in environmental models using sparse data: A case study, Applied Mathematics and Computation, Volume 17, Issue 4, November 1985, Pages 335-355.
  4. Rogers, C. C. M., Beven, K. J., Morris, E. M. & Anderson, M. G., Sensitivity analysis, calibration and predictive uncertainty of the Institute of Hydrology Distributed Model, 30/10/1985, In : Journal of Hydrology. 81, 1-2, p. 179-191.
  5. R.C.Spear, G.M.Hornberger, Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysis, Water Research, Volume 14, Issue 1, 1980, Pages 43-49.
  6. Heredia, M.B.; Prieur, C.; Eckert, N. (2020). Nonparametric estimation of aggregated Sobol' indices: application to a depth averaged snow avalanche model (Technical report). Inria Grenoble. hal-02868604.
  7. Tarantola, S.; Giglioli, N.; Jesinghaus, J.; Saltelli, A. (2002). "Can global sensitivity analysis steer the implementation of models for environmental assessments and decision-making?". Stochastic Environmental Research and Risk Assessment 16: 63–76. doi:10.1007/s00477-001-0085-x. 
  8. Prieur, C.; Viry, L.; Blayo, E.; Brankart, J-M. (2019). "A global sensitivity analysis approach for marine biogeochemical modeling". Ocean Modelling 139: 101402. doi:10.1016/j.ocemod.2019.101402. Bibcode2019OcMod.13901402P. https://hal.inria.fr/hal-01952797/file/Paper-Modecogel.pdf. 
  9. Puy, A.; Lo Piano, S.; Saltelli, A. (2020). "Current Models Underestimate Future Irrigated Areas". Geophysical Research Letters 47 (8). doi:10.1029/2020GL087360. Bibcode2020GeoRL..4787360P. 
  10. Borgonovo, E.; Lu, X.; Plischke, E.; Rakovec, O.; Hill, M.C. (2017). "Making the most out of a hydrological model data set: Sensitivity analyses to open the model black‐box". Water Resources Research 53 (9): 7933–7950. doi:10.1002/2017WR020767. Bibcode2017WRR....53.7933B. 
  11. P. Young, “Data-based mechanistic modelling, generalised sensitivity and dominant mode analysis,” Comput. Phys. Commun., vol. 117, no. 1–2, pp. 113–129, Mar. 1999.
  12. S. Razavi and H. V. Gupta, “A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory,” Water Resour. Res., vol. 52, no. 1, pp. 423–439, Jan. 2016.
  13. S. Razavi and H. V. Gupta, “A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application,” Water Resour. Res., vol. 52, no. 1, pp. 440–455, Jan. 2016.
  14. S. Razavi, R. Sheikholeslami, A. Haghnegahdar, and B. Esfahbod, “VARS-TOOL: A Comprehensive, Efficient, and Robust Sensitivity Analysis Toolbox,” Am. Geophys. Union, Fall Gen. Assem. 2016, Abstr. id. H11A-1287, 2016.
  15. Andrea Saltelli, Ksenia Aleksankina, William Becker, Pamela Fennell, Federico Ferretti, Niels Holst, Sushan Li, Qiongli Wu, Why so many published sensitivity analyses are false: a systematic review of sensitivity analysis practices, Environmental Modelling and Software, Volume 114, April 2019, Pages 29-39.
  16. Ecological Indicators, an Elsevier journal.
  17. A. J. Jakeman, R. A. Letcher, and J. P. Norton, “Ten iterative steps in development and evaluation of environmental models,” Environ. Model. Softw., vol. 21, no. 5, pp. 602–614, 2006.
  18. S. H. Hamilton et al., “A framework for characterising and evaluating the effectiveness of environmental modelling,” Environ. Model. Softw., vol. 118, pp. 83–98, Aug. 2019.
  19. J. C. Little et al., “A tiered, system-of-systems modeling framework for resolving complex socio-environmental policy issues,” Environ. Model. Softw., vol. 112, pp. 82–94, Feb. 2019.
  20. J. Badham et al., “Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities,” Environ. Model. Softw., vol. 116, pp. 40–56, Jun. 2019.
  21. Call for papers of Special Issue on Sensitivity analysis for environmental modelling, Environmental Modelling and Software, 2020.