Software uncertainty in integrated environmental modelling: the role of semantics and open science

de Rigo, Daniele (2013): Software uncertainty in integrated environmental modelling: the role of semantics and open science. Forthcoming in: Geophysical Research Abstracts , Vol. 15, (2013)

Abstract

Excerpt:

Computational aspects increasingly shape environmental sciences. Actually, transdisciplinary modelling of complex and uncertain environmental systems is challenging computational science (CS) and also the science-policy interface. Large spatial-scale problems falling within this category - i.e. wide-scale transdisciplinary modelling for environment (WSTMe) - often deal with factors for which deep-uncertainty may prevent usual statistical analysis of modelled quantities and need different ways for providing policy-making with science-based support. Here, practical recommendations are proposed for tempering a peculiar - not infrequently underestimated - source of uncertainty. Software errors in complex WSTMe may subtly affect the outcomes with possible consequences even on collective environmental decision-making. Semantic transparency in CS and free software are discussed as possible mitigations. [...]

• Item Type: MPRA Paper
• Language: English
• Keywords: software uncertainty; software errors; open science; free software; free scientific software; semantic array programming; data-transformation modelling; reproducible research; environmental modelling; complexity; uncertainty
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q51 - Valuation of Environmental Effects; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q54 - Climate ; Natural Disasters and Their Management ; Global Warming; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q57 - Ecological Economics: Ecosystem Services ; Biodiversity Conservation ; Bioeconomics ; Industrial Ecology; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory; C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods

References

[1] Casagrandi, R., Guariso, G., 2009. Impact of ICT in environmental sciences: A citation analysis 1990-2007. Environmental Modelling & Software 24 (7), 865-871. http://dx.doi.org/10.1016/j.envsoft.2008.11.013

[2] de Rigo, D., (exp.) 2013. Behind the horizon of reproducible integrated environmental modelling at European scale: ethics and practice of scientific knowledge freedom. F1000 Research. Submitted

[3] Gomes, C. P., 2009. Computational sustainability: Computational methods for a sustainable environment, economy, and society. The Bridge 39 (4), 5-13. http://www.nae.edu/File.aspx?id=17673

[4] Easterbrook, S. M., Johns, T. C., 2009. Engineering the software for understanding climate change. Computing in Science & Engineering 11 (6), 65-74. http://dx.doi.org/10.1109/MCSE.2009.193

[5] Hamarat, C., Kwakkel, J. H., Pruyt, E., 2012. Adaptive robust design under deep uncertainty. Technological Forecasting and Social Change. http://dx.doi.org/10.1016/j.techfore.2012.10.004

[6] Bankes, S. C., 2002. Tools and techniques for developing policies for complex and uncertain systems. Proceedings of the National Academy of Sciences of the United States of America 99 (Suppl 3), 7263-7266. http://dx.doi.org/10.1073/pnas.092081399

[7] Kandlikar, M., Risbey, J., Dessai, S., 2005. Representing and communicating deep uncertainty in climate-change assessments. Comptes Rendus Geoscience 337 (4), 443-455. http://dx.doi.org/10.1016/j.crte.2004.10.010

[8] de Rigo, D., Corti, P., Caudullo, G., McInerney, D., Di Leo, M., San-Miguel-Ayanz, J., 2013. Toward Open Science at the European scale: Geospatial Semantic Array Programming for Integrated Environmental Modelling. Geophysical Research Abstracts 15, EGU General Assembly 2013

[9] Rodriguez Aseretto, D., Di Leo, M., de Rigo, D., Corti, P., McInerney, D., Camia, A., San Miguel-Ayanz, J., 2013. Free and Open Source Software underpinning the European Forest Data Centre. Geophysical Research Abstracts 15, EGU General Assembly 2013

[10] de Rigo, D., Corti, P., Caudullo, G., McInerney, D., Di Leo, M., San-Miguel-Ayanz, J., (exp.) 2013. Supporting Environmental Modelling and Science-Policy Interface at European Scale with Geospatial Semantic Array Programming. In prep.

[11] Lempert, R. J., 2002. A new decision sciences for complex systems. Proceedings of the National Academy of Sciences of the United States of America 99 (Suppl 3), 7309-7313. http://dx.doi.org/10.1073/pnas.082081699

[12] Kandlikar, M., Risbey, J., Dessai, S., 2005. Representing and communicating deep uncertainty in climate-change assessments. Comptes Rendus Geoscience 337 (4), 443-455. http://dx.doi.org/10.1016/j.crte.2004.10.010

[13] Gober, P., Kirkwood, C. W., 2010. Vulnerability assessment of climate-induced water shortage in Phoenix. Proceedings of the National Academy of Sciences 107 (50), 21295-21299. http://dx.doi.org/10.1073/pnas.0911113107

[14] de Rigo, D., 2012. Semantic Array Programming for Environmental Modelling: Application of the Mastrave library. In: Seppelt, R., Voinov, A. A., Lange, S., Bankamp, D. (Eds.), International Environmental Modelling and Software Society (iEMSs) 2012 International Congress on Environmental Modelling and Software. Managing Resources of a Limited Planet: Pathways and Visions under Uncertainty, Sixth Biennial Meeting. pp. 1167-1176. http://www.iemss.org/iemss2012/proceedings/D3_1_0715_deRigo.pdf

[15] de Rigo, D., 2012. Semantic Array Programming with Mastrave - Introduction to Semantic Computational Modelling. http://mastrave.org/doc/MTV-1.012-1

[16] Free Software Foundation, 2012. What is free software? http://www.gnu.org/philosophy/free-sw.html (revision 1.118 archived at http://www.webcitation.org/6DXqCFAN3 )

[17] Stallman, R. M., 2009. Viewpoint: Why ”open source” misses the point of free software. Communications of the ACM 52 (6), 31-33. http://dx.doi.org/10.1145/1516046.1516058 (free access version: http://www.gnu.org/philosophy/open-source-misses-the-point.html )

[18] Lempert, R., Schlesinger, M. E., Jul. 2001. Climate-change strategy needs to be robust. Nature 412 (6845), 375. http://dx.doi.org/10.1038/35086617

[19] Shell, K. M., Nov. 2012. Constraining cloud feedbacks. Science 338 (6108), 755-756. http://dx.doi.org/10.1126/science.1231083

[20] van der Sluijs, J. P., 2012. Uncertainty and dissent in climate risk assessment: A Post-Normal perspective. Nature and Culture 7 (2), 174-195. http://dx.doi.org/10.3167/nc.2012.070204

[21] Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., Schellnhuber, H. J., Feb. 2008. Tipping elements in the earth’s climate system. Proceedings of the National Academy of Sciences 105 (6), 1786-1793. http://dx.doi.org/10.1073/pnas.0705414105

[22] Hastings, A., Wysham, D. B., Apr. 2010. Regime shifts in ecological systems can occur with no warning. Ecology Letters 13 (4), 464-472. http://dx.doi.org/10.1111/j.1461-0248.2010.01439.x

[23] Barnosky, A. D., Hadly, E. A., Bascompte, J., Berlow, E. L., Brown, J. H., Fortelius, M., Getz, W. M., Harte, J., Hastings, A., Marquet, P. A., Martinez, N. D., Mooers, A., Roopnarine, P., Vermeij, G., Williams, J. W., Gillespie, R., Kitzes, J., Marshall, C., Matzke, N., Mindell, D. P., Revilla, E., Smith, A. B., Jun. 2012. Approaching a state shift in earth’s biosphere. Nature 486 (7401), 52-58. http://dx.doi.org/10.1038/nature11018

[24] Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., Schellnhuber, H. J., Feb. 2008. Tipping elements in the earth’s climate system. Proceedings of the National Academy of Sciences 105 (6), 1786-1793. http://dx.doi.org/10.1073/pnas.0705414105

[25] Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., Stouffer, R. J., 2008. Stationarity is dead: Whither water management? Science 319 (5863), 573-574. http://dx.doi.org/10.1126/science.1151915

[26] Sloan, S., Pelletier, J., 2012. How accurately may we project tropical forest-cover change? A validation of a forward-looking baseline for REDD. Global Environmental Change 22 (2), 440-453. http://dx.doi.org/10.1016/j.gloenvcha.2012.02.001

[27] Nabuurs, G.J., van Putten, B., Knippers, T.S., Mohren, G. M.J., 2008. Comparison of uncertainties in carbon sequestration estimates for a tropical and a temperate forest. Forest Ecology and Management 256 (3), 237-245. http://dx.doi.org/10.1016/j.foreco.2008.04.010

[28] Green, D. G., Sadedin, S., Jun. 2005. Interactions mattercomplexity in landscapes and ecosystems. Ecological Complexity 2 (2), 117-130. http://dx.doi.org/10.1016/j.ecocom.2004.11.006

[29] de Rigo, D. 2012. Integrated Natural Resources Modelling and Management: minimal redefinition of a known challenge for environmental modelling. Excerpt from the Call for a shared research agenda toward scientific knowledge freedom, Maieutike Research Initiative. http://www.citeulike.org/groupfunc/15400/home

[30] Baker, R., Koch, F., Kriticos, D., Rafoss, T., Venette, R., van der Werf, W. (Eds.), 2012. Advancing risk assessment models for invasive alien species in the food chain: contending with climate change, economics and uncertainty. Bioforsk FOKUS 7. Bioforsk, Frederik A. Dahls vei 20, 1432 s, Norway. http://www.pestrisk.org/2012/BioforskFOKUS7-10_IPRMW-VI.pdf ↑

[31] de Rigo, D., Caudullo, G., San-Miguel-Ayanz, J., Stancanelli, G., 2012. Mapping European forest tree species distribution to support pest risk assessment. In: Baker, R., Koch, F., Kriticos, D., Rafoss, T., Venette, R., van der Werf, W. (Eds.), Advancing risk assessment models for invasive alien species in the food chain: contending with climate change, economics and uncertainty. Bioforsk FOKUS 7. Bioforsk, Frederik A. Dahls vei 20, 1432 s, Norway. http://www.pestrisk.org/2012/BioforskFOKUS7-10_IPRMW-VI.pdf

[32] Thompson, I. D., of the Convention on Biological Diversity, S., 2009. Forest resilience, biodiversity, and climate change : a synthesis of the biodiversity/resilience/stability relationship in forest ecosystems. Secretariat of the Convention on Biological Diversity. ISBN: 9292251376

[33] Center for International Forestry Research., FAO Regional Office for Asia and the Pacific, 2005. Forests and floods: drowning in fiction or thriving on facts? Center for International Forestry Research; Food and Agriculture Organization of the United Nations, Regional Office for Asia and the Pacific. http://www.worldcat.org/isbn/9793361646

[34] Bonan, G. B., Jun. 2008. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320 (5882), 1444-1449. http://dx.doi.org/10.1126/science.1155121

[35] Ferreira, L., Constantino, M. F., Borges, J. G., Garcia-Gonzalo, J., Aug. 2012. A stochastic dynamic programming approach to optimize Short-Rotation coppice systems management scheduling: An application to eucalypt plantations under wildfire risk in portugal. Forest Science, 353-365. http://dx.doi.org/10.5849/forsci.10-084

[36] de Rigo, D., Rizzoli, A. E., Soncini-Sessa, R., Weber, E., Zenesi, P., Dec. 2001. Neuro-dynamic programming for the efficient management of reservoir networks. In: Proceedings of MODSIM 2001, International Congress on Modelling and Simulation. Vol. 4. Modelling and Simulation Society of Australia and New Zealand, pp. 1949-1954. http://mpra.ub.uni-muenchen.de/42233/

[37] Bond, C. A., Champ, P., Meldrum, J., Schoettle, A., 2011. Investigating the optimality of proactive management of an invasive forest pest. In: Keane, R. E., Tomback, D. F., Murray, M. P., Smith, C. M. (Eds.), The future of high-elevation, five-needle white pines in Western North America: Proceedings of the High Five Symposium. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, pp. 295-302. http://www.treesearch.fs.fed.us/pubs/38241

[38] de Rigo, D., Castelletti, A., Rizzoli, A. E., Soncini-Sessa, R., Weber, E., Jul. 2005. A selective improvement technique for fastening neuro-dynamic programming in water resources network management. In: Ztek, P. (Ed.), Proceedings of the 16th IFAC World Congress. Vol. 16. International Federation of Automatic Control (IFAC), pp. 7-12. http://dx.doi.org/10.3182/20050703-6-CZ-1902.02172

[39] Phillis, Y. A., Kouikoglou, V. S., Jun. 2012. System-of-Systems hierarchy of biodiversity conservation problems. Ecological Modelling 235-236, 36-48. http://dx.doi.org/10.1016/j.ecolmodel.2012. 03.032

[40] Cavallo, A., Nardo, A., 2008. Optimal fuzzy management of reservoir based on genetic algorithm. In: Lowen, R., Verschoren, A. (Eds.), Foundations of Generic Optimization. Vol. 24 of Mathematical Modelling: Theory and Applications. Springer Netherlands, pp. 139-159. http://dx.doi.org/10.1007/978-1-4020-6668-9_2

[41] Castelletti, A., deRigo, D., Tepsich, L., Soncini-Sessa, R., Weber, E., Jul. 2008. On-Line design of water reservoir policies based on inflow prediction. In: Myung, C., Misra, P. (Eds.), Proceedings of the 17th IFAC World Congress. Vol.17. International Federation of Automatic Control (IFAC), pp. 14540-14545. http://dx.doi.org/10.3182/20080706-5-KR-1001.02463

[42] Kempeneers, P., Sedano, F., Seebach, L. M., Strobl, P., San-Miguel-Ayanz, J., Dec. 2011. Data fusion of different spatial resolution remote sensing images applied to Forest-Type mapping. IEEE Transactions on Geoscience and Remote Sensing 49 (12), 4977-4986. http://dx.doi.org/10.1109/TGRS.2011.2158548

[43] Sedano, F., Kempeneers, P., Strobl, P., McInerney, D., San-Miguel-Ayanz, J., Mar. 2012. Increasing spatial detail of burned scar maps using IRSAWiFS data for mediterranean Europe. Remote Sensing 4 (3), 726-744. http://dx.doi.org/10.3390/rs4030726

[44] de Rigo, D., Bosco, C., 2011. Architecture of a Pan-European Framework for Integrated Soil Water Erosion Assessment. Vol. 359 of IFIP Advances in Information and Communication Technology. Springer Boston, Berlin, Heidelberg, Ch. 34, pp. 310-318. http://dx.doi.org/10.1007/978-3-642-22285-6_34

[45] Voinov, A., Shugart, H. H., Jan. 2013. ’integronsters’, integral and integrated modeling. Environmental Modelling & Software 39, 149-158. http://dx.doi.org/10.1016/j.envsoft.2012.05.014

[46] Mendoza, G. A., Martins, H., Jul. 2006. Multi-criteria decision analysis in natural resource management: A critical review of methods and new modelling paradigms. Forest Ecology and Management 230 (1-3), 1-22. http://dx.doi.org/10.1016/j.foreco.2006.03.023

[47] O’Farrell, P. J., Anderson, P. M. L., May 2010. Sustainable multifunctional landscapes: a review to implementation. Current Opinion in Environmental Sustainability 2 (1-2), 59-65. http://dx.doi.org/10.1016/j.cosust.2010.02.005

[48] Dale, V. H., Beyeler, S. C., Aug. 2001. Challenges in the development and use of ecological indicators. Ecological Indicators 1 (1), 3-10. http://dx.doi.org/10.1016/S1470-160X(01)00003-6

[49] Gilbert, N., Sep. 2010. Balancing water supply and wildlife. Nature. http://dx.doi.org/10.1038/news.2010.505

[50] Morin, A., Urban, J., Adams, P. D., Foster, I., Sali, A., Baker, D., Sliz, P., 2012. Shining light into black boxes. Science 336 (6078), 159-160. http://dx.doi.org/10.1126/science.1218263

[51] Barnes, N., Jones, D., Nov. 2011. Clear climate code: Rewriting legacy science software for clarity. Software, IEEE 28 (6), 36-42. http://dx.doi.org/10.1109/MS.2011.113

[52] Sanders, R., Kelly, D., Jul. 2008. Dealing with risk in scientific software development. Software, IEEE 25 (4), 21-28. http://dx.doi.org/10.1109/MS.2008.84

[53] Cerf, V. G., 2012. Where is the science in computer science? Commun. ACM 55 (10), 5. http://dx.doi.org/10.1145/2347736.2347737

[54] Pincas, U., Feb. 2011. Program verification and functioning of operative computing revisited: How about mathematics engineering? Minds and Machines 21 (2), 337-359. http://dx.doi.org/10.1007/s11023-011-9237-z

[55] Sanders, P., 2009. Algorithm engineering an attempt at a definition. In: Albers, S., Alt, H., Nher, S. (Eds.), Efficient Algorithms. Vol. 5760 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 321-340. http://dx.doi.org/10.1007/978-3-642-03456-5_22

[56] Kleiner, K., 2011. Data on demand. Nature Climate Change 1 (1), 10-12. http://dx.doi.org/10.1038/nclimate1057

[57] Nature, 2011. Devil in the details. Nature 470 (7334), 305-306. http://dx.doi.org/10.1038/470305b

[58] Peng, R. D., 2011. Reproducible research in computational science. Science 334 (6060), 1226-1227.http://dx.doi.org/10.1126/science.1213847

[59] Cai, Y., Judd, K. L., Lontzek, T. S., 2012. Open science is necessary. Nature Climate Change 2 (5), 299. http://dx.doi.org/10.1038/nclimate1509

[60] Ghisla, A., Rocchini, D., Neteler, M., Frster, M., Kleinschmit, B., 2012. Species distribution modelling and open source GIS: why are they still so loosely connected? In: Seppelt, R., Voinov, A. A., Lange, S., Bankamp, D. (Eds.), International Environmental Modelling and Software Society (iEMSs) 2012 International Congress on Environmental Modelling and Software. Managing Resources of a Limited Planet: Pathways and Visions under Uncertainty, Sixth Biennial Meeting. pp. 1481-1488. http://www.iemss.org/iemss2012/proceedings/D6_0897_Ghisla_et_al.pdf

[61] Iverson, K. E., 1980. Notation as a tool of thought. Communications of the ACM 23 (8), 444-465. http://awards.acm.org/images/awards/140/articles/9147499.pdf

[62] Lehman, M. M., 1989. Uncertainty in computer application and its control through the engineering of software. J. Softw. Maint: Res. Pract. 1 (1), 3-27. http://dx.doi.org/10.1002/smr.4360010103

[63] Lehman, M. M., Ramil, J. F., 2002. Software uncertainty. In: Bustard, D., Liu, W., Sterritt, R. (Eds.), Soft-Ware 2002: Computing in an Imperfect World. Vol. 2311 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg, Ch. 14, pp. 477-514. http://dx.doi.org/10.1007/3-540-46019-5_14

[64] Hook, D., Kelly, D., 2009. Testing for trustworthiness in scientific software. In: Software Engineering for Computational Science and Engineering, 2009. SECSE ’09. ICSE Workshop on. IEEE, Washington, DC, USA, pp. 59-64. http://dx.doi.org/10.1109/SECSE.2009.5069163

[65] Hatton, L., 2007. The chimera of software quality. Computer 40 (8), 104-103. http://dx.doi.org/10.1109/MC.2007.292

[66] Hatton, L., 1997. The t experiments: errors in scientific software. Computational Science & Engineering, IEEE 4 (2), 27-38. http://dx.doi.org/10.1109/99.609829

[67] Hatton, L., 2012. Defects, scientific computation and the scientific method uncertainty quantification in scientific computing. Vol. 377 of IFIP Advances in Information and Communication Technology. Springer Boston, Berlin, Heidelberg, Ch. 8, pp. 123-138. http://dx.doi.org/10.1007/978-3-642-32677-6_8

[68] Lehman, M. M., 1996. Laws of software evolution revisited software process technology. In: Montangero, C. (Ed.), Software Process Technology. Vol. 1149 of Lecture Notes in Computer Science. Springer Berlin/Heidelberg, Ch. 12, pp. 108-124. http://dx.doi.org/10.1007/BFb0017737

[69] Oberkampf, W. L., DeLand, S. M., Rutherford, B. M., Diegert, K. V., Alvin, K. F., 2002. Error and uncertainty in modeling and simulation. Reliability Engineering & System Safety 75 (3), 333-357. http://dx.doi.org/10.1016/S0951-8320(01)00120-X

[70] Wilson, G., 2006. Where’s the real bottleneck in scientific computing? American Scientist 94 (1), 5+. http://dx.doi.org/10.1511/2006.1.5

[71] Rebaudengo, M., Reorda, M., Violante, M., 2011. Software-Level Soft-Error mitigation techniques. In: Nicolaidis, M. (Ed.), Soft Errors in Modern Electronic Systems. Vol. 41 of Frontiers in Electronic Testing. Springer US, pp. 253-285. http://dx.doi.org/10.1007/978-1-4419-6993-4_9

[72] Beaudette, D., 2008. Simple comparison of two Least-Cost path approaches. In: Open Source Software Tools for Soil Scientists. http://casoilresource.lawr.ucdavis.edu/drupal/node/544 (archived at: http://www.webcitation.org/6D0LHBRXW )