Here follows a very short excerpt from a position (working) paper aiming at quickly introducing by example the rationale behind Integrated Natural Resources Modelling and Management (INRMM). The library of INRMM related publications may be:

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Copyright (C) 2009,2010,2011,2012,2013 Daniele de Rigo. This document is licensed under a Creative Commons Attribution-NoDerivs 3.0 Italy License .

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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

Integrated Natural Resources Modelling and Management: minimal redefinition of a known challenge for environmental modelling.

Daniele de Rigo

Natural resources are intrinsically entangled in complex causal networks (Figure 1) whose management is increasingly complicated due to the need to reliably model the climate change along with the "feedbacks between the social and biophysical systems" [1] and due to huge economic and social impacts of their management policies - which could greatly benefit from the possibility to integrate risk assessment and multipurpose use optimization of different resources [2].

Water resources directly affect agriculture, drinking water and energy supply while also determining flood and drought risks, whose mitigation impose severe constraints to the effectiveness of seasonal water allocation. River catchments land cover influences the precipitation-runoff relationship and especially forest resources play a decisive role in exacerbating or mitigating moderate floods and soil erosion. While land cover directly affects soil erosion either positively (i.e. forests cover and good agricultural practices) or negatively (wildfire- or pest-degraded cover and bad agricultural practices [3]), climate and climate change affect soil erosion both indirectly by driving land cover changes and directly varying precipitation intensity and duration. Plant pest outbreaks also intensely affect land cover [4], either quantitatively (the plant species composition of forests and of agriculture areas) or qualitatively (e.g. sudden pest-induced disruption of forests).

Figure 1: An example (from [5]) of the typical complexity of natural resources relationships which also involve multi-scale integration due to heterogeneous spatial and temporal domains. "A series of domain-specific aspects of an environmental/anthropic system are shown along with their main connections (causal chain)" [5]. The causal network shows typical cyclic dependencies and is described as a motivational example for introducing the opportunity to adopt a semantic approach to environmental computational modelling. In particular, semantic array programming [5,6,2] is proposed as an useful paradigm for supporting complex environmental modelling integration. (Credit: Copyright (C) 2009,2010,2011,2012 Daniele de Rigo. Figure excerpted from [5] and belonging to the Mastrave project documentation).

At the same time, soil erosion influences water sediment transport, water resources quality and water storage loss [7]. These premises make improvement and integration of these natural resources – forest, soil, water resources – and land use management a high priority which needs to link many aspects, among which those related to renewable energy, in a multicriteria approach [8].

However, this integration poses challenging issues with respect to the effective exploitation of available data and updated description of physical subsystems (both of them are typically heterogeneous and frequently changing sets), to the discovery, reuse and adaptation of existing domain specific models for data-transformation and to the scalability of classical monolithic integration systems. The strategic goal of such an integration effort is to consistently move toward scientific reproducibility [9,10] in natural resources modelling and toward increased understandability of deep implications of INRMM for both citizens and policy-makers so to better support participatory decision-making in such a vital topic.

References

[1]. Perrings, C., Duraiappah, A., Larigauderie, A., Mooney, H. A., (2011). The biodiversity and ecosystem services Science-Policy interface. Science 331 (6021), 1139-1140. doi: 10.1126/science.1202400 .

[2]. de Rigo D. and Bosco, C. (2011). Architecture of a Pan-European Framework for Integrated Soil Water Erosion Assessment. Environmental Software Systems. Frameworks of eEnvironment, IFIP Advances in Information and Communication Technology 359, Chapter 34, 310-318. doi: 10.1007/978-3-642-22285-6_34 .

[3]. Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., Snyder, P. K. (2005). Global consequences of land use. Science, 309(5734), 570–574. doi: 10.1126/science.1111772 .

[4]. Logan, J. A., Régnière, J., Powell, J. A., (2003). Assessing the impacts of global warming on forest pest dynamics. Frontiers in Ecology and the Environment 1(3), 130-137. doi: 10.1890/1540-9295(2003)001[0130:ATIOGW]2.0.CO;2 .

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

[6]. de Rigo, D. (2012). Semantic Array Programming for Environmental Modelling: Application of the Mastrave Library. International Environmental Modelling and Software Society (iEMSs) 2012 International Congress on Environmental Modelling and Software - Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany .

[7]. Hansen, L., Hellerstein, D. (2007). The Value of the Reservoir Services Gained With Soil Conservation. Land economics 83(3), 285-301. doi: 10.3368/le.83.3.285 .

[8]. Angelis-Dimakis, A., Biberacher, M., Dominguez, J., Fiorese, G., Gadocha, S., Gnansounou, E., Guariso, G., Kartalidis, A., Panichelli, L., Pinedo, I., Robba, M. (2011). Methods and tools to evaluate the availability of renewable energy sources. Renewable and Sustainable Energy Reviews 15 (2), 1182-1200. doi: 10.1016/j.rser.2010.09.049 .

[9]. Peng, D. R. (2011). Reproducible Research in Computational Science. Science, 334(6060), 1226-1227. doi: 10.1126/science.1213847 .

[10]. 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. doi: 10.1126/science.1218263 . ⋅                                                                                                                             

Copyright (C) 2009,2010,2011,2012 Daniele de Rigo. This document is licensed under a Creative Commons Attribution-NoDerivs 3.0 Italy License .