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Copyright (C) 2009,2010,2011,2012,2013,2014,2015,2016,2017 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

How do we interpret and cope with the complex challenges of the changing global environment, culture and society? Among the many ways to do so, a key perspective considers the possibilities and limits of our Earth system as a whole. Mankind exploits Earth's resources to live, increasingly altering incredibly complex systems of systems. Over the centuries, our influence has grown in intensity and pervasivity, spanning at multiple scales over ecology, climate, transport and connectivity even among far (and until recently weakly interrelated) systems. This cascade of feedbacks among environmental and anthropic systems generates a wide array of ecological, geopolitical, social, health and cultural consequences. Changes in one system may reverberate faster than before in the other interconnected systems, with potentially reinforcing loops among multiple systems. Will the critical systems on which we depend be resilient to the potential impact of multiple disturbances, hazards and vulnerabilities, under partially unknown changing patterns of drivers and pressures?

Human population experienced a growth from approximately 1 billion people in 1800 to 5 billions in 1987, 6 billions in 1999 and 7 billions in 2012. Demand for food, energy and materials is growing with uneven regional patterns of health, nutrition, economic inequality, literacy and access to knowledge. The availability, dynamics and sustainability of the resources in the Earth system plays, literally, a vital role. Clean air, water and food security, energy, protection form natural and biological hazards, disaster risk management and mitigation, availability of materials and more immaterial goods, genetic conservation and preservation of strategic assets and heritage heavily depend on the state of Earth natural resources. In this respect, the multi-scale intricacy and huge stakes linked to the global economy are just a subset of the overall complexity we face. Unfortunately, the chain of deep consequences potentially associated to policy decisions affecting natural resources is definitely beyond the "common sense" and requires new cultural tools able to overcome disciplinary barriers and to support urgent decisions under high stakes and uncertainty. Building and transferring this transdisciplinary culture to the new generations is part of the challenge - which is not only technical and scientific but also cognitive, epistemological and educational.

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. These policies 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] as well as wildfires [5][6] and other disturbances [7][8] influence the connectivity of habitats and the overall landscape, 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 [9]) 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)" [9]. 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 [9,10,2] 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 [9] and belonging to the Mastrave project documentation).

At the same time, soil erosion influences water sediment transport, water resources quality and water storage loss [9]. These premises make improvement and integration of these natural resources – forest, soil, water resources – with agricultural resources and land use management a high priority which needs to link many aspects, among which those related to renewable energy and the bio-based economy, in a multicriteria approach [12]. Given the essential role of non-linearities and feedbacks among the nodes of this broad multi-scale network, even crisis preparedness, emergency response and recovery are inherently crossing disciplinary barriers.

However, this integration implies 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). Data uncertainty, incomplete and evolving knowledge of connections and feedbacks among physical systems (system of systems), modelling and software uncertainty [13] play a role (difficult to assess) in the discovery, reuse and adaptation of existing domain specific models for transdisciplinary data-transformations of interest and in the scalability of classical monolithic integration systems.

Apparently, no silver bullet is currently suitable to completely address this complex network of disciplinary and transdisciplinary uncertainty [14]. In this peculiar context, fully automatic scientific workflows still appear as far too simplistic – if not even potentially dangerous under critical decision-making processes. A viable mitigation strategy might rely on integrating an array of approaches, where human expertise remains an essential ingredient to supervise the growing automation and interdependency of specialised computational models.

Powerful computational methods exist for supporting the integrated management of natural resources and hazards, based on classical control-theory. However, most of them rely on automatic control formulations where a risk/cost function (perhaps multi-objective) has to be minimised.

On the other hand, complex modelling is sometime required in order for decision-makers and risk-assessors (or emergency operators) to be supported with integrated information difficult to estimate and extremely focused toward understandability. This is particularly the case when the extent of uncertainty and lack of data and knowledge prevents the equivalent of any pragmatically effective risk/cost function to be guessed. Under these circumstances of deep-uncertainty, scenario modelling may still be appropriate for supporting strategic and operational decision making.

It should be therefore clear how the nature of the problem described may occasionally slide toward a fuzzy boundary between proper robust management approaches and descriptive/hypothetic modelling ones.

Both modelling and management challenges may be also extremely intensive from a computational perspective. Their integrated application to transdisciplinary, often multi-scale problems with natural resources, define the domain of Integrate Natural Resources Modelling and Management (INRMM).

The strategic goal of such an integration effort is to consistently move toward scientific reproducibility [15,16] 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.


[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 , INRMM:8936554

[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 , INRMM:10793234

[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 , INRMM:764927

[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 , INRMM:10444147

[5] Lloret, F., Calvo, E., Pons, X., Díaz-Delgado, R., 2002. Wildfires and landscape patterns in the Eastern Iberian peninsula. Landscape Ecology 17 (8), 745-759. doi: 10.1023/a\%3a1022966930861 , INRMM:13380333

[6] Martín-Martín, C., Bunce, R. G. H., Saura, S., Elena-Rosselló, R., (2013). Changes and interactions between forest landscape connectivity and burnt area in Spain. Ecological Indicators 33, 129-138. doi: 10.1016/j.ecolind.2013.01.018 , INRMM:13399222

[7] Seidl, R., Fernandes, P. M., Fonseca, T. F., Gillet, F., Jönsson, A. M., Merganičová, K., Netherer, S., Arpaci, A., Bontemps, J.-D., Bugmann, H., González-Olabarria, J. R., Lasch, P., Meredieu, C., Moreira, F., Schelhaas, M.-J., Mohren, F., (2011). Modelling natural disturbances in forest ecosystems: a review. Ecological Modelling 222 (4), 903-924. doi: 10.1016/j.ecolmodel.2010.09.040 , INRMM:8136572

[8] Feser, F., Barcikowska, M., Krueger, O., Schenk, F., Weisse, R., Xia, L., (2014). Storminess over the North Atlantic and northwestern Europe - a review. Quarterly Journal of the Royal Meteorological Society, n/a. doi: 10.1002/qj.2364 , INRMM:13100822

[9] de Rigo, D. (2012). Semantic Array Programming with Mastrave - Introduction to Semantic Computational Modelling. , INRMM:11744308

[10] 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 , INRMM:12227965

[11] 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 , INRMM:11112810

[12] 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 , INRMM:7973259

[13] de Rigo, D., (2013). Software uncertainty in integrated environmental modelling: the role of semantics and open science. Geophysical Research Abstracts 15, 13292+. doi: 10.6084/m9.figshare.155701 , INRMM:12794802

[14] van der Sluijs, J. P., (2005). Uncertainty as a monster in the science-policy interface: four coping strategies. Water Science & Technology 52 (6), 87-92. , INRMM:11478404

[15] Peng, D. R. (2011). Reproducible Research in Computational Science. Science, 334(6060), 1226-1227. doi: 10.1126/science.1213847 , INRMM:10086724

[16] 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 , INRMM:10561432

Meta-information Database (INRMM-MiD).
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