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Challenges and opportunities for integrating lake ecosystem modelling approaches

Mooij, W.M. and Trolle, D. and Jeppesen, E. and Arhonditsis, G. and Belolipetsky, P. and Chitamwebwa, D.B.R. and Degermendzhy, A.G. and DeAngelis, D.L. and De Senerpont Domis, L.N. and Downing, A.S. and Elliott, J.A. and Fragoso Jr., C.R. and Gaedke, U. and Genova, S.N. and Gulati, R.D. and Håkanson, L. and Hamilton, D.P. and Hipsey, M.R. and Hoen ‘t, P.J. and Hülsmann, S. and Los, F.J. and Makler-Pick, V. and Petzoldt, T. and Prokopkin, I. and Rinke, K. and Schep, S.A. and Tominaga, K. and Dam Van, A.A. and Nes van, E.H. and Wells, S.A. and Janse, J.H. (2010) Challenges and opportunities for integrating lake ecosystem modelling approaches. Aquatic Ecology, 44, 633-667. ISSN 1386-2588.

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A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others (‘reinventing the wheel’). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available (‘having tunnel vision’). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its ‘leading principle’, there are many opportunities for combining approaches. We take the point of view that a single ‘right’ approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.

Item Type:Article
Institutes:Nederlands Instituut voor Ecologie (NIOO)
ID Code:7769
Deposited On:07 Oct 2010 02:00
Last Modified:31 Mar 2014 10:23

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