Challenges and opportunities for integrating lake ecosystem modelling approaches
Mooij, Wolf M.; Trolle, Dennis; Jeppesen, Erik; Arhonditsis, George; Belolipetsk, Pavel V.; Chitamwebwa, Deonatus B.R.; Degermendzhy, Andrey G.; DeAngelis, Donald L.; De Senerpont Domis, Lisette N.; Downing, Andrea S.; Elliott, J. Alex; Fragoso Jr., Carlos Ruberto; Gaedk, Ursula; Genova, Svetlana N.; Gulati, Ramesh D.; Hakanson, Lars; Hamilton, David P.; Hipsey, Matthew R.; Hoen, Jochem ‘t; Hulsmann, Stephan; Los, F. Hans; Makler-Pick, Vardit; Petzoldt, Thomas; Prokopkin, Igor G.; Rinke, Karsten; Schep, Sebastiaan A.; Tominaga, Koji; Van Dam, Anne A.; Van Nes, Egbert H.; Wells, A.; Janse, Jan H.. 2010 Challenges and opportunities for integrating lake ecosystem modelling approaches. Aquatic Ecology, 44. 633-667. 10.1007/s10452-010-9339-3Before downloading, please read NORA policies.
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
|Programmes:||CEH Topics & Objectives 2009 onwards > Water > WA Topic 3 - Science for Water Management > WA - 3.4 - Develop novel and improved methods to enable the sustainable management of freshwaters and wetlands|
|Additional Information:||This article in Aquatic Ecology is Open Access - please click on the OFFICIAL URL link to access full text of the publisher version.|
|Additional Keywords:||aquatic, food web dynamics, plankton, nutrients, spatial, lake, freshwater, marine, community, population, Hydrology, eutrophication, global change, climate warming, fisheries, biodiversity, management, mitigation, adaptive processes, non-linear dynamics, analysis, bifurcation, understanding, prediction, model limitations, model integration|
|Date made live:||30 Nov 2010 14:05|
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