George Arhonditsis

Dr. George Arhonditsis

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Research Focus & Metrics

Academic Career Length

27Years (2000-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2016 in Harmful Algae
Verified
Journal Article
Closed access
SWSusan B. Watson
Susan B. Watson
CMCarol Miller
Carol Miller
GAGeorge Arhonditsis
George Arhonditsis
GBGregory L. Boyer
Gregory L. Boyer
WCWayne Carmichael
Wayne Carmichael
SWSteven Wilhelm
Steven Wilhelm

1 August 2021 in Water Research
Verified
Journal Article
Closed access
JHJiacong Huang
Jiacong Huang
YZYinjun Zhang
Yinjun Zhang
Haijian BingHaijian Bing
Haijian Bing
Jian PengJian Peng
Jian Peng
FDFeifei Dong
Feifei Dong
GAGeorge Arhonditsis
George Arhonditsis

13 September 2016
Verified
Closed access
MRMichael Rode
Michael Rode
AWAndrew J. Wade
Andrew J. Wade
MCMatthew J. Cohen
Matthew J. Cohen
RHRobert T. Hensley
Robert T. Hensley
MBMichael J. Bowes
Michael J. Bowes
Seifeddine JomaaSeifeddine Jomaa
Seifeddine Jomaa

1 January 2004 in Marine Ecology Progress Series
Verified
Journal Article
Closed access
GAGeorge Arhonditsis
George Arhonditsis
MBMt Brett
Mt Brett

18 February 2015 in Environmental Science & Technology
Verified
Journal Article
Closed access
CWChristopher Wellen
Christopher Wellen
AKAhmad-Reza Kamran-Disfani
Ahmad-Reza Kamran-Disfani
GAGeorge Arhonditsis
George Arhonditsis

27 August 2010 in Aquatic Ecology
Verified
Journal Article
Open access
PDF file
WMWolf M. Mooij
Wolf M. Mooij
DTDennis Trolle
Dennis Trolle
Erik JeppesenErik Jeppesen
Erik Jeppesen
GAGeorge Arhonditsis
George Arhonditsis
PBPavel V. Belolipetsky
Pavel V. Belolipetsky
JJJan H. Janse
Jan H. Janse
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.