TY  - JOUR
A1  - van Gerven, Luuk P. A.
A1  - Brederveld, Robert J.
A1  - de Klein, Jeroen J. M.
A1  - DeAngelis, Don L.
A1  - Downing, Andrea S.
A1  - Faber, Michiel
A1  - Gerla, Daan J.
A1  - Janse, Jan H.
A1  - Janssen, Annette B. G.
A1  - Jeuken, Michel
A1  - Kooi, Bob W.
A1  - Kuiper, Jan J.
A1  - Lischke, Betty
A1  - Liu, Sien
A1  - Petzoldt, Thomas
A1  - Schep, Sebastiaan A.
A1  - Teurlincx, Sven
A1  - Thiange, Christophe
A1  - Trolle, Dennis
A1  - van Nes, Egbert H.
A1  - Mooij, Wolf M.
T1  - Advantages of concurrent use of multiple software frameworks in water quality modelling using a database approach
JF  - Fundamental and applied limnology : official journal of the International Association of Theoretical and Applied Limnology
N2  - Water quality modelling deals with multidisciplinary questions ranging from fundamental to applied. Addressing this broad range of questions requires multiple analysis techniques and therefore multiple frameworks. Through the recently developed database approach to modelling (DATM), it has become possible to run a model in multiple software frameworks without much overhead. Here we apply DATM to the ecosystem model for ditches PCDitch and its twin model for shallow lakes PCLake. Using DATM, we run these models in six frameworks (ACSL, DELWAQ, DUFLOW, GRIND for MATLAB, OSIRIS and R), and report on the possible model analyses with tools provided by each framework. We conclude that the dynamic link between frameworks and models resulting from DATM has the following main advantages: it allows one to use the framework one is familiar with for most model analyses and eases switching between frameworks for complementary model analyses, including the switch between a 0-D and 1-D to 3-D setting. Moreover, the strength of each framework - including runtime performance - can now be easily exploited. We envision that a community-based further development of the concept can contribute to the future development of water quality modelling, not only by addressing multidisciplinary questions but also by facilitating the exchange of models and process formulations within the community of water quality modellers.
KW  - Database Approach To Modelling
KW  - DATM
KW  - PCLake
KW  - PCDitch
KW  - OSIRIS
KW  - ACSL
KW  - R
KW  - GRIND
KW  - DUFLOW
KW  - DELWAQ
KW  - Modelling Framework
KW  - Model Implementation
KW  - Model Analysis
KW  - Differential Equations
KW  - Community-based Modelling
Y1  - 2015
U6  - https://doi.org/10.1127/fal/2015/0631
SN  - 1863-9135
VL  - 186
IS  - 1-2
SP  - 5
EP  - 20
PB  - Schweizerbart
CY  - Stuttgart
ER  - 
TY  - GEN
A1  - Mooij, Wolf M.
A1  - Trolle, Dennis
A1  - Jeppesen, Erik
A1  - Arhonditsis, George B.
A1  - Belolipetsky, Pavel V.
A1  - Chitamwebwa, Deonatus B. R.
A1  - Degermendzhy, Andrey G.
A1  - DeAngelis, Donald L.
A1  - Domis, Lisette Nicole de Senerpont
A1  - Downing, Andrea S.
A1  - Elliott, J. Alex
A1  - Fragoso Jr., Carlos Ruberto
A1  - Gaedke, Ursula
A1  - Genova, Svetlana N.
A1  - Gulati, Ramesh D.
A1  - Håkanson, Lars
A1  - Hamilton, David P.
A1  - Hipsey, Matthew R.
A1  - ‘t Hoen, Jochem
A1  - Hülsmann, Stephan
A1  - Los, F. Hans
A1  - Makler-Pick, Vardit
A1  - Petzoldt, Thomas
A1  - Prokopkin, Igor G.
A1  - Rinke, Karsten
A1  - Schep, Sebastiaan A.
A1  - Tominaga, Koji
A1  - Van Dam, Anne A.
A1  - Van Nes, Egbert H.
A1  - Wells, Scott A.
A1  - Janse, Jan H.
T1  - Challenges and opportunities for integrating lake ecosystem modelling approaches
T2  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - 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 traitbased 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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1326 
KW  - aquatic
KW  - food web dynamics
KW  - plankton
KW  - nutrients
KW  - spatial
KW  - lake
KW  - freshwater
KW  - marine
KW  - community
KW  - population
KW  - hydrology
KW  - eutrophication
KW  - global change
KW  - climate warming
KW  - fisheries
KW  - biodiversity
KW  - management
KW  - mitigation
KW  - adaptive processes
KW  - non-linear dynamics
KW  - analysis
KW  - bifurcation
KW  - understanding
KW  - prediction
KW  - model limitations
KW  - model integration
Y1  - 2010
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-429839
SN  - 1866-8372
IS  - 1326
ER  - 
TY  - JOUR
A1  - Mooij, Wolf M.
A1  - Trolle, Dennis
A1  - Jeppesen, Erik
A1  - Arhonditsis, George B.
A1  - Belolipetsky, Pavel V.
A1  - Chitamwebwa, Deonatus B. R.
A1  - Degermendzhy, Andrey G.
A1  - DeAngelis, Donald L.
A1  - Domis, Lisette Nicole de Senerpont
A1  - Downing, Andrea S.
A1  - Elliott, J. Alex
A1  - Fragoso Jr, Carlos Ruberto
A1  - Gaedke, Ursula
A1  - Genova, Svetlana N.
A1  - Gulati, Ramesh D.
A1  - Håkanson, Lars
A1  - Hamilton, David P.
A1  - Hipsey, Matthew R.
A1  - ‘t Hoen, Jochem
A1  - Hülsmann, Stephan
A1  - Los, F. Hans
A1  - Makler-Pick, Vardit
A1  - Petzoldt, Thomas
A1  - Prokopkin, Igor G.
A1  - Rinke, Karsten
A1  - Schep, Sebastiaan A.
A1  - Tominaga, Koji
A1  - Van Dam, Anne A.
A1  - Van Nes, Egbert H.
A1  - Wells, Scott A.
A1  - Janse, Jan H.
T1  - Challenges and opportunities for integrating lake ecosystem modelling approaches
JF  - Aquatic ecology
N2  - 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 traitbased 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.
KW  - aquatic
KW  - food web dynamics
KW  - plankton
KW  - nutrients
KW  - spatial
KW  - lake
KW  - freshwater
KW  - marine
KW  - community
KW  - population
KW  - hydrology
KW  - eutrophication
KW  - global change
KW  - climate warming
KW  - fisheries
KW  - biodiversity
KW  - management
KW  - mitigation
KW  - adaptive processes
KW  - non-linear dynamics
KW  - analysis
KW  - bifurcation
KW  - understanding
KW  - prediction
KW  - model limitations
KW  - model integration
Y1  - 2010
U6  - https://doi.org/10.1007/s10452-010-9339-3
SN  - 1573-5125
SN  - 1386-2588
VL  - 44
SP  - 633
EP  - 667
PB  - Springer Science + Business Media B.V.
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Kampe, Heike
A1  - König-Rinke, Marie
A1  - Petzoldt, Thomas
A1  - Benndorf, Jürgen
T1  - Direct effects of Daphnia-grazing, not infochemicals, mediate a shift towards large inedible colonies of the gelatinous green alga Sphaerocystis schroeteri
N2  - The influence of Daphnia galeata x hyalina grazing and of infochemicals released by the daphnids on the colony size and growth rate of the colonial gelatinous green alga Sphaerocystis schroeteri (Chlorococcales) was investigated in laboratory batch experiments run for 96 h. High zooplankton grazing pressure was exerted by a final concentration of 100 daphnids L-1 in the Daphnia treatments. Infochemicals were obtained by filtration (0.2 µm) of water from D. galeata x hyalina cultures (200 ind. L-1 exposed for 24 h). This filtrate was added to the S. schroeteri cultures in two concentrations corresponding to 7 and 50 daphnids L-1, respectively. The growth rate of S. schroeteri was neither affected significantly by direct Daphnia grazing nor by the presence of Daphnia infochemicals in comparison to the control. However, the portion of inedible S. schroeteri colonies (diameter > 50 µm) increased under direct grazing pressure, whereas the Daphnia infochemicals did not influence the colony size significantly. We conclude that the shift in colony size by direct zooplankton grazing denotes an effective defence mechanism against size selective feeding for colonial gelatinous green algae. This effective defence in combination with unchanged growth rates of the larger colonies (under non-limiting nutrient and light conditions) falsifies the assumption of a trade-off between minimising grazing losses and maximising growth by optimising the colony size.
Y1  - 2007
UR  - http://www.sciencedirect.com/science/journal/00759511
U6  - https://doi.org/10.1016/j.limno.2007.01.001
SN  - 0075-9511
ER  - 
TY  - JOUR
A1  - Janssen, Annette B. G.
A1  - Arhonditsis, George B.
A1  - Beusen, Arthur
A1  - Bolding, Karsten
A1  - Bruce, Louise
A1  - Bruggeman, Jorn
A1  - Couture, Raoul-Marie
A1  - Downing, Andrea S.
A1  - Elliott, J. Alex
A1  - Frassl, Marieke A.
A1  - Gal, Gideon
A1  - Gerla, Daan J.
A1  - Hipsey, Matthew R.
A1  - Hu, Fenjuan
A1  - Ives, Stephen C.
A1  - Janse, Jan H.
A1  - Jeppesen, Erik
A1  - Joehnk, Klaus D.
A1  - Kneis, David
A1  - Kong, Xiangzhen
A1  - Kuiper, Jan J.
A1  - Lehmann, Moritz K.
A1  - Lemmen, Carsten
A1  - Oezkundakci, Deniz
A1  - Petzoldt, Thomas
A1  - Rinke, Karsten
A1  - Robson, Barbara J.
A1  - Sachse, Rene
A1  - Schep, Sebastiaan A.
A1  - Schmid, Martin
A1  - Scholten, Huub
A1  - Teurlincx, Sven
A1  - Trolle, Dennis
A1  - Troost, Tineke A.
A1  - Van Dam, Anne A.
A1  - Van Gerven, Luuk P. A.
A1  - Weijerman, Mariska
A1  - Wells, Scott A.
A1  - Mooij, Wolf M.
T1  - Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective
JF  - Aquatic ecology : the international forum covering research in freshwater and marine environments
N2  - Here, we present a community perspective on how to explore, exploit and evolve the diversity in aquatic ecosystem models. These models play an important role in understanding the functioning of aquatic ecosystems, filling in observation gaps and developing effective strategies for water quality management. In this spirit, numerous models have been developed since the 1970s. We set off to explore model diversity by making an inventory among 42 aquatic ecosystem modellers, by categorizing the resulting set of models and by analysing them for diversity. We then focus on how to exploit model diversity by comparing and combining different aspects of existing models. Finally, we discuss how model diversity came about in the past and could evolve in the future. Throughout our study, we use analogies from biodiversity research to analyse and interpret model diversity. We recommend to make models publicly available through open-source policies, to standardize documentation and technical implementation of models, and to compare models through ensemble modelling and interdisciplinary approaches. We end with our perspective on how the field of aquatic ecosystem modelling might develop in the next 5-10 years. To strive for clarity and to improve readability for non-modellers, we include a glossary.
KW  - Water quality
KW  - Ecology
KW  - Geochemistry
KW  - Hydrology
KW  - Hydraulics
KW  - Hydrodynamics
KW  - Physical environment
KW  - Socio-economics
KW  - Model availability
KW  - Standardization
KW  - Linking
Y1  - 2015
U6  - https://doi.org/10.1007/s10452-015-9544-1
SN  - 1386-2588
SN  - 1573-5125
VL  - 49
IS  - 4
SP  - 513
EP  - 548
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Sachse, Rene
A1  - Petzoldt, Thomas
A1  - Blumstock, Maria
A1  - Moreira, Santiago
A1  - Paetzig, Marlene
A1  - Ruecker, Jacqueline
A1  - Janse, Jan H.
A1  - Mooij, Wolf M.
A1  - Hilt, Sabine
T1  - Extending one-dimensional models for deep lakes to simulate the impact of submerged macrophytes on water quality
JF  - Environmental modelling & software with environment data news
N2  - Submerged macrophytes can stabilise clear water conditions in shallow lakes. However, many existing models for deep lakes neglect their impact. Here, we tested the hypothesis that submerged macrophytes can affect the water clarity in deep lakes. A one-dimensional, vertically resolved macrophyte model was developed based on PCLake and coupled to SALMO-1D and GOTM hydrophysics and validated against field data. Validation showed good coherence in dynamic growth patterns and colonisation depths. In our simulations the presence of submerged macrophytes resulted in up to 50% less phytoplankton biomass in the shallowest simulated lake (11 m) and still 15% less phytoplankton was predicted in 100 m deep oligotrophic lakes. Nutrient loading, lake depth, and lake shape had a strong influence on macrophyte effects. Nutrient competition was found to be the strongest biological interaction. Despite a number of limitations, the derived dynamic lake model suggests significant effects of submerged macrophytes on deep lake water quality. (C) 2014 Elsevier Ltd. All rights reserved.
KW  - Lake model
KW  - Macrophytes
KW  - Water quality
Y1  - 2014
U6  - https://doi.org/10.1016/j.envsoft.2014.05.023
SN  - 1364-8152
SN  - 1873-6726
VL  - 61
SP  - 410
EP  - 423
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Mooij, Wolf M.
A1  - Brederveld, Robert J.
A1  - de Klein, Jeroen J. M.
A1  - DeAngelis, Don L.
A1  - Downing, Andrea S.
A1  - Faber, Michiel
A1  - Gerla, Daan J.
A1  - Hipsey, Matthew R.
A1  - Janse, Jan H.
A1  - Janssen, Annette B. G.
A1  - Jeuken, Michel
A1  - Kooi, Bob W.
A1  - Lischke, Betty
A1  - Petzoldt, Thomas
A1  - Postma, Leo
A1  - Schep, Sebastiaan A.
A1  - Scholten, Huub
A1  - Teurlincx, Sven
A1  - Thiange, Christophe
A1  - Trolle, Dennis
A1  - van Dam, Anne A.
A1  - van Gerven, Luuk P. A.
A1  - van Nes, Egbert H.
A1  - Kuiper, Jan J.
T1  - Serving many at once: How a database approach can create unity in dynamical ecosystem modelling
JF  - Environmental modelling & software with environment data news
N2  - Simulation modelling in ecology is a field that is becoming increasingly compartmentalized. Here we propose a Database Approach To Modelling (DATM) to create unity in dynamical ecosystem modelling with differential equations. In this approach the storage of ecological knowledge is independent of the language and platform in which the model will be run. To create an instance of the model, the information in the database is translated and augmented with the language and platform specifics. This process is automated so that a new instance can be created each time the database is updated. We describe the approach using the simple Lotka-Volterra model and the complex ecosystem model for shallow lakes PCLake, which we automatically implement in the frameworks OSIRIS, GRIND for MATLAB, ACSL, R, DUFLOW and DELWAQ. A clear advantage of working in a database is the overview it provides. The simplicity of the approach only adds to its elegance. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-sa/3.0/).
KW  - Modelling framework
KW  - Programming language
KW  - Differential equation
KW  - Community-based modelling
KW  - Database approach to modelling
KW  - DATM
Y1  - 2014
U6  - https://doi.org/10.1016/j.envsoft.2014.04.004
SN  - 1364-8152
SN  - 1873-6726
VL  - 61
SP  - 266
EP  - 273
PB  - Elsevier
CY  - Oxford
ER  -