TY  - GEN
A1  - Erra, Ramon Guevara
A1  - Velazquez, Jose L. Perez
A1  - Rosenblum, Michael
T1  - Neural Synchronization from the Perspective of Non-linear Dynamics
T2  - Frontiers in computational neuroscience / Frontiers Research Foundation
KW  - brain synchronization
KW  - non-linear dynamics
KW  - neural synchonization
KW  - brain rhythms
KW  - epilepsy
Y1  - 2017
U6  - https://doi.org/10.3389/fncom.2017.00098
SN  - 1662-5188
VL  - 11
PB  - Frontiers Research Foundation
CY  - Lausanne
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  - 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  - Schewe, Jacob
A1  - Levermann, Anders
T1  - A statistically predictive model for future monsoon failure in India
JF  - Environmental research letters
N2  - Indian monsoon rainfall is vital for a large share of the world's population. Both reliably projecting India's future precipitation and unraveling abrupt cessations of monsoon rainfall found in paleorecords require improved understanding of its stability properties. While details of monsoon circulations and the associated rainfall are complex, full-season failure is dominated by large-scale positive feedbacks within the region. Here we find that in a comprehensive climate model, monsoon failure is possible but very rare under pre-industrial conditions, while under future warming it becomes much more frequent. We identify the fundamental intraseasonal feedbacks that are responsible for monsoon failure in the climate model, relate these to observational data, and build a statistically predictive model for such failure. This model provides a simple dynamical explanation for future changes in the frequency distribution of seasonal mean all-Indian rainfall. Forced only by global mean temperature and the strength of the Pacific Walker circulation in spring, it reproduces the trend as well as the multidecadal variability in the mean and skewness of the distribution, as found in the climate model. The approach offers an alternative perspective on large-scale monsoon variability as the result of internal instabilities modulated by pre-seasonal ambient climate conditions.
KW  - monsoon failure
KW  - climate change
KW  - coupled climate model
KW  - stochastic model
KW  - non-linear dynamics
Y1  - 2012
U6  - https://doi.org/10.1088/1748-9326/7/4/044023
SN  - 1748-9326
VL  - 7
IS  - 4
PB  - IOP Publ. Ltd.
CY  - Bristol
ER  -