- J.A. Elliott (1)
- M.R. Flindt (1)
- C.R. Fragoso (1)
- G. Gal (1)
- D.P. Hamilton (1)
- P.C. Hanson (1)
- M.R. Hipsey (1)
- J. Hoen 't (1)
- J.H. Janse (1)
- E. Jeppesen (1)
- V. Makler-Pick (1)
- D.M. Marques (1)
- W.M. Mooij (1)
- A. Nielsen (2)
- T. Petzoldt (1)
- K. Rinke (1)
- M. Sondergaard (1)
- K. Tominaga (1)
- D. Trolle (2)
Effects of climate and nutrient load on the water quality of shallow lakes assessed through ensemble runs by PCLake
Nielsen, A. ; Trolle, D. ; Bjerring, R. - \ 2014
Ecological Applications 24 (2014)8. - ISSN 1051-0761 - p. 1926 - 1944.
ecosystem model pclake - danish lakes - phosphorus - state - eutrophication - restoration - equifinality - uncertainty - sensitivity - management
Complex ecological models are used to predict the consequences of anticipated future changes in climate and nutrient loading for lake water quality. These models may, however, suffer from nonuniqueness in that various sets of model parameter values may yield equally satisfactory representations of the system being modeled, but when applied in future scenarios these sets of values may divert considerably in their simulated outcomes. Compilation of an ensemble of model runs allows us to account for simulation variability arising from model parameter estimates. Thus, we propose a new approach for aquatic ecological models creating a more robust prediction of future water quality. We used our ensemble approach in an application of the widely used PCLake model for Danish shallow Lake Arreskov, which during the past two decades has demonstrated frequent shifts between turbid and clear water states. Despite marked variability, the span of our ensemble runs encapsulated 70–90% of the observed variation in lake water quality. The model exercise demonstrates that future warming and increased nutrient loading lead to lower probability of a clear water, vegetation-rich state and greater likelihood of cyanobacteria dominance. In a 6.0°C warming scenario, for instance, the current nutrient loading of nitrogen and phosphorus must be reduced by about 75% to maintain the present ecological state of Lake Arreskov, but even in a near-future 2.0°C warming scenario, a higher probability of a turbid, cyanobacteria-dominated state is predicted. As managers may wish to determine the probability of achieving a certain ecological state, our proposed ensemble approach facilitates new ways of communicating future stressor impacts.
A community-based framework for aquatic ecosystem models
Trolle, D. ; Hamilton, D.P. ; Hipsey, M.R. ; Bolding, K. ; Bruggeman, J. ; Mooij, W.M. ; Janse, J.H. ; Nielsen, A. ; Jeppesen, E. ; Elliott, J.A. ; Makler-Pick, V. ; Petzoldt, T. ; Rinke, K. ; Flindt, M.R. ; Arhonditsis, G.B. ; Gal, G. ; Bjerring, R. ; Tominaga, K. ; Hoen, J. 't; Downing, A.S. ; Marques, D.M. ; Fragoso, C.R. ; Sondergaard, M. ; Hanson, P.C. - \ 2012
Hydrobiologia 683 (2012)1. - ISSN 0018-8158 - p. 25 - 34.
climate-change - lake kinneret - phytoplankton - management - fish
Here, we communicate a point of departure in the development of aquatic ecosystem models, namely a new community-based framework, which supports an enhanced and transparent union between the collective expertise that exists in the communities of traditional ecologists and model developers. Through a literature survey, we document the growing importance of numerical aquatic ecosystem models while also noting the difficulties, up until now, of the aquatic scientific community to make significant advances in these models during the past two decades. Through a common forum for aquatic ecosystem modellers we aim to (i) advance collaboration within the aquatic ecosystem modelling community, (ii) enable increased use of models for research, policy and ecosystem-based management, (iii) facilitate a collective framework using common (standardised) code to ensure that model development is incremental, (iv) increase the transparency of model structure, assumptions and techniques, (v) achieve a greater understanding of aquatic ecosystem functioning, (vi) increase the reliability of predictions by aquatic ecosystem models, (vii) stimulate model inter-comparisons including differing model approaches, and (viii) avoid 're-inventing the wheel', thus accelerating improvements to aquatic ecosystem models. We intend to achieve this as a community that fosters interactions amongst ecologists and model developers. Further, we outline scientific topics recently articulated by the scientific community, which lend themselves well to being addressed by integrative modelling approaches and serve to motivate the progress and implementation of an open source model framework.