PCLake & PCLake

General Information
Acronym of the model	PCLake & PCLake+
Full name of the model	PCLake & PCLake+
Model components	Chemistry, Biology
Supported platforms	Windows
Programming Language	Diverse (C++, Matlab, R, Fortran, …)
Still maintained	Yes, by: NIOO, WUR & PBL
Most recent version	–
Model structure
Needs compilation, Executables are available
1D
Mass balance included
Model description
Model objective	Calculation of water quality of lakes Calculation of the critical nutrient loads of lakes Calculation of the effect of water management of lakes
Specific application	Lakes in the Netherlands (e.g. Lake Loosdrecht), Germany (e.g Muggelsee) , China (e.g. Taihu)
Background knowledge needed to run model	Water quality, ecological processes, basic mathematics
Basic procedures	See user manual
PCLake is a dynamic, mathematical model used to study eutrophication effects in shallow lakes and ponds. PCLake explicitly models the most important biotic groups and their interrelations, within the general framework of nutrient cycles. PCLake is used by both scientist and water managers. In 2019 the existing PCLake model has received a major overhaul, leading to the PCLake+ model which can also be applied to stratifying lakes. PCLake is composed of a set of coupled differential equations. With a large number of state variables (>100) and parameters (>300), the model may be characterized as relatively complex. The main biotic variables are phytoplankton and submerged aquatic vegetation, describing primary production. A simplified food web is made up of zooplankton, zoobenthos, young planktivorous and adult benthivorous fish and piscivorous fish. The main abiotic factors are related to transparency (suspended solids, humic substances) and the nutrients phosphorus (P), nitrogen (N) and silica (Si) in both water and sediment. Optionally, a wetland zone with marsh vegetation and water exchange with the lake can be included. PCLake has been generally calibrated for nutrient, transparency, chlorophyll concentrations and vegetation data on more than 40 European (but mainly Dutch) lakes, leading to a model calibrated and validated to predict ecological state shifts. Systematic sensitivity and uncertainty analysis have been performed as well.
Link to website/manual	Github, Wikipedia
Model characteristics
Input variables	Obligatory: lake hydrology, nutrient loading, lake dimensions and sediment characteristics. Optional: grazing rates, growth rates, stoichiometry
Input file format	depending on the language used
Output variables	Chlorophyll-a, transparency, cyanobacteria, vegetation cover and fish biomass, as well as the concentrations and fluxes of nutrients N, P and Si, and oxygen
Output file format	depending on the language used
Biogeochemical model components	Over 100 components. A summary of the most important: Nutrients: inorganic nutrients (NH4, NO3,PO4,Si) and organic nutrients (detrital or biotical) Phytoplankton: blue-greens, greens, diatoms Zooplankton Planktivorous fish Benthivorous fish Piscivorous fish Sediment food web: detritus, inorganic matter, benthos
Model structure/mathematical framework	Differential equations
Temporal resolution	1 day
Minimal spatial resolution	–
Variables needing calibration	The model is calibrated in a general manner based on 40 lakes to make it widely applicable without overfitting to any specific case.
Has successfully been used in
Climate Change Scenario	e.g. Mooij, W.M., Janse, J.H., De Senerpont Domis, L.N., Hülsmann, S., Ibelings, B.W., 2007. Predicting the effect of climate change on temperate shallow lakes with the ecosystem model PCLake. Hydrobiologia 584, 443-454.
Shallow Lake/Reservoir	e.g. Janse, J.H., Scheffer, M., Lijklema, L., Van Liere, L., Sloot, J.S., Mooij, W.M., 2010. Estimating the critical phosphorus loading of shallow lakes with the ecosystem model PCLake: Sensitivity, calibration and uncertainty. Ecological Modelling 221, 654-665. Janse, J.H., De Senerpont Domis, L.N., Scheffer, M., Lijklema, L., Van Liere, L., Klinge, M., Mooij, W.M., 2008. Critical phosphorus loading of different types of shallow lakes and the consequences for management estimated with the ecosystem model PCLake. Limnologica-Ecology and Management of Inland Waters 38, 203-219. Janssen, A.B.G., de Jager, V.C.L., Janse, J.H., Kong, X., Liu, S., Ye, Q., Mooij, W.M., 2017. Spatial identification of critical nutrient loads of large shallow lakes: Implications for Lake Taihu (China). Water Research 119, 276-287. Janssen, A.B.G., Teurlincx, S., An, S., Janse, J.H., Paerl, H.W., Mooij, W.M., 2014. Alternative stable states in large shallow lakes? Journal of Great Lakes Research 40, 813-826.
Deep lake/Reservoir	Janssen, A.B.G., Teurlincx, S., Beusen, A.H.W., Huijbregts, M.A.J., Rost, J., Schipper, A.M., Seelen, L.M.S., Mooij, W.M., Janse, J.H., 2019. PCLake+: A process-based ecological model to assess the trophic state of stratified and non-stratified freshwater lakes worldwide. Ecological Modelling 396, 23-32.
Oligotrophic water	Janse, J.H., Scheffer, M., Lijklema, L., Van Liere, L., Sloot, J.S., Mooij, W.M., 2010. Estimating the critical phosphorus loading of shallow lakes with the ecosystem model PCLake: Sensitivity, calibration and uncertainty. Ecological Modelling 221, 654-665. Janse, J.H., De Senerpont Domis, L.N., Scheffer, M., Lijklema, L., Van Liere, L., Klinge, M., Mooij, W.M., 2008. Critical phosphorus loading of different types of shallow lakes and the consequences for management estimated with the ecosystem model PCLake. Limnologica-Ecology and Management of Inland Waters 38, 203-219.
Mesotrophic water	Janse, J.H., Scheffer, M., Lijklema, L., Van Liere, L., Sloot, J.S., Mooij, W.M., 2010. Estimating the critical phosphorus loading of shallow lakes with the ecosystem model PCLake: Sensitivity, calibration and uncertainty. Ecological Modelling 221, 654-665. Janse, J.H., De Senerpont Domis, L.N., Scheffer, M., Lijklema, L., Van Liere, L., Klinge, M., Mooij, W.M., 2008. Critical phosphorus loading of different types of shallow lakes and the consequences for management estimated with the ecosystem model PCLake. Limnologica-Ecology and Management of Inland Waters 38, 203-219.
Eutrophic water	Janssen, A.B.G., de Jager, V.C.L., Janse, J.H., Kong, X., Liu, S., Ye, Q., Mooij, W.M., 2017. Spatial identification of critical nutrient loads of large shallow lakes: Implications for Lake Taihu (China). Water Research 119, 276-287.
Management support	Kuiper, J.J., Verhofstad, M.J.J.M., Louwers, E.L.M., Bakker, E.S., Brederveld, R.J., van Gerven, L.P.A., Janssen, A.B.G., de Klein, J.J.M., Mooij, W.M., 2017. Mowing submerged macrophytes in shallow lakes with alternative stable states: battling the good guys? Environmental management 59, 619-634. Kong, X., He, Q., Yang, B., He, W., Xu, F., Janssen, A.B.G., Kuiper, J.J., Van Gerven, L.P.A., Qin, N., Jiang, Y., Liu, W., Yang, C., Bai, Z., Zhang, M., Kong, F., Janse, J.H., Mooij, W.M., 2016. Hydrological regulation drives regime shifts: evidence from paleolimnology and ecosystem modeling of a large shallow Chinese lake. Global Change Biology 23, 737–754.
Countries in which the model has been applied	Netherlands, Germany, Turkey, Denmark, Ireland, Greece, China, New Zealand
Which institutes have applied the model	Netherlands environmental assessment agency (PBL) Netherland Institute for Ecology (NIOO) Wageningen University (WUR) Witteveen en Bos EcoConsultancy IGB-Berlin Waikato University NIWA
Has coding for	Sediment dynamics
Accessibility
Open-Source
Support	Manual is available
Can be coupled to the following models	Delft-3D
How can someone get access to this model	Github
Form was updated: 2019-07-15