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This study presents FABM-PCLake, a complete redesign of the PCLake aquatic ecosystem model, which we implemented into the Framework for Aquatic Biogeochemical Models (FABM). In contrast to the original model, which was designed for temperate, fully mixed freshwater lakes, the new FABM-PCLake represents an integrated aquatic ecosystem model that enables simulations of hydrodynamics and biogeochemical processes for zero dimensional, one-dimensional as well as three-dimensional heterogeneous environments. FABM-PCLake describes interactions between multiple trophic levels, including piscivorous, zooplanktivorous and benthivorous fish, zooplankton, zoobenthos, three groups of phytoplankton and rooted macrophytes. The model also accounts for oxygen dynamics and nutrient cycling for nitrogen, phosphorus and silicon, both within the pelagic and benthic domains. FABM-PCLake includes a two-way communication between the biogeochemical processes and the physics, where some biogeochemical state variables (e.g., phytoplankton) influence light attenuation and thereby the spatial and temporal distributions of light and heat. At the same time, the physical environment, including water currents, light and temperature influence a wide range of biogeochemical processes. The model enables studies on ecosystem dynamics in physically heterogeneous environments (e.g., stratifying water bodies, and water bodies with horizontal gradient in physical and biogeochemical properties), and through FABM also enables data assimilation and multi-model ensemble simulations. Examples of relevant model applications include climate change impact studies and environmental impact assessment scenarios for lakes and reservoirs worldwide.
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FABM-PCLake – linking aquatic ecology with
1
hydrodynamics
2
3
F. Hu
1,2*
, K. Bolding
1,3
, J. Bruggeman
3,4
, E. Jeppesen
1,5
, M.R. Flindt
2
, L. van
4
Gerven
6,7
, J.H. Janse
6,8
, A.B.G. Janssen
6,7
, J.J. Kuiper
6,7
, W.M. Mooij
6,7
, D.
5
Trolle
1,5
6
[1] Aarhus University, Department of Bioscience, Vejlsøvej 25, 8600 Silkeborg, Denmark
7
[2] University of Southern Denmark, Department of Biology, Campusvej 55, 5230 Odense M,
8
Denmark
9
[3] Bolding & Bruggeman ApS, Strandgyden 25, 5466 Asperup, Denmark
10
[4] Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, United
11
Kingdom
12
[5] Sino-Danish Center for Education and Research, University of Chinese Academy of
13
Sciences, Beijing
14
[6] Netherlands Institute of Ecology, Department of Aquatic Ecology, 6700 AB Wageningen,
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The Netherlands
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[7] Wageningen University, Department of Aquatic Ecology and Water Quality Management,
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6700 AA, The Netherlands
18
[8] PBL Netherlands Environmental Assessment Agency, Dept. of Nature and Rural Areas,
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3720 AH Bilthoven, The Netherlands
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*Corresponce to: Fenjuan Hu (fenjuan.hu@gmail.com)
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Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
Manuscript under review for journal Geosci. Model Dev.
Published: 3 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
2
Abstract
1
This study presents FABM-PCLake, a complete redesign of the PCLake aquatic ecosystem
2
model, which we implemented into the Framework for Aquatic Biogeochemical Models
3
(FABM). In contrast to the original model, which was designed for temperate, fully mixed
4
freshwater lakes, the new FABM-PCLake represents an integrated aquatic ecosystem model
5
that enables simulations of hydrodynamics and biogeochemical processes for zero-
6
dimensional, one-dimensional as well as three-dimensional heterogeneous environments.
7
FABM-PCLake describes interactions between multiple trophic levels, including piscivorous,
8
zooplanktivorous and benthivorous fish, zooplankton, zoobenthos, three groups of
9
phytoplankton and rooted macrophytes. The model also accounts for oxygen dynamics and
10
nutrient cycling for nitrogen, phosphorus and silicon, both within the pelagic and benthic
11
domains. FABM-PCLake includes a two-way communication between the biogeochemical
12
processes and the physics, where some biogeochemical state variables (e.g., phytoplankton)
13
influence light attenuation and thereby the spatial and temporal distributions of light and heat.
14
At the same time, the physical environment, including water currents, light and temperature
15
influence a wide range of biogeochemical processes. The model enables studies on ecosystem
16
dynamics in physically heterogeneous environments (e.g., stratifying water bodies, and water
17
bodies with horizontal gradient in physical and biogeochemical properties), and through
18
FABM also enables data assimilation and multi-model ensemble simulations. Examples of
19
relevant model applications include climate change impact studies and environmental impact
20
assessment scenarios for lakes and reservoirs worldwide.
21
22
1 Introduction
23
The field of aquatic ecosystem modelling has undergone waves of development during the
24
past decades, and models have grown in complexity in terms of ecosystem components and
25
processes included (Robson, 2014). However, even though hundreds of models have been
26
formulated for research or management purposes, only a handful has found frequent use and
27
ongoing development (Trolle et al., 2012). This reflects that many models are being built with
28
the same or similar properties, and thus that model development for the past decades has been
29
subject to some degree of “re-inventing the wheel” as discussed by Mooij et al (2010).
30
Another drawback of many aquatic ecosystem models is the typical discrepancy in
31
complexity between the ecosystem representation and the physical environment. Hence, few
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Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
Manuscript under review for journal Geosci. Model Dev.
Published: 3 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
3
studies have attempted to couple aquatic ecosystem dynamics including higher trophic levels
1
(e.g., fish) and explicit physical dynamics (one example is the study by Makler-Pick et al.
2
(2011)), which, however, is not readily available for further developments). High complexity
3
in ecosystem conceptualizations therefore generally comes at the expense of simple or no
4
hydrodynamic representation (e.g., PCLake (Janse and van Liere,1995; Janse, 2005; Janse et
5
al., 2008) and EcoPath (Christensen and Pauly, 1992)). By contrast, physically resolved
6
hydrodynamic models often include no or only simple ecosystem representations, and
7
disregard higher trophic levels. To avoid “re-inventing the wheel”, and to overcome this
8
discrepancy in complexity between the ecological and physical representation, a way forward
9
is to enable an easy coupling between existing ecosystem models and hydrodynamic models.
10
Thus, the complexity of the conceptual biogeochemical model and the physical representation
11
may readily be adapted to best suit the needs and purposes of a given study. To this end, we
12
implemented and modified a well-developed and widely applied ecosystem model, PCLake,
13
within FABM, the Framework for Aquatic Biogeochemical Models by Bruggeman and
14
Bolding (2014). FABM enables a flexible coupling of ecosystem processes in PCLake with a
15
selection of hydrodynamic models representing zero- to three-dimensional hydrodynamics.
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2 Implementation of PCLake in FABM
18
PCLake is originally a zero-dimensional ecological model for shallow lakes developed by
19
Janse and van Liere (1995) and it has been widely applied (for example, Stonevičius and
20
Taminskas, 2007; Mooij et al., 2009; Nielsen et al., 2014; further references in Mooij et al.,
21
2010). The model describes the dynamics of phytoplankton, macrophytes and a simplified
22
food web, and accounts for mass balances, represented by dry weight, nitrogen, phosphorus
23
and silicon cycling between the various components of the ecosystem. The original PCLake
24
model (documented in detail in Janse (2005)) contains detailed biological processes within the
25
water column and also a relatively advanced biogeochemical sediment module (describing
26
nutrient dynamics in the sediment top layer and exchanges with the water column), while
27
thermo- and hydrodynamics are not explicitly accounted for. The original model also includes
28
a marsh module describing (helophytic) marsh vegetation in a zone around a lake, which
29
attempts to account for interactions between open waters and a more highly vegetated marsh
30
area that may be present close to the shoreline in some lakes. The main purpose of the model
31
is to predict critical nutrient loadings, i.e. the loading where a shallow lake may switch
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Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
Manuscript under review for journal Geosci. Model Dev.
Published: 3 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
4
between a clear and a turbid state, related to a non-linear ecosystem response to nutrient
1
loading as a result of self-enhancing feedback mechanisms within the ecosystem.
2
FABM, in which we have now implemented PCLake, is a framework for biogeochemical
3
models of marine and freshwater systems (Bruggeman and Bolding, 2014). FABM enables
4
complex biogeochemical models to be developed as sets of stand-alone, process-specific
5
modules. These can be combined at runtime to create custom-tailored models. As outlined in
6
detail by Bruggeman and Bolding (2014), FABM divides the coupled advection-diffusion-
7
reaction equation that governs the dynamics of biogeochemical variables into two parts: a
8
reaction part (i.e., sink and source terms) provided by the biogeochemical models, and a
9
transport part handled by the hydrodynamic models. The transport part includes advection,
10
diffusion and potential vertical movements (sinking, floating and potentially active
11
movement), and also eddy-mixing, dilution and concentration processes. Therefore based on
12
local variables (including, for example, local light conditions, temperature and concentrations
13
of state variables) calculated by transport part, the biogeochemical models calculate sink and
14
source terms at current time and space and return the value to physical models via FABM.
15
Afterwards, FABM passes the information from biogeochemical modes to physical models
16
which will receive the sink and source term values and process next step of calculation taking
17
into consideration of feedback from biogeochemical models. FABM thereby enables model
18
applications of different physical representations (ranging 0D to 3D) without the need to
19
change the biogeochemical source code. Most of the pelagic state variables in a
20
biogeochemical model implemented in FABM will typically be transported by the
21
hydrodynamics. However, some pelagic variables, particularly relevant for higher trophic
22
levels such as fish (that may exhibit active movement based, for example, on the food source
23
availability), can be set as exempt from hydrodynamic transport or even include their own
24
custom time and space varying movement. On the other hand, all benthic state variables, such
25
as macrophytes (that need to be attached to a “benthic” grid cell), are always exempt from
26
hydrodynamic transport. Further detail on the concept of FABM is provided in Bruggeman
27
and Bolding (2014).
28
Besides PCLake, a series of large ecosystem models has been implemented in FABM. These
29
include representations of the European Regional Seas Ecosystem Model (ERSEM) and the
30
lake model Aquatic EcoDynamics (AED). But in contrast to PCLake, none of these include
31
higher trophic levels such as fish. FABM is written in Fortran2003 and therefore FABM-
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Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
Manuscript under review for journal Geosci. Model Dev.
Published: 3 February 2016
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Author(s) 2016. CC-BY 3.0 License.
5
PCLake is also implemented in Fortran2003. The key difference between the new FABM-
1
PCLake (Fig. 1) and the original PCLake conceptual model (e.g., Janse et al. 2010) is that
2
FABM-PCLake enables physical processes. Hence, a major advantage of FABM-PCLake is
3
that the detailed biogeochemical processes provided by PCLake can now be used to study
4
deep (i.e. stratifying) and spatially complex aquatic ecosystems. While the core of the overall
5
conceptual model of the PCLake “lake part” remains intact, the underlying mechanisms of
6
processes that relate to transport have changed. For example, while the resuspension of
7
detritus is derived from an empirical relation to lake fetch in the original PCLake (represented
8
by an arrow going from the bottom sediments to the water column in Fig. 1), resuspension in
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FABM-PCLake can now be derived from the actual bottom shear stress simulated by the
10
physical model. When implementing PCLake into FABM, a series of modifications relative to
11
the original PCLake model were made. This was done because some of the processes
12
parameterized in the original PCLake model can now be resolved explicitly by the
13
hydrodynamic models and the functionalities of FABM.
14
The main modifications are:
15
1) excluding the marsh module (as any two- or three-dimensional exchanges of solutes
16
can now be resolved by an explicit physical domain);
17
2) excluding the original loading, dilution and water level burial correction processes (as
18
this will now instead be resolved by the physical model and its boundary conditions);
19
3) excluding the original (and optional) forcing for dredging processes and fish
20
harvesting (as similar functionality is now provided through the state variable time
21
series forcing enabled by FABM);
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4) adding the option to make resuspension directly dependent on bottom shear stress
23
provided by the hydrodynamic model (in contrast to the original empirical
24
resuspension function, which was related only to the average lake fetch);
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5) extending the available options for describing light limitation functions for
26
phytoplankton.
27
To maintain the integrity of the original PCLake model, in terms of process rates that are
28
formulated on bases of daily average incoming light, we used the ability of FABM to provide
29
daily averages of photosynthetically active radiation (PAR) for the centre point in any given
30
water column cell. In total, the FABM-PCLake implementation comprises 57 state variables.
31
These include representations of oxygen dynamics, organic and inorganic forms of nitrogen,
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Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
Manuscript under review for journal Geosci. Model Dev.
Published: 3 February 2016
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Author(s) 2016. CC-BY 3.0 License.
6
phosphorus and silicon, three phytoplankton groups, one zooplankton and one zoobenthos
1
group, zooplanktivorous and zoobenthivorous fish (representing juveniles and adult fish,
2
respectively), piscivorous fish and submerged macrophytes (Fig. 1). A complete record of the
3
partial differential equations for each state variable can be found in the Supplementary
4
Material.
5
The code implementation involved a complete redesign and rewrite of the PCLake code into a
6
FABM compliant modular structure (see Fig. 2 and Supplementary material, supplementary
7
table S1), thus allowing FABM to acquire sink and source terms for each state variable
8
differential equation, and pass these for numerical solution and transportation by a physical
9
host model. By implementing the model in FABM, one can now combine different ecosystem
10
modules from different biogeochemical models available in FABM to suit the study purpose
11
(such as running the phytoplankton module from the AED model together with the
12
zooplankton module from the PCLake model to simulate the ecosystem for a particular case
13
study). Another important FABM feature is the ability to undertake data assimilation at
14
runtime, where simulated state variables can be “relaxed” to values of observations that are
15
read-in during a simulation. Hereby, one can enforce certain components of the ecosystem
16
(e.g., macrophyte seasonality), while simulating other parts of the ecosystem dynamically.
17
The model code was divided into modules of abiotic, phytoplankton, macrophytes and food
18
web dynamics. These modules were further sub-divided into water column (pelagic) and
19
sediment (benthic) domains. Concurrently, we developed an auxiliary module for FABM-
20
PCLake to handle the overall system processes. The system processes will typically influence
21
several other modules, and include resuspension, sedimentation and burial. In PCLake, burial
22
is included as a process that can prevent a net increase of sediment material by burial of a
23
small layer of sediment, equally thick as the layer that had been added to it. This material is
24
considered as buried in the deeper sediment and lost from the system.
25
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3 Model verification
27
To ensure that all biogeochemical processes have been implemented correctly through the
28
equations in FABM-PCLake, we verified the model by running a benchmark test case against
29
the original PCLake model. Hence, we compared output from the original PCLake model
30
(zero-dimensional, using the OSIRIS version, i.e. a C++ executable called from a Microsoft
31
Excel shell) with that from FABM-PCLake model executed with a zero-dimensional driver.
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Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
Manuscript under review for journal Geosci. Model Dev.
Published: 3 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
7
The models were applied with identical model initialization and parameterization, and the
1
same forcing and boundary conditions in terms of inflow, water temperature, light and
2
nutrient loads for a 5 year period. The initial values for state variables and model
3
parameterization were taken from the original PCLake version, which has been calibrated
4
using data from 43 European lakes (Janse et al., 2010), most of which were Dutch lakes, but
5
also included a few lakes from Belgium, Poland and Ireland. To ensure comparability, we left
6
the Marsh module in the original PCLake model turned off, and used the simple empirical
7
resuspension function (this function remains as an optional function in the FABM-PCLake
8
model, while we also implemented a bottom stress driven resuspension process) in the
9
FABM-PCLake version. Moreover, for the purpose of the benchmark test, processes that are
10
not included in the new FABM-PCLake, such as water column burial correction, dredging
11
and fish harvesting, were turned off in the original PCLake model. We found that there were
12
only marginal differences between the outputs of the two model versions, which could be
13
attributed to small differences in the numerical solvers of the models (Fig. 3). We therefore
14
conclude that the new FABM-PCLake implementation provides corresponding
15
representations of ecosystem dynamics, relative to the original PCLake model.
16
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4 Model features and perspectives
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The FABM-PCLake model is now able to run with a selection of hydrodynamic models
19
(which can be simply selected by the user), covering zero-dimensional (included with the
20
FABM source code), one-dimensional (e.g., the General Ocean Turbulence Model, GOTM
21
http://www.gotm.net, and the General Lake Model, GLM
22
http://aed.see.uwa.edu.au/research/models/GLM) as well as three-dimensional (e.g., the
23
General Estuary Transport Model, GETM www.getm.eu, Modular Ocean Model, MOM -
24
http://mom-ocean.org and work in progress - Nucleus for European Modelling of the Ocean,
25
NEMO http://www.nemo-ocean.eu, and The Unstructured Grid Finite Volume Community
26
Ocean Model, FVCOM - http://fvcom.smast.umassd.edu/fvcom ) hydrodynamic models. A
27
major advantage of this development is that the detailed ecological processes provided by
28
PCLake can now be used to study deep and spatially complex aquatic ecosystems. In addition,
29
it becomes possible to study the concept of critical nutrient loading for spatial heterogeneous
30
aquatic systems. This is important because the concept of regime shifts in ecosystems is
31
widely acknowledged in science and ecosystem management, while the effect of spatial
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Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
Manuscript under review for journal Geosci. Model Dev.
Published: 3 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
8
heterogeneity on the occurrence of regime shifts is poorly understood (Janssen et al., 2014).
1
Other key features enabled by FABM are:
2
1) the ability to replace one or several of the PCLake modules (e.g., phytoplankton) with
3
that from another ecosystem model available through FABM (e.g., ERGOM, ERSEM
4
or AED);
5
2) the ability to force time series for some state variables (i.e., data assimilation) while
6
others are left fully dynamic (e.g., one could force time series of macrophyte biomass,
7
and look at the response of fish, zooplankton, phytoplankton etc.);
8
3) the ability to run multiple models in an ensemble (e.g., for inter-model comparisons).
9
Whether run as a zero-, one- or three-dimensional model application, the model executable
10
will generate an output file of NetCDF format (*.nc), which can be opened and manipulated
11
by a range of software packages (e.g, Matlab, IDL) and a range of free NetCDF viewers, such
12
as PyNcView (http://sourceforge.net/projects/pyncview). The latter provides an easy-to-use
13
graphical user interface (GUI) for creation of animations and publication-quality figures (a
14
screenshot of visualization of FABM-PCLake state variables is demonstrated in Fig. 4).
15
16
Code availability
17
The model can be compiled and executed on Windows, Linux, and Mac OS machines, and is
18
open source and freely available under the GNU General Public License (GPL) version 2.
19
Source code, executables, and test cases can be downloaded directly from http://fabm.net, or
20
as git repositories (updated information on how to download the code from git repositories as
21
well as compiling the code for different platforms is available from the FABM wiki at
22
http://fabm.net/wiki). Contact persons for FABM-PCLake model: Fenjuan Hu
23
(fenjuan@bios.au.dk), Dennis Trolle (trolle@bios.au.dk), Karsten Bolding
24
(bolding@bios.au.dk). Contact persons for the original zero-dimensional PCLake model: Jan
25
H. Janse (jan.janse@pbl.nl), Wolf. M. Mooij (w.mooij@nioo.knaw.nl).
26
27
Acknowledgements
28
This study was supported by CLEAR (a Villum Kann Rasmussen Foundation, Centre of
29
Excellence project), and MARS (Managing Aquatic ecosystems and water Resources under
30
multiple Stress) funded under the 7th EU Framework Program, Theme 6 (Environment
31
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
Manuscript under review for journal Geosci. Model Dev.
Published: 3 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
9
including Climate Change), Contract No.: 603378 (http://www.mars-project.eu). A.B.G.
1
Janssen was funded by the Netherlands Organization for Scientific Research (NWO) project
2
no. 842.00.009. J.J. Kuiper and L.van Gerven were funded by the Netherlands Foundation for
3
Applied Water Research (STOWA) project no. 443237 and the Netherlands Environmental
4
Assessment Agency (PBL).
5
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Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
Manuscript under review for journal Geosci. Model Dev.
Published: 3 February 2016
c
Author(s) 2016. CC-BY 3.0 License.
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1
2
Fig. 1. Conceptual model of FABM-PCLake (FABM, Framework of Aquatic biogeochemical
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Models; PCLake, the implemented aquatic ecosystem model). Key state variables of the
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FABM-PCLake biogeochemical model and the interactions between these (represented by
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arrows); and an illustration of how a physical model may now transport biogeochemical state
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variables through explicit physical processes.
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Author(s) 2016. CC-BY 3.0 License.
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Fig. 2. The modular structure of the FABM-PCLake code. Each square box represents a
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FORTRAN module of FABM-PCLake (and these modules are interacting/communicating
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through FABMto simulate the processes illustrated by arrows in Fig.1). The brown coloured
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boxes are related to the sediment domain and the blue boxes to the water column domain.
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Note that all modules may be applied for 0-D to 3-D spatial domains. A detailed description
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of the contents of each module is provided in the Supplementary Material.
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Author(s) 2016. CC-BY 3.0 License.
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Fig. 3. Key time series outputs from a five-year simulation by the original PCLake model
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(PCLake-Original), and the new FABM-PCLake model (FABM-PCLake), represented by dry
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weight of total phytoplankton biomass, dry weight of zooplankton biomass, dry weight of
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macrophytes biomass, and the concentration of phosphate in the water column.
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Published: 3 February 2016
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Fig. 4. Visualization of FABM-PCLake state variables in PyNcView, exemplified by a two
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year period simulated by a one-dimensional FABM-PCLake application of a 20 m deep water
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column. State variables to be viewed are simply selected in the left panel, and figures can be
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viewed, manipulated and saved in the right panel and as detached figures (a detached figure is
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exemplified by the temperature plot).
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Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
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Published: 3 February 2016
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Author(s) 2016. CC-BY 3.0 License.
... To provide users with a standardised and easy-to-use workflow for model application and evaluation of aquatic ecosystems, the Water Ecosystems Tool (WET) was introduced in 2017 as a plugin for QGIS 2. x. (Nielsen et al., 2017). The plugin operated on top of the coupled one-dimensional hydrodynamic-ecosystem model GOTM-FABM-PClake (Bruggeman and Bolding, 2014;Hu et al., 2016;Umlauf et al., 2005). However, recent efforts by Schnedler- Meyer et al. (2020) have made significant advances to the designated aquatic ecosystem model FABM-PClake, producing an update representing a new generation, still FABM-compatible (enabling coupling to multiple physical models), completely modularised aquatic ecosystem model that allows flexibility in terms of food web configuration. ...
... GOTM facilitates a one-dimensional representation of the physical domain by accounting for hydrodynamic and thermodynamic essentials in the water column related to vertical mixing (Burchard and Bolding, 2001), and WET, via state-of-the-art conceptual representations, mimics the most important biotic and abiotic functions in the aquatic ecosystem. WET is based on the FABM-PCLake model (Hu et al., 2016), which again is a further development of the original PCLake model by Janse and Van Liere (1995). For a detailed description of WET, its legacy, complexity and concept, please see Schnedler- Meyer et al. (2020). ...
... A new feature that we proudly present is the possibility of utilising parts of the new modular configuration concept characterising the aquatic ecosystem model WET by Schnedler-Meyer et al. (2020), enabling configuration of different model complexities for a given application. When creating a new QWET project, users may now choose between three different templates, i.e. conceptual representations, for the ecosystem (Fig. 2): Template 1, a simple nutrient-phytoplankton-zooplankton-detritus (NPZD) representation; Template 2, a representation equivalent to FABM-PClake (Hu et al., 2016), including nutrients, three phytoplankton groups, zooplankton, zoobenthos, piscivorous, zooplanktivorous and benthivorous fish and macrophytes; and Template 3, an advanced version of Template 2 expanded with an additional zooplankton group. Several other conceptual representations may be of relevance to tailor case-specific needs, and QWET supports inclusion of additional templates upon request. ...
Article
Full-text available
We wish to introduce QWET, a new version of the free and open-source QGIS plugin for the aquatic ecosystem model WET. QWET is as a graphical user interface for the application, evaluation and experimentation of WET. Several new features have been incorporated since its predecessor and, here, we demonstrate elements of the new plugin by applying it to Danish Lake Ravn. Among others, we compare model simulations against observations and describe how the scenario platform now supports scheduling of state-variable manipulation, which allows users to explore lake or reservoir restoration interventions such as biomanipulation and oxygenation. With QWET, we seek to aid practitioners who do not possess the sufficient technical expertise to operate a state-of-the art complex model system, such as WET, and thereby hope to facilitate a wider use and adaptation of aquatic ecosystem models.
... With the development of FABM-PCLake (Hu et al. 2016), it has recently become possible to investigate the effects of depth heterogeneity in shallow lake modelling by coupling FABM-PCLake with the one-dimensional (1D) General Ocean Turbulence Model (GOTM, [Burchard et al. 1999]) into a single combined 1D model (GOTM-FABM-PCLake). In this study, we set up, calibrated, and validated GOTM-FABM-PCLake against a comprehensive data set on shallow, eutrophic Lake Hinge, Denmark, covering a 14-yr period (two 7-yr periods for calibration and validation, respectively). ...
... FABM-PCLake is a dynamic lake ecosystem model describing nutrient dynamics and interactions between multiple trophic levels within the water column and the top sediment ( Fig. 3; Hu et al. 2016). The latest source code of FABM-PCLake was downloaded in May 2019 with the modification of optional percentage winter fish kill (data available online). ...
... The effects of temperature on macrophyte growth were modeled by two exponential functions representing effects of temperature on production and maintenance respiration. With a higher base value for FIG. 3. Conceptual figure of the aquatic ecosystem model FABM-PCLake adapted from (Hu et al. 2016). Key state variables of FABM-PCLake are shown and their interactions represented by gray arrows. ...
Article
Full-text available
In recent years, considerable efforts have been made to restore turbid, phytoplankton‐dominated shallow lakes to a clear‐water state with high coverage of submerged macrophytes. Various dynamic lake models with simplified physical representations of vertical gradients, such as PCLake, have been used to predict external nutrient load thresholds for such non‐linear regime shifts. However, recent observational studies have questioned the concept of regime shifts by emphasizing that gradual changes are more common than sudden shifts. We investigated if regime shifts would be more gradual if the models account for depth‐dependent heterogeneity of the system by including the possibility of vertical gradients in the water column and sediment layers for the entire depth. Hence, bifurcation analysis was undertaken using the 1D hydrodynamic model GOTM, accounting for vertical gradients, coupled to the aquatic ecosystem model PCLake, which is implemented in the framework for aquatic biogeochemical modeling (FABM). First, the model was calibrated and validated against a comprehensive dataset covering two consecutive seven‐year periods from Lake Hinge, a shallow, eutrophic Danish lake. The autocalibration program Auto‐Calibration Python (ACPy) was applied to achieve a more comprehensive adjustment of model parameters. The model simulations showed excellent agreement with observed data for water temperature, total nitrogen, and nitrate and good agreement for ammonium, total phosphorus, phosphate, and chlorophyll‐a concentrations. Zooplankton and macrophyte coverage were adequately simulated for the purpose of this study, and in general the GOTM‐FABM‐PCLake model simulations performed well compared with other model studies. In contrast to previous model studies ignoring depth heterogeneity, our bifurcation analysis revealed that the spatial extent and depth limitation of macrophytes as well as phytoplankton chlorophyll‐a responded more gradually over time to a reduction in the external phosphorus load, albeit some hysteresis effects still appeared. In a management perspective, our study emphasizes the need to include depth heterogeneity in the model structure to more correctly determine at which external nutrient load a given lake changes ecosystem state to a clear‐water condition.
... It contains both ecosystems, lakes and wetlands, and can simulate a well-mixed water body and the sediment top-layer. The model describes the dynamics of phytoplankton, macrophytes and a simplified food web including zooplankton, zoobenthos, zooplanktivorous fish, benthivorous fish and piscivorous fish and accounts for mass balances, represented by dry weight, nitrogen, phosphorus and silicon cycling between the various components of the ecosystem (Hu et al., 2016). A zooplankton group in PCLake is treated as a common biomass pool rather than being calculated separately for individual species. ...
... The general equations (Eqs. S1-S6) for the zooplankton groups are given in Appendix S2. Hu et al. (2016) linked the ecological model with a hydrodynamic model and the new FABM-PCLake allowed the simulation of hydrodynamic and biogeochemical processes in zero, one as well as three dimensions. Nielsen et al. (2017) provided a graphical user interface for FABM-PCLake, a plugin named "WET" developed in the QGIS platform. ...
Article
Zooplankton is an essential part of the simulation in ecological process-based models and rigorous calibration of the zooplankton module lacks relevant modeling research that can predict the response of zooplankton biomass to varied environmental factors. The paper therefore builds a one-dimensional lake ecology model PCLake, which quantifies the dynamic effects on zooplankton in small water bodies distinguished by lake size and eutrophication status in warming climates. Based on the main geometric characteristics among a series of shallow water bodies, we constructed three lake models, namely, a northern lake with a larger area (> 0.1 ha) in Poland (Lake NL), a northern lake with a smaller (< 0.1 ha) area in Poland (Lake NS), and a southern lake with the smallest area in Croatia (Lake SS). Data from 2017 to 2018, including water temperature, dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), chlorophyll a (Chl a), and zooplankton, were used to calibrate and verify models for three shallow water body categories and uncertainty analyses were carried out to support the credibility of our models. Further, to discuss the potential driving forces of environmental factors on zooplankton, we set up a series of scenarios in which atmospheric temperature and nutrient input were changed. Zooplankton are only considered as a common pool and therefore only how biomass varied can be obtained. Warming resulted in a decline of zooplankton in the lakes located in Northern Europe, with peak decreases in zooplankton biomass more than four times higher in Lake NS than in Lake NL. In addition, due to multiple nutrient loading scenarios, incoming nitrogen and phosphorus concentrations were found to have a huge impact on zooplankton biomass in Lake NL. Specifically, relative to the original eutrophic level, the average annual biomass of zooplankton increased by 90% with a 75% increase in organic nitrogen over the original eutrophic level and decreased by more than 50% with a 75% decrease in inorganic phosphorus. Hence, lake size characteristics should be taken into account in management and restoration as they may be synergistic with in-lake biological and abiotic processes under complex environmental forces.
... Model analysis may help to clarify the impact of heat waves on lakes (Mooij et al., 2007). In this study, we used the state-of-the-art complex, dynamic biogeochemical model FABM-PCLake (Hu et al., 2016) coupled with the onedimensional, hydrodynamic General Ocean Turbulence Model (GOTM) (Burchard & Bolding, 2001) to quantitatively elucidate the impacts of summer heat waves on water quality attributes in a temperate stratified lake in Denmark. Our hypotheses were that (1) heat waves would increase the internal release of nutrients, (2) increases of phytoplankton biomass and cyanobacteria would potentially become a more dominant feature of the phytoplankton community in summer, and (3) increase of the summer heat wave frequency hampers recovery towards the pre-disturbance ecological state. ...
... Originally, PCLake was a zero-dimensional ecological model for non-stratified shallow lakes (Janse & Liere, 1995), and it accounted for the combined nutrient-food web dynamics in a completely mixed lake (Aldenberg et al., 1995;Janse, 1997Janse, , 2005. However, with the development of FABM-PCLake (Hu et al., 2016), it is possible to connect a re-design of PCLake (FABM-PCLake) with a hydrodynamic model, thereby enabling reproduction of both the vertical and temporal variations of aquatic ecosystems and providing more accurate predictions of the physical transfer of matter (Zhang et al., 2013) as coupled aquatic hydrodynamic-biogeochemical models typically simulate more realistic ecosystem state changes (Robson, 2014). ...
Article
Full-text available
The global surface temperature has increased by about 0.74°C over the past 100 years, and the frequency of extreme weather has increased as well. We used the state-of-the-art complex, dynamic, mechanistic model GOTM-FABM-PCLake to quantify the impacts of extreme summer warming on a summer-stratified temperate Danish lake. Simulated values of all calibrated parameters (water temperature, DO, NO3, NH4, TN, PO4, TP and Chl.-a) agreed well with observed values over the whole calibration and validation period and generally exhibited the same seasonal dynamics and inter-annual variations as the monitoring data. A series of climate scenarios with different summer heat wave frequencies and duration were set up to quantify the effects on the ecosystem state of the lake. Our simulations showed that summer surface mean TN will decrease with rising summer heat wave frequencies, while summer surface mean TP and Chl.-a and the biomass and proportion of cyanobacteria will increase. Following a summer heat wave, the lake approached baseline conditions in the autumn, but with increasing frequency of heatwaves the recovery period increased. Our results suggest that compliance with existing legislation, such as EU’s Water Framework Directive, will become increasingly challenging in a future scenario with increased temperatures and more frequent heatwaves.
... GOTM-WET simulates the key ecosystem dynamics with a strong vertical heterogeneity, and can be linked to the HYPE model to evaluate the impact of catchments dynamics on the reservoirs. The ecological module is based on the PCLake model with a fully closed biogeochemical cycling (carbon, nitrogen, phosphorus) and a typical foodweb structure of temperate lake/reservoir ecosystems (Hu et al., 2016;Janse, 2005). GOTM-WET was calibrated and validated against a five-year monitoring dataset (2011-2015, in YR3 andYH3) preceded by a one-year spin-up period (2010). ...
Article
Full-text available
Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso- and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015-2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modelling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.
... Catchment models have been established for simulating terrestrial and in-stream processes to model P loading from watersheds (e.g., Wade et al., 2002;Jackson-Blake et al., 2017). Lake models have also been thoroughly validated to simulate lake thermal stratification (Imberger and Patterson, 1981;Goudsmit et al., 2002;Tanentzap et al., 2007;Perroud et al., 2009;Thiery et al., 2014) and biological processes (Saloranta and Andersen, 2007;Hipsey et al., 2013;Hu et al., 2016). In parallel, recent developments in sediment diagenetic modeling (Katsev et al., 2006;Couture et al., 2010;McCulloch et al., 2013;Katsev and Dittrich, 2013;Torres et al., 2015;Gudimov et al., 2016;Couture et al., 2016;Doan et al., 2018) have enabled the use of such models in a better understanding of lake nutrient cycling processes. ...
Article
Lake Auburn, Maine, USA, is a historically unproductive lake that has experienced multiple algal blooms since 2011. The lake is the water supply source for a population of ~60,000. We modeled past temperature, and concentrations of dissolved oxygen (DO) and phosphorus (P) in Lake Auburn by considering the watershed and internal contributions of P as well as atmospheric factors, and predicted the change in lake water quality in response to future climate and land-use changes. A stream hydrology and P-loading model (SimplyP) was used to generate input from two major tributaries into a lake model (MyLake) to simulate physical mixing, chemical dynamics, and sediment geochemistry in Lake Auburn from 2013 to 2017. Simulations of future lake water quality were conducted using meteorological boundary conditions derived from recent historical data and climate model projections for high greenhouse-gas emission cases. The effect of future land development on lake water quality for the 2046 to 2055 time period under different land-use and climate change scenarios were also simulated. Our results indicate that lake P enrichment is more responsive to extreme storm events than increasing air temperatures, mean precipitation, or windstorms; loss of fish habitat is driven by windstorms, and to a lesser extent an increasing water temperature; and watershed development further leads to water quality decline. All simulations also show that the lake is susceptible to both internal and external P loadings. Simulation of temperature, DO, and P proved to be an effective means for predicting the loss of water quality under changing land-use and climate scenarios.
... There are two main types of water eutrophication prediction modeling: mechanism based water quality modeling method [6] and water quality modeling method based on data. For the mechanism modeling method, the model has developed from simple model of single layer, single chamber, single component and zero dimension to multi-layer, multi chamber, multi-component and three-dimensional complex model, and has been gradually applied to pollution control and ecosystem management of rivers and lakes [7][8][9]. However, the mechanism of many systems in the real world is still unclear. ...
Article
With the acceleration of industrialization and urbanization, most lakes and reservoirs have been in eutrophication state. Eutrophication of water body will produce a series of environmental problems, among which cyanobacteria bloom is one of the most studied and seriously polluted problems. It is of great significance to effectively control the occurrence of cyanobacteria blooms by predicting and simulating the outbreak process of cyanobacteria blooms and accurately forecasting the relevant governance departments. However, there are two problems in the existing analysis of algal blooms: on the one hand, it is difficult to consider the impact of other factors on cyanobacteria blooms by taking chlorophyll concentration as the main influencing factor, and it is also unable to determine the relationship between various factors. On the other hand, only based on the field monitoring data research, lack of comprehensive analysis of the whole water area. The remote sensing image can reflect the change of the whole water area, but the traditional analysis method is difficult to deal with the massive remote sensing data effectively. In this study, eutrophication level was used as characterization index of cyanobacteria bloom, and the remote sensing image and its inversion map were taken as the main research data, and a new method of cyanobacteria bloom prediction based on four-dimensional (4D) fractal CNN was proposed. The prediction model uses 4D fractal CNN to extract the features of multi factor remote sensing images, capture the temporal and spatial characteristics and the interaction between multiple factors, and predict the eutrophication level of water body. In this study, a total of 216 remote sensing images of Taihu Lake Basin were selected from 29 groups with fine weather from 2009 to 2010 obtained by MODIS satellite. The simulation results show that the method proposed in this paper has excellent prediction performance, and the accuracy rate of 85.71% is better than that of common 3D CNN and 4D CNN models.
... D'autres modèles basés sur le modèle décrit par Vollenweider, mettent en relation les concentrations moyennes en nutriments, en chlorophylle α, la transparence de l'eau, en prenant des caractéristiques physiques des lacs comme la profondeur moyenne ou le temps de rétention des éléments (Canfield Jr et Bachmann, 1981;Jones et Bachmann, 1976;Larsen et Mercier, 1976;Reckhow et Chapra, 1983). Le modèle présenté ci-dessous (Figure 1.3) est le modèle PCLake développé dans les lacs peu profonds par Janse et al. (1997), et adapté par la suite dans de nombreuses publications (Hu et al., 2016;Janse et al., 2010;Mooij et al., 2007). Le principe de ce modèle est de créer un modèle dynamique entre les différents cycles des nutriments, le phytoplancton, les plantes aquatiques et le réseau trophique. ...
Thesis
Les étangs piscicoles sont des milieux souvent enrichis en nutriments dans le but d’accroître la productivité global du système afin d’augmenter la biomasse de poisson. De fortes concentrations en nutriments peuvent entraîner une eutrophisation de l’étang conduisant à une perte de la biodiversité de l’étang et à une dominance du phytoplancton. Ce changement peut être caractérisé par un seuil critique, appelé point de basculement, où un changement significatif de la richesse en espèces et/ou de l’abondance survient dans plusieurs groupes taxonomiques. Les points de basculement ont été déterminés dans les étangs de la Dombes et du Forez. (1) Dans un premier temps, les points de basculement ont été déterminés dans différent groupes taxonomiques grâce à trois méthodes statistiques différentes en utilisant cinq indices de diversité, ceci afin d’évaluer les meilleures méthodes d’analyses. (2) Dans un deuxième temps, les changements pluriannuels des points de basculement ont été évalués en relation avec les concentrations en nutriments, les plantes aquatiques et les conditions météorologiques. (3). Par la suite, les points de basculements chez les Odonates ont été déterminés en relation avec des gradients d’eutrophisation du système, la richesse des plantes aquatiques et le recouvrement végétal, et la production piscicole. (4) Pour finir, le rôle des différentes pratiques piscicoles dans les étangs comme la fertilisation, l’addition de nourriture artificielle, le chaulage, et la mise en assec des étangs, a été étudié en relation avec la production piscicole, la diversité en espèces des plantes aquatiques, et l’eutrophisation du système. Nos résultats ont montré une importante variation des points de basculement suivant les différentes méthodes statistiques et les indices de diversité utilisés. Pour tous les groupes taxonomiques, les plantes aquatiques se sont révélées être les plus influencées par l’eutrophisation dans les étangs piscicoles. Les points de basculement ont montré une importante diminution de la diversité en espèces des plantes aquatiques et du recouvrement végétal, liés aux concentrations en nutriments qui dirige la compétition entre les producteurs primaires, à savoir le phytoplancton et les plantes aquatiques. Les points de basculement sont donc liés directement aux deux équilibres stables de dominance des plantes aquatiques ou du phytoplancton. Toutefois, les points de basculements peuvent varier significativement suivant les années, principalement due aux conditions météorologiques qui surviennent au printemps. De plus, la diversité en espèces et l’abondance des Odonates ont montré être négativement influencés par une trop forte eutrophisation du système. Les points de basculement sont très importants pour les gestionnaires des étangs qui pourront ainsi gérer leurs étangs de manière à garder un milieu équilibré et ainsi apporter le moins d’intrants possibles. Nos résultats ont ainsi montré qu’une mise en assec est la pratique permettant d’optimiser au mieux le système : productivité piscicole élevée, richesse des plantes aquatiques élevée et faible concentration de chlorophylle α. Ainsi, pour atteindre un bon état écologique de l’étang, une mise en assec des étangs doit s’effectuer toutes les quatre à cinq ans. Par conséquent, il est ainsi possible de prédire grâce aux points de basculement, le moment où l’état d’équilibre de l’étang va basculer vers une dominance phytoplanctonique. Des points de basculement ont ainsi été déterminés dans le but de maintenir une forte diversité de plantes aquatiques et un fort recouvrement végétal, pour préserver une forte biodiversité des étangs et une bonne productivité piscicole.
Article
Full-text available
Eutrophication of lake ecosystems is a pervasive global environmental problem, exacerbated by urbanization, industrialization, and intensification of agriculture. Excess loading of the macronutrients nitrogen and phosphorus from a myriad of human activities in catchment areas has forced many lake ecosystems into turbid, eutrophic states from which natural recovery is slow. A wide array of tools is available to lake managers to accelerate the process of lake restoration. These range from external measures to reduce nutrient loading, to internal measures designed to either retain nutrients in sediments or remove them from the lake ecosystem. In this preface to the Special Issue Restoration of eutrophic lakes: current practices and future challenges, we briefly review the currently available approaches to lake restoration and assess their global prevalence through a literature survey. The results show that traditional restoration methods such as aeration and chemical inactivation of phosphorus by aluminum continue to be widely used and reported in the scientific literature. The popularity of biomanipulation appears to be in decline, while studies into newly developed, in-lake nutrient inactivation methods are expanding. Hypolimnetic withdrawal, on the other hand, remains a comparatively rare technique. The 16 original research articles in the Special Issue showcase the current state-of-the-art in knowledge of these approaches and their efficacy. We conclude by discussing the key relevant future challenges in the field of lake restoration science. Of these, the need to unite diverse fields of knowledge to develop quantitative models of lake ecosystem responses to restoration measures under a changing climate is paramount. We also emphasize the ongoing need for interaction between scientists, managers, and stakeholders in lake restoration.
Model Dev. Discuss., doi:10
  • Geosci
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-260, 2016
A general framework for aquatic biogeochemical Note that all modules may be applied for 0-D to 3-D spatial domains
  • J Bruggeman
  • K Bolding
Bruggeman, J. and Bolding, K. 2014. A general framework for aquatic biogeochemical Note that all modules may be applied for 0-D to 3-D spatial domains. A detailed description 7 of the contents of each module is provided in the Supplementary Material.