Ecological dimensions in the SAF Why do we need the ecological dimension? Methodology and data inputs Outcomes of a study site application (SSA) Management implications SPICOSA Training Support Pack Material produced by Jakob Walve Presentation outline

Why do we need the ecological dimension? - There is an environmental problem to be solved! In SPICOSA, problems related to e.g. eutrophication and declining fish stocks are addressed. - We want to achieve sustainable development, with acceptable environmental impact and status, e.g. according to Water Framework Directive. In SPICOSA, e.g. the development of fisheries and mussel farming are explored. SPICOSA Training Support Pack Material produced by

Methodology: Systems thinking is the key Issue resolution: What is the problem? What are the objectives? System definition: System boundaries? Key ecological processes? Data needs / availability: What data are available or can be made available? What will available data allow or restrict? What new data can be collected? -The main idea is to make better use of existing data. Conceptual Model: description of relationships between system components, from expert knowledge. Forms the basis for the problem solving through numerical modelling. Often has to be simplified: problem scaling. Formulation and Appraisal, i.e. Mathematical and Numerical modelling: Inputs from data are modulated by ecological transformation processes, described by mechanistic (process) or empirical (relational) knowledge, in a modelling software. Usually the objective is to determine the response of a few system properties to certain management options. Important steps are Calibration and Validation of the model using data from the studied system. SPICOSA Training Support Pack

Issue resolution and System Definition Example of Policy Issue and important ecological processes illustrated with Overall Conceptual model: SPICOSA Training Support Pack From SPICOSA Deliverable D.3.2. SAF Protocol on CZ System Design (fig. 3.1) Loch Fyne, a 60 km long fiordic sea loch on the west coast of Scotland Example Policy Issue “Managing Loch Fyne so as to Maximize the Value of Ecosystem Goods and Services to the Local Economy”

Methodology: Systems thinking is the key Conceptual Model: an example SPICOSA Training Support Pack From SPICOSA Deliverable D.3.2. SAF Protocol on CZ System Design Example of ”fuzzy” conceptual model of phytoplankton growth The ”fuzzy” model has to be made more precise by identifying and distinguishing state variables (stocks), fluxes and their controls

Methodology: Systems thinking is the key Conceptual Model: example expanded to include a feedback loop SPICOSA Training Support Pack From SPICOSA Deliverable D.3.2. SAF Protocol on CZ System Design The conceptual model has been expanded to include a feedback loop: the recirculation of nutrients from grazers

Methodology: Systems thinking is the key Conceptual Model: expanded to include boundary conditions SPICOSA Training Support Pack From SPICOSA Deliverable D.3.2. SAF Protocol on CZ System Design The conceptual model is expanded to include boundary conditions The boundary condition will be quantified inside a ‘convertor’ rather than as a stock – because boundary conditions are outside the dynamic model and should not be changed as a result of what happens inside the model. Notice also that there has to be one exchange flow for every stock in the model.

Methodology: Systems thinking is the key Data needs/availability: identifying the data needs in the conceptual model for phytoplankton SPICOSA Training Support Pack From SPICOSA Deliverable D.3.2. SAF Protocol on CZ System Design Identifying data needs in the conceptual model for phytoplankton. In addition to this, there is need for calibration and validation data

Methodology: Systems thinking is the key Conceptual Model: simplified version SPICOSA Training Support Pack From SPICOSA Deliverable D.3.2. SAF Protocol on CZ System Design (fig. 8.2) The model often has to be or can be simplified. The model should be made as simple as possible, but still has to capture the essential dynamics of the system in relation to the objectives of the model

Methodology applied in a case study Issue resolution: Example Himmerfjärden SSA, Sweden SPICOSA Training Support Pack Himmerfjärden: A brackish estuary in the Baltic Sea. Problems: Reduced value due to relatively low water transparency, and loss of macrophytes. There is risk for cyanobacterial blooms if nitrogen loads are reduced. There is a general need to meet WFD requirements (ecological statsus is moderate to poor according to present classification). Export of nutrients to the Baltic Sea. Specific ecological questions: What can be achieved by different measures (STP, agriculture). What will be the effect of improvements in the open Baltic Sea? What is the effect of moved STP discharge point (potentially affecting availability of nutrients in different parts of the estuary)? 10 km Water area 232 km 2 Mean depth 17 m Maximum depth ~52 m Salinity 4-7 Freshwater from 9 brooks and diffuse runoff 10 m 3 s -1 From Lake Mälaren 7 m 3 s -1 From large sewage treatment plant (STP) 1.5 m 3 s -1 Open Baltic Sea

The model boundaries were defined according to the existing divisions into drainage basins. We chose this area to simplify the model. This is the most impacted area. The black dot indicate sewage treatment plant (STP) discharge. The boundaries for the ecological model are the shorelines, i.e we do not model drainage basin processes, but estimate load reductions according to various scenarios of land use System Definition: Example Himmerfjärden SSA, Sweden The sea area was divided into three sub-basins according to natural dividers (sills) and available data (sampling stations shown as red dots) The main fresh water input comes from Lake Mälaren through the city of Södertälje (most of Lake Mälaren discharge is through Stockholm). The large drainage basin of L.Mälaren is not shown here.

Conceptual Model: Himmerfjärden example, water exchange The water exchange conceptual model for the three sub-basins was first divided into only two depth layers, but was later developed into a three-layer model. This gave a more realistic model reflecting the actual sill depths between the basins. Still, of course, it is a simplification of the real world. The numerical water-exchange model is heavily data-dependent: Salinity data is used to calculate flows according to mass-balance. 0-7 m 7-13 m m 0-7 m 7-13 m RainLocal runoff STP 0-10 m m HAs HAm HAb NAs NAm NAb HIs HIm HIb Lake Mälaren runoff Baltic Sea Rain m m HallsfjärdenNäslandsfjärdenHimmerfjärden pr Box name Volume (Mm 3 ) Salinity (avg. for ) depth Legend for model:

SPICOSA Training Support Pack Version 2: Version 1: Conceptual Model: Himmerfjärden example, Ecology This version was the initial ecological conceptual model of the System Design step The link to water transparency Secchi depth was not shown, but was thought to be linked by empirical relationship with chlorophyll This is how the model was actually developed as a first version. The first operational version was however further simplified (next slide...)

SPICOSA Training Support Pack Nitrogen loading Total Nitrogen concentration Nitrogen retention Water exchange Secchi depth (water transparency) Conceptual Model version3: Model version3: Secchi depth is estimated according to empirical relationship between nitrogen concentration and Secchi depth

SPICOSA Training Support Pack Nitrogen loading Total Nitrogen concentration Nitrogen retention Water exchange Secchi depth (water transparency) Conceptual Model version3: with main links to socioeconomic model shown Model version3: with main links to socioeconomic model shown Secchi depth is estimated according to empirical relationship between nitrogen concentration and Secchi depth Cost estimation of load reductions Economic valuation of gains

SPICOSA Training Support Pack WE Input dataFW inflow calculation Nitrogen input data Nitrogen calibration data Nitrogen-data export function WE Const. Basin 1 Basin 2 Basin 3 Flow calculation Water balance Salt balance Salinity error calculation Nitrogen balance In practise: Overview of ExtendSim layout for Himmerfjärden Ecological model WE = Water exchange model FW = Fresh water Basin1 Salt balance Example

SPICOSA Training Support Pack Extend layout for Ecological model

SPICOSA Training Support Pack Variations in boundary conditions, nitrogen input and water exchange explain most of the variations in total nitrogen concentration. The biology added is a loss of nitrogen during the spring bloom, seen as a sudden drop in modeled nitrogen concentration (blue line) in spring. This model serves mainly one purpose: to calculate total nitrogen concentrations and from these the water transpareny (Secchi depth) Himmerfjärden example Hindcast (validation) results: Himmerfjärden example Loss of N during spring phytoplankton blooms

SPICOSA Training Support Pack Load from STP reduced from 10 mg/l to 4 mg/l Nitrogen loadTotal nitrogen concentration Secchi depth Total nitrogen reduced from 390 to 320 µg/l in Himmerfjärden, the largest basin Secchi depth increased from 2.8 to 3.5m To Socio- economic model The model will have to be developed according to the Conceptual model version 2 (or some other idea with a simpler model) to anwer questions about nitrogen fixing cyanobacteria and chlorophyll concentrations Scenario 1 1. Reference scenario: 10 mg nitrogen per liter from sewage treatment plant (STP) 2. Improved sewage treatment scenario: 4 mg N/ L from STP Results of two scenario runs in the preliminary version of the water-exchange/ecological model Scenario 2 STP Nitrogen loads to the model basin Himmerfjärden

Numerical modelling: Lessons learnt -Start simple: construct “Ball-park model” that works (is possible to run) and that is successively developed to a more advanced stage with tests at each stage -Save new versions, and document the changes (at least briefly) SPICOSA Training Support Pack

Management implications The model can be an important tool, but since it is a simplification, and has certain objectives, it cannot answer everything. It may be more or less uncertain depending on how far scenarios are taken. The model will most likely be one decision support tool among others! The most important “tool” is a good general and expert knowledge of the system! The model will not replace this! The model may highlight certain data needs. The model may reduce data needs, but more likely it will be helpful in prioritizing which data to collect. Model may give results that the model does not itself answer how to handle, e.g the costs for a Secchi depth improvement are higher than calculated gains, but may partly result from the fact that qualitative benefits may be difficult to value. Or that measures reducing eutrophication also decrease yield of fisheries. Or that banning of commercial fisheries in favour of tourist fishery may result in higher profits, but may be politically difficult. SPICOSA Training Support Pack