Map source: A. Engebretsen

Modellering av næringsstoff-transport i Morsa vassdraget: Kobling av modellers usikkerhet ved bruk av Bayesianske nettverk

Modelling of nutrient transport in the Morsa catchment: integrating model uncertainty using Bayesian networks

Alexander Engebretsen(UiO), Koji Tominaga(UiO), David N. Barton(NINA), Tom Andersen(UiO), Jostein Starrfelt(NIVA), Øyvind Kaste(NIVA) og Rolf D. Vogt(UiO) MILJØ2015 Parallel session 8 - Thursday 7 February 2013

Management measures

Nutrient run-off

Lake water quality

MyLake

SWAT

Presentation overview

Management context Catchment runoff modeling using SWAT Water quality modeling using MyLake Transdisciplinary communication using Bayesian network model Conclusions

Overview

Management challenge • ca. 1 billion NOK spent on measures for reducing total PO4 concentrations in Morsa catchment

• need to comply with WFD «good ecological status» • need to identify effectiveness of measures by modeling also confounding background trends and fluxes

Skarbøvik et al., 2011

To

tal f

osfa

t µg/

L

Vestre Vansjø

60% forest 14% agriculture

Presentation overview

Management context Catchment runoff modeling using SWAT Water quality modeling using MyLake Transdisciplinary communication using Bayesian network model Conclusions

Overview

Soil and Water Assessment Tool (SWAT) (Neitsch, Arnold et al. 2009)

•Process based •Dynamic •Runs on a daily time scale

•Distributed in space •Simulates changes in loading of pollutants to water based on farmer management practices

Integrating different spatial variation in nutrient pressure>state>impact

Hydrological responce unit (HRU) (land cover & soil type & slope)

Map source: A. Engebretsen

Sub-basin 438 in Huggenes creek

MYLAKE water quality model

SWAT run-off model

1. Calibrate discharge

2. Calibrate sediment loading

3. Calibrate Phosphorus loading

4. Run scenarios

Abatement actions modeled by SWAT

• Vegetated filter strips • Reduction in P fertilizing • Constructed wetlands • Point source removal • Changes in crop rotations • No autumn plowing • Etc.

Presentation overview

Management context Catchment runoff modeling using SWAT Water quality modeling using MyLake Transdisciplinary communication using Bayesian network model Conclusions

Overview

• MyLake Model – handles freezing – 1-dimensional (by depth) – physically based with simple

biology (algae growth)

• Empirical Model for WFD indicators

Lake modelling domain

Runoff

Lake physics & chemistry

Lake algae status

Model calibration is about fitting to the field observation

After calibration make simulations Consider all possible tunings for both SWAT and MyLake

Proper model connections, better simulation

Tota

l P (µ

g/L)

Ch

l a (µ

g/L)

The ‘naïve’ scenario with model parameter uncertainty (shown summer concentrations)

VFS effectiveness with all par. uncertainties

Presentation overview

Management context Catchment runoff modeling using SWAT Water quality modeling using MyLake Transdisciplinary communication using Bayesian network model Conclusions

Overview

Graphical synthesis of SWAT-MyLake simulation for «buffer strip» effectiveness: conditional

probability distributions

Mean summer total P (µg L-1)

Example 1: using Bayesian belief networks for deductive scenario analysis of «buffer strip» effectiveness

Example 2 – using conditional probabilities for inductive reasoning about measures

Current buffer strips are only marginally more likely to lead to low eutrophication than a situation without buffer strips (0m). 5m and 10m are equally effective

low eutrophication is unlikely in the summer months

low eutrophication is more likely in recent years

predictions are sensitive to model parameter uncertainty

Equally likely model parameters can lead to different water quality predictions

Suppose we predict very low eutrophication levels - what are the most likely explanations among confounding effects?

Presentation overview

Management context Catchment runoff modeling using SWAT Water quality modeling using MyLake Transdisciplinary communication using Bayesian network model Conclusions

Overview

SWAT MyLake TP, SS, Q

Best way to represent SWAT’s variation? - according to TP? - according to SS? Our strategy: find SWAT parameter sets that give MyLake extreme response: - total P loading total P in lake - SS loading total P in lake

Work in progress