Mathematical models in predicting the fate of Lake Pyhäjärvi waters

Transcription

1 Mathematical models in predicting the fate of Lake Pyhäjärvi waters Pori Timo Huttula, Inese Huttunen, Olli Malve, Anita Pätynen and Janne Ropponen SYKE

2 Recent projects and modelling (besides our present project) Benchmark Models for the Water framework directive (EU-BMW, ) Remote sensing and measuring technology in coupling of process-based catchment and lake models (CatchLake) 1 & 2 projects (TEKES, ) LakeState and LLR development (ME, ) Models in support of river basin management planning (Life+ Gisbloom, ) Adaptive strategies to Mitigate the Impacts of Climate Change on European Freshwater Ecosystems (EU REFRESH, ) Models in solving aquatic problems (SA, )

3 Modelling philosophy: the chain of models approach Catchment model Process model Habitat model User interface Baltic sea model Groundwater model Lake model River model Coastal zone model Water flows through the different models and tracers flow along with the water. Process models can act on the tracers, or calculate small scale flow, and e.g. habitat models depend on the other models Catchment model: WSFS-Vemala, INCA, SWAT, SOBEK River model: SOBEK, River2D, COHERENS Habitat model: Delft HABITAT, River2D Lake model: COHERENS, MyLake, PROTECH, LLR Process models: biological models, sediment models, Elmer Coastal zone model: COHERENS Baltic sea model: COHERENS 9

4 WSFS-Vemala In on line hydrological forecasting system (WSFS) covering whole country Data download from Based on HBV water balance solver Several unique features (snow cover data assimilation, soil moisture content simulation, ice cover of lakes and rivers, on line calibration etc..) TP loading - ICECREAM simulates each field in Finland TN loading VEMALA-N simulates for each 3rd level sub catchment 6 crop/land use classes VEMALA results are shown: for each water quality observation point available in Hertta-data base For each lake bigger than 1 ha

5

6 WSFS: Water balance components of PJ in 2013 and 2014

7 Seasonal shift in nutrient loading from Eurajoki to Baltic Sea Most important for the lake s ecosystem is seasonal shift in loading from spring flood peak to autumn-winter runoff events Discharge, m 3 month Seasonal shift in discharge Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TP loading - seasonal shift Note: these Figures contain net loading from Eurajoki catchment to the Baltic Sea TP loading, t month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

8 Simulated daily values of TP in Pyhäjärvi Mean annual concentration present : 26,60 ug/l climate change + business as usual (A1B) ,21 ug/l LLR worst case was about 24 ug/l Winter concentrations are higher in climate change run, because loading during the winter is increasing. Some unexplainably lower concentration in first year of simulation.

9 MyLake-model in Pyhäjärvi MyLake was employed to create daily thermal profiles and ice-break and ice-formation days for PROTECH model Calibration required tuning the wind sheltering effect and adjusting the melting snow albedo Pätynen et al Modelling the impact of higher temperature on the phytoplankton of a boreal lake. Boreal Environment Research 19:

10 Water temperature and ice conditions

11 PROTECH-model Phytoplankton RespOnse To Environmental CHange (Reynolds et al. 2001) PROTECH was applied to estimate the impact of higher temperatures on phytoplankton growth in lake Pyhäjärvi Pätynen et al Modelling the impact of higher temperature on the phytoplankton of a boreal lake. Boreal Environment Research 19:

12 LS-model Lake Box-model Targets for loading reduction Main processes between nutrient loading, algae and zooplankton Observed and fitted algal biomass concentrations The darker gray area correspond to 95% predictive limits of the fitted model. Solid line is the median algae concentration. Lighter gray gives 95% prediction limits for the observations. (Example year from BMW WP6 Delivery Report)

13 LakeLoadResponse (LLR) model tool Calculates the amount of external nutrient loading reduction that is needed to achieve good water quality in a lake. The reduction is calculated according to the Good/Moderate class boundary of TN, TP, chlorophyll a and/or phytoplankton biomass Models (statistical / steady state): Chapra s (1975) model for retention of total phosphorus and nitrogen Hierarchical linear chlorophyll a model (Bayesian approach) Logistic linear regression model for phytoplankton blooming (TP and TN) With Bayesian inference we can get more realistic assessment of the prediction uncertainty / Confidence of compliance of water quality standards The LLR model tool has been developed within the Finnish Environment Institute (SYKE) to ease the use of models in lake management planning. Helps identifying the needed cost-effective management actions. The model tool is freely available in the internet: 28

14 MyLake scenario results in PJ INCA-P and WSFS-Vemala results as input Intensive agriculture scenarios 1 and 2: No subsidies, Effetive agriculture, increase of oil crops by 25% Green scenarios 3 and 4: Nature conservation, change of 20% of arable land to forests 29

15 REFRESH: LLR scenario results LLR - probabilistic 0D lake model: relationship between external/internal N+P loading and ecological status Baseline/observed Pyhäjärvi Bad 0 % Poor 0 % Moderate 50.9 % Good 48.8 % High 0.3 % ECHAM5-KNMI Pyhäjärvi Bad 0 % Poor 0 % Moderate 62.7 % Good 37.2 % High 0.2 % ECHAM5-KNMI+LC1 Pyhäjärvi Bad 0 % Poor 1.7 % Moderate 86.3 % Good 11.6 % High 0.5 % Status in changing climate and with different land use scanerios Chl-a ug/l ECHAM5-KNMI+LC2 Pyhäjärvi Bad 0 % Poor 1.7 % Moderate 84.7 % Good 12.9 % High 0.7 % Chl-a ug/l ECHAM5-KNMI+LC3 Pyhäjärvi Bad 0 % Poor 0 % Moderate 16.2 % Good 71.1 % High 12.7 % Chl-a ug/l ECHAM5-KNMI+LC4 Pyhäjärvi Bad 0 % Poor 0 % Moderate 27.5 % Good 71.3 % High 1.2 % Target: 7 ug/l chla Chl-a ug/l Chl-a ug/l Chl-a ug/l 30

16 Baseline SMHI-BCM Measure 40% incr. in winter vegetation Future climate CC KNMI Future climate CC KNMI + measure Worst case CC KNMI + LC1 Worst case CC KNMI+LC1+measure Best case CC SMHI-BCM+LC4 Best case CC SMHI-BCM+LC4+measure TotP concentration ug/l REFRESH: LLR results 30 P concentration distributions 25 Target P 18 ug/l The LLR model results indicate that we can be almost certain that in the best (worst) case scenario the WFD compliance is (not) met

17 COHERENS modelling tool COupled Hydrodynamical Ecological model for REgioNal Shelf seas, RBINS-MUMM, Belgium 3D finite difference, s-layer, multi-purpose numerical model designed for application in coastal and shelf seas, estuaries, lakes and reservoirs Open source, available to the public since 2000 Multi-platform, extremely good documentation (1500+ pages) Modular design Physical core selectable simulation modes, dimensions, numerical schemes Flexible and expandable Biological/ecological module Sedimentation module Wave modules Tracers Processes Actively developed, constantly evolving (latest version V2.5.1 available since August/2013) Contact person in SYKE: Janne Ropponen 32

18 COHERENS-results Animation Model with 100 m*100 m cell size, 20 layers Steady SW wind 1.4 m/s Solar radiation computed SPM in mg/l River discharge as in 1993 Simulation starts Surface layer Depth integrated Results show Time and space scales Mesocale circulation Diurnal variation Transport features

19 Resuspension modelling in present project Wave measurements in L. Taihu and L. Pyhäjärvi Modification of SMB- equations, where wave length depends on fetch, wind velocity and water depth Wave induced bottom shear is dependent on wave length and water viscosity Good results with wave heights. Challenges with wave periods

20 Assessment tools for solving aquatic problems related to the pollution from agriculture Active in July 1, 2012 June 30, 2014 Funding: Academy of Finland and JSPS WP 1: Project coordination (months 1 24) WP 2: Atmospheric modelling (coupling with lake model) WP 3: Flow and wave measurements and modelling Task 1: Wave measurements and modelling Task 2: External and internal loading Task 3: Seto Inland Sea flow modelling Task 4: Data assimilation WP 4: Statistical analysis between land use and river water quality WP5. Dissemination of the project results OU/

21 Economic aspects included Example: GISBLOOM Pilot-areas

22 Conclusion A large number of different models have been applied for various purposes The give crucial information for the scientific and managerial use The active research collaboration by different actors has facilitated this activity Long data sets and special data collection campaigns have made it possible to apply models There are automated modelling runs and modelling results available Simple models for managerial use Advanced and complicated models for research purposes The vast majority of the models have been based on science approach in future also socio economic aspects will be included Lake Pyhäjärvi with is catchment is an excellent and productive example about focusing