Web based map service VESINETTI to integrate data and models for participatory river basin management 5 th Nordic WFD meeting, Iceland

Transcription

1 Web based map service VESINETTI to integrate data and models for participatory river basin management 5 th Nordic WFD meeting, Iceland Olli Malve 1, Anita Etholen 1, Sanna Helttunen 3, Tuomo Kauranne 2, Matti Lindholm 1, Heikki Mäkinen 5, Timo Pyhälahti 1, Pasi Roti 2, Matti Silvennoinen 2, Vincent Westberg 4, Sari Väisänen 1 1 Finnish Environment Institute, 2 Arbonaut Oy Ltd, 3 The Association for Water and Environment of Western Uusimaa, 4 Centre for Economic Development, Transport and Environment in South Ostrobotnia 5 Päijät-Hämeen Vesijärvisäätiö Foundation

2 BACKGROUND Eutrophication poses the biggest threat to the Finnish inland and coastal waters, with highest nutrient loads from agriculture. Climate change may strengthen symptoms of eutrophication. At present, the planning of WFD programs of measures is based on data retrieval, analyses, decision and dissemination protocols which are not fully compatible and optimal for the cost-efficient and participatory implementation of WFD. AIMS The GISBLOOM project will demonstrate integrated VESINETTI web based map service which are compatible and optimal in the identification of cost efficient program of measures and in the engagement of public to participatory monitoring and management. MAIN EU POLICY(IES) TARGETED Water Framework Directive (2000/60/EC) Marine Strategy Framework Directive (2008/56/EC) Nitrates Directive

3 GisBloom project Participatory monitoring, forecasting, control and socio-economic impacts of eutrophication and algal blooms in river basin districts (LIFE09 ENV/FI/000569) Introduces, demonstrates and evaluates a web-based map service Vesinetti

4 VESINETTI web based map service Designed to improve understanding of algal blooms eutrophication and their responses to management measures nutrient load reduction climate change in lakes and coastal areas

5 VESINETTI (continued) Combines nationwide Data Models on Climate (data from Met inst) Hydrology (real time data and modelling) Land-use (corine, slices) Management measures (measure library...) Costs and benefits (measure monitoring, esitimations) Nutrient loads (point sources, diffuse sources) Water quality responses (LLR-model)

6 VESINETTI (continued) Facilitates participatory river basin management Provides integrated, web-based tools and map services for Monitoring of surface waters Ecological and cost-benefit analyses for most measures Real-time forecasts for algal blooming in river basins and around Finnish lakes on a daily basis. Selection of cost-efficient measures for river basin management plans in nine pilot areas

7 The themes of demonstration in nine pilot river basins (or lakes within the basins) 1. Conceptual modeling of different scenarios 2. Estimation of internal loading and corresponding effects 3. Estimation of non-point loading and corresponding effects 4. Modeling of recovery from eutrophication and forecasting of algal blooms 5. Identification of cost-efficient program of measures estimation of socio-economic impacts 6. Ecological classification based on model forecasts 7. Implementation of GIS services for monitoring of algal blooms 8. Implementation of integrated participatory Web services and tools for managers and stakeholders

8 PROJECT LOCATION Helsinki, Joensuu, Lahti, Lohja, Vasa, Tvärminne - Finland Demonstrations in 9 pilot areas: Lapuanjoki, Hiidenvesi, Pohjanpitäjänlahti, Pien-Saimaa, Vanajavesi, Säkylän Pyhäjärvi, Lahden Vesijärvi, Vantaanjoki river and -estuary, Karvianjoki, Temmesjoki BUDGET INFO Total amount: % EC Co-funding: 49,12 % DURATION Start: 1/10/2010 End: 30/9/ Olli Malve, SYKE

9 Data Management Scenarios Analyses GIS Data Base IMPLEMENTATION IN THREE STEPS Models and Web Services Ecological and Socio- Economic Forecasts and Impact Estimates Public Understanding, Motivation and Skills Climate Scenarios 1. Collation and Analyses of Data 2. Implementation of Models, Tools and Services 3. Demonstration in River Basin Management Cost-Efficient Implementation of Management Good Ecological Status and Usability of Water Body

10 10

11 Web based data dissemination tool 11 Map platform to publish pilot area data, project tools and results. Contains basemaps and categorized thematic map layers Clickable features with extra information Real-time data loading from multiple sources

12 Technology 12 Based on open source GIS and UI libraries Works in standard web browser Supports OGC (Open Geospatial Consortium) standards like WMS (Web Map Service) for raster map data WFS (Web Feature Service) for vector map data WPS (Web Processing Service) for server side processing like running models Multiple map layers can be viewed, also timelines.

13 Lake based info window Lake based info window gathers data together and contains: General lake information Categorized information about observations, operations and results of analysis Real time models, pre-defined scenarios and results. 13

14 Example: Defining input parameters for LLR (Lake Load Response) model 14

15 Example: Viewing results of LLR model 15

16 Hydromorfological information (name, lake type, mean depth, volume, surface area A) Hydrological information (outflow, Q) Water quality information (P and N concentrations (C P /C N ) and loads (L P and L N ), chl-a concentration C Chla ) INPUT Biological information (total algal biomass, new: selective fishing data) FROM GISBLOOM DATABASE FROM VEMALA MODEL C P / N LP N Q v A / NUTRIENT RETENTION Chapra s model s C P/N = concentration of TP and TN (μg L -1 ) L = nutrient load (kg d -1 ) Q = discharge (m 3 s- 1 ) v s = retention coefficient (m d- 1 ) A = surface area (m 2 ) CHLOROPHYLL RESPONSE Linear mixed effects model CChla_ijk β0ijk β1ijk log(c P ) β2ijk log(c N ) β3ijk log(c P ) log(c N ) fixedeffects uk+u1 j k u2 j k+u3 j k + v j k εijk random effectsof type random effectsof lakes error term PROBABILITY OF PHYTOPLANKTON BLOOMING Logistic regression model Logit(Pr) 0 1 log(c P) 2 log(c C Chla = chlorophyll a concentrations (μg L -1 ), log-scale β j = regression coefficients, fixed u i = regression coefficients, random Pr(y=1) = probability of bloom N ) OUTPUT Predicted TP, TN, Chlorophyll a concentrations Probability of bloom Required nutrient load reduction

17 TN load kg/d RESULT LLR Model Calculates the amount of external nutrient loading reduction that is needed to achieve good water quality in a lake Current nutrient loadings, target load and TP and TN loading reductions needed in lake Kirkkojärvi. Current load kg d -1 Target load kg d -1 Reduction kg d -1 % TP TN With 35 % reduction of TP load the totp concentration will get under Good/Moderate target level. With 31 % reduction of TN load the totn concentration will get under Good/Moderate target level Chl-a estimates as a function of nutrient loadings Kirkkojärvi Chla with current TP and TN loadings 23.5 ug/l Chla (ug/l) Probabilities of achieving certain ecological status in lake Kirkkojärvi. Good or very good status Moderate, bad or poor status With current load -50 % TP -80 % TP 24 % 52 % 86% 76 % 48 % 14% Good/Mod 12 Poor/Bad TP load kg/d With current TP/TN loading this waterbody is at least in poor status with a probability of 76 % according to chl a level. With 50 % reduction of TP load the compliance would be achieved in 52 % probability and with 80 % reduction the probability would be 86 %. The TP/TN load combinations of the target level line would get the chl a concentration under WFD limit with 50 % confidence. LLR can be used in ecological classification

18 The KUTOVA+ model (under development) A spreadsheet model for determining the most cost-effective measures for phosphorus loading reductions on a catchment scale The main outcomes are cost-effectiveness of single measures the most cost-effective combination of measures estimated effect of the selected combination of measures on P loading total cost of the combination of measures

19 The KUTOVA+ model The results (1)

20 The KUTOVA+ model The results (2)

21 EXPECTED RESULTS Environmental: Decreased frequency and extend of algal blooms. Adaptation of management to climate change impacts. Outputs: Engagement of public to participatory monitoring and management Improved understanding and forecasting of algal blooms Identification of cost-efficient management measures Integrated, web-based tools and services Demonstration in the management of pilot river basins Acceptance of measures Contact informationt: Senior Research Scientist Olli Malve, Phone: Research Engineer Pia Rotko, Phone: Finnish Environment Institute P.O.Box 140, FI Helsinki

22 PROJECT S IMPLEMENTORS Coordinating Beneficiary: Finnish Environment Institute Beneficiaries (5): Arbonaut Ltd., Päijät-Häme Vesijärvisäätiö Foundation, The Association for Water and Environment of Western Uusimaa (LUVY), Zoological Station, University of Helsinki (Tvärminne), Centre for Economic Development, Transport and Environment in South Ostrobotnia. Partners: Local management projects together with associated stakeholders