AQUAREHAB – WP6 Ludek Blaha (RECETOX) Jean-Marc brignon (INERIS) Geraldine Ducos (INERIS) Broekx Steven (VITO-RMA) Campling Paul (VITO-RMA) Seuntjens Piet (VITO-RMA) Haest Pieter Jan (VITO-RMA) Jaroslav Slobodnik (EI) Corina Carpentier (EI) Nilsson, Bertel (GEUS) Troldborg, Lars (GEUS) Giuliano Di Baldassarre (IHE) Linh Hoang (IHE) Girma Yimer (IHE) Zhu Xuan (IHE) Ann van Griensven (IHE) 2ndAQUAREHAB Consortium meeting, Delft, NL January 14-15, 2010

AQUAREHAB Geraldine Ducos (INERIS) Linh Hoang (IHE) Pieter Jan Haest (VITO-RMA) Lars Troldberg (GEUS) Corina Carpentier (EI) Linh Hoang (IHE) Zhu Xuan (IHE) Seleshi Yalew (IHE) WP6

CONTENT WP 6 Objectives WP 6 Tasks WP 6 Deliverables WP 6 Results Pollution list Fate models Inventory of data on remediation measures Inventory of toxicological data Inventory of DSS systems Draft design of REACH-ER WP 6 Conclusions

OBJECTIVES The objective of WP6 is to develop a generic collaborative management tool ‘REACH-ER’ that can be used by stakeholders, citizens or water managers to evaluate the ecological and economical effects of different remedial actions on waterbodies. How important are innovative technologies compared to more conventional measures? Ecological: potentially large impact on local and/or basin scale, on substances which are problematic. Economic: cost-effective compared to conventional measures.

TASKS T6.1 Fate model integrating the fluxes of chemicals at river basin scale: T6.1.1 Odense T6.1.2 Scheldt river T6.1.3 Senne river T6.2. Ecological effect assessment of chemicals in river basins T6.2.1 Ecotoxicological database (MU) T6.2.2.Ecotoxicological database (MU) T6.3 Economical analyses of water quality remediation measures T6.3.1 Information from existing remediation measures T6.3.2 Information from “AQUAREHAB” measures T6.4 Integration of fate, effect assessment and economic analyses in a management tool REACH-ER T6.4.1 identification of the pollutant list T6.4.2 Conceptual design T6.4.3 Optimisation issues T6.4.4 Implementation T6.4.5 Application on Scheldt and Odense river T6.5 Rehabilitation Guidelines

DELIVERABLES D.6.1 (Month 12): Conceptual fate model framework D.6.1 (Month 18): Economic assessments methodology applied to the cases D.6.3 (Month 24): Coupled fate-ecological modelling framework for pollutants D.6.4 (Month 30): Coupled fate-ecological-economical modelling framework for rehabilitation technologies D.6.5 (Month 30): Management tool for rehabilitation technologies D.6.6 (Month 36): Guidance document on rehabilitation and restoration technologies D.6.7 (Month 42): Report on evaluation of management scenarios for rehabilition technologies for critical areas within the Scheldt and the Odense river

CONTENT WP 6 Objectives WP 6 Tasks WP 6 Deliverables WP 6 Results Pollution list Fate models Inventory of toxicological data Inventory of data on remediation measures Stakeholder analysis for DSS system Draft design of REACH-ER WP 6 Conclusions

POLLUTANT LIST Piet Seuntjes, VITO-RMA

Aquarehab substances POLLUTANT LIST DoW Surface water + groundwater Nitrate Pesticides Chlorinated aliphatic hydrocarbons (CAH) Other substances: BTEX, chlorobenzenes, metals, … Surface water + groundwater WP6: model substances

Selection of substances POLLUTANT LIST Selection of substances Criteria Aquarehab substance group WFD priority chemicals Registration period Presence in pilot river basins (Scheldt, Odense) Ecological relevance Data from other projects (Modelkey, Socopse, Score-PP, Footprint) Moderately sorbing compounds

Selection POLLUTANT LIST Green: suitable Orange: moderately suitable Red: not suitable

Selected substances WP6 POLLUTANT LIST Selected substances WP6 Nitrate Pesticides: Isoproturon, Simazine, Terbutylazine, MCPA, Bentazon, Glyphosate, AMPA, Mecoprop. The selection of the pesticides was done based on their occurrence in EU rivers, their presence on the market, their inclusion in the WFD priority substance list, their physical chemical properties (moderately sorbing), and the existence of physical and chemical data from other EU projects. Chlorinated aliphatics: trichloro-ethylene. This substance is considered the model substance for the CAHs. It was chosen because of the presence on the WFD priority substance list and information collected in the SCORE-PP project BTEX: toluene and benzene. These substances are representative for the BTEXs. They were chosen because of their revelance for groundwater pollution and their inclusion in the WFD list (benzene). Nonylphenol, DEHP: these substances were chosen for a dedicated study related to the Zenne river case where they occur and have a strong ecological relevance as evidenced in the Modelkey project. They are also included in the WFD priority substances list.

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FATE MODELING: Scheldt river Pieter Jan Haest (UA – VITO) Piet Seuntjens, Steven Broekx and Paul Campling (VITO)

FATE MODELING: Scheldt river Modelling tool: PCRaster Area: 20000 km² Pollutants: Nitrate (+Pesticide) Resolution: 1 km2 Time step: monthly Purpose of the modelling Fate for nitrate and pesticides Link to AQUAREHAB WP’s 15

Implementation FATE MODELING: Scheldt river PCRaster environmental modelling language Raster based GIS, suitable for distributed dynamic modelling Easy to modify code Easy to replace/update Series of maps Tables with temporal data Time series 16

Nutrient fluxes FATE MODELING: Scheldt river Preliminary results for nitrogen in the soil and groundwater: N storage in soil [kg/km2] N storage in shallow groundwater [kg/km2] N storage in deep groundwater [kg/km2] 17

Nutrient fluxes FATE MODELING: Scheldt river Preliminary results for nitrogen in the river network: N-load [kg/year] at the outflow point of the Scheldt basin 18

WP6 19

FATE MODELING: Senne river Claudio Avella (UNESCO-IHE/University of Milano) Girma Yimer (UNESCO-IHE) Ann van Griensven (UNESCO-IHE)

FATE MODELING: Senne river Modelling tool: SWAT + HEC-RAS Area: 1100 km² Pollutants: CAH, Nitrate Resolution: 30 m data Time step: daily Purpose of the modelling: Model the transport of pollutants from ‘Vilvoode-Machelen’ site -> REACH-ER View pollution from Vilvoorde-Machelen in relation to the urban pollution and operations of the WWTP’s of Brussels -> Scheldt model. Compute nitrate loads and nitrification/denitrification processes -> Scheldt model. Link to AQUAREHAB WP’s WP3-WP7: Vilvoorde/Machelen (remedial technology + MODFLOW model) 21

CASE STUDY: SENNE RIVER BASIN, BELGIUM FATE MODELING: Senne river CASE STUDY: SENNE RIVER BASIN, BELGIUM

CASE STUDY: SENNE RIVER BASIN, BELGIUM FATE MODELING: Senne river CASE STUDY: SENNE RIVER BASIN, BELGIUM Vilvoorde/ Machelen Area: 1011 km2 Average flow: 9 m3/sec Average velocity: 0.2 – 0.3 m/sec Receives waste-water from 1.4 mln of inhabitants: Land use Agricultural 48% Urbanised 38% Pasture 8% Forest 6% Soil Loam 5 models: SWAT: Rainfall-runoff, nitrate KOSIM: Sewer/WWTP in Brussels MODFLOW: Groundwater/contaminant flux HEC-RAS: Hydrodynamic river model downstream AQUASIM model for denitrification in river bed

FATE MODELING: Senne river SWAT MODEL SWAT (Soil and Water Assessment Tool) is a conceptual hydrological model that works on daily time step. It can simulate hydrological processes as well as water quality and sediment transportation and processes at soil phase, taking into account for weather conditions and land management. Upland Processes Channel/Flood Plain Processes

HEC-RAS MODEL FUTURE DEVELOPMENT A hydraulic model of the last streams of the river is being built. HEC-RAS model also include a water-quality module. The two models will be linked to have a global model of the river. FUTURE DEVELOPMENT Calibration/validation for the flows Run SWAT with sub-daily time step Calibration/validation for nitrate Link to Scheldt estuary model

Groundwater flow and Transport modeling (Girma Yimer) FATE MODELING: Senne river Groundwater flow and Transport modeling (Girma Yimer) DONE: The groundwater flow and Transport modeling of Vilvoorde-Machelen region has been carried out by VITO (Touchant, Bronders et al. 2007) and VUB (Boel 2008) TO DO: - Implementing transport models that incorporates multiple chemical and biological reactions (e.g. RT3D) at finer scale - Uncertainty and probabilistic risk assessment

FATE MODELING: Odense river Linh Hoang (UNESCO-IHE) Lars Troldborg (GEUS) Ann van Griensven (UNESCO-IHE)

FATE MODELING: Odense river Modelling tool: SWAT / MIKE-SHE-DAISY Area: ~1000 km² Pollutants: Nitrate, pesticides Resolution: 30 m data Time step: daily Purpose of the modelling: Compute nitrate and pesticide pollutions to the river Odense Evalute effectiveness of restored wetlands to reduce pollution to the river Link to AQUAREHAB WP’s WP1(+WP7): Removal of pesticides and nitrate 28

FATE MODELING: Odense river Area: approx. 1,050 km2, including 1,015 km of watercourse The River Odense, which is about 60 km long and drains a catchment of 625 km2, is the largest river Population: 246,000, 10% not serviced by sewerage system Monthly precipitation: 40 mm (April)- 90 mm (December/January) Soil type: clay soil (51%), sandy soil (49%) Land use: Farmland (68%), urban area (16%, woodland (10%) and natural/ semi-natural areas (6%)

FATE MODELING: Odense river Pressure on water quality Atmosphere Industry 25 WWTPs > 30PE 489 stormwater outfalls, 204 from combined and 285 from separate sewerage system WWTPs and stormwater outfalls Households 1870 registered farms in 2000 960 is livestock holdings Livestock density: 0.9 unit/ha Agriculture

FATE MODELING: Odense river Data collection No. Data Purpose 1 Catchment data (topology, geology, land use, soil map) Build catchment models 2 Meteorological data Input for catchment models 3 Hydrological data (discharge, groundwater head) Calibrate hydrological models 4 Water quality data Calibrate water quality models 5 Pollutant loadings from point sources (households, industries, WWTPs, etc) Inputs for water quality models 6 Diffuse source data (Agriculture and farming data)

FATE MODELING: Odense river Integrate wetland model in catchment models Build SWAT model Integrate in SWAT Integrate in DAISY-MIKE SHE Update SWAT model Update the existing DAISY-MIKE SHE model Compare the performance of 2 models

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Inventory of toxicological data: conceptual design Ludek Blaha, Karel Brabec, Martina Nešporová blaha@recetox.muni.cz Masaryk University, Faculty of Science, RECETOX (Research Centre for Environmental Chemistry and Ecotoxicology), www.recetox.cz

Ecotoxicological assessment CA model for community Species Sensitivity Distribution (SSD) One compound - organisms have variable sensitivities (example: diethyl phthalate, DEP)

Ecotoxicological assessment CA model for community Species Sensitivity Distribution (SSD) Diethyl phthalate - distribution of sensitivities (based on ECx, NOECs…) Frequency % species 100 % 50 % 0 % Increasing concentration Increasing concentration

Ecotoxicological assessment Species Sensitivity Distribution - APPLICATIONS 1) PROSPECTIVE – EQC definition 5 % can be lost (95% protected) Safe concentration

Potentially affected fraction (PAF) of community Ecotoxicological assessment Species Sensitivity Distribution - APPLICATIONS 1) PROSPECTIVE – EQC definition 2) RETROSPECTIVE – Relative risk evaluation Potentially affected fraction (PAF) of community 5 % can be lost (95% protected) Safe concentration Measured (modelled) concentrations

Ecotoxicological assessment Species Sensitivity Distribution – AQUAREHAB application (mixtures) PAF (risks) 0.60 0.35 0.25 (Modelled) conc. of 3 compounds: conc. 1 / conc. 2. / conc. 3 MIXTURE PAF – msPAF (multisubstance PAF) – e.g. dissimilar mode of action (response addition) msPAF = 1 – (1-0.6)*(1-0.35)*(1-0.25) = 0.8 => 80% of species will be affected

WP6 40

Economical assessment Geraldine Ducos (INERIS) Steven Broekx (VITO)

Economical assessment Concept design & planning of activities Inventory of data on existing remediation measures Question: Costs of AQUAREHAB measures????

WP6 43

Stakeholde consultation of DSS systems Steven Broekx (VITO)

Results of stakeholder consultation Potential end users at all levels: national, subbasin, administrations Check potential added value What do we need and what do we not need? General conclusions on consultation: It is time consuming and requires resilience. (up untill now: 6 presentations, 8 interviews,…). Not every administration is equally happy about an integrative approach esp. those who execute the projects (interference with own policy, cost-effective sollution could implicate a shift in budgets). If you want people to use it, they need to be involved from the start and have impact on outline.

What is not needed Stakeholder consultation We do not need: Additional work: new reporting demands, run complicated models New models doing the same things but different: “we have models for basic water quality parameters, water quantity (floods)”

What is needed Stakeholder consultation We do need: Estimate how far we reach in achieving different targets with certain measures. (compose and compare scenario’s) Support in prioritisation. (now: a lot of expert judgement) Impact of measures on different water aspects (biological quality and links to physico-chemical quality and hydromorphology), cfr. bufferstrips: a single aspect approach is disadvantageous Local vs. central reporting levels: handle different scales Upstream-downstream impacts Transparancy in data, indicate uncertainty is important. Possibility for users to insert data.

Conclusions for set up Stakeholder consultation A strict integration with models reduces added value. (no user interface embedded in models) Waterbody vs. river basin: both are needed. Take into account multi-objective impacts: quality (ecological + physico-chemical), quantity Modular: easy to extend DSS with other modules Transparency: possibility to go back to basic figures and possibility to insert own figures. Time frame: some measures take long time to be at full impact (decades)

Draft design of REACH-ER Piet Seuntjens (VITO) Paul Campling (VITO) Steven Broekx (VITO) Ann van Griensven (UNESCO-IHE)

REACH-ER (1) Driving forces Responses (2) (3) Pressures (4) State Impact Responses (1) (2) (3) (4)

REACH-ER Identification of components Drivers: List of pollutant, pollution maps (e.g. maps of pesticide use) Pressures (“Hazard” “fluxes): e.g.MODFLOW model State: Fate models (“Exposure”): e.g. SWAT/PCRASTER -> Covert time series to statistical descriptors -> Probabilities of concentrations at various locations Impacts: ecotoxicoligy “Risk” -> Species Sensitivity Distribution -> Potentially affected fraction (PAF) Responses: - WP7 models -> remedial measures database/rules Optimisation/Multi-criteria analysis

REACH-ER REQUIREMENTS Spatial visualisation (GIS) Shape files for river reaches, fluxes Time dynamic variability within a year changes over the years/decades Web-based: accessible over the internet (Uncertainties) Model independent (OpenMI compliant) 2002 2003 2004 2005

+ Link to groundwater + Link to models + Remedial measure database + Optimisation

Conclusions Starting of modelling for all cases Inventories for: Remedial measures Toxicological data DSS Systems Stakeholder consultation for DSS First design of DSS Planning of further activities

Questions/issues Link with other modelling activities (Odense wetlands, Vilvoorde/Machelen site)! Data (&tools) for pesticide fate modelling Several ecologically effective pollutants NOT modeled/studies in AQUAREHAB Upscaling issues, general conclusions Temperature dependence on ecotoxicology Estimation of benefits? Costs for AQUAREHAB measures? Use of existing DSS such as MODELKEY? Case specific versus generic data/rules

THANK YOU!!! Ludek Blaha (RECETOX) Jean-Marc brignon (INERIS) Geraldine Ducos (INERIS) Broekx Steven (VITO-RMA) Campling Paul (VITO-RMA) Seuntjens Piet (VITO-RMA) Haest Pieter Jan (VITO-RMA) Jaroslav Slobodnik (EI) Corina Carpentier (EI) Nilsson, Bertel (GEUS) Troldborg, Lars (GEUS) Giuliano Di Baldassarre (IHE) Linh Hoang (IHE) Girma Yimer (IHE) Zhu Xuan (IHE) Ann van Griensven (IHE)