Deliverable. Due date of deliverable: Actual submission date:

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1 LIFE + Environment Policy and Governance Project name: Participatory monitoring, forecasting, control and socio-economic impacts of eutrophication and algal blooms in River Basin Districts (GISBLOOM) Project reference: LIFE09 ENV/FI/ Duration: October 1 st, 2010 September 30 th, 2013 Deliverable Sub-action 3.1 Report on the production of water quality and algal blooming forecasts for the public with the WSFS-VEMALA- LakeState model and the forecast production system Beneficiary: Finnish Environment Institute (SYKE) Contributors: Markus Huttunen, Inese Huttunen, Vanamo Seppänen, Marie Korppoo, Bertel Vehviläinen(SYKE) Due date of deliverable: Actual submission date: With the contribution of the LIFE financial instrument of the European Community Page 1/26

2 Content Introduction... 3 Methodology... 3 Phosphorus model development... 3 Nitrogen model development... 6 Nutrient and chlorophyll-a forecast production Delivery of the forecasts Examples of the forecasts References Appendix 1: Nash Sutcliffe coefficients RR for chlorophyll-a simulation Page 2/26

3 Introduction The WSFS-Vemala model simulates hydrology and water quality for all river basins in Finland. The simulated water quality parameters are total phosphorus, total nitrogen and suspended solids. The model simulates on a daily time-step nutrient leaching from land areas, incoming loads to each one hectare or larger lakes, nutrient transport in rivers and finally loading into the sea. Each agricultural plot is simulated separately taking into account slope, soil type and plant of the field. The model is used for real time simulation and forecasting of water quality. In the action 3.1 the WSFS-Vemala model and LakeState chlorophyll-a simulation model were integrated. The resulting integrated model is run daily for providing chlorophyll-a forecasts for all lakes about, which includes all 1 ha and larger lakes in Finland. A set of important and well observed lakes was selected among all lakes and forecast for those lakes are provided for the public. The nutrient and chlorophyll-a forecasts for all lakes are provided for experts in the user interface of the integrated WSFS-Vemala-LakeState model. Methodology The work in action 3.1 includes the further development of the WSFS-Vemala model for phosphorus and nitrogen simulations, the integration of the WSFS-Vemala with the LakeState model, the production of nutrient and chlorophyll-a forecasts and the delivery of the forecasts to the experts and to the public. In the WSFS-Vemala model phosphorus simulations were developed further by integrating WSFS-Vemala with the field-scale Icecream model, which was also further developed from its original version. For the nitrogen simulation, WSFS-Vemala was developed further towards a process based model including nitrate (NO 3 ) and organic nitrogen. WSFS-Vemala was then integrated with the LakeState model. WSFS-Vemala produces simulated and forecasted nitrogen and phosphorus concentrations in the lakes and based on these the LakeState model simulates chlorophyll-a concentrations in the lakes. Chlorophyll-a concentrations are used as an indicator for the risk of algal blooms. With the integrated WSFS-Vemala-LakeState model daily nutrient and chlorophyll-a forecasts were produced for all lakes. Forecasts for a selected set of lakes are provided to the public via several interfaces and the forecasts for all the lakes are provided only for experts via the user interface of the model. Phosphorus model development Phosphorus (P) is simulated with the ICECREAM model, which has been coupled with WSFS- Vemala. ICECREAM is a field-scale model, simulating nutrient leaching from arable land. It is based on CREAMS (Knisel 1980) and GLEAMS (Knisel 1993) models, applied to Finnish conditions by Rekolainen and Posch (1993), and further developed for simulation of phosphorus leaching by Page 3/26

4 Tattari et al. (2001), Yli-Halla et al. (2005), Bärlund et al. (2009) and Jaakkola et al. (2012). A schematic presentation of the phosphorus transformations in the model is shown in figure 1. Figure 1. Simulation of phosphorus flows in the ICECREAM model. In order to better simulate leaching of phosphorus from the 1,100,000 field plots in Finland, some equations in ICECREAM have been modified to statistical equations representing Finnish fields. The initial inorganic P content of the soil is calculated according to the following equation: (1) where soil inorganic P is in mg kg -1, Plab is labile phosphorus in mg kg -1 and clay% is the percentage of clay in the soil. The change in the soil P-test value over a year, which is used for calculating the balance between different P pools is calculated as follow: (2) where STP is the soil P-test value (mg kg -1 ) at the end of the year, STP 0 is the soil P-test value at the beginning of the year and P bal is the change in the total P content of the soil during the year (kg ha - 1 ). The dissolved P concentration in surface runoff is calculated according to equation 3 and depends on the soil P-test value. (3) where Pw is the phosphorus concentration in surface runoff (mg L -1 ). The materials for deriving equations 1, 2 and 3 are shown in table 1. Page 4/26

5 Table 1. Data for deriving equations 1-3. Use of the data Data References Equation 1: Initial inorganic phosphorus content in the soil Equation 2: Change in the soil P-test value over a year Equation 3: Dissolved P concentration in surface runoff Total inorganic phosphorus, acid ammonium acetate extractable phosphorus, and clay content of 23 mineral soils in Finland Change in soil P-test values during 8-14 years lasting field trials including 18 non-fertilized fields, removal of P in crop yield Acid ammonium acetate extractable P from 15 Finnish mineral soils, dissolved P concentration in runoff from rainfall simulations of the soils Peltovuori 2002, Saarela et al Saarela et al. 2003, Saarela et al Uusitalo and Aura 2005 For coupling ICECREAM with WSFS-Vemala the hydrological simulation needed to give similar results in both models. For this purpose, the total evapotranspiration for ICECREAM is now partly calculated by WSFS-VEMALA. During the growing season transpiration and evaporation are calculated by ICECREAM but corrected with a correction factor in order to reach the same yearly total evapotranspiration as in WSFS-VEMALA. Also snow water equivalent and precipitation / snow melt are inputs from WSFS-VEMALA. The effect of soil frost has been emphasized by reducing the soil infiltration during the frost period. Further minor changes have been made to the model. These changes affect the length of the frost period, the functioning of soil macropore flow, the movement of labile phosphorus in the soil, the growth of winter wheat and grass, the erosion of grass covered soil and the calculation of plant transpiration. For calibration and development of the ICECREAM model, experimental data from four Finnish agricultural fields has been used. These experiments have been published by Turtola and Kemppainen (1998), Koskiaho et al. (2002), Puustinen et al. (2005), Uusitalo et al. (2007) and Turtola et al. (2007). Most of the parameters used are adopted from the previous versions of ICECREAM (Tattari et al. 2004, Bärlund et al. 2009). The ICECREAM model is originally designed for simulating mineral soils only. However, in some pilot areas peat is a common soil type of agricultural fields. For example, in Lapuanjoki and Karvianjoki areas, a fourth of the arable land is peat. For this reason, it is necessary to be able to simulate phosphorus loading also from organic fields. ICECREAM s extension for simulating peat soils is under development. Page 5/26

6 Nitrogen model development In this project, a new version of the nitrogen model, VEMALA-N, was developed. In the new model, nitrate (NO 3 - ) and organic nitrogen are described separately. Ammonium (NH 4 + ) leaching is neglected in the VEMALA-N model at the moment. Ammonium loading represents a small fraction (around 6%) of total nitrogen (TN) loading on average from Finnish river catchments (Mattsson et al. 2005). Although, NH 4 + leaching is neglected, the NH 4 + storage in the soil is modelled and linked to organic nitrogen and nitrate in the soil. Nitrate is simulated using a semi-process based model. In the VEMALA-N model, six land uses / crop classes are defined: spring cereals, winter cereals, grassland, root crops, green fallow and forest. The nitrogen processes included in the soil model are mineralization, nitrification, denitrification, immobilization, plant uptake, fertilizer input and decay and nitrogen leaching. The schematic presentation of the VEMALA-N and conceptual hydrological model is presented in the Figure 2. Most of the nitrogen processes in the soil are simulated as first order kinetic processes that depend on the mass of the nitrogen fractions in the soil, soil temperature and soil moisture (Table 2). The most appropriate function relating mineralization and soil temperature is a logistic function, which has an S-shape as suggested by Dessureault-Rompre et al. (2010). According to this function, mineralization is low at low soil temperatures around 0 C, mineralization rises rapidly between 5 and 15 C to finally reach a plateau around 20 C. A parabolic function is used to describe the effect of soil moisture on mineralization as suggested by Myers et al. (1982) and Paasonen-Kivekäs et al. (2009). The maximum rate of mineralization is found around field capacity. In VEMALA-N, denitrification depends on the NO 3 - availability and soil moisture in the soil. Soil temperature is not included yet in this version, mainly to be able to reach high denitrification values during low temperature periods as reported by Martikainen et al. (2002).According to them, emissions of N 2 O (originating from nitrification and denitrification) during winter can be on average 57 % of the annual flux. Both mineral and organic soils have substantial N 2 O production close to zero temperatures. The immobilization process in the model varies with inorganic N storages in the soil (NO 3 - and NH 4 + ), soil moisture and soil temperature. However, Martikainen et al. (2002) reported that immobilization responded weakly to soil temperature changes between +0.5 C and +15 C. The growth of the plants biomass is related to air temperature sums over the vegetative season (Rekolainen and Posch 1993). Nitrogen uptakes by plants are simulated using a daily nitrogen demand taking into account daily plant biomass growth and soil moisture stress. Mass balances in the soil of NO 3 - and NH 4 + are simulated for each time step by the equations found in Table 2. NO 3 - concentration in the soil solution is simulated by assuming that all the NO 3 - storage is dissolved in the soil water of the simulated soil layer. The NO 3 - concentration in the groundwater is assumed to be constant in time, but different for agricultural and non-agricultural areas. Further developments of the VEMALA-N model include the simulation of ammonium leaching from agricultural and non-agricultural areas. Page 6/26

7 The organic nitrogen model was modified from the concentration-runoff relationship model to a model better linked to the conceptual hydrological model. Indeed, in the new version, subsurface and base flow are characterised by different organic nitrogen concentrations. VEMALA-N model simulates total nitrogen (TN) concentrations and loads on a daily basis for all Finnish catchments including Gisbloom pilot areas Example of the simulated and observed TN concentrations for Vantaanjoki are presented in Figure 3. Figure 2. Schematic presentation of VEMALA-N and hydrological model Page 7/26

8 Figure 3. Example of simulated and observed TN concentrations in Vantaanjoki ( ) Page 8/26

9 Table 2. VEMALA-N model processes Process Equation Reference Parameter Definition Value Mineralization Wade et al.(2002), Johnsson (1990) Page 9/26 M org k min k temp k soilm Mass of organic Nitrogen mineralization rate coefficient of temperature effect coefficient of soil moisture effect Nitrfication Wade et al.(2002) M NH4 Mass of Ammonium k nitr nitrfification rate Denitrification This study k denitr denitrification rate k soilmden coefficient of soil moisture effect for denitrification Immobilization NO 3 = Wade et al.(2002) M NO3 Mass of Nitrate k immobn immobilization rate of Nitrate Immobilization NH 4 = Wade et al.(2002) k immoba immobilization rate of Ammonium Soil temperature coef. k temp Q = Tsoil T , ift soil T 1 Rankinen et al. (2004) Q 10 change of rate with a 10 o C change in soil temperature This study T soil soil temperature kg ha -1 for agriculture 2500 k ha -1 for forest day -1 for agriculture day -1 for forest day -1 for agriculture, day -1 for forest day -1 for agriculture day -1 for forest 1.0 if relative soil moisture >80%, 0.0 if relative soil moisture 80% day -1 for agriculture for forest day -1 for agriculture for forest T 1 threshold temperature 20 C Soil moisture coef. Myers et al. (1982) b parameter of curvature 2.1 = ) soil moist soil moisture from hydrological model, mm wilting p soil moisture content at wiliting point mm field c soil moisture content at field capasity mm Biomass growth Rekolainen and B mat Biomass at maturity input data, kg/m 2 Posch (1993) w plant dependent growth parameter for cereals, 1.0 for grass, 3.0 for root crops NO - 3 plant uptake ( - ) Knisel (1993) C N Nitrogen content of the crop biomass input data, % NH + 4 plant uptake )( - ) Knisel (1993) k NO3 Ratio of plant uptake of NO This study k soilmplup coefficient of soil moisture effect for plant uptake

10 Process Equation Reference Parameter Definition Value NO - 3 fertilizer appl. Karvonen and Varis F undis,no3 NO 3 fertilizer in undissolved form = (1992) k d dissolution rate 0.15 day -1 NH 4 + fertilizer appl. = Karvonen and Varis (1992) F undis,nh4 NH 4 fertilizer in undissolved form NO - 3 mass balance = + This study Dep NO3 NO 3 atmospheric deposition input data NH + 4 mass balance = This study Dep NH4 NH4 atmospheric deposition input data Subsurface leaching This study PUW Plant unavailable water mm subs flow subsurface flow from hydrological model, mm US upper water storage from hydrological model, mm Groundwater leaching mg l -1 for agriculture, This study c gw NO3 concentration in groundwater flow = mg l -1 for forest gw flow groundwater flow from hydrological model, mm Page 10/26

11 Nutrient and chlorophyll-a forecast production Nutrient forecasts are produced for a 6 months forecast period. The weather input for the first 15 days is based on ECMWF weather forecast. The weather input for the rest of the 6 months period is based on historical climatology. The forecast period is simulated with 50 different weather inputs, which provide a variation of different possible weather patterns for the forecast period. After processing nutrient forecasts the LakeState model is run for each lake using the daily simulated total phosphorus and total nitrogen as an input. The LakeState model is a hierarchical, linear mixed effects regression model model and it gives probability limits for the simulated chlorophyll-a concentration (Fig. 4). The hierarchy of the model means that it uses both the data from the study lake and from the lakes of same type to make the predictions. The main basis for the usage of the hierarchical model is that lakes within the same type are assumed to have similar chlorophyll a response to changing nutrient concentrations. It is also assumed that data from one lake type covers a wider range of observation values than that from a single lake. The dataset used for constructing LakeState chlorophyll-a model parameter estimates consists of 2246 Finnish lakes with all together in-lake observations of growing season chlorophyll a, total nitrogen and total phosphorus concentrations from years 1990 to The dataset covers large scale of trophic states and different sizes of water bodies. See Malve 2007 for detailed model description. Over the historical period the probability range is due to the uncertainty in the parameterization of the LakeState model. Over the forecast period the probability range is due to the parameterization and the uncertainty in the phosphorus and nitrogen input concentrations (Figs. 5 and 6). The weather observations, temperature and precipitation are updated daily from the Finnish Meteorological Institute (FMI). The weather forecast from ECMWF is updated twice per day. As an input, the model uses also weather radar data from FMI and hydrological observations from SYKE. The nutrient and chlorophyll-a forecasts are updated daily and therefore they show also responses of sudden weather phenomena, such as heavy rainfall which brings nutrients into lakes and causes risk of algae bloom.

12 Figure 4: An example of the chlorophyll-a simulation. Figure 5: An example of the nitrogen simulation and forecast. Page 12/26

13 Figure 6: An example of the phosphorus simulation and forecast. Delivery of the forecasts The forecasts are delivered on several platforms in order to reach the public. The used platforms are Vesinetti, SYKE s hydrological forecasts www-pages and JärviWiki. Vesinetti (Fig. 7) is implemented in the GISBLOOM project and nutrient and chlorophyll-a results are presented there with other results from GISBLOOM. SYKE s www-pages for hydrological forecasts (Fig. 8) are traditionally used for delivering real time hydrological forecasts. The main page of the hydrological forecasts is visited over times per year and therefore also nutrient and chlorophyll-a forecasts have a good visibility there. JärviWiki (Fig. 9) is a site for general information on lakes and algal bloom observations are also provided. For the public, the forecasts are provided for a set of lakes, which are well observed and where the model simulation is reliable. For the experts, simulation results and forecasts are provided for all lakes on the user interface. In order to use model results for example for a small lake where no nutrient or chlorophyll-a observations are available, an expert evaluation of the goodness and reliability of the results is needed. Therefore not all results can be provided to the public automatically. This expert interface (Fig. 10) is the same user interface as for the hydrological forecasts. The user interface is already available for all users in the environmental administration and for about 200 users outside the environmental administration. Page 13/26

14 Figure 7: An example of the nutrient and chlorophyll-a forecasts in the Vesinetti. Figure 8: Main page of the SYKE's hydrological forecasts Page 14/26

15 Figure 9: Chlorophyll-a forecasts in the JärviWiki. Figure 10: User interface of the WSFS-Vemala model. Page 15/26

16 Examples of the forecasts In the following pictures there are examples of the chlorophyll-a simulations for the summer In these pictures, the grey background shows the historical variation and the violet line represents the daily mean of the historical variation. The mean of the year 2012 simulation is presented as a blue line. The green area shows the variation of 50% of the simulated values and the 90 % variation is presented in yellow. These pictures represent mostly the historical simulation, forecast is only provided at the end of November. Black dots represent observed values, in most of the cases observations are within 50 % variation limits of the model. In Appendix 1 the goodness of fit values are presented for the best simulated lakes. Page 16/26

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21 References Bärlund, I., Tattari, S., Puustinen, M., Koskiaho, J. Yli-Halla, M. & Posch, M Soil parameter variability affecting simulated fieldscale water balance, erosion and phosphorus losses. Agricultural and Food Science 18: Dessureault-Rompre,J., Zebarth,B.J., Georgallas,A., Burton,D.L., Grant,C.a. and Drury,C.F Temperature dependence of soil nitrogen mineralization rate: Comparison of mathematical models, reference temperatures and origin of the soils. Geoderma, 157, Jaakkola, E., Tattari, S., Ekholm, P., Pietola, L., Posch, M., Bärlund, I Simulated effects of gypsum amendment on phosphorus losses from agricultural soils. Agricultural and Food Science 21: Karvonen, T., Varis, E., Mathematical models in crop production. University of Helsinki Department of plant production: 218. Knisel, W., ed CREAMS, A field-scale model for chemicals, runoff, and erosion from agricultural management systems. Conservation Research Report 26. Washington, D.C.: USDA. Knisel, W GLEAMS: Groundwater loading effects of agricultural management systems. Version Publication No. 5. Athens, Georgia: University of Georgia, Department of Biological and Agricultural Engineering, Coastal Plain Experiment Station. Koskiaho, J., Kivisaari, S., Vermeulen, S., Kauppila, R., Kallio, K. & Puustinen, M Reduced tillage: Influence on erosion and nutrient losses in a clayey field in southern Finland. Agricultural and Food Science in Finland 11: Malve O Water quality prediction for river basin management. PhD. thesis, Helsinki University on Technology. 126 s. Martikainen, P.J., Regina, K., Syväsalo, E., Laurila, T., Lohila, A., Aurela, M., Silvola J., Kettunen, R., Saarnio, S., Koponen, H., Jaakkola, T., Pärnä, A., Silvennoinen, H., Lehtonen, H., Peltola, J., Sinkkonen, M. & Esala, M Agricultural soils as a sink and source of greenhouse gases: A research Page 21/26

22 consortium (AGROGAS). Finnish Global Change Research Programme FIGARE: ISBN Myers, R.J.K., C.A. Campbell, and K.L. Weier Quantitative relationship between net nitrogen mineralization and moisture content of soils. Can. J. Soil Sci. 62: Paasonen-Kivekäs, M., Peltomaa, R., Vakkilainen P., & Äijö, H Maan vesi- ja ravinnetalous ojitus, kastelu ja ympäristö: 452 p., ISBN Peltovuori, T Phosphorus extractability in surface soil samples as affected by mixing with subsoil. Agricultural and Food Science in Finland 11: Puustinen, M., Koskiaho, J. & Peltonen, K Influence of cultivation methods on suspended solids and phosphorus concentrations in surface runoff on clayey sloped fields in boreal climate. Agriculture, Ecosystems and Environment 105: Rankinen, K., Karvonen, T., Butterfield, D A simple model for predicting soil temperature in snow-covered and seasonally frozen soil: model description and testing Hydrology and Earth System Sciences 8 (4), Rekolainen, S. & Posch, M Adapting the CREAMS model for Finnish conditions. Nordic Hydrology 24(5): Saarela, I., Järvi, A., Hakkola, H. & Rinne, K Phosphorus status of diverse soils in Finland as influenced by long-term P fertilization 1. Native and previously applied P at 24 experimental sites. Agricultural and Food Science in Finland 12: Saarela, I., Järvi, A., Hakkola, H. & Rinne, K Phosphorus status of diverse soils in Finland as influenced by long-term P fertilization 2. Changes of soil test values in relation to P balance with references to incorporation depth of residual and freshly applied P. Tattari, S., Bärlund, I., Rekolainen, S., Posch, M., Siimes, K., Tuhkanen, H-R. & Yli-Halla, M Modeling sediment yield and phosphorus transport in Finnish clayey soils. Transactions of the ASAE 44(2): Page 22/26

23 Turtola, E. & Kemppainen, E Nitrogen and phosphorus losses in surface runoff and drainage water after application of slurry and mineral fertilizer to perennial grass ley. Agricultural and Food Science in Finland 7: Turtola, E., Alakukku, L., Uusitalo, R. & Kaseva, A Surface runoff, subsurface drainflow and soil erosion as affected by tillage in a clayey Finnish soil. Agricultural and Food Science 16: Uusitalo, R. & Aura, E A rainfall simulation study on the relationships between soil test P versus dissolved and potentially bioavailable particulate phosphorus forms in runoff. Agricultural and Food Science 14: Uusitalo, R., Turtola, E. & Lemola, R Phosphorus losses from a subdrained clayey soil as affected by cultivation practices. Agricultural and Food Science 16: Wade, A.J., Durand, P., Beaujouan, V., Wessel, W. W., Raat, K. J., Whitehead, P. G., Butterfield, D., Rankinen, K. and Lepisto, A., A nitrogen model for European catchments: INCA, new model structure and equations. Hydrol. Earth Syst. Sci., 6, Yli-Halla, M., Tattari, S., Bärlund, I., Tuhkanen, H.-R., Posch, M., Siimes, K. & Rekolainen, S Simulating processes of soil phosphorus in geologically young acidic soils of Finland. Transactions of the ASAE 48(1): Page 23/26

24 Appendix 1: Nash Sutcliffe coefficients RR for chlorophyll-a simulation Nash Sutcliffe model efficiency coefficients for the best simulated lakes. Maximum value of the coefficient is 1. Lake ID Lake name Chlorophyll-a RR Kuopasjärvi Iso-Petäinen Suuri Hietajärvi Kuusijärvi Savijärvi Saamainen Juumajärvi Palonselkä Matalajärvi Karhujärvi Toplanen Moksjärvi Mekrijärvi Kaljasjärvi Heräjärvi Kumpunen Poikkeusjärvi Kutemainen Villasenjärvi Kilpijärvi Saapunki Mallos Vehkajärvi Saarisjärvi Niemenjärvi Märkjärvi Suolajärvi Puolankajärvi Valkjärvi Pieni-Myhi Iso-Torava 0.23 Page 24/26

25 Lake ID Lake name Chlorophyll-a RR Gålisjön Ylimmäinen Ilomantsinjärvi Kirventeenjärvi Hämeenjärvi Rautajärvi Hunttijärvi Jaurakaisjärvi Iso Siikajärvi Kousanjärvi-Keskinen Näläntöjärvi Muonionjärvi Kytäjärvi Halijärvi Vessanjärvi Hepolampi Jäljänjärvi Lievestuoreenjärvi Saraavesi Humalalampi Koskio Alaset Halmejärvi, Lohnajärvi Kerteselkä Källträsket Haapajärvi Petäjäjärvi Huuhtjärvi Elämäjärvi Oijusluoma Poosjärvi Kanteleenjärvi Sonnanjärvi Iso-Koirajärvi Salajärvi Sääksjärvi Alajärvi Iso-Haaranen 0.09 Page 25/26

26 Lake ID Lake name Chlorophyll-a RR Vuojärvi Syntymäinen 0.09 Page 26/26