Modelling agricultural nutrient loading from Finnish watersheds

Transcription

1 Modelling agricultural nutrient loading from Finnish watersheds Inese Huttunen, Markus Huttunen, Marie Korppoo, Bertel Vehviläinen, SYKE Maataloustieteen päivät Viikki, Helsinki

2 Contents VEMALA modelling system description ICECREAM model description ICECREAM validation at different scales (LOHKO-II, RavinneRenki) Agricultural loading results Summary and discussion LOHKO-II 2

3 Structure of the national scale nutrient loading model - VEMALA Main concept to simulate the system from the field to the Baltic Sea VEMALA model consists of several sub-models: Conceptual hydrological model Terrestrial models: Regression model relating concentration and runoff Catchment scale semi-process based N leaching model ICECREAM field scale process based model River transport model Lake nutrient mass balance model Model simulates N,P, SS daily concentrations and loads New VEMALA version simulates bioavailable fractions of nutrients - TOC, PO 4 3-, PP, Porg, NO 3-, NH 4+, Norg, phytoplankton, O 2 3

4 National scale data used in VEMALA meteorological data (daily air temperature and precipitation, FMI), hydrological data (daily discharge and water levels, SYKE), water quality monitoring data (SYKE), agricultural field data for all fields in Finland (from Soil testing laboratories (40%) or estimated from community level data): soil texture from Soil testing laboratories or Finnish Soil database, Slope from DEM (10mx10m) or from laser scanned topography (2m x 2m), Crop from field plot register for each year, P-test value from Soil testing laboratories, Amount of manure based on animal number for each community, Mineral fertilizer based on sold fertilizer in Finland annual point loads from the Compliance Monitoring Data System (VAHTI) Loading from scattered settlements from Built environment information system and specific loading per person

5 Top and bottom soil layers Transpira tion Evapora ation Water and nutrient flow in ICECREAM model Precipitation ICECREAM is a field scale model which simulates water, N, P cycle in the soil (based on CREAMS, GLEAMS) ICECREAM has three water flow components: Surface runoff (SCS curve number method) Macropore flow Infiltration through the soil layers Surf ace run off θ f1 θ w1 θ f2 θ w2 SCScurve Infilt rati on Percolati on 10 layres Infiltration through the micropores is simulated by Richard s equation Exchange between macro and micropores should be simulated Macr opore flow Drainage

6 Nitrogen processes in ICECREAM model N processes simulated are: mineralization, nitrification, denitrification, volatization, immobilization, plant uptake, fixation Mainly processes are by first order rate depending on the N fraction storage in the soil, and soil moisture and soil temperature coefficients Plant growth is simulated by growth day degree method Mineralization/immobilization of manure and plant residues, depends on C:N ratio 6

7 Phosphorus processes in ICECREAM P processes simulated: mineralization, immobilization, adsorption to soil particles related to clay content in the soil, P uptake depending on N:P ratios in the crops, transport with erosion, leaching with infiltration ICECREAM simulates transport of PP and DRP fraction, transport of both P fractions mainly happens with surface runoff and macropore flow 7

8 Validation at field and catchment scales - LOHKO and LOHKO-II projects ICECREAM model is validated against 1) field scale measurements and 2) river observations IN LOHKO project the field management data from farmers 183 fields with total area of 691 ha was collected Each field separately was simulated with ICECREAM model by using crop management data from each farmer In LOHKO-II sub-processes like N uptake, mineralization are validated. Also the costs of mitigation measures for farmers will be simulated by combined ICECREAM and Taloustohtori (LUKE) tool Forest loading Field management data from farmers Loading from fields VEMALA ICECREAM Field scale loading to farmers N,P concentrations for valiadation to sensor data 8

9 Yield growth development of N storage in the soil in ICECREAM model The N fertilizer coefficient is based on Michaelis-Menten formula, which describes the rate of the enzymatic reactions, by relating growth rate coefficient to the NO 3 concentration (or mass): coef N = v max NO 3,mass k m +NO 3,mass where NO 3,mass is NO 3 mass in the soil layer (kg ha -1 ), V max =1.8 is maximum value of growth coefficient, k m =20 is the NO 3 mass in the soil at which growth rate parameter is ½ of the maximum rate. 9

10 Yield, kg/ha Simulated and observed spring wheat yield with different N applications Yield data for spring wheat, barley and oat with different fertilizer applications (N=90 kg/ha, N=120 kg/ha, N=160 kg/ha) has been received from Hankkija oy Observed N step trial yield data for longer period ( ) has been provided by YARA Suomi oy to validate the combined effect of soil moisture and different N applications N=90 kg/ha N=120 kg/ha N=160 kg/ha Simulated, Elimäki Observed, Elimäki Simulated and observed spring wheat yield with different N applications for year 2016 at Elimäki test field (data from Hankkija oy) 10

11 DRP loading, g/ha/year PP loading, g/ha/yr Validation of TP loading in ploughing vs direct sowing Observed in Aurajoki field, clay soil, 8% slope, high P-test 24 Surface runoff is increasing in direct sowing Erosion is decreasing in direct sowing PP is decreasing in direct sowing Dissolved phosporus is increasing quite much in observed field Only one field of observations of direct sowing effect There is a serious need for more observation data of direct sowing effect! PP, observed PP, simulated DRP, observed DRP, simulated Puustinen, M., Koskiaho, J., & Peltonen, K. (2005). 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,

12 Providing field scale erosion and loading data to farmers The project provided field scale results to each farmer by mail in form of maps and tables N,P and erosion results for different crop management ploughing, reduced tillage, catch crop, direct sowing, different fertilizer amount Following crops are simulated spring wheat, barley, autumn wheat, fallow, grass crops are under development Numeric knowledge of P, N, SS loading helps farmer to chose the fields where the crop management changes are most needed Map of the simulated erosion kg/ha/yr for one field plot (management ploughing) 12

13 NO 3 leaching, kg/ha/yr Simulating NO 3 leaching during organic fertilizer applications (summer and autumn) Luke Maaninka group (Mari Räty, Perttu Virkajärvi et al.) has provided observed daily N,P and runoff data for grass plots on coarse soil for summer and autumn slurry applications ( ) NO 3 leaching is higher in autumn spreading both in observed and simulated cases NO 3 leaching during 2012 is extermely high, because 1) 2011 was perustamisvuosi, 2) higher soil temperatures in autumn, low soil frost depth etc. It is not explained only by higher runoff NO 3 leaching in slurry summer and autumn spreading NO3_summer_spr_sim NO3_autumn_spr_sim NO3_summer_spr_obs NO3_autumn_spr_obs 13

14 Runoff, mm NO 3 leaching, kg/ha/year NO 3 loading transported with surface runoff is low on coarse soils Only 11% of NO 3 load is transported by surface runoff from coarse soil grass field (simulated share is 8%) Surface runoff is 37% of total runoff (simulated share is 39%) For clay fields share of NO 3 load in surface runoff is higher (around 20%, Paasonen-Kivekäs et al., 1999) Also in RavinneRenki the aim is to provide field scale results to farmers NO 3 loading distribution between surface and lysimeter runoff NO3_surf_run_obs NO3_lysimeter_obs NO3_surf_run_sim Surface and lysimeter runoff distribution NO3_lysimeter_sim Surf_run_obs Lysimeter_obs Surf_run_sim Lysimeter_sim 14

15 Validation of field scale model at catchment scale (LOHKO, LOHKO-II) Continuous water quality measurement stations provide unique data sets for loading calculations and model validation Lepsämänjoki river continuous measurement data series are for NSE for TP loads is 0.86, Maximum TP loads are underestimated, NSE for TP concentrations is 0.68, maximum contratrations are quite well simulated 15

16 Real-time national scale nutrient loading system VEMALA model simulates the source apportionment of the nutrient loading TP loading from agriculture to the Baltic Sea is 42% (mean ) Nutrient loading results for also Baltic Sea sub-basins, also for each inland lake FI/Vesi/Vesitilanne_ja_ennusteet/Ravinnekuormitus Urban nutrient runoff 0 % Scattered settlements 20 % TP loading Point sources 4 % Atmospheri c deposition 2 % Agriculture 42 % Natrural background loading from forests 26 % Forestry 3 % Natural background loading from fields 3 % 16

17 Summary and discussion VEMALA/ICECREAM modelling system can be used to simulate agricultural nutrient loading on field, catchment and national scale and can be used also for the scenario simulations Model results have been widely used in the implemetation of WFD in planning river basin management plans by regional environmental centers In LOHKO-II and RavinneRenki project we provide field scale N, P loading results to the farmers Model simulates inflow of bioavailable fractions, besides the TP, TN, to the inland water bodies and coastal areas The model can use detailed information of each field, if the information is available (exact fertilizer application, soil textures, crop rotations, management dates) Scale: the field scale is chosen as a simulation unit, because agricultural mitigation measures requires to be simulated and implemented at a farm level Uncertainty must be considered, uncertainty is caused by process decsription uncertainties, model parameter uncertainties (each field is a challange), uncertainty in input data (fertilizer amount unknown) 17

18 Thank you! 18