Workshop on climatic analysis and mapping for agriculture Bologna, June 2005 Josef Eitzinger, Herbert Formayer, Mirek Trnka Andreas Schaumberger, Karl Buchgraber Institute of Meteorology Working Group Agrometeorology University of Natural Ressources and Applied Life Sciences (BOKU) ( In Cooperation with Institute of Grassland Management, Gumpenstein Grassland yield and drought impact modelling using GIS in Austria

Agriculture in Austria Land use : Grassland: ~ 1.9 Mio ha (22%) Arable land: ~ 1.4 Mio ha (17%) Forests: ~ 3.3 Mio ha (40%) Others: ~ 1.8 Mio ha (21%) Agricultural land use mostly limited by: topography, temperature soil conditions Precipitation

Fig. 1 : Distribution of managed grasslands in Austria

Background and aim of the project Increasing drought damage in Austrian grassland Need for agricultural drought monitoring of grasslands for insurance High spatial variabilities of impacting factors : need for high spatial resolution (field scale) Gap on quality and quantity of spatial data resources : need for simplified models Variability of management and growth conditions : Need for high time resolution (daily base) Operational application of the model

Why using simplified models ? Fig.2 : Uncertainties and model complexity

Fig.3 : Methodology of model system development

The model system - background

1. Soil crop water balance (FAO approach (Allen et al., 1998)

The FAO grass reference ET where ETo =grass reference evaporation-transpiration [mm d -1 ] Rn = net radiation at the crop surface [MJ m -2 d -1 ] G = soil heat flux density [MJ m -2 d -1 ] T = air temperature at 2 m height [  C] u2= wind speed at 2 m height [m s -1 ] Es= saturation vapour pressure [kPa] Ea= actual vapour pressure [kPa] Es-ea= saturation vapour pressure deficit [kPa]  = slope vapour pressure curve [kPa  C -1 ]  = psychometric constant [kPa  C -1 ]

Fig.4 : Actual evapotranspiration for water stressed conditions

a. Calculation of daily water balance of the upper soil layer (no transpiration assumed) : D e,i = D e,i-1 - [P i - RO i ] - I i /f w + E i /f ew + DP i D e, i-1 = potential cumulative evapotranspiration of the previous day D e,i = potential cumulative evapotranspiration of the actual day P i = precipitation RO i = runoff I i = irrigation E i = evaporation DP e,i = drainage F w = proportion of water infiltration at soil surface during irrigation F ew = proportion of wet soil surface during irrigation Crop water balance for multiple soil layers and crop water stressed conditions

b. Calculation of daily water balance of the rootet layer (only transpiration including crop water stress is considered) : D r,i = D r,i-1 - DP e,i - CR i + ET c,i + DP i whereas : D r, i-1 = soil water content at the previous day D r,i = soil water change of actual day CR i = capillary rise ET c,i = evapotranspiration DP e,i = drainage from upper layer (Eq. a) DP i = drainage downward

and : 0  De,i  TAW TAW = 1000 [FC WP] Z r RAW = p TAW ET c = K s K cb Et o K s = [TAW - Dr,i] / [TAW - RAW] (K s  1) where : TAW = total available soil water in soil layer FC = field capacity of soil layer WP = wilting point of soil layer Z r = soil layer thickness RAW = readily plant available soil water in layer p = proportion of TAW reduction till water stress occurs Et o = potential evapotranspiration [calculated by FAO Penman-Monteith or Hargreaves; Allen et al., 1998] K cb = basal crop coefficient [calculated, Allen et al., 1998] K s = coefficient of reduction of transpiration through crop water stress

Fig.5 : The soil water stress factor

Fig. 6 : Model testing results (site Gumpenstein)

2. The empirical grassland yield model GRAM (Trnka, Eitzinger, 2005) Fig. 7 : Combination of the FAO model and GRAM

Dry matter yield of meadows (optimized for specific regions) : GRAM Equation (example) : DM (kg/ha) = where N= nitrogen application D= duration of growing cycle Cut= number of grass cut Te = effective temperature Ge = effective radiation

Fig. 8 : Validation results of GRAM

3. Spatial interpolation of Precipitation, Radiation and Evapotranspiration on daily base Used meteorological Stations in Austria Fig. 10 : All meteorological data Red stations with radiation Fig. 10 : All meteorological data Red stations with radiation

Spatial interpolation of Precipitation On daily base no additional dependencies ( e.g. station height) can be found in the precipitation data. This can be explained by the effects of convective processes. Therefore a one step interpolation with Kriging was used. To get reliable results with this approach, a high station density and good data quality is essential. We used ~ 850 precipitation stations with an average distance of ~ 10 km. The yearly sums of the interpolated daily precipitation fields on each raster element show a similar height dependence as found in other studies in Austria. Therefore the station density seems to be high enough. To get better precipitation fields, additional information about sub scale variability on daily base should be used for interpolation (e.g. Radar-sounding). Fig. 11 : Precipitation and elevation

Spatial interpolation of Radiation Used Information  Radiation Station ~ 130  Digital Elevation Model 50 m Resolution  GIS Radiation Model „Solar Analyst V1.0“ Results from the Radiation Model Clear sky insolation for every day and DEM element including the effect of:  Inclination  Orientation  Height effect  Shading effects

Spatial interpolation of Radiation Interpolation scheme:  Calculation of the ratio clear sky insolation to flat lowland insolation (Iref) on every DEM-element for every day (Rpot).  Calculation of the ratio actual insolation to clear sky insolation at every radiation station and day (Ract).  Interpolation of Ract using Kriging.  Multiplying Ract*Rpot*Iref to get the actual insolation for every DEM-Element for each day. Fig. 12 : Result on daily solar Radiation distribution in Austria

Spatial interpolation of Evapotranspiration Interpolation scheme:  Calculation of grass reference evapotranspiration (ETr) based on Penman Monteith on all station with sufficient meteorological data (~ 130 Station)  Estimating ETr from average water vapour deficit with regression on other stations (~150)  Calculation a linear height dependence from all stations  Calculating the residuals from station data to linear height model  Interpolation of the residuals using Kriging  Applying the linear height model to the DEM  Add the interpolated residuals and the height model  Multiply the Result with Rpot

Spatial interpolation of Evapotranspiration Fig. 13 : Regression between daily average water vapour deficit and grass reference evapotranspiration (ETr) Fig. 14 : Height dependence of ETr in August 2003 in Austria estimated from all station data

Results of GIS implementation

Fig. 15 : Evaporation distribution of grassland April

Fig. 16 : Transpiration distribution of grassland April

Fig. 17 : Actual Transpiration distribution of grassland April

Fig. 18 : Soil water content of grassland April

Fig. 19 : GRAM water availability factor of grassland June

Fig. 20 : GRAM weekly development of grassland growth factor April 2003

Fig. 21 : GRAM long term water stress factor of grassland June

Fig. 22 : GRAM grassland dry matter yield, 2nd cut, 2003

Fig. 23 : GRAM grassland dry matter yield, total, 2003

Conclusions GIS implementation of a simplified grassland water balance and yield model showed reliable results on verification sites. Promises to be a powerful tool for agricultural drought impact detection (e.g. for drought insurance). There still remain several factors of uncertainty (interpolation methods, representativeness of soil and weather data, grassland composition and GRAM) Further improvement of soil input data, spatial precipitation will increase accuracity Further years of verification needed. Operational test application of the GISmodel starting with 2006 in Austria.

Thank you !