Comparison of spatial interpolation techniques for agroclimatic zoning of Sardinia (Italy) Cossu A., Fiori M., Canu S. Agrometeorological Service of Sardinia Viale Porto Torres, 119 – Sassari (Italy) Tel – Fax WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

Aims  Integrate the interpolated information in SAR Geographic Information System  Using GIS for agroclimatic and ecosystems characterization of Sardinia  Evaluate several methods for precipitation and temperature spatial interpolation in Sardinia WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

Reference activity Reference activity Experiences in COST 719 indicated the path that the authors have followed WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

The GIS SAR GIS dataset is based on: SOIL information: land use, high resolution DEM (20 mt), ecopedological data WEATHER information: climatic data for 250 thermopluviometric stations for a 50 years period. agrometeorological data with high temporal resolution for 60 stations from SATELLITE information: NOAA and LANDSAT images. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

A few information about Sardinia  Sardinia is the second Mediterranean island, with approximately 2.5 million hectares with 1.6 million people. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)  Sardinia is an ancient island and the long erosion processes caused the absence of high mountains. The territory is characterized above all by extended plateaus with elevation ranges between 300 and meters.  The intensive agricultural areas are localized in the plains of west coast, but the widest hilly areas are interested by vineyards (36000 ha), olive groves (40000 ha) and intensive livestock breeding (1.5 million hectares with 3.5 million ewes).

The Sardinian Environment A typical Agricultural Ecosystem in plain coast areas WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

The Sardinian Environment Husbandry Ecosystem in hilly areas WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

The Sardinian Environment Natural Ecosystem in mountain areas WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

The sea around the island is a source of climatic variation determining a coast-inland pattern. The latitudinal development of island (approximately 200 km) gives a climatic North-South pattern. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) The elevation is particularly important on the east coast where steep mountains near the sea trigger East-West autumn precipitation pattern. Sardinian climate patterns

Data and Methods 59 temperature stations for the 1961-’90 period 59 temperature stations for the 1961-’90 period 199 precipitation stations for the same period 199 precipitation stations for the same period Interpolation techniques: Interpolation techniques:  Inverse Distance Weighted  Ordinary Kriging  Co-kriging  Linear Multiregressive Analysis with Residuals Kriging WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) The statistic comparison has been made on: The multiregression has been applied on elevation, latitude, longitude and sea distance on 250-meter grid points. The residuals of the regression model has been interpolated by Ordinary Kriging.

Methods validation approach A selection of stations, based on qualitative parameters, have been divided in two sets:  A training set where the interpolation techniques have been applied to give the parameters of the interpolating functions;  A verifying independent set in order to evaluate the goodness of the above functions; The assessment of each method has been carried out through Root Mean Square Error (RMSE) and the Determination Coefficient r 2 between observed and predicted data for each station of the verifying set; WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

For temperature field the methods have been verified using 39 meteorological stations for training set and 20 stations for the verifying set. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Training stationsVerifying stations Temperature interpolation The selection of stations was made about a random process

Temperature interpolation MONTH KRIGINGCo-KRIGINGMULTIREGRESSIONIDW TMINTMAXTMINTMAXTMINTMAXTMINTMAX JANUARY FEBBRUARY MARCH APRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER Root Mean Square Error The lowest Root Mean Square Error has been obtained with Multiregression method WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

Temperature interpolation Determination coefficient and F significance (p=0.05) MONTH KRIGINGCo-KRIGINGMULTIREGRESSIONIDW TMINTMAXTMINTMAXTMINTMAXTMINTMAX JANUARY n.s n.s FEBBRUARY n.s MARCH n.s n.s n.s. APRIL n.s n.s n.s. MAY n.s n.s n.s. JUNE n.s n.s n.s. JULY n.s n.s n.s n.s. AUGUST n.s n.s n.s. SEPTEMBER n.s n.s n.s. OCTOBER n.s n.s n.s. NOVEMBER n.s DECEMBER The typical summer anticyclonic regime (high pressure stabilized on the entire island) makes temperature distribution dependent from microclimatological effects. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Multiregression shows also the most significative determination coefficients No method is able to represent the summer spatial distribution of maximum temperature

December maximum temperature WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Kriging r 2 =0.26 Co-Kriging r 2 =0.25 IDW r 2 =0.33 Multiregr. r 2 =0.84 The december maximum temperature interpolation exhibits the largest differences between multiregressive and the other methods. There is no significant difference between Kriging, Co-Kriging and IDW maps. The multiregressive method applied on geomorphological variables shows the highest determination coefficient and allows a high spatial resolution

WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) December maximum temperature interpolated on all stations with Multiregressive method

July maximum temperature WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Kriging r 2 =0.08 n.s. Co-Kriging r 2 =0.09 n.s. IDW r 2 =0.09 n.s. Multiregr. r 2 =0.23 n.s. According with previous results, in July no method is able to predict the spatial distribution of the temperature

In this case the methods have been verified using 149 meteorological stations for training set and 50 stations for the verifying set. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Training stationsVerifying stations Precipitation interpolation The selection of stations was made about a random process

Precipitation interpolation Root Mean Square Error MONTHKRIGINGCo-KRIGINGMULTIREGRESSIONIDW JANUARY FEBBRUARY MARCH APRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER TOTAL Generally the lowest Root Mean Square Error has been obtained with Multiregressive method. In summer the Co- Kriging (with elevation as co- variable) is the best interpolator. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

Precipitation interpolation Determination coefficient and F significance (p=0.05) MONTHKRIGINGCo-KRIGINGMULTIREGRESSIONIDW JANUARY FEBBRUARY MARCH APRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER TOTAL All the techniques give significant results, but the best fits have been obtained with the stochastic and multiregressive methods, with small differences among them. The multiregressive method performs better on winter months where the distribution of precipitation is function of geomorphological parameters. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

Precipitation interpolation MONTHMODELNUGGETSILLRANGEr2r2 JANUARY Exponential FEBBRUARY Exponential MARCH Exponential APRIL Exponential MAY Exponential JUNE Exponential JULY Spherical AUGUST Spherical SEPTEMBER Exponential OCTOBER Spherical NOVEMBER Exponential DECEMBER Exponential TOTAL Exponential For the stochastic methods, also the semivariogram model appears to be season- dependent. The spatial autocorrelation analysis is made with GS+7 software WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

Annual precipitation WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Kriging r 2 =0.65 Co-Kriging r 2 =0.65 IDW r 2 =0.60 Multiregr. r 2 =0.69 Annual precipitation is best interpolated through multiregression analysis but with small statistical differences in comparison to the stochastics methods. Nevertheless, the precipitation field on climatic scale seems to be function of geomorphological parameters but the randomness of this field does not suggest the use of this method with a high spatial resolution of the geomorphological variables grid.

Annual precipitation interpolated on all stations with Co-Kriging WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

June precipitation WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Kriging r 2 =0.64 Co-Kriging r 2 =0.64 IDW r 2 =0.59 Multiregr. r 2 =0.49 Summer precipitations are often due to small scale convective systems, so the multiregressive method is not able to predict the spatial distribution.

WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) June precipitation interpolated on all stations with Co-Kriging

Some consideration on interpolation techniques The best temperature interpolator is the multiregression analysis with residual kriging. Summer maximum temperatures, however, are poorly described by all the above methods. Due to its intrinsic randomness the precipitation field is more difficult to interpolate. The performances of the different methods appear to be season- dependent. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)

Agroclimatic applications... Spatial interpolation of precipitation and temperature fields is the base of an agroclimatic analysis WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) The results obtained on temperature field are applied to build preliminary maps of some agroclimatic indexes

Indexes: Climatic Degree Days  T>0°C WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) This map shows the annual degree days calculated on monthly mean temperature above 0°C. About 65% of the island is over 5500 degree days. (degree days are important to evaluate the crop adaptability in specific areas)

WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Indexes: Climatic Degree Days  T>10°C This map shows the areas with annual degree days calculated on monthly mean temperature above 10°C. About 67% of the island is over 2000 degree days

WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Indexes: Huglin index (index of climatic suitability for grapevine based on degree days) Sardinia have few areas with Huglin value under 1500 (above 0.2%), suitable for producing champagne-type wine and early table grape. Over 30% of the island shows Huglin values over 2500, suitable for producing dessert wine.

WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005) Indexes: Annual Evapotranspiration The Campidano plain and the north west of Barbagia are the areas with highest value of evapotranspiration (evapotranspiration is calculated by Hargreaves Samani equation on monthly interpolated temperature data)

Thank you for attention! WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (Bologna, Italy, June 2005)