Geographical Information Systems and eostatistics for Modelling Radioactively Contaminated Land Areas

The Chernobyl plume contaminated vast lands of Europe with radiocaesium ( 137Cs) in 1986 because of the deposition of radionuclides on the ground by wet and dry deposition processes. Nevertheless, in a nuclear emergency, contamination data may be very sparse and there is need to make rapid and scientifically supported decisions. Here we analyze the rainfall field, an important precursor of the wet deposition, during the passage of the plume. Thus, estimating rainfall spatial variability can help to identify possible contaminated areas and associated risks when rainfall exceeded a given threshold. In this paper, we show that the conditional probabilities of exceeding threshold rainfall values could be spatially assessed using the mutual benefits of linking geostatistical and geographical information system (GIS) to quantify the evaluation of the risk involved in decision making. In particular, the non-parametric geostatistic technique, termed Indicator Kriging (IK), enables one to efficiently estimate the probability that the true value exceeds the threshold values by means of the indicator coding transform. Afterward, GIS has been used to find the areas probably affected by wash-out (probability >0.5 that rainfall is above a certain threshold). The experimental study has been focused on a test site in Beneventan agroecosystem (Southern Italy) to model the spatial uncertainty over a continuous area from sparse rainfall data. This enabled to generate probability maps delineating area potentially affected by to contamination to be monitored after wet deposition of Chernobyl releases.