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The AAPG/Datapages Combined Publications Database
Environmental Geosciences (DEG)
Abstract
DOI: 10.1306/eg.02290504029
Hydroinformatics system for pollutant
potential
leaching spatial uncertainty assessment
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Nazzareno Diodato1
1Monte Pino Research Observatory on Climate and Landscape, Contrada Monte Pino, 82100 Benevento, Italy; [email protected]
AUTHORS
Nazzareno Diodato was born in Benevento, Italy, in 1966 and was educated at Naples Federico II University. Between 1989 and 2003, he followed postgraduate courses in meteorology, statistics and environmental sciences. Since that time, he has been the director of the Monte Pino Research Observatory, which is registered in TEMS (Terrestrial Ecosystem Monitoring Sites) network of the FAO (Food and Agriculture Organization), United Nations. At present, he is also an established author in the geosciences field, collaborating with the Scientific Computing and Data Analysis Group of Sannio University for integrating environmental resource and climate modelling with statistical and geostatistical techniques. His most recent papers appeared in Elsevier, Springer, Wiley, and American Society and Civil Engineering international journals. He had recently taken part in the MedCLIVAR (Mediterranean Climate Variability) Project, including the reconstruction of the Mediterranean past climate variability and extreme and natural hazards, the description of patterns and mechanism characterizing its space-time variability.
ACKNOWLEDGMENTS
I gratefully acknowledge the three reviewers, Robert J. Menzie, Robert S. Young, and David T. King, for providing helpful suggestions.
I am very grateful to Dr Marcello Tedesco that facilitated the data collection used
in this work.
ABSTRACT
Accounting for uncertainty in risk assessments to nutrient exposure can take numerous forms. A novel procedure for mapping design leaching risk (L) at subregional basin scale is presented, which is based on soil-water balance (SWB) and a geostatistical approach incorporated in the Geographic Information System-hydroinformatic structure to quantify the exchange probability of plant-available soil water, which can be taken as a measure to determine the nutrient and contaminant leaching risk of a site. Although spatial variability in precipitation surplus at local and intermediate scales may not be critical for water-resource assessment, it is critical for contaminant transport because focused water surplus and preferential flow allow contaminants to migrate rapidly through the unsaturated zone to underlying aquifers. The leaching data are transformed to a binary response (0,1), referred to as coindicators,
which is consistent with a soft description of leaching
used
in this study to mitigate the contaminant
potential
spatial uncertainty in the aquifer. Nonlinear multivariate geostatistical methods, known as coindicator kriging, enable this uncertainty to be converted to an estimated probability, where the true value exceeds the leaching threshold. The experimental approach was made to a test site in the Tammaro subhumid agricultural landscape (south Italy) for the incorporation of measurement errors and change of support in uncertainty leaching modeling that were based on long-term hydrological water balance for natural site conditions. In this way, about 220 km2 (85 mi2) (33%) of the total 675 km2 (260 mi2) of the Tammaro Basin were classified as areas sensitive to nutrient and contaminant leaching. The advantage of this approach is that it is quick and applicable in regions that lack a large pollution data set, especially in developing countries.
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