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Case study for investigating groundwater and the future of mountain spring discharges in Southern Italy

Journal of Mountain Science 2017年第9期14卷 1791-1800页

作者： Nazzareno DIODATO gianni bellocchi Francesco FIORILLO Gerardo VENTAFRIDDA Science and Technology Department University of Sannio 82100 Benevento Italy Met European Research Observatory 82100 Beneuento Italy UMR Ecosysteme Frairial INRA VetAgro Sup 63000 Clermont-Ferrand France Acquedotto Pugliese S.p.A. 70121 Bari Italy

Groundwater extraction is used to alleviate drought in many habitats. However, widespread drought decreases spring discharge and there is a need to integrate climate change research into resource management and action. Accurate estimates of groundwater discharge may be valuable in improving decision support systems of hydrogeological resource exploitation. The present study performs a forecast for groundwater discharge in Aquifer?s Cervialto Mountains(southern Italy). A time series starting in 1883 was the basis for longterm predictions. An Ensemble Discharge Prediction(EDis P) was applied, and the progress of the discharge ensemble forecast was inferred with the aid of an Exponential Smoothing(ES) model initialized at different annual times. EDisP-ES hindcast model experiments were tested, and discharge plume-patterns forecast was assessed with horizon placed in the year 2044. A 46-year cycle pattern was identified by comparing simulations and observations, which is essential for the forecasting purpose. ED is P-ES performed an ensemble mean path for the coming decades that indicates a discharge regime within ± 1 standard deviation around the mean value of 4.1 m^3 s^(-1). These fluctuations are comparable with those observed in the period 1961-1980 and further back, with changepoints detectable around the years 2025 and 2035. Temporary drought conditions are expected after the year 2030.

关键词： Caposele(Italy) Ground water Drought Ensemble forecast Exponential smoothing Spring discharge

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Assessment of the Spatial Uncertainty of Nitrates in the Aquifers of the Campania Plain (Italy)

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American Journal of Climate Change 2013年第2期2卷 128-137页

作者： Nazzareno Diodato Libera Esposito gianni bellocchi Luisa Vernacchia Francesco Fiorillo Francesco Maria Guadagno Environmental Geology Department University of Sannio Benevento Italy Met European Research Observatory HyMex-GEWEX Experiment World Climate Research Programme Benevento Italy Met European Research Observatory HyMex-GEWEX Experiment World Climate Research Programme Benevento Italy Grassland Ecosystem Research Unit French National Institute of Agricultural Research Clermont-Ferrand France

We present a non-parametric hydro-geostatistical approach for mapping design nitrate hazard in groundwater. The approach is robust towards the uncertainty of the parametric models used to map groundwater pollution. In particular, probability kriging (PK) estimates the probability that the true value of a pollutant exceeds a set of threshold values using a binary response variable (probability indicator). Such soft description of the pollutant can mitigate the uncertainty in pollutant concentration mapping. PK was used for assessing nitrate migration hazard across the Campania Plain groundwater (Southern Italy) as exceeding typical critical values set to 25 and 50 mg.L-1. Cross-validation indicated that the PK is more suitable than ordinary kriging (OK), which yields large uncertainty in absolute values prediction of nitrate concentration. This means that spatial variability is critical for contaminant transport because critical contaminants concentration could be exceeded due to preferential flows allowing the pollutant to migrate rapidly through the caveats aquifer. Accordingly with PK application, about 250 km2 (40% of the total600 km2 of the Campania Plain) were classified as very sensitive areas (western zone) to maximum permissible concentration of nitrates (>50 mg.L-1). When the probability to exceed 25 mg.L-1 was considered, the contaminated surface increased to 70% of the total area.

关键词： Campania Plain (Italy) Nitrate Probability Kriging

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