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...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.展开更多
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...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.展开更多
Soil erosion by water is a serious threat for the Mediterranean region.Raindrop impacts and consequent runoff generation are the main driving forces of this geomorphic process of soil degradation.The potential ability...Soil erosion by water is a serious threat for the Mediterranean region.Raindrop impacts and consequent runoff generation are the main driving forces of this geomorphic process of soil degradation.The potential ability for rainfall to cause soil loss is expressed as rainfall erosivity,a key parameter required by most soil loss prediction models.In Italy,rainfall erosivity measurements are limited to few locations,preventing researchers from effectively assessing the geography and magnitude of soil loss across the country.The objectives of this study were to investigate the spatio-temporal distribution of rainfall erosivity in Italy and to develop a national-scale grid-based map of rainfall erosivity.Thus,annual rainfall erosivity values were measured and subsequently interpolated using a geostatistical approach.Time series of pluviographic records(10-years)with high temporal resolution(mostly 30-min)for 386 meteorological stations were analysed.Regression-kriging was used to interpolate rainfall erosivity values of the meteorological stations to an Italian rainfall erosivity map(500-m).A set of 23 environmental covariates was tested,of which seven covariates were selected based on a stepwise approach(mostly significant at the 0.01 level).The interpolation method showed a good performance for both the cross-validation data set(R^(2)_(cv)=0.777)and the fitting data set(R^(2)=0.779).展开更多
文摘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.
文摘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.
文摘Soil erosion by water is a serious threat for the Mediterranean region.Raindrop impacts and consequent runoff generation are the main driving forces of this geomorphic process of soil degradation.The potential ability for rainfall to cause soil loss is expressed as rainfall erosivity,a key parameter required by most soil loss prediction models.In Italy,rainfall erosivity measurements are limited to few locations,preventing researchers from effectively assessing the geography and magnitude of soil loss across the country.The objectives of this study were to investigate the spatio-temporal distribution of rainfall erosivity in Italy and to develop a national-scale grid-based map of rainfall erosivity.Thus,annual rainfall erosivity values were measured and subsequently interpolated using a geostatistical approach.Time series of pluviographic records(10-years)with high temporal resolution(mostly 30-min)for 386 meteorological stations were analysed.Regression-kriging was used to interpolate rainfall erosivity values of the meteorological stations to an Italian rainfall erosivity map(500-m).A set of 23 environmental covariates was tested,of which seven covariates were selected based on a stepwise approach(mostly significant at the 0.01 level).The interpolation method showed a good performance for both the cross-validation data set(R^(2)_(cv)=0.777)and the fitting data set(R^(2)=0.779).
