Interactive comment on “ Estimating annual effective infiltration coefficient and groundwater recharge for karst aquifers of the southern Apennines ”

This is an interesting paper that addresses an important topic – the quantification of groundwater recharge in karst aquifers. The figures are nicely done and are helpful for understanding the methods and results. As outlined in my review comments, I think that the paper introduces some confusion by not clearly emphasizing that groundwater recharge and infiltration are two different processes. Also, there are a few concerns related to the water budget equation and assumptions used to simplify it.


Introduction
Karst aquifers host important groundwater resources for human and agricultural use in many areas of the world and include natural landscapes and ecosystems with great geo-and biodiversities (Goldscheider, 2012).For regions in southern Italy, these aquifers are the primary source of drinking water and a strategic resource for socio-economic and environmental development (Allocca et al., 2007); moreover their groundwater resources play a primary role in regulating the hydro-ecological regime of rivers.In this area, the public water supplies of major cities, such as Naples, which has approximately 1.0 million inhabitants, and many small towns and countless settlements are fed by large and small karst springs.Karst groundwater resources have also been utilised since the Roman epoch for drinking water (for example Augustan Aqueduct, dated 33-12 BC) and thermal and mineral water.These aquifers are currently important sources also for several bottling plants.Hence, the correct estimation at various space-time scales of groundwater recharge processes in karst systems, taking into account atmospheric decadal variability (De Vita et al., 2012a), is a fundamental and challenging issue to be investigated to properly manage groundwater as well as surface resources with respect to the EU Water Framework Directive (European Commission, 2000).
A wide range of direct and indirect approaches to estimate groundwater recharge processes, with the degree of approximation depending on different space-time scales, have been proposed (Scanlon et al., 2002 and references therein).Examples include lysimeter measurements, soil moisture budgets and effective infiltration coefficients, as well as water table rise, tracer and remote sensing methods.At a regional scale, groundwater recharge can be assessed by multi-disciplinary analyses of hydrological time series plus hydrogeological and geomorphological data in a GIS environment to estimate the endogenous and exogenous variables affecting the recharge processes (Andreo et al., 2008;Dripps and Bradbury, 2010).Introduction

Conclusions References
Tables Figures

Back Close
Full For many karst areas around the world, assessment of the groundwater recharge has been carried out by estimating the annual effective infiltration coefficient (AEIC).The AEIC is defined as the ratio between the groundwater outflow and the effective rainfall volumes in a specified time (usually monthly or yearly) and at the aquifer scale (Drouge, 1971;Bonacci, 2001).In karst aquifers, the AEIC is controlled by several factors, among which the composition of carbonate rocks, fracturing degree, development of epikarst and hypokarst processes, slope steepness, land use and covering soil type can be basically recognised.The measurement reported by Kessler (1965) in Hungarian karst areas had a value of 51.6 %.For Greek karst, Burdon (1965) found a value of 45.2 % in the Parnassos-Ghiona aquifer, which is consistent with 49.8 % estimated by Soulios (1984) for the same area.In addition, Drouge (1971) estimated a coefficient of approximately 50 % in the Saugras Basin in France.For different calcareous groundwater basins in Croatia, AEIC values ranging from 35 % to 70 % (Vilimonovic, 1965) and from 35 % to 76 % with a mean of 57 % (Bonacci, 2001) were also found.Finally, for other non-European countries, a value of 27 % was assessed for the dolomitic basin of Tennesee (Sodeman and Tysinger, 1965).
In Italy, Boni et al. (1982) reported an AEIC value of 70 % for some karst aquifers in the central Apennines, while Celico (1983) and Allocca et al. (2007) heuristically assessed AEIC values up to 90 % for karst aquifers of the southern Apennines, taking their typical summit flat and endorheic morphologies into account.
The aim of this study was to assess the average annual groundwater recharge of the main karst aquifers of the southern Apennines (Italy) by estimating the mean AEIC for four representative karst aquifers.This assessment was conceived as a key aspect of groundwater hydrology in karst aquifers of southern Apennines which will provide an effective tool to estimate annual groundwater recharge.To achieve this objective, we carried out an integrated approach based on the hydrological budget applied to precipitation, evapotranspiration and spring discharge time series, as well as geomorphological settings, land use and type of soil cover analyses.Introduction

Conclusions References
Tables Figures

Back Close
Full The paper is organised as follows: after a description of the issue and a review of the literature in Sect. 1, we present the hydrogeological characteristics of the karst aquifers of the southern Apennines in Sect. 2 followed by the data and methods, results, discussion and concluding remarks in Sects.3, 4 and 5, respectively.

Apennines
The southern Apennines consist of a series of mountain ranges in which karst aquifers form the major massifs (Fig. 1).In this area, karst aquifers cover approximately 8560 km 2 (Fig. 1) and consist mainly of Triassic-Liassic dolomites, Jurassic limestones and Paleogene marly limestones of the Mesozoic carbonate platform series, which were tectonically deformed and piled up in the fold-and-thrust belt Apennines structure during the Miocene orogenic phases (Patacca and Scandone, 2007).The karst aquifers of the southern Apennines in several cases are characterised by large flat surface and endorheic zones on the top and exhibit an average inclination of structurally controlled slopes of approximately 30 • -35 • , related to the morphological evolution of original fault line scarps (Brancaccio et al., 1978;Bull, 2007).Moreover, given their proximity to volcanic centres (Fig. 1), these aquifers were singularly covered by variable thicknesses of ash-fall pyroclastic deposits (De Vita et al., 2006, 2012b) that erupted during the Quaternary, whose presence influences epikarst development (Celico et al., 2010).

Conclusions References
Tables Figures

Back Close
Full basically controlled by the geometry of stratigraphic or tectonic contacts with these units of lower permeability, being generally oriented toward the lowest point of the hydrogeological boundary (Celico, 1983;Allocca et al., 2007), where basal springs are located (Fig. 1).In these zones, the groundwater circulation can also feed alluvial and detrital aquifers in lateral contact with karst aquifers.Other minor stratigraphic or tectonic factors subdivide the basal groundwater circulation inside karst aquifers.These include faults with low-permeability damage zones or intervals in the carbonate series with marly or argillaceous composition that compartmentalise the aquifers in basin-inseries systems (Celico, 1983;Celico et al., 2006).
A subordinate perched groundwater flow also occurs in the surficial part of karst aquifers, where stratigraphic and structural factors or the presence of small karst conduits can generate high-altitude seasonal and ephemeral springs characterised by mean annual discharges generally lower than 0.01 m 3 s −1 .The groundwater recharge of karst aquifers occurs by diffuse-direct net infiltration through the epikarst (autogenic recharge) and concentrated-secondary infiltration of runoff formed on the surrounding or overlying non-karst terrains (allogenic recharge).For several karst aquifers of the southern Apennines, the mean annual groundwater flow was assessed mostly on the basis of short-duration time series or few measurements of spring discharges.
The climatic characteristics of the southern Apennines and their temporal variability strongly control the recharge processes in karst aquifers, and both are controlled by the North Atlantic Oscillation (De Vita et al., 2012a).The climate of this sector of Italy varies from Mediterranean type (Csa) in the coastal sector to Mediterranean mild climate (CSb) in the inland areas (Geiger, 1954).The spatial distribution of mean annual precipitation is chiefly influenced by the orographic effect (Henderson-Sellers and Robinson, 1986)  lower precipitations down to 700-900 mm are recorded because of the rain shadow effect.

Data and methods
Analyses were carried out in a large sector of the southern Apennines covering approximately 19 339 km 2 , corresponding to the regional hydrological network of the National Hydrographic and Tidal Service, Department of Naples.We assessed the basic hydrogeological characteristics that control groundwater recharge in karst aquifers over this territory (Fig. 1): aquifer extension, outcropping lithology, morphological settings (slope angle distribution and summit flat/endorheic areas), land use and type of soil cover.We also collected and analysed the precipitation and air temperature time series recorded by all monitoring stations functioning over the same territory in the period 1926-2012.Moreover, four sample karst aquifers were identified to estimate the AEIC on the basis of the availability of significant spring discharge time series and representativeness of the lithological and morphological settings (Fig. 1): the Matese (a), Accellica (a), Terminio and Cervialto karst aquifers.Although not numerous, the examined sample aquifers are the only ones for which long time series of spring discharges are available in the southern Apennines.

Aquifer lithology, covering soil type, land use and geomorphological data
On the basis of preceding hydrogeological studies carried out for singular aquifers and synthesised in reviews of regional relevance (Celico, 1983;Allocca et al., 2009), 40 principal karst aquifers were identified (Fig. 1).The outcropping lithology of the karst aquifers were assessed by analysing hydrogeological maps of southern Italy, 1 : 250 000 scale (Allocca et al., 2007).
To analyse the types of soil covering such aquifers, the Land System Map of the Campania Region, 1 : 250 000 scale (www.risorsa.info),and the Ecopedological Map of Introduction

Conclusions References
Tables Figures

Back Close
Full Italy, 1 : 250 000 scale (www.pcn.minambiente.it),were consulted and data from Corine Land Cover 2006 (www.eea.europa.eu)were collected to analyse land use.A 20 m grid spacing digital elevation model was constructed to examine the morphological features of the karst aquifers, giving special attention to slope angle and extension of the endorheic watersheds.The above-mentioned spatial data were implemented in a geographical information system, which allowed us to analyse the spatial frequency of such parameters for each examined karst aquifer.

Hydrological data
The Nevertheless, more than 50 % of the monitoring stations worked for longer than 30 yr and approximately 10 % of the stations ran for more than 70 yr.Another issue of this monitoring network was the prevailing distribution of stations in the lower-middle altitude ranges (0-600 m a.s.l.), which is a limiting factor in assessing hydrological data at the highest altitude ranges.Time series were analysed to reconstruct regional distribution models of mean annual precipitation, air temperature and effective precipitation, thereby accounting for variations due to orographic control of mountain ranges (Roe, 2005;Houze, 2012) and altitude (Vuglinski, 1972;Brunsdon et al., 2001) in a GIS environment.For the effective precipitation data set, a linear correlation analysis between mean annual values and altitude of each station was carried out identifying subzones with a homogeneous relationship between effective precipitation and altitude.For each effective precipitation 10134 Introduction

Conclusions References
Tables Figures

Back Close
Full zone, an empirical model was calculated by means of a linear regression weighted by the number of years of functioning of each station (Carroll and Ruppert, 1988).
To estimate the mean annual effective precipitation over the period 1926-2012, the actual evapotranspiration was calculated for each rain gauge station by Turc's formula (Turc, 1954), the reliability of which was confirmed by other studies in the Mediterranean area (Santoro, 1970;Boni et al., 1982): where ETR j is the mean annual actual evapotranspiration (mm) for the j rain gauge station; AP j is the mean annual precipitation (mm) for the j rain gauge station; and AT j is the mean annual air temperature ( • C) for the j air temperature-rain gauge station.
The actual evapotranspiration was also calculated for those rain gauge stations not provided with an air temperature sensor.In this case, the mean air annual temperature (AT j ) was estimated by the empirical linear regression model with the altitude, which was found to be unique at the regional scale and statistically robust.
Finally, the mean annual effective precipitation (AEP j ) for the j rain gauge station was calculated by To estimate the mean annual spring discharges, the discharge time series of basal springs of the sample karst aquifers were analysed (Fig. 1).Specifically for the Matese (a) karst aquifer, the Maretto and Torano springs were considered (recording period from 1967-2000 and 1957-2000, respectively).karst aquifer and a unique case for the duration of its time series (recording period 1921-2012), was considered (De Vita et al., 2012a).For the Accellica (a) karst aquifer, we analysed the Avella and Ausino-Ausinetto springs (recording period 1967-1989).

Hydrological budget of karst aquifers and AEIC estimation
The AEIC was estimated for each of the four sample karst aquifers, by applying the hydrological budget equation to mean the values for the period 1926-2012: where AP is the mean annual precipitation, U i is the mean annual indirect inflow discharge, ETR is the mean annual actual evapotranspiration, R is the mean annual runoff, IE is the mean annual direct net infiltration, U u is the mean annual indirect outflow discharge, Q s is the mean annual spring discharges, Q t is the mean annual tapped discharge and ±∆W r is the interannual variation of groundwater reserves.
Because interannual variations of groundwater reserves (±∆W r ) are negligible in the long-term, Eq. ( 1) can be simplified as follows: Groundwater inflows (U i ) and losses (U u ) through juxtaposed alluvial and detrital aquifers were estimated by the application of Darcy's law (Darcy, 1856) to the hydrogeological parameters of the adjoining aquifers.
The mean AEIC was estimated for each karst aquifer as the ratio between the mean annual outflow (V outflow = U u +Q s +Q p ) and the annual mean inflow (V inflow = AP − ETR +U i ), where both were related to the whole extension of the aquifer:

Conclusions References
Tables Figures

Back Close
Full Furthermore, due the peculiar morphological setting of karst aquifers, summit flat areas (slope angle ≤ 5 • ) and endorheic watersheds, in which the infiltration value reaches the maximum value (AEIC = 100 %), were identified and measured (Fig. 3d).The annual effective infiltration coefficient for the slope part (AEIC S ), in non-endorheic conditions and with slope angle greater than 5 • , was therefore calculated by the following formula: where A T is the total area of the karst aquifer (km 2 ), and A E is the cumulative extension of summit flat areas and/or endorheic watersheds (km 2 ).
This estimation was considered useful for a comprehensive understanding of the hydrological role of karst aquifers, and thus also for taking into account a general assessment of runoff formation along karst slopes (Horvat and Rubinic, 2006) by estimating the annual runoff coefficient (ARC), which is the complementary part of the AEIC (ARC = 100 − AEIC S ).
To test the sensitivity of the AEIC estimation due to the annual variability of inflow and outflow volumes, minimum and maximum values of the AEIC and AEIC S were estimated by considering the 95 % confidence limits of the effective precipitation and air temperature empirical models.

Aquifer extensions and lithology
Aquifer extensions and the lithology of the 40 karst aquifers were assessed by analysing regional hydrogeological maps (Allocca et al., 2007).Specifically, the four sample karst aquifers were shown to be representative, both by their significant extensions and their outcropping lithology (Fig. 2d): Matese (a) (120 km 2 ; 97 % limestone Introduction

Conclusions References
Tables Figures

Soil type, land use and geomorphological features
Our analysis of the soil types covering karst aquifers identified the loamy sand type (coded as LS in Fig. 2a) as the prevailing one with a percentage greater than 90 % for three of the four karst aquifers considered, which is consistent with Naclerio et al. (2008) and Naclerio et al. (2009).A fraction of a coarser soil type, 14 % of sandy loam soils (coded as SL in Fig. 2a), was identified for the Terminio karst aquifer according to other studies carried out at a detailed scale (Allocca et al., 2008) and the proximity to the Somma-Vesuvius volcano, which led to the deposition both of greater thicknesses of ash-fall pyroclastic deposits (De Vita et. al., 2006) and coarser grain sizes.
Land use varied among four principal typologies: woodland, meadowland, areas without vegetation cover and urban areas.Specifically, the woodland and meadowland classes were the dominant ones in the four sample karst aquifers, extending for approximately 85 % and 14 % of the total area, respectively (Fig. 2b).
We found the sample karst aquifers to have extensions of summit flat areas and endorheic zones (Fig. 2d) varying from 43 % in the case of Terminio to 0 % Accellica (a), with intermediate values of approximately 35 % and 20 % for Matese (a) and Cervialto, respectively.Moreover, the cumulative distributions of slope angle were found to be similar across the sample (Fig. 2c) and other aquifers (Fig. 3c), showing a similar median value of 25 • .
Considering the 40 karst aquifers identified at a regional scale (Fig. 1), the soil type was notably homogeneous (Fig. 3a) with a prevalence of sand in each category.We found average land use values of 69 % for woodland, 25 % for meadowland, 5 % for areas without vegetation and 1 % for urban areas (Fig. 3b).The morphological settings of all karst aquifers showed very similar cumulative distributions of slope angles, Introduction

Conclusions References
Tables Figures

Back Close
Full with a median of 25 • and a modal value ranging within 20 • -25 • .In contrast, the most frequent higher value slope angle class was 30 • -35 • , according to the typical morphological setting due to the erosional evolution of fault-line scarps in carbonate mountains of the southern Apennines (Brancaccio et al., 1978;Bull, 2007).Significant differences were observed in the distribution and extent of the summit flat and endorheic areas (Fig. 3d and Table 2) according to the different structural settings of the karst aquifers.More extended summit flat and endorheic watersheds were detected in the northern and in the southern parts of the study area.In particular, more than 40 % of the total area of the Terminio and Alburni karst aquifers (Fig. 3d and Table 2) were characterised by summit flat area and endorheic watersheds, and hence by a total effective infiltration.

Effective precipitation estimation
Despite the apparent homogeneous distribution of rain gauges and air temperature stations over the territory (Fig. 4a and b), the assessment of the spatial distribution of these stations revealed an inhomogeneous scattering with altitude, with a dominant presence in the lower-middle ranges (Fig. 4c and d).This scarcity of a monitoring network at higher altitude ranges was recognised as a principal issue to overcome to assess the groundwater recharge of karst aquifers, which have a mountainous morphology extending up to the highest altitudes.In fact, the statistical comparison between the altitude of the monitoring stations and the karst aquifers showed that 50 % of these areas lie at altitudes between 800 and 2280 m a.s.l., where only 10 % of rain gauge and air temperature stations are located (Fig. 4c and d).
Therefore, to estimate groundwater recharge at a regional scale, a distributed model of AEP was reconstructed by considering the spatial variability due to both orographic and altitudinal controls.By analysing the correlation of AEP data with the altitudes of the rain gauge stations, we found three homogeneous effective precipitation zones according to the orographic barrier effect (Vuglinski, 1972;Brunsdon et al., 2001) of the Apennine chain.An upwind zone, extending from the coastline to the principal Apennine morphological divide, and two downwind zones eastward of the same divide Introduction

Conclusions References
Tables Figures

Back Close
Full were identified (Figs. 5 and 6), which resulted from a rain shadowing effect (Roe, 2005).
For each zone, a specific linear regression model, weighted by the years of functioning of each rain gauge station was identified between AEP and altitude (Fig. 5a, b, and  c).These models showed that the AEP values progressively increase with altitude even considering three different empirical laws across the Apennine chain, which were always statistically significant (r min = 0.714 and Prob.t Student max < 0.1 %).According to the dominant control of the altitude, only one linear regression model was found between air temperature and altitude (Fig. 5d) which was statistically significant (r = −0.856and Prob.t Student < 0.1 %).
On the basis of such findings, a distributed model of mean annual effective precipitations (AEP) was reconstructed by integrating the three effective precipitation zones in a GIS layer (Fig. 6).For the upwind effective pluviometric zone, the recorded values of mean annual effective precipitation ranged between 373 mm and 1606 mm, but varied from 200 to 1010 mm for the two downwind zones.

AEIC and AEIC S estimations
From the estimation of the variables forming Eqs. ( 5) and ( 6), the AEIC and AEIC S were estimated for the four sample karst aquifers (Table 1), which also took into account the uncertainties due to the linear regression models (95 % confidence limits) of annual effective precipitation and altitude (Fig. 7).Considering the results related to the mean value of the regression models, similar values of the AEIC were found for the Terminio, Cervialto and Matese (a) karst aquifers, corresponding to 79 %, 71 % and 69 %, respectively, whereas a value of 50 % was calculated for the Accellica (a) karst aquifer.This difference appeared to be mainly correlated to the different lithology, which is prevailingly dolomitic, and the lack of summit flat and endorheic areas for the latter case.Corresponding AEIC S and ARC values (Table 1 and Fig. 7) were estimated as ranging from 50 % to 64 % and from 50 % to 36 %, respectively.Introduction

Conclusions References
Tables Figures

Back Close
Full

Assessment of groundwater recharge
To generalise the results obtained for the four sample karst aquifers, a correlation analysis was carried out among the estimated parameters: AEIC, limestone area, summit flat and endorheic area, woodland area, loamy sand soil type area and mean slope angle.Due to the similarity of their values, both for the four sample karst aquifers and the other ones (Figs. 2 and 3), the correlation analysis revealed a negligible role of the last three parameters on AEIC variability.Consequently, we found a multiple linear regression to empirically correlate the mean AEIC to the basic controlling variables, namely limestone area (L%) and summit flat and endorheic area (E%): which was statistically significant (r = 0.984; Prob.F Fisher = 3.0 %; Standard errors of 5.92, 0.06 and 0.07 for the intercept, first and second coefficient, respectively).The preceding equation confirms the insight that the flat and endorheic area is a factor affecting the mean AEIC more strongly than lithology (outcrop of karst rocks).
We estimated the AEIC and AEICs values for the 40 regional karst aquifers by applying the empirical Eqs. ( 6) and (7) (Table 2).The minimum estimated AEIC value was calculated for the Circeo karst aquifer (48 %); the maximum value was found for the Terminio karst aquifer (78 %), with a residual of 1 % respect to that directly calculated (Table 1) and a mean global value of 59 %.
The estimation of the AEIC and the AEP values for each karst aquifer allowed the assessment of the respective mean annual groundwater recharge (Table 2).To validate this empirical estimation, the recharge value calculated by Eq. ( 7) for the four sample karst aquifers was compared with the outflow discharges.The resulting residuals between the predicted recharge and measured outflow was considered to be negligible, ranging between 0 % and 10 %, and thus supporting the reliability of the empirical estimations.Moreover, the correlation between the estimated mean annual groundwater recharge and outflow assessed for 18 of the 40 karst aquifers on the basis of nonsystematic spring discharges measurements, which were carried out during the 70's 10141 Introduction

Conclusions References
Tables Figures

Discussion and conclusions
To assess mean annual groundwater recharge in karst aquifers of the southern Apennines, our approach focused on the estimation of the AEIC as a practical tool, which was already established for karst aquifers in other countries.
The applied methods were oriented to account for the lack of temporal and spatial hydrological time series, namely the availability of significant spring discharge measurements and effective precipitation in the high altitude ranges.Consequently, the results are based on all existing hydrological data.
A contribution to reconstruct a regional distributed model of effective precipitation that also accounts for orographic barrier and altitude controls of the Apennine chain is provided by identifying three homogeneous zones in which singular empirical laws exist relative to altitude.This approach is proposed as a simpler and more direct method for assessing distributedly effective precipitation, which is not based on geostatistical analyses of rainfall (Goovaerts, 2000;Marquínez et al., 2003) but on the recognition of the orographic barrier and altitude controls (Vuglinski, 1972;Brunsdon et al., 2001).
By applying the hydrological budget equation to effective precipitation and spring discharge data, the estimations of the AEIC for four sample karst aquifers varied from 50 % to 79 % with a mean value of 67 % and are comparable with those in the European and peri-Mediterranean areas (Burdon, 1965;Vilimonovic, 1965;Drouge, 1971;Bonacci, 2001).Because of the more accurate assessment of mean annual groundwater outflow and inflow volumes for the four sample karst aquifers, as well as for the duration of time series, the calculated values advance our knowledge regarding the AEIC of karst aquifers in the southern Apennines.
Using a correlation analysis of other factors recognisable as affecting groundwater recharge in sample karst aquifers, such as lithology, morphological settings, land use Introduction

Conclusions References
Tables Figures

Back Close
Full and covering soil type, we found a significant empirical relationship between AEIC, lithology and summit flat and/or endorheic areas.Owing to the similarity of the other karst aquifers identified in the regional study area, an empirical estimate of the mean AEIC was also proposed for those aquifers.We therefore present a method to assess groundwater recharge of karst aquifers at a regional scale.
The proposed approach highlights another complementary aspect related to the annual runoff estimation of the slope areas, which is particularly relevant for the management of surficial water resources and furnishes values of mean ARC varying from 36 % to 50 %, matching those estimations carried out for Dinaric karst aquifers (Horvat and Rubinic, 2006) and some river basins of the southern continental Italy (Del Giudice et al., 2012).
The methodology is presented as a reliable system for modelling the groundwater recharge of karst aquifers at regional and mean annual scales in the case of a large territory with discontinuous and absent hydrological monitoring.It can be conceived as a deeper understanding of groundwater hydrology in karst aquifers and a first step to overcome the lack of spring discharges and piezometric levels time series.The application of this method would thus permit the design of appropriate management models for groundwater and surface resources of karst aquifers as well as the setting up of accurate strategies to mitigate the effects of climate change.This achievement would allow balancing environmental needs and the societal impacts of water uses, as required by the EU Water Framework Directive (European Commission, 2000).Introduction

Conclusions References
Tables Figures

Back Close
Full  Full  Table 2. Data and estimations of AEIC, AEIC s , ARC and mean annual groundwater recharge for karst aquifers of the study area.In the last column, the mean annual groundwater outflow, estimated for some of the karst aquifers by other hydrogeological studies (Celico, 1983;Allocca et al., 2007)  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | of the Apennine mountain ranges on humid air masses moving eastward from the Tyrrhenian Sea.According to the location of the Apennine chain, higher orographic precipitation occurs in the western sector, with maximum values up to 1700-2000 mm along the Apennines ridge itself.Eastward of the Apennines ridge, Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | For the Terminio aquifer, we analysed the Cassano Irpino (recording period 1965-2010), Serino (recording period 1887-2010) and Baiardo and Salza Irpina springs (recording period 1970-2000).For the Cervialto aquifer, the Sanità spring, which represents the sole outflow of the entire 10135 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Table 1 .
AEIC and mean AEIC S estimation for the investigated sample karst aquifers.Values are related to the mean value of the AEP linear regression models with altitude.
are reported; values estimated in this study for the four sample karst aquifers (ID 17a, 27, 31a and 32) are reported.