# Infiltration and Short-Time Recharge in Deep Karst Aquifer of the Salento Peninsula (Southern Italy): An Observational Study

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area Description

## 3. Materials and Methods

_{1}(t) + s

_{2}(t)]/2

_{1}and s

_{2}are the available soil moisture density measurements (in m

^{3}/m

^{3}at 2 cm and 35 cm underground, respectively) and h represents an effective soil thickness, which has been determined as the value that minimizes the total possible negative output for I (varying h between 5 and 100 cm by a 5 cm step). The value of h = 40 cm found in this way has been then used in the final evaluations of I. The range of soil moisture content per square meter (0–160 mm) obtained with this value of h in Equation (3) is consistent with the range of variability observed in Salento maps of soil moisture content [17]. Equation (2) has been used at monthly and daily resolution in the following paragraphs.

_{i}) and W(t

_{i}+nt), and < > indicates the averaged value over the time series. C(nt) has then been normalized dividing by C(0).

## 4. Results

^{2}. Attributing half of the variance to the measured E flux, this translates into an uncertainty of about 0.02 mm per day (taking three standard deviations as maximum uncertainty). In the worst case of all errors summing up, it means about 0.6 mm per month. Considering a minimum of 0.2 mm error in the resolution of the rain gauge, we have a minimum uncertainty of the order of 1 mm per month in the infiltration estimation, apart from the soil storage correction. However, the uncertainty can be quite large due to the missing data in the considered period. An estimation of the total measurement uncertainty considering both instrumentation errors and missing data has been suggested in [23], where the missing data uncertainty was estimated as proportional to both the contribution of the single measurement error and the total number of missing data (the total number of missing data per year is always less than 10%). The seasonal/yearly average/total values for P,I, and soil moisture at 2 cm in the short study period 2009–2011 are shown in Table 2, where the error interval now represents the total estimated measurement uncertainty from [23] (not the “climatic” standard deviation of the whole 2003–2016 period as in the long term averages of Table 1).

## 5. Discussion

#### 5.1. Estimations of the Seasonal/Yearly Net Infiltration

#### 5.2. Infiltration, Precipitation Distribution and Aquifer Recharge

#### 5.3 Water Management Concerns

## Supplementary Materials

## Acknowledgments

_{2}Observe” project, funded by the European Union’s Seventh Framework Program.

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Location and geological map of Salento. Legend:

**1**, coarse calcarenites and clayey-marls (Quaternary);

**2**, fine calcarenites (Tertiary);

**3**, limestone (Cretaceous);

**4**, faults;

**5**, fresh water/brackish water interface;

**6**, brackish water/saltwater interface;

**7**, CNR ISAC-Lecce micrometeorological base;

**8**, selected wells of “progetto Tiziano”; and

**9**, rain gauge stations of the Civil Protection Agency. Vertical scale magnified ten times in A–A′ section.

**Figure 2.**Frequencies N of the calculated 90% footprints in one year (2010), expressed as distances in km from the measurement mast, from 30-minute averaged data of the ISAC-Lecce database.

**Figure 3.**Surface infiltration (T=P−E) in the dry (April–September, red triangles) and wet (October–March, blue circles) season. Values in meters. Dashed lines are linear regressions. Seasonal HY-INT index (+) for the dry season (non-dimensional). The continuous line is a linear regression with a 90% confidence of increasing trend by the Mann–Kendall test.

**Figure 4.**Surface infiltration T=P−E (+) and net infiltration I from Equation (2) (o) for the overlapping period of analysis (2009–2011). Values in meters. The continuous lines have been added for visual clarity only.

**Figure 5.**Sum of the sensible and latent heat fluxes (H+λE, daily averages, vertical axis) versus the available energy flux (net radiation minus soil heat flux: Rn-G, horizontal axis) for the 2009–2011 period. Data from the ISAC-Lecce database. All values in watts per square meter. Dashed line: x = y.

**Figure 6.**The 2009–2011 monthly variations in the piezometric level of well 12IIIS W versus total monthly infiltration I calculated by Equation (2) in: (

**a**) the same month; (

**b**) one month before; (

**c**) two months before; and (

**d**) three months before. All values in meters. The continuous lines are linear regressions, the dashed lines represent x = y.

**Figure 7.**The 2009–2011 monthly variations in the averaged piezometric levels of all five wells W versus total monthly infiltration I calculated by Equation (2) in: (

**a**) the same month; (

**b**) one month before; (

**c**) two months before; and (

**d**) three months before. All values in meters. The continuous lines are linear regressions, the dashed lines represent x = y.

**Figure 8.**Cross-covariance functions C(nt) between the daily increments in the piezometric level for the closest well 12IIIS and daily net infiltration as calculated by Equation (2) (+), and corrected by vanishing negative values (o). Continuous lines added for visual clarity.

**Figure 9.**Same of Figure 8, but for the averaged piezometric increments of all the wells.

**Figure 10.**The 2009–2011 monthly precipitations (mm) measured at CNR-ISAC base and five selected rain gauges of the regional Civil Protection network.

**Figure 11.**December 2009 daily precipitations (mm) measured at CNR-ISAC base and five selected rain gauges of the regional Civil Protection network.

**Figure 12.**June 2010 daily precipitations (mm) measured at CNR-ISAC base and five selected rain gauges of the regional Civil Protection network.

**Table 1.**Total precipitation P, total surface infiltration (T=P−E, where E is evapotranspiration) and average surface soil moisture (measured at 2 cm depth), all averaged over the dry season, the wet season and the whole year in the period 2003–2016. The uncertainties represent the standard deviations in the 2003–2016 period. Data from the ISAC-Lecce database.

(2003–2016 Period) | Precipitation (m) | Surface Infiltration (m) | Soil Moisture (m/m) |
---|---|---|---|

Dry season (April–September) | 0.25 ± 0.13 | 0.06 ± 0.13 | 0.11 ± 0.05 |

Wet season (October−March) | 0.45 ± 0.13 | 0.33 ± 0.13 | 0.25 ± 0.05 |

Year | 0.70 ± 0.13 | 0.39 ± 0.13 | 0.18 ± 0.05 |

**Table 2.**Total precipitation P, total net infiltration (I from Equation (2)) and average surface soil moisture (measured at 2 cm depth), all averaged over the dry season, the wet season and the whole year in the overlapping period 2009–2011. The uncertainties take into account the measurement errors and the missing data. Data from the ISAC-Lecce database.

(2009–2011 Period) | Precipitation (m) | Net Infiltration (m) | Soil Moisture (m/m) |
---|---|---|---|

Dry season (April–September) | 0.36 ± 0.04 | 0.12 ± 0.04 | 0.14 ± 0.04 |

Wet season (October–March) | 0.36 ± 0.02 | 0.28 ± 0.02 | 0.23 ± 0.04 |

Year | 0.72 ± 0.06 | 0.40 ± 0.06 | 0.19 ± 0.04 |

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**MDPI and ACS Style**

Delle Rose, M.; Martano, P.
Infiltration and Short-Time Recharge in Deep Karst Aquifer of the Salento Peninsula (Southern Italy): An Observational Study. *Water* **2018**, *10*, 260.
https://doi.org/10.3390/w10030260

**AMA Style**

Delle Rose M, Martano P.
Infiltration and Short-Time Recharge in Deep Karst Aquifer of the Salento Peninsula (Southern Italy): An Observational Study. *Water*. 2018; 10(3):260.
https://doi.org/10.3390/w10030260

**Chicago/Turabian Style**

Delle Rose, Marco, and Paolo Martano.
2018. "Infiltration and Short-Time Recharge in Deep Karst Aquifer of the Salento Peninsula (Southern Italy): An Observational Study" *Water* 10, no. 3: 260.
https://doi.org/10.3390/w10030260