# Aosta Valley Mountain Springs: A Preliminary Analysis for Understanding Variations in Water Resource Availability under Climate Change

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Case Studies

#### 2.1.1. Promise Spring

#### 2.1.2. Alpe Perrot Spring

#### 2.1.3. Promiod Spring

#### 2.1.4. Cheserod Spring

#### 2.2. Data

#### 2.3. Hydro-Meteorological Data Analysis

^{2}, defined by Equations (1)–(3)

^{2}factor describes the proportion of the variation in the dependent variable that can be predicted from the independent variable(s). R

^{2}varies between 0 and 1; when its value is 0, the model used does not explain the data at all, whereas, when the value is 1, the model explains the data perfectly [30].

#### 2.4. Rainfall Time Series Analysis

#### 2.5. Trend Analysis of Flow Rate Long-Term Series

_{MK}are

_{j}and Y

_{i}are data at time points j and i (j < i) respectively, n is the length of the time series, t

_{p}is the number of ties for the p

_{th}value, and q is the number of tied values. The sign of the trend is represented by the sign of standardized test statistic (Z

_{MK}).

_{Sen}, and the intercept, a

_{Sen}, of the trend line, are given by

_{i}is the data point at time t

_{i}.

## 3. Results

#### 3.1. Hydro-Meteorological Data Analysis Results

#### 3.2. Rainfall Time Series Analysis Results

#### 3.3. Trend Analysis Results

_{MK}> 0, b

_{sen}> 0). In contrast, the Promiod spring showed a reduction in discharge over time (Z

_{MK}< 0, b

_{sen}< 0).

_{sen}< 0) and for both Least Square Linear Regression tests (b

_{LSLR}< 0), expressing the decreasing trend in the discharge amount over the selected time interval. The positive trends described by the statistical tests performed for the Cheserod, Promise, and Perrot springs validate the increasing amount of water discharged during the detected period. Moreover, for all case studies data recorded, the low p-values (<0.05) provide the statistical significance of monotonic trend for the springs’ datasets.

_{LSLR}), followed by Alpe Perrot and Promise springs.

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Aosta Valley region sketch map. Yellow triangles indicate the weather stations, blue points the mountain springs locations.

**Figure 2.**Geological and morphological characterization of the Promise spring. The superficial catchment area of the spring was calculated using the SAGA GIS toolbox hydrology algorithms within QGIS 3.16.3 software.

**Figure 3.**Geological and morphological characterization of Alpe Perrot spring. The superficial catchment area of the spring was calculated using the SAGA GIS toolbox hydrology algorithms within QGIS 3.16.3 software.

**Figure 4.**Geological and morphological characterization of Promiod spring. The superficial catchment area of the spring was calculated using the SAGA GIS toolbox hydrology algorithms within QGIS 3.16.3 software.

**Figure 5.**Geological and morphological characterization of Cheserod spring. The superficial catchment area of the spring was calculated using the SAGA GIS toolbox hydrology algorithms within QGIS 3.16.3 software.

**Figure 6.**Graphs representing the correlation between seasonal values of cumulative discharge (x-axis) and cumulative precipitation (y-axis) for each hydrogeological season from 2012 to 2019. The coefficient of determination (${R}^{2}$) for the linear regression of the two datasets in each graph is shown. (

**a**) Promise spring; (

**b**) Alpe Perrot spring; (

**c**) Promiod spring (

**d**); Cheserod spring.

**Figure 7.**Time series plots of rainfall amount (

**a**,

**b**), intensity (

**c**,

**d**) and rainy days (

**e**,

**f**) over the analyzed period taking into account the two best-correlated weather station—springs study cases: Alpe Perrot—Champdepdraz (

**a**,

**c**,

**e**) and Promiod—Saint Vincent (

**b**,

**d**,

**f**). The linear interpolation trend is represented by a red line and the regression equation is shown in each plot.

**Figure 8.**Time series of discharge (

**a**,

**b**) of the two sets of best-correlated weather station—springs study cases: Alpe Perrot—Champdepdraz (

**a**) and Promiod—Saint Vincent (

**b**). The linear interpolation trend is represented by a red line and the regression equation is shown on each plot.

Weather Stations: | Aymavilles—Vieyes | Saint Vincent—Terme | La Thuile—Villaret | Champdepraz—Chevrère | |||
---|---|---|---|---|---|---|---|

Place: | Vieyes | Terme | Villaret | Chevrère | |||

Municipality: | Aymavilles | Saint Vincent | La Thuile | Champdepraz | |||

Basin: | s. Grand’Eyvia | Dora Baltea | s. Ruitor | s. Chalamy | |||

Spring: | Cheserod spring | Promiod spring | Promise spring | Alpe Perrot spring | |||

Elevation (m a.s.l.): | 1139 | 626 | 1488 | 1260 | |||

Latitude (WGS84) | 45.6497° | 45.7495° | 45.7095° | 45.6835° | |||

Longitude (WGS84) | 7.2508° | 7.6526° | 6.95609° | 7.61357° | |||

Weather Station–Spring distance | 6650 m | 5147 m | 1342 m | 612 m | |||

Aymavilles—Vieyes | Saint Vincent—Terme | La Thuile—Villaret | Champdepraz—Chevrère |

Promise | Alpe Perrot | Promiod | Cheserod | |
---|---|---|---|---|

1° h.y. | 09/03/2012–12/04/2013 | 12/03/2012–25/03/2013 | 29/02/2012–19/04/2013 | 17/12/2011–26/03/2013 |

(recharge season; discharge season) | (09/03/2012–08/05/2012; 09/05/2012–12/04/2013) | (12/03/2012–05/06/2012; 06/06/2012–25/03/2013) | (29/02/2012–21/05/2012; 22/05/2012–19/04/2013) | (17/12/2011–06/07/2012; 07/07/2012–26/03/2013) |

2° h.y. | 13/04/2013–09/03/2014 | 26/03/2013–14/03/2014 | 20/04/2013–08/02/2014 | 27/03/2013–23/05/2014 |

(recharge season; discharge season) | (13/04/2013–06/05/2013; 07/05/2013–09/03/2014) | (26/03/2013–24/05/2013; 25/05/2013–14/03/2014 | (20/04/2013–18/05/2013; 19/05/2013–08/02/2014) | (27/03/2013–25/08/2013; 26/08/2013–23/05/2014) |

3° h.y. | 10/03/2014–15/03/2015 | 15/03/2014–13/03/2015 | 09/02/2014–13/03/2015 | 24/05/2014–02/05/2015 |

(recharge season; discharge season) | (10/03/2014–21/04/2014; 22/04/2014–15/03/2015) | (15/03/2014–30/05/2014; 31/05/2014–13/03/2015) | (09/02/2014–23/05/2014; 24/05/2014–13/03/2015) | (24/05/2014–05/06/2014; 06/06/2014–02/05/2015) |

4° h.y. | 16/03/2015–26/02/2016 | 14/03/2015–20/03/2016 | 14/03/2015–17/02/2016 | 03/05/2015–09/04/2016 |

(recharge season; discharge season) | (16/03/2015–08/05/2015; 09/05/2015–26/02/2016) | (14/03/2015 -17/06/2015; 18/06/2015–20/03/2016) | (14/03/2015–21/05/2015; 22/05/2015–17/02/2016) | (03/05/2015–11/07/2015; 12/07/2015–09/04/2016) |

5° h.y. | 27/02/2016–10/03/2017 | 21/03/2016–09/03/2017 | 18/02/2016–11/02/2017 | 10/04/2016–13/05/2017 |

(recharge season; discharge season) | (27/02/2016–27/04/2016; 28/04/2016–10/03/2017) | (21/03/2016–06/06/2016; 07/06/2016–09/03/2017) | (18/02/2016 -10/06/2016; 11/06/2016–11/02/2017) | (10/04/2016–07/09/2016; 08/09/2016–13/05/2017) |

6° h.y. | 11/03/2017–05/04/2018 | 10/03/2017–24/03/2018 | 12/02/2017–17/02/2018 | 14/05/2017–12/04/2018 |

(recharge season; discharge season) | (11/03/2017–20/04/2017; 21/04/2017–05/04/2018) | (10/03/2017–01/06/2017; 02/06/2017–24/03/2018) | (12/02/2017–12/06/2017; 13/06/2017–17/02/2018) | (14/05/2017–07/06/2017; 08/06/2017–12/04/2018) |

7° h.y. | 06/04/2018–02/04/2019 | 25/03/2018–31/03/2019 | 18/02/2018–31/03/2019 | 13/04/2018–06/06/2019 |

(recharge season; discharge season) | (06/04/2018–02/05/2018; 03/05/2018–02/04/2019) | (25/03/2018–04/06/2018; 05/06/2018–31/03/2019) | (18/02/2018–11/05/2018; 12/05/2018–31/03/2019) | (13/04/2018–23/07/2018; 24/07/2018–06/06/2019) |

**Table 3.**Results of different methods used to assess the trend in spring discharge. * MK = Mann–Kendal; b

_{LSLR}= Least Square Linear Regression slope; b

_{sen}= Sen’s slope.

$\mathbf{MK}*\mathbf{Standardized}\mathbf{Test}\mathbf{Statistic}\left({\mathit{Z}}_{\mathit{M}\mathit{K}}\right);$$\mathbf{Hour}\mathbf{Interval}\mathbf{Time}\mathbf{Series}(\mathbf{l}/\mathbf{s})$ | $\mathbf{Sen}\u2019\mathbf{s}\mathbf{Slope}\mathbf{Test}({\mathit{b}}_{\mathit{s}\mathit{e}\mathit{n}}*);$$\mathbf{Hour}\mathbf{Interval}\mathbf{Time}\mathbf{Series}(\mathbf{l}/\mathbf{s})$ | p-Value | $\mathbf{Linear}\mathbf{Regression}({\mathit{b}}_{\mathit{L}\mathit{S}\mathit{L}\mathit{R}}*);$$\mathbf{Average}\mathbf{Daily}\mathbf{Discharge}\mathbf{Time}\mathbf{Series}(({{\displaystyle \sum}}^{}{\mathbf{m}}^{3}/\mathbf{d}\mathbf{a}\mathbf{y})/\mathbf{y}\mathbf{e}\mathbf{a}\mathbf{r})$ | $\mathbf{Linear}\mathbf{Regression}({\mathit{b}}_{\mathit{L}\mathit{S}\mathit{L}\mathit{R}}*);$$\mathbf{Monthly}\mathbf{Cumulate}\mathbf{Time}\mathbf{Series}({\mathbf{m}}^{3}/\mathbf{m}\mathbf{o}\mathbf{n}\mathbf{t}\mathbf{h})$ | |
---|---|---|---|---|---|

Promise | 87.02 | 3.66 × 10^{−5} | 0.00 | 25.643 | 130.2 |

Alpe Perrot | 33.13 | 6.39 × 10^{−5} | 0.00 | 87.107 | 176.91 |

Promiod | −23.04 | −5.08 × 10^{−6} | 0.00 | −0.214 | −6.23 |

Cheserod | 69.81 | 9.68 × 10^{−5} | 0.00 | 219.32 | 378.44 |

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**MDPI and ACS Style**

Gizzi, M.; Mondani, M.; Taddia, G.; Suozzi, E.; Lo Russo, S. Aosta Valley Mountain Springs: A Preliminary Analysis for Understanding Variations in Water Resource Availability under Climate Change. *Water* **2022**, *14*, 1004.
https://doi.org/10.3390/w14071004

**AMA Style**

Gizzi M, Mondani M, Taddia G, Suozzi E, Lo Russo S. Aosta Valley Mountain Springs: A Preliminary Analysis for Understanding Variations in Water Resource Availability under Climate Change. *Water*. 2022; 14(7):1004.
https://doi.org/10.3390/w14071004

**Chicago/Turabian Style**

Gizzi, Martina, Michele Mondani, Glenda Taddia, Enrico Suozzi, and Stefano Lo Russo. 2022. "Aosta Valley Mountain Springs: A Preliminary Analysis for Understanding Variations in Water Resource Availability under Climate Change" *Water* 14, no. 7: 1004.
https://doi.org/10.3390/w14071004