SOURCE: A semi-automatic tool for spring monitoring data analysis and aquifer characterization

It has become increasingly necessary to optimise mountain groundwater resource management and 14 comprehend resource recharging systems from a hydrogeological perspective in order to formulate adequate 15 resource protection strategies. Analysing mountain spring behaviour and aquifer characteristics can be time 16 consuming, so new automated techniques and software tools are needed to estimate hydrogeological 17 parameters and understand exhaustion dynamics of groundwater resources. 18 This paper introduces SOURCE, a new semi-automatic tool that automates the hydrogeological 19 characterisation of water springs and provides proper estimations of the vulnerability index, as well as 20 autocorrelation and cross-correlation statistical coefficients. SOURCE rapidly processed input data from the 21 Mascognaz 1 spring (Aosta Valley) water probes and meteorological station to provide graphical outputs and 22 values for the main hydrodynamic parameters. 23 Having a single software package that contains all the main methods of water spring analysis could potentially 24 reduce analysis times from a few days to a few hours. 25


Introduction 27
Mountain aquifers represent one of the largest and most valuable sources of water in northern Italy and are 28 necessary to meet the water needs of the population. In recent decades, different hydrological issues, such as 29 the gradual drying up of many springs, low discharge rates during dry months, and formerly perennial springs 30 becoming seasonal, have been reported in studies from throughout the Italian Alps and Apennines (Cambi and     Analysing spring discharge hydrographs is one of the most useful tools when studying mountain springs and 118 defining aquifer characteristics, such as the type and quantity of its groundwater reserves.

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There have been several studies on recession curve modelling, each establishing different mathematical 120 relationships between the water spring discharge parameter (Q) and recording time (t). Boussinesq (1904) and 121 Maillet (1905) proposed two different analytical formulas that describe the dependence of the flow rate at a where Qt (m 3 /s) is the flow rate value at t ≠ t0, Q0 is the flow rate at t = t0 and α is the recession coefficient, a 128 constant that depends only on the aquifer hydraulic systems, as shown below.
Maillet showed that the recession of a spring can be represented by an exponential formula, implying a linear 130 relationship between the hydraulic head and flow rate: where the recession coefficient α can be determined using the following equation.  Properly identifying the vulnerability level of a mountain aquifer and its associated springs is necessary to 169 protect aquifers from potential pollution sources and preserve water quality over time.

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Aquifers are fed by rainfall, snowmelt and surface runoff, collectively known as neo-infiltration water. Water 171 from these sources infiltrates the ground and becomes part of the underground flow. Because neo-infiltration 172 water can transport pollutants into groundwater systems, the rate at which neo-infiltration water enters 173 groundwater systems and its velocity toward a spring must be accurately evaluated.

Code 200
The dynamics of mountain groundwater resource depletion are heavily influenced by climate conditions.

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Annual variations in snow and rain precipitation impact the hydrodynamic characteristics and exhaustion 202 modalities of springs. As such, it is necessary to develop new automated techniques that will allow researchers 203 to estimate the main parameters of mountain aquifers quickly and accurately. In order to propose a new, 204 advanced, semi-automatic tool for spring characterisation that uses available parameter datasets, the above-  To be correctly input into SOURCE, Excel files must have a first sheet named "Spring_data" that contains the 221 water spring data and a second sheet named "Meteo_data" that contains the meteorological data. With this 222 format, it is possible to run the script and set parameters using the proposed GUI interface (Fig. 3)   The second possible graphical output is recession curves (Fig. 5). The aquifer parameters, calculated using the

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Understanding the correlation period between precipitation and spring response requires eliminating the 283 influence of the winter recharge period until the peak due to melting. Springs do not respond to winter and 284 spring precipitation; the water that arrives at springs depends on the melting process, which is correlated with 285 temperature fluctuations rather than precipitation. Comparing data from different years within the selected period revealed that the lag time between rainfall and 291 discharge tended to be short. As shown in Fig. 9, the considered lag time was 3 days; analysing all years within 292 the considered period resulted in a maximum value of 4 days and a minimum value of 1 (Tab. 5).

Conclusions 299
New automated tools can potentially be applied to estimate aquifer hydrogeological parameters and monitor 300 water spring behaviour. The effects of climate change on mountain springs can be intense, and tools are needed 301 to guarantee a correct understanding of the dynamics of available resource exhaustion.

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In this paper, SOURCE is an advanced semi-automatic Python tool that automates the hydrogeological 303 characterisation of spring aquifers. This tool was tested through an analysis of the Mascognaz 1 mountain 304 Figure 9: Cross correlation between rainfall and flow rate 19 springs. Graphical outputs, as well as hydrodynamic parameter values (e.g., VESPA index and auto-and cross-305 correlation coefficients) for an aquifer, can be obtained from SOURCE. These graphs and values are crucial 306 for understanding the hydrogeological processes that characterise spring aquifers and for developing a proper 307 groundwater resource management strategy.

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Unlike the software currently available through various university centres (e.g., RC software and the USGS 309 GW Toolbox), the proposed tool provides an accurate estimation of the vulnerability index and also provides 310 a recorded signal analysis using autocorrelation and cross-correlation statistical functions. A single software 311 package that contains all of the main methods of water spring analysis has the potential to significantly reduce 312 analysis times.

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The SOURCE intuitive interface allowed not only researchers and hydrogeologists, but also non-expert users 314 to test the software and correctly use its functionalities for mountain springs analysis.

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SOURCE is an open-source software tool, and the code is available for free download at 316 https://www.diati.polito.it/ricerca/aree/geologia_applicata_geografia_fisica_e_geomorfologia.

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The authors are open to all comments and advice from users that could help to further implement the code and 318 improve the performances. The authors declare that they have no known competing financial interests or personal relationships that 324 could have appeared to influence the work reported in this paper.

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The hourly recorded data used to support the findings of this study have not been made directly available 327 because they are ownership of Politecnico di Torino. However, they are reported as graphs.