English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Mountain springs represent one of the largest and most precious sources of potable water in Italy, necessary to meet the water needs of the population. Optimizing the present and future management strategies of mountain groundwater resources has become increasingly necessary. The accuracy and frequency of the flow rate ( Q ) measurements determine and restrict the processes that can be studied using spring hydrograph and recession curve analysis. Therefore, to properly define mountain aquifers’ hydrogeological properties, it turns out important to highlight the variation of the error in the estimation of the hydrogeological parameters as the time interval of sampling varies. In this paper, recession curve analysis was performed on two different mountain springs (Spring 1 and Spring 2) of north-western Italy, firstly considering available 4-h resolution measuring data and subsequently by resampling data to simulate longer sampling intervals of 1, 3, 7, 15, and 30 days.  The resulting distribution of errors introduced by longer acquisition intervals underlined how the percentage error increases with increasing acquisition interval. For obtaining an adequate estimation of mountain aquifer hydrodynamic parameters, in place of continuous hourly data, 1-day and 3-day sampling intervals with associated errors respectively lower than 5% and 10% were found to be valid.

Reliability of spring recession curve analysis as a function of the temporal resolution of the monitoring dataset