
Pore water isotope fingerprints to understand the spatiotemporal groundwater recharge variability in ungauged watersheds
Author(s) -
Mattei Alexandra,
Barbecot Florent,
Goblet Patrick,
Guillon Sophie
Publication year - 2020
Publication title -
vadose zone journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.036
H-Index - 81
ISSN - 1539-1663
DOI - 10.1002/vzj2.20066
Subject(s) - groundwater recharge , watershed , hydrology (agriculture) , environmental science , groundwater , water balance , aquifer , geology , computer science , geotechnical engineering , machine learning
Reliable groundwater recharge quantification at the regional scale (e.g., watershed or subwatershed) is fundamental to sustainable water resource management. Although modeling at the watershed scale is gaining wide support, the long‐term monitoring needed for model calibration is often not readily available, as many watersheds worldwide remain ungauged. In response to this situation, we propose a new approach to estimate groundwater recharge at the watershed scale. This approach is fast and accurate and takes into account the existing variability without requiring long‐term monitoring. Only a single field campaign to acquire soil water content and pore water isotopic composition depth profiles is needed. The principle is to extend a physically based, one‐dimensional unsaturated zone flow model from the local (i.e., profile) to the watershed scale, using an index method for distributed recharge based on a GIS. The methodology was validated in a gauged watershed, where previous studies have estimated recharge using a spatialized water balance model calibrated using long‐term discharge monitoring data. Scaling was investigated by comparing recharge values obtained using the local‐scale approach at 10 study sites within the watershed with coinciding values obtained at the watershed scale. Recharge values were similar in terms of both dynamics and quantity. Using the pore water isotopic fingerprint of ungauged watersheds is therefore confirmed to be a suitable approach for understanding spatiotemporal recharge variability.