A groundwater isoscape (δD, δ18O) for Mexico

Numerous studies have shown that precipitation isocapes drive δD and δ18O patterns in surficial waters and in terrestrial food webs. While the GNIP (Global Network for Isotopes in Precipitation) dataset provided a key foundation for linking precipitation-terrestrial isoscapes globally, it has insufficient spatial coverage in many countries like Mexico. To overcome this limitation, we hypothesized that shallow phreatic groundwaters in Mexico could be used as an isotopic integrator of long-term seasonally weighted precipitation inputs to the landscape to aid in calibrating spatial H and O isotope datasets for terrestrial, biological and hydrological research. Groundwater was sampled from 234 sites in Mexico at ~ 50 km latitudinal spacing to obtain high spatial resolution and country-wide coverage for the construction of a groundwater isoscape. Our data revealed that shallow groundwater infiltration in Mexico appears largely unaffected by evaporation and reflects seasonally weighted precipitation inputs. These precipitation inputs are primarily biased to summertime when highest rainfall occurs, but a small degree of post-precipitation evaporation revealed a lower d-excess zone that corresponded to the interior semi-arid ecozone. We developed a predictive general linear model (GLM) for hydrogen and oxygen isotopic spatial patterns in Mexican groundwater and then compared the results to a validation subset of our field data, as well external data reported in the literature. The GLM used elevation, latitude, drainage basin (Atlantic vs. Pacific), and rainfall as the most relevant predictive variables. The GLM explained 81% of the overall isotopic variance observed in groundwater, 68% of the variance within our validation subset, and 77% of the variance in the external data set. Our predictive GLM is sufficiently accurate to allow for future ecological, hydrological and forensic isoscape applications in Mexico, and may be an approach that is applicable to other countries and regions where GNIP stations are lacking.