Links & Resources

Links & Resources

Recent G@GPS Publications

Haldorsen, S., van der Ploeg, M., Cendon, D.I., Chen, J., Chkir Ben Jemaa, N., Gurdak, J.J., Purtschert, R., Tujchneider, O., Vaikmae, R., Perez, M., and Zouari, K., 2016, Groundwater and global palaeoclimate signals (G@GPS), Episodes – Journal of International Geoscience, 39(4),DOI:10.18814/epiiugs/2016/v39i4/103888.

Velasco, E.M., Gurdak, J.J., Dickinson, J.E., Ferre, T.P.A., and Corona, C., 2015, Interannual to multidecadal climate forcings on groundwater resources of the West Coast of the U.S., Journal of Hydrology: Regional Studies, special issue on the Water-Energy-Food Nexus of the Asia-Pacific Region.

Jasechko, S., Lechler, A., Pausata, F. S. R., Fawcett, P. J., Gleeson, T., Cendón, D. I., Galewsky, J., Le Grande, A. N., Risi, C., Sharp, Z. D., Welker, J. M., Werner, M., and Yoshimura, K. (2015). Glacial–interglacial shifts in global and regional precipitation δ18O. Climate of the Past 11, 1375-1393.

Pärn J., Vaikmäe R., Raukas A. and Bauert H. (eds). (2015) 4th Annual Meeting of G@GPS IGCP 618 Project, Estonia, 5-9 July 2015. Abstracts and Field guide book Tallinn University of Technology, Tallinn, 60 pp. (ISBN 978-9949-430-88-8)

Iverach C., Cendón D., Hankin S., Lowry D., Fisher R., France J., Nisbet E., Baker A., Bryce K. (2015), Assessing Hydraulic Connectivity Between Unconventional Gas Developments and Adjacent Aquifers Using Methane Isotopes, Dissolved Organic Carbon and Tritium. Scientific Reports, v. 5, p. 15996.

Scheiber L., Ayora C., Vázquez-Suñé E., Cendón D.I., Soler A., Custodio E., Baquero J.C. (2015). Recent and old groundwater in the Niebla-Posadas regional aquifer (southern Spain): Implications for its management. Journal of Hydrology, 523, 624-635.



New program to calculate local meteoric water lines (LMWL) by Hughes ad Crawford (2012):

Hughes, C. E., and Crawford, J., 2012, A new precipitation weighted method for determining the meteoric water line for hydrological applications demonstrated using Australian and global GNIP data: Journal of Hydrology, v. 464–465,  p. 344-351.

The relationship between d2H and d18O in precipitation at a site, known as the local meteoric water line (LMWL), is normally defined using an ordinary least squares regression (OLSR) which gives equal weighting to all data points regardless of the precipitation amount they represent. However, smaller precipitation amounts are more likely to have a lower D-excess due to re-evaporation of raindrops below the cloud-base or biases in the sampling method. In this paper we present an equation for a precipitation amount weighted least squares regression (PWLSR) that will correct these biases for use in groundwater and surface hydrology applications. New LMWL equations are presented for Australian sites in the Global Network of Isotopes in Precipitation (GNIP), where the PWLSR consistently produces a LMWL with a larger gradient than the OLSR.

Perth and Alice Springs exhibit the largest change in slope. This is consistent with the higher frequency of small monthly precipitation amounts with low D-excess values occurring at these sites in summer for Perth and throughout the year for Alice Springs.

The PWLSR method was also applied to 288 stations in the GNIP data base (N > 36) and the difference between the slopes of the LMWLs (Da = slopePWLSR–slopeOLSR) calculated for these stations. The mean change in slope, Da was 0.12 with 56% of sites showing an increase in slope or positive Da value and 44% having a decrease in slope or negative Da. Sites with Mediterranean climates showed the greatest increase in slope. The magnitude of the change in slope followed some general trends showing a positive correlation with average d2H and d18O composition and rainfall variability, and negative correlation with period of record (N).

Contact or go to if you want a copy of the paper or supplementary data which has the meteoric water lines for all GNIP sites. CLICK HERE to download an executable which calculates meteoric water lines using all the methods mentioned in the paper for your rainfall data.