Dr. Chinchu Mohan
Post Doctoral Researcher, University of Saskatchewan, Canada
Prof Andrew Western
University of Melbourne, Australia
Anthropogenic climate change alters the hydrological cycle, impacting water resources in multiple ways. As groundwater recharge is the most climate sensitive component of groundwater hydrology, understanding processes governing recharge and making a reliable estimate is an important step towards groundwater management under climate change. While several attempts have been made to estimate the climate change impact on groundwater recharge, the uncertainties involved in the predictions have not been well addressed.
This study aims to quantitatively evaluate the prediction uncertainty of global groundwater recharge in using CMIP5 climate data. In this study an empirical model of diffuse pluvial groundwater recharge (0.50x0.50; annual time step; 1960-2100) was forced with bias corrected climatological outputs from Hadley Centre Global Environmental Model version 2 Earth Systems model - (HadGEM2 -ES); and the Max-Planck-Institute Earth System Model-Low Resolution model (MPIESM-LR).
This study was carried out under RCP 4.5 and 8.5 and three ensemble members with varying initial conditions of each of the GCMs were also used to evaluate the GCM internal uncertainty. The results show that, the global groundwater recharge is expected to increase by
2080 under both RCP 4.5 and 8.5, however the rate of increase is highly region specific.
Across the globe ~53% of the area is having an increase in recharge, while 38% are expected to have decreased recharge by 2080. However, the future recharge estimates in most regions show significant variations with RCPs, GCMs and even between ensemble members of the same GCM. Despite the prediction uncertainty was region specific, global average it varied between 10 and 15 mm/y. The regions with highest percentage change in recharge (either increase or decrease) were the ones with highest variation in prediction.
This study clearly shows that the prediction uncertainty within different ensemble members are comparable with the uncertainties between RCPs or between GCMs. Thus, neglecting the prediction variance between ensemble members seriously compromises our understanding of the future state of groundwater systems across the globe.
Given the unknown future, one of the best possible ways ahead is to use multiple ensemble members along with multiple GCMs in groundwater recharge projections so that the uncertainty bounds are better understood.