WITHDRAWN - Quantifying Future Groundwater Depletion, Climate Change and Irrigation Demand
Monday, December 4, 2017: 4:10 p.m.
101 C (Music City Center)
A warming world, population rise, and increased demands for irrigation all compound to create pressures on groundwater resources. Multi-model integration is needed to evaluate these interactions, combining available observations with crop, climate and hydrological models. Major agricultural regions of the USA (e.g., High Plains aquifer) and North India (e.g., Punjab) rely heavily on groundwater, therefore better quantification and predictions of the impact of climate change on groundwater resources are urgently needed. Here we provide an assessment of historical and future climate change impacts on groundwater storage using two novel modeling approaches, machine learning and Bayesian model. We demonstrate the integration of climatic, crop and hydrological determinants of groundwater storage change in the High Plains and Alluvial aquifer of Punjab, India. The model is calibrated using historical (1980-2012) climate, streamflow, ocean temperature observations, and simulated crop water demand. Model runs using climate model projections and irrigation demand are used to simulate changes in future groundwater storage. The climate data is used directly in the model, also in a land surface hydrology model to predict streamflow, and in a biophysical crop model to predict irrigation demand. Climate data from two GCMs each with scenarios RCP 4.5 and 8.5 are used to generate future scenario inputs. The models are run in a high performance parallel computing environment to obtain estimates of future groundwater level change for hundreds of wells across each aquifer. Based on this combined climate-agriculture-groundwater model, changes in future groundwater storage are projected up to 2049. These results will be useful for identifying the locations of future groundwater stress, which will have implications for sustainable agricultural production, and will help inform management decisions in a rapidly changing and resource-constrained world. Overall, the major findings will be useful for immediate use to researchers and will improve decision-making in stakeholder communities.