Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions

Soheil Nozari, Ryan T. Bailey, Erin M.K. Haacker, Zachary T. Zambreski, Zaichen Xiang, Xiaomao Lin

Research output: Contribution to journalArticlepeer-review

Abstract

The steady overexploitation of the Ogallala Aquifer underlying the U.S. High Plains Region has put irrigated crop production at risk, particularly in the Southern and Central High Plains. To manage this issue properly, a data-driven modeling framework is developed and tested that is fast to employ and yet provides reliable long-term groundwater level (GWL) forecasts as a function of climatological and anthropogenic factors. The modeling framework uses the random forests (RF) technique in combination with ordinary kriging, and is tested in Finney County in southwest Kansas. The introduction of groundwater withdrawal potential as a new surrogate for pumping intensity enables the RF model to capture decline in groundwater depletion rate as the system progresses towards aquifer depletion and/or as a result of well retirement policies. The RF model is executed from 2017 to 2099 for 20 different downscaled global climate models (GCMs) for the two representative concentration pathways (RCP) scenarios of 4.5 and 8.5. The results show the aquifer will cease to support irrigated agriculture in most of the county by 2060 under status quo management and average climate conditions. Moreover, climate will likely shift the aquifer's depletion time frame by 15 years or less in most of the study area. The long-term combined impact of well retirement plans and climate conditions on groundwater depletion trends imply well retirement policies do not lead to sustained groundwater savings. This study demonstrates the capacity of machine learning models to serve as a rapid assessment tool, informing policymakers about future groundwater availability in intensively irrigated regions and under different climate and management conditions.

Original languageEnglish (US)
Article number128511
JournalJournal of Hydrology
Volume614
DOIs
StatePublished - Nov 2022

Keywords

  • Climate change
  • Groundwater depletion
  • Groundwater management
  • Irrigation
  • Machine learning
  • Ogallala aquifer

ASJC Scopus subject areas

  • Water Science and Technology

Fingerprint

Dive into the research topics of 'Employing machine learning to quantify long-term climatological and regulatory impacts on groundwater availability in intensively irrigated regions'. Together they form a unique fingerprint.

Cite this