Influence of Irrigation Drivers Using Boosted Regression Trees: Kansas High Plains

Susan E. Lamb, Erin M.K. Haacker, Samuel J. Smidt

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Groundwater levels across parts of western Kansas have been declining at unsustainable rates due to pumping for agricultural irrigation despite water-saving efforts. Accelerating this decline is the complex agricultural landscape, consisting of both categorical (e.g., management boundaries) and numerical (e.g., crop prices) factors that drive irrigation decisions, making integrated water budget management a challenge. Furthermore, these factors frequently change through time, rendering management strategies outdated within relatively short time scales. This study uses boosted regression trees to simultaneously analyze categorical and numerical data against annual irrigation pumping to determine the relative influence of each factor on groundwater pumping across both space and time. In all, 45 key water use variables covering approximately 19,000 groundwater wells were tested against irrigation pumping from 2006 to 2016 across five categories: (1) management/policy, (2) hydrology, (3) weather, (4) land/agriculture, and (5) economics. Study results showed that variables from all five categories were included among the top 10 drivers to irrigation, and the greatest influence came from variables such as irrigated area per well, saturated thickness, soil permeability, summer precipitation, and pumping costs (depth to water table). Variables that had little influence included regional management boundaries and irrigation technology. The results of this study are further used to target the factors that statistically lead to the greatest volumes of groundwater pumping to help develop robust management strategy suggestions and achieve water management goals of the region.

Original languageEnglish (US)
Article numbere2020WR028867
JournalWater Resources Research
Issue number5
StatePublished - May 2021


  • Ogallala Aquifer
  • decision making
  • groundwater management
  • machine learning
  • water policy
  • water use

ASJC Scopus subject areas

  • Water Science and Technology


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