Earth and Atmospheric Sciences, Department of


Date of this Version



Lamb, S. E., Haacker, E. M. K., & Smidt, S. J. (2021). Influence of irrigation drivers using boosted regression trees: Kansas High Plains. Water Resources Research, 57, e2020WR028867.



© 2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution License,


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 Furthermore, these factors frequently change through time, rendering management strategies outdated within relatively short time scales.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.

Plain Language Summary Water use for crops has lowered groundwater levels in western Kansas. Past studies have shown that this water use is driven by many factors spanning policy, economics, and the physical environment. Because of this complexity, it has been difficult to fully understand which factors most drive irrigation use relative to each other. This study uses a machine-learning model to rank the influence of 45 factors on irrigation pumping. These factors are analyzed over space (∼19,000 wells across western Kansas) and time (2006–2016). Based on this study, drivers to water use include total irrigated area, summer rainfall, and depth to the water table. Factors that have little influence include management district boundaries and irrigation system type. These results are used to make water management suggestions for the region.