Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020WR028867 |
Influence of Irrigation Drivers Using Boosted Regression Trees: Kansas High Plains | |
Susan E. Lamb; Erin M.K. Haacker; Samuel J. Smidt | |
2021-03-22 | |
发表期刊 | Water Resources Research |
出版年 | 2021 |
英文摘要 | 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 Furthermore, these factors frequently change through time, rendering management strategies outdated within relatively short timescales. 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 2006‐2016 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 ten 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. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/320953 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Susan E. Lamb,Erin M.K. Haacker,Samuel J. Smidt. Influence of Irrigation Drivers Using Boosted Regression Trees: Kansas High Plains[J]. Water Resources Research,2021. |
APA | Susan E. Lamb,Erin M.K. Haacker,&Samuel J. Smidt.(2021).Influence of Irrigation Drivers Using Boosted Regression Trees: Kansas High Plains.Water Resources Research. |
MLA | Susan E. Lamb,et al."Influence of Irrigation Drivers Using Boosted Regression Trees: Kansas High Plains".Water Resources Research (2021). |
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