Econometric Estimation of Groundwater Depth Change for the High Plains Aquifer

Authors

Jonathan R. Sims, University of Nebraska-LincolnFollow

First Advisor

Lilyan Fulginiti

Date of this Version

11-2017

Citation

J. R. Sims, Econometric Estimation of Groundwater Depth Change for the High Plains Aquifer, MS Thesis, Department of Agricultural Economics, University of Nebraska–Lincoln, 2017. Accessed [mm/dd/yyyy] from http://digitalcommons.unl.edu/agecondiss/

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Agricultural Economics, Under the Supervision of Professor Lilyan E. Fulginiti. Lincoln, Nebraska: November 2017.

Copyright (c) 2017 Jonathan R. Sims

Abstract

This article presents a new method for estimating changes in depth to groundwater at a yearly, county level and incorporates these estimates as the dependent variable of econometric models for the High Plains aquifer. The High Plains (Ogallala) aquifer underlies eight states in the central United States and is the primary source of irrigation water for this large food producing region. The stock of groundwater is a finite, non-renewable resource with minimal recharge in most areas. Many fields of study, including hydrology and agricultural economics, are interested in depth to groundwater changes because they serve as a proxy for estimating groundwater stock changes. Economic data exist at the yearly, county level, but there are currently no yearly estimates for depth to groundwater changes making it difficult to reliably utilize economic optimization and production models that depend on groundwater data. Including the new estimates generated in this study as the dependent variable with climate, recharge, and irrigation as independent variables in panel econometric models (Pooled OLS, Random Effects, and Fixed Effects) with counties as the individuals produced statistically significant results. Further, models were found which consistently performed best when comparing coefficients and predicted values with outside estimates from hydrology studies.

Advisor: Lilyan E. Fulginiti