Journal: Ott, T.J., Majumdar, S., Huntington, J.L., Pearson, C., Bromley, M., Minor, B.A., ReVelle, P., Morton, C.G., Sueki, S., Beamer, J.P., & Jasoni, R.. (2024). Toward Field-Scale Groundwater Pumping and Improved Groundwater Management using Remote Sensing and Climate Data. Agricultural Water Management, 302, 109000.https://doi.org/10.1016/j.agwat.2024.109000

Groundwater overdraft in the western United States has prompted water managers to develop groundwater management plans that include mandatory reporting of groundwater pumping (GP). However, most irrigation systems in this region are not equipped with irrigation water flow meters to record GP and performing quality control of the available metered GP data is difficult due to the scarcity of reliable secondary GP estimates. We hypothesize that Landsat-based actual evapotranspiration (ET) estimates from OpenET can be used to predict GP and aid in quality control of the metered GP data. The objectives of this study are to:

1. Pair OpenET estimates of consumptive use (Net ET, i.e., actual ET less effective precipitation) and metered annual GP data from Diamond Valley, Nevada, and Harney Basin, Oregon;
2. Evaluate linear regression and machine learning models to establish the GP vs Net ET relationship; and
3. Compare GP estimates at the field- and basin-scales.

Results from using a bootstrapping technique showed that the mean absolute errors and root mean square errors for field-scale GP depth are ∼11 % and ∼14 % across Diamond Valley and Harney Basin based on the OpenET ensemble mean, which showed the highest skill among all the OpenET ET models. Moreover, the regression models explained 50 %-70 % variance in GP depth and ∼90 % variance in GP volumes. Our GP volume estimates are also within 7% and 17% of the total reported and measured volumes in Diamond Valley and Harney Basin, respectively, and the estimated average irrigation efficiency of 87 % aligns with known center-pivot system efficiencies. Additionally, the OpenET ensemble proves to be useful for identifying discrepancies in metered GP data, which are subsequently flagged as outliers. Results from this study illustrate usefulness of satellite-based ET estimates for estimating GP and metered GP data quality control and have the potential to help estimate historical GP.

Keywords: groundwater pumping; remote sensing; evapotranspiration; irrigation; machine learning; consumptive use

Title: Nevada Water Initiative

Major Goals: The primary goals will be to estimate water use across the state associated with agriculture and natural groundwater discharge areas, and perform data collection and monitoring that will be used for water use model development and validation. These estimates will be foundational for updating groundwater withdrawals, consumptive use, water budgets, and future hydrologic modeling activities across the state. Additional activities in collaboration with Nevada Division of Water Resources (NDWR) and U.S. Geological Survey (USGS) will focus on providing climate, water use, and Geographic Information System (GIS) data and analyses to support groundwater recharge, water availability, and water demand assessments across the state, and water budget assessments in priority basins.

Agencies involved:

Installing the correct environment and running the scripts

We would like to express our gratitude for the support received from the State of Nevada / U.S. Department of the Treasury (grant number 27042), United States Geological Survey (USGS) NASA Landsat Science Team (grant number 140G0118C0007), USGS Water Resources Research Institute (grant G22AC00584-00), Desert Research Institute Maki Endowment, and Windward Fund. Our appreciation extends to the open-source software and data communities for their generosity in making their resources available to the public. We also acknowledge the invaluable contribution of the OpenET consortium and respective funding partners, Google Earth Engine, Nevada Division of Water Resources, and the Oregon Water Resources Department for providing the necessary computational resources and datasets pertaining to ET, groundwater pumping, places of use, and other shapefiles used in this research. We also extend our gratitude to Dr. Richard G. Niswonger (USGS) for his helpful suggestions on handling discrepancies in the metered pumping data and to Marty Plaskett and Mark Moyle (Diamond Valley farmers) for their interest and support in our efforts. Furthermore, we are thankful to our colleagues and families for their unwavering motivation and support throughout this endeavor. It is important to note that any opinions, findings, conclusions, or recommendations presented in this manuscript are solely those of the authors and do not necessarily reflect the viewpoints of the funding agencies.

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