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AridGW Characterizing The Relationship Between Evapotranspiration and Groundwater Decline Across Arid Agricultural Regions: Outputs and Analysis
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| Created: | May 21, 2026 at 7:30 p.m. (UTC) | |
| Last updated: | May 28, 2026 at 3:19 p.m. (UTC) (Metadata update) | |
| Published date: | May 28, 2026 at 3:19 p.m. (UTC) | |
| DOI: | 10.4211/hs.b5ef5485b54e486ab4488d60158cce9a | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Published |
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Abstract
Groundwater is the primary water source for irrigated agriculture in many arid regions. Consequently, groundwater declines in cultivated drylands can threaten the sustainability of irrigation. However, little is known about why groundwater levels decline rapidly in some areas and more slowly in others. Here, we develop an accessible and reproducible workflow that integrates open-source, high-resolution data for groundwater-level records, evapotranspiration, and precipitation data to explore the relationship between agriculture and groundwater decline. We show that groundwater declines tend to be most rapid in areas where evapotranspiration exceeds precipitation. Our finding holds across multiple agricultural regions, spanning from southern California to eastern Arkansas. Our results suggest that regions where evapotranspiration exceeds precipitation are at elevated risk of groundwater depletion, and could be good areas to intensify future monitoring efforts. Altogether, our analyses demonstrate how climate and satellite data can be useful predictors of groundwater decline in cultivated drylands, and may provide a promising proxy for groundwater withdrawals where well metering data is absent.
Subject Keywords
Coverage
Spatial
Temporal
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Content
README.md
title: "AridGW-Archive-README" author: "Henry Oliver" date: "2026-05-22" output: html_document
Characterizing The Relationship Between Evapotranspiration and Groundwater Decline Across Arid Agricultural Regions
Abstract
Groundwater is the primary water source for irrigated agriculture in many arid regions. Due to limited precipitation, water withdrawals in these regions often outpace groundwater recharge, leaving them vulnerable to accelerating groundwater-level declines. This project aims to understand how the expansion of cultivated land and irrigation intensity relate to groundwater-level changes in arid regions of the United States. Satellite imagery offers a way to track agricultural practices and model their relationship to groundwater trends; however, accessing and analyzing data requires specialized software and coding experience, creating a technical barrier that prevents many users. To address this challenge, we develop a reproducible workflow that integrates groundwater-level records with remotely sensed evapotranspiration (ET) and precipitation data. Using ET:Precipitation ratios as a proxy for irrigation intensity, we analyze relationships between water use, aridity, and groundwater decline across multiple sites. Statistical analyses, including Spearman's rank correlation, and visualizations of inter-annual trends are used to quantify these relationships. This project improves understanding of the drivers of groundwater depletion in arid regions while reducing technical barriers to groundwater–agriculture research. The resulting workflow can support entities such as the Food and Agriculture Organization (FAO), Sustainable Development Goal 6 (SDG 6), and regional groundwater agencies in advancing sustainable water management.
Purpose
This archive holds the intermediate and final analysis outputs associated with the study described above. All outputs are reproducible by following the workflow documented at: https://github.com/meds-AridGW/ET_GW_analysis_workflow
Contents
et_precipt_ratio_nkm (n = 1, 2, 4, 10)
Annual groundwater and ET:Precipitation data for each monitoring well site, aggregated within a circular buffer of radius n km. These are intermediate outputs linking groundwater level trends to remotely sensed ET, precipitation, aridity, and land use variables. One file is produced per buffer size.
Files: et_precipt_ratio_1km.csv, et_precipt_ratio_2km.csv, et_precipt_ratio_4km.csv, et_precipt_ratio_10km.csv
| Column | Description |
|---|---|
year_value |
Calendar year of the observation |
site_id |
Unique identifier for each groundwater monitoring well site |
gw_trend_m_per_yr |
Long-term groundwater level trend at the site, in meters per year; negative values indicate decline |
region |
Regional grouping associated with the well site |
mean_et |
Mean evapotranspiration within the buffer area for the given year (mm/yr), derived from OpenET |
mean_precip |
Mean precipitation within the buffer area for the given year (mm/yr), from CHIRPS |
et_precip_ratio |
Ratio of ET to precipitation; used as a proxy for irrigation intensity |
AI_1km |
Aridity index value within a 1 km buffer around the well site |
aridity_class_1km |
Aridity classification based on the aridity index |
pct_cultivated |
Percentage of land classified as cultivated within the buffer area for the given year, derived from USDA Cropland Data Layer |
site_summary_nkm (n = 1, 2, 4, 10)
Site-level averages across the 2000–2020 study period for each of the 50 monitoring well sites. One file is produced per buffer size.
Files: site_summary_1km.csv, site_summary_2km.csv, site_summary_4km.csv, site_summary_10km.csv
| Column | Description |
|---|---|
site_id |
Unique identifier for each groundwater monitoring well site |
gw_trend_m_per_yr |
Average rate of groundwater level change at the site, in meters per year; derived from et_precipt_ratio_nkm |
region |
Regional grouping associated with the well site |
mean_et |
Mean annual evapotranspiration across the study period (mm/yr), from OpenET |
mean_precip |
Mean annual precipitation across the study period (mm/yr), from CHIRPS |
et_precip_ratio |
Mean ratio of ET to precipitation across the study period |
spearman_sensitivity_table.csv
Contains Spearman rank correlation coefficients (ρ) and associated p-values between groundwater decline rates and ET:Precipitation ratios, calculated for each region across all four buffer sizes. Positive ρ values indicate that higher ET:Precipitation ratios are associated with faster groundwater decline; negative values indicate the opposite. This table is used to assess sensitivity of the correlation to the choice of spatial buffer size.
| Column | Description |
|---|---|
region |
Regional grouping associated with the well sites |
rho_1km |
Spearman ρ between groundwater decline and ET:Precipitation ratio within the 1 km buffer |
p_value_1km |
P-value associated with rho_1km |
rho_2km |
Spearman ρ within the 2 km buffer |
p_value_2km |
P-value associated with rho_2km |
rho_4km |
Spearman ρ within the 4 km buffer |
p_value_4km |
P-value associated with rho_4km |
rho_10km |
Spearman ρ within the 10 km buffer |
p_value_10km |
P-value associated with rho_10km |
Data Access
Data from these tables came from the following sources. Detailed information on data access, downloads, and reproduction of tables can be found in this github repository: https://github.com/meds-AridGW/ET_GW_analysis_workflow.
USGS Well Data
Groundwater monitoring site metadata and associated groundwater level records are hosted on HydroShare. Download the CSV and place it in data/gw_data/:
Groundwater Site Data – HydroShare
USDA Cropland Cultivation Data
Cultivation data are sourced from the USDA Cropland Data Layer (CDL), available for 2008–2020. Download the National CDL zip files for each year and place them in data/cultivation/. Once in place, unzip each file.
Cropland Data Layer – USDA
OpenET Evapotranspiration Data
ET data are extracted via the OpenET Polygon Raster API. An account and personal API key are required. See evapotranspiration/open-et-timeseries.ipynb for extraction instructions.
CHIRPS Precipitation Data
Precipitation data are sourced from the CHIRPS v3.0 dataset (daily satellite-based rasters, 1981–present). No account or API key is required. Run precipitation/chirps_API.ipynb to download TIFF files to a chirps_data/ folder.
Aridity Index Data
Data were obtained from the Global Aridity Index and Potential Evapotranspiration (ET₀) Database: Version 3.1, available through Figshare. Download and unzip the Global-AI_ET0_annual_v3_1 folder. This analysis uses the annual raster file ai_v31_yr.tif.
Authors
Henry Oliver, Marie Tolteca, Richard Montes-Lemus, Austin Martinez
Acknowledgements
UC Santa Barbara Bren School, Dr. Scott Jasechko, USDA, USGS, OpenET, CHIRPS
Credits
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
|---|---|---|---|---|
| Richard Montes-Lemus | UCSB | |||
| Marie Tolteca | UCSB | |||
| Austin Martinez | UCSB |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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