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Jillian M Deines

Stanford University | Postdoctoral Scholar

Subject Areas: Hydrology, Water Management, Sustainable Agriculture, Irrigation, Remote Sensing

 Recent Activity

ABSTRACT:

This resource is a repository of the map products for the Annual Irrigation Maps - High Plains Aquifer (AIM-HPA) dataset produced from Landsat satellite data in Deines et al. 2019. The maps cover a 608,260 km2 area across the High Plains Aquifer in the United States. AIM-HPA provides annual irrigation maps for 34 years (1984-2017). Please see Deines et al. 2019 for full details. If needed, copies can be requested from the author at jillian.deines@gmail.com.

Preferred citation:
Deines, J.M., A.D. Kendall, M.A. Crowley, J. Rapp, J.A. Cardille, and D.W. Hyndman. 2019. Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine. Remote Sensing of Environment 233:111400. DOI: 10.1016/j.rse.2019.111400

Map Metadata:
Map products are projected in EPSG:5070 - CONUS Albers Equal Area, NAD83
Resolution: 30 m
Raster value key:
0 = NoData, outside of study boundary
1 = Irrigated
2 = Not irrigated

Corresponding author: Jillian Deines, jillian.deines@gmail.com

Disclaimer: Irrigation maps are produced for research purposes and have an associated classification accuracy estimated to be ~91%. Overall, they are able to capture about 85% of the variation in county irrigated area statistics provided by USDA NASS. Please see Deines et al. 2019 for further detail on the methods underlying map production and estimated accuracies across years.

Note:
AIM-HPA can also be accessed directly from Google Earth Engine via the following publicly shared asset ID: "projects/h2yo/IrrigationMaps/AIM/AIM-HPA/AIM-HPA_Deines_etal_RSE_v01_1984-2017"
Example: var aimhpa = ee.Image("projects/h2yo/IrrigationMaps/AIM/AIM-HPA/AIM-HPA_Deines_etal_RSE_v01_1984-2017");
Example GEE code editor script for exporting maps from GEE: https://code.earthengine.google.com/e7623e04f410879b5d16b724dee94d0c

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ABSTRACT:

Preferred citation:
Hyndman, DW, T Xu, JM Deines, G Cao, R Nagelkirk, A Vina, W McConnell, B Basso, A Kendall, S Li, L Luo, F Lupi, D Ma, JA Winkler, W Yang, C Zheng, and J Liu. 2017. Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling. Geophysical Research Letters, 44. DOI: 10.1002/2017GL074429

We developed a new systems modeling framework to quantify the influence of changes in land use, crop growth, and urbanization on groundwater storage for Beijing, China. This framework was then used to understand and quantify causes of observed decreases in groundwater storage from 1993 to 2006, revealing that the expansion of Beijing'’s urban areas at the expense of croplands has enhanced recharge while reducing water lost to evapotranspiration, partially ameliorating groundwater declines. Please see Hyndman et al. 2017 for full details.

This repository contains assembled model input data not easily acquired through cited sources, model-subcomponent output such as annual land use rasters, and the MODFLOW groundwater model files which integrates these subcomponents.

Groundwater Model and Data
The MODFLOW groundwater model files and associated data can be found in the "Groundwater Model Files" folder. This includes well observation data, input recharge data, as well as data stored within the groundwater model such as pumping data and aquifer top and bottom. See the readme.txt within the folder and Hyndman et al. 2017 for additional detail.

Annual Land Use Rasters
The "Annual land use rasters" folder contains annually modeled land use. The key for land use codes is in LandUse_codeKey.csv. For methods, see Hyndman et al. 2017.

Contact: David Hyndman, hyndman@msu.edu

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ABSTRACT:

This resource is a repository of the map products for the Annual Irrigation Maps - Republican River Basin (AIM-RRB) dataset produced in Deines et al. 2017. It also provides the training and test point datasets used in the development and evaluation of the classifier algorithm. The maps cover a 141,603 km2 area in the northern High Plains Aquifer in the United States centered on the Republican River Basin, which overlies portions of Colorado, Kansas, and Nebraska. AIM-RRB provides annual irrigation maps for 18 years (1999-2016). Please see Deines et al. 2017 for full details.

Preferred citation:
Deines, J.M., A.D. Kendall, and D.W. Hyndman. 2017. Annual irrigation dynamics in the US Northern High Plains derived from Landsat satellite data. Geophysical Research Letters. DOI: 10.1002/2017GL074071

Map Metadata
Map products are projected in EPSG:5070 - CONUS Albers NAD83
Raster value key:
0 = Not irrigated
1 = Irrigated
254 = NoData, masked by urban, water, forest, or wetland land used based on the National Land Cover Dataset (NLCD)
255 = NoData, outside of study boundary

Training and test point data sets supply coordinates in latitude/longitude (WGS84). Column descriptions for each file can be found below in the "File Metadata" tab when the respective file is selected in the content window.

Corresponding author: Jillian Deines, jillian.deines@gmail.com

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 Contact

Mobile 5132907489
Mobile 5132907489
Email (Log in to send email)
Website https://jdeines.github.io
Resources
All 0
Collection 0
Composite Resource 0
Generic 0
Geographic Feature 0
Geographic Raster 0
HIS Referenced Time Series 0
Model Instance 0
Model Program 0
MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
Web App 0
Composite Resource Composite Resource

ABSTRACT:

This resource is a repository of the map products for the Annual Irrigation Maps - Republican River Basin (AIM-RRB) dataset produced in Deines et al. 2017. It also provides the training and test point datasets used in the development and evaluation of the classifier algorithm. The maps cover a 141,603 km2 area in the northern High Plains Aquifer in the United States centered on the Republican River Basin, which overlies portions of Colorado, Kansas, and Nebraska. AIM-RRB provides annual irrigation maps for 18 years (1999-2016). Please see Deines et al. 2017 for full details.

Preferred citation:
Deines, J.M., A.D. Kendall, and D.W. Hyndman. 2017. Annual irrigation dynamics in the US Northern High Plains derived from Landsat satellite data. Geophysical Research Letters. DOI: 10.1002/2017GL074071

Map Metadata
Map products are projected in EPSG:5070 - CONUS Albers NAD83
Raster value key:
0 = Not irrigated
1 = Irrigated
254 = NoData, masked by urban, water, forest, or wetland land used based on the National Land Cover Dataset (NLCD)
255 = NoData, outside of study boundary

Training and test point data sets supply coordinates in latitude/longitude (WGS84). Column descriptions for each file can be found below in the "File Metadata" tab when the respective file is selected in the content window.

Corresponding author: Jillian Deines, jillian.deines@gmail.com

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Composite Resource Composite Resource
Quantifying changes in water use and groundwater availability in Beijing: Supporting data for Hyndman et al. 2017
Created: Aug. 18, 2017, 5:54 a.m.
Authors: David Hyndman · Jillian Deines · Tianfang Xu · Guoliang Cao · Ryan Nagelkirk · Andres Vina · William McConnell · Bruno Basso · Shuxin Li · Anthony Kendall · Lifeng Luo · Doncheng Ma · Frank Lupi · Julie Winkler · Wu Yang · Chunmiao Zheng · Jianguo Liu

ABSTRACT:

Preferred citation:
Hyndman, DW, T Xu, JM Deines, G Cao, R Nagelkirk, A Vina, W McConnell, B Basso, A Kendall, S Li, L Luo, F Lupi, D Ma, JA Winkler, W Yang, C Zheng, and J Liu. 2017. Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling. Geophysical Research Letters, 44. DOI: 10.1002/2017GL074429

We developed a new systems modeling framework to quantify the influence of changes in land use, crop growth, and urbanization on groundwater storage for Beijing, China. This framework was then used to understand and quantify causes of observed decreases in groundwater storage from 1993 to 2006, revealing that the expansion of Beijing'’s urban areas at the expense of croplands has enhanced recharge while reducing water lost to evapotranspiration, partially ameliorating groundwater declines. Please see Hyndman et al. 2017 for full details.

This repository contains assembled model input data not easily acquired through cited sources, model-subcomponent output such as annual land use rasters, and the MODFLOW groundwater model files which integrates these subcomponents.

Groundwater Model and Data
The MODFLOW groundwater model files and associated data can be found in the "Groundwater Model Files" folder. This includes well observation data, input recharge data, as well as data stored within the groundwater model such as pumping data and aquifer top and bottom. See the readme.txt within the folder and Hyndman et al. 2017 for additional detail.

Annual Land Use Rasters
The "Annual land use rasters" folder contains annually modeled land use. The key for land use codes is in LandUse_codeKey.csv. For methods, see Hyndman et al. 2017.

Contact: David Hyndman, hyndman@msu.edu

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Composite Resource Composite Resource
Annual Irrigation Maps - High Plains Aquifer (AIM-HPA, Deines et al. 2019)
Created: Aug. 27, 2019, 5:09 a.m.
Authors: Deines, Jillian M · Kendall, Anthony D · Morgan A. Crowley · Rapp, Jeremy · Jeffrey A. Cardille · Hyndman, David William

ABSTRACT:

This resource is a repository of the map products for the Annual Irrigation Maps - High Plains Aquifer (AIM-HPA) dataset produced from Landsat satellite data in Deines et al. 2019. The maps cover a 608,260 km2 area across the High Plains Aquifer in the United States. AIM-HPA provides annual irrigation maps for 34 years (1984-2017). Please see Deines et al. 2019 for full details. If needed, copies can be requested from the author at jillian.deines@gmail.com.

Preferred citation:
Deines, J.M., A.D. Kendall, M.A. Crowley, J. Rapp, J.A. Cardille, and D.W. Hyndman. 2019. Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine. Remote Sensing of Environment 233:111400. DOI: 10.1016/j.rse.2019.111400

Map Metadata:
Map products are projected in EPSG:5070 - CONUS Albers Equal Area, NAD83
Resolution: 30 m
Raster value key:
0 = NoData, outside of study boundary
1 = Irrigated
2 = Not irrigated

Corresponding author: Jillian Deines, jillian.deines@gmail.com

Disclaimer: Irrigation maps are produced for research purposes and have an associated classification accuracy estimated to be ~91%. Overall, they are able to capture about 85% of the variation in county irrigated area statistics provided by USDA NASS. Please see Deines et al. 2019 for further detail on the methods underlying map production and estimated accuracies across years.

Note:
AIM-HPA can also be accessed directly from Google Earth Engine via the following publicly shared asset ID: "projects/h2yo/IrrigationMaps/AIM/AIM-HPA/AIM-HPA_Deines_etal_RSE_v01_1984-2017"
Example: var aimhpa = ee.Image("projects/h2yo/IrrigationMaps/AIM/AIM-HPA/AIM-HPA_Deines_etal_RSE_v01_1984-2017");
Example GEE code editor script for exporting maps from GEE: https://code.earthengine.google.com/e7623e04f410879b5d16b724dee94d0c

Show More