CLOUD COMPUTING FOR DROUGHT

MONITORING WITH GOOGLE EARTH ENGINE

Landsat 8 John Abatzoglou

Katherine Hegewisch

Alex Peterson

Donny VanSant

Rick Allen

Ayse Kilic

Tyler Erikson

David Thau

Noel Gorelick

Rebecca Moore

Mike Hobbins

Jim Verdin

Justin Huntington

Britta Daudert

Charles Morton

Dan McEvoy

Andy Joros

Landsat 8

Introduction• Collaboration with Google Earth Engine Team

• DRI and U-Idaho received two Google Earth Engine Faculty Research grants in 2014 to develop

software and provide guidance for monitoring of drought and evapotranspiration (ET)

• One of many results – a web application so anyone can process and visualize map and time series

and users can download results

MODIS April – October 2014 Median NDVI made with Earth Engine in about 7 seconds

Landsat for Vegetation Water Use and

Drought Monitoring• Remote sensing using Landsat is arguably the only way to detect vegetation stress and

ET at field scales over large areas

• Landsat pixel size (30m x 30m) is optimal for evaluating individual fields, riparian zones, and meadows (1985-pres)

• MODIS pixel size (250m x 250m) is optimal for regional analysis (2000-pres)

• To better understand if vegetation changes are natural or anthropogenic we need ~30+ years of satellite data, and paired with climate archives

• Better understanding vegetation and ET varies with climate at field and regional scales will increase the effectiveness of biological and hydrological monitoring plans, and drought monitoring

Landsat MODIS

Cloud Computing with Climate and Remote Sensing Data

• Develop a tool to better understand the long term spatial and temporal variability of ET from irrigated agriculture and groundwater dependent ecosystems (riparian areas, wetlands, springs)

• Rely on Landsat satellite imagery (16 day return intervals) to compute vegetation indices and energy balanced based ET

• Rely on gridded weather data to estimate PPT and ETo

• Problem – lots and lots of data and processing..

• 21 scenes for NV

• 1000+ Landsat images per path/row since 1985

• Equates to >20,000 images to process..

Google Earth Engine Cloud Computing• Google has the entire archive of Landsat and MODIS imagery and CFSR, NLDAS, and downscaled NLDAS

gridded weather data available for massive parallel processing in the cloud

• This technology has changed the paradigm of how we process and analyze satellite imagery and gridded

weather data

• https://earthengine.google.org/#timelapse/v=40.86687,-117.50682,8.988,latLng&t=2.85

Java Script and

Python

Application

Programming

Interface (API)

Max 30m Landsat 8 NDVI 5/2014 to 10/2014

Landsat and Drought Monitoring

Lovelock, Nevada – Humboldt River Basin• No groundwater pumping for irrigation (too salty)

• Very little storage upstream

• Extremely sensitive to persistent hydrologic drought

-Growing Season Max NDVI (30m Pixels) – Computed using Google Earth Engine

-Google hosts the entire 40yr+ Landsat archive and provides parallel cloud computing

2011 2013 2014

Wet Dry Drier

Landsat

~0%

water

delivery

Monitoring Spring declines – Needle Point Spring, UT• GW modeling is a necessary tool for assessing and predicting capture of natural

SW/GW discharge• https://earthengine.google.org/#timelapse/v=38.74288,-114.04747,10.812,latLng&t=0.61

Needle Point Spring stopped flowing

in 2001

Pumpers are point fingers at each

other as to who is responsible

Hearing just held at NV State

Engineer’s Office

Hydrologic Modeling & Remote Sensing• Groundwater modeling and remote sensing

tools are mutually supportive tools• Modeling supports the remote sensing

• Remote sensing supports the GW modeling

• Newly developed front end web application

to mine the Landsat and other remote

sensing and gridded weather data archives

in the cloud to evaluate change, and better

understand causality of change

Google Earth Engine Landsat Time

Series Tool – Climate Engine

Demo of Climate EngineHopefully a tool relevant at field and regional scales

www.climateengine.org – still in development..

• Show last summer MODIS vegetation vigor for NW NV (map)

Demo of CLIM Engine and some other tools that can be

useful for field level inventories and drought assessments

• Show case studies on Climate Engine

• Show PDSI for last year (case study link)

• Maggie Creek Restoration (Landsat summer NDVI time series)

• Range allotment by South Fork (MODIS summer time series, summer PPT)

• -115.92 E , 40.56 N

• Indian Valley dry vs drier year map and time series

• Impacts of pumping on Needle Point Spring vegetation (daily Landsat time

series)

Landsat 8, Launched Feb 11, 2013

Contact Information:

Justin.Huntington@DRI.edu

775-673-7670

Many thanks to:

You

Collaborators

Google

BLM

USGS/NASA

Landsat Science Team

NV Division of Water Resources

University of Idaho

Drought and Past Precipitation & Temperature

• Precipitation in NW NV is extremely variable from year to year

• Only a dozen years are near the10 inch average

• Recent droughts are relatively short compared to the 30s and 50s droughts

• Current temperatures are higher than the 30s drought; high temperatures increase severity of

droughts ( i.e. drought feedbacks)

*

Figures modified from Mike Dettinger, USGS

Precipitation 5yr avg.

Temperature 5yr avg.

Temperature Trend

Precipitation

Extended Droughts

Drought Evolution and Monitoring

Drought evolution is complex: it has multiple drivers and develops and

recovers at different time scales

Long Term

vs.

Short Term

Drought

Hydrologic

vs.

Soil Moisture/

Rangeland

Drought

Blue = Wet : Red = Dry

Jan – Dec 2014 Precipitation Anomaly July – October 2014 Precipitation Anomaly

• Fish Lake Valley example of pairing Landsat NDVI with PPT and pumping

• Groundwater is primary source of water for irrigation in the valley

• Test – can we see changes in greasewood NDVI due to pumping?

• https://earthengine.google.org/#timelapse/v=37.82067,-118.03078,10.812,latLng&t=2.86

Fish Lake Pumping and Wetland / Greasewood Flat

Vegetation

Result – Fish Lake Pumping &

Greasewood Phreatophytes

• Arlemont Ranch well (117 S01 E35 35CC 1) measured by NDWR

• Digitized polygon around well, ~ 0.25 miles across; is largerly comprised of greasewood

• Evaluated spatial average Aug-Sept NDVI, PRISM PPT, and water levels

• NDVI declining; GW levels declining

Avg. PPT = 5in/yr

Pre

cip

ita

tio

n F

rac

tio

n o

f N

orm

al

2013 July-Aug Max NDVI 2014 July-Aug Max NDVI

Indian Valley supports the largest

Sage-Grouse lek in NV (i.e. aggregation

of males dancing for females)

Indian Valley, NV

Remote Sensing for Sage-Grouse Sensitive Areas

Capture of Groundwater Discharge

• Appropriation of the full perennial yield assumes capture all the natural groundwater discharge

• By design, long-term groundwater pumping causes a lowering of the water table and reduces groundwater ET (ETg)• Capture of ETg is put to beneficial use (for humans)

• Capture of ETg reduces vegetation vigor and biological diversity

• In most cases, groundwater appropriation is based on the ETg from phreatophyte vegetation

Sources of Water to a Pumped Well

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.00 10 20 30 40 50 60

TIME, IN YEARS

FR

AC

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N O

F P

UM

PIN

G R

AT

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GW storage

“Capture”

- capture of SW and ETg

Theis (1940) “All water discharged by wells is balanced by a loss of

water somewhere else”

“the idea of safe yield…in which the size

of a development if it is less than or equal

to the recharge is considered to be ‘safe’

is fallacious”

“Often streams are depleted long before

the pumping reaches the magnitude of

recharge.”

“…if pumping equals recharge (or discharge),

eventually streams, marshes, and springs dry up”

“Despite being discredited repeatedly in the literature,

safe yield continues to be used as the basis of water-

management policies, leading to continued ground-

water depletion, stream dewatering, and loss of

wetland and riparian ecosystems.”

Monitoring, Management, Mitigation (3M) Plans

• Baseline and future hydrologic and biological monitoring (~7yrs)

• Establishment of groundwater management actions• Staged development

• Trigger levels

• Pumping schedules

• Assess response of ecosystems to withdraw

• Refinement of unreasonable adverse effects

• Mitigation measures• Operational adjustment

• Change in pumping location

• Reduction in pumping / curtailment

• Provide alternative water source

Shoshone Ponds

Stipulation Requirements for Hydrologic Monitoring

• Monitor stream / spring discharge

• Monitor vegetation vigor

• Separate impacts of climate from

pumping effects

• Remote sensing, including both aerial

photography and satellite imagery

“However, currently available technology does

not provide sufficient precision to detect short-

term changes in vegetation that may be

induced by groundwater withdrawal at the fine

scales necessary to meet the monitoring

requirements of the Plan. Instead, permanent

line transect data will be used to detect these

fine-scale vegetation changes.”

Shoshone Ponds