State of USDA Science: Water Management and Water Conservation

Dale Bucks, Susan Moran, Dave Goodrich, Mark Weltz, and Numerous ARS Scientists

USDA Science Related to Water Management and Water Conservation

Covered in This Presentation

Near surface soil moisture Root zone soil moisture Snowmelt and runoff Water and energy balance Water quality Precipitation forecasting Weather generation Land cover assessment Vegetation and water stress CO2 flux

0 5 10 15 20 25 30 35 40 45 50 55 60

12 January 1997 23 March 1997

Percent Volumetric Soil Moisture

Tombstone Tombstone

Soil near saturation Soil dry

Near Surface Soil Moisture Maps Derived from Synthetic Aperture Radar (SAR) Images

Soil Moisture Field Experiments (SMEX)

USDA/ARS Watershed Experiment Sites Diverse vegetation, topography, soils, climate (Iowa, 2002;

Oklahoma, Georgia, 2003; Arizona, Idaho, 2005)

Approach Intensive sampling (satellite/airborne/ground) Short time duration (~1 month) Aircraft underflights of AMSR to scale from in-situ to satellite

footprint and evaluate heterogeneity Study spatial/temporal soil moisture dynamics and effects of

vegetation, temperature, texture & topography on soil moisture accuracy

Measurements Soil moisture (gravimetric, probe) Soil bulk density, texture, surface roughness Biomass, Soil temperature (IR, probe) Airborne (PSR-C, AESMIR, ESTAR, PALS) Ground-based radiometers

SGP

Iowa

Georgia

Idaho

Arizona

-98.35 -98.30 -98.25 -98.20 -98.15 -98.10 -98.05 -98.00 -97.95 -97.90

34.95

-98.35 -98.30 -98.25 -98.20 -98.15 -98.10 -98.05 -98.00 -97.95 -97.90

34.95

-98.35 -98.30 -98.25 -98.20 -98.15 -98.10 -98.05 -98.00 -97.95 -97.90

34.95

-98.35 -98.30 -98.25 -98.20 -98.15 -98.10 -98.05 -98.00 -97.95 -97.90

34.95

-98.35 -98.30 -98.25 -98.20 -98.15 -98.10 -98.05 -98.00 -97.95 -97.90

34.95

PSR-C and PALS airborne radiometer imagery

USDA/ARS Watershed Experiment Sites

Aqua AMSR-E Watershed Soil Moisture Validation Projects

SMEX02 (June 2002, Ames, Iowa) -- Experiment Plan http://hydrolab.arsusda.gov/smex02/smex02.htm

Little Washita, OK

Little River, GA

Walnut Gulch, AZ

Reynolds Creek, ID

AMSR-E SMEX03,05 U.S. Soil Moisture Validation Sites

-110 .25 -110 .15 -110 .05 -109 .95 -109 .8531 .50

31 .60

31 .70

31 .80

31 .90

-117.00 -116.90 -116.80 -116.70 -116.6042.95

43.05

43.15

43.25

43.35

-83.90 -83.80 -83.70 -83.60 -83.5031.40

31.50

31.60

31.70

31.80

-98 .30 -98.20 -98.10 -98.00 -97.9034.65

34.75

34.85

34.95

35.05

a) L ittle W ash ita , O K b) L ittle R iver, G A

d) R eyno lds C reek , IDc) W alnu t G u lch , A Z

Longitude W (Degrees)

Latit

ude

N (

Deg

rees

)

+ R ain gage E x isting S M site

+ R ain gage E x isting S M S ite

+ R ain gage E x isting S M S ite

+ R ain gage E x isting S M S ite+ +

+ +

AMSR-E Soil Moisture Validation

Global Soil Moisture Monitoring 2010

•AMSR is better than the past

•A lower frequency instrument is needed

•HYDROSOptimal frequencyBetter spatial

resolution than previous missions

20022002 19851985

1 2 3 5 10 20 30 50Low

High

Frequency (GHz)

Sen

siti

vity

Bare

AquaAqua Meteorological Meteorological Satellites Satellites

20102010

HYDROSHYDROS

Vegetated

INSTRUMENT: •Low frequency •Antenna technology to provide 10 km resolution

PARTNERS: NASA, MIT, JPL, DOD, IPO, Italy, Canada, and Science Team (ARS)

HYDROS provides the first global view of Earth's changing soil moisture and land surface freeze/thaw conditions, leading to breakthroughs in weather and climate prediction and in the understanding of processes linking water, energy, and carbon cycles, which enhances our agricultural competitiveness.

HYDROS was submitted to the NASA Earth System Science Pathfinder Program. It has been selected to serve as an alternative to the selected missions, should they encounter difficulties during initial development phases. New science and application priorities could affect selection.

Global Soil Moisture Monitoring 2010

MODIS 250 m Processing System OverviewNASA – DAAC Data Sets

MODIS 250 m. HDF files

• USGS database

• Pyrenees digital database

Meteorological

real time data

Digital Basis

• DEM’s

• Basin contours

• Ground control points

• GIS / Computer Codes

Snow Maps

Snow DepletionCurves

Snow Cover Tables

Product Users • U.S. Bureau of Reclamation (USA)

• Elephant Butte Irrigation District (USA)

• ENHER, Barcelona (Spain)

SRM Model

Snowmelt Runoff Forecasts

Internet Zone

Level 1b

HDF Files

Preprocessing

• HDF extraction

• Geometric correction

• Radiometric correction

HDF Tools• Webwinds (NASA-JPL)

• MS2GT (Wisconsin University)

• Commercial: IDL, ENVI

Snowmelt Runoff: MODIS and Modeling

RG1 2001 SRM ForecastRio Grande at Del Norte , 3,414.5 km 2

0

20

40

60

80

100

120

140

160

180

0 30 60 90 120 150 180

days f rom 1 April to 30 September

daily

dis

char

ge m

3/s

Forecasted

Measured

Forecasted and Measured Daily Streamflow of Rio Grande at Del Norte Using SRM with No

Updating – 2001 Snowmelt Season

• Obtained from conditions of an average year: 1976 (temperature and precipitation)

• Snow cover derived from 2001 conditions measured by MODIS satellite snow maps

Forecasted volume: 682.1 Hm3

Measured volume: 808.2 Hm3

V= 16.9 %

Snowmelt Runoff: MODIS and Modeling

5 km5 km

11

TA (z=50m)TA (z=50m)

GOESGOES

MODIS MODIS

TRADTRAD

CoverCover

ABLABL

LandsatLandsat

LandsatLandsat

60 m60 m

22

2-STAGE FLUX DISAGGREGATION PROCEDURE

2-STAGE FLUX DISAGGREGATION PROCEDURE

Evapotranspiration: Optical Remote Sensing and Modeling

DISALEXI – OUTPUT AT 30M RESOLUTION

DISALEXI – OUTPUT AT 30M RESOLUTION

ER01ER01

ER05ER05

ER09ER09

ER13ER13

El R

en

o, O

K 2

July

1997

El R

en

o, O

K 2

July

1997

1 GOES pixel1 GOES pixel

Evapotranspiration: Optical Remote Sensing and Modeling

DISALEXI – VALIDATION DISALEXI – VALIDATION Comparison of DisALEXI disaggregated surface energy

fluxes with eddy covariance measurements at same locationsComparison of DisALEXI disaggregated surface energyfluxes with eddy covariance measurements at same locations

Rn

G

H

ET

Rn

G

H

ET

0

200

400

600

0 200 400 600

Eddy Covariance Flux (W/m^2)Eddy Covariance Flux (W/m2)Eddy Covariance Flux (W/m2)Dis

AL

EX

I Flu

x (

W/m

2)

Dis

AL

EX

I Flu

x (

W/m

2)

xx

xx

Flux components

Flux components

Evapotranspiration: Optical Remote Sensing and Modeling

Water Quality: Sediment, Nutrients, and Chlorophyll

Water Quality: Sediment, Nutrients, and Chlorophyll

Landsat Image Derived Image

Lake Chicot, Arkansas

Seasonal Precipitation Forecast Nov-Dec-Jan 2002

Daily Precipitation Forecasting

Daily Precipitation Forecasting

Combining Remote Sensing and Modeling for Grassland Assessment: SEHEM - Spatially Explicit Hydro-Ecological Model

Distributed meteorological and precipitation data

Distributed elevation, soil, vegetation & model calibration

information

Real time, distributed simulations of the diurnal, seasonal and multi-year pattern of plant growth, soil water and energy fluxes

Satellite spectral data for model

calibration and validation

Leaf Area IndexLeaf temperatureSoil temperature

Visible Radiative Transfer Model

Thermal RadiativeTransfer Model

Plant GrowthSubModel

HydrologicSubModel

SEHEM: Spatially Explicit Hydro-Ecological Model

SEHEM Calibration Procedure

Maximum energy conversionefficiency and initial root biomass

Surface reflectanceand temperature

An Example of SEHEM Output

1990mean = 92.5 1991

mean = 66.0

1992mean = 89.5

1993mean = 65.9

1994mean = 50.7

1995mean = 75.2

1996mean = 76.8

1997mean = 47.1

1998mean = 82.9

1999mean = 91.8

20

40

60

80

100

120

140

160

AnnualNet PrimaryProduction1990-1999

Key component of most hydrologic models

Land Cover and Land Cover Change

Remotely sensed imagery

transformed into land cover

Multi-decadal RS Land Cover Change

Urbanization - 277% Increase

RUNOFFSEDIMENT

inte

nsi

ty

time

RAINFALL

415% basin increase inMesquite from ’73-’86

timeru

no

ff

HYDROLOGICMODELS

LandCover

DEM

Soils

Hydrologic Impacts of Land Cover Change using AGWA

High urban growth

1973-1997

San Pedro River Basin

<<WY >>WY

Water yield change between 1973 and 1997

SWAT Results

Sierra Vista Subwatershed

KINEROS Results

1997 Land Cover

Concentrated urbanization

Using SWAT and KINEROS for integrated watershed assessment Land cover change analysis and impact on hydrologic response

Pre-urbanization

Post-urbanization

1973Runoff

1997Runoff

Optical Remote Sensing: WDI Predicts Large-Scale Grassland CO2 Flux

1 – 1.099

(-.5) – (-.599)

(-.4) – (-.499)

(-.3) – (-.399)

(-.2) – (-.299)

(-.1) – (-.199)

0 - .199

.2 - .299

.3 - .399

.4 - .499

.5 - .599

.6 - .699

.7 - .799

.8 - .899

.9 - .999

1 – 1.099

(-.5) – (-.599)

(-.4) – (-.499)

(-.3) – (-.399)

(-.2) – (-.299)

(-.1) – (-.199)

0 - .199

.2 - .299

.3 - .399

.4 - .499

.5 - .599

.6 - .699

.7 - .799

.8 - .899

.9 - .999

1 – 1.099

(-.5) – (-.599)

(-.4) – (-.499)

(-.3) – (-.399)

(-.2) – (-.299)

(-.1) – (-.199)

0 - .199

.2 - .299

.3 - .399

.4 - .499

.5 - .599

.6 - .699

.7 - .799

.8 - .899

.9 - .999

1 – 1.099

CO2 Flux (mg m-2 s-1)

1 – 1.099

(-.5) – (-.599)

(-.4) – (-.499)

(-.3) – (-.399)

(-.2) – (-.299)

(-.1) – (-.199)

0 - .199

.2 - .299

.3 - .399

.4 - .499

.5 - .599

.6 - .699

.7 - .799

.8 - .899

.9 - .999

1 – 1.099

(-.5) – (-.599)

(-.4) – (-.499)

(-.3) – (-.399)

(-.2) – (-.299)

(-.1) – (-.199)

0 - .199

.2 - .299

.3 - .399

.4 - .499

.5 - .599

.6 - .699

.7 - .799

.8 - .899

.9 - .999

1 – 1.099

(-.5) – (-.599)

(-.4) – (-.499)

(-.3) – (-.399)

(-.2) – (-.299)

(-.1) – (-.199)

0 - .199

.2 - .299

.3 - .399

.4 - .499

.5 - .599

.6 - .699

.7 - .799

.8 - .899

.9 - .999

1 – 1.099

CO2 Flux (mg m-2 s-1)CO2 Flux (mg m-2 s-1)

260 (1993) 274 (1994)

242 (1998) 269 (1999)

R2 = 0.73

287

274

272256

242

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-T/Tp

WD

I

R2 = 0.73R2 = 0.73

287

274

272256

242

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-T/Tp

WD

I

R2 = 0.73

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-T/Tp

R2 = 0.90

CO

2(m

g/m

2 ) p

lan

t u

pta

ke

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-T/Tp

R2 = 0.90

CO

2(m

g/m

2 ) p

lan

t u

pta

ke

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

WDI

MAD = 0.24

CO

2(m

g/m

2/s

) fl

ux

ne

t C

O2

loss

fro

m s

oil

ne

t C

O2

pla

nt

up

tak

e

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

WDI

MAD = 0.24

CO

2(m

g/m

2/s

) fl

ux

ne

t C

O2

loss

fro

m s

oil

ne

t C

O2

pla

nt

up

tak

e

ArizonaNew

Mexico

Texas

Oklahoma

Maricopa Agricultural Center

Walnut Gulch Experimental Watershed

Jornada Experimental Range

Little Washita River Experimental Watershed

Maricopa Agricultural Center, Walnut Gulch Experimental Watershed, Jornada Experimental Range and Little Washita River Experimental Watershed

Temple, TX

Boise, ID

Coshocton, OH

El Reno, OK

Oxford, M SStillwater, OK

T ifton, GA

Treynor, IA

Tucson, AZ

University Park, PA

Watkinsville, GA

Woodward, OK

Columbia, M OBeltsville, M D

ARS Watershed Locations

State of Science USDA Science: Summary of Primary Target and Agricultural

Applications for Remote Sensing/Decision Support Systems

Primary Applications:Soil moisture

Drought and water scarcity predictions Variations in local weather, precipitation, and water resources Water quality indicators Global climate change effects Etc.

Primary Target: Agriculture, Water and the EnvironmentPrimary Goal: Clean and Abundant Water