state of usda science: water management and water conservation dale bucks, susan moran, dave...
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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