Post on 04-Jan-2016
Remote sensing for surface water hydrology
• RS applications for assessment of hydrometeorological states and fluxes– Soil moisture, snow cover, snow water equivalent,
evapotranspiration, vegetation cover and water content, land surface energy balance, water quality
• The above parameterize numerous physical, conceptual, and empirical models of surface water dynamics, such as runoff, infiltration, and streamflow
• Can runoff/streamflow be directly observed and quantified with RS?
Not with any current technology
NRCS* Curve number methodData and Parameters
• Digital Elevation model
• Watershed delineation
• Land use / land cover
• Soil hydrologic group
• Precipitation data
• Streamflow record
• Stream baseflow estimation
• Antecedent moisture condition
= CurveNumber}
* NRCS – Natural Resources Conservation Service
Essential observations of a surface water system
Precipitation (rainfall)
Infiltration
Runoff
Streamflow
Infiltration
Soil moistureRS directquantificationPassive microwave
methods
very coarse spatial resolutionpoor temporal resolution
expensive data
moderate spatial resolutionexcellent temporal resolution
free data
RS proxycharacterization
Landscape stateand energy flux
Data and Methodology
• Remote Sensing DataMODIS NASA’s Moderate Resolution Imaging Spectroradiometer - Surface temperature (LST)- Albedo- Vegetation state
- NDVI (Normalized Difference Vegetation Index)- EVI (Enhanced Vegetation Index)- User derived MSI (Moisture Stress Index) and others
AMSR-E Advanced Microwave Scanning Radiometer - Soil Moisture (resolution issues?)- Vegetation water content and roughness
General methodology
• MODIS time-series landscape biophysicals – High temporal resolution (daily but composited as 8 and 16 day
products)– Moderate spatial resolution (0.25 - 1km2 pixel dim)
• NEXRAD radar (Stage III, MPE) precipitation estimates• USGS gauged streamflow records
Model parameterization based on:
http://malibusurfsidenews.com/blog/uploaded_images/USGS_Pic2488r-764415.jpg
NEXRAD MPE radar estimate of hourly precipitation rate for 4 July 2006 (21:00 GMT) for Sandies Creek watershed and surrounding region. Rates ranged from 0.0 mm/hr (black pixel) to 14.6 mm/hr (white pixel) for cells within the watershed
Daytime LST (8 day composite) for the Sandies Creek watershed for the period 18 - 25 February 2002. Mean temperatures for this period ranged from 24.9 C (dark pixels) to 29.3 C (light pixels).
NDVI (16 day composite) image of the Sandies Creek watershed for the period 18 February – 6 March 2002. Dark-toned and light-toned pixels represent low and high NDVI values (stressed vegetation vs healthy), respectively.
How is LST coupled to soil moisture (or vice versa)
• Heat flux from the earth’s surface– Radiative flux (long wave thermal 9-13 μm)– Sensible heat flux (convection and conduction)– Latent heat flux (phase change)
• Is soil surface emissivity affected by soil moisture? would this affect radiative, sensible, or latent heat loss?
• Would a loss or gain of near-surface soil moisture likely impact sensible or latent heat flux?
From: http://upload.wikimedia.org/wikipedia/en/6/69/LWRadiationBudget.gif
Coupling vegetation to soil moisture
A Typical Vegetation Reflectance Spectra
0
0.1
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0.5
0.6
350
472
594
716
838
960
1082
1204
1326
1448
1570
1692
1814
1936
2058
2180
2302
2424
Wavelength
Ref
lect
ance
visible near infrared middle infrared
Leaf structure Leaf water content
Leaf chemistry
Spectral response of leaf drydown as % water loss
0
0.1
0.2
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0.7
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350
493
636
779
922
1065
1208
1351
1494
1637
1780
1923
2066
2209
2352
2495
Wavelength (nm)
Reflecta
nce
0%
15%
25%
32%
41%
55%
100%
nir
rednir
rednirNDVI
LLCC
EVIbluerednir
rednir
1
21
red
Spectral response of leaf drydown as % water loss
0
0.1
0.2
0.3
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0.6
0.7
0.8
350
493
636
779
922
1065
1208
1351
1494
1637
1780
1923
2066
2209
2352
2495
Wavelength (nm)
Reflecta
nce
0%
15%
25%
32%
41%
55%
100%
Band 2 Band 7Band 6
62
62
MbMb
MbMbNDWI
2
6
Mb
MbMSI
2
7mod
Mb
MbMSI
Development of a benchmark model (CN) for Sandies Creek for 2004
RS Model Development (2004)
• 6 MODIS parameters (LSTday, LSTnight, NDVI, EVI, NDWI, MSI) x 2 states (raw, deseasoned) x 3 antecedent offsets (0, 8, 16 days) = 36 regressors evaluated (plus precipitation)
• Streamflow log transformed (normality assumptions)
• Final model: Prec, LSTdayr(1), EVIr(0)
-3
-2
-1
0
1
2
3
4
logQ
Act
ual
-6 -4 -2 0 2 4 6 8 10 12
logQ Predicted P<.0001 RSq=0.84
RMSE=0.6876
Final equation:
ITPQ 331.7192.0439.0957.0log where Q = streamflow, P = precipitation, T = LST, and I = EVI
All β1,2,3 estimates significant at P < 0.0001β0 estimate significant at P < 0.04
2002 – 07* time series of daytime LST and precipitation
-10
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60
Jan-
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May
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Sep
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Jan-
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May
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Date
Tem
per
atu
re (
C)
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Mea
n d
aily
pre
cip
itat
ion
(m
m)
precipitationLST-dayLST-day deseasonedseasonal mean
Sandies Creek calibration and validation results
Model period E log series Bias
Calibration All (n = 174) 0.677 0.207 (-0.471)*
2002 (n = 43) 0.616 1.037 (-0.399)*
2003 (n = 42) 0.477 -0.467
2004 (n = 45) 0.705 -0.516
2005 (n = 44) 0.785 -0.627
Validation All (n = 57) 0.453 -0.322
2006 (n = 46) -0.028 -0.593
2007 (n = 11) 0.871 -0.293
Calibration Validation
* Exclusion of July 2002 flood event
Sandies Creek validation results (linear space)