New remote sensing methods of calculating surface water volumes

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New remote sensing methods of calculating surface
watervolumes

• Doug Alsdorf, UCLA
• Funded by NASA SENH, THP, LBA

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Outline

• The Global Water Cycle
• Soil Moisture, Precipitation, and Surface Water
• Why Wetlands Are a Key Target for Remote Sensing
• Spaceborne Measurements of Changes in Surface
  Water Volumes
• Challenges for GRACE
• Distribution of Potential Fields Knowledge
  Across Very Different Disciplines

Global volumetric measures are the key
Alsdorf, D. and D. Lettenmaier, Science,
1485-1488, 2003. Alsdorf, D., D. Lettenmaier, C.
Vörösmarty, and the NASA Surface Water Working
Group, EOS Transactions of the AGU, 269-276, 2003.
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Water Energy Fluxes in Global Water Cycle
DSfp Qout Qgw (P-E) Qin
From Land Cover Land Use Change Missions
From Precipitation (GPM, TRMM), Clouds
(CloudSat), and Soil Moisture Missions

• Global Needs
• Surface water area for evaporation direct
  precipitation
• DS and Q

From Soil Moisture Mission (e.g., SMOS, HYDROS)
Qtot DSmc DSfp Qtr Qatm Qsoil Qgw
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The Difficulty of In-Situ Measuring of Q and
DSof Wetlands
Singular gauges are incapable of measuring the
flow conditions and related storage changes in
these photos whereas complete gauge networks are
cost prohibitive. The ideal solution is a
spatial measurement of water heights from a
remote platform.
Non-Channelized Flow
100 Inundated!
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The Amazon Floodplain

• What is the role of wetland, lake, and river
  water storage as a regulator of biogeochemical
  cycles, such as carbon and nutrients?
• Rivers outgas as well as transport C. Ignoring
  water borne C fluxes, favoring land-atmosphere
  only, yields overestimates of terrestrial C
  accumulation
• Water Area x CO2 Evasion Basin Wide CO2 Evasion

(L. Hess photos)
Richey, J.E., J.M. Melack, A.K. Aufdenkampe, V.M.
Ballester, and L.L. Hess, Outgassing from
Amazonian rivers and wetlands as a large tropical
source of atmospheric CO2, Nature, 416, 617-620,
2002.
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Lack of In-Situ Knowledge

• Lake Calado The only floodplain lake, of 8000,
  where the water balance has been measured
  throughout annual hydrograph.
• 7 Gauges on major rivers, cannot define flow
  across floodplain Itapeau to Manacapuru 12,000
  inundated km2 1/2 Maryland, 634 USGS gauges,
  Potomac at D.C. 400 m3/s Negro40000 m3/s
• Worlds largest river, yet discharge and storage
  change are poorly known.

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Interferometric SIR-C Measurements of Water Level
Changes
Interferogram
Amplitudes
ALSDORF, D.E., J.M. MELACK, T. DUNNE, L.A.K.
MERTES, L.L. HESS and L.C. SMITH, Interferometric
radar measurements of water level changes on the
Amazon flood plain, Nature, v. 404, p. 174-177
2000. ALSDORF, D.E., L.C. SMITH, and J.M.
MELACK, Amazon water level changes measured with
interferometric SIR-C radar, IEEE Transactions on
Geoscience and Remote Sensing, v. 39, p. 423-431,
2001.
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Flow Network Extracted with GIS
Flow path networks are extracted from radar
mosaic. Water level drops are attached to the
flow paths using GIS. Flow path distances
between each drop and the Amazon River channel
are recorded.
65 km
5 cm
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Amazon Flow Path Distance
Water level drops are governed by flow path
distance, not by water body size.
Complex Algorithm Compared to simple algorithm,
there is a clearer pattern of diminishing drop
with increasing flow path distance. cc0.69 rms
upper2.18 cc0.71 rms middle1.54 cc0.71 rms
lower2.07
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Amazon Floodplain Storage Change from SAR
Muskingum Flow Routing Predicts downstream
hydrograph from reach storage, inflow and outflow
(i.e., continuity). Main Stem Qstorage Qin
Qtrib Qex Qout Floodplain Qstorage Qprecip
Qup - Qex Qevap
Qex Qfp2ms - Qms2fp Qex at average flow
corresponding to October 1994 is estimated at
6500 m3/s (Richey et al. 1989). Key Assumes
floodplain water levels and changes equal main
channel gauge. But, interferometric SAR
demonstrates that water levels are NOT equal.
Predicted water drops in cm
Interferometric SAR Method Spatially integrates
local measures of water levels changes on the
floodplain to estimate Qex. Requires flow
network, extracted from SAR or DEM, and local
water level changes (i.e., from interferometric
SAR). Yields 4600 m3/s (Alsdorf, 2003). Key
Assume one day in mid-October, 1994 when
interferometric observations were acquired is
similar to averages over 15 years in Muskingum
assumes extrapolation beyond SIR-C swath is
correct. Needs more data!
Difference is 30! If indicative of inundation
and recession, average annual error could be
equivalent to entire Mississippi River flow!
Richey, J.E., L.A.K. Mertes, T. Dunne, R.L.
Victoria, B.R. Forsberg, A.C.N.S. Tancredi, and
E. Oliveira, Sources and routing of the Amazon
River flood wave, Global Biogeochemical Cycles,
3, 191-204, 1989. Alsdorf, D.E., Water
storage and discharge across the central Amazon
floodplain measured with GIS and remote sensing
imagery, Annals of the Association of American
Geographers, 93, 55-66, 2003.
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Measured Floodplain Storage Change from
Interferometric JERS-1 SAR
12 Jul 96 15 Apr 96
29 Jun 97 2 Apr 97
11 Apr 93 26 Feb 93
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Floodplain Hydraulics
12 Jul 96 15 Apr 96
29 Jun 97 2 Apr 97
High Water
High Water
11 Apr 93 26 Feb 93
DEM
Initial Rising Water
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Channel Slope and Discharge from SRTM
SRTM
Water Slope from SRTM
Channel Geometry from SAR

Q
Mannings n
Observed96297 m3/s Estimated93498 m3/s
Hendricks, Alsdorf, Pavelsky, and Sheng, Channel
Slope from SRTM Water Surface Elevations in the
Amazon Basin, AGU Abstract, 2003
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Storage and Discharge from Radar Altimetry
Presently, altimeters are configured for
oceanographic applications, thus lacking the
spatial resolution that may be possible for
rivers and wetlands.
Water Slope from Altimetry
Classified SAR Imagery
DS

Birkett, C.M., Contribution of the TOPEX NASA
radar altimeter to the global monitoring of large
rivers and wetlands, Water Resources
Res.,1223-1239, 1998. Birkett, C.M., L.A.K.
Mertes, T. Dunne, M.H. Costa, and M.J. Jasinski,
Surface water dynamics in the Amazon Basin
Application of satellite radar altimetry, Journal
of Geophysical Research, 2003.
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Detectability of Modeled Monthly Changes in
Terrestrial Water Storage
Orange bars are changes in total soil and snow
water storage modeled by the Global Soil Wetness
Project. Error bars represent the total
uncertainty in GRACE-derived estimates, including
uncertainty due to the atmosphere, post glacial
rebound, and the instrument itself. Modified
from Rodell and Famiglietti 1999.
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GRACE, Hydrology, and Potential Field Modeling

• Modeling software has been developed during past
  3 decades for mass (and magnetic susceptibility)
  distributions in the earths crust
• Software is oriented with satellite observations
  in mind and includes both forward and inverse
  approaches
• Some Important References
• von Frese, R.R.B., W.J. Hinze, L.W. Braile and
  A.J. Luca, Spherical earth gravity and magnetic
  anomaly modeling by Gauss_Legendre quadrature
  integration, J. Geophys., 49, 234-242, 1981.
• von Frese, R.R.B., W.J. Hinze and L.W. Braile,
  Spherical earth gravity and magnetic anomaly
  analysis by equivalent point source inversion,
  Earth Planet. Sci. Lett., 53, 69-83, 1981.
• Dyment, J., and J. Arkani-Hamed, Equivalent
  source magnetic dipole revisited, Geophys. Res.
  Letters, 25, 2003-2006, 1998.
• Ongoing modeling research includes methods using
  global basis functions, infinite dimensional
  spherical harmonic basis functions (pers. comm.
  M. Purucker)

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Example Equivalent Point Source

• Inverse method of finding mass variations based
  on gravity anomalies
• Challenges
• spatially scaling the predicted mass variations
  to linear rivers and associated floodplains
• Including the temporal component
• Converting from geodetic framework to geophysical

von Frese, R.R.B., W.J. Hinze and L.W. Braile,
Spherical earth gravity and magnetic anomaly
analysis by equivalent point source inversion,
Earth Planet. Sci. Lett., 53, 69-83, 1981.
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Conclusions

• Lack of Q and ?S measurements cannot be
  alleviated with more gauges (e.g., wetlands
  diffusive flow).
• Instead, satellite perspective is best suited to
  measure the spatial variations in Q and ?S
• Radar altimetry ( imagery), interferometric SAR,
  and GRACE provide volumetric measures
• Challenges include incorporation of existing
  gravity modeling software within surface water
  mass and flux balance models.