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Calhoun: The NPS Institutional Archive
Faculty and Researcher Publications Funded by Naval Postgraduate School
2007
Snow properties in relation to runoff
Rittger, Karl
http://hdl.handle.net/10945/52418
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Presentation by Karl Rittger
This work is supported by Naval Postgraduate School Award N00244-‐07-‐1-‐113 and NASA Cooperative Agreement
NNG04GC52A
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Mo#va#on � In snowmelt dominated river basins, snow properties near peak accumulation are used to assess spring and summer runoff. Forecast models rely on estimates of the water stored in the snowpack to determine the contribution of snowmelt to runoff.
� Current operational runoff forecasts (DWR & NWS) assume stationary relationship between sparse point measurements of snow water equivalent and runoff � Spring runoff forecasts use snow course measurements taken near the 1st of each month
� Analysis of snow course measurements show non-‐stationarity ie. trends � Howat and Tulaczyk (2005) find decreasing and increasing SWE trends dependent on both latitude and elevation
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Snow Water Equivalent in the Sierra Nevada
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Can we improve runoff forecas#ng by integra#ng remote sensing sources? � Snow Covered Area
� From satellites � MODIS
� Daily at 500m � Landsat
� Every 16 days at 30m
� Snow Water Equivalent � Telemetered pillows
� Daily measurements
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Spring Runoff in the Sierra Nevada for the last 100 years
Based on monthly unimpaired runoff volumes, we selected a set of years during the Landsat TM historical record (1985-‐2007) that encompass 80% of the range of variability in runoff during the last century. An average family uses 0.25 to 1.0 acre-‐feet a year
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Study Area – Loca#on and Topography
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Topographic Characteris#cs � Elevation
� American lower � Kern higher
� Aspect � Kern south facing
� Slope � Similar � Kern slightly steeper
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Spring Runoff for each Watershed We estimate the fraction of snow in each 30 m pixel for the American, San Joaquin and Kern watersheds for five years that represent the minimum, quartiles and maximum April, May, and June unimpaired runoff. Recent years have produced similar variability in runoff, and fractional snow cover is estimated from MODIS for these years at 500 m resolution.
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Satellite Spectral Bands Landsat
MODIS
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Landsat Thema#c Mapper (TM and ETM+)
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Moderate Resolu#on Imaging Spectroradiometer (MODIS)
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Top of Atmosphere Reflectance for Landsat Lλ = "gain" * QCAL + "offset“ Lλ = ((LMAXλ -‐ LMINλ)/(QCALMAX-‐QCALMIN)) * (QCAL-‐QCALMIN) + LMINλ
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6S radia#ve transfer code (hQp://6s.ltdri.org)
� Developed by the Laboratoire d'Optique Atmospherique. The code permits calculations of near-‐nadir (down-‐looking) aircraft observations, elevated surfaces, non lambertian surface conditions, absorbing gases, Rayleigh scattering, and aerosol scattering effects. The spectral resolution is 2.5 nm. � Primarily used for LUTs for MODIS
� Kotchenova et al. 2006 � Kotchenova and Vermote 2007
� List of other atmospheric radiative transfer codes
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Physical Background � Fraction of photons from target reach satellite sensor. � Typically 80% at 0.85 µm and 50% at 0.45µm � Photons lost though absorption and scattering � Absorption from
� Aerosols (small) or atmospheric gasses � Principally O3, H2O, O2, CO2, CH4, N2O
� Scattering
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Surface Reflectance using 6S Signal perturbed by gaseous absorption and scattering by molecules and aerosols Absorption by atmospheric gases: O3, H2O, O2, CO2,CH4, and N2O
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Surface reflectance to TOA reflectance for Landsat
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Solar Zenith and Eleva#on
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Snow Covered Area from Spectral Unmixing
� Roberts et al, 1998 � Painter et al, 2003
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SWE from snow pillows San Joaquin 3/8/2004
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(Roughly?) Es#ma#ng SWE for a River Basin � Fassnacht et al, 2003
� Hypsometric Interpolation with inverse weighted distance interpolation of the residuals
� Spreads snow into the ocean
� Blended SWE � Multiply by SCA
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Results from 2004 near peak SWE
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SCA and Eleva#on
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SWE and Eleva#on
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Snow Covered Area totals Landsat
MODIS
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Snow Water Equivalent totals
American
San Joaquin
Kern A
merican
San Joaquin
Kern
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Correla#on of SCA, SWE and blended SWE with Runoff
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Conclusion � Although snow water equivalent interpolations are influenced by data availability, when combined with remote sensing it can be useful in predicting stream flow. These techniques can provide water managers with more accurate volumes of water stored in snowpack
� Further work will investigate alternative interpolation methods as well as utilize space-‐time interpolated MODIS snow cover to provide basin SWE estimates over the season