Snow Properties Relation to Runoff Presentation by Karl Rittger This work is supported by Naval Postgraduate School Award N00244-07-1-113 and NASA Cooperative Agreement NNG04GC52A We investigate the relationship of snow-covered area (SCA) to snow water equivalent (SWE) in Sierra Nevada watersheds with varying latitudes, orientations, and elevations. In addition to snow-covered area, we estimate the spatial distribution of snow water equivalent. The correlations of these estimates with streamflow are evaluated for each of the watersheds.  

Motivation 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 In the mathematical sciences, a stationary process is a stochastic process whose probability distribution is the same for all times or positions. As a result, parameters such as the mean and variance, if they exist, also do not change over time or position. As an example, white noise is stationary. However, the sound of a cymbal crashing is not stationary because the acoustic power of the crash (and hence its variance) diminishes with time. Stationarity is used as a tool in time series analysis, where the raw data are often transformed to become stationary, for example, economic data are often seasonal and/or dependent on the price level. Processes are described as trend stationary if they are a linear combination of a stationary process and one or more processes exhibiting a trend. Transforming this data to leave a stationary data set for analysis is referred to as de-trending.

Snow Water Equivalent in the Sierra Nevada

Can we improve runoff forecasting by integrating 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

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

Study Area – Location and Topography

Topographic Characteristics Elevation American lower Kern higher Aspect Kern south facing Slope Similar Kern slightly steeper

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.

Satellite Spectral Bands Landsat MODIS

Landsat Thematic Mapper (TM and ETM+) TM RGB, sensor description Satellite sources include MODIS which provides daily imagery at 500m spatial resolution, and Landsat which provides imagery every 16 days at 30m spatial resolution. In California’s Sierra, daily snow pillow measurements of snow water equivalent are the main source of in-situ information.

Moderate Resolution Imaging Spectroradiometer (MODIS) MODIS RGB, sensor description

Top of Atmosphere Reflectance for Landsat Lλ = "gain" * QCAL + "offset“ Lλ = ((LMAXλ - LMINλ)/(QCALMAX-QCALMIN)) * (QCAL-QCALMIN) + LMINλ

6S radiative transfer code (http://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 Largest errors w/ polarization

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

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 Graphs from 6S

Surface reflectance to TOA reflectance for Landsat

Solar Zenith and Elevation

Snow Covered Area from Spectral Unmixing Fractional snow covered area is estimated using multiple endmember spectral unmixing from MODSCAG and TMSCAG at 500m and 30m respectively. These algorithms are based on MEMSCAG (Multiple Endmember Snow Covered Area and Grain Size) and use MODIS bands 1-7 or TM bands 1-5 & 7(Painter et al, 2003, RSE). Landsat TM saturates frequently saturates over snow in visible bands. Saturation in Bands 1-3 are modeled as 100% snow whereas saturation in 1 or 2 of the bands are modeled with 5 or 4 bands respectively to estimate fractional SCA. Landsat TM surface reflectance is modeled using 6S and accounts for elevation.   Roberts et al, 1998 Painter et al, 2003

SWE from snow pillows San Joaquin 3/8/2004

(Roughly?) Estimating 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 Snow water equivalent is interpolated from snow pillows using the Hypsometric method with inverse weighted distance interpolation of the residuals (Fassnacht et al, 2003, WWR). Difficult to assess accuracy w/o ground truth

Results from 2004 near peak SWE

SCA and Elevation SCA in the San Joaquin is very similar in varying size snow years as is the high elevation Kern, whereas SCA in the American differs greatly from year to year. Therefore, larger runoff years have deeper and/or denser snowpack at high elevations.  

SWE and Elevation Interpolation methods are limited by data input. The interpolated SWE maps shown above for the San Joaquin incorrectly depict higher depths of snow at lower elevations than at higher elevations. The lower elevation snow pillows measure more snow due to the orographic effect which is not accounted for by the interpolation. A greater number and better placement of snow pillows allow the American and Kern to be modeled more realistically.  

Snow Covered Area totals Landsat MODIS

Snow Water Equivalent totals Kern American San Joaquin Kern American San Joaquin

Correlation of SCA, SWE and blended SWE with Runoff We find significant values of rho for SWE and blended SCA x SWE for 30 m resolution and significant values of rho for blended SCA x SWE at 500 m resolution. These tests show that snow products at higher resolution give more information about spring runoff than lower resolution data. They also show that snow products that take advantage of both in-situ measurements and remote sensing are more informative.  

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