Assimilating Remotely Sensed Snow Observations into a Macroscale Hydrologic Model Kostas Andreadis and Dennis Lettenmaier UW-UBC Fall Hydrology Workshop Oct. 1 2004

Importance of Snow Snow plays key role in hydrologic cycle As much as 90% of annual streamflow is snowmelt driven in the western US In situ observations are unable to capture temporal and spatial variability of snow processes Large-scale observation strategies focus on remote sensing

Satellite Remote Sensing of Snow Snow Cover Extent (SCE) Visible wavelength sensors (GOES, AVHRR, MODIS etc) Cloud-free conditions required Lack of any information about water storage Snow Water Equivalent (SWE) Passive microwave sensors (SSM/I, AMSR-E etc) Coarse spatial resolution Wet snow and metamorphism greatly affect signal

EOS-era Satellites MODerate resolution Imaging Spectroradiometer (MODIS) was launched on board EOS-Terra in 1999 Provides a variety of land surface data, including snow cover extent Advanced Microwave Scanning Radiometer (AMSR-E) is on board the EOS-Aqua satellite Has been producing daily SWE datasets since February 2004

Model-based approaches for prediction of snow properties Additional information about snow properties can be obtained by land surface hydrology models Uncertainty in forcing data and/or model parameters Nonlinearity and scale of modeled processes

Motivation Data assimilation offers the framework to : optimally combine models and remote sensing observations account for the limitations of both Availability of improved remotely sensed data products (MODIS, AMSR-E) increases the potential for snow data assimilation

<Propagation Step> Data assimilation Initial State Observation Available? Hydrologic Model <Propagation Step> YES NO Forcing Data Assimilation Algorithm <Update Step> Observation Operator Observation

Ensemble Kalman Filter Propagation Step Analysis Step Propagation Step yαt yfi,t yfi,t+1 Time tk-1 tk tk+1

Variable Infiltration Capacity (VIC) Snow Model Solves energy and water balance over grid cells Subgrid variability in topography and vegetation (indirect representation of snow areal extent) Two layer energy and mass balance model Surface layer simulates energy exchanges with the atmosphere Pack layer acts as storage and simulates deeper snowpacks Accounts for processes like snow interception and densification

Experimental Design Study area selected: Snake River basin Assimilation of MODIS SCE data for period 2000-2003 MODIS daily SCE product @ 500 m spatial resolution Assimilation of AMSR-E SWE data for 2004 AMSR-E daily SWE product dissagregated @ 12.5 km resolution

Model Implementation VIC snow model hourly simulations at a spatial resolution of 1/8o Ensemble of model states are generated by perturbing precipitation and temperature forcings Precipitation perturbed by log-normally distributed spatially correlated random fields Temperature range and daily mean are perturbed with Gaussian random fields

Snow Depletion Curve We need a functional that relates model SWE with observed SCE Developed by Anderson (1973) and used currently at NWS Luce et al. 1999 Separate SDC for combinations of vegetation and elevation classes VIC simulated SWE data and MODIS imagery used to calculate Wmax parameter Gamma distributions fitted to infer the shape of the SDC

Snow Depletion Curve Vegetation classes: Forest Shrublands Grasslands Elevation classes: z < 1500 (m) 1500 < z < 2000 (m) z > 2000 (m) Figures show MODIS Snow Covered Area (SCA) versus simulated Snow Water Equivalent (SWE) in mm

MODIS Assimilation Results Comparison of percentage agreement between MODIS and simulations Days included based on a 50% cloud threshold

SWE Updating in MODIS SCE Assimilation Snapshots of simulated SWE (with/without assimilation) and MODIS SCE The enKF is able to consistently update SWE, by only using SCE information

MODIS Assimilation Results Comparison with SNOTEL and COOP station snow observations Stations with an elevation difference from the grid box, greater than 200 m were excluded (124 stations left from 257 originally)

MODIS Assimilation Results Mean peak seasonal SWE Filter estimated peak SWE values are closer to station values for 58 out of 66 available stations

SWE Validation SWE Percentile Relative Mean Squared Error (RMSE) for all available stations for each winter season enKF improved estimates for about half the stations However, performance was worse for the rest of the stations

Mean of Snow Water Equivalent Differences from SNOTEL with Time

Mean SWE RMSE variability with elevation and time Elevation zones are the same as those for the SDC March 1st was taken as the separating date between periods

Snow Water Equivalent SWE and SCE time series at West Yellowstone SNOTEL station (2008 m, VIC RMSE = 0.18 and enKF RMSE = 0.25) enKF SNOTEL VIC MODIS

Snow Water Equivalent SWE and SCE time series at Beaver Reservoir SNOTEL station (1545 m, VIC RMSE = 0.25 and enKF RMSE = 0.19) VIC enKF SNOTEL MODIS

AMSR-E Assimilation Results SWE Percentile RMSE as a function of maximum SNOTEL SWE enKF RMSE was improved for 31 out of 66 available stations

Limitations More accurate observation operator is required (e.g. use of independent SWE data to develop SDC) Non-continuity of SCE (0 to 1) – Assimilation has no effect during full snow coverage Modelling of both background and observation errors Water balance errors imposed by temperature biases

Questions?