Jeff Deems Western Water Assessment National Snow and Ice Data Center University of Colorado. Tom Painter NASA Jet Propulsion Laboratory

Transcription

1 Jeff Deems Western Water Assessment National Snow and Ice Data Center University of Colorado Tom Painter NASA Jet Propulsion Laboratory and the ASO Team

2 The NASA JPL Airborne Snow Observatory Snow Water Equivalent Riegl Q1560 ALS 800 khz Pulse Rate 2 lasers with fore/ar poineng Snow Albedo CASI Imaging Spectrometer μm 2m spaeal resolueon from 4000m aso.jpl.nasa.gov Retrieve topography: snow- free & snow- on Difference gives snow depth SWE from assimilaeon of modeled density field constrained by observaeons SWE variaeon primarily due to depth

3 Snow depth & SWE from LiDAR majority of spaeal variability in SWE is due to snow depth depth can be measured by differeneal elevaeon mapping collect snow- free & snow- covered data sets classify & remove vegetaeon subtract snow- free from snow- covered apply obs/modeled density SWE = depth * density SNOTEL/manual obs + snow model can esemate density well SNOWPACK model, courtesy CAIC LiDAR- derived snow depth, Colorado (Deems et al., 2013)

4 Snow Albedo from Hyperspectral Imagery Visible Near IR Reflectance Wavelength (μm)

5 Riegl Q1560 ALS Twin lasers Long range 5000m+ AGL Fore/aR poineng enhances coverage & geometry in steep terrain Wide FOV allows large overlap or efficient planning Full waveform processing improves subcanopy surface deteceon

6 ASO Compute System opera1onal products 24- hour data product latency products: snow depth SWE (using snow pillow + modeled densiees) broadband albedo LiDAR/spectrometer workflows are interdependent LiDAR DSM used for terrain correceon spectrally snow- free areas forced to zero snow depth

7 ASO DemonstraEon Missions /14/15 2 Primary study basins: Tuolumne River, Yosemite National Park, CA Uncompahgre River, CO Snow- off August, 2012 Snow- on Spring weekly flights in CA monthly flights in CO

8 Example Results: 2014 & 2015 Tuolumne Basin, CA Yosemite NaMonal Park h"p://wwa.colorado.edu

9 Tuolumne River Basin SWE aso.jpl.nasa.gov

10 Tuolumne River Basin SWE aso.jpl.nasa.gov

11 Tuolumne River Basin SWE aso.jpl.nasa.gov

12 Tuolumne River Basin SWE aso.jpl.nasa.gov

13 Tuolumne River Basin SWE aso.jpl.nasa.gov

14 Tuolumne River Basin SWE aso.jpl.nasa.gov

15 Tuolumne River Basin SWE aso.jpl.nasa.gov

16 Tuolumne River Basin SWE aso.jpl.nasa.gov

17 Tuolumne River Basin SWE aso.jpl.nasa.gov

18 Tuolumne River Basin SWE aso.jpl.nasa.gov

19 Tuolumne River Basin SWE aso.jpl.nasa.gov

20 Tuolumne River Basin SWE Tioga Pass, CA 16 April 2015

21 Tuolumne River Basin SWE aso.jpl.nasa.gov

22 Tuolumne River Basin SWE aso.jpl.nasa.gov

23 Tuolumne River Basin SWE aso.jpl.nasa.gov

24 Tuolumne River Basin SWE aso.jpl.nasa.gov

25 Tuolumne River Basin SWE aso.jpl.nasa.gov

26 Tuolumne River Basin SWE aso.jpl.nasa.gov

27 Tuolumne Basin SWE & Reservoir Inflow

28 Banded SWE Report Uncompahgre River above Ridgway, 30 April 2015

29 ApplicaEons IntegraMng ASO with water management

30 Hetch Hetchy Reservoir OperaEons balancing streamflow, water supply, & hydropower needs

31 Tuolumne Basin Hydrology Modeling SWE/Met StaMons & PRMS Model Units h"p://wwa.colorado.edu ASO SWE & Albedo aggregated to PRMS hydrologic response units Courtesy Bruce McGurk McGurk Hydrologic

32 PRMS inflow forecast June 1 Forecast + ASO Obs Inflow Raw PRMS Inflow results Hetch Hetchy Reservoir Tuolumne River, CA 2013 PrecipitaEon and temperature were forecasted from 6/1 Simulated and observed inflow are similar from 5/15 through 6/1, the forecast date Modeled SWE (red) corrected by ASO SWE (blue) McGurk Hydrologic

33 PRMS inflow June 1 Forecast + ASO Obs Inflow Raw PRMS Inflow forecast 2250 results 1750 Hetch Hetchy Reservoir Tuolumne River, CA /15/2013 5/16/2013 5/17/2013 Obs. HH Inflow (cfs) Raw PRMS basin_cfs ASO PRMS basin_cfs 5/18/2013 5/19/2013 5/20/2013 5/21/2013 5/22/2013 5/23/2013 5/24/2013 5/25/2013 5/26/2013 5/27/2013 ASO-corrected PRMS Inflow 5/28/2013 5/29/2013 5/30/2013 5/31/2013 6/1/2013 6/2/2013 6/3/2013 6/4/2013 6/5/2013 6/6/2013 6/7/2013 6/8/2013 6/9/2013 6/10/2013 6/11/2013 6/12/2013 6/13/2013 6/14/2013 6/15/2013 6/16/2013 Observed inflow 28 TAF less than inieal prediceon ASO- corrected forecast closer to observed Reservoir operaeons adjusted (drar reduced) arer ASO forecast HH reached top of gates, was full no water lost to spill McGurk Hydrologic

34 Tuolumne Basin Hydrology Modeling Results: 2014 h"p://wwa.colorado.edu McGurk Hydrologic

35 Current ASO products LiDAR High resolueon DEM & DSM Tree/canopy height maps Terrain morphology Water body mask Surface roughness Snow depth SWE SCA and SC% ElevaEon band SWE map & report SWE Eme series Snowline esemaeon Spectrometer Land surface albedo High resolueon imagery (RGB) High resolueon land cover classificaeon Snow albedo

36 SWE & SCA Reports For integraeon with exiseng operaeonal data needs Banded SWE products Map & tabular Basin aggregate SWE & SCA

37 ASO Expansion: California State of CA & water manager support for full Sierra Nevada coverage

38 Uncompahgre River Grand Mesa Rio Grande/Conejos R. (Colorado Water ConservaMon Board) Wyoming: (State Engineer s Office) Wyoming Range Wind River Range Sierra Madre Medicine Bow Mountains Idaho ASO Expansion: Southern Rockies West side of Wyoming Range h"p://wwa.colorado.edu

39 Megafire Disaster Response: NASA JPL and USDA Forest Service inter- agency collaboraeons ObjecEve: characterize pre, aceve, & post- burn condieons of megafires, to observe ecosystem properees influencing fire probability, behavior & recovery to inform management Coverage: California King (2014) and Rim (2013) megafires Data colleceon, processing & disseminaeon (wildfire.jpl.nasa.gov) is a collaboraeve effort that aids disaster response & post- fire recovery planning: IdenEfy endangered species habitat Water quality assessment Erosion assessment Removal of hazardous logs Timber harveseng A)#MASTER# #1#thermal#infrared#band,#a# proxy#for#nirops#(~#1#m#resoludon)# #used#in#acdve#fire#management# perimeter#mapping# AVIRIS hyperspectral improves burn area imaging over Rim Fire B)#NIROPS#progression#with#MODIS# muldlband#thermal#infrared#acdve# fire#pixels#(~#1000#m#resoludon)#can# be#used#to#calculate#fire#intensity# C)#MASTER#mulDband#thermal#infrared# (35#m#resoluDon)# LiDAR before (left) and after (right) King Fire Increasing#InformaDon#

40 New OpportuniEes in Water Management snow & weather paoerns are increasingly different from past years dust on snow climate warming new tools are becoming available to support resilient management of water resources expanded measurements of the snow energy balance airborne & satellite mapping of snow depth & albedo high resolueon weather forecasts integrated snowmelt & hydrology forecasts

41 Contact: Jeff Deems Tom Painter aso.jpl.nasa.gov