modis satellite image of sierra nevada snowcover big data and mountain water supplies roger bales...
Post on 17-Dec-2015
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MODIS satellite image of Sierra Nevada snowcover
Big data and mountain water
supplies
Roger BalesSNRI, UC Merced
& CITRIS
Example: forecasting the amount & timing of spring/summer snowmelt runoff in mountain rivers
Uses of data: hydropower scheduling, water allocations for agriculture & cities, dam operations, forest management, drought & flood planning
Past : reliance on historical runoff data, measurements at a few index sites, statistical correlations
Future: blending data from satellites, wireless sensor networks, advanced modeling tools
Available now: technology, satellite data, prototype ground data, strong community interest
Missing pieces: operational-quality wireless sensor networks, cyberinfrastructure to clean/integrate data & deliver custom information for decision support
R. Bales
infiltration
evapotranspiration
snowmelt
streamflow
sublimation
ground & surface water exchange
precipitation
Water balance – fluxes Reservoirs: Snowpack storageSoil-water storage
Myths:
We can, with a high degree of skill, estimate or predict the magnitude of these fluxes & reservoirs
Better hydrologic modeling using existing data sources will yield significant improvement
R. Bales
Example: forecasting the amount & timing of spring/summer snowmelt runoff in mountain rivers
Uses of data: hydropower scheduling, water allocations for agriculture & cities, dam operations, forest management, drought & flood planning
Past : reliance on historical runoff data, measurements at a few index sites, statistical correlations
Future: blending data from satellites, wireless sensor networks, advanced modeling tools
Available now: technology, satellite data, prototype ground data, strong community interest
Missing pieces: operational-quality wireless sensor networks, cyberinfrastructure to clean/integrate data & deliver custom information for decision support
R. Bales
Observed changes in water cycle go beyond historical levels
Knowles et al.,2006
-2.2 std devsLESS as snowfall
+1 std devMORE as snowfall
less snow more rain
Mote, 2003
TRENDS (1950-97) in April 1 snow-water content at
western snow courses
less spring snowpack
earlier snowmelt
Stewart et al., 2005
Combined stresses:Climate warmingLandcover changePopulation pressures
R. Bales
Example: forecasting the amount & timing of spring/summer snowmelt runoff in mountain rivers
Uses of data: hydropower scheduling, water allocations for agriculture & cities, dam operations, forest management, drought & flood planning
Past : reliance on historical runoff data, measurements at a few index sites, statistical correlations
Future: blending data from satellites, wireless sensor networks, advanced modeling tools
Available now: technology, satellite data, prototype ground data, strong community interest
Missing pieces: operational-quality wireless sensor networks, cyberinfrastructure to clean/integrate data & deliver custom information for decision support
R. Bales
Empirical & regressionmethods
Volume forecasts
Precipitation forecast
Decision making
Ground data
Seasonal water-supply forecasting – current
R. Bales
Example: forecasting the amount & timing of spring/summer snowmelt runoff in mountain rivers
Uses of data: hydropower scheduling, water allocations for agriculture & cities, dam operations, forest management, drought & flood planning
Past : reliance on historical runoff data, measurements at a few index sites, statistical correlations
Future: blending data from satellites, wireless sensor networks, advanced modeling tools
Available now: technology, satellite data, prototype ground data, strong community interest
Missing pieces: operational-quality wireless sensor networks, cyberinfrastructure to clean/integrate data & deliver custom information for decision support
R. Bales
Energy balance modeling scheme
solar longwavemeteorological
data albedo vegetation
xy
t
snow
energybalancemodel
vegetation
topographysoils
data cube precipitation
Time
SWEpixel by
pixel SWE & SCA
pixel by pixel runoff potential
keep it simple – but not too simple!
here is where the big data & information processing comes in
R. Bales
Example: forecasting the amount & timing of spring/summer snowmelt runoff in mountain rivers
Uses of data: hydropower scheduling, water allocations for agriculture & cities, dam operations, forest management, drought & flood planning
Past : reliance on historical runoff data, measurements at a few index sites, statistical correlations
Future: blending data from satellites, wireless sensor networks, advanced modeling tools
Available now: technology, satellite data, prototype ground data, strong community interest
Missing pieces: operational-quality wireless sensor networks, cyberinfrastructure to clean/integrate data & deliver custom information for decision support
R. Bales
lidar
A new generation of integrated measurements
satellite snowcover
low-cost sensors
Process research & advanced modeling tools
wireless sensor
networks
R. Bales
Example: forecasting the amount & timing of spring/summer snowmelt runoff in mountain rivers
Uses of data: hydropower scheduling, water allocations for agriculture & cities, dam operations, forest management, drought & flood planning
Past : reliance on historical runoff data, measurements at a few index sites, statistical correlations
Future: blending data from satellites, wireless sensor networks, advanced modeling tools
Available now: technology, satellite data, prototype ground data, strong community interest
Missing pieces: operational-quality wireless sensor networks, cyberinfrastructure to clean/integrate data & deliver custom information for decision support
R. Bales
Basin-wide deployment of hydrologic instrument clusters – American R. basin
Strategically place low-cost sensors to get spatial estimates of snowcover, soil moisture & other water-balance components
Network & integrate these sensors into a single spatial instrument for water-balance measurements.
in progress
R. Bales
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