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

Turning unknowns into knows through new water information systems

Research support: NSF, NASA, CA-DWR, SCE, CITRIS

R. Bales