MODIS satellite image of Sierra Nevada snowcover Big data and mountain water supplies Roger Bales SNRI, 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 storage Soil-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., std devs LESS as snowfall +1 std dev MORE as snowfall less snow more rain Mote, 2003 TRENDS ( ) in April 1 snow-water content at western snow courses less spring snowpack earlier snowmelt Stewart et al., 2005 Combined stresses: Climate warming Landcover change Population 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 & regression methods 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 solarlongwave meteorological data albedovegetation x y t snow energy balance model vegetation topography soils data cube precipitation Time SWE pixel 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