MODIS satellite image of Sierra Nevada snowcover

1. Big data and mountain water supplies Roger Bales SNRI, UC Merced & CITRIS MODIS satellite image of Sierra Nevada snowcover

2. 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

3. Water balance – fluxes Reservoirs: Snowpack storage Soil-water storage evapotranspiration precipitation 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 infiltration snowmelt sublimation streamflow ground & surface water exchange R. Bales

4. 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

5. TRENDS (1950-97) in April 1 snow-water content at western snow courses Knowles et al., 2006 Mote, 2003 -2.2 std devs LESS as snowfall +1 std dev MORE as snowfall Observed changes in water cycle go beyond historical levels less spring snowpack less snow more rain earlier snowmelt Combined stresses: Climate warming Landcover change Population pressures Stewart et al., 2005 R. Bales

6. 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

7. Seasonal water-supply forecasting – current Precipitation forecast Decision making Empirical & regression methods Volume forecasts Ground data R. Bales

8. 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

9. energy balance model vegetation topography soils pixel by pixel SWE & SCA SWE pixel by pixel runoff potential Time Energy balance modeling scheme data cube meteorological data snow precipitation solar longwave albedo vegetation t y x keep it simple – but not too simple! here is where the big data & information processing comes in R. Bales

10. 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

11. A new generation of integrated measurements low-cost sensors wireless sensor networks satellite snowcover lidar Process research & advanced modeling tools R. Bales

12. 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

13. 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 in progress Network & integrate these sensors into a single spatial instrument for water-balance measurements. R. Bales

14. Turning unknowns into knows through new water information systems Research support: NSF, NASA, CA-DWR, SCE, CITRIS R. Bales