Challenges & opportunities: water resources information systems Roger Bales, Professor University of California, Merced CITRIS Corporate Sponsor Day May 2, 2006

Problem: how to modernize California’s water information systems Challenges: Widely dispersed decision making & growing, heterogeneous demand for information Decades old technology in use, with only modest, limited programs underway to upgrade systems Collect Store Search Retrieve Analyze Present Societal “pulls” (for this project)

Technological advances offer opportunities Incoming price signals Availability of satellite remote sensing information Development of inexpensive, low-power sensors & powerful, ground-based sensor networks Maturing of physically based, spatially distributed hydrologic models Technology “Push”: (CITRIS technology being used/developed etc)

Bridge the gaps with current practice Priority needs & opportunities Incoming price signals Integrate the science Bridge the gaps with current practice Technology “Push”: (CITRIS technology being used/developed etc)

Existing point measurements fail to sample spatial variability Snow water equivalent measurements Other examples precipitation soil moisture snow albedo vegetation properties evapotranspiration

Plot-scale controls on snow distribution snow depth, cm 106 94 82 70 58 112 100 88 76 64 snow depth, cm elevation solar radiation vegetation density slope wind exposure relations differ at small catchment vs. regional watershed scale (Molotch et al., 2004)

Snow vegetation / interactions open crown edge under canopy Valles Caldera, 2005 66 76 86 96 106 116 126 136 day of year snowpack depth, cm 20 40 60 80 100 120 140

Soil moisture follows same pattern as snow high low low high

Measuring mountain water cycle at the basin scale satellite remote sensing multiple instrument clusters in a basin flux tower Instrument cluster Flux tower or meteorological station Embedded sensor network Sap flow array Stream & groundwater sensors Data & communication hub

Embedded sensor network for mountain water cycle Sensors snow depth air temperature relative humidity solar radiation soil moisture soil temperature … Pod microcomputer data storage radio battery solar cell One node Mother pod signal/data to/from other nodes signal/data network data & control data logger & IP connection via phone, radio or direct signal/data to/from UC Merced

Pod & snow pinger at Gin flat sensor network

Mother pod, data logger & snow pinger at Gin flat embedded sensor network Snowcourse SNOTEL

Sierra Nevada fractional snow cover from satellite: 3/7/04 We want a time series of these.

Integration of satellite & ground-based measurement systems & modeling Some opportunities: snowcover extent & water equivalent soil moisture precipitation streamflow/runoff forecasting

Energy balance modeling scheme data cube incident solar air temp relative humidity wind speed SWE albedo SCA longwave t y x energy balance LSM vegetation topography soils Time SWE pixel by pixel SWE & SCA pixel by pixel runoff potential basin potential runoff Time

Scaling mountain water balance Blending measurements from multiple scales basin SWE precip radiation EB ground/RS topography ground soil moisture micromet bedrock soils remote sensing SCA albedo vegetation soil moisture snow distribution fluxes micromet plot/hillslope infiltration & recharge

Applications: snowmelt modeling, Marble Fork of the Kaweah River (Molotch et al., GRL, 2004) Melt flux = (Rnetmq + Tdar)SCA net radiation > 0 degree days > 0 snow covered area From Noah mq = energy to water depth conversion, 0.026 cm W-1 m2 day-1 ar = conversion parameter, based on wind, humidity, roughness

Magnitude of snowmelt: modeled – observed snow water equivalent SWE difference, cm AVIRIS albedo Tokopah basin, Sierra Nevada assumed albedo assumed w/ update Basin-average albedo estimated from remotely-sensed AVIRIS (Airborne Visible/Infrared Imaging Spectroradiometer) data specific to the catchment typically differed by 20% from albedo estimated using a common snow-age based empirical relation, as often used in climate or hydrologic models. Using the AVIRIS albedo estimates in a distributed snowmelt model that explicitly includes net solar radiation resulted in a much more accurate estimate of the timing and magnitude of snowmelt as compared to the same model with the empirical albedo. Model improvement was most significant in areas and at times where incident solar radiation was relatively high and temperatures low. Molotch, N. P., T. H. Painter, R. C. Bales, and J. Dozier, Incorporating remotely sensed snow albedo into spatially distributed snowmelt modeling, Geophysical Research Letters, 31, L03501 DOI:10.1029/2003GL019063, 2004.

Bridging the gaps & integrating the science – next steps Embedded sensor networks critical need for prototype deployments develop communications & systems for data integration Data & information systems address need for user-oriented integration of heterogeneous data for decision support applications develop digital watershed tools & technologies Economic impact of project (especially for the Corporate Sponsors)

Who benefits Benefit: enhance billions of dollars of decisions annually by reducing uncertainty & enabling efficient water management Partners: State/federal water managers Water information providers Regional/local water managers (irrigation, urban, hydropower) Research community Private sector Public policy or social impact of this CITRIS project