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

2. 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)

3. 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)

4. 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)

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

6. 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)

7. 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

8. Soil moisture follows same pattern as snow
high
low
low
high

9. 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

10. 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

11. Pod & snow pinger at Gin flat sensor network

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

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

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

15. 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

16. 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

17. 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, cm W-1 m2 day-1
ar = conversion parameter, based on wind, humidity, roughness

18. 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: /2003GL019063, 2004.

19. 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)

20. 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