Transcript CITRIS_bales_CSD-2006-04

Challenges & opportunities: water
resources information systems
Roger Bales, Professor
University of California, Merced
CITRIS Corporate Sponsor Day
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Problem: how to modernize California’s
water information systems
Collect
Challenges:
Widely dispersed decision
making & growing,
heterogeneous demand
for information
Decades old technology in
use, with only modest,
limited programs
underway to upgrade
systems
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Present
Store
Analyze
Search
Retrieve
Incoming price signals
Technological advances offer
opportunities
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
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Incoming price signals
Priority needs & opportunities
Integrate the science
Bridge the gaps with current practice
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Existing point measurements fail to
sample spatial variability
Snow water equivalent measurements
Other examples
 precipitation
 soil moisture
 snow albedo
 vegetation
properties
 evapotranspiration
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Plot-scale controls on snow distribution
3300
3390
3450
snow depth, cm
112
106
100
94
N
88
82
2 km
76
4 km
snow depth, cm
elevation
solar
radiation
wind exposure
vegetation
density
slope
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relations differ at small
catchment vs. regional
watershed scale
(Molotch et al., 2004)
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64
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Snow vegetation / interactions
Valles Caldera, 2005
snowpack depth, cm
140
120
crown edge
100
80
open
60
40
under canopy
20
0
66
76
86
96
106 116
day of year
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126
136
Soil moisture follows same
pattern as snow
high
low
low
high
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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
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Embedded sensor network for
mountain water cycle
signal/data to/from other nodes
One node
Sensors
snow depth
air temperature
relative humidity
solar radiation
soil moisture
soil temperature
…
Pod
microcomputer
data storage
radio
battery
solar cell
signal/data
Mother pod
microcomputer
radio
battery
solar cell
data logger &
IP connection via
phone, radio or
direct
network data
& control
signal/data to/from UC Merced
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Pod & snow
pinger at Gin flat
sensor network
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Mother pod, data
logger & snow pinger
at Gin flat embedded
sensor network
Snowcourse
SNOTEL
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Sierra Nevada fractional snow cover from
satellite: 3/7/04
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Integration of satellite & ground-based
measurement systems & modeling
Some opportunities:
– snowcover extent & water equivalent
– soil moisture
– precipitation
– streamflow/runoff forecasting
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Energy balance modeling scheme
data
cube
SWE
albedo SCA
incident
solar
air
longwave temp
relative wind
humidity speed
t
y
x
vegetation
energy
balance
LSM
topography
soils
basin
potential
runoff
SWE
Time
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pixel by
pixel runoff
potential
pixel by
pixel SWE
& SCA
Time
Scaling mountain water balance
Blending measurements from multiple scales
basin
ground/RS
remote sensing
SWE
precip
radiation
EB
topography
SCA
albedo
vegetation
soil moisture
ground
soil moisture
micromet
bedrock
soils
micromet
plot/hillslope
infiltration &
recharge
snow distribution
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fluxes
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
mq = energy to water depth conversion, 0.026 cm W-1 m2 day-1
ar = conversion parameter, based on wind, humidity, roughness
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Magnitude of snowmelt: modeled –
observed snow water equivalent
SWE difference, cm
AVIRIS
albedo
Tokopah basin,
Sierra Nevada
assumed w/
update
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assumed
albedo
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
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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
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