Transcript Hydrologic Predictability in the Mississippi River Basin

Publication of a large-scale hydrologic
data set using the SDSC SRB
NPACI All Hands Meeting
March 19, 2003
Edwin P. Maurer
University of Washington
Departments of Civil and
Environmental Engineering and
Atmospheric Sciences
Public Interest in Hydrologic Variability
Droughts
$6-8 billion/yr
Extreme Events
Floods $5 billion/yr
Source: NOAA, Hydrologic Info. Ctr.
Prospects for Future
Forecast for Future:
Deluge or Drought
“floods and droughts…will become
increasingly common as the world
grows warmer” – NYT, 8/28/2002
“effects…include increases in
rainfall rates and increased
susceptibility of semiarid regions
to drought.” – U.S. Climate Action
Report, 2002
Motivation for Development of Hydrologic Dataset
1) Large and growing costs imply
potential societal benefit to
predictability
2) Better observation and prediction
of climate signals and their
teleconnections to land areas
increases predictive skill
3) Improved understanding of
continental-scale hydrologic
variability through data collection
and modeling
Understanding the Land-Surface Water Budget
Examine variability in water budget components
(Near Surface) Water Balance Equation
dW
Q  PE
dt
Need long records of observations to
define variability and predictability
E P
W
Q
Precipitation and Evaporation Observations
Precipitation appears well
defined, generally since 1948
Ameriflux (flux towers)
provides measurements of
E, since mid 1990’s
U.S. Station density: 1 per 700 km2
U.S. Station Density: 1 per 130,000 km2
Snow Water and Soil Moisture Observations
• About 600 SNOTEL sites in
western US
• Snow water content
measured since 1977
•Spatial coverage poor at
continental scale
Source: A. Robock, Rutgers U.
Runoff (Streamflow) Observations
• Streamflow in the U.S. measured at roughly
7,000 active gauging stations.
• Stations represent regulated flow conditions
• Streamflow is a spatially integrated quantity
Source: U.S.G.S.
Need for Modeling
Variability of water budget components
cannot be determined with observations
dW
Q  PE
dt
To derive W and E, use P (and T) to drive a
hydrologic model
Reproduce observed Q
By water balance, E must be close
Physically-based land surface representation
can provide information on variability
Hydrologic Model
VIC Model Features:
•Developed over 10 years
•Energy and water budget
closure at each time step
•Multiple vegetation classes in
each cell
•Sub-grid elevation band
definition (for snow)
•Subgrid infiltration/runoff
variability
Use of Existing GIS Products
Soil parameters: derived
from Penn State State
STATSGO in the U.S., FAO
global soil map elsewhere.
Land Cover/Vegetation:
from the University of
Maryland 1-km Global
Land Cover product
(derived from AVHRR)
Production of the Hydrologic Dataset
Using VIC model, simulation run for 50
years at 3-hour time step
Input
Time series of spatial data
One terabyte of output archived
Derived Data Set Characteristics
•50-years
•3-hour time step
•1/8 degree (~12 km) resolution
•77,000 grid cells through domain
• Variables include all
water and energy
budget components
• Long term spatial
data set allows
characterization of
variability
Use and Sharing of the Dataset
•The database allowed the in-house
investigation of predictability of runoff,
resulting in several journal articles.
•Currently updating dataset.
•Ongoing use at the University of Washington,
using SRB as permanent storage site.
•This unique dataset is of interest to many
others in Climate modeling, Forecasting, and
Hydrologic Modeling communities.
Data Storage and Access
Storage challenge:
•Each File (3-hourly) ~500MB (compressed)
•19 variables x 51 years=969 files
• ~500GB space needed for 3-hourly files (+
80GB for daily).
•netCDF format used (compression to 2-D)
•Desired ftp-type storage
Discovered (by word of mouth) that SDSC
SRB could provide a storage system for:
•Large storage space for archiving data
•Permanent space allocation
•Accessible location for other users
Storing the Dataset
• Uploading files – first began investigating
SDSC SRB in late 2001. Our local system
limitations required postprocessing in 4
stages.
• PC-based software problematic (SRB
Browser, later replaced by InQ), esp. for
multiple file transfers and using PC/UNIX
network interface.
• Installation and use of UNIX utilities worked
better. Understanding “containers”
“collections” etc. outside of “ftp” mentality
– learning curve.
• Final uploading required assistance,
generously provided by SDSC personnel.
Subsequent updating has gone smoothly
with minimal outside assistance.
Retrieving Data from the Collection
• As with uploading, early PC-based software problematic
• Later InQ better, but PC-based retrieval restricted usability
(large file sizes, saving across networks)
• Browser-based mySRB good for examining archive but not
functional for downloading files
• UNIX utilities
allow scripting
for accessing
multiple
variables and
years.
• This is how the
data is almost
always used.
Geographic Distribution of Users
•On-line (voluntary) registration
form records many users
•Since the publication of the
journal article in November 2002,
over 40 unique users have
registered.
Precipitation/Land Cover Experiment
Differing VIC soil moisture
fields used to initialize a
mesoscale atmospheric
model over Florida
Shows changes in
precipitation due to soil
moisture and vegetation
(Marshall et al. 2002,
Colorado State Univ.)
Regional Application of the dataset
•Paucity of land surface
observations for validating
weather prediction and
climate models.
•VIC model output used as
“pseudo-observations”
•This graphic from a study
headed at UCSC/Scripps
(Roads et al., 2002)
Use in Diagnosing Climate Model Bias
Betts at al (2003) studied the
annual cycle of the land surface
water and energy balance over the
Mississippi River basin.
Assessed the systematic biases in
the surface energy and water
budgets of NASA-DAO atmospheric
general circulation model and
ECMWF reanalysis (ERA-40).
Used the current dataset as
benchmark measurements for
assessing model biases.
Summary and Future Plans
The SDSC SRB has provided a valuable service
to this project and the hydrologic community,
allowing the permanent storage and sharing of
our data set with other researchers.
As software improved and we gained experience
in retrieving data, users from many disciplines
have been able to successfully access the data.
Future data set development will benefit from this
experience, to provide:
Better file formats for selective downloading
Smaller files for ease of use
THANK YOU!