Hydrology Modeling in Alaska: Model Documentation Template
(please fill out as much as possible)
Your name: Scott Lindsey
Model name: Various, NWSRFS, CHPS, Sacramento Soil Moisture Accounting,
Snow-17, etc
Authors: Various
Source code location (if public): NOAA/NWS Office of Hydrologic Development
http://www.weather.gov/oh/
Citations and URLs for basic documentation:
http://www.nws.noaa.gov/oh/hrl/nwsrfs/users_manual/htm/xrfsdocpdf.php
http://www.weather.gov/oh/hrl/chps/index.html
Source code language:
Fortran and C for NWSRFS
Java for CHPS
For the following section, you may wish to use appropriate keywords such as:
Physically-based, statistical, lumped parameter, spatially distributed, transitive
model, equilibrium model, implicit, semi-implicit, explicit, TOPMODEL based,
finite element, finite differences, routing, bottom boundary condition, parallel
code, Richardson equation, optimization, forecast, etc
Model type and/or conceptual framework:
SAC-SMA is a physically based, lumped parameter, conceptual soil moisture
accounting model that divides rainfall and computed snow melt sources into
surface runoff, direct inflow, impervious area inflow, interflow, supplemental
baseflow, and primary baseflow.
Data needed to run the model (inputs): point precipitation and temperature inputs
along with river stage (other meteorological inputs can be ingested to compute
evapotranspiration).
Parameters and how they are derived: Variety of soil moisture parameters
(snowmelt characteristic parameters for SNOW model) are developed using
historical point precipitation, temperature and flow data.
Spatial element used to lump inputs and outputs: elevation, aspect
Sub-models (i.e. snow or ground thermodynamics):
Snow model is a temperature index model
Rainfall/runoff transformation mechanism:
Sacramento (SAC-SMA) model moves water to channel, Unit Hydrograph model
time distributes surface runoff into channel.
Runoff routing within spatial elements and to basin outlet:
Unit Hydrograph model moves water from channel to outlet.
Several routing models available to move water from basin outlet downstream to
next forecast point
Method for including sub-grid scale processes:
Resolution (possible & prudent):
We apply NWSRFS to basins as small as 15 mi2 up to 15000 mi2. Obviously for
a lumped parameter model, 15 mi2 is on the small side and 15000 mi2 is on the
large side, but given the data network that we have in Alaska for both streamflow
and for forcings of temperature and precipitation, NWSRFS has been
successfully used at both ends of this range.
Method of deriving topography:
GIS used as a preprocessor to subdivide watersheds into elevation zones that
represent early snowmelt, mid to late snowmelt and glacier areas. This is done
during the calibration process
Calibration approaches:
Interactive iterative process. Inputs for temperature and precipitation are
prepared from historical data for a time period that overlaps with the historical
flow data. Initial parameter estimates are developed using GIS, regionalized
guidance from previous calibrations and model guidance from documentation.
Parameters are iteratively changed to minimize errors such as RMSE and to best
represent all flow regimes
Treatment of frozen ground:
SAC-SMA has a frozen ground component, but we have not used it.
Publications using this model: (small sample for Sacramento Model – many more
for the Snow-17 model not included)
Conference Articles
1. Bryce D. Finnerty, Michael B. Smith, Dong-Jun Seo, Victor Koren, Glenn
Moglen, Sensitivity of the Sacramento Soil Moisture Accounting Model to
Space-Time Scale Precipitation Inputs from NEXRAD, unknown - Office of
Hydrology Office, February 2008
2. GEORGAKAKOS, KONSTANTINE P., AND HUDLOW, MICHAEL D.,
QUANTITATIVE PRECIPITATION FORECAST TECHNIQUES FOR USE
IN HYDROLOGIC FORECASTING, PROCEEDINGS, TECHNICAL
CONFERENCE ON MITIGATION OF NATURAL HAZARDS THROUGH
REAL-TIME DATA COLLECTION SYSTEMS AND HYDROLOGIC
FORECASTING, WMO, STATE OF CALIFORNIA DEPARTMENT OF
WATER RESOURCES, AND NOAA, 12-13SACRAMENTO,
CALIFORNIA, September 1983
3. KRAJEWSKI, WITOLD F., AND HUDLOW, MICHAEL D., EVALUATION
AND APPLICATION OF REAL-TIME METHOD TO ESTIMATE MEAN
AREAL PRECIPITATION FROM RAIN GAGE AND RADAR DATA,
PROCEEDINGS, TECHNICAL CONFERENCE ON MITIGATION OF
NATURAL HAZARDS THROUGH REAL-TIME DATA COLLECTION
SYSTEMS AND HYDROLOGICAL FORECASTING, WMO, STATE OF
CALIFORNIA DEPARTMENT OF WATER RESOURCES, AND NOAA,
18-19SACRAMENTO, CALIFORNIA, September 1983
4. M.B. Smith*, V.I. Koren, E. Wells, D. Wang, and Z. Zhang, EVALUATION
OF THE ADVANTAGES OF THE CONTINUOUS SAC-SMA MODEL,
Presented at 15th Conference on Hydrology, AMS, January 9-14, 2000,
Long Beach, CA, January 2000
Journal Articles
1. FINNERTY, BRYCE D., SMITH, MICHAEL B., SEO, DONG-JUN,
KOREN, VICTOR, AND MOGLEN, GLENN, SPACE-TIME SCALE
SENSITIVITY OF THE SACRAMENTO SOIL MOISTURE ACCOUNTING
MODEL TO DISTRIBUTED INPUTS FROM RADAR-GAGE
PRECIPITATION FIELDS, JOURNAL OF HYDROLOGY, , , December
1997
2. Richard M. Anderson, Victor I. Koren, Seann M. Reed, Using SSURGO
Data to Improve Sacramento Model a Priori Parameter Estimates, Journal
of Hydrology, 320, 103-116, January 2006
3. Koren, Victor, Parameterization of SAC-SMA model specifically for dry
basins. Part I: Derivation of Climate adjustment relationships, A priori
parameter estimation improvement using new data sources, December
2008
4. Koren, Victor, Parameterization of SAC-SMA model specifically for dry
basins. Part II: CONUS-wide climate adjusted grids and their tests in the
distributed modeling, A priori parameter estimation improvement using
new data sources, March 2009
Strengths and Weaknesses in Alaska applications:
For real-time river forecasting in Alaska, the combination of models such as the
SAC-SMA and SNOW-17 have proven to be robust and work quite well for wellcalibrated basins. We also use a simple glacier model because warping the
snow model parameters to represent glaciers did not work well. This does give
us a tool for hydrologic forecasting, but NWSRFS does have some significant
weaknesses.
Those weaknesses include an architecture that was designed for a mainframe
computer with binary database files and fortran code. Adding new models to this
framework is very difficult. Data streams are optimized for ingesting point data
and computing areal means. Gridded forcing data is difficult to use within the
current framework. Extracting model states from the binary files is also not easily
accomplished. Finally, moving toward or investigating a more distributed
hydrologic modeling approach is very difficult within NWSRFS.
In contrast, CHPS-FEWS is modular, configurable and built with a service
oriented architecture. Initially, we will have the same models available in CHPS
that we currently use, but the opportunity to expand and experiment with new
models, data sources and forecast methods (i.e. ensemble inputs) will greatly
expand as a result of the migration to CHPS.