National Weather Service (NWS) River Forecast Centers (RFCs) frequently
use the Snow-17 and the Sacramento Soil Moisture Accounting (SAC-SMA) models
to assist with flood forecasting. Although these models were developed
using physical reasoning, they are conceptual models for which parameters
cannot be measured directly in the field. The most accurate parameter estimates
are still derived through calibration against observed streamflow; however,
the use of GIS data can also help. An ArcView application called the Calibration
Assistance Program (CAP) has been developed to extract useful information
from soil property, forest cover, satellite snow cover, rainfall climatology,
and potential evaporation data layers.
Thirteen National Weather Service River Forecast Centers (RFCs) in the
United States are responsible for producing both short and long-term river
flow forecasts. There are currently about 4,000 forecast points in the
United States, some along the main stems of large rivers and others along
tributaries. To produce point forecasts, major river basins are typically
subdivided into basins ranging in size from 300 - 5000 km2.
Lumped rainfall-runoff and snow melt (where necessary) models are typically
applied to each basin, and the forecasted flows at basin outlets are routed
through the river network to downstream forecast points.
Two conceptual watershed models are commonly used to simulate runoff
in each basin modeled: Snow-17 (Anderson, 1973) for snow accumulation and
ablation and the Sacramento Soil Moisture Accounting Model (SAC-SMA) (Burnash
al., 1973) for translating rain-plus-snowmelt into runoff. Model calibration
is essential to the successful application of these conceptual watershed
Calibration of conceptual models at RFCs is an ongoing process. There
remain a number of basins in the United States where conceptual models
have not been applied, but where application of these models may provide
future benefits in forecasting. Model calibrations may be needed when an
RFC adds a new forecast point or when there is a transition to a newer
model technology. For instance, many RFCs have moved away from the empirical
Antecedent Precipitation Index (API) type model (NWSRFS, 2001, II.3) in
favor of the SAC-SMA conceptual model. A significant advantage of continuous,
conceptual models over event API models is that they can be used for long
term hydrologic forecasts (weeks to months in advance). Long term forecasts
are valuable to water managers dealing with a wide variety of issues including
reservoir regulation, hydro-power generation, navigation, and Endangered
Species Act implementation. Additional re-calibration needs may arise if
there are changes to basin land use or significant changes in the operational
rain gage network over time.
Preparing a properly calibrated hydrologic model for river forecasting
involves significant effort. Basin calibration requires (1) data preparation,
(2) derivation of initial estimates for model parameters, and (3) manual
or automatic calibration or both using observed streamflow data to refine
model parameter estimates.
The Calibration Assistance Program (CAP) is an application designed
to assist the calibrator in deriving initial parameter estimates. The ArcView
extension (AvCAP) described in this paper replaces an older GRASS-based
(USACERL, 1983) application. In addition to maintaining equivalent functionality
to the old CAP, the new application includes enhanced functionality and
new underlying data sets. The development of AvCAP focuses only on aspects
of calibration that require GIS capabilities. The numerous other steps
in the calibration process are currently supported by other programs in
the National Weather Service River Forecast System (NWSRFS, 2001). Table
1 lists programs used to help with various aspects of calibration.
Table 1. Programs Used During Model Calibration
|Calibration Assistance Program (CAP, AvCAP)
|Integrated Hydrologic Automated Basin Boundary
||Delineate basin boundaries and compute drainage
areas. Also includes tools to assist in deriving synthetic unit hydrographs.
|Historical Data Browser (HDB)
||Locate data stations; retrieve descriptive station
information, period of record, brief statistical summaries, etc.; extract
data for use in other applications
|Precipitation Preliminary Processing (PXPP)
||Use for station consistency checks; compute
mean monthly station values; replace missing data
|Interactive Double Mass Analysis (IDMA)
||Graphical assistance for determining when consistency
corrections are required
|Mean areal precipitation (MAP), mean areal precipitation
from NEXRAD fields (MAPX), Mean areal temperature (MAT), and Mean areal
potential evaporations (MAPE)
||Compute time series of basin mean values for
|Manual Calibration Program (MCP), Interactive
Calibration Program (ICP)
||ICP provides and interface for viewing model
states, outputs, and flow hydrographs; ICP runs MCP with user specified
|Parameter Optimization Program (OPT)
||Automatic parameter optimization program
The remainder of this paper is split into three main sections: (1) a
description of the current capabilities of AvCAP, with emphasis on how
information derived from AvCAP is used in the SNOW-17 and SAC-SMA models,
(2) a brief description of the key computational features and GUI components
not discussed in section 1, and (3) concluding remarks and possible future
DERIVING MODEL PARAMETERS USING AvCAP
Pre-processed data layers delivered with the software are a unique feature
of AvCAP. Data delivered with AvCAP can be displayed and queried using
standard ArcView and Spatial Analyst functionality. In addition, there
are custom tools to do the following tasks:
Although some (not all) of the functionality described here is fairly routine
and could be accomplished manually by an experienced ArcView user, a goal
of AvCAP customization is to simplify parameter estimation for the non-expert
user. Further explanation of these functions (below) is organized based
on how the derived information is used in the hydrology models. Area-elevation
curve derivation, basin subdivision, forest cover analysis, and the display
of snow cover data are used to derive Snow-17 model inputs. Functions related
to precipitation analysis are useful in deriving station weights for precipitation
analysis in mountainous areas. Mean estimates of evaporation, evaporation
adjustment factors, and soil-based SAC-SMA estimates are used in SAC-SMA
Derive area-elevation curves
Derive elevation-precipitation plots
Subdivide basins into elevation zones
Display defined zones on top of other data layers (e.g. precipitation,
Compute basin or zone statistics for:
Long-term Mean Precipitation (monthly, seasonal, annual)
Long-term Mean Potential evaporation (monthly, annual, seasonal)
Potential evaporation adjustment factors
Soil-based estimates for 11 SAC-SMA parameters
Percent of each forest type
Automatic Theme manipulation to reduce the tedium of loading and setting
the legends for data sets of interest (e.g. snow cover, forest types, etc.)
Before elaborating on the AvCAP functionality, a summary of the key
data sets that support AvCAP functions is provided in Table 2. These data
were collected from several sources, as indicated in Column 4 of Table
2. Several of these data sets are described further in the discussions
Table 2 Data Sets Used by AvCAP
||400 -m digital elevation model
||NOHRSC 15 arc-second data projected into Albers, NOHRSC
created the 15 arc-second DEM by resampling USGS 3 arc-second DEM data
(also known as 1:250K data or 1 degree quadrangle data)
||Percent forest cover for ~ 1 km pixels
||Forest cover types for CONUS (~ 1km pixels)
|Pptann, pptjan, pptfeb, pptmar, etc.
||PRISM Annual and monthly precipitation [inches] (~ 5 km grid cells)
|Peann, pejan, pefeb, pemar, etc
||Annual and monthly potential evaporation [inches] (~ 10 km grid cells)
||Koren et al. (1998)
|PE adjustment grids (~ 10 km grid cells)
||Koren et al. (1998)
|SAC-SMA Parameter Grids
||11 grids (1 km resolution)
||Koren et al. (2000); Duan et al. (2001)
|soil depth, soil texture, and hydrologic soil group grids
||grids (1 km resolution), texture (11 layers) and hydrologic soil group
grids have multiple values assigned to each cell
|Snow cover grids
||Remote sensing images from NOHRSC indicating snow, no snow, or cloud
cover for specific days and geographic windows. Data are from 1990 - 1995
||NOHRSC CD-ROM received at OHD July 8, 2000
||Windows defining the spatial extent of snow cover grids
||Created at OHD from images on NOHRSC CD-ROM
||RFC basin boundary Shapefile.
||State boundaries -- used for reference only.
||RFC boundary or buffered RFC boundary for reference
||EPA's river reach file 1 (RF1)
||RF1 files were edited by NOHRSC for use in IHABBs; at HL, files were
imported into Arc/Info format and then converted to Shapefiles; further
editing was done to ensure that streams in coastal areas extend past the
edge of the DEM and into an ocean or great lake
Snow Model Parameters
Basin Subdivision with the Assistance of Snow Cover and Forest Type
In mountainous basins with significant snowfall, it is often useful
to divide a basin into two or more elevation zones for modeling. This helps
to capture variations in hydrometeorology and basin hydrology that are
highly correlated with elevation. In a basin subdivided into three elevation
zones, the highest zone might be characterized by significant spring snowmelt
contribution to runoff in all years, while the middle zone may only contribute
significant spring snowmelt during years with above average snow accumulation,
and the lowest zone might be an area where most of the precipitation falls
as rain. A zonal subdivision may also attempt to capture variation in basin
characteristics such as landcover, landuse, soil type, slope, and drainage
Figure 1c shows a three zone division for the headwaters of the Skykomish
River. The zones were defined by the NWS Northwest River Forecast Center
(NWRFC -- Figure 1a) to provide hydrologic forecasts at USGS steamflow
gage 12134500 (Skykomish River near Gold Bar, WA). The Skykomish River
is a tributary of the Snohomish River shown in Figure 1b and drains out
the Cascade Mountains into western Washington. The Skykomish Basin will
be used to illustrate examples throughout this paper. Elevations in the
Skykomish Basin range from 96 m to 2267 m and the elevation breaks selected
for zone subdivision are 610 m and 1067 m. An important distinction should
be made between the division of headwater basin such as the Skykomish River
Basin into elevation zones and the division of a larger river basin like
the Snohomish into subbasins for modeling. The division of larger river
basins into subbasins for modeling involves an entirely different set of
criteria, which are not discussed here.
Figure 1. (a) Location of the Northwest River Forecast Center (NWRFC),
one of 13 River Forecast Centers in the United States, (b) Snohomish River
Basin, and (c) Elevation Zones for Skykomish River Basin above Gold Bar,
Dividing a basin into elevation zones for modeling is a subjective procedure.
Satellite snow cover images and forest type information available through
AvCAP are intended to help in defining elevation zones. One suggested procedure
for determining a useful separating elevation for the upper zone is to
(1) use satellite snow cover images from a number of dates in conjunction
with elevation data to determine average snowline elevations for these
dates, (2) label runoff hydrographs with these elevation values, and (3)
determine the elevation above which significant snowmelt occurs in all
but the years with very small snow accumulation (Advanced NWSRFS Calibration
Workshop, 2000). With snow cover and DEM data loaded by AvCAP, representative
elevation values of DEM cells on the border of snow cover can be determined
manually by using the standard Spatial Analyst
however, this approach may become tedious. An alternative is to use the
Spatial Analyst contouring function to create elevation contours that can
be overlaid on the snow images.
Example elevation zones and contours overlaid on satellite snow cover
images are shown in Figure 2 for two dates in 1990. In Figure 2a from
February 26, 1990, 82% of the upper zone is covered with snow, 67% of the
middle zone, and 29% of the lower zone. The same values for June 21, 1990,
(Figure 2b) are 40%, 24%, and 7% respectively. Due to the coarseness of
the satellite snow cover images and the far from perfect correlation between
elevation and snow cover in this basin, estimating typical snowline elevations
from this satellite data remains somewhat subjective.
Figure 2. Satellite snow cover for (a) February 26, 1990, and (b)
June 21, 1990. Selected contours correspond to elevation zones from Figure
In AvCAP, the CAP à Snow Data
menu option facilitates the process of viewing snow cover images by automatic
loading and setting legends for the historical snow cover images developed
at the National Operational Hydrologic Remote Sensing Center (NOHRSC).
Currently only a limited period of historical images from 1990 - 1995 are
stored in the AvCAP database, a limiting factor when there is a need to
assess snow coverage in years with high, medium, and low snowfall amounts.
Future modifications to AvCAP should make it easier for RFCs to incorporate
new data into this database.
Forest cover type may also be a useful indicator of climate regime and
hence a useful separation level for dividing a basin into zones. Forest
types shown in Figure 3 indicate a good correlation with the zone definitions
in the Skykomish Basin. The upper zone is dominated by Fir-spruce, the
middle zone is a mixture of Douglas-fir, Fir-spruce, and Ponderosa Pine,
and the lower zone is dominated by Douglas-fir. Forest type and forest
percent grids delivered with AvCAP were obtained from NOHRSC, but the data
set was originally created at the USDA Southern Forest Experiment Station
(Zhu and Evans, 1994). Using the Zhu and Evans (1994) 1 km percent forest
cover and forest type grids, AvCAP computes the percent forest cover in
each basin and zone of interest, as well as the distribution of the percent
forested area covered by each forest type. Forest type percentages for
the Skykomish Basin are also shown in Figure 3.
Figure 3. Forest Types in the Skykomish River Basin
Other Uses of Forest Data
Another possible use for the forest type and forest density information
accessible through AvCAP is in determining initial values for the Snow-17
model parameters MFMAX and MFMIN (Anderson, 1996). MFMAX and MFMIN are
melt factors occurring on June 21 and December 21 respectively. Anderson
(1996) explains that forest cover is often a good indicator of melt rates
and provides suggested initial ranges for MFMAX and MFMIN based on general
forest cover categories: (1) quite dense coniferous forest, (2) mixed forest
-- coniferous plus open and/or deciduous, (3) predominantly deciduous,
and (4) open.
The percent forest cover estimates computed by CAP may also be helpful
in determining estimates for the effective forest cover (EFC) parameter
that is used in the evaporation calculations during snow cover situations.
The EFC parameter is essentially the percent of the basin covered by coniferous
forest. When there is snow cover, evapotranspiration demand is reduced
by the fraction of the basin in which there is snow cover but no forest
In mountainous basins, it is common for precipitation to fall in the
form of snow at high elevations and as rain at lower elevations because
temperature tends to decrease with elevation. The Snow-17 model allows
for the computation of the basin rain-snow elevation above and below which
precipitation will be typed as snow or rain, respectively. Rain-snow elevation
is determined using observed temperature estimates at a station with known
elevation, a user specified lapse rate, and a user specified threshold
temperature (temperature separating rain from snow). To determine the percent
of a basin or zone over which precipitation falls as snow and the percent
of a basin over which precipitation falls as rain, a fixed area-elevation
curve can be used in conjunction with the time-varying rain-snow elevation
AvCAP computes area-elevation curves for either the entire basin or
a selected zone within the basin. An area-elevation curve for the upper
portion of the Skykomish River Basin is shown in Figure 4. Because the
capabilities of the ArcView Chart type document are fairly limited, AvCAP
implements X-Y plots (area-elevation and precipitation-elevation curves)
using a custom document type called CAPGraph based on the ArcView View
GUI. Data points to construct the area-elevation curve (black triangles)
are determined by dividing the basin into equal intervals and then computing
the mean elevation in that interval. The user has control over the number
of intervals to use. The green triangles represent the elevation values
at the 10th, 50th, and 90th percentiles
of the drainage area.
Figure 4. Area-elevation Curve for the Skykomish River Basin
A user has the option to export area-elevation curve data in an ASCII
format that can be used directly as input to the Snow-17 model. In addition
to this option, there are several other noteworthy Buttons and Tools available
when a CAPGraph document is being viewed. These features are summarized
in Table 3.
Table 3. Buttons and Tools Available from the CAPGraph GUI
||Displays an x-y coordinate list of the points
displayed on the plot. This list can be copied and pasted to a text editor.
||Write area-elevation data to an ASCII format
readable by Snow 17.
||Statistics: Reports the elevation values below
which 10%, 50%, and 90% of the basin lies.
||Standard Tools borrowed from the
ArcView View GUI. These tools can be used to add text to the CAPGraph window;
draw points, lines or polygons; select, move, or delete graphic objects
(note: everything shown in the CAPGraph window is a graphic object); pan;
zoom in; and zoom out.
||Return the graph coordinates (X,Y)
of any mouse location clicked in the CAPGraph window. These coordinates
are different from the "View" coordinates displayed on the right portion
of the ArcView Toolbar.
An important component of hydrologic model calibration is preparation
of a mean areal precipitation (MAP) time series. When a basin is subdivided
into elevation zones, an MAP time series must be created for each zone.
In mountainous areas, long term mean rainfall amounts my vary significantly
over short distances due to the effects of elevation and aspect relative
to typical storm tracks. Thus, the use of a weighting scheme that depends
only on geographic location (e.g. Thiessen polygons) is inappropriate.
To derive MAP estimates in mountainous areas, it is common to first
assign relative station weights based on knowledge of the relative importance
of location versus elevation in how well stations are correlated with one
another. Once relative weights are assigned, the actual station weights
are computed in such a way that the long term mean precipitation predicted
by applying the station weights to gaged time series is equal to the long
term mean precipitation estimated for the basin. Long term mean basin precipitation
is typically determined using an isohyetal map; however, values derived
from the isohyetal map may need adjustment to reflect the period of record
covered by the available station data. In addition, mean precipitation
estimates derived from isohyetal maps are typically verified using regional
water balance analysis, sometimes resulting in precipitation adjustments
to produce regionally consistent evaporation estimates. Ensuring that precipitation
means computed with station weights are equal to the long term precipitation
means is important in efforts to minimize bias between MAPs computed for
calibration and MAPs computed for operational forecasting.
AvCAP simply provides estimates of the long-term basin mean for annual,
monthly, and seasonal precipitation using gridded PRISM data (PRISM, 2001).
In this way, the national PRISM rainfall grids serve as the isohyetal maps
in AvCAP. Computed values of seasonal (winter versus summer) long-term
means are used when winter time orographic effects make elevation a more
important factor in determining relative station correlations than during
summer convective storms. In these situations, different station weights
may be assigned during winter and summer seasons.
Currently, the X-Y precipitation-elevation plotting capability in AvCAP
is limited to using PRISM precipitation grids and not station precipitation
data. An example annual precipitation-elevation graph produced by
AvCAP is shown in Figure 5. Although not used directly to estimate
model parameters, a graph of this type is helpful in selecting stations
for analysis and assigning station weights. Knowing that elevation
plays a key role in defining precipitation variability for a particular
basin, a calibrator might place more weight on a high elevation station
to define the precipitation MAP for an upper zone, even if the station
lies outside the basin boundary. The algorithm to derive the data
points shown in Figure 5 is to slice the basin into a user defined number
of elevation intervals, compute an average precipitation and elevation
within each interval, and plot the resulting set of data pairs. For reference,
the red triangles are plotted using the lowest precipitation and elevation
values and the highest elevation and precipitation values in the basin.
The precipitation-elevation line does not necessarily pass through these
points because the high elevation location may not be exactly the same
as the high precipitation location.
Figure 5. Precipitation-Elevation Curve for the Skykomish River Basin
One area where a little bit of additional AvCAP automation might save
substantial time is in assigning relative station weights. Assigning relative
station weights is still a subjective process; however, customized tools
to display station anomalies and station correlations would simplify this
process. Another useful precipitation related AvCAP addition would be to
include station data in precipitation-elevation plots like that shown in
Figure 5 and to provide the option to produce seasonal precipitation-elevation
Parameters Used in the Sacramento Model (SAC-SMA)
The AvCAP database includes several useful parameter grids covering
the conterminous United States. These grids include 12 mean monthly and
a mean annual potential evaporation (PE) grids, 12 PE adjustment factor
grids, and 11 Sacramento model parameter grids. The AvCAP function that
uses these grids simply computes mean, maximum, and minimum values of these
parameters for basins and/or zones of interest, and presents the results
to the user in a tabular format.
Koren et al. (1998) used information from seasonal and annual free water
surface evaporation maps in NOAA Technical Report 33 (Farnsworth et
al., 1982) and mean monthly station data from NOAA Technical Report
34 (Farnsworth and Thompson, 1982) to derive parameters for an equation
that predicts seasonal variability of mean daily PE. These parameters were
used to derive the monthly PE grids delivered with AvCAP. Summing the monthly
grids yield results consistent with the annual and seasonal maps in NOAA
Technical Report 33.
In application of the SAC-SMA model, it is common to multiply PE by
a potential evaporation adjustment factor to account for seasonal variations
in vegetation activity. The product of the PE and PE adjustment values
is referred to as the PE demand. Koren et al. (1998) describe the
method used to develop PE adjustment factor grids. The method uses an empirical
function relating PE adjustment factors to green vegetation fraction data.
This empirical relationship was developed using PE adjustment factors derived
from calibration of the SAC-SMA model and monthly values of green vegetation
fraction data from National Center for Environmental Prediction (NCEP)
data sets (Gutman and Ignatov, 1998). Figure 6 shows the monthly values
of PE, PE adjustment, and potential evapotranspiration demand computed
for the Skykomish basin using AvCAP.
Figure 6. PE, PE Adjustments, and PE Demand for the Skykomish River
Koren et al. (2000) describe a method to estimate 11 of the SAC-SMA
parameters using soil texture, soil depth, and Soil Conservation Service
(SCS) hydrologic soil group data (also described in Duan et al.,
2001). This method is based on the hypothesis that the SAC-SMA model
parameters do correlate to physical soil properties in some way, even though
these properties cannot be measured directly. The main motivation for deriving
soil-based SAC-SMA parameter estimates was for use in distributed modeling
research; however, the derived grids and the basic soil grids used in their
derivation are provided in AvCAP as supplemental information for the calibration
of lumped models. These grids show regional variations in soil properties/parameters
that might be useful for a calibrator to identify regional trends in calibration
parameters. A common calibration strategy is to concentrate on a group
of hydrologically connected basins. Parameters derived for one basin are
often a useful starting point for estimating parameters in a similar nearby
basin (HRC, 1999). AvCAP computes mean values from the SAC-SMA parameter
grids for selected basins or zones, which may be used as a reasonable starting
point for calibration when no better estimates are available. Figure 7a
shows soil texture for the upper layer in the Snohomish area, and 7b shows
the SAC-SMA parameter Upper Zone Tension Water Maximum (UZTWM). As expected,
there is some spatial correlation between these two maps, but the correlation
is not perfect because texture information for additional soil layers,
total soil depth data, and hydrologic soil group data also go into the
calculation of UZTWM.
Figure 7. (a) Surface soil texture and (b) estimated UZTWM (mm) for
the Snohomish area.
AVCAP COMPUTATIONAL PROCEDURES AND GUI INTERFACE COMPONENTS
AvCAP programs are written in Avenue and distributed as an ArcView Extension.
The programs require ArcView 3.1 and Spatial Analyst 1.1. Nearly all of
the AvCAP functionality is accessible through a "CAP" menu item that is
added to the View GUI by the extension. The CAP menu is illustrated in
Figure 8. A summary of the main program features is provided below. More
detailed descriptions, including illustrations of the Dialogs displayed
for user input are provided in the online documentation (AvCAP, 2001).
Figure 8. CAP Menu
1. Setup: The Setup item must be run to get started, but will
typically only need to be run once. Setup creates a main View for analysis,
loads data, and associates an object tag with the main View for data management
purposes. The View object tag is a Dictionary object that stores data Theme
definitions. Other programs check this Dictionary for required data inputs.
The use of an object tag is preferable to a global variable because it
is saved with the project and will persist into subsequent user sessions.
2. Pre-compute Statistics: The main purpose of this option is
efficiency. Typically, RFCs have already defined basin boundaries for several
hundred basins. These basin boundaries will not change often so it is computationally
efficient to pre-compute statistics from the underlying parameter grids
and store the basin results in tables. If an elevation subdivision is made
for a particular basin, the actual computation of the statistics for that
subdivision will be made using Item 6. With no subdivisions, Item 6 simply
reads values from pre-computed data tables.
3. Create Subview for Selected Basin: Setup generates an RFCView
intended for viewing multiple basins and regional parameter trends. More
detailed analysis of individual basins is initiated using the Create Subview
for Selected Basin option. This creates a new View in which localized (clipped)
data sets (e.g. basin boundary, streams, forest types, elevation, etc.)
are viewed. Items 4-7 are only accessible to the user when a Subview is
the active ArcView document.
4. Plot Graph: This item gives the user the option to plot an
area-elevation curve or a PRISM precipitation-elevation curve. If a basin
has been subdivided, there is an option to plot either curve for the entire
basin or a selected zone. Because the capabilities of the ArcView Chart
type document are fairly limited, AvCAP implements X-Y plots (area-elevation
and precipitation-elevation curves) using a custom document type called
CAPGraph based on the ArcView View GUI. Key features of this DocGUI were
described above in relation to parameter estimation for Snow-17.
5. Subdivide Basin: This item provides a Dialog allowing the
user to select elevation intervals for subdivision. A grid and polygon
layer defining the zones are created and added to the user's Subview.
6. Statistics: Provides a Dialog that allows the user to select
which statistics to compute and display for the basin of interest.
7. Snow Data: Provides a Dialog listing snow cover images that
are available for the area of interest. Selected images are automatically
loaded and displayed using a pre-defined legend.
CONCLUSIONS AND FUTURE PLANS
AvCAP provides assistance to hydrologists in managing and examining
spatial data sets and in estimating initial parameters for the Snow-17
and SAC-SMA conceptual models. AvCAP is intended to be used in conjunction
with an existing suite of NWS calibration procedures.
A key aspect of AvCAP is the compilation of the supporting data sets
listed in Table 2. Most of these data sets cover the conterminous United
States, although some are relatively coarse in resolution. Use of the flexible
and widely used ArcView environment allows users to incorporate their own
more detailed data if available for an area of interest.
AvCAP is still in the early stages of development, and the intent is
to continue exploring ways that GIS technology can assist with both current
future calibration methodologies. Several possible enhancements to AvCAP
have been suggested in this paper, and there are others being considered.
Linkage to other NWSRFS calibration programs is one area where small AvCAP
enhancements may yield significant simplifications to the overall calibration
process. For example, automatic mapping of climate stations selected using
Historical Data Browser (HDB) and display of mean monthly and annual station
statistics as well as station correlation values from the Preliminary Precipitation
Processor (PXPP) would be very useful functions. The formal requirements
process now in place at NWS may help to identify aspects of the calibration
process which need the most improvement.
Advanced NWSRFS Calibration Workshop (2000). Held at North Central River
Forecast Center (NCRFC), July 17-21, 2000.
Anderson, E.A. (Nov. 1973). "National Weather Service River Forecast
System -- Snow Accumulation and Ablation Model," NOAA Technical Memorandum
NWS Hydro-17, Silver Spring, MD.
Anderson, E. (1996). "Initial Parameter Values for the Snow Accumulation
and Ablation Model," NWSRFS User's Manual IV.2.2,<http://hsp.nws.noaa.gov/oh/hrl/nwsrfs/users_manual/htm/formats.htm>,
AvCAP (2001). "CAP User Manual (ArcView Prototype v. 1.1)," <http://hsp.nws.noaa.gov/oh/hrl/calb/cap/capmanual.html>
Burnash, R.J., R.L. Ferral, and R.A. McGuire (1973). A Generalized Streamflow
Simulation System Conceptual Modeling for Digital Computers, U.S. Department
of Commerce National Weather Service and State of California Department
of Water Resources.
Duan, Q., J. Schaake, and V. Koren (2001). "A Priori Estimation of Land
Surface Model Parameters," Land Surface Hydrology, Meteorology, and
Climate: Observations and Modeling, Water Science and Application Volume
Farnsworth, R.K., and E.S. Thompson (1982) "Mean Monthly, Seasonal,
and Annual Pan Evaporation for the United States," NOAA Technical Report
NWS 34, Washington, D.C., 82 pp.
Farnsworth, R.K., E.S. Thompson, and E.L. Peck (1982). "Evaporation
Atlas for the Contiguous 48 United States," NOAA Technical Report NWS 33,
Gutman, G., and A. Ignatov (1998). "The derivation of the green vegetation
fraction from NOAA/AVHRR data for use in numerical weather prediction models,"
Int. J. Remote Sensing, 19, 8, 1533-1543.
HRC - Hydrologic Research Center (May 1999). "Calibration of the Sacramento
Soil Moisture Accounting Model," Video Series produced for the National
Koren, V., J. Schaake, Q. Duan, M. Smith, and S. Cong, Unpublished Report:
"PET Upgrades to NWSRFS, Project Plan," unpublished report, August 13,
Koren, V.I., M. Smith, D. Wang, Z. Zhang (2000). "Use of Soil Property
Data in the Derivation of Conceptual Rainfall-Runoff Model Parameters,"
80th Annual Meeting of the AMS, Long Beach, CA, January 10-14, 2000.
NWSRFS User's Manual Documentation (June, 2001). <http://hsp.nws.noaa.gov/oh/hrl/nwsrfs/users_manual/htm/formats.htm>
PRISM (2001). <http://www.ocs.orst.edu/prism/prism_new.html.
USACERL, U.S. Army Corps of Engineers Construction Engineering Research
Laboratory (1983). GRASS 4.1 User's Reference Manual, USACERL, Champaign,
IL, 556 pp.
Zhu, Z., and D.L. Evans (1994). "U.S. Forest Types and Predicted Percent
Forest Cover from AVHRR Data," Photogrammetric Engineering and Remote
Sensing, 60, 5, pp. 525-531.
Dr. Seann M. Reed
National Weather Service Office of Hydrologic Development
1325 East-West Highway w/ OHD1
Silver Spring, MD 20877
(301) 713-0640 x161