Coupling HEC-HMS with Atmospheric Models for Prediction
of Watershed Runoff
M. L. Anderson, M. ASCE1; Z.-Q. Chen, M. ASCE2; M. L. Kavvas, M. ASCE3; and Arlen Feldman, M. ASCE4
Abstract: The operation of reservoirs in the Sierra Nevada mountains of California for flood control relies on forecasts of reservoir
inflows. In the past, accurate forecasts of the reservoir inflows resulting from watershed runoff have been made, but only after the water
has entered the main channel. During flooding events, this limits the amount of time available for the implementation of emergency
management procedures. Translating precipitation forecasts into runoff forecasts can greatly improve the runoff-forecast lead time. The
operational National Center for Environmental Prediction Eta model provides 48-h-ahead forecasts of precipitation in 6-h intervals in a
40⫻40 km gridded form. In this study, the mesoscale model, MM5, is used to transfer the Eta forecast data down to the appropriate space
and time scales required to link the Eta model precipitation forecast results to the watershed model, HEC-HMS, for runoff prediction. An
initial diagnostic study of this procedure has been performed on the Calaveras River watershed in Northern California. Initial results
indicate that: 共1兲 model parameterization choice in MM5 is necessary to refine the precipitation forecasts; 共2兲 the method shows promise
for generating 48-h-ahead forecasts of reservoir inflows; and 共3兲 calibration of the HEC-HMS model with distributed precipitation is
necessary for this methodology. This paper presents the study results along with a discussion of the methodology.
DOI: 10.1061/共ASCE兲1084-0699共2002兲7:4共312兲
CE Database keywords: Runoff forecasting; Watersheds; California.
Introduction
The operation of reservoirs in the Sierra Nevada mountains of
California for flood control rely on forecasts of reservoir inflows.
In the past, accurate forecasts of the reservoir inflows resulting
from watershed runoff have been made, but only after the water
had entered the main channel. During flooding events, this limits
the amount of time available for the implementation of emergency management procedures. An example of this limitation and
its consequences occurred during the December 1996/January
1997 flooding events over Northern California 共National Weather
Service 1997兲. This study examines the possibility of increasing
runoff-forecast lead time through the use of precipitation forecasts.
Recent efforts in river forecasting have focused on quantifying
rainfall amounts from radar images 共Charley 1986; James et al.
1993; Coontz 1994; Mimikou 1996兲. Research has also progressed in the continued development of models capable of predicting the spatial and temporal evolution of the flood wave as it
1
Researcher, Civil and Environmental Engineering, Univ. of California at Davis, Davis, CA 95616.
2
Researcher, Civil and Environmental Engineering, Univ. of California at Davis, Davis, CA 95616.
3
Professor, Civil and Environmental Engineering, Univ. of California
at Davis, Davis, CA 95616.
4
Head, Research Division, United States Army Corps of Engineers,
Hydrologic Engineering Center, Davis, CA 95616.
Note. Discussion open until December 1, 2002. Separate discussions
must be submitted for individual papers. To extend the closing date by
one month, a written request must be filed with the ASCE Managing
Editor. The manuscript for this paper was submitted for review and possible publication on December 3, 1999; approved on October 16, 2001.
This paper is part of the Journal of Hydrologic Engineering, Vol. 7, No.
4, July 1, 2002. ©ASCE, ISSN 1084-0699/2002/4-312–318/$8.00⫹$.50
per page.
moves down the channel 共Yapo et al. 1993; Franchini and Lamberti 1994; Lamberti and Pilati 1996兲. However, these reported
studies are limited to the time frame of radar images and water
that is already in the main channel. The only way to gain additional lead time in runoff forecasting is to gain precipitation information ahead of its occurrence.
One way in which this can be accomplished is by translating
precipitation forecasts into runoff forecasts. The National Center
for Environmental Prediction 共NCEP兲 Eta model 共Staudenmaier
1996a,b兲 provides 48-h-ahead forecasting of precipitation in 6-h
intervals in a 40⫻40 km2 gridded form over the entire United
States. Through the use of a mesoscale model, MM5, and a
rainfall-runoff model, HEC-HMS, this information can be translated into runoff forecasts with a 48-h lead time. Accurate runoff
forecasts of this nature would greatly improve the lead time necessary for emergency management procedures such as evacuations to be carried out.
A perfect forecast of runoff peak, volume, and timing would
be the ideal scenario for such a methodology. However, effective
modifications to emergency management procedures can be made
with a less than perfect forecast. The implementation of a series
of emergency management measures may be made as the forecast
inflows increase. Because there is a range of flows that apply to a
given set of measures, the runoff forecast should be accurate
enough to fall into the correct flow range. That is, the predicted
inflow and the observed inflow should trigger the same set of
emergency management measures.
As a means of evaluating this approach, a feasibility study has
been completed on the Calaveras River watershed in Northern
California. Eta model forecasts were obtained to provide input
and boundary conditions for the mesoscale model, MM5. The
MM5 model was then run to provide a refined precipitation forecast with spatial and temporal scales suitable for use in the United
States Army Corps of Engineers 共USACE兲 watershed model
HEC-HMS. HEC-HMS was run with the precipitation forecast
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Table 1. Grid Points of Corners of AWIPS Grid #212
(I, J)
共1, 1兲
共1, 129兲
共185, 129兲
共185, 1兲
Fig. 1. Schematic diagram of process for obtaining precipitation
forecasts. Runoff forecasts from HEC-HMS.
data in order to provide a 48-h forecast of runoff entering a reservoir on the Calaveras River.
The results of this study are presented in this paper, which
starts with an overview of the methodology used to translate the
Eta model precipitation forecasts into HEC-HMS runoff forecasts.
After describing the computer models MM5 and HEC-HMS that
are used in the study, a description of the Calaveras River watershed used in the case study is presented, including its representation in HEC-HMS. Results of the study are then presented and
discussed. The paper concludes with an assessment of the methodology including future directions for its further development.
Methodology and Model Descriptions
A schematic of the methodology for translating precipitation forecasts into runoff forecasts is shown in Fig. 1. Eta-model products
are first downloaded from NCEP’s Web site. This information
provides the input and boundary conditions for the mesoscale
model, MM5, which can then be considered to be nested into the
Eta model. The mesoscale model, MM5, is run to produce 48
one-hour forecasts of precipitation depths on a grid covering the
Calaveras River basin that will be used by the watershed model,
HEC-HMS. In the following subsections, the Eta model forecast
products, MM5, and HEC-HMS models are described, including
the input that is necessary for this simulation methodology.
Eta Model Forecast Products
A series of operational Eta models for atmospheric numerical
forecasts have been developed for use in the U.S. 共Burks and
Staudenmaier 1996; Janish and Weiss 1996; Schneider et al.
1996兲. The gridded Eta model results can be found over the
AWIPS 共Advanced Weather Interactive Processing System兲 grids
Latitude/longitude
12.190 N/133.459 W
54.536 N/152.856 W
57.290 N/49.385 W
14.335 N/65.091 W
in GRIB format. GRIB 共Gridded Binary兲 is a general purpose,
bit-oriented data exchange format that is an efficient vehicle for
transmitting large volumes of gridded data to automated centers
over high-speed telecommunication lines using modern protocols.
A GRIB decoder is required to read and to process the forecast
data in their raw formats.
NCEP releases two forecasts at 00Z and 12Z UTC 共Coordinated Universal Time兲 each day. The letter Z means that the time
is the local time at the zero degree longitude 共Greenwich meridian兲. For hydrological application, it may be necessary to convert
the UTC time to a local time. Each forecast has a lead time of 48
h with time intervals of 6, 3, or 1 h. The 6-h interval data is
available to the public from the NCEP Data Repository Site at
ftp.ncep.noaa.gov.
The operational Eta model at the time of the study has a 32 km
horizontal resolution and 45 vertical layers, and it runs over a
domain that encompasses nearly all of North and Central
America, including surrounding oceans and Alaska and Hawaii.
Initial conditions are provided by the Eta Data Assimilation System 共EDAS兲, which runs on a 3-h forecast/analysis/update cycle
for 12 h prior to the start time of a model run. Boundary conditions are provided by the previous cycle’s Aviation model 共AVN兲
run of the NCEP Global Spectral Model.
NCEP uses an Eta model postprocessor to generate the outputs
over several AWIPS 共Advanced Weather Interactive Processing
System兲 grids that are more useful to meteorologists and hydrologists than the original Eta model outputs. The gridded Eta model
output are further grouped into 3D output files and surface output
files. AWIPS grid #212 was used for this study. This grid is defined on a Lambert Conformal projection with a nominal horizontal grid resolution of 40 km and grid dimensions of 185⫻129.
The latitude/longitude locations of the corner points of this grid
are given in Table 1. This domain covers most of North America
and the nearby oceans, including the 48 contiguous United States,
the southern half of Canada, and most of Mexico.
Mesoscale Model MM5
The MM5 modeling system is the fifth generation mesoscale
model developed by the National Center for Atmospheric Research 共NCAR兲 and the Pennsylvania State Univ. It is a globally
relocatable model that can be run under either hydrostatic or
nonhydrostatic dynamic frameworks. The nonhydrostatic
Table 2. MM5 Parameterizations used in Forecast Study
MM5 parameterization
Atmospheric radiation
Boundary layer
Soil model
Precipitation—nonconvective
Precipitation—convective
Scheme chosen
Dudhia
Blackadar
Multilayer
Explicit three-phase
Grell
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Table 3. Rain Gauges used in Calibration of Calaveras Watershed
HEC-HMS Model
Rain Gauge
Esparanza
Railroad Flat
Sheep Ranch
New Hogan Reservoir
Robidart Ranch
Latitude (°)
Longitude (°)
38.242
38.314
38.210
38.155
38.137
⫺120.497
⫺120.543
⫺120.462
⫺120.814
⫺121.030
the NCEP Eta model forecast on AWIPS grid #212, which has a
spatial resolution of approximately 40 km. The AWIPS data set
provides three-dimensional forecasts of temperatures, wind vectors, relative humidity, geopotential heights, and surface pressures
every 6 h for a total of 48 h. Nested within the outer grid is a
34⫻34 cell inner grid with a resolution of 4 km. The inner grid
spans the Calaveras River basin and its immediate surrounding
areas 共Fig. 2兲 and uses relaxation boundary conditions. The computational time interval is 20 s and the time interval of the MM5
precipitation output is set to one hour to coincide with the data
requirements of HEC-HMS.
The parameterizations used in the MM5 simulation of the Calaveras basin are shown in Table 2. For this initial study, an evaluation of the impact of using different parameterizations within
MM5 was not performed. A complete description of the parameterizations available in the MM5 modeling system can be found
in Grell et al. 共1994兲.
Fig. 2. Map of California showing location of Calaveras River and
MM5 model outer and inner grid boundaries
framework, used in this study, allows the model to be used at a
few-kilometer scale 共Dudhia 1993兲.
The state variables of the MM5 modeling system with the
nonhydrostatic framework include pressure, temperature, density,
and wind velocities. Parameterized processes include advection,
diffusion, radiation, boundary-layer processes, surface-layer processes, cumulus convection, and routines for all three phases of
water in the atmosphere. Several options exist for the parameterization of moist convection and boundary layer processes for the
simulation of atmospheric phenomena at different scales and different characteristics 共Kavvas and Chen 1998兲.
The MM5 model for the Calaveras River basin is a 32-layer
model with two nested grids identified as the outer grid and inner
grid. The 31⫻31 node outer grid has a spatial resolution of 12 km
and spans central California, part of Nevada near Lake Tahoe, and
part of the Pacific Ocean by San Francisco and Half Moon Bays
共Fig. 2兲. It receives its time-dependent boundary conditions from
HEC-HMS
HEC-HMS is the updated version of the USACE rainfall-runoff
model 共USACE-HEC 1998兲. It utilizes a graphical user interface
to build a watershed model and to set up the precipitation and
control variables for simulation. For this project, the Calaveras
River watershed in California was modeled with a focus on the
upper watershed that provides inflow into New Hogan Reservoir.
A map of the location of the watershed is shown in Fig. 2.
The watershed model created in HEC-HMS follows the form
of the Sacramento District Corps office HEC-1 forecast model of
the basin 共USACE 1987兲. This model utilizes one subbasin above
New Hogan Reservoir that provides runoff into the reservoir.
Below the reservoir, there is a routed channel reach and another
subbasin that provides runoff to a sink point at Bellota. Bellota is
a point on the lower Calaveras River where a flow and stage
gauging station is located. A schematic description of the basin is
shown in Fig. 3.
4. Calibration
HEC-HMS Model
Table
Model parameter
Fig. 3. HEC-HMS schematic description of Calaveras River basin
Volume moisture deficit
Wet Front Suction
Conductivity
% impervious
Time of concentration
Storage coefficient
Recession constant
Threshold flow
Coefficients
for
Calaveras Watershed
Calibrated value
0.1 in.
0.1 in.
0.125 in./h
22%
5.5 h
0.5
0.25
3,000 cfs
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Fig. 4. Calibrated model versus observed New Hogan Reservoir inflows
In Fig. 3, the outline of the watershed and branches of the
upper Calaveras River are shown underneath the schematic components of the watershed model. The upper Calaveras watershed
is represented by the subwatershed component Upper Basin. This
subwatershed component provides inflows into New Hogan Reservoir, which is represented with a triangle. A second subwatershed is made up of the watershed below the reservoir and is titled
Lower Basin. The gauging station at Bellota is represented in the
watershed model as a sink, and the river reach between New
Hogan Reservoir and Bellota is represented by a channel reach
that routes streamflow using the Muskingum method. For this
study, the inflows into New Hogan Reservoir from the upper watershed were the only components examined.
In order to use the gridded precipitation component of HECHMS, a GIS-based spatial representation of the watershed is required. This spatial representation can be created using a set of
UNIX-based ARC/INFO routines called GridParm 共USACE-HEC
1996兲. This representation treats the basin as a collection of cells
that each have a response time based upon their relative location
to the subwatershed outlet. Flow is routed from cell to cell based
upon topography determined from the DEM using the ModClark
method 共USACE 1996兲. One set of infiltration parameters is used
for all the cells in the watershed. With this representation, HECHMS can transform spatial representations of rainfall data into
runoff at the subwatershed outlet.
Application
Calibration and Verification of HEC-HMS
The calibration period for the HEC-HMS model of the Calaveras
basin was a 48-h period from February 8 to 9, 1999. Rainfall data
were obtained from five rain gauges in the Calaveras Basin listed
in Table 3. Calibration and verification were performed using
rainfall data from these five ground-based rain gauges as point
gauges. Final calibration parameters for the HEC-HMS model are
shown in Table 4. The model uses a Green-Ampt infiltration/loss
parameterization, the ModClark hydrograph transformation routine, and a recession base flow component. The initial loss and
initial flow are treated as initial conditions and vary from simulation to simulation. A plot of the observed inflow into New
Hogan Reservoir versus the model simulated inflow is shown in
Fig. 4. Note that the observed and model simulated flows match
well, except that the peak of the simulated flow is slightly later
than the observed flow. The opposite occurs for the verification
run.
For verification purposes, a second 48-h period from February
16 to 18, 1999, was used. A plot of the observed versus predicted
inflow into New Hogan Reservoir is shown in Fig. 5. Note that
the predicted peak is early compared to the observed flow. The
value in the ModClark hydrograph transformation routine was set
to match the timing of the calibration and verification peaks as
close as possible. Using this set of parameters for the Calaveras
River watershed model, shown in Table 4, an application of the
runoff forecasting process was conducted.
Runoff Forecast Generation
In order to demonstrate the use of Eta model forecast data to
generate runoff forecasts with HEC-HMS, a 48-h forecast period
from January 19 to 21, 1999, was selected. During this time period, rain gauges measured between 2 and 3 in. of rainfall in the
upper part of the Calaveras basin.
Fig. 6 shows the 48-h Eta forecasts of precipitation in the
Calaveras River basin and its surrounding region for the above-
Fig. 5. Verification results on predicted versus observed New Hogan Reservoir inflows
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Fig. 6. Eta model forecast: 6-h accumulated precipitation over Calaveras River Basin from 1999-01-19 – 12:00Z to 1999-01-21 – 12:00Z
Fig. 7. Sample of MM5 model forecasts of 1-h accumulated precipitation over Calaveras River basin
mentioned time period of the study. There are eight plots in Fig.
6. Each plot represents the spatial distribution of the accumulated
precipitation during a 6-h time period. Each square in the plots
represents one AWIPS grid. These AWIPS grids have a size of
roughly 40⫻40 km. In these plots, the heavy black outline indicates the boundary of the Calaveras River basin. The gray shades
indicate the 6-h accumulated precipitation in mm in each grid. Six
AWIPS grids cover the Calaveras River watershed, as shown in
Fig. 6. Within each plot of Fig. 6, the number of grids is 4 in the
vertical and 5 in the horizontal directions.
The 48-h-ahead forecast of precipitation by the MM5 model
covered the time period of 12:00 GMT January 19, 1999 to 12:00
GMT January 21, 1999. A sample of six hourly plots of the spatial
distribution of the hourly precipitation depths generated by MM5
is shown in Fig. 7. In these plots, the heavy black outline indicates the boundary of the Calaveras River basin. The gray shades
indicate the precipitation in mm/h in each grid. The spatial and
temporal evolution of the precipitation fields in Calaveras River
basin and its surrounding region are shown clearly in these plots.
Using the precipitation inputs represented in Figs. 6 and 7,
HEC-HMS was run with the gridded precipitation routine using
the calibrated values shown in Table 4. For the Eta model forecast
precipitation, the precipitation values were interpolated bilinearly
in space and divided evenly in time in order to obtain hourly
precipitation data on the HEC-HMS precipitation grid. A plot of
observed inflows into New Hogan Reservoir compared with the
HEC-HMS model using the Eta model precipitation, the MM5
model precipitation, and point gauge precipitation is shown in
Fig. 8. Because of the interpolation in space and time on the Eta
model forecast precipitation, the resulting precipitation values are
too small to lead to any runoff using the point gauge calibrated
model parameters of HEC-HMS. This can be seen in Fig. 8,
where the Eta model runoff prediction is a recession curve only.
Fig. 8. Comparison of observed runoff to HEC-HMS forecast runoff
into New Hogan Reservoir using Eta model precipitation forecast,
MM5 model precipitation forecast, and point gauge precipitation
measurements
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Fig. 9. Comparison of observed versus forecasted precipitation for three sites in Calaveras Basin
Using the refined precipitation data from the MM5 simulation, the
predicted runoff forecast is significantly less than the observed
values, but does show a runoff response to the precipitation. Specifically, the magnitude of the forecast runoff is only 67% of the
observed runoff and the forecast peak is 4 h later than the observed peak. The underprediction of the magnitude may be due in
part to the fact that the calibration and verification were performed with point rain gauge data. Such a calibration may not be
appropriate for gridded precipitation applications of HEC-HMS.
In order to see if the forecast model could represent the appropriate runoff, the HEC-HMS model was run again with rainfall
data taken from the five gauges used in the calibration process.
The results are shown in Fig. 8. Note in Fig. 8 that, when the
point precipitation gauge data is used with HEC-HMS, the flow is
much better represented. The peak and timing of the peak are well
simulated with the point gauge data. However, it must be noted
that the results from the point rain gauge data can only be obtained after the rain events have been recorded by the rain gauges.
This does not provide any lead time in terms of a forecast runoff.
However, the MM5 results are forecast up to 48 h before the
actual rain event occurs.
As a further investigation into the differences between observed and forecast runoff by HEC-HMS, the differences between
the MM5 forecast precipitation and point gauge precipitation values were compared. The point rainfall gauges used here are a
subset of the rainfall gauges that were used to produce the model
calibration and verification results of Figs. 4 and 5, and of forecast performance comparisons of Fig. 8. The rainfall hyetographs
for the three gauges are compared with the nearest neighbor grid
points within the MM5 simulation and are shown in Fig. 9. As
can be seen from all of these plots, MM5 underpredicts the early
part of the storm and overpredicts the later part of the storm. The
MM5 total storm depths for all grid points fall in the range of 1– 4
in. with the nearest neighbor points to the gauges having similar
depth totals between 2 and 3 in. The overall peaks of the rainfall
intensities produced by MM5 also match those of the observed
rainfall. The differences indicate that the calibration and verification process should include an evaluation of the different parameterizations in MM5 for representing precipitation processes.
Such an evaluation would likely lead to a more refined precipitation forecast. Even without such an evaluation, the current results
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point to a promising way of obtaining runoff predictions with a
lead time much greater than is currently available.
Conclusions
Runoff forecasts from precipitation forecasts can be obtained
using the methodology presented here. Eta model forecast products can be refined using the mesoscale atmospheric model MM5
to obtain hourly precipitation values over a 48-h period. The
methodology presented here can obtain a runoff forecast within
minutes of obtaining the precipitation forecast from the MM5
model. The process can be automated, yielding a valuable tool for
reservoir management. The methodology was demonstrated for a
48-h forecast period in January 1999 in the Calaveras watershed
in Northern California. Inflows to New Hogan Reservoir were
predicted using Eta model forecast data and a refined precipitation
forecast using the mesoscale model MM5. Because the Eta model
precipitation forecast is given in 6-h intervals, the precipitation
forecast must be distributed into an hourly format to be used in
the current formulation of HEC-HMS.
With the HEC-HMS model, which was calibrated by means of
point gauge precipitation data, the timing and magnitude of the
forecast peak in the runoff hydrograph were underestimated when
the point gauge calibrated HEC-HMS model was driven by spatially distributed MM5 rainfall forecasts. However, when the
point gauge calibrated HEC-HMS used point gauge rainfall for
the same storm, the magnitude and timing of the peak runoff were
matched. This would indicate that it is necessary to calibrate the
HEC-HMS model with spatially distributed rainfall when using
the model in the forecasting framework presented here. When the
HEC-HMS model is calibrated and verified using the gridded
precipitation data from MM5, runoff predictions will likely improve. The MM5 model can also be changed in terms of the
choice of land surface model, boundary layer model, and local
precipitation parameterization. These changes can be used to fine
tune the precipitation forecast.
The accuracy of the forecast for use in driving emergency
management directives may not need to match the peak inflow
exactly. The runoff forecast should, however, cross the appropriate thresholds so as to cause the appropriate directives to be enacted. Further work is required in order to provide quantitative
measures for this type of forecast accuracy. Improved accuracy in
terms of matching the timing and magnitude of the peak inflow
and total volume of runoff would provide more information to
reservoir operators for flood control releases. As a result, a new
tool will be available for the prediction of runoff, which will
provide an improved lead time for better reservoir operations as
well as providing more lead time to act on emergency management directives.
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