guidelinesforsensitivit5yandautocalibrationinswat
TRANSCRIPT
![Page 1: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/1.jpg)
1
Guidelines for Using the Sensitivity Analysis and Auto-calibration
Tools for Multi-gage or Multi-step Calibration in SWAT
by
Michael W. Van Liew
Department of Biological Systems Engineering
University of Nebraska-Lincoln
Lincoln, NE
and
Tamie L. Veith
USDA ARS
Pasture Systems and Watershed Management Research Unit
University Park, PA
![Page 2: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/2.jpg)
2
Table of Contents
I. Overview……………… …………………………………………....3
II. Sensitivity Analysis Tool…………………………………………...4
III. Sensitivity Analysis Set-up and Execution………………...…….5
IV. The Shuffled Complex Evolution Algorithm…………………. 10
V. Auto-calibration Set-up and Execution…………………………11
VI. Auto-calibration Output………………………………………...22
VII. Calibration of Water Quality Constituents…………………...24
VIII. References……………………………………………………...26
VIII. Appendix A…………………………………………………….28
![Page 3: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/3.jpg)
3
I. Overview
The ArcSWAT 2.3 Interface for SWAT 2005 consists of a number of tools that can
be used to assist model users in evaluating parameter sensitivity, aid in model
calibration, and assess parameter uncertainty. These tools were developed by Van
Griensven (2005) and in recent years have been employed increasingly by SWAT
users worldwide. The sensitivity analysis tool is helpful to model users in
identifying parameters that are most influential in governing streamflow or water
quality response. The sensitivity analysis tool in the ArcSWAT Interface allows
model users to conduct two types of analyses. The first analysis may help to
identify parameters that improve a particular process or characteristic of the model,
while the second analysis identifies the parameters that are affected by the
characteristics of the study watershed and those to which the given project is most
sensitive (Veith and Ghebremichael, 2009).
The auto-calibration option provides a powerful, labor-saving tool that can be used
to substantially reduce the frustration and uncertainty that often characterize
manual calibrations (Van Liew et al., 2005). The auto-calibration tool currently
available in the ArcSWAT Interface allows users to determine optimal parameter
values for multiple output variables for a delineated project, based upon observed
data at a single gage during a one-step auto-calibration. In some cases, however,
multi-gage observed data may be available for implementing a distributed
approach to calibration, where observed and simulated outputs are compared at
multiple points on a watershed. Moreover, it may be advantageous to employ a
multi-step approach to auto-calibration, if optimal parameter sets are needed for a
handful of output variables such as streamflow, sediment, and nutrients.
This training module assists model users in implementing the sensitivity analysis
tool outside of the ArcSWAT Interface. The module also provides guidelines for
performing multi-gage and/or multi-step auto-calibrations by running the program
in a project directory instead of the interface. All project files are created in the
interface and then selected files related to auto-calibration are subsequently
modified manually before running the project in MS DOS. An overview of the
necessary steps for building the auto-calibration files for a multi-gage calibration is
presented in Appendix A. Practical guidelines are also included in this module to
help expedite the auto-calibration process. Before proceeding with the instructions
![Page 4: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/4.jpg)
4
provided in this training module, it is suggested that model users review Section 14
“SWAT Simulation” of the ArcSWAT 2.3 Interface for SWAT 2005 User’s Guide
developed by Winchell et al. (2009). Users may also find it helpful to read “How
to: applying and interpreting the SWAT auto-calibration tools” (Veith and
Ghebremichael, 2009) that defines and explains the main outputs of the tools
available in the auto-calibration interface.
Once a project has been constructed and the warm-up and calibration periods have
been established, an initial simulation can be performed to verify that data have
been correctly input to the project. The input.std and output.std files are
particularly useful in this regard. The latter is helpful in verifying overall output
values for hydrology, crop growth and biomass production. The user should also
copy the project directory that was created in the ArcSWAT interface to a separate
directory. With the inclusion of a SWAT executable in that directory, the project
can then be run in MS DOS mode. Creating a duplicate copy of the project can
also serve to test auto-calibrated and manually adjusted streamflow or water
quality parameter sets while the original auto-calibration run is in progress.
II. Sensitivity Analysis Tool
Model users are often faced with the difficult task of determining which
parameters to calibrate so that the model response mimics the actual field,
subsurface, and channel conditions as closely as possible. When the number of
parameters in a model is substantial as a result of either a large number of sub-
processes being considered or because of the model structure itself, the calibration
process becomes complex and computationally extensive (Rosso, 1994;
Sorooshian and Gupta, 1995). In such cases, sensitivity analysis is helpful to
identify and rank parameters that have significant impact on specific model outputs
of interest (Saltelli et al., 2000).
Sensitivity analysis demonstrates the impact that change to an individual input
parameter has on the model response and can be performed using a number of
different methods (Veith and Ghebremichael, 2009). The method in the ArcSWAT
Interface combines the Latin Hypercube (LH) and One-factor-At-a-Time (OAT)
sampling (Van Griensven, 2005). During sensitivity analysis, SWAT runs
(p+1)*m times, where p is the number of parameters being evaluated and m is the
![Page 5: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/5.jpg)
5
number of LH loops. For each loop, a set of parameter values is selected such that
a unique area of the parameter space is sampled. That set of parameter values is
used to run a baseline simulation for that unique area. Then, using one-at-a-time
(OAT), a parameter is randomly selected, and its value is changed from the
previous simulation by a user-defined percentage. SWAT is run on the new
parameter set, and then a different parameter is randomly selected and varied.
After all the parameters have been varied, the LH algorithm locates a new
sampling area by changing all the parameters (Veith and Ghebremichael, 2009).
The sensitivity analysis tool in ArcSWAT has the capability of performing two
types of analyses. The first type of analysis uses only modeled data to identify the
impact of adjusting a parameter value on some measure of simulated output, such
as average streamflow. The second type of analysis uses measured data to provide
an overall “goodness of fit” estimation between the modeled and the measured
time series. The first analysis may help to identify parameters that improve a
particular process or characteristic of the model, while the second analysis
identifies the parameters that are affected by the characteristics of the study
watershed and those to which the given project is most sensitive (Veith and
Ghebremichael, 2009).
III. Sensitivity Analysis Set-up and Execution
By entering the ArcSWAT Interface Sensitivity Analysis Window, the user first
specifies the SWAT simulation that will be used for performing the sensitivity
analysis and the location of the subbasin where observed data where be compared
against simulated output. This is illustrated in Figure 1. The user then enters the
desired input settings and observed data file, as shown in Figure 2. NINTVAL, m,
is the number of sub-ranges into which each parameter range is divided. If m = 10,
then one Latin Hypercube loop will sample a parameter of range 0 to 1.0 within the
subrange of 0.0 to 0.1, 0.1 to 0.2 and so on. In the input window, OAT refers to
the percentage of change in the parameter value that will be used in the variations
within the parameter range. If a range is from 0 to 1.0, a 5% parameter change will
vary by 0.05*(1.0 – 0.0) = 0.05 units. Selection of the number of LH loops and the
parameter change percentage should be made jointly, as they are both a function of
the parameter ranges (Veith and Ghebremichael, 2009). The user then enters the
parameters that will be selected for the sensitivity analysis, adjusts lower and upper
![Page 6: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/6.jpg)
6
parameter bounds if necessary, and then selects the observed data set that will be
compared to the modeled output, if desired (Figure 3).
Sensitivity Input Window
Analysis Location: Select
from the SWAT simulation
list a simulation for
performing the sensitivity
analysis
Subbasin: Select a
subbasin within the
project where observed
data will be compared
against simulated output
Figure 1. Input of the SWAT simulation project and subbasin location
![Page 7: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/7.jpg)
7
Sensitivity Input Window
Hypercube intervals
(Alpha_Bf): 10 intervals of
0-0.1, 0.1-0.2 … 0.9-10.0
OAT change (Alpha_Bf):
Changes by 5% x (1.0 -
0.0) = 0.05
Initial value of 0.13
becomes 0.08 or 0.18
Figure 2. Input of the sensitivity analysis parameter settings
Sensitivity Input Window
Select Parameters for
conducting sensitivity
analysis
Lower bound = 0.0
Upper bound = 10.0
Adjust if necessary
Observed Data File Name
Figure 3. Input of the observed data, selected parameters and associated lower and
upper bounds
![Page 8: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/8.jpg)
8
In the Output window, the user designates whether or not the sensitivity analysis
will be run on just modeled output, on observed versus simulated output, or both.
For either case, the user selects which parameters will be selected for the
sensitivity analysis and also indicates whether concentrations or loads will be
evaluated for parameters governing water quality (Figure 4). If modeled output is
selected, the user must specify whether the sensitivity analysis will be performed
using average modeled output, such as streamflow, or the percent of time that the
modeled output is less than a user specified threshold value. If observed versus
simulated output is selected, the user must specify the type of objective function
that will be used for the analysis, either the sum of squares or the sum of squares
ranked (Figure 4). Once completed, the user writes files to the project directory.
Sensitivity Analysis Output WindowOutput Evaluation:
Comparison variable(s)
Objective Function:
Select optimization
method
Write Input Files to
Project Directory
Select Concentrations or
Loads for Water Quality
Select Average Modeled
Output (e.g., streamflow)
Or Percent of Time
output is < a threshold
value
Figure 4. Selections for output parameter sensitivity and/or observed versus
simulated output sensitivity
The sensitivity analysis is run in the directory by double clicking on an SWAT
executable that is added to the project directory. Upon completion of the
sensitivity analysis, a number of files are generated in the output. The sensout.out
![Page 9: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/9.jpg)
9
file provides a summary of the inputs used for the analysis and a listing of the
ranked parameters (Figures 5 and 6). The parameter producing the highest average
percentage change in the objective function value is ranked as most sensitive
(Veith and Ghebremichael, 2009).
Main Output: Sensout.dat
Input Data:
Objective and
Response
Functions
List of Parameters
Figure 5. A listing of the input data and objective and response functions in the
senout.out file.
![Page 10: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/10.jpg)
10
Main Output: Sensout.dat
Parameter Ranking
Figure 6. A listing of the parameter ranking in the sensout.out file.
IV. The Shuffled Complex Evolution Algorithm
ArcSWAT includes a multiobjective, automated calibration procedure that was
developed by Van Griensven and Bauwens (2003). The calibration procedure is
based on a Shuffled Complex Evolution Algorithm (SCE-UA; Duan et al., 1992)
and a single objective function. In a first step, the SCE-UA selects an initial
population of parameters by random sampling throughout the feasible parameter
space for “p” parameters to be optimized, based on given parameter ranges. The
population is partitioned into several communities, each consisting of “2p+1”
points. Each community is made to evolve based on a statistical “reproduction
process” that uses the simplex method, an algorithm that evaluates the objective
function in a systematic way with regard to the progress of the search in previous
iterations (Nelder and Mead, 1965). At periodic stages in the evolution, the entire
population is shuffled and points are reassigned to communities to ensure
information sharing. As the search progresses, the entire population tends to
converge toward the neighborhood of global optimization, provided the initial
population size is sufficiently large (Duan et al. 1992). The SCE-UA has been
widely used in watershed model calibration and other areas of hydrology such as
![Page 11: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/11.jpg)
11
soil erosion, subsurface hydrology, remote sensing, and land surface modeling, and
has generally been found to be robust, effective, and efficient (Duan 2003).
In the optimization scheme developed for SWAT 2005, parameters in the model
that affect hydrology or water quality can be changed in either a lumped (over the
entire watershed) or distributed (for selected subbasins or HRUs) way. In addition,
the parameters can be modified by replacement, by addition of an absolute change
or by a multiplication of a relative change. Besides weight assignments for output
variables that can be made in multi-objective calibrations (e.g., 50% streamflow,
30% sediment, and 20% nutrients), the user can specify a particular objective
function that is minimized. The objective function is an indicator of the deviation
between a measured and a simulated series (Van Griensven and Bauwens 2003).
Available objective function options in the auto-calibration tool include the sum of
squares of residuals and the sum of squares of residuals ranked. The former
represents the classical mean square error method that aims at matching a
simulated time series to a measured series while the latter represents the fitting of
the frequency distributions of the observed and simulated series.
The auto-calibration tool in SWAT can be run in either the Parasol or the Parasol
with Uncertainty Analysis mode. In the Parasol mode, the tool searches for an
optimal calibration set as described above. In the Parasol with Uncertainty
Analysis mode, each simulation that was performed during the Parasol
optimization process is grouped into a “good” or “not good” category. These
categories are based on whether or not the objective function value of the run falls
within a user-defined confidence interval (CI). This confidence interval is defined
by the user input of “90%”, “95%”, or “97.5%” probability and the corresponding
statistic of either a chi-square or Bayesian distribution (Veith and Ghebremichael,
2009).
V. Auto-calibration Set-up and Execution
By entering the ArcSWAT Interface Auto-Calibration and Uncertainty Input
Window, the user first specifies the SWAT simulation that will be used for
performing the auto-calibration and the location of the subbasin where observed
data where be compared against simulated output. This is illustrated in Figure 7.
![Page 12: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/12.jpg)
12
The user then enters the desired optimization settings, observed data file, and
method of calibration, as shown in Figure 8.
Auto-calibration Input Window
Analysis Location: Select
from the SWAT simulation
list a simulation for
performing the calibration
Subbasin: Select a
subbasin within the
project where observed
data will be compared
against simulated output
Figure 7. Input of the SWAT simulation project and subbasin location
Auto-calibration Input Window
Optimization Settings
MAXN = Maximum number
of trials before optimization is
terminated
IPROB = sets the threshold
for ParaSol:
1 = 90% CI
2 = 95% CI
3 = 97.5% CI
Calibration Method:
ParaSol or ParaSol
with Uncertainty
Analysis
Observed Data File Name
Figure 8. Input of optimization settings, observed record, and calibration method
![Page 13: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/13.jpg)
13
It is recommended that the user rely upon the default values listed for the
optimization settings in the Input Window, with the exception of two variables,
MAXN and IPROB. The choice made for selecting MAXN, the maximum number
of trials allowed before optimization is terminated, can be particularly useful in
regulating the duration of the auto-calibration. For example, by noting the project
run time (normally several seconds to a few minutes for the warm-up plus
calibration periods), a user can estimate the time required to complete the
automated calibration, based on MAXN. Depending upon the size and complexity
of the project, a cursory auto-calibration run can be made by setting MAXN equal
to 500 to 1000. A more comprehensive auto-calibration can be made by setting
MAXN to 3000 or more. The user may also choose to vary the threshold value of
the confidence interval, IPROB, which in turn will impact the number of “good”
parameter sets that are generated when running the tool in the Parasol with
Uncertainty Analysis mode. Figure 9 shows the listing of optimization settings
that are created in the parasolin.dat file, including MAXN and IPROB.
Figure 9. A listing the optimization settings created in the parasolin.dat file
Because the ArcSWAT Interface does not properly load the observed data file at
this time, the user must add this file directory to the project directory. Figure 10
illustrates values of the input record for observed daily streamflow. A SWAT
project involving a two gage auto-calibration would require two such files. Figure
11 presents values of the input record for observed monthly streamflow and
sediment at a single gage.
![Page 14: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/14.jpg)
14
Auto-calibration Input Window:
Observed Daily RecordYear of
observed
record
Observed
Daily
Streamflow
in cms
Julian day
of
observed
record
Figure 10. Input file for observed daily streamflow
Auto-calibration Input Window:
Observed Monthly Record
Observed
Monthly
Sediment
Load
(tons/day)
Observed
Monthly
Streamflow
(cms)
Month of
observed
record
Year of
observed
record
Figure 11. Input file for observed monthly streamflow and sediment record
![Page 15: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/15.jpg)
15
The next step in preparing the auto-calibration run is to select the model
parameters that will be calibrated and their associated lower and upper bounds.
In most cases it is suggested that the user select the default values listed for lower
and upper calibration bounds, unless field data are available to provide more
specific information regarding parameter ranges. The input window for parameter
selection is given in Figure 12. A complete list of streamflow, sediment, and
nutrient parameters that can be calibrated in the tool is presented by Van Griensven
(2005). Suggested streamflow parameters that are governed by responses to
snowmelt and rainfall are presented in Figure 13.
Auto-calibration Input Window
Select Parameters for
calibration
Adjust initial lower and
upper bounds, if necessary
(note: minimum lower bound
for SURLAG = 0.5)
Figure 12. Input window for parameter selection
![Page 16: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/16.jpg)
16
Figure 13. A suggested listing of parameters for calibrating streamflow
Parameters in SWAT may be grouped by basin, subbasin, or HRU response. For
distributed parameter modeling, basin parameters, such as surlag and sftmp, are not
usually allowed to vary spatially. On the other hand, some or all of the subbasin
and HRU parameters can be designated to vary regionally, depending upon the
location of available measured data. A two gage approach for the auto-calibration
of subbasin and HRU parameters is illustrated in Figure 14.
Figure 14. A two gage approach to auto-calibration of subbasin and HRU
parameters
![Page 17: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/17.jpg)
17
Since the current version of the ArcSWAT Interface auto-calibration tool only
allows model parameters to be calibrated against measured data at a single gage,
the following procedure is suggested for creating the changepar file so that a
regionally varied model calibration can be achieved. To construct the necessary
information for this file, a multi-step ArcSWAT interface iteration is required in
order to group parameters that vary by HRUs or subbasins with the corresponding
calibration point within the project. This can be accomplished by employing the
“Select HRUs\LU” radio button in the auto-calibration input panel to select all
HRUs or subbasins associated with a given calibration point for each parameter to
be calibrated (Figure 15). Writing the input files from the interface builds that
portion of the changepar.dat file corresponding to the first calibration point. The
newly created changepar.dat file must now be renamed in the project directory to
prevent it from being overwritten. Upon selecting the second calibration point in
the project, “Select HRUs\LU” is again employed to group all HRUs or subbasins
for each parameter that corresponds to that second point. By once again writing
the input files, a second changepar.dat file is created. Manually combining the two
files in the project directory creates the information necessary to perform a
regionally varied auto-calibration, as shown in Figure 16. Model users should
carefully review the newly created changepar.dat file to ensure that all the desired
parameters are included in the file and that there are no duplicate listings.
![Page 18: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/18.jpg)
18
Auto-calibration input: Multigage Changepar file is created by combining two or more changepar files that are
specific for certain subbasins or HRUs in the project
For parameters that vary by
HRU, select All Land Uses, Soils,
and Slopes for Subbasins that
are relevant to a particular gage
For parameters that vary by
Subbasin, select All Subbasins
that are relevant to a particular
gage
Figure 15. Using the select HRU/LU button to select model parameters by
subbasins or HRUs
Auto-calibration input: Multigage Changepar file is created by combining two or more changepar files that are
specific for certain subbasins or HRUs in the project
Subbasins
for gage 2
Subbasins
for gage 1
HRUs for
gage2
HRUs for
gage 1
Figure 16. An example multigage changepar.dat file
![Page 19: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/19.jpg)
19
Following the construction of the changepar.dat file, the Auto-calibration Output
pane in the ArcSWAT Interface is used to enter information to perform calibration
output evaluations. This includes the parameter or parameters to be calibrated, the
type of objective function (sum or squares or sum of squares ranked), the weight
assignments for output variables that can be made in multi-objective calibrations,
and the selection of either concentration or load calibration for water quality
parameters (Figure 17). In addition to the parasolin.dat and changepar.dat files, a
third file created when the input files are written in the interface is objmet.dat. As
shown in Figure 18, this file defines the variables and methods that will be used for
optimization.
Auto-calibration Output Window
Output Evaluation: Select
parameter to be calibrated
Objective Function:
Select optimization
method
Write Input Files to
Project Directory
Select Concentrations or
Loads for Water Quality
Calibration
Figure 17. Selection of settings for calibration output evaluations
![Page 20: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/20.jpg)
20
Auto-calibration input file: Objmet
Code number for
Autocalfile in .fig
Concentration or load
Objective function
method
Given weight for
objective function
Code number for
calibration variable
Figure 18. A description of the optimization control variables and methods in
objmet.dat.
Two additional flies, referred to as file.cio and fig.fig, are modified when the input
files are written to the project directory. File.cio is the overall command file for
the project, while fig.fig is the command file governing the routing of water and
pollutants from the landscape to the stream reaches. Examples of these two files
are presented in Figures 19 and 20, respectively. Variable ICLB in file.cio governs
the type of run that is performed by the model, such as default, parameter
sensitivity, or auto-calibration. Once the fig.fig file is created in the directory, it
must be modified manually to account for multiple calibration gages. Each
calibration location requires two lines: the command code line and the observed
data file name. Figure 20 illustrates a fig.fig file consisting of a two gage
calibration. In the first “autocal” command line located at the bottom of the file,
“16” represents the command code for auto-calibration, “77” is the hydrograph
storage location for the reach being calibrated (corresponding to reach 1 of the
project), “1” represents the number of the auto-calibration file, and “0” represents a
daily time-step registration. Line two of the file, colq1.dat, is the name of the file
containing the observed flow record that corresponds to reach 1. In line three of
![Page 21: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/21.jpg)
21
the fig file, “68” is the hydrograph storage location for the reach being calibrated
(corresponding to reach 3 of the project) and “2” represents the number of the
autocalibration file. Line four of the file, colq2,dat, is the observed file name
corresponding to reach 3.
Auto-calibration input file: Filecio
ICLB =AutoCalibration
Default = 0
Sensitivity = 1
Optimization = 2
Optimization with
uncertainty = 3
Bestpar = 4
NYSKIP = Warm-up
Number of years
simulated
Figure 19. Designation of auto-calibration options in file.cio
![Page 22: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/22.jpg)
22
Auto-calibration input file: fig
Autocal Command Code
and Observed Data Files
for 2 Gage Locations
Figure 20. Location of the autocal command code and observed data files in the
fig.fig file
Auto-calibration Output
Parasolout.out, goodpar.out, bestpar.out, and autocal1.out are output files that are
particularly useful for viewing upon completion of the auto-calibration run.
Multiple gages that are calibrated will result in multiple autocal.out files. Output
from the first three of these files is described by Veith and Ghebremichael (2009).
In brief, parasolout.out lists the maximum and minimum parameter values for all
solutions in the Parameter Uncertainty Analysis. Goodpar.out is a record of all
solutions having an objective function value within the Uncertainty Analysis
Confidence Inverval, while bestpar.out represents the parameter set with the
optimal solution among all the goodpar.out solutions. The autocal1.out file
provides a listing of the simulated streamflow and pollutants at the calibration
point for the designated time step used during the run. It is generated during the
last run of the auto-calibration. Sample output files are shown in Figures 21-23.
![Page 23: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/23.jpg)
23
Auto-calibration output file: Parasolout
Parameter
Uncertainty
Ranges
Calibration
Figure 21. Example output file for parasolout.out
Auto-calibration output file: goodparand bestpar
CalibrationParameter listings
Figure 22. Example output files for goodpar.dat and bestpar.out
![Page 24: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/24.jpg)
24
Auto-calibration output file: Autocal
Parameter
Uncertainty
RangesCalibrationMonthly Streamflow
Monthly Sediment Load
Figure 23. Example output file for autocal1.out
Once the auto-calibration has been completed, the user can rerun the project with
the bestpar.dat data set with ICBN = 4 in file.cio. Simulated streamflow generated
from the autocal.out file can in turn be compared against measured streamflow at
the monthly or daily time step. Users may find that manual fine tuning following
auto-calibration provides moderate improvement in comparison of measured
versus simulated streamflow. Use of a manual approach following auto-calibration
accentuates the tradeoffs that exist in achieving total mass balance, reasonable
hydrograph responses, and adequate representation of the range in flows (Van
Liew et al., 2005). Fine tuning often tends to provide better matches for the lower
flows without a significant sacrifice in maintaining a good representation of the
peak flows.
Calibration of Water Quality Constituents
Two alternatives exist for employing the auto-calibration feature in the model.
One possibility is to use the multi-objective function developed by van Griensven
(2005) to simultaneously calibrate streamflow and selected water quality variables,
which include sediment, nitrogen, and phosphorus. The other possibility is to
![Page 25: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/25.jpg)
25
calibrate streamflow and water quality constituents separately in a multi-step
fashion. Cursory testing of the two methods suggests that one method may not
necessarily be better than the other. Cursory testing also suggests that it may be
preferable to calibrate water quality constituents at the monthly instead of daily
time step, due to apparent convergence limitations that seem to exist at the daily
time scale.
If the multi-objective function approach is employed, the following changes in the
input files are needed. First of all, the observed record must include measured
values for both streamflow and the water quality constituent(s), as illustrated in
Figure 11. Sediment or nutrient values are input as loads at the monthly time scale.
Second, the objmet.dat file must be amended to indicate water quality calibration
and a given weight for the objective function must be assigned (Figure 18) (van
Griensven, 2005). Third, the changepar.dat file must also be modified to include
the desired parameters that are to be calibrated.
An alternative to the multi-objective function is to use a multi-step calibration
procedure. This procedure is described briefly as follows. Following auto-
calibration and manual fine tuning of the parameters governing the streamflow
calibration, all parameter values in bestpar.out need to be rewritten to their
respective input files. This can readily be accomplished by using the Manual
Calibration Helper in the interface to update project HRUs or subbasins with
appropriate parameter values related to streamflow. Most values can then be
verified by rerunning the model and reviewing the input.std file.
The auto-calibration tool can once again be employed to search for optimal
parameter values that govern sediment loading in SWAT, based upon a specified
measured record. Manual adjustments following the auto-calibration may be
warranted to achieve a suitable sediment calibration. Updating the input files with
the Manual Calibration Helper could then be undertaken, before proceeding with
the calibration of nutrient constituents in the model.
![Page 26: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/26.jpg)
26
References
Duan, Q. D. 2003. “Global optimization for watershed model calibration.”
Calibration of watershed models, Water Science Applied Series,
Vol. 6, Q. Duan, et al., eds., AGU, Washington, D.C., 89–104.
Duan, Q., Gupta, V. K., and Sorooshian, S. 1992. “Effective and efficient
global optimization for conceptual rainfall-runoff models.”
Water Resour. Res., 28, 1015–1031.
Nelder, J. A., and Mead, R. A. 1965. “Simplex method for function
minimization.” Comput. J., 7, 308–313.
Rosso R. 1994. An introduction to spatially distributed modelling of basin
response. In Advances in Distributed Hydrology, Rosso R Peano A
Becchi I, Bemporad GA (eds). Water Resources Publications: Fort
Collins; 3–30.
Saltelli A, Scott EM, Chan K, Marian S. 2000. Sensitivity Analysis. John
Wiley & Sons: Chichester.
Sorooshian S, Gupta VK. 1995. Model calibration. In Computer Models
of Watershed Hydrology, Singh VP (ed). Water Resources Publications:
Highlands Ranch, Colorado, USA; 23–63.
Van Griensven, A., and Bauwens, W. 2003. “Multiobjective autocalibration
for semidistributed water quality models.” Water Resour. Res.,
39(12), 1348.
Van Griensven, A. 2005. Sensitivity, auto-calibration, uncertainty and model
evaluation in SWAT2005. Unpublished report.
![Page 27: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/27.jpg)
27
Veith, T.L. and L.T. Ghebremichael. 2009. How to: applying and interpreting the
SWAT Auto-calibration tools. In: Fifth International SWAT Conference
Proceedings. August 5-7, 2009 (Proceedings).
Van Liew, M. W., J. G. Arnold, and D. D. Bosch. 2005. Problems and potential of
autocalibrating a hydrologic model. Transactions of the ASAE 48(3):1025-1040.
Van Liew, M.W., T.L.Veith, D.B. Bosch, and J.G. Arnold. 2007. Suitability of
SWAT for the Conservation Effects Assessment Project: comparison of USDA
Agricultural Research Service Watersheds. J. of Hydrologic Engineering.
12(2):173-189.
Winchell, M., R. Srinivasan, M. di Luzio, J. Arnold, 2007: ArcSWAT interface for
SWAT 2005. User’s Guide. Blackland Research Center, Texas Agricultural
Experiment Station, Temple.
![Page 28: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/28.jpg)
28
Appendix A. Overview of steps for building auto-calibration files for
a multi-gage calibration
1. Select simulation and subbasin
2. Construct observed data files for
selected time step. (Data files can be
built in Excel, saved as a space
delimited file that ends with a
“.dat” extension.)
Format for monthly observations:
Year (1x5i), month (3x2i), 3x,
measured values (1X11F.3)
Format for daily observations:
Year (1X5i), day (2X, 3i), 3x,
measured values (1X11F.3)
3. Select optimization settings and
calibration method and input
observed data file.
4. Select parameters for calibration.
Adjust initial boundaries
Auto-calibration Input Window:
Observed Monthly Record
Observed
Monthly
Sediment
Load
(tons/day)
Observed
Monthly
Streamflow
(cms)
Month of
observed
record
Year of
observed
record
Sample monthly streamflow and
sediment record
Auto-calibration Input Window
Select Parameters for
calibration
Adjust initial lower and
upper bounds, if necessary
(note: minimum lower bound
for SURLAG = 0.5)
Auto-calibration Input Window
Analysis Location: Select
from the SWAT simulation
list a simulation for
performing the calibration
Subbasin: Select a
subbasin within the
project where observed
data will be compared
against simulated output
Auto-calibration Input Window
Optimization Settings
MAXN = Maximum number
of trials before optimization is
terminated
IPROB = sets the threshold
for ParaSol:
1 = 90% CI
2 = 95% CI
3 = 97.5% CI
Calibration Method:
ParaSol or ParaSol
with Uncertainty
Analysis
Observed Data File Name
![Page 29: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/29.jpg)
29
5. Use the HRU/LU button to select
model parameters by subbasin
or HRUs associated with gage 1.
6. In the auto-calibration output
window, write files to project
directory. Enter project directory
and rename changepar.dat file
(e.g., changepar1.dat).
7. Repeat step 5 using the
HRU/LU button to select
model parameters by subbasin
or HRUs associated with gage 2.
8. Select calibration output
evaluations and rewrite input
files to project directory.
9. Combine contents of
changepar1.dat created from
step 6 with the newly created
changepar.dat file in the
project directory. Verify that
there are no duplicate
parameter listings.
Auto-calibration input: Multigage Changepar file is created by combining two or more changepar files that are
specific for certain subbasins or HRUs in the project
Subbasins
for gage 2
Subbasins
for gage 1
HRUs for
gage2
HRUs for
gage 1
Auto-calibration Output Window
Output Evaluation: Select
parameter to be calibrated
Objective Function:
Select optimization
method
Write Input Files to
Project Directory
Select Concentrations or
Loads for Water Quality
Calibration
Auto-calibration input: Multigage Changepar file is created by combining two or more changepar files that are
specific for certain subbasins or HRUs in the project
For parameters that vary by
HRU, select All Land Uses, Soils,
and Slopes for Subbasins that
are relevant to a particular gage
For parameters that vary by
Subbasin, select All Subbasins
that are relevant to a particular
gage
Auto-calibration input: Multigage Changepar file is created by combining two or more changepar files that are
specific for certain subbasins or HRUs in the project
For parameters that vary by
HRU, select All Land Uses, Soils,
and Slopes for Subbasins that
are relevant to a particular gage
For parameters that vary by
Subbasin, select All Subbasins
that are relevant to a particular
gage
![Page 30: GuidelinesforSENSITIVIT5YANDAUTOCALIBRATIONINSWAT](https://reader034.vdocuments.pub/reader034/viewer/2022051817/547ff8ecb37959532b8b59c9/html5/thumbnails/30.jpg)
30
10. Include observed data
file names in fig.fig file in the
project directory.
Format for observed data file:
(10X, up to13 characters with
last 4 ending in “.dat”)
11. Add second line for
for gage 2 in the objmet.dat
file. Verify correct control
variables for each line.
12. Designate appropriate
variables in file.cio file and
proceed with program
execution.
Auto-calibration input file: fig
Autocal Command Code
and Observed Data Files
for 2 Gage Locations
Auto-calibration input file: Filecio
ICLB =AutoCalibration
Default = 0
Sensitivity = 1
Optimization = 2
Optimization with
uncertainty = 3
Bestpar = 4
NYSKIP = Warm-up
Number of years
simulated
Auto-calibration input file: Objmet
Code number for
Autocalfile in .fig
Concentration or load
Objective function
method
Given weight for
objective function
Code number for
calibration variable