<p>Soil and water assessment tool model (SWAT model) is a
widely used model when dealing with large and complex watershed simulations. To
correctly predict runoff of a watershed, auto-calibration methods are applied.
Among all the platforms, SWAT CUP is widely used in the SWAT model community.
The new web-based calibration platform: SWATShare is also gaining its
popularity due to the benefits of user-friendly interface, access to
high-performance computing resources, and collaborative interface. While the
algorithm implemented in SWAT CUP is Sequential Uncertainty Fitting version 2
(SUFI2), Sorting Genetic Algorithm II (NSGA-II) is the algorithm employed by
SWATShare. There is a limited amount of research comparing the model
performance between these two calibration algorithms and platforms. </p>
<p> </p>
<p>This study aims to examine whether the performances of
calibrated models are providing equally reliable results. Thirty US watersheds are
studied in this research, SWAT models were calibrated using seven years of
rainfall data and outflow observations from 2001 to 2007, and then the models
were validated using three years of historical records from 2008 to 2010.
Inconsistency exists between different algorithms calibrated parameter sets,
and the percentage difference between parameter values ranges from 8.7% to
331.5%. However, in two-thirds of the study basins, there is no significant
difference between objective function values in two algorithms calibrated
models. Correlations are examined using values of parameters and watershed
features. Among all the features and parameters, Length of reach and GW_DELAY,
CH_N2 and ALPHA_BF, climate zone and GWQMN, SFTMP and NSE have medium
correlation exist in both SWATShare and SWAT CUP calibrated models among 30
watersheds. The correlation coefficient difference between them are less than
0.1. When visualizing results by Ecoregions, KGE and NSE are similar in
calibrated models from both tools. </p>
<p> </p>
<p>The
initial parameter range used for SWAT CUP calibration could lead to satisfactory
results with greater than 0.5 objective function values. However, the parameter
values of the calibrated model might not be presenting a real physical
condition since they are out of the realistic range. The inaccurate parameter
values might lead to lower objective function values in the validation. The
objective function values can be improved by setting the range of parameter
values to match the realistic values. </p>
<p> </p>
<p> </p>
<p>By
comparing two tools, SWATShare accurately calibrates parameter values to a
realistic range using default range in most cases. For those models with an
unsatisfactory result from SWATShare, the objective function values could be
improved after specifying the parameters to the best-fit range given by SWAT
CUP results. Also, for those watersheds which have similar satisfactory
calibrated objective values from both tools, constraining the parameter to a
reasonable range could generate a new calibrated model that performs as well as
the original one. Using the approach to constrain parameter values to a
realistic range gradually can exclude some statistically satisfactory but
physically meaningless models. Comparing two auto-calibration software,
SWATShare accurately calibrates parameter values to a realistic range using
default range in most cases. Also, in some of the ecoregions, the best
parameter sets in SWATShare fall in a more physically meaningful range. Overall, the newly emerged
platform, SWATShare, is found to have the capability of conducting good SWAT
model calibration. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12730688 |
| Date | 29 July 2020 |
| Creators | Kuan Hung Lin (9179471) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/COMPARATIVE_ANALYSIS_OF_SWAT_CUP_AND_SWATSHARE_FOR_CALIBRATING_SWAT_MODELS/12730688 |
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