Title: | Physically based distributed hydrological model calibration based on a short period of streamflow data: Case studies in four Chinese basins |
Authors: | Sun, W., Y. Wang, G. Wang, X. Cui, J. Yu, D. Zuo and Z. Xu |
Year: | 2017 |
Journal: | Hydrology and Earth System Sciences |
Volume (Issue): | 21(1) |
Pages: | 251-265 |
Article ID: | |
DOI: | 10.5194/hess-21-251-2017 |
URL (non-DOI journals): | |
Model: | SWAT |
Broad Application Category: | hydrologic only |
Primary Application Category: | calibration, sensitivity, and/or uncertainty analysis |
Secondary Application Category: | hydrologic assessment |
Watershed Description: | 5,629 km^2 Jinjiang River, 1,089 km^2 Donghe River, 8,843 km^2 Heihe River and 32,535 km^2 Yalongjiang River, located respectively in southeast, central, northwest, and west central China. |
Calibration Summary: | |
Validation Summary: | |
General Comments: | |
Abstract: | Physically based distributed hydrological models
are widely used for hydrological simulations in various environments.
As with conceptual models, they are limited in
data-sparse basins by the lack of streamflow data for calibration.
Short periods of observational data (less than 1 year)
may be obtained from fragmentary historical records of previously
existing gauging stations or from temporary gauging
during field surveys, which might be of value for model calibration.
However, unlike lumped conceptual models, such
an approach has not been explored sufficiently for physically
based distributed models. This study explored how the use of
limited continuous daily streamflow data might support the
application of a physically based distributed model in datasparse
basins. The influence of the length of the observation
period on the calibration of the widely applied soil and water
assessment tool model was evaluated in four Chinese basins
with differing climatic and geophysical characteristics. The
evaluations were conducted by comparing calibrations based
on short periods of data with calibrations based on data from
a 3-year period, which were treated as benchmark calibrations
of the four basins, respectively. To ensure the differences
in the model simulations solely come from differences
in the calibration data, the generalized likelihood uncertainty
analysis scheme was employed for the automatic calibration
and uncertainty analysis. In the four basins, contrary to the
common understanding of the need for observations over a
period of several years, data records with lengths of less
than 1 year were shown to calibrate the model effectively,
i.e., performances similar to the benchmark calibrations were
achieved. The models of the wet Jinjiang and Donghe basins
could be effectively calibrated using a shorter data record
(1 month), compared with the dry Heihe and upstream Yalongjiang
basins (6 months). Even though the four basins
are very different, when using 1-year or 6-month (covering
a whole dry season or rainy season) data, the results show
that data from wet seasons and wet years are generally more
reliable than data from dry seasons and dry years, especially
for the two dry basins. The results demonstrated that this idea
could be a promising approach to the problem of calibration
of physically based distributed hydrological models in data sparse
basins, and findings from the discussion in this study
are valuable for assessing the effectiveness of short-period
data for model calibration in real-world applications. |
Language: | English |
Keywords: | |