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| Titel |
SWAT use of gridded observations for simulating runoff – a Vietnam river basin study |
| VerfasserIn |
M. T. Vu, S. V. Raghavan, S. Y. Liong |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 8 ; Nr. 16, no. 8 (2012-08-16), S.2801-2811 |
| Datensatznummer |
250013429
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| Publikation (Nr.) |
 copernicus.org/hess-16-2801-2012.pdf |
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| Zusammenfassung |
| Many research studies that focus on basin hydrology have applied the SWAT model
using station data to simulate runoff. But over regions lacking robust
station data, there is a problem of applying the model to study the
hydrological responses. For some countries and remote areas, the rainfall
data availability might be a constraint due to many different reasons such
as lacking of technology, war time and financial limitation that lead to
difficulty in constructing the runoff data. To overcome such a limitation,
this research study uses some of the available globally gridded high
resolution precipitation datasets to simulate runoff. Five popular gridded
observation precipitation datasets: (1) Asian Precipitation Highly Resolved
Observational Data Integration Towards the Evaluation of Water Resources
(APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3)
Precipitation Estimation from Remote Sensing Information using Artificial
Neural Network (PERSIANN), (4) Global Precipitation Climatology Project
(GPCP), (5) a modified version of Global Historical Climatology Network (GHCN2)
and one reanalysis dataset, National Centers for Environment
Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to
simulate runoff over the Dak Bla river (a small tributary of the Mekong
River) in Vietnam. Wherever possible, available station data are also used
for comparison. Bilinear interpolation of these gridded datasets is used to
input the precipitation data at the closest grid points to the station
locations. Sensitivity Analysis and Auto-calibration are performed for the
SWAT model. The Nash-Sutcliffe Efficiency (NSE) and Coefficient of
Determination (R2) indices are used to benchmark the model performance.
Results indicate that the APHRODITE dataset performed very well on a daily
scale simulation of discharge having a good NSE of 0.54 and R2 of 0.55,
when compared to the discharge simulation using station data (0.68 and
0.71). The GPCP proved to be the next best dataset that was applied to the
runoff modelling, with NSE and R2 of 0.46 and 0.51, respectively. The
PERSIANN and TRMM rainfall data driven runoff did not show good agreement
compared to the station data as both the NSE and R2 indices showed a
low value of 0.3. GHCN2 and NCEP also did not show good correlations. The
varied results by using these datasets indicate that although the gauge
based and satellite-gauge merged products use some ground truth data, the
different interpolation techniques and merging algorithms could also be a
source of uncertainties. This entails a good understanding of the response
of the hydrological model to different datasets and a quantification of the
uncertainties in these datasets. Such a methodology is also useful for
planning on Rainfall-runoff and even reservoir/river management both at
rural and urban scales. |
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