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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 368
Proc. IAHS, 368, 221–226, 2015
https://doi.org/10.5194/piahs-368-221-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 368, 221–226, 2015
https://doi.org/10.5194/piahs-368-221-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 May 2015

06 May 2015

Simulating hydrological processes in a sub-basin of the Mekong using GBHM and RS data

W. Wang1, H. Lu1,2, B. Gao4, Y. Jiao3, and Z. Pang5 W. Wang et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System Science, Tsinghua University, Beijing, 100084, China
  • 2Joint Center for Global Change Studies, Beijing, 100875, China
  • 3Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China
  • 4School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, China
  • 5Remote Sensing Center, Institute of Water Resources and Hydropower Research, Beijing, 100044, China

Keywords: Distributed hydrological model, TRMM, Mekong River, GLDAS

Abstract. This paper presents simulations of daily hydrological process of the Mun River, the largest tributary of the Mekong, with a geomorphology-based hydrological model (GBHM) driven by two forcing sets: traditional station data and grid data derived from remote sensing and GLDAS products. Driven by the station data, the Mun-GBHM model is successfully calibrated against the discharge observed in 1991, but the model accuracy decreases with the increase of simulation time during the validation period of 1992–1999. Driven by the TRMM rainfall and other meteorological data from GLDAS, using the same parameters as above, the model performs reliably at both the monthly and daily scale. Moreover, when the model is calibrated with one year of gridded data, its performance can be further improved. Our results demonstrate that TRMM and GLDAS are able to drive the GBHM so providing reliable hydrologic predictions in such data-poor or ungauged basins.

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