TY - JOUR
T1 - Evaluation of remotely sensed precipitation and its performance for streamflow simulations in basins of the Southeast Tibetan Plateau
AU - Wang, Sheng
AU - Liu, Suxia
AU - Mo, Xingguo
AU - Peng, Bin
AU - Qiu, Jianxiu
AU - Li, Mingxin
AU - Liu, Changming
AU - Wang, Zhonggen
AU - Bauer-Gottwein, Peter
N1 - Publisher Copyright:
© 2015 American Meteorological Society.
PY - 2015
Y1 - 2015
N2 - Four satellite-based precipitation products [TMPA real time (T-rt), its gauge-adjusted version (T-adj), Climate Prediction Center (CPC) morphing technique (CMORPH) real time (C-rt), and its gauge-adjusted version (C-adj)] were evaluated by a gauge-based synthesis dataset. Further, these products along with the CMORPH gauge-satellite blended version (C-ga), which is virtually C-adj in precipitation ungauged regions and is controlled by gauge analysis over regions of a dense station network, were intercompared with daily streamflow predicted by the distributed vegetation interface processes (VIP) model in the Lhasa and Gongbo basins of the southeast Tibetan Plateau. Results show these satellite-based products perform better in summer than in other seasons. Relative to the gauge-based synthesis dataset, for areal precipitation of the Lhasa basin from 2007 to 2010, biases of C-rt and T-rt are -10.49% and 157.88%, respectively. Biases of C-adj and T-adj are 3.42% and 24.12%, respectively. The C-rt bias is underestimation of the volume of observed rainfall correctly detected and overestimation of the volume of falsely alarmed rainfall, while T-rt bias comes from overestimation of the volume of observed rainfall correctly detected. Simulation efficiencies of stream discharges driven by T-adj and C-adj are better than those by T-rt and C-rt, which are consistent with the accuracies of these products. With benchmarked model parameters using the gauge-based dataset, C-adj presents well for simulation, while T-adj needs parameter recalibration to achieve good skills. Compared to T-adj and C-adj, better simulation could be obtained by C-ga in precipitation-gauged regions.
AB - Four satellite-based precipitation products [TMPA real time (T-rt), its gauge-adjusted version (T-adj), Climate Prediction Center (CPC) morphing technique (CMORPH) real time (C-rt), and its gauge-adjusted version (C-adj)] were evaluated by a gauge-based synthesis dataset. Further, these products along with the CMORPH gauge-satellite blended version (C-ga), which is virtually C-adj in precipitation ungauged regions and is controlled by gauge analysis over regions of a dense station network, were intercompared with daily streamflow predicted by the distributed vegetation interface processes (VIP) model in the Lhasa and Gongbo basins of the southeast Tibetan Plateau. Results show these satellite-based products perform better in summer than in other seasons. Relative to the gauge-based synthesis dataset, for areal precipitation of the Lhasa basin from 2007 to 2010, biases of C-rt and T-rt are -10.49% and 157.88%, respectively. Biases of C-adj and T-adj are 3.42% and 24.12%, respectively. The C-rt bias is underestimation of the volume of observed rainfall correctly detected and overestimation of the volume of falsely alarmed rainfall, while T-rt bias comes from overestimation of the volume of observed rainfall correctly detected. Simulation efficiencies of stream discharges driven by T-adj and C-adj are better than those by T-rt and C-rt, which are consistent with the accuracies of these products. With benchmarked model parameters using the gauge-based dataset, C-adj presents well for simulation, while T-adj needs parameter recalibration to achieve good skills. Compared to T-adj and C-adj, better simulation could be obtained by C-ga in precipitation-gauged regions.
KW - Hydrology
KW - Observational techniques and algorithms
KW - Physical Meteorology and Climatology
KW - Remote sensing
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U2 - 10.1175/JHM-D-14-0166.1
DO - 10.1175/JHM-D-14-0166.1
M3 - Article
AN - SCOPUS:84950154220
SN - 1525-755X
VL - 16
SP - 2577
EP - 2594
JO - Journal of Hydrometeorology
JF - Journal of Hydrometeorology
IS - 6
ER -