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首页»科研成果» 2015
叶爱中、段青云等在 HYDROLOGICAL PROCESSES 上发表论文
全球院
Aizhong Ye1,2,*, Qingyun Duan1,2, John Schaake3, Jing Xu1,2, Xiaoxue Deng1,2, Zhenhua Di1,2, Chiyuan Miao1,2 and Wei Gong1,2
1 State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China 2 Joint Center for Global Change Studies, Beijing, 100875, China 3 Consultant, Annapolis, MD, USA ABSTRACT For water supply, navigational, ecological protection or water quality control purposes, there is a great need in knowing the likelihood of the river level falling below a certain threshold. Ensemble streamflow prediction (ESP) based on simulations of deterministic hydrologic models is widely used to assess this likelihood. Raw ESP results can be biased in both the ensemble means and the spreads. In this study, we applied a modified general linear model post-processor (GLMPP) to correct these biases. The modified GLMPP is built on the basis of regression of simulated and observed streamflow calculated on the basis of canonical events, instead of the daily values as is carried out in the original GLMPP. We conducted the probabilistic analysis of post-processed ESP results falling below pre-specified low-flow levels at seasonal time scale. Raw ESP forecasts from the 1980 to 2006 periods by four different land surface models (LSMs) in eight large river basins in the continental USA are included in the analysis. The four LSMs are Noah, Mosaic, variable infiltration capacity and Sacramento models. The major results from this study are as follows: (1) a modified GLMPP was proposed on the basis of canonical events; (2) post-processing can improve the accuracy and reduce the uncertainty of hydrologic forecasts; (3) post-processing can help deal with the effect of human activity; and (4) raw simulation results from different models vary greatly in different basins. However, post-processing can always remove model biases under different conditions.
KEY WORDS: low flow;ensemble forecast;post-processer;NLDAS data
PUBLISHED BY: HYDROLOGICAL PROCESSES, 2015， 29 (10):2438-2453 SOURCE: http://onlinelibrary.wiley.com/doi/10.1002/hyp.10374/abstract |
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