A new statistical precipitation downscaling method with Bayesian model averaging: a case study in China

2015 ◽  
Vol 45 (9-10) ◽  
pp. 2541-2555 ◽  
Author(s):  
Xianliang Zhang ◽  
Xiaodong Yan
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Downscaling Method

scholarly journals Comparison of Three Statistical Downscaling Methods and Ensemble Downscaling Method Based on Bayesian Model Averaging in Upper Hanjiang River Basin, China

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jiaming Liu ◽  
Di Yuan ◽  
Liping Zhang ◽  
Xia Zou ◽  
Xingyuan Song
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Support Vector ◽  
Simulation Accuracy ◽  
Downscaling Method ◽  
Hanjiang River

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA) method combined with three statistical downscaling methods, which are support vector machine (SVM), BCC/RCG-Weather Generators (BCC/RCG-WG), and Statistics Downscaling Model (SDSM), is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB). The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error) shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.

scholarly journals Real-time flood forecasting coupling different postprocessing techniques of precipitation forecast ensembles with a distributed hydrological model. The case study of may 2008 flood in western Piemonte, Italy

2013 ◽  
Vol 13 (2) ◽  
pp. 211-220 ◽  
Author(s):  
D. Cane ◽  
S. Ghigo ◽  
D. Rabuffetti ◽  
M. Milelli
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Hydrological Model ◽  
Good Description ◽  
Model Averaging ◽  
Region Of Interest ◽  
Precipitation Forecast ◽  
Distributed Hydrological Model ◽  
Probabilistic Forecasts

Abstract. In this work, we compare the performance of an hydrological model when driven by probabilistic rain forecast derived from two different post-processing techniques. The region of interest is Piemonte, northwestern Italy, a complex orography area close to the Mediterranean Sea where the forecast are often a challenge for weather models. The May 2008 flood is here used as a case study, and the very dense weather station network allows us for a very good description of the event and initialization of the hydrological model. The ensemble probabilistic forecasts of the rainfall fields are obtained with the Bayesian model averaging, with the classical poor man ensemble approach and with a new technique, the Multimodel SuperEnsemble Dressing. In this case study, the meteo-hydrological chain initialized with the Multimodel SuperEnsemble Dressing is able to provide more valuable discharge ranges with respect to the one initialized with Bayesian model averaging multi-model.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models
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