Details

Autor(en) / Beteiligte

• Mandal, Sohom
• Srivastav, Roshan K.
• Simonovic, Slobodan P.

Titel

Use of beta regression for statistical downscaling of precipitation in the Campbell River basin, British Columbia, Canada

Ist Teil von

• Journal of hydrology (Amsterdam), 2016-07, Vol.538, p.49-62

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2016

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •A statistical downscaling model is developed based on beta regression.•Precipitation state in river basin is calculated.•Proposed model reduces multidimensionality of explanatory variables.•The proposed model adds value to GCM simulation.•The model performs well in terms of preserving temporal and spatial dependence. Impacts of global climate change on water resources systems are assessed by downscaling coarse scale climate variables into regional scale hydro-climate variables. In this study, a new multisite statistical downscaling method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics. The beta regression based downscaling method includes two main steps: (1) prediction of precipitation states for the study area using classification and regression trees, and (2) generation of precipitation at different stations in the study area conditioned on the precipitation states. Daily precipitation data for 53years from the ANUSPLIN data set is used to predict precipitation states of the study area where predictor variables are extracted from the NCEP/NCAR reanalysis data set for the same interval. The proposed model is applied to downscaling daily precipitation at ten different stations in the Campbell River basin, British Columbia, Canada. Results show that the proposed downscaling model can capture spatial and temporal variability of local precipitation very well at various locations. The performance of the model is compared with a recently developed non-parametric kernel regression based downscaling model. The BR model performs better regarding extrapolation compared to the non-parametric kernel regression model. Future precipitation changes under different GHG (greenhouse gas) emission scenarios also projected with the developed downscaling model that reveals a significant amount of changes in future seasonal precipitation and number of wet days in the river basin.