Abstract
A binary logistic model is developed for probabilistic prediction of a wet or dry day based upon daily rainfall data from 1981 to 2008 taken from 25 stations of Bagmati River basin. The predictor variables included in the model are daily relative humidity, air surface temperature, sea level pressure, v-wind which are expressed as principal components of 9 grids of the National Centers for Environmental Protection (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis data with resolution of 2.50×2.50. Principal component analysis is used to reduce the dimension of the predictors in the presence of spatial correlations between grids and thus reduce their multicollinearity effect. The result depicts that the model has 86.4 percent predictive capability in the analysis period (1981-2000) and 86.1 in the validation period (2001-2008) along with support of receiver operating characteristic (ROC) analysis. The results demonstrate that the first two principal components of relative humidity are the key predictor variables with respective odds ratios (ORs) of 4.18 and 3.61, respectively. The other statistically significant predictors are the second principal component of v-wind with OR 1.43, the second and first principal components of air surface temperature with ORs 1.38 and 0.76, respectively and the first principal component of sea level pressure with OR 0.44. Goodness-of-fit test, ROC analysis and other main diagnostic tests showed that the fitted logistic model is characterized by good fits for analysis as well as validation period.
Highlights
Climate is a very important natural process that affects human life and the environment
When a multicollinearity test was performed with the predictand as a continuous variable, the results showed multicollinearity problem with high variance inflation factors (VIF) associated with several predictors
Thereafter, two predictors namely GPH1 and Precipitable Water_1 (PW1) were excluded as predictor variables
Summary
Climate is a very important natural process that affects human life and the environment. The extreme rainfall when happens may cause serious damage with great socioeconomic losses by heavy floods or by prolong droughts [2]. This situation drives us to have a sound methodology and technique to understand such phenomena correctly as far as possible. General Circulation Model (GCM) is one of the recent climate models to observe the impact and to predict the climate change. GCM outputs are not suitable for direct use to assess the climate change impact at local level because of their oversimplification in terms of coarse resolution input information, equations and others [3]. GCM uses information on orography, land surface or other at coarse resolution.
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