Study of the Bagmati River in Nepal as Religious Tourist Attraction: Quantile Regression approach for Assessment of Rainfall pattern over it (Journal of Tourism and Himalayan Adventures, 2019)
Quantile regression is an emerging statistical tool used to explain the relationship between response and predictor variables. It provides more robust and efficient estimators, on the conditional median or other quantiles of the response variable, compared to OLS. This study aims to establish relationship between monthly rainfall data in the Bagmati River and some large-scale climate predictor variables using this technique over the period 1981-2000. Quantile regression model reveals the relationship between the monthly rainfall and each of seven predictor variables. Geopotential height 1, Mean sea level pressure 1, Precipitable water 1 showed a greater effect ranging from q = 0.25 to q = 0.950. Each of these seven predictor variables has tendency to affect the rainfall uniquely to each other. Impact of climate change on these predictors may adverse effect on water tourism and religious tourism along the Bagmati River. Thus, the use of quantile regression is very important to determine the effect of the above seven predictors in a model to explain the monthly rainfall behavior over the river.