Modeling a frost index in Kansas, USA

Abstract

A frost index is a calculated value that can be used to describe the state and the changes in the weather conditions. Frost indices affect not only natural and managed ecosystems, but also a variety of human activities. In addition, they could indicate changes in extreme weather and climate events. Growing season length is one of the most important frost indices. In this report, growing season lengths were collected from 23 long-term stations over Kansas territory. The records extended to the late 1800s for a few stations, but many started observations in the early 1900s. Though the start dates of the records were different, the end dates were the same (2009). To begin with, time series models of growing season length for all the stations were fitted. In addition, by using fitted time series models, predictions and validation checking were conducted. Then a regular linear regression model was fitted for the GSL data. It removed the temporal trend by doing regression on year and it showed us the relationship between GSL and elevation. Finally, based on a penalized likelihood method with least angle regression (LARS) algorithm, spatial-temporal model selection and parameter estimation were performed simultaneously. Different neighborhood structures were used for model fitting. The spatial-temporal linear regression model obtained was used for interpreting growing season length of those stations across Kansas. These models could be used for agricultural management decision-making and updating recommendations for planting date in Kansas area.