Abstract

Climate change could affect botanical tourism by altering the plant phenology (e.g., flowering and leaf coloring date) and the physical comfort of tourists. To date, few studies have simultaneously considered the influence of plant phenology and physical comfort on the travel suitability of botanical tourism. Taking Beijing as an example, this study used phenological data of 73 species from 1963 to 2017 to construct a phenological ornamental index (POI) according to the flowering and leaf coloring date of ornamental plant. The climate comfort index (CCI) of tourism was calculated by using meteorological data of the corresponding periods. Finally, the travel suitability index (TSI) was constructed by integrating the two indices (POI and CCI). The POI showed that the best period for spring flower viewing was from April 4 to May 10, while the best period for autumn leaves viewing was from October 11 to November 6 on average. According to the variation of the CCI within the year, the most comfortable period for spring tourism was matched with the best period for spring flower viewing (April 4 to June 1), but the most comfortable period for autumn tourism (September 4 to October 19) was earlier than the best period for autumn leaves viewing. The TSI indicated that the best periods for spring and autumn botanical tourism were April 7 to May 10 and October 10 to November 7, respectively. Based on the climate data under different scenarios (representative concentration pathways 4.5 and 8.5), we simulated the climate and phenological suitability for botanical tourism in the next thirty years. The results showed that the best period for spring botanical tourism during 2040–2050 was earlier and the period for autumn botanical tourism was later than that in the past 55 years. Meanwhile, the duration would shorten by 2–7 days for both seasons. This study provided a reference for assessing the impact of global climate change on the best season of botanical tourism.

Highlights

  • Climate change could affect botanical tourism by altering the plant phenology and the physical comfort of tourists

  • According to the variation of the climate comfort index (CCI) within the year, the most comfortable period for spring tourism was matched with the best period for spring flower viewing (April 4 to June 1), but the most comfortable period for autumn tourism (September 4 to October 19) was earlier than the best period for autumn leaves viewing. e travel suitability index (TSI) indicated that the best periods for spring and autumn botanical tourism were April 7 to May 10 and October 10 to November 7, respectively

  • Based on the climate data under different scenarios, we simulated the climate and phenological suitability for botanical tourism in the thirty years. e results showed that the best period for spring botanical tourism during 2040–2050 was earlier and the period for autumn botanical tourism was later than that in the past 55 years

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Summary

Introduction

Climate change could affect botanical tourism by altering the plant phenology (e.g., flowering and leaf coloring date) and the physical comfort of tourists. Few studies have simultaneously considered the influence of plant phenology and physical comfort on the travel suitability of botanical tourism. In the late 1960s, advances in biometrics and computer technology led to the rapid development of climate comfort indices based on human heat exchange models [19, 20], such as perceived temperature (PT) [21], standard effective temperature (SET) [22], and physiological equivalent temperature (PET) [23], which comprehensively considered the meteorological factors (air temperature, relative humidity, wind speed, and solar radiation), human metabolic rate, clothing, and individual parameters. Scientists developed a universal thermal climate index (UTCI), which was regarded as one of the most comprehensive indices for calculating heat stress in outdoor space [24, 25]. e input data for calculating UTCI included meteorological and nonmeteorological (metabolic rate and clothing thermal resistance) data [26]

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