Precipitation and Minimum Temperature are Primary Climatic Controls of Alpine Grassland Autumn Phenology on the Qinghai-Tibet Plateau

Abstract

Autumn phenology is a crucial indicator for identifying the alpine grassland growing season’s end date on the Qinghai-Tibet Plateau (QTP), which intensely controls biogeochemical cycles in this ecosystem. Although autumn phenology is thought to be mainly influenced by the preseason temperature, precipitation, and insolation in alpine grasslands, the relative contributions of these climatic factors on the QTP remain uncertain. To quantify the impacts of climatic factors on autumn phenology, we built stepwise linear regression models for 91 meteorological stations on the QTP using in situ herb brown-off dates, remotely sensed autumn phenological metrics, and a multi-factor climate dataset during an optimum length period. The results show that autumn precipitation has the most extensive influence on interannual variation in alpine grassland autumn phenology. On average, a 10 mm increase in autumn precipitation during the optimum length period may lead to a delay of 0.2 to 4 days in the middle senescence date (P < 0.05) across the alpine grasslands. The daily minimum air temperature is the second most important controlling factor, namely, a 1 °C increase in the mean autumn minimum temperature during the optimum length period may induce a delay of 1.6 to 9.3 days in the middle senescence date (P < 0.05) across the alpine grasslands. Sunshine duration is the third extensive controlling factor. However, its influence is spatially limited. Moreover, the relative humidity and wind speed also have strong influences at a few stations. Further analysis indicates that the autumn phenology at stations with less autumn precipitation is more sensitive to precipitation variation than at stations with more autumn precipitation. This implies that autumn drought in arid regions would intensely accelerate the leaf senescence of alpine grasslands. This study suggests that precipitation should be considered for improving process-based autumn phenology models in QTP alpine grasslands.

The important role of soil moisture in controlling autumn phenology of herbaceous plants in the Inner Mongolian steppe

“…It is possible that rather than air temperature, leaf temperature could be a better predictor of leaf senescence (Liu et al, 2020). Wu et al (2018) also revealed a contrasting effect of daytime and nighttime warming on autumn leaf senescence of plants in the Northern Hemisphere using ground phenological records and satellite greenness data, indicating a more complex response of plant autumn phenology to thermal conditions than presented in existing phenological models (An et al, 2020).…”

“…In addition, it was unlikely that model parameters calibrated on the basis of any single site could be utilized consistently at other sites (Table 3), suggesting that site‐specific model calibrations are not reliable for spatial extrapolation (Botta, Viovy, Ciais, Friedlingstein, & Monfray, 2000; Tao et al, 2018). For predicting regional phenology, it is essential to build spatially independent models for all sites covering the entire region, which is, however, technically inefficient (An et al, 2020). Going forward, a way to obtain better predictions at the regional scale is to establish a generalized model over the whole region that is driven by both climatic and genotypic factors (Liang, 2016).…”

Trends of Freezing Period And Its Main Cause On The Qinghai-Tibetan Plateau From 1961 To 2018

“…Alpine meadow, as the dominant ecosystem on the QTP, is highly sensitive to ). An et al (2020) found the daily minimum air temperature can be a key factor for controlling autumn phenology in alpine grasslands, which a 1°C increase in the mean autumn minimum temperature during the optimum length period may induce a delay of 1.6 to 9.3 days in the middle senescence date across the alpine grasslands.…”

Spatiotemporal Variation of Osmanthus fragrans Phenology in China in Response to Climate Change From 1973 to 1996

“…Higher preseason precipitation could also alter spring phenology ( Dai et al, 2013 ; Piao et al, 2019 ; Zhou et al, 2019 ), because more precipitation may increase the heat demand of spring phenology ( Fu et al, 2014 ). Photoperiod may also influence bud burst, but generally in a minor way ( Chen et al, 2017 ; An et al, 2020 ). To date, the mechanisms by which photoperiod affects phenology remain unexplored experimentally ( Liu et al, 2016a ).…”