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

Abstract

Autumn phenology variation in temperate grassland is a direct indicator of land degradation, and a phenology‐based model has been used to simulate human‐induced land degradation. It is important to reveal how environmental factors influence autumn phenology of herbaceous plants. In this study, we examined the effects of temperature, photoperiod, and soil moisture on the leaf withering date (LWD) of four herbaceous species (Xanthium sibiricum, Plantago asiatica, Iris lactea, and Taraxacum mongolicum) at 15 sites in the Inner Mongolian steppe from 1981 to 2012. Then, we developed two process‐based models coupling the effects of the three factors based on an existing cold degree–days (CDD)‐based model for predicting the LWD of the four species. The new models showed better accuracy than CDD model during validation. The model structure and parameters suggested that soil moisture likely influenced LWD nonlinearly and presented a multiplicative interaction with temperature and photoperiod. In addition, photoperiod had divergent effects on the accumulation of cold temperature for different species and sites. For X. sibiricum, the photoperiod only determined the start of cold temperature accumulation. Considering the carryover effect of spring phenology did not improve model performance. Our study also showed that local adaptation of plants was commonly ignored when using same parameters in model calibration with observation data from multiple sites; however, model calibrations using site‐specific data were less stable, and the parameters could not be extrapolated across space. It will be necessary to establish a generalized regional model driven by both climatic and genotypic factors in future research.

Earlier leaf senescence dates are constrained by soil moisture

“…Furthermore, the sensitivity of vegetation to soil moisture has substantially increased since 1982 (Li, Migliavacca, et al, 2022). Previous studies found that not only temperature, but also plant-available water affect autumn phenological processes (Fu et al, 2022;Luo et al, 2021;Tao et al, 2021;Wu et al, 2022). For instance, Tao et al (2021) found that soil moisture considerably constrained the autumn phenology of herbaceous plants, which means that the accuracy of the CDD model could be improved using soil moisture based on in situ data.…”

“…Previous studies found that not only temperature, but also plant-available water affect autumn phenological processes (Fu et al, 2022;Luo et al, 2021;Tao et al, 2021;Wu et al, 2022). For instance, Tao et al (2021) found that soil moisture considerably constrained the autumn phenology of herbaceous plants, which means that the accuracy of the CDD model could be improved using soil moisture based on in situ data. However, a generalized autumn phenology that takes into account soil moisture, and which can be applied at large scales, is still lacking.…”

Multimodel simulations revealed strong spatial heterogeneity in temperature‐, photoperiod‐ and moisture‐driven modes of autumn phenology in Northern Hemisphere grasslands (25° N–55° N)

“…Several studies have demonstrated its better performance in modeling leaf coloration and fall for many woody species compared to the CDD model (Archetti et al, 2013; Delpierre et al, 2009; Keenan & Richardson, 2015); furthermore, according to the significant influence of spring phenology on autumn phenology of some woody species, Keenan and Richardson (2015) further modified it by considering the effect of the beginning of the growing season on autumn phenology, which also slightly improved the accuracy of autumn phenology simulation in Harvard Forest. But the limited influence of leaf unfolding date on leaf senescence was found for herbaceous plants in China (Ren, Vitasse, et al, 2022; Tao et al, 2021). Meanwhile, Lang et al (2019) developed another model by using the interaction of photoperiod and minimum temperature to nonlinearly determine the daily leaf senescence rate of plants, which also exhibited an improved accuracy of leaf senescence compared with previous models for both woody and herbaceous species.…”

“…Meanwhile, Lang et al (2019) developed another model by using the interaction of photoperiod and minimum temperature to nonlinearly determine the daily leaf senescence rate of plants, which also exhibited an improved accuracy of leaf senescence compared with previous models for both woody and herbaceous species. Additionally, the incorporation of water factors can also ameliorate the simulation performance of autumn phenology of herbaceous species compared with temperature‐based models only (Lang et al, 2019; Ren et al, 2019; Tao et al, 2021).…”

“…Simultaneously, almost all models above are established with limited in situ observations at the species level. Although differences in phenological responses to climate change have been diagnosed among species (Lang et al, 2019; Ren et al, 2019; Tao et al, 2021), the spatial pattern of autumn phenology modeling performance at the hemispheric scale is still unclear. In this study, we comprehensively compared the current widely used autumn models and some improved versions in simulating the end of the growing season (EOS) retrieved from remote sensing data over northern grasslands, with the aim of weighing their accuracies and exploring the spatial heterogeneity of grassland autumn phenology drives.…”