# Early Evidence That Soil Dryness Causes Widespread Decline in Grassland Productivity in China
## 5 Conclusions  
We used simulation results from a terrestrial ecosystem model to analyze the moderating effect of soil drought on GPP changes in Chinese grasslands to explore the conditional probability of ecosystem GPP loss due to soil drought and to analyze the dominance of terrestrial-atmosphere moisture constraints on ecosystem GPP. The main conclusions are as follows:  
1. No significant trends were found for soil moisture in the historical or future periods, and its long-term change was mainly reflected through interannual fluctuations. Soil moisture showed a highly significant positive correlation with ecosystem GPP in the time series, indicating that when soil water decreases, it causes a decrease in ecosystem GPP. Moreover, the correlation between SM and GPP was higher in the warm season than annually, and higher in the future period than in the historical period, representing a stronger constraint on GPP in Chinese grasslands in the warm season and a deeper constraint in the future period than in the historical period. 2. Using the LPJmL model's soil moisture data at different soil depths and analyzing their relationship with ecosystem GPP, it was found that the correlation between shallow-soil moisture (0-50 cm) and GPP was clearly higher than that of deeper soils, and the probability of an ecosystem GPP deficit due to a shortage of soil water in the shallow layer was much higher than that of soil water in the middle and deep layers. 3. In probabilistic terms, soil drought has a higher probability of initiating the loss of ecosystem GPP than atmospheric drought, with moisture scarcity originating from the soil becoming the main aspect that constrains ecosystem GPP. In the future, with the rapid rise of global VPD, the probability of ecosystem GPP loss induced by atmospheric drought increases and overtakes soil drought as the main water constraint in some regions. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/land12020484/s1](https://www.mdpi.com/article/10.3390/land12020484/s1)]([https://www.mdpi.com/article/10.3390/land12020484/s1](https://www.mdpi.com/article/10.3390/land12020484/s1)), Figure S1. **Author Contributions:** Conceptualization, Z.H., Z.S. and P.H. ; methodology, X.M. ([PERSON]) and P.H. ; software, T.D., P.H. and N.W. ; validation, P.H. and X.M. ([PERSON]); formal analysis, P.H. ; investigation, P.H. and X.M. ([PERSON]); resources, P.H. ; data curation, P.H. ; writing--original draft preparation, [PERSON] ([PERSON]), Z.H., S.M. and P.H. ; writing--review and editing, [PERSON] ([PERSON]), [PERSON] ([PERSON]), J.M., Y.Z. and P.H. ; visualization, P.H. ; supervision, P.H. ; project administration, Z.S. and P.H. ; funding acquisition, Z.S. and P.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Foundation items: National Natural Science Foundation of China, No. 32060408; National Basic Resources Survey Special, No. 2017 FY100200; the Scientific Innovation Project of Postgraduates of Xinjiang Uygur Autonomous Region, China, No. XJ2021G169. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. Data Availability Statement: Data are already published and publicly available, datasets utilized for this research are as follows: The ISMIP dataset: [[https://esg.pik-potsdam.de/projects/isimip/](https://esg.pik-potsdam.de/projects/isimip/)]([https://esg.pik-potsdam.de/projects/isimip/](https://esg.pik-potsdam.de/projects/isimip/)), accessed on 6 December 2022. The authors declare no conflict of interest. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Vegetation vulnerability and resistance to hydrometeorological stresses in water- and energy-limited watersheds based on a Bayesian framework. _Catena_**2021**, _196_, 104879. [CrossRef]
* [PERSON] and [PERSON] (1992) [PERSON]; [PERSON] Applied nonparametric regression. _Economica_**1992**, _58_, 538.