# Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China
## 5 Conclusions  
This study developed an analysis framework to quantitatively assess the influence of _SM_ on _WIE_. Based on the analysis framework, this paper found that:  
1. _SM_ had an overall negative effect on the _WIE_ in the terrestrial ecosystems during the last 30 years. In the _NPP_ pathway, _SM_ significantly affected _WIE_, and the sensitivity coefficient ranged from \(-68.66\) to \(43.19\). However, in the _ET_ pathway, _SM_completely negatively affected _WUE_. Under the dual effects of _ET_ and _NPP_ pathways, _SM_ negatively affected _WUE_ in 84.62% of the area, whereas it showed a positive influence on _WUE_ in the remaining 15.38% of the area. Thus, although _SM_ had a positive effect on the _WUE_ of individual regions, it still had an overall negative effect on the _WUE_ of each climatic zone. 2. The variation in _SM_ could lead to a significant _WUE_ variability, especially in Northeast and Northwest China. During the growing season in the past 30 years, the average and maximum variability of _WUE_ caused by variation in _SM_ were 28.02% and 109.29%, respectively. Regarding different climatic zones, _SM_ in CTMP had the greatest effect on _WUE_, whereas it had the least impact on _WUE_ in PTMP. In addition, the lower the forest covers, the higher the variability of _WUE_ caused by changes in _SM_. The _WUE_ of forest ecosystems is more resistant to changes in _SM_, whereas the _WUE_ of non-forest vegetation is more sensitive to changes in _SM_. These findings deepened our understanding of _WUE_ changes and their underlying mechanisms, thereby offering important insight for predicting the response of ecosystems to climate change. All authors contributed greatly to this work. Conceptualization, X.H. ; methodology, X.H. ; software, J.Z. ; writing--original draft preparation, J.Z. and X.F. ; validation, H.H. ; formal analysis, X.F. and Y.L. ; writing--review and editing, X.H. ; project administration, X.H. ; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (U1903114) and the Cross Team Project of the \"Light of West China\" Program of CAS (no: E0284101). Not applicable. The GLDAS dataset was obtained from the NASA Global Land Data Assimilation System (GLDAS) Noah model (GLDAS_NOAH025\(M\)2_0, [[https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)]([https://disc.gsfc.nasa.gov/datasets](https://disc.gsfc.nasa.gov/datasets)), accessed on 10 October 2020). The monthly net primary productivity (NPP) dataset of China's terrestrial ecosystems in the north of 18\({}^{\circ}\)N was obtained from the Global Change Research Data Publishing & Repository of China ([[http://www.geodoi.ac.cn/WebEn/CategoryList.aspx](http://www.geodoi.ac.cn/WebEn/CategoryList.aspx)]([http://www.geodoi.ac.cn/WebEn/CategoryList.aspx](http://www.geodoi.ac.cn/WebEn/CategoryList.aspx)), accessed on 24 September 2020). The classification dataset of the soil texture (sand, silt, and clay content in 0-100 cm soil depth) and the soil moisture data based on the microwave remote sensing in China during 2002 and2011 wereprovided by the \"National Tibetan Plateau Data Center\" of China ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)), accessed on 13 May 2020). The dataset of soil hydraulic parameters in China was provided by the \"National Cryosphere Desert Data Center of China\" ( [[http://www.crensed.ac.cn/portal/](http://www.crensed.ac.cn/portal/)]([http://www.crensed.ac.cn/portal/](http://www.crensed.ac.cn/portal/)), accessed on 21 August 2020). The observed _ET_ and _NPP_ (_NEE_) data in eight flux towers of China were provided by ChinaFLUX ([[http://www.crenren.org.cn/](http://www.crenren.org.cn/)]([http://www.crenren.org.cn/](http://www.crenren.org.cn/)), accessed on 15 September 2021).