# Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China
## 4 Discussion  
### The Reliability of Evaluation Results  
In most cases, _SM_ dominates _ET_, especially in water-limited regions [54; 55]. To explore the coupling relationship between _SM_ and _ET_, researchers proposed a simple parameterization schematic, in which the _SM_ and _ET_ of a forest presented a linear relationship [56]. Recently, studies have proposed a conceptual framework for the dependence of the evaporative fraction (_EF_) on _SM_. It is suitable to analyze the soil moisture control on energy partitioning [50; 51]. Based on this coupling relationship between _SM_ and _EF_, this study established an evaluation framework for the feedback relationship between the _WUE_ and _SM_. The uncertainty of this evaluating framework mainly originated from two aspects:  
Figure 5: Percentage change in water use efficiency (_\(\Delta\)WUE_) caused by soil moisture variation in the terrestrial ecosystem in the growing season. Panels **(a,b)** represent the spatial variation characteristics of the _\(\Delta\)WUE_ and _\(\Delta\)WUE_\({}_{\max}\) of the entire terrestrial ecosystem, respectively. Panel **(c)** represents _\(\Delta\)WUE_ and _\(\Delta\)WUE_\({}_{\max}\) in different climatic zones. Panel **(d)** shows the scatter plot between the variation in _WUE_ and the ratio of tree cover to no-tree vegetation cover. The abbreviations of MT, SST, MST, NST, WTMP, MTMP, CTMP, PTMP, and PSF represent Marginal tropical zone, south subtropics zone, middle subtropics zone, north subtropics zone, warm temperate zone, middle temperate zone, colder zone, temperate zone in Qinghai–Tibet Plateau and the sub-rigid zone in Qinghai–Tibet Plateau (PSF), respectively.  
(1) the reliability of the datasets used and (2) the uncertainty of the parameterized expression of \(EF\) and \(SM\).  
Previous studies have shown that evaporation, \(LE\), \(H\), and \(SM\) data in the GLDAS dataset have good applicability in China [57; 58]. Our verification results (Figure 2) also showed that the datasets used in this study were, overall, reliable, despite some overestimation or underestimation. The analysis method for the coupling relationship between \(EF\) and \(SM\) has already been applied [51]; however, this framework needs to fit two parameters (namely, \(\theta_{c}\) and \(\theta_{r}\)), which may cause uncertainty. Given this, the study directly used two determined parameters, namely, \(\theta_{l}\), and \(\theta_{r}\), provided by the dataset of soil hydraulic parameters in 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). This study also introduced a dimensionless parameter A. Thus, this study only fit one parameter (A), which could avoid the uncertainty caused by the need to fit two parameters in the original equation and eliminate the error caused by possible overfitting. This study used the monthly \(SM\) and \(EF\) data from 1985 to 2000 to fit the multi-year average monthly parameter A. The monthly \(SM\) from 2001 to 2014 was estimated using Equation (4) and then verified using \(SM\) data of GLDAS (Figure 2d). The overall trend of change in estimated \(SM\) was close to the GLDAS \(SM\) data, and the \(R^{2}\), RMSE, MAPE, and NSE of this simulation were 0.99, 0.01 m\({}^{3}\cdot\)m\({}^{-3}\), 18%, and 0.98, respectively. Although there were still some simulation errors, the estimation was generally reliable. Furthermore, the trend of change in the estimated value was consistent with the GLDAS \(SM\) data; thus, the functional relationship between \(SM\) and \(EF\) was credible overall.