## 3 Results
### The Comparison and Modeling of FLDAS and GLDAS ET and SSM Products  
Figure 9 shows correlation maps between FLDAS and GLDAS ET (Figure 9a) and SSM (Figure 9b) products. It is noted that some regions (areas in China, Egypt, Saudi Arabia) are not shown due to a lower confidence value (\(<\)95%) in the statistical significance test. In general, FLDAS ET/SSM products have strong consistency with those of GLDAS. However, some BWh and BWk regions (North Africa, south of the Arabian Peninsula, west of China, west coast of South America) demonstrate less correlation, while some areas in Egypt even showing a negative correlation (small map in Figure 9b). Compared with Figure 1b, these regions are mostly covered by desert or have no biome data recorded. Moreover, the comparisons between satellite-based products (NASA-USDA SSM and MODIS ET) and model products obtained from FLDAS and GLDAS over arid regions (BSh, BSk, BWh and BWk) are presented in Figure 10 (SSM) and Figure 11 (ET), respectively. It is noted that both models tend to overestimate SSM (linear regression coefficient \(<\)1) but underestimate ET (linear regression coefficient \(>\)1), which is indicated by the positive (SSM) and negative (ET) biases (\(\psi\)). The values of \(\left|\psi\right|\) and \(\psi\) are nearly identical for all plots in Figure 10. The absolute values of \(\psi\) and \(\left|\psi\right|\) are also almost identical in the cold areas (BSK and BWk), while different in the hot areas (BSh and BWh). This means model outputs of ET sometimes are estimated as being higher than ET observed from MODIS satellites in the hot areas but not in the cold areas. For SSM products (Figure 10), satellite SSM values fall into the range of around [0, 25], while ranges of [12, 48] and [0, 45] are set for FLDAS and GLDAS, respectively. For both FLDAS and GLDAS, the hot areas (BSh and BWh) have better consistency with NASA-USDA products than the cold areas (BSK and BWk). Compared with GLDAS, FLDAS achieves better consistency in the hot areas with higher R\({}^{2}\), yet is slightly worse in the cold areas. For ET products (Figure 11), the highest consistency is found in BSh, yet the lowest consistency in BWk. FLDAS has slightly better coherency with MODIS ET data than GLDAS in all regions. Figure 8: The GRACE Water Equivalent Height (cm) of time series for regions in Figure 7: (**a**) Region A; (**b**) Region B; (**c**) Region C and (**d**) Region D from 2002 to 2020, with pink areas covering 2010 to 2019. The blue dotted line are the observations; red dotted line is the regression line; the red/blue arrows indicate decreasing/increasing trends over the period of 2010–2019, respectively. Source: [[https://ccar.colorado.edu/grace/jpl.html](https://ccar.colorado.edu/grace/jpl.html)]([https://ccar.colorado.edu/grace/jpl.html](https://ccar.colorado.edu/grace/jpl.html)). Figure 9: The correlation maps (correlation coefficient) between FLDAS and GLDAS in **(a)** ET and **(b)** SSM products calculated by monthly anomalies values during the years 2001–2019. Only regions with a confidence level of 95% are shown. Figure 10: Comparisons of NASA-USDA SSM versus SSM values obtained from FLDAS and GLDAS for all types of arid regions. The solid black and red lines represent the one to one line and regression line, respectively. Figure 11: Comparisons of MODIS ET versus ET values obtained from FLDAS and GLDAS for all types of arid regions. The solid black and red lines represent the \(y=10^{\circ}\)x line and regression line, respectively. As shown in Figure 9, strong disagreement between FLDAS and GLDAS products mainly exists in the Egypt territory. It is not decided which product has better performance due to a lack of ground observations. Therefore, the satellite-based dataset becomes a relatively reliable source of monitoring SSM and ET in this region. However, the satellite-based dataset is occasionally inaccessible due to atmospheric conditions (e.g., clouds) and sensor operating issues.