## 3 Results
### LDAS-Monde Impact
#### 3.2.2 Assimilation Impact on LAI and Soil Moisture
Figure 7: Seasonal average (from January to June) maps of precipitation [kg\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)] and LAI [m\({}^{2}\cdot\)m\({}^{-2}\)] over January 2001 to June 2018. From left to right: precipitation, LAI model, LAI analysis, LAI analysis-model difference. The latter column shows the impact of assimilating LAI and SWI on the simulated LAI. LDAS-ERA5 configuration is used. Figure 8: Same as Figure 7, but from July to December. Figure 9: Monthly average values of LAI from 1 January 2001 to June 2018: model (blue line), satellite product (green circles), analysis (red line) in the LDAS-ERA5 configuration. Analysis increments for the LAI and for soil moisture from the LDAS-ERA5 configuration in (i) the second layer of soil (\(w_{2}\), between 1 and 4 cm), (ii) the fourth layer (\(w_{4}\), between 10 and 20 cm), and (iii) the sixth layer (\(w_{6}\), between 40 and 60 cm) are averaged for the period of January 2001 to June 2018. They are presented in Figure 12. Overall, the analysis removes the LAI in the central part (SS)  
Figure 11: Seasonal (**a**) RMSD and (**b**) correlation of LAI analysis when compared to the observations considering the three climatic regions (SH, SS, and SG) over January 2001 to June 2018 for the LDAS-ERA5 configuration. Figure 10: Seasonal (**a**) SDD (**b**) bias (**c**) RMSD and (**d**) correlation between LAI from either LDAS-ERA5 (solid lines) or LDAS-ERA1 (dashed lines) model (blue) and analysis (red) and observed LAI over January 2001 to June 2018.  
of the domain. However, small spots of increases are noticeable in both the south-western (SG) and north-eastern part (SS) of the domain. Regarding the \(w_{2}\) soil moisture values, the analysis adds water in some parts of the domain (SH, SG, and the eastern part of SS) and removes water in the central part. For \(w_{4}\) and \(w_{6}\), i.e., for deeper soil layers, the impact is less pronounced (in agreement with the above mentioned study of the Jacobians), but a perceptible north-eastward oriented drying is still observed. It is worth mentioning that the dark blue point in the north-western part of Figure 12a (positive LAI increments) around Mopti in Mali corresponds to the Niger inner delta, including wetlands and irrigated areas. Irrigation is not accounted for in the version of ISBA used in this study and the analysis tends to compensate for this model weakness. This area is also visible in the map of the observed LAI (Figure 1b). While Figure 12 illustrates analysis increments for the LDAS-ERA5 configuration, the LDAS-ERA1 one follows the same description (not shown). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & **Jan.** & **Feb.** & **Mar.** & **Apr.** & **May** & **Jun.** & **Jul.** & **Aug.** & **Sep.** & **Oct.** & **Nov.** & **Dec.** \\ \hline BF & 59,724 & 59,723 & 59,724 & 59,715 & 58,914 & 53,840 & 48,140 & 48,006 & 46,022 & 54,265 & 56,406 & 56,406 \\ \hline SH & 18,684 & 18,683 & 18,684 & 18,679 & 18,674 & 18,678 & 17,638 & 17,644 & 17,646 & 17,646 & 17,646 & 17,646 \\ \hline SS & 19,440 & 19,440 & 19,440 & 19,440 & 19,440 & 19,440 & 18,334 & 18,357 & 18,319 & 18,360 & 18,360 & 18,360 \\ \hline SD & 21,600 & 21,600 & 21,600 & 21,596 & 20,800 & 15,722 & 12,168 & 12,005 & 10,057 & 18,259 & 20,400 & 20,400 \\ \hline \end{tabular}
\end{table} 
Table 4: Seasonal number of LAI values (N) from 1 January 2001 to 30 June 2018 for the LDAS-ERA5 configuration. Figure 12: Averaged analysis increments for January 2001 to June 2018. Four control variables are illustrated: (**a**) leaf area index and soil moisture in (**b**) the second (WG2, 1–4 cm), (**c**) fourth (WG4, 10–20 cm), and (**d**) sixth (WG6, 40–60 cm) layer of soil for the LDAS-ERA5 configuration. As mentioned above, the impact of assimilation on SSM is relatively weaker than on LAI. This can be seen, for instance, in Figure 13a, which shows the correlation values between SSM from the openloop and the analysis (for both LDAS-ERA5 and LDAS-ERAI configurations) and the SSM estimates from CGLS.