## 3 Results and Discussion
### Validation
#### 3.2.2 Inter-Annual Variability of LAI and WG2  
Drought monitoring is an important area of research nowadays. Our interest was focused on the short-term responses of the vegetation to agricultural drought. The ability of the modeling system to simulate inter-annual variability has also been demonstrated in the Pannonian Basin. Eight years (2008-2015) were simulated using assimilation and open-loop runs. These eight years were used as a baseline for calculating the monthly anomalies for the year 2012 when an extremely severe drought affected Hungary. Most of 2012 was characterised by drought. It was also recorded dry for two months, March, and August, accompanied by severe frosts in February and prolonged heatwaves in summer. The normalised anomaly was calculated as noted in [46]:  
\[AnoX=\frac{X-\left<X\right>}{stdev\ (X)} \tag{8}\]  
where <X> stands for the monthly average and _stdev (X)_ indicates the standard deviation. \(X\) can denote variables such as LAI or root-zone soil moisture (WG2), respectively. \(AnoX\) could be calculated for monthly or 10-day periods for simulations and satellite data. When the root-zone soil moisture was examined, SWI-10 was used from the satellite, which is a recursive formulation for the exponential filter of SWI over 10 days and correlates well with WG2.  
Figure 7 shows monthly AnoLAI values calculated from satellite products and the simulations (open-loop and assimilation). Both experiments were able to reproduce the extremely low LAI anomalies in the summer of 2012, and the root-zone soil moisture anomalies (Figure 8) were represented only by the assimilation run in the summer and autumn of 2012. Higher soil moisture deficits were observed over the Carpathian Basin between July and September by the satellite and assimilation products. In early summer, a positive anomaly was observed in the southern and eastern part of the domain for GEOV1 LAI, while the assimilation was unable to maintain this higher LAI anomaly in these regions. This may be due to a negative anomaly in assimilated WG2 values in spring. The lack of precipitation reached 6% of the March average. Drought occurred at the beginning of the growing phase of the vegetation, which reduced crop productivity.  
Figure 6: Time series of the residuals, SAT-OP: blue, SAT-ASS*: orange.  
Figure 9 illustrates the time series of AnoLAI and AnoWG2 in Hegyhatsai calculated with LDAS and open-loop model runs as well as satellite and in-situ data. Instead of AnoWG2, AnoSWI-10 was calculated based on the satellite SWI-10 information. AnoWG2 was also determined with the in-situ soil moisture data for Hegyhatsai. The wet years of 2010 and 2014, as well as the drought in 2012, were very well represented by the simulations and the observations. In winter 2013, the AnoLAI values showed opposite behaviors. Satellite values became positive, while the simulated ones were negative over that period. Higher GEOV1 LAI values were noticed in winter and early spring of 2013 (see the mean values on Figure 5, and this was observed at Hegyhatsai by the satellite data (not shown)) compared to the seasonal mean; however, the open-loop run was not able to localise this extreme behavior. At the same time, LDAS was able to compensate by assimilation of LAI. Higher AnoWG2 in-situ series were detected at the beginning of 2008, which did not appear in the rest of the data. In the second half of 2010, heavy precipitation fell in the region, causing high soil moisture anomalies. The simulations were able to  
Figure 8: Monthly anomalies for the year 2012 from May to September: ASCAT/SWI-10 product (first row) assimilated WG2 (second row) and open-loop WG2 (third row).  
Figure 7: Monthly anomalies for the year 2012 from May to September: GEOV1 LAI product (first row), assimilated LAI (second row) and open-loop LAI (third row).  
follow the processes and capture the maximum values; however, the in-situ measurements showed a wider positive anomaly, while the rest of AnoWG2 data declined rapidly.