# Impacts of Compound Hot-Dry Events on Vegetation Productivity over Northern East Asia
## 3 Results
### Performance of CLM5 in Simulating the Eco-Hydrological Processes  
Before exploring the mechanisms of vegetation responses to the CHD events using CLM5, the performance of CLM5 in modeling the eco-hydrological variables, including ET, SM, and GPP, is compared with the observed data. As shown in Figure 3, the spatial distributions of annual ET, SM, and GPP simulated by CLM5 are similar to the observed distributions. The spatial correlation coefficient between  
Figure 2: (**a**) The areas with compound hot–dry (CHD) events in summer from 2003 to 2019 across northern East Asia. Anomalies of (**b**) daily maximum temperature (T\({}_{\rm max}\)), (**c**) standardized precipitation index (SPI), (**d**) gross primary productivity (GPP), (**e**) evapotranspiration (ET), and (**f**) soil moisture (SM) in the top 10 cm of soil for the four selected CHD events compared to the averages during 2003–2019. simulated and observed ET is 0.81. CLM5 underestimates ET in most parts of the study area. The Sikhote-Alin Mountain Region bordering the Sea of Japan is underrated, with a bias of more than \(-\)200 mm/year (Figure 3g). The ET bias in spring is higher than in other seasons and higher in woodlands than grasslands (Table 3). The spatial correlation of surface SM between simulation and observation is 0.59. The model underestimates SM near Daxinganling and Lake Baikal while overestimating SM in some eastern parts of China (Figure 3h). The spatial correlation coefficient of annual GPP between simulation and observation is 0.68, with a higher value for woodlands than grasslands. GPP is overestimated chiefly in the northern part of 53\({}^{\circ}\) N. It is underestimated in some of the southern parts of 53\({}^{\circ}\) N, mainly a transition zone of different ecosystems or dominated by deciduous forests (Figure 3i). The model's performance varies in seasons and various types of vegetation. It simulates well the GPP of woodlands in spring and fall, with the spatial correlation coefficients between simulation and observation at 0.71 and 0.87, respectively. Also, it performs well in simulating the summer GPP of grassland, with a spatial correlation coefficient of 0.63 (Table 3). Figure 3: Annual means of the observed evapotranspiration (ET) and soil moisture (SM) in the top 10 cm of soil from GLEAM (**a,b**), observed gross primary productivity (GPP) from FluxSat (**c**), simulated ET, SM, and GPP by CLM5 (**d–f**) during 2003–2019, and the differences between simulation and observation (**g–i**). Correlations (Pearson coefficient) of ET (**j**), SM in the top 10 cm (**k**), and GPP (**l**) between CLM5 and observational datasets on a daily scale from 2003 to 2019. Bare soil and cropland are marked in grey. Figure 3j-1 show the temporal correlation between the simulated and observed datasets on a daily scale. The results show that the model simulates the variation in ET over time very well, with the correlation coefficients ranging from 0.65 to 0.96 and the average value reaching 0.87. CLM5 also performs well in simulating GPP variation, with correlation coefficients ranging from 0.55 to 0.96 and the average value reaching 0.96. Comparatively speaking, the variation in SM is not captured by CLM5, nor are those of ET and GPP, especially in the northeastern part, occupied mainly by woodland. The CLM5 can capture the responses of ET, SM, and GPP changes well in the four CHD events. Figure 4 shows the changes in these three variables between the _Control_ and _SE_Clim_ simulations. The spatial patterns and magnitudes are similar to those derived from the observed anomalies in the four events (Figure 2d-f). In particular, the simulated anomalies derived from the _Control_ simulation also show similar spatial patterns. However, there are discrepancies between the simulated and observed data regarding the changes in ET and GPP in certain areas southeast of Lake Baikal. The model shows decreases in ET and GPP in these regions, whereas the observations show contrasting trends.