## 4 Discussion  
### Driving Factors for the Spatial Heterogeneity of Vegetation in the MHRB  
The driving forces for the spatial heterogeneity of vegetation vary significantly across different regions [17; 23]. The explanatory power of land use was the greatest for the spatial heterogeneity of the vegetation in the MHRB. In the MHRB, forests had much higher LAI and GPP values than other land use types (Figure 9h). The values of LAI and GPP were lower in water, barren, and impervious surfaces. The mean annual LAI value of forest was about twice that of grassland and cropland. Therefore, the expansion of forests is beneficial to increase vegetation cover in the MHRB. The most important climate indicator in explaining the spatial differences of vegetation distribution in the MHRB was the humidity index. In most areas of north China, moisture is thought to be a major factor influencing the changes in vegetation [50; 51]. The lowest LAI and GPP values were found in dry areas with annual precipitation below 407 mm and a humidity index under 0.41 (Figure 9a-c). Limited by water availability, vegetation in these drier areas has lower coverage and is susceptible to the adverse effects of drought [52; 53]. In these arid regions, ecological engineering implementation should prioritize projecting or enhancing vegetation with lower biomass and coverage. The distribution of forests corresponded to the areas with higher values of precipitation and humidity index (Figure 2), and forest growth often requires more water consumption. For afforestation, areas with precipitation above 407 mm and a humidity index above 0.41 can be given priority. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Climatic Indicators** & **MLHRB** & **MBSHRB** & **MYDHRB** & **MDQHRB** & **MZYHRB** & **MZWHRB** \\ \hline Temperature (\({}^{\circ}\)C yr\({}^{-1}\)) & 0.3 & 0.15 & 0 & 0.23 & 0 & 2.42 \({}^{*}\) \\ Precipitation (mm yr\({}^{-1}\)) & 0.83 & 1.52 & 2.05 \({}^{*}\) & 1.67 & 2.42 \({}^{*}\) & 0.61 \\ Humidity index (yr\({}^{-1}\)) & \(-\)0.08 & 0.91 & 1.36 & 1.52 & 1.44 & 0.68 \\ VPD (kPa yr\({}^{-1}\)) & \(-\)0.15 & \(-\)0.91 & \(-\)1.06 & \(-\)1.74 & \(-\)1.52 & 1.36 \\ ETo (mm yr\({}^{-1}\)) & 2.58 \({}^{*}\) & 1.67 & 0.38 & \(-\)2.5 \({}^{*}\) & 1.21 & 0.45 \\ Solar radiation (W m\({}^{-2}\) yr\({}^{-1}\)) & \(-\)0.38 & 0.23 & 0 & \(-\)2.35 \({}^{*}\) & \(-\)1.29 & \(-\)1.89 \\ \hline \end{tabular} Note: \({}^{**}\) indicates that the sen’s slope is significant (\(p<0.05\)).  
\end{table} 
Table 6: Climate change (Sen’s slope) in the MHRB from 2001 to 2018. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Vegetation** & **Climatic** & & & & & \\
**Indexes** & **Indicators** & **MLHRB** & **MBSHRB** & **MYDHRB** & **MDQHRB** & **MZYHRB** & **MZWHRB** \\ \hline \multirow{6}{*}{LAI} & Temperature & \(-\)0.17 & \(-\)0.08 & \(-\)0.2 & \(-\)0.43 & \(-\)0.27 & \(-\)0.05 \\  & Precipitation & 0.52 \({}^{*}\) & 0.58 \({}^{*}\) & 0.51 \({}^{*}\) & 0.29 & 0.19 & 0.08 \\  & Humidity & 0.43 & 0.49 \({}^{*}\) & 0.48 \({}^{*}\) & 0.23 & 0.24 & 0.11 \\  & urD & \(-\)0.44 & \(-\)0.25 & \(-\)0.47 \({}^{*}\) & \(-\)0.16 & \(-\)0.08 & \(-\)0.22 \\  & ETo & \(-\)0.01 & \(-\)0.01 & \(-\)0.31 & 0.01 & \(-\)0.27 & \(-\)0 In the MHRB, the correlation coefficients between humidity index and ETo were significantly negative in all sub-basins (Table 7). As ETo increased from 842.5 mm to 1099.97 mm, LAI constantly declined (Figure 9d), mostly because the humidity index would fall as ETo increased. With an increase in ETo, the climate tends to become drier [54], thus adversely affecting vegetation growth in water-scarce areas. The decline of ETo is also thought to be beneficial for vegetation growth in semi-arid grassland [55]. When the solar radiation was more than 167.45 W m\({}^{-2}\), values of LAI and GPP continued to decrease with the increase in solar radiation (Figure 9f), which was mainly due to the drier climate in these areas and the fact that higher solar radiation frequently correlated with less precipitation.