## 2 Materials and Methods
### Model Experiments  
We conducted two sets of model experiments to explore the sensitivity of soil stratification and root distribution (parameter \(a\) in Equation (10)) to soil hydrology and vegetation growth: (1) Soil stratification schemes. Two soil stratification schemes were tested (Table 1). In Scheme 1, the root zone was divided into uniform 10 cm layers, with layer thickness increasing below the root zone. In Scheme 2, the soil layer thickness gradually increased from the surface to the bottom. This approach, adapted from previous studies [66; 67],  
Figure 3: Location of the study site and the percentage of plantation areas in the Loess Plateau at a spatial resolution of 0.1\({}^{\circ}\) (source: adapted from [62]).  
avoided the occurrence of wet-dry oscillations caused by larger soil layer thickness. In both schemes, the maximum thickness of any individual layer was less than 2 m. (2) Parameter \(a\) Schemes. We tested seven values for parameter \(a\) (0.1, 0.2, 0.3, 0.5, 0.7, 1.0, and 3.0 m), which controls the vertical distribution of fine roots. The physical parameterization options selected for this study are shown in Table 2. All experiments were conducted from 1 January 2014 to 31 December 2018, with 30 model spin-up iterations to ensure model equilibrium. Only the output from the final iteration was analyzed. Model performance was assessed by comparing the simulated soil water content, leaf area index, and root distribution against observations.  
Following the sensitivity experiments, the optimal soil stratification scheme and parameter \(a\) were selected for long-term simulations. To investigate the importance of plant-water interactions, we conducted two additional experiments: one with dynamic vegetation disabled (\"nodynamic\") and one with it enabled (\"dynamic\"). The differences in the results between the two simulations reflected the effect of plant-water interactions. These experiments were conducted from 1 January 1980 to 31 December 2020, driven by the CMFD forcing data and seven spin-up loops. Outputs from the seventh loop were used for analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Scheme 1**} & \multicolumn{4}{c}{**Scheme 2**} \\ \hline
**Sequence** & **Thickness** & **Total Thickness** & **Sequence** & **Thickness** & **Total Thickness** \\ \hline
1–124 & 0.1 & 12.4 & 1–2 & 0.05 & 0.10 \\
125–147 & 0.2 & 17.0 & 3 & 0.06 & 0.16 \\
148–154 & 0.4 & 19.8 & 4 & 0.10 & 0.26 \\
155–156 & 0.6 & 21.0 & 5 & 0.14 & 0.40 \\
157–166 & 0.8 & 29.0 & 6–28 & 0.20 & 5.0 \\
167–184 & 1.0 & 47.0 & 29–68 & 0.40 & 21.0 \\
185–194 & 1.2 & 59.0 & 69 & 0.60 & 21.6 \\
195–204 & 1.4 & 73.0 & 70 & 0.80 & 22.4 \\
205–214 & 1.6 & 89.0 & 71–81 & 1.00 & 33.4 \\
215–234 & 1.8 & 125.0 & 82–91 & 1.20 & 45.4 \\
235–239 & 2 & 135.0 & 92–95 & 1.40 & 51.0 \\ - & - & - & 96–100 & 1.60 & 59.0 \\ - & - & - & 101–105 & 1.80 & 68.0 \\ - & - & - & 106–138 & 2.00 & 134.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil stratification schemes in the sensitivity experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameterization Options** & **Used in This Study** \\ \hline Dynamic vegetation & 2: Leaf area predicted \\ Dynamic root & 1: Dynamic root \\ Canopy stomatal resistance & 1: Ball–Berry \\ Surface runoff & 3: Original surface runoff \\ Supercooled liquid water & 1: No interaction \\ Frozen soil permeability & 1: Linear effect \\ Radiation transfer & 1: Modified two-stream \\ Snow surface albedo & 2: CLASS \\ Snow–rain partitioning & 1: Jordan91 \\ Soil water retention & 1: Van Genutchen \\ Snow/soil temperature time scheme & 1: Semi-implicit \\ \hline \hline \end{tabular}
\end{table}
Table 2: Noah-MP parameterization options selected.