*4.3. Pattern Difference and Time Variation*

#### 4.3.1. Spatial Validation

Figure 5 shows the CC and RMSE distribution of soil moisture, evaporation, and air temperature at 2 m against the remote sensing or reanalysis data from 15 July to 30 November. The simulations and validation data are processed as daily averages to avoid the daily cycle dominating the evaluation measure. And bilinear interpolation in space was performed for the simulations to match with the validation data.

When comparing the soil moisture, WRF-H exhibits a lower correlation and a larger RMSE than WRF-S in most areas, especially in the west part of SRTR (Figure 5a–d). Due to the wetter soil moisture simulation, more evapotranspiration was simulated by WRF-H. Result in a slightly low CC and high RMSE simulated by WRF-H than WRF-S (Figure 5e–h). But the WRF-H exhibits advantages in simulation temperature at 2 m, with lower RMSE at the same CC. From the spatial distribution of soil moisture and evaporation, the simulations are similar in the eastern part of the Three Rivers. In contrast, the simulation of WRF-H is worse than WRF-S in the central and western regions.

After spatial validation, WRF-Hydro improved the simulation of temperature and deteriorated the humidity and evapotranspiration. The WRF-Hydro does not show the advantages in space scale as it does at the point scale. It is probably because the CMA stations were built in the more inhabitable areas of the plateau, such as in the valleys. These places are usually crossed by rivers and have a high soil moisture content. And these wetter areas cannot be resolved by GLEAM data at 0.25◦ resolution but can be identified by the 4 km WRF-Hydro model.

**Figure 5.** Evaluation of soil moisture, evapotranspiration, and air temperature at 2 m over the SRTR. (**a**) Spatial distribution of correlation coefficient (CC) between satellite retrievals or reanalysis data and soil moisture simulations from WRF-H during the study period; (**b**) Same as (**a**), but for spatial distribution of RMSE; (**c**,**d**) Same as (**a**,**b**), but simulations are from WRF-S; (**e**–**h**) Same as (**a**–**d**), but for evapotranspiration; (**i**–**l**) Same as (**a**–**d**), but for air temperature at 2 m. The dots in (**a**) and (**c**) denote the correlation coefficient and are significantly above the 95% confidence level, and the correlation coefficient in each grid point of (**e**,**g**,**i**,**k**) passed the significance test.

cient in each grid point of (**e**,**g**,**i**,**k**) passed the significance test.

The anomaly correlation is convenient to detect similarities in the patterns of departures. And it avoids the influence of bias in the grid-based validations as they are essentially a modeled product. Figure 6a illustrates that WRF-H exhibits a higher anomaly correlation coefficient (0.955 versus 0.941). They reflect the spatial distribution of soil moisture improvements due to lateral soil water flow simulated by WRF-Hydro. Models that do not incorporate this process may lack the ability to reproduce anomaly patterns of soil moisture. Figure 6b,c show that the anomaly correlation coefficient scores achieved by WRF-H and WRF-S were close, and scores will be lower in winter. Soil water lateral flow has little effect on evapotranspiration and 2 m air temperature patterns. The anomaly correlation is convenient to detect similarities in the patterns of departures. And it avoids the influence of bias in the grid-based validations as they are essentially a modeled product. Figure 6a illustrates that WRF-H exhibits a higher anomaly correlation coefficient (0.955 versus 0.941). They reflect the spatial distribution of soil moisture improvements due to lateral soil water flow simulated by WRF-Hydro. Models that do not incorporate this process may lack the ability to reproduce anomaly patterns of soil moisture. Figure 6b,c show that the anomaly correlation coefficient scores achieved by WRF-H and WRF-S were close, and scores will be lower in winter. Soil water lateral flow has little effect on evapotranspiration and 2 m air temperature patterns.

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**Figure 5.** Evaluation of soil moisture, evapotranspiration, and air temperature at 2 m over the SRTR. (**a**) Spatial distribution of correlation coefficient (CC) between satellite retrievals or reanalysis data and soil moisture simulations from WRF−H during the study period; (**b**) Same as (**a**), but for spatial distribution of RMSE; (**c**,**d**) Same as (**a**,**b**), but simulations are from

**Figure 6.** Anomaly correlation coefficients of (**a**) soil moisture against SMAP, (**b**) evapotranspiration against GLEAM, (**c**) air temperature at 2 m against CMFD. **Figure 6.** Anomaly correlation coefficients of (**a**) soil moisture against SMAP, (**b**) evapotranspirationagainst GLEAM, (**c**) air temperature at 2 m against CMFD.

#### 4.3.2. Spatial Distribution

Figure 7 compares the total precipitation, average soil moisture, latent heat and sensible heat fluxes simulated by WRF-H and WRF-S. The spatial distribution of the two sets of the models is similar (Figure 7a,b). The difference (Figure 7c) is prominent in local areas but small when averaged across the region, consistent with other study [43]. The characteristics of the soil moisture, in contrast, are opposite to the spatial distribution

of precipitation. WRF-H spatially overestimates soil moisture throughout the SRTR and, more significantly, in the northern part of the SRTR (Figure 7f). The result is that WRF-S simulates soil moisture as wet in the southeast and dry in the northwest (Figure 7e), while WRF-H will hamper such a spatial distribution feature (Figure 7d). itation. WRF-H spatially overestimates soil moisture throughout the SRTR and, more significantly, in the northern part of the SRTR (Figure 7f). The result is that WRF-S simulates soil moisture as wet in the southeast and dry in the northwest (Figure 7e), while WRF-H will hamper such a spatial distribution feature (Figure 7d).

Figure 7 compares the total precipitation, average soil moisture, latent heat and sensible heat fluxes simulated by WRF-H and WRF-S. The spatial distribution of the two sets of the models is similar (Figure 7a,b). The difference (Figure 7c) is prominent in local areas but small when averaged across the region, consistent with other study [43]. The characteristics of the soil moisture, in contrast, are opposite to the spatial distribution of precip-

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4.3.2. Spatial Distribution

**Figure 7.** Spatial distribution of precipitation amounts from (**a**) WRF−H, (**b**) WRF−S, (**c**) WRF−h minus WRF−S over the study period. (**d−f**), (**g−i**) and (**j−l**) are the same as (**a−c**), but for averaged soil moisture (0~10 cm), averaged latent heat flux and sensible heat flux, respectively. **Figure 7.** Spatial distribution of precipitation amounts from (**a**) WRF-H, (**b**) WRF-S, (**c**) WRF-h minus WRF-S over the study period. (**d–f**), (**g–i**) and (**j–l**) are the same as (**a–c**), but for averaged soil moisture (0~10 cm), averaged latent heat flux and sensible heat flux, respectively.

The spatial distribution of latent heat has the same characteristics as soil moisture (Figure 7g–i), while the sensible heat flux is the opposite (Figure 7j–l). Changes in the distribution of sensible and latent heat fluxes affect the boundary layer development and influence the precipitation structure. Overall, the pattern of spatial changes in soil moisture, sensible and latent heat flux is quite similar. The temporal variation will be analyzed The spatial distribution of latent heat has the same characteristics as soil moisture (Figure 7g–i), while the sensible heat flux is the opposite (Figure 7j–l). Changes in the distribution of sensible and latent heat fluxes affect the boundary layer development and influence the precipitation structure. Overall, the pattern of spatial changes in soil moisture, sensible and latent heat flux is quite similar. The temporal variation will be analyzed next.

#### next. 4.3.3. Time Variation

4.3.3. Time Variation Figure 8 demonstrates simulated daily variable averages on the TRSR from 15 July to 30 November. Although the precipitations from both models match over the entire analysis period, the soil moisture simulated by WRF-H is more humid than WRF-S during the Figure 8 demonstrates simulated daily variable averages on the TRSR from 15 July to 30 November. Although the precipitations from both models match over the entire analysis period, the soil moisture simulated by WRF-H is more humid than WRF-S during the analysis period. Latent and sensible heat flux declines with seasonal changes, and even negative values emerge in sensible heat flux. The heat fluxes in the Tibetan Plateau are more susceptible to the freeze-thaw process than the high-latitude frozen soil regions [95]. The latent heat flux simulated by WRF-H is greater than WRF-S before the soil freezes, but the sensible heat flux is more petite than WRF-S. After the soil is frozen, WRF-H coincides with the curve of the WRF-S simulation. Due to the energy balance, the surface skin temperature simulation also showed differences before freezing.

temperature simulation also showed differences before freezing.

**Figure 8.** Temporal variations of the SRTR basin average in (**a**) precipitation, (**b**) soil moisture in the first soil layer (0~10 cm), (**c**) latent heat flux, (**d**) sensible heat flux, (**e**) surface skin temperature, (**f**) albedo. **Figure 8.** Temporal variations of the SRTR basin average in (**a**) precipitation, (**b**) soil moisture in the first soil layer (0~10 cm), (**c**) latent heat flux, (**d**) sensible heat flux, (**e**) surface skin temperature, (**f**) albedo.

The albedo map shows that the two models' albedo is relatively stable in summer and dramatic in autumn and winter. The steady increase of albedo in summer is caused by the decrease in soil moisture and phenology. The change in albedo due to inconsistent precipitation simulation time is small—the accumulation and sublimation of snowfall The albedo map shows that the two models' albedo is relatively stable in summer and dramatic in autumn and winter. The steady increase of albedo in summer is caused by the decrease in soil moisture and phenology. The change in albedo due to inconsistent precipitation simulation time is small—the accumulation and sublimation of snowfall cause the fluctuated albedo. In September, due to differences in surface temperature simulations, the albedo of the WRF-H is higher than the WRF-S, then the albedo of the two models coincide as the temperature difference decreases.

analysis period. Latent and sensible heat flux declines with seasonal changes, and even negative values emerge in sensible heat flux. The heat fluxes in the Tibetan Plateau are more susceptible to the freeze-thaw process than the high-latitude frozen soil regions [95]. The latent heat flux simulated by WRF-H is greater than WRF-S before the soil freezes, but the sensible heat flux is more petite than WRF-S. After the soil is frozen, WRF-H coincides with the curve of the WRF-S simulation. Due to the energy balance, the surface skin

WRF-H is more consistent with the WRF-S in winter because of the weakening of overland flow and subsurface lateral flow. The decrease of soil hydraulic conductivity of frozen soil reduced the subsurface flow. Furthermore, most winter precipitation falls to the ground in snowfall and dissipates through sublimation, rarely infiltrating into the soil layer. The snowmelt will contribute to discharges mainly during the rainy and peak flow periods [70].

#### *4.4. Precipitation Structure and Boundary Layer Variables* straight lines represent the fitted curves. The simulations of WRF-H and WRF-S are simi-
