4.4.1. Precipitation Frequency-Intensity Structure

periods [70].

Figure 9 shows the fitting results of GPM precipitation, WRF-H, and WRF-S. The circle points represent the natural double pairs with different rainfall intensities, and the straight lines represent the fitted curves. The simulations of WRF-H and WRF-S are similar, but WRF-H is slightly closer to the GPM. According to Equation (7), the fitted α is 2.39, 2.74 and 2.8, and the fitted β is 26.63, 9.9 and 9.12 for GPM, WRF-H and WRF-S, respectively. The difference between simulation and GPM, WRF-H is reduced by 15% compared to WRF-S in a and β is reduced by 5%. The fitting results show that soil water lateral flow in WRF-H makes the frequency-intensity structure closer to the observation. Since convectivepermitting is used in both models, the boundary layer that triggers convection needs to be further evaluated. lar, but WRF-H is slightly closer to the GPM. According to Equation (7), the fitted α is 2.39, 2.74 and 2.8, and the fitted β is 26.63, 9.9 and 9.12 for GPM, WRF-H and WRF-S, respectively. The difference between simulation and GPM, WRF-H is reduced by 15% compared to WRF-S in a and β is reduced by 5%. The fitting results show that soil water lateral flow in WRF-H makes the frequency-intensity structure closer to the observation. Since convective-permitting is used in both models, the boundary layer that triggers convection needs to be further evaluated.

*Water* **2021**, *13*, x FOR PEER REVIEW 17 of 25

models coincide as the temperature difference decreases.

*4.4. Precipitation Structure and Boundary Layer Variables* 

4.4.1. Precipitation Frequency-Intensity Structure

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

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

WRF-H is more consistent with the WRF-S in winter because of the weakening of

Figure 9 shows the fitting results of GPM precipitation, WRF-H, and WRF-S. The cir-

cle points represent the natural double pairs with different rainfall intensities, and the

**Figure 9.** The precipitation frequency over the SRTR is binned by the hourly intensity and its double exponential fit line. **Figure 9.** The precipitation frequency over the SRTR is binned by the hourly intensity and its double exponential fit line.

#### 4.4.2. Boundary Layer Variables

4.4.2. Boundary Layer Variables In Figure 10a, WRF-H shows a higher convective available potential energy (CAPE)in most of the unfreezing period, and also that coupling hydrology processes make convectional energy more readily available in SRTR. The amplitude of convective inhibition (CIN) did not change in the unfreezing or freezing period between the two simulations. WRF-H exhibits a slightly lower lifting condensation level (LCL) in most of the unfrozen In Figure 10a, WRF-H shows a higher convective available potential energy (CAPE)in most of the unfreezing period, and also that coupling hydrology processes make convectional energy more readily available in SRTR. The amplitude of convective inhibition (CIN) did not change in the unfreezing or freezing period between the two simulations. WRF-H exhibits a slightly lower lifting condensation level (LCL) in most of the unfrozen period, indicating a low cloud base. The level of free convection (LFC) fluctuates violently in the unfrozen season but becomes stable when it uplifts to the top of model layers in the frozen season. The improvement of precipitation structure is mainly attributed to the increase of CAPE caused by wetter soil.

period, indicating a low cloud base. The level of free convection (LFC) fluctuates violently in the unfrozen season but becomes stable when it uplifts to the top of model layers in the frozen season. The improvement of precipitation structure is mainly attributed to the in-

crease of CAPE caused by wetter soil.

**Figure 10.** Temporal variations of the TRSR basin averaged values for the boundary layer elements (**a**) CAPE, (**b**) CIN, (**c**) LCL, (**d**) LFC. The curve on the lower half corresponds to the Y−axis on the left, with the red line representing WRF−H and the blue line representing WRF−S. The curve on the upper half corresponds to the y−axis on the right side, and the red line represents the bias of WRF−H relative to WRF−S. **Figure 10.** Temporal variations of the TRSR basin averaged values for the boundary layer elements (**a**) CAPE, (**b**) CIN, (**c**) LCL, (**d**) LFC. The curve on the lower half corresponds to the Y−axis on the left, with the red line representing WRF−H and the blue line representing WRF−S. The curve on the upper half corresponds to the y−axis on the right side, and the red line represents the bias of WRF−H relative to WRF−S.

#### **5. Discussion 5. Discussion**

We evaluated the runoff simulation skills of uncoupled WRF-Hydro and the effect of lateral flow of soil water on the atmosphere by an enhanced fully coupled WRF-Hydro model in SRTR. This is one of the earliest studies using WRF-Hydro in the SRTR, which is located on the Tibetan Plateau and is an important watershed for ecological conservation and water management in China. The results reveal the following main conclusions: We evaluated the runoff simulation skills of uncoupled WRF-Hydro and the effect of lateral flow of soil water on the atmosphere by an enhanced fully coupled WRF-Hydro model in SRTR. This is one of the earliest studies using WRF-Hydro in the SRTR, which is located on the Tibetan Plateau and is an important watershed for ecological conservation and water management in China. The results reveal the following main conclusions:


correlation coefficient. These findings show the significance of lateral flow in soil moisture simulation.

3. The coupled WRF-Hydro results in an increase in latent heat flux, a decrease in sensible heat flux, and a decrease in soil surface temperature due to the moist soil. The change in turbulent heat flux gives the WRF-Hydro simulation an enormous CAPE and easier convection, reducing precipitation intensity-frequency errors.

The NSE of runoff in this study is not high, perhaps because the agility of WRF-Hydro might be unnecessarily constrained by its complex process [96]. This is probably the reason that NSE is low in some watersheds in similar studies [97–100]. The calibrated model parameters (Zmax = 50) in this study are to offset the impact of continuously excessive precipitation on runoff production. The extra precipitation stored in the conceptual groundwater bucket during the simulation period will deteriorate runoff simulation in the next period, for the discharge of baseflow will increase the runoff in winter. If the simulation time is increased and the accumulated precipitation overestimation is large enough, then the WRF-Hydro cannot obtain an accurate runoff through calibration and the NSE will decrease as a result. It suggests that the output of the GCM is not recommended as a driver for multi-year runoff hindcasting in SRTR. Accurate precipitation-driven data, such as CMFD, is necessary for such simulation. However, using Zmax = 50 does not worsen the coupled simulation, as the parameter does not affect soil moisture and cannot further influence weather processes.

The lateral flow from WRF-Hydro leads to wetter soils in the SRTR, similar to the semiarid environment in the USA [101]. For areas where WRF-Hydro tends to overestimate the soil moisture, it is recommended to calibrate the parameters affecting soil moisture simulation, such as the reference infiltration factor (REFKDT). This study did not calibrate soil moisture because the calibrated parameters could artificially lead to better simulation results for one variable than the other.

The limitations of the study are mainly threefold. First, the overestimation of GCM precipitation in SRTR [102] resulted in its inability to be used as a force to simulate multiyear runoff. Secondly, although the soil texture dataset [67] has the highest resolution in China right now, the small number of sampling points on SRTR leads to a larger uncertainty in soil texture than in other regions in China. Thirdly, the CMA station was established in a habitable place on the plateau. These areas tend to be low-lying, with moist soils and rivers passing through them. This means that the stations are located where the lateral flow of soil water flows in, not out. More observations are needed where lateral soil water flows out.

The low but acceptable NSE of runoff simulation in the study shows the potential of WRF-hydro in the hydrological simulation of the SRTR. Meanwhile, the high anomaly correlation coefficient scores achieved by WRF-Hydro in soil moisture simulations, as well as the closer to observed precipitation intensity-frequency structure, suggest that the WRF-Hydro module is worthy of being incorporated in convective-scale simulations. Reducing the precipitation overestimation of the GCM in SRTR is urgently needed for the future application of the coupled WRF-Hydro model in SRST.

#### **6. Conclusions**

In the present study, we set up two sets of experiments to investigate the prospects of WRF-Hydro in the hydrological forecasting and its impacts on land-atmosphere interaction of SRTR. Results reveal the WRF-Hydro has shown potential in runoff prediction in SRTR. Coupled WRF-Hydro with soil water lateral flow increases wet soil bias in the western part of the SRTR, but improves the soil moisture anomaly pattern. The coupled model also enhances CAPE, and produces a more reliable precipitation intensity-frequency structure. Overall, our results illustrate the effect of WRF-Hydro in the coupled hydrologyatmosphere simulation system. GCM with less overestimation of precipitation in SRTR to drive coupled WRF-Hydro is desirable for future work.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10.3 390/w13233409/s1, Table S1. Primary WRF, Noah-MP, and WRF-Hydro parameters were used in the simulation [103–110].

**Author Contributions:** Conceptualization, X.M.; Data curation, G.L., H.C., Z.L., Y.M. and L.Z.; Formal analysis, G.L.; Methodology, G.L., X.M. and E.B.; Project administration, X.M.; Resources, X.M.; Software, G.L.; Validation, X.M., L.S., Z.L. and L.Z.; Visualization, G.L.; Writing—original draft, G.L.; Writing—review & editing, X.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Chinese National Science Foundation Programs: 41930759, the auspices of the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA2006010202), Chinese National Science Foundation Programs: 41822501,the Science and Technology Research Plan of Gansu Province (20JR10RA070), the Chinese Academy of Youth Innovation and Promotion, CAS (Y201874), iLEAPs (integrated Land Ecosystem-Atmosphere Processes StudyiLEAPS), and the National Key Research and Development Program of China (2016YFB0501303).

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** The numerical model simulations upon which this study is based are too extensive to archive or transfer. Instead, we provided all the information needed to replicate the simulations; we used model version WRF-Hydro v5.0.3. The model code, compilation script, initial and boundary condition files, WRF-Hydro routing grid files, and the settings (namelist) are available from the authors.

**Acknowledgments:** We acknowledge the modeling support from the WRF-Hydro Community. We thank the team of China Meteorological Administration, the team of Global Precipitation Measurement (GPM) Mission, the team of Global Land Evaporation Amsterdam Model (GLEAM), the team of The Soil Moisture Active Passive Mission (SMAP), and the National Tibetan Plateau Data Center for providing data for model validation. As well as thanks to the European Centre for Medium-Range Weather Forecasts for providing reanalysis data.

**Conflicts of Interest:** The authors declare no conflict of interest.

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