*4.3. Advantages and Limitations of GWR*

The adjusted R2 value from the GWR model is 0.88, and the *AIC* value is −540.58. Compared with the OLS model (R<sup>2</sup> = 0.54, *AIC* <sup>=</sup> −504.22), the *AIC* value is reduced by 36.36 and the degree of fit is higher, but there are regional differences. The regional statistics of R2 shows that (Figure 7b) the largest value occurred in Wuhai City, Inner Mongolia (K) R2 = 0.96, followed by Hohhot City (H) R<sup>2</sup> = 0.92, and the two prefecture-level cities with the smallest R2 were Hinggan League (B) and Bayan. In Bayannur City (I), R2 is 0.64 and 0.67, respectively. The difference in the accuracy of regional fitting may be related to the influence of elevation changes on the distribution of other factors in the large east-west span of the study area. Areas with small R2 (A, B, I) have an average elevation of less than 800 m, and the average single factor *q*-value is 0.17. The average elevation of Wuhai City (K) and Hohhot City (H) are 1193 m and 1379 m, respectively, and the average *q*-value is 0.32 and 0.28, respectively. On the other hand, the area of each city is quite different, so is the statistical sample size, and the collinearity of the factors within the region may be another reason for the low fit in Hinggan League (B) and Bayannur City (I) [11,53,54].

#### *4.4. Future Directions*

Compared with traditional statistical models, we quantified the non-linear responses of independent variables and their interactions to SPEI change, without input of complex parameters. Further research may include: (1) using long-term SPEI data and more accurate PET calculation methods, such as the Penman and Hargreaves–Samani formula to produce more generalizable drought-driven results; (2) refining the spatial scale both horizontally and vertically, especially in eastern Inner Mongolia and western Mongolia, to generate results at higher resolutions.

#### **5. Conclusions**

Based on the multi-source data at the 110 meteorological stations, DEM, and vegetation types in Inner Mongolia and its surrounding areas, this study investigated the spatiotemporal variation of SPEI during the growing season in Inner Mongolia from 2000 to 2018. Through the introduction of time rate of change in SPEI, we used Geodetector and GWR models to screen the main controlling factors and then effectively quantified the impact of the factors on drought changes and the results are of great significance for

drought-driven research. We made the following conclusions. (1) The SPEI in the growing season from 2000 to 2018 in Inner Mongolia showed a spatial variation pattern from dry west to wet east. The area with light drought accounts for the largest proportion in the whole region. (2) The inter-annual variation of SPEI shows an upward trend and the area of elevated SPEI accounted for 79.70% of the study area. These results indicate that the drought condition became alleviated with time during the growing season in Inner Mongolia. (3) The drought changes in Inner Mongolia were generally controlled by natural factors, with nonlinear interaction between factors enhancing drought impact. The aggravated drought in the central and western regions of the study area, such as Alxa League, Ulanqab City, Baotou City, and Wuhai City, were mainly driven by a synergy of hot air temperature, scarce precipitation, and high elevation, with significant impact from soil and LUCC at an elevation of 800~1300 m. The results from this study should be helpful for decision-making and management of regional water resources.

**Author Contributions:** Conceptualization, B.J.; formal analysis, Y.Q.; funding acquisition, G.Y.; methodology, M.Z.; software, M.L.; supervision, T.Z. and G.Y.; validation, X.Z.; visualization, Y.Q.; writing—original draft, B.J.; writing—review & editing, Y.Q., X.Z. and T.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (41801099), the second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0307), and the Key Research and Development Program of Sichuan (2022YFS0491).

**Data Availability Statement:** The meteorological data are available at http://data.cma.cn, accessed on 12 June 2022. The DEM are available at https://earthexplorer.usgs.gov/, accessed on 11 June 2022. The Population density data are available at https://landscan.ornl.gov/, accessed on 12 June 2022. The vegetation types data, land cover data, soil texture are available at https://www.resdc.cn/, accessed on 12 June 2022. Main rivers and county stations are available at http://ngcc.sbsm.gov.cn, accessed on 13 June 2022.

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

#### **References**

