**1. Introduction**

Drought is a major natural disaster severely affecting the ecosystem and humans [1–3]. It is generally represented by soil water shortage and has long periods, wide range, occurs frequently, and affects large populations [4]. China is largely an agricultural country facing frequent droughts, which cause huge economic losses [5–7]. Therefore, strengthening drought monitoring, especially on a large scale with high spatiotemporal continuity, is necessary, and it can facilitate real-time dynamic capturing of drought occurrence and its development process and provide a reference for decision making to undertake timely and effective mitigation measures.

Previously, studies have been conducted on methods to monitor and evaluate droughts objectively, accurately, and quantitatively [1]. Generally, several drought assessment indicators are constructed using observation factors, such as precipitation, temperature,

**Citation:** Wu, W.; Li, R.; Shao, J. Assessment of Regional Spatiotemporal Variations in Drought from the Perspective of Soil Moisture in Guangxi, China. *Water* **2022**, *14*, 289. https://doi.org/10.3390/ w14030289

Academic Editors: Alban Kuriqi and Aizhong Ye

Received: 16 November 2021 Accepted: 10 January 2022 Published: 19 January 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

evaporation, and runoff [1,8]. However, these indicators do not consider the hydrological problems of subsurface soil and further divide the integrity of the water cycle to some extent. Soil moisture is a key physical quantity in climate studies [9–11]. It not only regulates the balance between material and energy exchange during land–air interactions [12], but it is also the most direct water source for natural ecosystems. Vegetation growth and development is extremely sensitive to changes in soil moisture [13,14], which can change the water–energy balance between land and air by affecting the surface albedo, soil thermal parameters, evaporation, and transpiration [12], and change the structure of the atmospheric boundary layer. Thus, soil moisture can both cause climate change and can be affected by climate change [13]. Soil droughts are mostly caused by a lack of soil moisture. The soil moisture content has a crucial relationship with the drought intensity in any region [15] and has a further direct impact on vegetation growth and agricultural production [14]. Therefore, considering soil moisture during drought monitoring using remote sensing is necessary.

Many direct methods, such as the gravimetric method, are accurate but expensive and are used to estimate soil moisture [15,16]. Additionally, indirect estimates based on microwave [8,17] or near-infrared band remote sensing data are also efficient approaches to estimate soil moisture. Some highly advanced soil moisture remote sensing products, such as Soil Moisture Active Passive [18] and Soil Moisture and Ocean Salinity [19] by the National Aeronautics and Space Administration, and European Space Agency's Climate Change Initiative Soil Moisture [20] have been developed and widely used globally for drought studies. However, some studies have indicated that the accuracy of soil moisture estimates can be enhanced by combining microwave and optical remote sensing [5].

Currently, TerraClimate, a dataset of high-spatial resolution (~4-km, 1/24◦) monthly climatic water balance for regional and global terrestrial surfaces during 1958–2018 [21], provides new types of soil moisture assimilation data, which have been previously applied to monitor soil droughts [22]. Considering the regional and seasonal dependence, the ability of TerraClimate data to capture soil moisture anomalies and their variabilities corresponds to other properties used to characterize the soil conditions [21]. The subsequent results can support TerraClimate as an indicator of soil water status; additionally, it can be used to develop new indicators of soil drought.

The present study was conducted in the Guangxi Zhuang Autonomous Region (hereinafter referred to as Guangxi). The shallow soil layer and its poor water holding capacity in Guangxi results in a complex runoff generation and confluence, thereby causing frequent regional floods and droughts for many years [23]. Studying the characteristics and risks of regional droughts in this region is thus urgently required. To study the impacts of climate change on soil droughts, soil moisture as an indicator of soil drought should be considered. Presently, little research has been conducted on the point-scale measurement of soil moisture; therefore, high-resolution distribution data of soil moisture are required for agriculture management, water management, and drought and flood monitoring in Guangxi.

In this study, we calculated the standardized soil moisture index (SSMI) based on the precipitation and temperature data of Guangxi for 1990–2018 and analyzed its variations, period, frequency, and other characteristics. Later, we analyzed the spatial variation characteristics of two typical droughts. Finally, we discussed the correlation between soil moisture anomaly and ocean temperature, which provides scientific reference for drought monitoring and early warning in Guangxi. The main aims of this study were: (1) to study the long-term trends and seasonal differences in soil droughts in Guangxi, (2) to discuss the spatial variation characteristics of soil droughts, and (3) to preliminarily explore the teleconnection factors affecting soil drought dynamics.

#### **2. Materials and Methods**

#### *2.1. Study Area*

Guangxi (extending from 20◦54 N–26◦24 N to 104◦26 E–112◦04 E) is located in South China (Figure 1) and to the southeast of the Yunnan–Guizhou Plateau, west of the Guangdong and Guangxi hills, and south of the North Bay. The terrain of this region is flat in the middle and south areas, which are in turn surrounded by mountains and plateaus, and the average altitude of the area is 802 m. An inclining trend is observed in the entire terrain from northwest to southeast. As a typical subtropical monsoon humid area, the annual precipitation in Guangxi is abundant (range 1500~2000 mm), with uneven spatiotemporal distribution, and the average annual temperature is relatively high, between 16~23 ◦C. Furthermore, karst developed hills and depressions are widely distributed [9]. Due to the special geological environment of karst areas in Guangxi, atmospheric precipitation can easily leak into the deep underground layer and become deeply buried groundwater, forming a pattern of water and soil separation, resulting in drought on the surface due to soil water shortage. At present, the development of rocky desertification in karst areas in Guangxi has become the most serious eco-environmental problem, restricting the sustainable development of Southwest China, and soil humidity is the key factor Therefore, the study of soil moisture in Guangxi has become an important measure for the ecological restoration and reconstruction of the region.

**Figure 1.** Geographical location of Guangxi. (**a**) The position of Guangxi in China, the green area in the picture is Guangxi; (**b**) altitude map; (**c**) land-use types derived from the European Space Agency.

#### *2.2. Soil Moisture Data*

Monthly TerraClimate precipitation data from January 1990 to November 2018 were used in this study. The data spatial resolution was 1/24◦ (~4-km). TerraClimate includes the requisite variables for calculating energy-based reference potential evapotranspiration and a water balance model [21]. TerraClimate uses satellite and climatic data that can be integrated and has the characteristics of high accuracy, a wide detectable range, and high spatiotemporal resolution [21]. In this study, the soil moisture mentioned includes all water below the surface except groundwater, rather than only plant root or surface soil water. Further, soil moisture data were acquired from TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, http://www.climatologylab.org/terraclimate (accessed on 11 August 2019).

#### *2.3. Standardized Soil Drought Index*

SSMI is a standardized anomaly of remotely sensed soil moisture data from 1990 to 2018. We used soil moisture data in TerraClimate to calculate the SSMI to characterize agricultural drought.

$$\text{SSMI}\_{i,j} = \frac{SM\_{i,j} - S\overline{M\_j}}{\partial\_j}.$$

Here, *i* is the observation year from 1990 to 2018, *j* is the observation month from January to December, and *SMj* and *∂<sup>j</sup>* are the average and standard deviation of soil humidity in month *j*, respectively. A detailed description of the this method can be found in the previous studies [24,25]. SSMI is dimensionless and is used to detect drought. When SSMI is greater than 0, it can be considered that it is wetter than that in the same period of many years; otherwise, it is drier. In this study, the drought situation levels, including slight (SSMI range: −0.5 to 0), moderate (−1 to −0.5), severe (−1.5 to −1), and extreme droughts (−2 to −1.5). If the SSMI value is lower than −1.5 in a certain month from 1990 to 2018, it represents an extreme drought event.
