Soil moisture is an important state variable of the terrestrial system and plays a critical role in land-atmosphere interactions [1
]. Its spatial and temporal distributions have strong effects on water, energy, and biogeochemical balances [3
]. In recent decades, soil moisture has been widely used in drought monitoring [4
], hydrological modeling [5
], vegetation changing [6
], and weather forecasting [7
]. Consequently, it is critical to understand soil moisture variability and quantify its driving forces.
Soil moisture is generally driven by climate, particularly precipitation and temperature [9
]. Precipitation is the main source of soil moisture, while temperature affects soil moisture by controlling evapotranspiration [10
]. Recent and future global warming and the potential acceleration of the water cycle may increase uncertainty in soil moisture variability [11
]. In addition, anthropogenic effects, which are commonly characterized by vegetation change, also have crucial effects on soil moisture [12
]. It is reported that humans have altered approximately 41% of the land surface by shifting from natural vegetation into agriculture and settlement [13
]. Vegetation change can alter soil infiltration and field capacity, thus affecting soil moisture [14
]. All these factors together affect soil moisture through complex interactions. Furthermore, many investigations have demonstrated the non-linear effects of climate and vegetation on soil moisture [15
], as well as the multiple causations among these variables [3
]. In this context, quantifying individual effects of these factors on soil moisture variability remains a challenge.
Several studies tried to identify the controlling factors of soil moisture based on correlations or multi-linear regressions [9
]. However, these approaches are insufficient to infer causality and are commonly affected by auto-correlation, nonlinearity, and cross-correlation between variables [18
]. Notably, a non-linear Granger-causality framework has been proposed and applied in the land-atmosphere system [18
], in which, the traditional linear ridge regression model of the Granger-causality method is substituted with a non-linear random forest algorithm. The non-linear Granger-causality framework can not only prevent over-fitting but also incorporate the non-linear nature of land-atmosphere interactions. Furthermore, in recent decades, remote sensing and models provided long-term data of soil moisture, climate, and vegetation, which facilitates applying sophisticated data-driven methods to investigate the response of soil moisture to climate and vegetation.
Xinjiang Uygur Autonomous Region (hereafter as Xinjiang), located in Northwest China, is an arid area with extremely scarce water resources. The fragile water-based ecosystem makes it highly sensitive to climate change and human disturbances. It is reported that the temperature and precipitation in this area showed an increasing trend over the past half-century [19
]. In addition, with the development of social economy, natural vegetation and bare land was replaced with cropland and construction land [20
]. Land cover and climate change unavoidably lead to soil moisture variability. Hence, understanding soil moisture variability and quantifying the individual effects of climate and vegetation change on soil moisture are important for water resources management and land use planning. However, although recently there are increasing studies on soil moisture in Xinjiang [21
], studies have neither investigated the relationship between vegetation and soil moisture, nor quantified the effects of climatic factors (i.e., precipitation and temperature) and vegetation on soil moisture variability. In this study, we combined the non-linear Granger-causality framework with multi-decadal reanalysis data to gain a deeper insight into the contribution of climate and vegetation change to soil moisture variability. The main aims of this study are: (1) To investigate soil moisture variability during the past three decades; (2) to quantify the direct and lagged effects of precipitation, temperature, and vegetation on soil moisture; (3) to quantify the effects of climatic extremes on soil moisture, and (4) to explore the influential factors for the effects of precipitation, temperature, and vegetation on soil moisture.
2. Study Area
Xinjiang, located in the Eurasian hinterland, comprises an area of 1.66 million km2
extending between 34°15′ N and 49°10′ N, 73°20′ E and 96°25′ E (Figure 1
). It is the core area of the Silk Road Economic Belt and the ecological environment has attracted wide attention.
There are two main basins (the Tarim and Junggar basins) lying between three high mountain ranges stretching across the northern (Altay Mountains), middle (Tianshan Mountains), and southern (Kunlun Mountains) parts of the study area. This typical mountain-basin system creates contrasting environments, including high mountain forest, middle mountain forest-grassland, low mountain desert, agricultural oasis, and natural shrub-grassland [23
Xinjiang is far from oceans and is surrounded by high mountain ranges, which leads to an arid climate [24
]. The average annual precipitation for the entire study area is about 150 mm and the spatial distribution is uneven with high precipitation in mountain areas and low in the plains. Xinjiang has a typical continental climate with four distinct seasons, while the mean annual temperature ranges between 2.5 and 10 °C.
For most previous studies about the climate effects on soil moisture, the relationships between soil moisture and climate factors were generally qualitatively analyzed based on correlation analysis or multi-linear regressions [9
]. This study, by contrast, quantified the causal effects of climate factors and vegetation on soil moisture variability based on a non-linear Granger-causality framework which can cope with non-linearity and cross-correlation between variables [18
]. The transformation from qualitative analysis to quantitative analysis has advanced the research about the effects of climate and vegetation on soil moisture variability.
We note that the MERRA-Land soil moisture product used in this study is the shallow depth of soil moisture. However, previous study found that shallow depth observations (upper 5 cm of soil at most) can be a representative value for soil moisture dynamics at the root zone layer (max 2 m) [52
]. In addition, many studies analyzed the relationship between shallow soil moisture and vegetation, and found that vegetation plays a non-negligible role in the temporal dynamics of the soil moisture for the shallow depth [6
]. Hence, quantifying the effects of vegetation on shallow soil moisture is feasible.
Precipitation, temperature, and vegetation contributed differently to soil moisture. Among these three factors, precipitation was the primary factor driving soil moisture variability, followed by temperature and vegetation. Previous studies also found that soil moisture was more controlled by precipitation than temperature in an arid region based on correlation analysis or multi-linear regressions analysis [10
]. In Xinjiang, since loam and sandy loam take up more than 90% of the study area according to FAO textural classification scheme [54
], the infiltration rate of soil is relatively high. Besides, Xinjiang is an arid region and the initial soil moisture is low. Hence, the conversion rate of precipitation into soil moisture is high, and thus precipitation has strong effects on soil moisture. For temperature, temperature indirectly affects soil moisture through evapotranspiration, while soil moisture also has effects on evapotranspiration [3
]. In arid areas, low soil moisture strongly constrains evapotranspiration variability, thus resulting in relatively small effects of temperature on soil moisture. Compared to precipitation and temperature, vegetation had weaker impacts on soil moisture variability. The reason may be mainly attributed to their small area [12
In the study area, the spatial pattern of soil moisture was similar to that of the precipitation (Figure 3
a,d). In areas with low precipitation, the initial soil moisture is relatively low and the infiltration rate is relatively high, thus the increased precipitation may totally transfer into soil moisture [56
]. As precipitation continues to increase, soil gets wet and infiltration rate declines [57
], thus the portion of precipitation into soil moisture gradually decreases, while the portion of precipitation into runoff gradually increases [58
]. Hence, the effects of precipitation on soil moisture decreased with precipitation and soil moisture increasing. In addition, the effects of precipitation decreased as the elevation increased. The declining relation is a result of the negative relationship between precipitation and elevation in the study area [24
], as also shown in Figure 3
a,d, i.e., soil moisture and precipitation in low elevation area were lower than that in high elevation area.
Except for precipitation, temperature, and vegetation, there are other factors such as wind speed or agricultural practices that affect soil moisture variability. Figure 8
a shows the spatial pattern of the explained variance (R2
) of soil moisture variability based on a full random forest model in which all variables (precipitation, temperature, climatic extremes, NDVI, and lagged variables) were included as predictors. In about 73% of the study area, more than 40% of the variance of soil moisture could be explained by these variables. However, the variance of soil moisture explained in other areas, such as the southern (Kunlun Mountains) and eastern (Turpan Basin and Lop Nor) regions, was below 25%. We hypothesize two potential reasons: (a) the uncertainty in the observations used as target and predictors is typically larger at high altitudes (especially in the Kunlun Mountains) due to snow and ice cover, and (b) there are some regions where soil moisture may be predominantly driven by other variables (such as wind speed). Evapotranspiration is a function of wind speed, therefore wind speed could increase the soil moisture evaporation loss [59
]. For further analysis, we downloaded the in situ wind speed data from the China Meteorological Administration (CMA) (http://www.nmic.gov.cn/
) and calculated the annual mean wind speed during 1982–2015 (Figure 8
b). Comparison of Figure 8
a,b reveals that the regions with low explained variance corresponded to the regions with high wind speed especially in the northern part of Xinjiang, which suggests that wind speed may have a strong effect on soil moisture in these regions. In addition, many studies have found that the Turpan Basin and Lop Nor have a climate with frequent strong winds [60
]. Hence, the low explained variance in the Turpan Basin and Lop Nor may be because the wind speed was not taken into account in the random forest model.