Characteristics of Agricultural Droughts and Spatial Stratified Heterogeneity and Dependence of Dominant Factors in Inner Mongolia Autonomous Region, China

Yang, Simin; Quan, Quan; Liang, Weijia; Liu, Tiejun

doi:10.3390/atmos12101249

Cited by 3 publications

(1 citation statement)

References 22 publications

(18 reference statements)

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“…Subsequently, numerous scholars have employed this model to investigate spatial heterogeneity and changes in the driving factors of surface SM. For example, Yang et al [21], Xu et al [22], and Yang et al [23] used the GD model to assess the spatiotemporal patterns of SM on the Inner Mongolian Plateau, the Ili River Basin, and the Shiyang River Basin, respectively, offering a novel research perspective for the analysis of the spatiotemporal patterns of SM. Due to the sensitivity of the GD model relative to spatial discretization methods and the number of strata, determining both can be relatively complex.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor

Yin,

Song,

Zhu

et al. 2023

Remote Sensing

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Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor

Yin,

Song,

Zhu

et al. 2023

Remote Sensing

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Temporal and Spatial Distribution Characteristics of Drought and Its Influence on Vegetation Change in Xilin Gol, China

Chen

Wang

et al. 2022

Atmosphere

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Drought hinders economic and social growth in many areas of China, especially in livestock-dominated Xilin Gol League in Inner Mongolia. Most studies exclusively utilize rainfall to measure drought. To clarify the spatial and temporal distribution characteristics and evolution rules of meteorological drought, monthly observation data from nine meteorological stations in Xilin Gol were used to calculate the (effective drought index, EDI). We studied the spatiotemporal pattern of drought and its influence on vegetation in Xilin Gol using the Mann–Kendall test, (empirical orthogonal function, EOF) decomposition, and quantitative representation. (1) The annual average EDI declined by 0.029/10a, and Xilin Gol experienced an average of 0.5 drought occurrences every year. (2) A normal incidence in Xilin Gol is 67.17–72.65%, and that of severe drought is 0.02–0.99%. (3) Xilin Gol’s drought intensity is mostly concentrated in the central, northeast, and southwest regions, especially southwest and central. (4) The first two principal feature vectors in Xilin Gol contributed 52.75% and 14.38% to the variance. (5) The average (normalized differential vegetation index, NDVI )of desert, typical, and meadow steppe increased, especially in typical steppe (0.034/10a). (6) In Xilin Gol, the NDVI–EDI correlation coefficient ranges from −0.642 to 0.888, with an average of 0.392. Only 1.7% of the areas are adversely linked.

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Analyzing Driving Factors of Drought in Growing Season in the Inner Mongolia Based on Geodetector and GWR Models

Yan-bin

Zhang

et al. 2022

Remote Sensing

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As an important ecological security barrier in northern China, the Inner Mongolia Autonomous Region (hereinafter referred to as Inner Mongolia) is seriously affected by drought. It is of great significance to characterize the spatial distribution of drought and identify the influencing factors of drought. Due to complex interactions among drought driving factors, it is difficult to quantify the contribution of each driving factor to drought using linear correlation analysis alone. In this study, we used the Standardized Precipitation Evapotranspiration Index (SPEI) as a quantitative indicator of drought to discuss the spatiotemporal variation of drought during growing seasons in the Inner Mongolia from 2000 to 2018. We quantitatively characterized mode, scope, and intensity of changes in SPEI caused by drought-influencing factors such as weather, water, topography, soil, and human activities using the Geodetector and Geographically Weighted Regression (GWR) models. We concluded that about 20.3% of the region showed a downward trend in SPEI, with the fastest rate of decline in the central and western Inner Mongolia. Air temperature, precipitation, elevation, and distance to rivers are the main controlling factors in drought change, and the factor interactions showed nonlinear enhancement. The drought driving effect was obvious in Alxa League, Wuhai City, Ulanqab City, and Baotou City. The results will help us to understand the effects of the driving factors on drought and eventually help policymakers with water-resource management.

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Characteristics of Agricultural Droughts and Spatial Stratified Heterogeneity and Dependence of Dominant Factors in Inner Mongolia Autonomous Region, China

Cited by 3 publications

References 22 publications

Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor

Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor

Temporal and Spatial Distribution Characteristics of Drought and Its Influence on Vegetation Change in Xilin Gol, China

Analyzing Driving Factors of Drought in Growing Season in the Inner Mongolia Based on Geodetector and GWR Models

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