Spatial and Temporal Variability of Soil Moisture and Its Driving Factors in the Northern Agricultural Regions of China

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Spatial and Temporal Variability of Soil Moisture and Its Driving Factors in the Northern Agricultural Regions of China


1. Introduction

Climate change is the most concerning global issue today, and extreme weather events occur frequently around the world. The water cycle accelerates in the context of climate change, which has brought enormous challenges to sustainable water resources management and socio-economic systems. As the key factor in the land–atmosphere system, soil moisture represents the signal of dryness and wetness, regulates the water and energy balance through evaporation, runoff, and infiltration [1,2], and can cause climate effects similar to the El Niño–Southern Oscillation (ENSO) [3].
Soil moisture is influenced by a series of complex factors, such as climate, soil type, topography, and agricultural production activities, which govern the irrigation water demand of crops and affect agricultural grain yields. Northern China contributes 58% of the national grain production, with more than 60% of the arable land and only 19% of the total water resources [4,5,6]. However, the region, presenting a higher warming rate, is seriously sensitive to climate change with the phenomena of earlier spring, later autumn, and significantly higher minimum temperature in winter [7].
The dry and wet studies in China mainly focus on the northern regions, especially in the NW. Some studies suggested that arid areas will become wet, and semi-arid and semi-humid regions will change to arid by mainly employing precipitation data. Hu et al. [8] pointed out that arid regions have a trend of wetness based on the standardized precipitation index (SPI); Huang et al. [9] revealed that some semi-arid areas evolved into dried states due to the weakening of the East Asian monsoon in summer. Moreover, a large number of previous studies showed that soil moisture in Northern China had a downward tendency in the 20th century, such as Ma et al. [10], who presented the intensive dryness over this region by comparing SWI, precipitation, PDSI, and soil moisture. Wang et al. [11] revealed a drying trend from 1950 to 2006 in the NE China to the north-central China. Additionally, researchers also analyzed the driving factors of soil moisture variation in Northern China. Zhu et al. [12] indicated that precipitation and temperature were the key drivers in different climatic zones by using multivariate nonparametric analysis; Bai et al. [13] used maximum covariance to analyze the correlation between soil moisture and precipitation and temperature. Finally, relative contribution rate analysis was applied to quantify the impact of temperature and precipitation on the soil moisture changes. Currently, the commonly used methods for soil moisture monitoring include remote sensing, model simulation, and oven drying methods. With the features of a wide range and spatiotemporal continuity of satellite remote sensing monitoring, researchers carried out soil moisture retrievals at different scales using optical, infrared, and active and passive microwave sensors [14,15,16]. However, remote sensing monitoring for soil moisture has the disadvantage of short data time series, shallow observation depth, and coarse spatial resolution [17,18]. Models for simulating soil moisture include hydrological models, land surface models, and neural network models. Li et al. [19] ran a hydrological model to depict the spatiotemporal characteristics of soil moisture in China. However, the model approach is complex and unstable [20]. The oven drying method, which is simple to operate, has high measurement accuracy [21] and is often used as a calibration standard for other observation methods.

Previous studies lacked long time series and high-quality soil moisture observations, and most of them focused on administrative districts, which paid less attention to the overall spatiotemporal assessment of soil moisture and its driving factors at the national scale. Therefore, this study used regression analysis and partial correlation analysis to reveal the spatial and temporal variability of soil moisture based on the measured data by oven drying method and corresponding daily meteorological data (temperature, wind speed, sunshine hours, relative humidity, precipitation, etc.) from 150 agro-meteorological stations in Northern China during 1980–2021. Specifically, the whole area was divided into five subregions, and the soil moisture of the growing season (April to October) in each of which was analyzed. The results of the study provide a scientific basis for strengthening agricultural management and promoting regional high-quality development. Naturally, the temporal and spatial distribution of soil moisture has a significant effect on the land–atmosphere water circulation and heat balance. Precise information on soil moisture can help improve the accuracy of seasonal climate and weather prediction. However, since the 21st century, regional differences in soil moisture changes emerged, especially the changes in precipitation patterns in China in recent years, which may have an impact on soil moisture, so there is an urgent need to clarify the latest evolutionary patterns and driving mechanisms of soil moisture in China.


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