Mapping the primary factors driving spatiotemporal variations of surface soil moisture from multi-dimensional zonality in the Yellow River Basin of China

Mapping the primary factors driving spatiotemporal variations of surface soil moisture from multi-dimensional zonality in the Yellow River Basin of China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mapping the primary factors driving spatiotemporal variations of surface soil moisture from multi-dimensional zonality in the Yellow River Basin of China Linghua Hu, Yiming Xie, Yinzhe Liu, Siyuan Chen, Haifang Yu, Qianlong Bie, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5330305/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Spatiotemporal variations of soil moisture are affected by a combination of factors many factors including climate, vegetation, human activities, of which the primary factors vary greatly in different geographical zonal dimension in the Yellow River Basin. To identify and map the dominant factors driving the spatiotemporal variation of surface soil moisture in the Yellow River Basin across different zonality from 2003 to 2018, relationships between spatiotemporal variations of soil moisture and driving factors (precipitation, evaporation, NDVI (Normalized Difference Vegetation Index) and land use) were analyzed from two geographical dimensions: longitude and altitude. The results revealed that: (1) The spatial distribution of surface soil moisture in the Yellow River Basin exhibited a pattern of " higher values in the east and west, and lower values in the middle". Temporally, surface soil moisture in the Yellow River Basin showed a noteworthy upward trend from 2003 to 2018, with an average change rate of 0.00066m³/m³·yr-1 over the past 16 years. As altitude ascended, the rate of surface soil moisture initially exhibited an increase from 0.00061 m³/m³·yr⁻¹ to 0.00078 m³/m³·yr⁻¹, followed by a decline to 0.00035 m³/m³·yr⁻¹. However, above altitudes of 4500 meters, the rate once again rose, reaching 0.00084 m³/m³·yr⁻¹. (2) Among the three driving factors, climate, NDVI and land use accounted for 45%, 18% and 8% of the regional surface soil moisture variations, respectively. Climate controlling factors are mainly concentrated in the southwest, south, east and northeast, NDVI controlling factors are mainly concentrated in the central Loess Plateau and the northern Hetao plain, and land use controlling factors are mainly distributed in and around some big cities. Additionally, 29% of the area was controlled by the combined effects of these three factors, with no dominant controlling factor evident with scattered distribution. (3) From the perspective of multi-dimensional zonality, the degree of climate influence is high in the east and west, low in the middle, and increases with the increase of altitude. The influence degree of vegetation increased first and then decreased from west to east. The influence degree was greater in the central area, and the influence increased first and then decreased slightly with the altitude. The peak value appeared in the middle altitude area at 1000m. And the degree of influence of human activity intensity is slightly lower in the central part. Earth and environmental sciences/Hydrology Earth and environmental sciences/Ecology/Climate change ecology surface soil moisture spatiotemporal evolution Random Forest Attribution analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Soil moisture is an important physical property of soil, playing a pivotal role in the soil-plant-atmosphere continuum. It not only influences the flow of soil matter and energy, but also actively participates in the nutrient cycle of the soil system [ 1 ]. Furthermore, soil moisture significantly affects various aspects such as runoff [ 2 ], soil microorganisms [ 3 ], biogeochemical cycles [ 4 ], and agriculture [ 5 , 6 ]. Mapping the spatiotemporal and drivers of spatiotemporal variation of surface soil moisture can provide a scientific basis for agricultural production, water resources management and ecological protection [ 7 , 8 ]. Previous studies have demonstrated distinct spatiotemporal variations in soil moisture. Globally, tropical regions exhibit higher average soil moisture, while deserts and semi-arid regions tend to have lower soil moisture [ 9 ]. Monitoring of global soil moisture changes from 1948 to 2010 revealed a long-term decreasing trend in soil moisture [ 7 ]. Soil moisture has been identified as being influenced by many factors such as climate, vegetation, soil types and topography [ 10 ], and primary control factors vary significantly in different regions and scales. In a small scale, land use and topography were suggested to exert significant effects on soil moisture, whereas climate factors were considered as dominated driving factors on a larger scale [ 11 ]. Wang Yunqian et al. [ 12 ] discovered relationships between variations in soil moisture and climate factors among different land types. Pan Yanxia et al. [ 13 ] found differences in driving factors of soil moisture between the rainy and dry periods, where soil texture plays a significant role during the dry periods, while local vegetation and topography have greater influence during the rainy periods. From the perspective of geography, longitude, latitude and altitude determine regional variations of climate, vegetation and human activities, and therefore cause the multi-dimensional zonality of soil moisture including latitudinal, longitude and altitudinal zonality. Mapping the variations and drivers of spatiotemporal variation of soil moisture from multi-dimensional zonality can provide a macroscopic geographical law to gain insight into regional variations in the variations and drivers of spatiotemporal variation of soil moisture in the Yellow River Basin. The Yellow River Basin is characterized by a fragile ecological environment, with notable water-related ecological issues [ 14 ]. In recent years, the advancement of remote sensing and GIS technology have facilitated the acquisition of soil moisture data, leading to significant progress in understanding the spatiotemporal evolution and cause analysis of soil moisture[ 15 – 17 ]. Numerous studies have demonstrated that the close relationship between soil moisture variations and vegetation, climate, and human activities in the Yellow River Basin. Fan Keke et al. revealed that, rising temperature driven by global warming caused contribute to a decrease in soil moisture in the Yellow River Basin[ 18 ]. Li Sha et al. explained the response mechanism of soil moisture to precipitation and actual evapotranspiration in the Basin[ 19 ]. Omer et al. highlighted the diverse impacts of different land use types on water content in the Yellow River Basin[ 20 ]. However, limited researches have comprehensively analyzed how these three factors collectively influence surface soil water, particularly in quantifying the interaction of these factors on soil water in different regions. This challenge hinders the analysis of the driving forces behind the spatiotemporal evolution of soil water in the Yellow River Basin. To gain macroscopic geographical laws into regional variations in the variations and drivers in the Yellow River Basin, this study intends to use trend analysis, Mann-Kendall trend analysis, partial correlation analysis and random forest algorithm to discover the spatiotemporal variations and their relationships with driving factors (precipitation, evaporation, NDVI (Normalized Difference Vegetation Index) and land use), and then identify and map the primary factors driving the spatiotemporal variation of surface soil moisture from the regional scale and from three geographical dimensions: latitude, longitude and altitude zonality in the Yellow River Basin. 2. Materials and Methods 2.1. Study Area The Yellow River Basin is situated in between 95°53' to 119°05' E longitude, 32°10' to 41°50' N latitude, covering a total area of 752,000 km². It traverses several provinces, including Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan and Shandong provinces (Fig. 1 ). The topography of the Yellow River Basin exhibits a west-to-east variation. The western region is dominated by the Qinghai-Tibet Plateau and characterized by rugged terrain and towering mountains, The central part comprises the Loess Plateau and the Inner Mongolia Plateau, featuring relatively high and gentle terrain. The lower reaches of the Yellow River flow through the North China Plain characterized by flat and low terrain, with some sections exhibiting meandering river channels. The vegetation in the western Yellow River Basin mainly consists of coniferous forests and alpine meadows. In the central area, grasslands and deciduous broad-leaved forests are predominant, while the lower reaches are primarily covered by deciduous broad-leaved forests. The Yellow River Basin experiences a monsoon climate area, with uneven distribution of precipitation throughout the year. More than 60% of the annual precipitation occurs from June to September, while other months receive relatively less rainfall. Precipitation decreases from east to west and from south to north. This pattern of precipitation distribution increases the vulnerability of the region to both drought and flood disasters. 2.2. Data Collection and Pre-Processing 2.2.1. Surveyed sites data The measured data of soil moisture at stations of the Loess Plateau were obtained from the National Earth System Science Data Center ( http://www.geodata.cn ), which was the annual average data from 1992 to 2019. We selected the normalized measured data of 37 stations to match and verify the remote sensing data to evaluate the accuracy of the remote sensing soil moisture data (Fig. 1 ). 2.2.2. Soil moisture data In this study, land surface soil moisture data of China were obtained from National Tibetan Plateau ( http://data.tpdc.ac.cn ). The dataset encompasses monthly data from 2003 to 2018, with a spatial resolution of 0.05°. To investigate the spatiotemporal variations of surface soil moisture in the Yellow River Basin, we specifically extracted the surface soil moisture data from the Yellow River Basin within the dataset. In this study, soil moisture data of each month were summarized and synthesized on the basis of each pixel, and then averaged to represent the average soil moisture content of each pixel in each year, that is, the annual soil moisture content. In order to validate the accuracy of soil moisture data in the Yellow River Basin, a correlation analysis was performed using data from the 37 surveyed sites and the homologous remote sensing data. The analysis showed a strong relationship between the remote sensing data and the surveyed sites data, with a correlation coefficient of 0.75 (P < 0.01). Figure 2 presents a scatter diagram that visually represents the relationship between the soil moisture data obtained from remote sensing and the surveyed sites. The figure provides evidence of the correspondence between the two datasets and supports the accuracy of the remote sensing data in capturing soil moisture variations in the study area. 2.2.3. Climate data The precipitation and evapotranspiration data used in this study were obtained from the National Earth System Science Data Center ( http://www.geodata.cn ), which is part of the National Science & Technology Infrastructure of China. The spatial resolution of the data is 1 km, providing detailed monthly precipitation and evapotranspiration information about the Yellow River Basin. To obtain annual climate data sets, the monthly climate data, including precipitation and evapotranspiration, were aggregated on an annual basis for each year. 2.2.4. Vegetation data NDVI used in this study was derived from the monthly grid data of China's Vegetation index spanning from 2003 to 2018. This data was obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences ( http://www.resdc.cn/ ), The spatial resolution of the data is 1 km. In this study, the monthly NDVI data were used to calculate annual NDVI data by pixel on a pixel-by-pixel basis using maximum synthesis method. By using the maximum NDVI selected by this method, the influence of cloudy and fogy atmospheric conditions were minimized, resulting in a more robust measurement of regional vegetation cover intensity [ 21 ]. 2.2.5. Land use data The Land use data used in this study were obtained from the annual China Land Cover Dataset (CLCD) [ 22 ], The CLCD provided land cover information with a spatial resolution of 30 meters. In this study, the Human Activity Intensity of Land Surface (HAILS) approach proposed by Xu Yong et al. [ 23 ], was employed to assign different construction lands equivalent conversion coefficients to various land use types. This method captures the comprehensive impacts of human activities on the land surface system. The HAILS method has been widely applied to quantify the intensity of human activities in the Loess Plateau [ 24 ], the middle and lower reaches of the Yangtze River [ 25 ] to explore the influence of human activities on eco-geographical environment in different regions. 2.3. Methods 2.3.1. Trend Analysis The linear regression model was used in this study to describe the linear correlation between the two variables. Specifically, the study focuses on the correlation between time and the surface soil moisture data of the Yellow River Basin. By fitting a linear regression model, the trend of the surface soil moisture annual changes over time was analyzed. The slope of the linear regression model is an important parameter that reflects the characteristics of surface soil moisture variation with time series. If the slope is positive, it indicates that the surface soil moisture value of the Yellow River Basin exhibits an upward trend throughout the study period. Conversely, if the slope is negative, it suggests a decreasing trend in the surface soil moisture value. The slope of 0, indicates that there is no significant change in the surface soil moisture value of the Yellow River Basin during the study period. By analyzing the slope of the linear regression model, the temporal variations of surface soil moisture can be illustrated in the Yellow River Basin. 2.3.2. Mann-Kendall test The Mann-Kendall (M-K) method is a non-parametric statistical test widely used to assess the significance of trends in time series data. It offers several advantages, including not requiring samples to follow a specific distribution and being less susceptible to the influence of outliers. In this paper, the M-K method was employed to test the significance of interannual variation trend of surface soil moisture in the Yellow River Basin. The calculation principle of the M-K method is as follows: Here x j represents the time series variable and n represents the number of samples. The sign’s function (denoted as sgn(θ)) is used to determine the overall direction of the trend in the time series. When sgn(θ) returns 1, it indicates a positive and increasing trend. Conversely, a value of -1 represents a negative and decreasing trend. A value of 0 is used to indicate no change in the trend. The sign function can be defined as follows: $$\:sgn\left({\theta\:}\right)=\left\{\begin{array}{c}1\:\:\:\:\:\:\:\:\:\left(\theta\:>0\right)\\\:0\:\:\:\:\:\:\:\:\:\left(\theta\:=0\right)\\\:-1\:\:\:\:\:\:\:\:\left(\theta\:<0\right)\end{array}\right.$$ 2 Mann(1945) and Kendall(1975) demonstrated that when n ≥ 8, the statistic S approximately follows a normal distribution with a mean of 0 and a variance of: $$\:Var\left(S\right)=\frac{n\left(n-1\right)\left(2n+5\right)}{18}$$ 3 The Z values corresponding to different S intervals in the Mann-Kendall statistic formula are as follows: $$\:{Z}_{c}=\left\{\begin{array}{c}\frac{S-1}{\sqrt{Var\left(S\right)}}\:\:\:(S>0)\\\:0\:\:\:\:\:\:\:\:\:\:(S=0)\\\:\frac{S+1}{\sqrt{Var\left(S\right)}}\:\:\:(S<1)\end{array}\right.$$ 4 ZC follows the standard normal distribution, and the indicators used to measure the trend size are: ZC 1.96: Indicates a significantly large positive trend. 2.3.3. Partial correlation analysis Partial correlation analysis is used to assess the influence of one factor on another factor while controlling the effects of other factors [ 26 , 27 ]. It allows for the specific association between two variables while accounting for the effects of other factors. Partial correlation analysis has been employed to investigate the relationships among vegetation, climate, human activities, as well as the temporal and spatial changes of soil moisture. This analysis helps in understanding the independent contributions of vegetation, climate, and human activities to the changes observed in soil moisture at different spatial and temporal scales. 2.3.4. Random forest model Random forest is an ensemble learning method that falls under the category of machine learning algorithms. It can be utilized for various tasks, including classification and regression [ 28 ]. One of the key functionalities of random forests is to assess the importance of features. At each node of the random forest, the reduction in the Gini index ΔGk is calculated for each influencing factor, denoted as k. By summing up the ΔGk values across all nodes of the random forest, summing up the ΔGk values across all trees, and subsequently taking the average value, we can obtain the importance of the influence factor k, referred to as variable importance (VI) [ 29 ]: $$\:VI=\frac{\sum\:_{h=1}^{n}\sum\:_{j=1}^{l}{\varDelta\:}_{Gkhj}}{\sum\:_{k=1}^{m}\sum\:_{h=1}^{n}\sum\:_{j=1}^{l}{\varDelta\:}_{Gkhj}}$$ 5 Currently, there are two important measurement indices for Random Forest: the average impurity reduction index based on the Gini index and the average accuracy reduction index based on out-of-pocket data replacement. In this study, we utilized the average impurity reduction index and implemented the Random Forest algorithm using the Scikit-learn package to calculate the feature importance (VI). A higher VI value indicates a greater contribution of the feature to the model. For this paper, we selected surface soil moisture in the Yellow River Basin as the dependent variable. We identified four indicators related to soil moisture changes, namely precipitation, NDVI, land use, and evapotranspiration, as the driving factors. To determine the contribution rates of these driving factors to the dependent variables, we calculated the VI for each driving factor. By examining the VI values at different pixels within the Yellow River Basin, we were able to identify the main driving factors that significantly influence the variations in surface soil moisture. Moreover, we explored the interaction among multiple driving factors to understand how they collectively impact the spatiotemporal changes of surface soil moisture in the Yellow River Basin. 3. Results 3.1. Spatial pattern of surface soil moisture in the Yellow River Basin The annual variation of surface soil moisture in the Yellow River Basin ranged from 0.040 to 0.541m³/m³ during the period of 2003–2018 (Fig. 3 .a). The spatial pattern exhibits a distinct pattern characterized by higher values in the eastern and western parts of the basin and lower values in the central region. Specifically, the eastern part of the basin and the Bohai Sea, as well as the western and southwestern Tibetan Plateau, exhibits higher surface soil moisture values ranging from 0.365 to 0.541 m³/m³. In contrast, lower values occur in the southeastern region of the Ordos Plateau, the Mu Us Sandy Land, the Loess Plateau and a small portion of the western area, ranging from 0.040 to 0.071 m³/m³ (0.040 ~ 0.071m³/m³). As altitude increases, the surface soil moisture initially experiences an increase and then subsequently decreases (Fig. 3 .b). Between 0 and 3000 meters, the average surface soil moisture fluctuates around 0.08 m³/m³. In a higher altitude range of 3000–4000 meters, the average surface soil moisture increased to 0.104m³/m³. However, with further elevation gain, the average surface soil moisture decreases to 0.092 m³/m³. 3.2. Temporal variation and altitudinal differentiation of surface soil moisture in the Yellow River Basin from 2003 to 2018 3.2.1. Variation trend of surface soil moisture in the Yellow River Basin from 2003 to 2018 The surface soil moisture in the Yellow River Basin increased significantly from 2003 to 2018, with an average annual change rate of 0.00066 m³/m³·yr-1 over the past 16 years (Fig. 4 , Fig. 5 .a). In the study, we carried out the significance level test and spatial mapping of the variation trend of surface soil water in the Yellow River Basin (Fig. 5 .b). It was found that the surface soil moisture presented an extremely significant or significant decline trend along the east and southeast of the Yellow River Basin. Northern part of Shaanxi, some areas of Hetao plain and Inner Mongolia showed the insignificant trend. However, the Loess Plateau area from the middle of Shaanxi to the north of Shanxi showed an extremely significant or significant increasing trend. Also, the Qinghai-Tibet Plateau located in the west of the Yellow River Basin was mainly dominated by an extremely significant increasing trend. And the differences between the south and north were obvious. Most of the northern parts represented extremely significant or significant increase, and yet the southern region showed a mixed situation of significant increase and no significant increase. Generally, the surface soil moisture in the Yellow River Basin mainly showed an increasing trend which varied from region to region. 3.2.2. Altitudinal variation of surface soil moisture in the Yellow River Basin from 2003 to 2018. According to the scatter density map illustrating the relationship between the annual change rate of surface soil moisture and altitude in the Yellow River Basin from 2003 to 2018, distinct variations were observed among different altitudes. In the eastern plain areas of the Yellow River Basin, characterized by altitudes below 500m, there was a significant decreasing trend in the surface soil moisture (Fig. 6 .a), with Theil-Sen trend test values ranging approximately from − 0.006 to 0 m³/m³·yr-1. On average, the change rate of soil moisture in the low altitudinal range of 0-500m was generally negative in the whole Yellow River Basin (Fig. 6 .b). In the western Yellow River Basin, surface soil moisture exhibited an overall increasing trend within the altitudinal range of 2500-5000m, with a more significant increase observed in the 1000-1500m range. The Theil-Sen test values reached 0.003m³ /m³·yr-1, with certain areas reaching 0.004-0.005m³/m³·yr-1. In some areas near 3000m elevation, the change rate of surface soil water exceeded 0.006m³/m³·yr-1. However, within the 4000-4500m range in a small part of the region, a significant negative growth in the change rate of surface soil water was observed, with a growth rate lower than − 0.006 m³/m³·yr-1. In summary, areas with a decreasing trend in surface soil moisture were primarily distributed in low altitude plain areas, as well as a few high-altitude regions. Conversely, areas with an increasing trend were predominantly found in middle to high altitude areas, with a higher degree of internal differentiation observed in the high-altitude regions compared to the middle and low-altitude areas. 3.3. Influence of different driving factors on the variations of the spatiotemporal variations of soil moisture in the Yellow River Basin from 2003 to 2018 3.3.1. Correlation between different driving factors and the spatiotemporal changes in surface soil moisture in the Yellow River Basin from 2003 to 2018 In this study, we examined the relative importance of precipitation, evapotranspiration, NDVI and land use factors on the surface soil moisture in the Yellow River Basin investigated throughout the study period. The R2 range for fitting the four factors to soil moisture changes in random forest model is 0.65–0.99, with an average value of 0.89. Considering the combined effects of precipitation and evapotranspiration as climate factor. Notably, climate emerged as the most influential factor driving overall changes in surface soil moisture in the Yellow River Basin from 2003–2018 (Fig. 7 .a). It exhibited a substantial average VI value of 0.467. Among climate, the contribution of precipitation to surface soil moisture in the Yellow River Basin was more significant, with an average VI value of 0.278. In specific regions such as the Bayan Kera Mountain in southwest of the Yellow River Basin, the Weihe River basin in the south and North China Plain in the southeast of the Yellow River Basin, precipitation had a significant influence on surface soil moisture, with values exceeding 0.5. However, in areas like the Hetao Plain, western Ordos Plateau and Wushaoling region, the contribution of precipitation to surface soil moisture was relatively small. On the whole, precipitation exhibited a positive correlation with surface soil moisture, except for an insignificant negative correlation observed only in the southern part of the Loess Plateau (Fig. 7 .b). This indicates that increased precipitation generally leads to an increase in surface soil moisture in the Yellow River Basin. In contrast, Evapotranspiration had a minimal impact on the changes in surface soil moisture in the Yellow River Basin, with an average VI value of only 0.188. Only specific regions, such as the southwest Tibetan Plateau, the northern Hetao plain and the central part of the Ordos Plateau, were influenced by evapotranspiration. In contrast to precipitation, evapotranspiration showed a predominantly negative correlation with surface soil moisture, meaning that higher evapotranspiration rates were associated with lower surface soil moisture. The contribution of NDVI to surface soil moisture in the Yellow River Basin ranked second after climate, with an average VI value of 0.306. However, it is important to note the presence of distinct spatial variations in its impacts. In the middle part of the Yellow River Basin and the northern part of the Loess Plateau, zones with high vegetation importance were identified, characterized by an average VI value of 0.639, which extended from the northeast to the southwest. In the Hetao plain, located in the northern part of the Yellow River Basin, vegetation also played a significant role in influencing surface soil moisture, with a VI value surpassing that of precipitation. In contrast, in the North China Plain in the southeast of the Yellow River Basin, the Bayan Kera Mountain area, the Mu Us Sandy Land and the southeastern Ordos Plateau, the contribution of vegetation to surface soil moisture was gradually give way to the increasing influence of precipitation and evapotranspiration factors. Overall, a positive relationship between vegetation and surface soil moisture was observed in the Yellow River Basin (Fig. 7 .b), indicating that higher vegetation coverage played a significant role in increasing surface soil moisture. In the Yellow River Basin, the distribution of surface soil moisture in the area most influenced by land use exhibited a scattered and blocky pattern, and the relationship between the two factors was complex. In capital cities such as Yinchuan, Hohhot, Jinan, a negative correlation was observed between land use and surface soil moisture, However, in cities like Xining, Lanzhou, Taiyuan, Xi 'an, Zhengzhou and in the Hetao plain area, a weak positive correlation was found. 3.3.2. The meridional differentiation and elevation differentiation of each controlling factor for surface soil moisture in the Yellow River Basin from 2003 to 2018 It can be seen from Fig. 8 .a that the VI value of climate factor from west to east presents a significant feature of "high in the east and west, low in the middle" along with longitude, that is, precipitation and evapotranspiration mainly affect the western plateau mountain area and the eastern plain area. In addition, VI value shows an increasing trend with the increase of altitude, and it is generally lower in the middle altitude area of 1500-2500m. VI value of NDVI increases first and then decreases with longitude from west to east, and the central region is most affected (Fig. 8 .b), that is, vegetation factors mainly affect the Loess Plateau in the central region. In addition, VI value increases with elevation at 0-2000m, and decreases continuously with elevation at altitudes greater than 1000m. The VI value of land use increased gradually from west to east along with longitude, which was consistent with the distribution of human activity intensity. In addition, VI value of land use gradually decreases with the increase of altitude, and VI value of land use is larger in low-altitude plain areas where human activities are more intense. 3.3.3. Comprehensive mapping of dominant factors driving the spatiotemporal variation of surface soil moisture in the Yellow River Basin from 2003 to 2018 By overlaying the importance of the three factors, a comprehensive map was generated to depict the dominant factors driving the spatiotemporal variation of surface soil moisture in the Yellow River Basin from 2003 to 2018 (Fig. 9 , 10 ). The analysis revealed that the spatiotemporal variations in surface soil moisture from 2003 to 2018 were primarily driven by the combination of climate factors including precipitation and evapotranspiration. Climate emerged as the dominant factor controlling surface soil moisture changes in 45% of the Yellow River Basin, with a VI value ranging between 0.5–0.96, particularly in the southern Yellow River Basin and the southeastern Ordos Plateau. Vegetation, as a dominant factor, controlled 18% of the Yellow River Basin, exhibiting a control area mainly distributed in the northern part of the Loess Plateau, the middle and back parts of the Hetao Plain and certain western areas of the Hetao Plain, with VI values ranging from 0.5 to 0.95. Human activity factors dominated 8% of the Yellow River Basin, displaying sporadic and small regional agglomerations that were mostly consistent with the distribution of urban construction hotspots in the region. Additionally, approximately 29% of the land area was affected by a combination of climate, vegetation and human activities, without a clear dominant control factor. 4. Discussion The spatial distribution of annual mean surface soil moisture in the Yellow River Basin exhibited a distinct trend of higher values in the eastern and western parts of the basin and lower values in the central region. The areas with higher values were primarily located in the easternmost part of the Yellow River Basin, the upper Tibetan Plateau area, the middle Hetao Plain area and the border area between Shaanxi and Gansu province in the middle and southern regions. Conversely, the areas with lower values were found in the middle and northern part of the basin and the Mu Us Sandy Land. This pattern aligns with the findings of Liu Minghui et al. [ 30 ]. The presence of high surface soil moisture in the eastern part of the Yellow River Basin can be attributed to the relatively high precipitation in that region [ 31 ]. Conversely, the high values observed in the western part of the basin can be attributed to the lower degree of soil evapotranspiration in that area [ 32 ]. Moreover, the region in Qinghai Province, characterized by higher altitudes, predominantly consists of alpine soil with larger soil porosity, facilitating greater retention of surface soil water. Also the existence of frozen soil in the high altitude area of the western Plateau was conducive to the stability of soil moisture content in the Qinghai-Tibet Plateau. This can be explained by the mechanism of permafrost preventing water penetration, melting ice providing soil water and low evapotranspiration [ 33 ]. In contrast, the arid environment and low vegetation coverage contribute to lower surface soil moisture in the middle and lower reaches of the Yellow River Basin, the middle and northern regions, and the Ermu Us Sandy Land. From 2003 to 2018, surface soil moisture in the Yellow River Basin experienced a significant overall increase. However, notable decreases were observed along the eastern and southeastern parts of the basin. The variations in surface soil moisture can be primarily attributed to the combined effects of climate, vegetation, and human activities with climate emerging as the dominant factor account for 45% of the changes in surface soil moisture in the Yellow River Basin. This dominance was particularly evident in the eastern, southern, southwestern, and northern regions of the Yellow River Basin. The upper reaches of the Yellow River Basin had exhibited a significant trend of warming and increased humidity since 1997 [ 34 ], which aligns with the observed upward trend in surface soil moisture in the western and southwestern Tibetan Plateau. In areas experiencing a decrease in surface soil moisture within the Yellow River Basin, climate factors exert a dominant influence, indicating that reduced precipitation and evapotranspiration contributed to the decline in surface soil moisture. In the lower reaches of the Yellow River Basin, the decrease in precipitation was the primary climate factor driving the reduction in soil moisture [ 35 ], while in the Hetao area, the increase in total evapotranspiration led to a decrease in surface soil moisture. In addition, climate was also the dominant factor of soil moisture change in the Mu Us Sandy Land region, which was also consistent with the research conclusions of Hu et al [ 36 ]. These regions should closely monitor the impacts of climate change on the ecological environment, particularly the effects of drought and evapotranspiration variations on surface soil moisture. The dominant area where vegetation influences surface soil moisture changes in the Yellow River Basin was concentrated in the middle of the Yellow River Basin, the northern region of the Loess Plateau, east windstorm area in Ningxia, and the Hetao Plain. Vegetation restoration in these areas had shown promising results in increasing surface soil moisture and improving the overall ecological environment. Previous studies, such as the research conducted by Cai Yifei et al and Guo Yanju et al [ 37 , 38 ], had confirmed these findings. The implementation of the "returning farmland to forest" project since around 1998 had led to gradual improvements in vegetation coverage on the Loess Plateau from 1990 to 2020. This, in turn, has resulted in better vegetation growth conditions and a significant increase in vegetation coverage in the Loess Plateau [ 37 , 39 ]. These findings helped explain the observed significant or extremely significant increase in surface soil moisture in the Loess Plateau region, spanning from central Shaanxi to north-central Shanxi, as documented in this study. In the Hetao Plain, higher vegetation coverage, lower soil sand content and a reduced risk of wind erosion had contributed to a significant influence of vegetation on surface soil moisture in Hetao Plain [ 40 ], as identified in this study. However, it should be noted that in the early stage of vegetation recovery, there may be a decline in surface soil moisture due to the strong absorption of groundwater by vegetation [ 41 ], and the specific situation requires further discussion. The study also highlighted the significance of human activities in the reduction of surface soil moisture reduction in the Yellow River Basin, particularly in provincial capitals like Xi 'an, Hohhot, Lanzhou, Xining and other low-altitude plain areas in the eastern part of the basin. In these regions, the combined VI for human activities and vegetation reached 0.632, indicating their substantial influence. The impact of human activities on surface soil moisture can be understood from two perspectives. Firstly, afforestation projects contributed to increase coverage [ 37 , 39 ], which had a positive effect on augmentation of surface moisture. Secondly, urbanization led to the expansion of construction land and the consequent heat island effect. As urbanization accelerates, large-scale human activities and inappropriate land use practices contributed to soil erosion [ 42 – 44 ], ultimately resulting in the reduction of soil moisture. Recently, scholars have conducted an analysis of the temporal variation trend of precipitation over the past 60 years, providing predictions for the next 30 years in the Yellow River Basin [ 45 ]. These findings are valuable in predicting future changes in surface soil moisture in the Yellow River Basin considering the strong interaction between climate factors and surface soil moisture. However, it is crucial to acknowledge the significant positive impact of vegetation factor on surface soil moisture and the complex relationship between human activities and soil moisture variations. The variation trend of surface soil moisture in the Yellow River Basin is driven by climate, vegetation, and human activities, with different dominant factors prevailing in different regions. To promote ecological protection and facilitate high-quality development in the Yellow River Basin, it is essential to develop corresponding soil conservation measures that consider the comprehensive factors and dominant drivers. 5. Conclusions This study employed various methods to investigate the temporal and spatial changes as well as the driving factors influencing surface soil moisture in the Yellow River Basin. The following conclusions were drawn: 1) The spatial distribution pattern of surface soil moisture in the Yellow River Basin generally exhibited a trend of higher values in the eastern and western parts of the basin and lower values in the central region. Surface soil moisture was relatively low in the altitude range of 0-3000m, while it tended to be higher in the altitudinal range of 3000m and above. Furthermore, as elevation increasing, the surface soil moisture demonstrated a pattern of initial increasing and then decreasing. On a temporal scale, surface soil moisture in the Yellow River Basin showed a noteworthy upward trend from 2003 to 2018, with an average change rate of 0.00066 m³/m³·yr-1 over the past 16 years. However, this trend varied depending on the altitude ranges. As altitude increased, the change rate of surface soil moisture in the Yellow River Basin showed a trend of first increasing, then decreasing, and then increasing again in middle and high-altitude areas, from 0.00061m³/m³·yr-1 to 0.00078m³/m³·yr-1 initially, then decreased to 0.00035m³/m³·yr-1, and then increased to 0.00084m³/m³·yr-1 in the altitude areas above 4500m. while the lower altitude areas of 0-500m in the east of the Yellow River Basin exhibited a significant downward trend in surface soil moisture. However, there was still a slight upward trend in the low altitude areas of the entire Yellow River Basin. 2) The spatiotemporal variations of the surface soil moisture in the Yellow River Basin were influenced by a combination of climate, vegetation, and human activities. Among these three factors, climate, as a leading driver, accounted for 45% of the total areas in the spatiotemporal variations of surface soil moisture within the Yellow River Basin, which mainly affects the eastern and western parts of the Yellow River Basin. Vegetation contributed to 18% of the variability, which mainly affected the middle of the Yellow River Basin. Human activities contributed 8%, with no clear spatial pattern. Additionally, 29% of the region was influenced by the combined effects of climate, vegetation, and human activities, with no dominant evident control factor. 3) From the perspective of multi-dimensional zonality, the degree of climate influence is high in the east and west and low and high elevations, low in the middle. The influence degree of vegetation increased first and then decreased from west to east. The influence degree was higher in the central area, and the influence increased first and then decreased slightly with the altitude. And the influence of human activities increases gradually with the decrease of coastal proximity and altitude. Declarations Conflicts of Interest: All authors declare that there is not any personal or financial conflicts of interest. Funding: This research was funded by the Natural Science Foundation of Henan (GrantNo.232300420165). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Author Contribution Zhao, F., Hu, L., Xie, Y. and Liu, Y. are in charge of conceptualization ; Hu, L. is in charge of methodology; Hu, L., Xie, Y., Liu, Y. and Chen, S. are in charge of data curation and visualization; Hu, L., Zhao, F., Yu, H., Chen, S. and Zhao, Y. are in charge of writing-review and editing. All authors have read and agreed to the published version of the man-uscript. Data Availability Statement: The data that support the findings of this study are available from the authors upon reasonable request. 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Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Jan, 2025 Reviews received at journal 13 Jan, 2025 Reviews received at journal 10 Jan, 2025 Reviewers agreed at journal 09 Jan, 2025 Reviewers agreed at journal 07 Jan, 2025 Reviewers invited by journal 22 Nov, 2024 Editor assigned by journal 22 Nov, 2024 Editor invited by journal 14 Nov, 2024 Submission checks completed at journal 13 Nov, 2024 First submitted to journal 25 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5330305","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375877578,"identity":"df4c232b-acf7-4aad-8c60-ce326d57fc68","order_by":0,"name":"Linghua Hu","email":"","orcid":"","institution":"1\tCollege of Geography and Environmental Science, Henan University","correspondingAuthor":false,"prefix":"","firstName":"Linghua","middleName":"","lastName":"Hu","suffix":""},{"id":375877579,"identity":"82ab25c6-a099-42fe-9a14-825058ea6b59","order_by":1,"name":"Yiming Xie","email":"","orcid":"","institution":"1\tCollege of Geography and Environmental 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07:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5330305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5330305/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-96129-w","type":"published","date":"2025-04-10T16:05:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68730977,"identity":"8ff09ab9-e497-4275-b3a6-8ced06b3ad99","added_by":"auto","created_at":"2024-11-11 12:30:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155023,"visible":true,"origin":"","legend":"\u003cp\u003eThe scope and topography of the Yellow River Basin.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/5d5506a8d0e6baec35c3fb99.png"},{"id":68730972,"identity":"80cdf26a-a017-4bab-8209-b467ff53474b","added_by":"auto","created_at":"2024-11-11 12:30:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43167,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of relationship between remote sensing data and surveyed sites data of soil moisture in the Yellow River Basin.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/1e5effd2005536eed5f0fa7c.png"},{"id":68730973,"identity":"1af5753b-8247-4a84-94a3-66181088ed3a","added_by":"auto","created_at":"2024-11-11 12:30:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eSpatial distribution map of average surface soil moisture in the Yellow River Basin from 2003 to 2018. \u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e. Bar chart of average surface soil moisture at different altitudes in the Yellow River Basin.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/3cc6105fff0b5441a044c835.png"},{"id":68730974,"identity":"75ae851b-653c-40af-9048-11de40a92ab5","added_by":"auto","created_at":"2024-11-11 12:30:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53414,"visible":true,"origin":"","legend":"\u003cp\u003eTrend chart of surface soil moisture variations in the Yellow River Basin from 2003 to 2018.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/7a55af6280f11207fe252f26.png"},{"id":68730976,"identity":"12f83eab-3cc4-4624-b73d-d65189e9c1b9","added_by":"auto","created_at":"2024-11-11 12:30:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":159306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eChange rate of the surface soil moisture in the Yellow River Basin from 2003 to 2018. \u003cstrong\u003eb\u003c/strong\u003e. Significance test results of surface soil moisture change trend in the Yellow River Basin from 2003 to 2018.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/a39fd1c3585776ad548753b7.png"},{"id":68731245,"identity":"92449d32-810c-47a4-8415-6aa3e410bc38","added_by":"auto","created_at":"2024-11-11 12:38:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":155895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eScatter plot of the change rate of pixels at different altitudes in the Yellow River Basin from 2003 to 2018. \u003cstrong\u003eb\u003c/strong\u003e. Change rate of surface soil water at different elevations in the Yellow River Basin from 2003 to 2018.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/39e5ea8d2259674edf4a1aa0.png"},{"id":68731242,"identity":"51bcf3b1-89c6-44c1-958a-ece5cc4a56a1","added_by":"auto","created_at":"2024-11-11 12:38:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":130325,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eThe importance of the four factors (NDVI, precipitation, land use and evapotranspiration) on surface soil moisture in the Yellow River Basin from 2003 to 2018. \u003cstrong\u003eb\u003c/strong\u003e. The correlation between the four factors and surface soil moisture in the Yellow River Basin from 2003 to 2018.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/42c89466552265bbd8ba5f49.png"},{"id":68730978,"identity":"1062add0-ac46-4c49-9588-edf1a0554879","added_by":"auto","created_at":"2024-11-11 12:30:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1118396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e The VI value of climate varies with longitude and altitude. \u003cstrong\u003eb.\u003c/strong\u003eThe VI value of NDVI varies with longitude and altitude. \u003cstrong\u003ec.\u003c/strong\u003e The VI value of Land use varies with longitude and altitude.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/d86890a2261f2e683296af62.png"},{"id":68730981,"identity":"e06fa2b3-10bc-4db6-bec8-ceff916d1de6","added_by":"auto","created_at":"2024-11-11 12:30:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":189016,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive mapping of dominant factors driving the spatiotemporal variation of surface soil moisture in the Yellow River Basin from 2003 to 2018.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/cfac095eb88f26961436738f.png"},{"id":68730980,"identity":"b0257601-125e-4c11-b2e9-f97655299733","added_by":"auto","created_at":"2024-11-11 12:30:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":137189,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive mapping of the dominant factors of spatiotemporal variation of surface soil moisture in the Yellow River Basin with longitude and altitude changes from 2003 to 2018.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/dcb869780da42c08a9b528b0.png"},{"id":80558632,"identity":"1aa1ac94-c808-41e3-9f32-012c13f86a0c","added_by":"auto","created_at":"2025-04-14 16:15:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2883249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5330305/v1/a309adf2-c5b2-4c6e-b409-11c1350a1b58.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping the primary factors driving spatiotemporal variations of surface soil moisture from multi-dimensional zonality in the Yellow River Basin of China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSoil moisture is an important physical property of soil, playing a pivotal role in the soil-plant-atmosphere continuum. It not only influences the flow of soil matter and energy, but also actively participates in the nutrient cycle of the soil system [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Furthermore, soil moisture significantly affects various aspects such as runoff [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], soil microorganisms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], biogeochemical cycles [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and agriculture [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Mapping the spatiotemporal and drivers of spatiotemporal variation of surface soil moisture can provide a scientific basis for agricultural production, water resources management and ecological protection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous studies have demonstrated distinct spatiotemporal variations in soil moisture. Globally, tropical regions exhibit higher average soil moisture, while deserts and semi-arid regions tend to have lower soil moisture [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Monitoring of global soil moisture changes from 1948 to 2010 revealed a long-term decreasing trend in soil moisture [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Soil moisture has been identified as being influenced by many factors such as climate, vegetation, soil types and topography [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and primary control factors vary significantly in different regions and scales. In a small scale, land use and topography were suggested to exert significant effects on soil moisture, whereas climate factors were considered as dominated driving factors on a larger scale [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Wang Yunqian et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] discovered relationships between variations in soil moisture and climate factors among different land types. Pan Yanxia et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] found differences in driving factors of soil moisture between the rainy and dry periods, where soil texture plays a significant role during the dry periods, while local vegetation and topography have greater influence during the rainy periods. From the perspective of geography, longitude, latitude and altitude determine regional variations of climate, vegetation and human activities, and therefore cause the multi-dimensional zonality of soil moisture including latitudinal, longitude and altitudinal zonality. Mapping the variations and drivers of spatiotemporal variation of soil moisture from multi-dimensional zonality can provide a macroscopic geographical law to gain insight into regional variations in the variations and drivers of spatiotemporal variation of soil moisture in the Yellow River Basin.\u003c/p\u003e \u003cp\u003eThe Yellow River Basin is characterized by a fragile ecological environment, with notable water-related ecological issues [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In recent years, the advancement of remote sensing and GIS technology have facilitated the acquisition of soil moisture data, leading to significant progress in understanding the spatiotemporal evolution and cause analysis of soil moisture[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Numerous studies have demonstrated that the close relationship between soil moisture variations and vegetation, climate, and human activities in the Yellow River Basin. Fan Keke et al. revealed that, rising temperature driven by global warming caused contribute to a decrease in soil moisture in the Yellow River Basin[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Li Sha et al. explained the response mechanism of soil moisture to precipitation and actual evapotranspiration in the Basin[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Omer et al. highlighted the diverse impacts of different land use types on water content in the Yellow River Basin[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, limited researches have comprehensively analyzed how these three factors collectively influence surface soil water, particularly in quantifying the interaction of these factors on soil water in different regions. This challenge hinders the analysis of the driving forces behind the spatiotemporal evolution of soil water in the Yellow River Basin.\u003c/p\u003e \u003cp\u003eTo gain macroscopic geographical laws into regional variations in the variations and drivers in the Yellow River Basin, this study intends to use trend analysis, Mann-Kendall trend analysis, partial correlation analysis and random forest algorithm to discover the spatiotemporal variations and their relationships with driving factors (precipitation, evaporation, NDVI (Normalized Difference Vegetation Index) and land use), and then identify and map the primary factors driving the spatiotemporal variation of surface soil moisture from the regional scale and from three geographical dimensions: latitude, longitude and altitude zonality in the Yellow River Basin.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study Area\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe Yellow River Basin is situated in between 95\u0026deg;53\u0026apos; to 119\u0026deg;05\u0026apos; E longitude, 32\u0026deg;10\u0026apos; to 41\u0026deg;50\u0026apos; N latitude, covering a total area of 752,000 km\u0026sup2;. It traverses several provinces, including Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan and Shandong provinces (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The topography of the Yellow River Basin exhibits a west-to-east variation. The western region is dominated by the Qinghai-Tibet Plateau and characterized by rugged terrain and towering mountains, The central part comprises the Loess Plateau and the Inner Mongolia Plateau, featuring relatively high and gentle terrain. The lower reaches of the Yellow River flow through the North China Plain characterized by flat and low terrain, with some sections exhibiting meandering river channels.\u003c/p\u003e\n \u003cp\u003eThe vegetation in the western Yellow River Basin mainly consists of coniferous forests and alpine meadows. In the central area, grasslands and deciduous broad-leaved forests are predominant, while the lower reaches are primarily covered by deciduous broad-leaved forests. The Yellow River Basin experiences a monsoon climate area, with uneven distribution of precipitation throughout the year. More than 60% of the annual precipitation occurs from June to September, while other months receive relatively less rainfall. Precipitation decreases from east to west and from south to north. This pattern of precipitation distribution increases the vulnerability of the region to both drought and flood disasters.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Data Collection and Pre-Processing\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1. Surveyed sites data\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe measured data of soil moisture at stations of the Loess Plateau were obtained from the National Earth System Science Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geodata.cn\u003c/span\u003e\u003c/span\u003e), which was the annual average data from 1992 to 2019. We selected the normalized measured data of 37 stations to match and verify the remote sensing data to evaluate the accuracy of the remote sensing soil moisture data (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2. Soil moisture data\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn this study, land surface soil moisture data of China were obtained from National Tibetan Plateau (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.tpdc.ac.cn\u003c/span\u003e\u003c/span\u003e). The dataset encompasses monthly data from 2003 to 2018, with a spatial resolution of 0.05\u0026deg;. To investigate the spatiotemporal variations of surface soil moisture in the Yellow River Basin, we specifically extracted the surface soil moisture data from the Yellow River Basin within the dataset. In this study, soil moisture data of each month were summarized and synthesized on the basis of each pixel, and then averaged to represent the average soil moisture content of each pixel in each year, that is, the annual soil moisture content.\u003c/p\u003e\n \u003cp\u003eIn order to validate the accuracy of soil moisture data in the Yellow River Basin, a correlation analysis was performed using data from the 37 surveyed sites and the homologous remote sensing data. The analysis showed a strong relationship between the remote sensing data and the surveyed sites data, with a correlation coefficient of 0.75 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents a scatter diagram that visually represents the relationship between the soil moisture data obtained from remote sensing and the surveyed sites. The figure provides evidence of the correspondence between the two datasets and supports the accuracy of the remote sensing data in capturing soil moisture variations in the study area.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3. Climate data\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe precipitation and evapotranspiration data used in this study were obtained from the National Earth System Science Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geodata.cn\u003c/span\u003e\u003c/span\u003e), which is part of the National Science \u0026amp; Technology Infrastructure of China. The spatial resolution of the data is 1 km, providing detailed monthly precipitation and evapotranspiration information about the Yellow River Basin. To obtain annual climate data sets, the monthly climate data, including precipitation and evapotranspiration, were aggregated on an annual basis for each year.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.4. Vegetation data\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eNDVI used in this study was derived from the monthly grid data of China\u0026apos;s Vegetation index spanning from 2003 to 2018. This data was obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.resdc.cn/\u003c/span\u003e\u003c/span\u003e), The spatial resolution of the data is 1 km. In this study, the monthly NDVI data were used to calculate annual NDVI data by pixel on a pixel-by-pixel basis using maximum synthesis method. By using the maximum NDVI selected by this method, the influence of cloudy and fogy atmospheric conditions were minimized, resulting in a more robust measurement of regional vegetation cover intensity [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.5. Land use data\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe Land use data used in this study were obtained from the annual China Land Cover Dataset (CLCD) [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e], The CLCD provided land cover information with a spatial resolution of 30 meters. In this study, the Human Activity Intensity of Land Surface (HAILS) approach proposed by Xu Yong et al. [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], was employed to assign different construction lands equivalent conversion coefficients to various land use types. This method captures the comprehensive impacts of human activities on the land surface system. The HAILS method has been widely applied to quantify the intensity of human activities in the Loess Plateau [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], the middle and lower reaches of the Yangtze River [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] to explore the influence of human activities on eco-geographical environment in different regions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Methods\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1. Trend Analysis\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe linear regression model was used in this study to describe the linear correlation between the two variables. Specifically, the study focuses on the correlation between time and the surface soil moisture data of the Yellow River Basin. By fitting a linear regression model, the trend of the surface soil moisture annual changes over time was analyzed. The slope of the linear regression model is an important parameter that reflects the characteristics of surface soil moisture variation with time series. If the slope is positive, it indicates that the surface soil moisture value of the Yellow River Basin exhibits an upward trend throughout the study period. Conversely, if the slope is negative, it suggests a decreasing trend in the surface soil moisture value. The slope of 0, indicates that there is no significant change in the surface soil moisture value of the Yellow River Basin during the study period. By analyzing the slope of the linear regression model, the temporal variations of surface soil moisture can be illustrated in the Yellow River Basin.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2. Mann-Kendall test\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe Mann-Kendall (M-K) method is a non-parametric statistical test widely used to assess the significance of trends in time series data. It offers several advantages, including not requiring samples to follow a specific distribution and being less susceptible to the influence of outliers. In this paper, the M-K method was employed to test the significance of interannual variation trend of surface soil moisture in the Yellow River Basin. The calculation principle of the M-K method is as follows:\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eHere x\u003csub\u003ej\u003c/sub\u003e represents the time series variable and n represents the number of samples. The sign\u0026rsquo;s function (denoted as sgn(\u0026theta;)) is used to determine the overall direction of the trend in the time series. When sgn(\u0026theta;) returns 1, it indicates a positive and increasing trend. Conversely, a value of -1 represents a negative and decreasing trend. A value of 0 is used to indicate no change in the trend. The sign function can be defined as follows:\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:sgn\\left({\\theta\\:}\\right)=\\left\\{\\begin{array}{c}1\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(\\theta\\:\u0026gt;0\\right)\\\\\\:0\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(\\theta\\:=0\\right)\\\\\\:-1\\:\\:\\:\\:\\:\\:\\:\\:\\left(\\theta\\:\u0026lt;0\\right)\\end{array}\\right.$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eMann(1945) and Kendall(1975) demonstrated that when n\u0026thinsp;\u0026ge;\u0026thinsp;8, the statistic S approximately follows a normal distribution with a mean of 0 and a variance of:\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:Var\\left(S\\right)=\\frac{n\\left(n-1\\right)\\left(2n+5\\right)}{18}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe Z values corresponding to different S intervals in the Mann-Kendall statistic formula are as follows:\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:{Z}_{c}=\\left\\{\\begin{array}{c}\\frac{S-1}{\\sqrt{Var\\left(S\\right)}}\\:\\:\\:(S\u0026gt;0)\\\\\\:0\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(S=0)\\\\\\:\\frac{S+1}{\\sqrt{Var\\left(S\\right)}}\\:\\:\\:(S\u0026lt;1)\\end{array}\\right.$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eZC follows the standard normal distribution, and the indicators used to measure the trend size are:\u003c/p\u003e\n \u003cp\u003eZC \u0026lt; -1.96: Indicates a significantly large negative trend.\u003c/p\u003e\n \u003cp\u003e-1.96\u0026thinsp;\u0026le;\u0026thinsp;ZC\u0026thinsp;\u0026le;\u0026thinsp;1.96: Indicates no significant trend.\u003c/p\u003e\n \u003cp\u003eZC\u0026thinsp;\u0026gt;\u0026thinsp;1.96: Indicates a significantly large positive trend.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.3. Partial correlation analysis\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePartial correlation analysis is used to assess the influence of one factor on another factor while controlling the effects of other factors [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. It allows for the specific association between two variables while accounting for the effects of other factors. Partial correlation analysis has been employed to investigate the relationships among vegetation, climate, human activities, as well as the temporal and spatial changes of soil moisture. This analysis helps in understanding the independent contributions of vegetation, climate, and human activities to the changes observed in soil moisture at different spatial and temporal scales.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.4. Random forest model\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eRandom forest is an ensemble learning method that falls under the category of machine learning algorithms. It can be utilized for various tasks, including classification and regression [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. One of the key functionalities of random forests is to assess the importance of features. At each node of the random forest, the reduction in the Gini index \u0026Delta;Gk is calculated for each influencing factor, denoted as k. By summing up the \u0026Delta;Gk values across all nodes of the random forest, summing up the \u0026Delta;Gk values across all trees, and subsequently taking the average value, we can obtain the importance of the influence factor k, referred to as variable importance (VI) [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]:\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$$\\:VI=\\frac{\\sum\\:_{h=1}^{n}\\sum\\:_{j=1}^{l}{\\varDelta\\:}_{Gkhj}}{\\sum\\:_{k=1}^{m}\\sum\\:_{h=1}^{n}\\sum\\:_{j=1}^{l}{\\varDelta\\:}_{Gkhj}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eCurrently, there are two important measurement indices for Random Forest: the average impurity reduction index based on the Gini index and the average accuracy reduction index based on out-of-pocket data replacement. In this study, we utilized the average impurity reduction index and implemented the Random Forest algorithm using the Scikit-learn package to calculate the feature importance (VI). A higher VI value indicates a greater contribution of the feature to the model.\u003c/p\u003e\n \u003cp\u003eFor this paper, we selected surface soil moisture in the Yellow River Basin as the dependent variable. We identified four indicators related to soil moisture changes, namely precipitation, NDVI, land use, and evapotranspiration, as the driving factors. To determine the contribution rates of these driving factors to the dependent variables, we calculated the VI for each driving factor. By examining the VI values at different pixels within the Yellow River Basin, we were able to identify the main driving factors that significantly influence the variations in surface soil moisture. Moreover, we explored the interaction among multiple driving factors to understand how they collectively impact the spatiotemporal changes of surface soil moisture in the Yellow River Basin.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Spatial pattern of surface soil moisture in the Yellow River Basin\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe annual variation of surface soil moisture in the Yellow River Basin ranged from 0.040 to 0.541m\u0026sup3;/m\u0026sup3; during the period of 2003\u0026ndash;2018 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.a). The spatial pattern exhibits a distinct pattern characterized by higher values in the eastern and western parts of the basin and lower values in the central region. Specifically, the eastern part of the basin and the Bohai Sea, as well as the western and southwestern Tibetan Plateau, exhibits higher surface soil moisture values ranging from 0.365 to 0.541 m\u0026sup3;/m\u0026sup3;. In contrast, lower values occur in the southeastern region of the Ordos Plateau, the Mu Us Sandy Land, the Loess Plateau and a small portion of the western area, ranging from 0.040 to 0.071 m\u0026sup3;/m\u0026sup3; (0.040\u0026thinsp;~\u0026thinsp;0.071m\u0026sup3;/m\u0026sup3;). As altitude increases, the surface soil moisture initially experiences an increase and then subsequently decreases (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.b). Between 0 and 3000 meters, the average surface soil moisture fluctuates around 0.08 m\u0026sup3;/m\u0026sup3;. In a higher altitude range of 3000\u0026ndash;4000 meters, the average surface soil moisture increased to 0.104m\u0026sup3;/m\u0026sup3;. However, with further elevation gain, the average surface soil moisture decreases to 0.092 m\u0026sup3;/m\u0026sup3;.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003e3.2. Temporal variation and altitudinal differentiation of surface soil moisture in the Yellow River Basin from 2003 to 2018\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003cp\u003e3.2.1. Variation trend of surface soil moisture in the Yellow River Basin from 2003 to 2018\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe surface soil moisture in the Yellow River Basin increased significantly from 2003 to 2018, with an average annual change rate of 0.00066 m\u0026sup3;/m\u0026sup3;\u0026middot;yr-1 over the past 16 years (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.a). In the study, we carried out the significance level test and spatial mapping of the variation trend of surface soil water in the Yellow River Basin (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.b). It was found that the surface soil moisture presented an extremely significant or significant decline trend along the east and southeast of the Yellow River Basin. Northern part of Shaanxi, some areas of Hetao plain and Inner Mongolia showed the insignificant trend. However, the Loess Plateau area from the middle of Shaanxi to the north of Shanxi showed an extremely significant or significant increasing trend. Also, the Qinghai-Tibet Plateau located in the west of the Yellow River Basin was mainly dominated by an extremely significant increasing trend. And the differences between the south and north were obvious. Most of the northern parts represented extremely significant or significant increase, and yet the southern region showed a mixed situation of significant increase and no significant increase. Generally, the surface soil moisture in the Yellow River Basin mainly showed an increasing trend which varied from region to region.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2. Altitudinal variation of surface soil moisture in the Yellow River Basin from 2003 to 2018.\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAccording to the scatter density map illustrating the relationship between the annual change rate of surface soil moisture and altitude in the Yellow River Basin from 2003 to 2018, distinct variations were observed among different altitudes. In the eastern plain areas of the Yellow River Basin, characterized by altitudes below 500m, there was a significant decreasing trend in the surface soil moisture (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.a), with Theil-Sen trend test values ranging approximately from \u0026minus;\u0026thinsp;0.006 to 0 m\u0026sup3;/m\u0026sup3;\u0026middot;yr-1. On average, the change rate of soil moisture in the low altitudinal range of 0-500m was generally negative in the whole Yellow River Basin (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.b). In the western Yellow River Basin, surface soil moisture exhibited an overall increasing trend within the altitudinal range of 2500-5000m, with a more significant increase observed in the 1000-1500m range. The Theil-Sen test values reached 0.003m\u0026sup3; /m\u0026sup3;\u0026middot;yr-1, with certain areas reaching 0.004-0.005m\u0026sup3;/m\u0026sup3;\u0026middot;yr-1. In some areas near 3000m elevation, the change rate of surface soil water exceeded 0.006m\u0026sup3;/m\u0026sup3;\u0026middot;yr-1. However, within the 4000-4500m range in a small part of the region, a significant negative growth in the change rate of surface soil water was observed, with a growth rate lower than \u0026minus;\u0026thinsp;0.006 m\u0026sup3;/m\u0026sup3;\u0026middot;yr-1. In summary, areas with a decreasing trend in surface soil moisture were primarily distributed in low altitude plain areas, as well as a few high-altitude regions. Conversely, areas with an increasing trend were predominantly found in middle to high altitude areas, with a higher degree of internal differentiation observed in the high-altitude regions compared to the middle and low-altitude areas.\u003c/p\u003e\n \u003c/div\u003e\u003cspan\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003e3.3. Influence of different driving factors on the variations of the spatiotemporal variations of soil moisture in the Yellow River Basin from 2003 to 2018\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3.3.1. Correlation between different driving factors and the spatiotemporal changes in surface soil moisture in the Yellow River Basin from 2003 to 2018\u003c/p\u003e\n \u003c/span\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn this study, we examined the relative importance of precipitation, evapotranspiration, NDVI and land use factors on the surface soil moisture in the Yellow River Basin investigated throughout the study period. The R2 range for fitting the four factors to soil moisture changes in random forest model is 0.65\u0026ndash;0.99, with an average value of 0.89.\u003c/p\u003e\n \u003cp\u003eConsidering the combined effects of precipitation and evapotranspiration as climate factor. Notably, climate emerged as the most influential factor driving overall changes in surface soil moisture in the Yellow River Basin from 2003\u0026ndash;2018 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.a). It exhibited a substantial average VI value of 0.467. Among climate, the contribution of precipitation to surface soil moisture in the Yellow River Basin was more significant, with an average VI value of 0.278. In specific regions such as the Bayan Kera Mountain in southwest of the Yellow River Basin, the Weihe River basin in the south and North China Plain in the southeast of the Yellow River Basin, precipitation had a significant influence on surface soil moisture, with values exceeding 0.5. However, in areas like the Hetao Plain, western Ordos Plateau and Wushaoling region, the contribution of precipitation to surface soil moisture was relatively small. On the whole, precipitation exhibited a positive correlation with surface soil moisture, except for an insignificant negative correlation observed only in the southern part of the Loess Plateau (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.b). This indicates that increased precipitation generally leads to an increase in surface soil moisture in the Yellow River Basin. In contrast, Evapotranspiration had a minimal impact on the changes in surface soil moisture in the Yellow River Basin, with an average VI value of only 0.188. Only specific regions, such as the southwest Tibetan Plateau, the northern Hetao plain and the central part of the Ordos Plateau, were influenced by evapotranspiration. In contrast to precipitation, evapotranspiration showed a predominantly negative correlation with surface soil moisture, meaning that higher evapotranspiration rates were associated with lower surface soil moisture.\u003c/p\u003e\n \u003cp\u003eThe contribution of NDVI to surface soil moisture in the Yellow River Basin ranked second after climate, with an average VI value of 0.306. However, it is important to note the presence of distinct spatial variations in its impacts. In the middle part of the Yellow River Basin and the northern part of the Loess Plateau, zones with high vegetation importance were identified, characterized by an average VI value of 0.639, which extended from the northeast to the southwest. In the Hetao plain, located in the northern part of the Yellow River Basin, vegetation also played a significant role in influencing surface soil moisture, with a VI value surpassing that of precipitation. In contrast, in the North China Plain in the southeast of the Yellow River Basin, the Bayan Kera Mountain area, the Mu Us Sandy Land and the southeastern Ordos Plateau, the contribution of vegetation to surface soil moisture was gradually give way to the increasing influence of precipitation and evapotranspiration factors. Overall, a positive relationship between vegetation and surface soil moisture was observed in the Yellow River Basin (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.b), indicating that higher vegetation coverage played a significant role in increasing surface soil moisture.\u003c/p\u003e\n \u003cp\u003eIn the Yellow River Basin, the distribution of surface soil moisture in the area most influenced by land use exhibited a scattered and blocky pattern, and the relationship between the two factors was complex. In capital cities such as Yinchuan, Hohhot, Jinan, a negative correlation was observed between land use and surface soil moisture, However, in cities like Xining, Lanzhou, Taiyuan, Xi \u0026apos;an, Zhengzhou and in the Hetao plain area, a weak positive correlation was found.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e3.3.2. The meridional differentiation and elevation differentiation of each controlling factor for surface soil moisture in the Yellow River Basin from 2003 to 2018\u003c/p\u003e\n \u003cp\u003eIt can be seen from Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.a that the VI value of climate factor from west to east presents a significant feature of \u0026quot;high in the east and west, low in the middle\u0026quot; along with longitude, that is, precipitation and evapotranspiration mainly affect the western plateau mountain area and the eastern plain area. In addition, VI value shows an increasing trend with the increase of altitude, and it is generally lower in the middle altitude area of 1500-2500m. VI value of NDVI increases first and then decreases with longitude from west to east, and the central region is most affected (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.b), that is, vegetation factors mainly affect the Loess Plateau in the central region. In addition, VI value increases with elevation at 0-2000m, and decreases continuously with elevation at altitudes greater than 1000m. The VI value of land use increased gradually from west to east along with longitude, which was consistent with the distribution of human activity intensity. In addition, VI value of land use gradually decreases with the increase of altitude, and VI value of land use is larger in low-altitude plain areas where human activities are more intense.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e3.3.3. Comprehensive mapping of dominant factors driving the spatiotemporal variation of surface soil moisture in the Yellow River Basin from 2003 to 2018\u003c/p\u003e\n \u003cp\u003eBy overlaying the importance of the three factors, a comprehensive map was generated to depict the dominant factors driving the spatiotemporal variation of surface soil moisture in the Yellow River Basin from 2003 to 2018 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). The analysis revealed that the spatiotemporal variations in surface soil moisture from 2003 to 2018 were primarily driven by the combination of climate factors including precipitation and evapotranspiration. Climate emerged as the dominant factor controlling surface soil moisture changes in 45% of the Yellow River Basin, with a VI value ranging between 0.5\u0026ndash;0.96, particularly in the southern Yellow River Basin and the southeastern Ordos Plateau. Vegetation, as a dominant factor, controlled 18% of the Yellow River Basin, exhibiting a control area mainly distributed in the northern part of the Loess Plateau, the middle and back parts of the Hetao Plain and certain western areas of the Hetao Plain, with VI values ranging from 0.5 to 0.95. Human activity factors dominated 8% of the Yellow River Basin, displaying sporadic and small regional agglomerations that were mostly consistent with the distribution of urban construction hotspots in the region. Additionally, approximately 29% of the land area was affected by a combination of climate, vegetation and human activities, without a clear dominant control factor.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe spatial distribution of annual mean surface soil moisture in the Yellow River Basin exhibited a distinct trend of higher values in the eastern and western parts of the basin and lower values in the central region. The areas with higher values were primarily located in the easternmost part of the Yellow River Basin, the upper Tibetan Plateau area, the middle Hetao Plain area and the border area between Shaanxi and Gansu province in the middle and southern regions. Conversely, the areas with lower values were found in the middle and northern part of the basin and the Mu Us Sandy Land. This pattern aligns with the findings of Liu Minghui et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The presence of high surface soil moisture in the eastern part of the Yellow River Basin can be attributed to the relatively high precipitation in that region [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Conversely, the high values observed in the western part of the basin can be attributed to the lower degree of soil evapotranspiration in that area [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, the region in Qinghai Province, characterized by higher altitudes, predominantly consists of alpine soil with larger soil porosity, facilitating greater retention of surface soil water. Also the existence of frozen soil in the high altitude area of the western Plateau was conducive to the stability of soil moisture content in the Qinghai-Tibet Plateau. This can be explained by the mechanism of permafrost preventing water penetration, melting ice providing soil water and low evapotranspiration [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In contrast, the arid environment and low vegetation coverage contribute to lower surface soil moisture in the middle and lower reaches of the Yellow River Basin, the middle and northern regions, and the Ermu Us Sandy Land.\u003c/p\u003e \u003cp\u003eFrom 2003 to 2018, surface soil moisture in the Yellow River Basin experienced a significant overall increase. However, notable decreases were observed along the eastern and southeastern parts of the basin. The variations in surface soil moisture can be primarily attributed to the combined effects of climate, vegetation, and human activities with climate emerging as the dominant factor account for 45% of the changes in surface soil moisture in the Yellow River Basin. This dominance was particularly evident in the eastern, southern, southwestern, and northern regions of the Yellow River Basin. The upper reaches of the Yellow River Basin had exhibited a significant trend of warming and increased humidity since 1997 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which aligns with the observed upward trend in surface soil moisture in the western and southwestern Tibetan Plateau. In areas experiencing a decrease in surface soil moisture within the Yellow River Basin, climate factors exert a dominant influence, indicating that reduced precipitation and evapotranspiration contributed to the decline in surface soil moisture. In the lower reaches of the Yellow River Basin, the decrease in precipitation was the primary climate factor driving the reduction in soil moisture [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], while in the Hetao area, the increase in total evapotranspiration led to a decrease in surface soil moisture. In addition, climate was also the dominant factor of soil moisture change in the Mu Us Sandy Land region, which was also consistent with the research conclusions of Hu et al [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These regions should closely monitor the impacts of climate change on the ecological environment, particularly the effects of drought and evapotranspiration variations on surface soil moisture.\u003c/p\u003e \u003cp\u003eThe dominant area where vegetation influences surface soil moisture changes in the Yellow River Basin was concentrated in the middle of the Yellow River Basin, the northern region of the Loess Plateau, east windstorm area in Ningxia, and the Hetao Plain. Vegetation restoration in these areas had shown promising results in increasing surface soil moisture and improving the overall ecological environment. Previous studies, such as the research conducted by Cai Yifei et al and Guo Yanju et al [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], had confirmed these findings. The implementation of the \"returning farmland to forest\" project since around 1998 had led to gradual improvements in vegetation coverage on the Loess Plateau from 1990 to 2020. This, in turn, has resulted in better vegetation growth conditions and a significant increase in vegetation coverage in the Loess Plateau [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These findings helped explain the observed significant or extremely significant increase in surface soil moisture in the Loess Plateau region, spanning from central Shaanxi to north-central Shanxi, as documented in this study. In the Hetao Plain, higher vegetation coverage, lower soil sand content and a reduced risk of wind erosion had contributed to a significant influence of vegetation on surface soil moisture in Hetao Plain [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], as identified in this study. However, it should be noted that in the early stage of vegetation recovery, there may be a decline in surface soil moisture due to the strong absorption of groundwater by vegetation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and the specific situation requires further discussion.\u003c/p\u003e \u003cp\u003eThe study also highlighted the significance of human activities in the reduction of surface soil moisture reduction in the Yellow River Basin, particularly in provincial capitals like Xi 'an, Hohhot, Lanzhou, Xining and other low-altitude plain areas in the eastern part of the basin. In these regions, the combined VI for human activities and vegetation reached 0.632, indicating their substantial influence. The impact of human activities on surface soil moisture can be understood from two perspectives. Firstly, afforestation projects contributed to increase coverage [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], which had a positive effect on augmentation of surface moisture. Secondly, urbanization led to the expansion of construction land and the consequent heat island effect. As urbanization accelerates, large-scale human activities and inappropriate land use practices contributed to soil erosion [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], ultimately resulting in the reduction of soil moisture.\u003c/p\u003e \u003cp\u003eRecently, scholars have conducted an analysis of the temporal variation trend of precipitation over the past 60 years, providing predictions for the next 30 years in the Yellow River Basin [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These findings are valuable in predicting future changes in surface soil moisture in the Yellow River Basin considering the strong interaction between climate factors and surface soil moisture. However, it is crucial to acknowledge the significant positive impact of vegetation factor on surface soil moisture and the complex relationship between human activities and soil moisture variations. The variation trend of surface soil moisture in the Yellow River Basin is driven by climate, vegetation, and human activities, with different dominant factors prevailing in different regions. To promote ecological protection and facilitate high-quality development in the Yellow River Basin, it is essential to develop corresponding soil conservation measures that consider the comprehensive factors and dominant drivers.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study employed various methods to investigate the temporal and spatial changes as well as the driving factors influencing surface soil moisture in the Yellow River Basin. The following conclusions were drawn:\u003c/p\u003e\n\u003cp\u003e1) The spatial distribution pattern of surface soil moisture in the Yellow River Basin generally exhibited a trend of higher values in the eastern and western parts of the basin and lower values in the central region. Surface soil moisture was relatively low in the altitude range of 0-3000m, while it tended to be higher in the altitudinal range of 3000m and above. \u0026nbsp;Furthermore, as elevation increasing, the surface soil moisture demonstrated a pattern of initial increasing and then decreasing. On a temporal scale, surface soil moisture in the Yellow River Basin showed a noteworthy upward trend from 2003 to 2018, with an average change rate of 0.00066 m³/m³·yr-1 over the past 16 years. However, this trend varied depending on the altitude ranges. As altitude increased, the change rate of surface soil moisture in the Yellow River Basin showed a trend of first increasing, then decreasing, and then increasing again in middle and high-altitude areas, from 0.00061m³/m³·yr-1 to 0.00078m³/m³·yr-1 initially, then decreased to 0.00035m³/m³·yr-1, and then increased to 0.00084m³/m³·yr-1 in the altitude areas above 4500m. while the lower altitude areas of 0-500m in the east of the Yellow River Basin exhibited a significant downward trend in surface soil moisture. However, there was still a slight upward trend in the low altitude areas of the entire Yellow River Basin.\u003c/p\u003e\n\u003cp\u003e2) The spatiotemporal variations of the surface soil moisture in the Yellow River Basin were influenced by a combination of climate, vegetation, and human activities. Among these three factors, climate, as a leading driver, accounted for 45% of the total areas in the spatiotemporal variations of surface soil moisture within the Yellow River Basin, which mainly affects the eastern and western parts of the Yellow River Basin. Vegetation contributed to 18% of the variability, which mainly affected the middle of the Yellow River Basin. Human activities contributed 8%, with no clear spatial pattern. Additionally, 29% of the region was influenced by the combined effects of climate, vegetation, and human activities, with no dominant evident control factor.\u003c/p\u003e\n\u003cp\u003e3) From the perspective of multi-dimensional zonality, the degree of climate influence is high in the east and west and low and high elevations, low in the middle. The influence degree of vegetation increased first and then decreased from west to east. The influence degree was higher in the central area, and the influence increased first and then decreased slightly with the altitude. And the influence of human activities increases gradually with the decrease of coastal proximity and altitude.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eAll authors declare that there is not any personal or financial conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the Natural Science Foundation of Henan (GrantNo.232300420165). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhao, F., Hu, L., Xie, Y. and Liu, Y. are in charge of conceptualization ; Hu, L. is in charge of methodology; Hu, L., Xie, Y., Liu, Y. and Chen, S. are in charge of data curation and visualization; Hu, L., Zhao, F., Yu, H., Chen, S. and Zhao, Y. are in charge of writing-review and editing. All authors have read and agreed to the published version of the man-uscript.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eHe, Q., He, Y. \u0026amp; Bao, W. Variations changes of surface soil moisture in arid and semi-arid mountainous areas. \u003cem\u003eJ. Mt. 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Arid Areas\u003c/em\u003e. \u003cstrong\u003e39\u003c/strong\u003e (03), 708\u0026ndash;722 (2022).\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"surface soil moisture, spatiotemporal evolution, Random Forest, Attribution analysis","lastPublishedDoi":"10.21203/rs.3.rs-5330305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5330305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpatiotemporal variations of soil moisture are affected by a combination of factors many factors including climate, vegetation, human activities, of which the primary factors vary greatly in different geographical zonal dimension in the Yellow River Basin. To identify and map the dominant factors driving the spatiotemporal variation of surface soil moisture in the Yellow River Basin across different zonality from 2003 to 2018, relationships between spatiotemporal variations of soil moisture and driving factors (precipitation, evaporation, NDVI (Normalized Difference Vegetation Index) and land use) were analyzed from two geographical dimensions: longitude and altitude. The results revealed that: (1) The spatial distribution of surface soil moisture in the Yellow River Basin exhibited a pattern of \" higher values in the east and west, and lower values in the middle\". Temporally, surface soil moisture in the Yellow River Basin showed a noteworthy upward trend from 2003 to 2018, with an average change rate of 0.00066m\u0026sup3;/m\u0026sup3;\u0026middot;yr-1 over the past 16 years. As altitude ascended, the rate of surface soil moisture initially exhibited an increase from 0.00061 m\u0026sup3;/m\u0026sup3;\u0026middot;yr⁻\u0026sup1; to 0.00078 m\u0026sup3;/m\u0026sup3;\u0026middot;yr⁻\u0026sup1;, followed by a decline to 0.00035 m\u0026sup3;/m\u0026sup3;\u0026middot;yr⁻\u0026sup1;. However, above altitudes of 4500 meters, the rate once again rose, reaching 0.00084 m\u0026sup3;/m\u0026sup3;\u0026middot;yr⁻\u0026sup1;. (2) Among the three driving factors, climate, NDVI and land use accounted for 45%, 18% and 8% of the regional surface soil moisture variations, respectively. Climate controlling factors are mainly concentrated in the southwest, south, east and northeast, NDVI controlling factors are mainly concentrated in the central Loess Plateau and the northern Hetao plain, and land use controlling factors are mainly distributed in and around some big cities. Additionally, 29% of the area was controlled by the combined effects of these three factors, with no dominant controlling factor evident with scattered distribution. (3) From the perspective of multi-dimensional zonality, the degree of climate influence is high in the east and west, low in the middle, and increases with the increase of altitude. The influence degree of vegetation increased first and then decreased from west to east. The influence degree was greater in the central area, and the influence increased first and then decreased slightly with the altitude. The peak value appeared in the middle altitude area at 1000m. And the degree of influence of human activity intensity is slightly lower in the central part.\u003c/p\u003e","manuscriptTitle":"Mapping the primary factors driving spatiotemporal variations of surface soil moisture from multi-dimensional zonality in the Yellow River Basin of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 12:30:11","doi":"10.21203/rs.3.rs-5330305/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-24T11:36:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-13T14:14:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-10T21:33:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284573311356278399843143386678094331966","date":"2025-01-09T14:33:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43743070451586528941548173689138018185","date":"2025-01-08T01:32:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-23T03:06:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-23T02:54:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-14T09:14:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T12:01:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-25T07:15:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5266172-4394-4c0d-a2c3-1cdc7828f0fc","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40020716,"name":"Earth and environmental sciences/Hydrology"},{"id":40020717,"name":"Earth and environmental sciences/Ecology/Climate change ecology"}],"tags":[],"updatedAt":"2025-04-14T16:09:22+00:00","versionOfRecord":{"articleIdentity":"rs-5330305","link":"https://doi.org/10.1038/s41598-025-96129-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-10 16:05:23","publishedOnDateReadable":"April 10th, 2025"},"versionCreatedAt":"2024-11-11 12:30:11","video":"","vorDoi":"10.1038/s41598-025-96129-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-96129-w","workflowStages":[]},"version":"v1","identity":"rs-5330305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5330305","identity":"rs-5330305","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}