The spatiotemporal variations of soil water content and soil temperature and the influences of precipitation and air temperature at the daily, monthly, and annual timescales in China

Chen, Xinguo; Li, Yi; Chau, Henry Wai; Zhao, Huichao; Li, Min; Lei, Tianjie; Zou, Yufeng

doi:10.1007/s00704-020-03092-9

Cited by 13 publications

(12 citation statements)

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“…The application of classical ML models to evaluate ET ref has recently attracted the attention of many researchers, and it has been confirmed that ML models provide better results than empirical equations [ 5 , 72 ]. The performance of recommended models in the temperature-based method is demonstrated in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Commonly, BARO is one of the most precise empirical equations for computing ET ref and is commonly used across Iran due to low requirements and its higher precision for weather data [ 14 ]. In most studies, the ML models also enhance the accuracy of computing ET ref using temperature-based models [ 72 , 73 ]. Nevertheless, this study indicates that temperature-based LSSVR1 and ANFPSO1 models do not improve the precision when assessing ET ref , because the R 2 values of these models had almost the same statistical results as BARO ( Table 3 ).…”

Section: Resultsmentioning

confidence: 99%

“…Recent studies have confirmed that the variation in ET ref can be clarified by temperature and solar radiation. Therefore, temperature-based empirical equations might be expected to provide better results for computing ET ref [ 14 , 21 , 72 ]. Similarly, Feng et al [ 75 ] also found that ML models had a higher accuracy for modeling ET ref than the empirical models in China.…”

Section: Resultsmentioning

confidence: 99%

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Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

Sharafi¹,

Ghaleni²,

Scholz³

2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

Sharafi¹,

Ghaleni²,

Scholz³

2023

Heliyon

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“…This result agreed well with Li et al [30]; their results showed that cotton stalk weight was positively correlated with soil moisture at depths of 0-10 to 40-50 cm at 11, 9, 10, 9 and 10 sites, while negative correlations were also detected between soil moisture at different depths and cotton yield indices. Previous studies have emphasized the importance of soil moisture on crop yields [42,43]. The plant height, growth period duration, 1000-kernel weight and kernel number per ear could be positively or negatively related to θ v at certain depth.…”

Section: The Best Multivariate Linear Regression Equationsmentioning

confidence: 99%

Quantitative Analysis of Winter Wheat Growth and Yields Responding to Climate Change in Xinjiang, China

Lin

et al. 2021

Water

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The knowledge of climate change effects on variations of winter wheat yields are crucial for productions. Our objectives were to investigate the relationship between yield-related indices of winter wheat and the related climatic variables (selected using variance inflation factors) at the 20 sites of Xinjiang, China over 1981–2017. The background of climate and yield changes was analyzed from temporal and spatial respects. The number of independent climatic variables was selected with the variance inflation factor method to remove the multicollinear feature. The Pearson correlation was conducted between the first difference values of climatic variables and yield-related indices of winter wheat (namely plant height, growth period duration, 1000-kernel weight, kernel number per ear, biomass and yield) to find the key climatic variables that impacted winter wheat growth and yields. The multi-variate linear and nonlinear functions were established step by step using the selected key climatic variables. The best function was determined for each site (significant for p < 0.05). From the results, there were general wetter and warmer trends of the climatic variables. Correspondingly, shortened winter wheat phenology and increased growth and yields were observed for most sites. Still, the climatic trends had mixed effects on winter wheat yields. The effects of precipitation, mean air temperature and relative humidity on plant height and growth period duration agreed well. Different sites had different major climatic drivers for winter wheat growth or yields, and the best functions of growth and yields could be linearly or nonlinearly, mostly described by multi-variate functions. The winter wheat growth or yield indices were also found to be closely connected with the soil water content status at the eight sites. The relationship between winter wheat growth or yield and climate provided useful references for forecasting crop production and for projecting the impact of future climate changes.

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“…Chen et al. (2020) found that the deep ST is closely correlated with air temperature. H. X. Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

The Persistence and Reemergence of Atmospheric Anomaly Signals in Soil Temperature

2022

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Land surface is one of the important components of the earth climate system, and soil temperature (ST) is a key factor of land surface processes. As an indicator of heat storage in land surface, ST plays an important role in land-atmosphere interactions. The anomalies in ST can change surface sensible flux, latent flux, longwave radiation, etc., and further affect weather and climate on multiple time scales (Q. Hu & Feng, 2004;Mahanama et al., 2008;Wu & Zhang, 2014). Moreover, ST can change the locations and intensities of precipitation by affecting the development of convection (Fan, 2009;Y. Liu & Avissar, 1999;Xue et al., 2018), and also facilitates the occurrence of the extreme heat events by local land-atmosphere coupling and changing atmospheric circulation (Gómez et al., 2016).The anomalies of ST usually depend on atmospheric anomalies, which are resulted from the anomalous sea surface temperature and sea ice, etc., and then ST anomalies can affect subsequent atmosphere by the persistence of the anomalies. For example, anomalous ST in winter can affect the precipitation in following spring (Tang et al., 1987), and ST in late spring correlates well with summer precipitation (Mahanama et al., 2008). Y. H. Wang et al. (2013) revealed that ST in May over Northwest China can affect summer precipitation by changing the East Asian summer monsoon. Ma (1995) found that there is a close relationship between antecedent ST anomalies and the subsequent floods in the Yangtze-Huaihe River Basin. Xue et al. (2018) demonstrated that spring land surface and subsurface temperature anomalies have important effects on the subsequent downstream droughts/floods in late spring-summer over North America and East Asia.The relationship between ST and subsequent atmosphere depends on the persistence of antecedent ST anomalies, which is also known as ST memory. ST anomalies can persist for a period ranging from 1 month to years, with the dependence of season, soil depth and climate regime, etc (Y. Liu & Avissar, 1999;K. Yang & Zhang, 2016). For example, in cold regions, ST anomalies in autumn can be stored in the frozen soil during the winter, and then reappear at the surface during the thaw (Matsumura & Yamazaki, 2012;Schaefer et al., 2007

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The spatiotemporal variations of soil water content and soil temperature and the influences of precipitation and air temperature at the daily, monthly, and annual timescales in China

Cited by 13 publications

References 75 publications

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

Quantitative Analysis of Winter Wheat Growth and Yields Responding to Climate Change in Xinjiang, China

The Persistence and Reemergence of Atmospheric Anomaly Signals in Soil Temperature

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