Soil moisture temporal stability and spatio‐temporal variability about a typical subalpine ecosystem in northwestern China

Zhu, Xi; He, Zhibin; Du, Jun; Chen, Longfei; Lin, Pengfei; Tian, Qian

doi:10.1002/hyp.13737

Cited by 8 publications

(16 citation statements)

References 77 publications

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“…Due to evaporation and uptake of water by plant roots, there was a low SWC average across time in the surface soil layer (10 cm) (Figure 4). This result was consistent with findings in other study areas (Zhu et al, 2020). With increasing soil depth, the average SWC first increased and then decreased in the 20–50 cm and 50–220 cm soil depth profiles, respectively, suggesting that soil to a depth of 50 cm has a buffering effect on SWC (Zhu, Cao, & Shao, 2019).…”

Section: Resultssupporting

confidence: 94%

“…The result in Figure 6 was not completely consistent with that shown in Figure 5, indicating that the calculation of soil thickness has a certain influence on the temporal stability layer of SWC. This may be also confirmed by the finding of Zhu et al (2020), who noted that correlations were stronger between two adjacent layers than between two non‐adjacent layers. Therefore, it is suggested that reasonable soil layering should be determined according to the required precision of the study when analysing temporal stability of SWC.…”

Section: Resultssupporting

confidence: 81%

“…In addition, the number of sites showing temporal stability in SWC did not increase with increasing soil depth. This result is partially consistent with that of Huang et al (2018) (0–300 cm) but inconsistent with that of Zhu et al (2020) (0–70 cm).…”

Section: Resultssupporting

confidence: 63%

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Spatio‐temporal variability of multi‐layer soil water at a hillslope scale in the critical zone of the Chinese Loess Plateau

Wang

et al. 2020

Hydrological Processes

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Understanding the spatio-temporal variability of soil water content (SWC) in the Critical Zone (CZ) is essential to balance plant-water benefits and to enhance the ability to monitor and predict drought. In current study, the spatial variability and temporal stability of multi-layer SWC to a depth of 500 cm (35 soil layers) at 27 sampling sites were evaluated at a hillslope scale. A total of 64 SWC measurements were obtained from 28 March, 2017 to 23 December, 2018. We hypothesized the existence of a "special point" that is temporally stable both in horizontal direction (which means different layers) and in vertical direction (which means different profiles). We found a clear seasonal dynamic pattern of mean SWC for all sites in the 0-500 cm profile, with SWC varying from 4.0 to 24.8%. In the horizontal layer, both the 130-cm and 140-cm layers contained a maximum number of temporal stability sites (N = 4) which all located at H 11 , H 13 , H 14 and H 26. In the vertical profile, H 19 was the most stable site, followed by H 10 and H 26 , with 15, 10 and 6 stable layers, respectively. The decreasing order of SWC temporal stability was 200-500 cm, 0-500 cm and 0-200 cm, which means the temporal stability of SWC in 0-500 cm profile was more dependent on the 0-200 cm sub-profile. At a three-dimension scale, the most temporal stability layer and site was 180-cm of H 1 and H 21 of 220-cm, which confirmed our hypothesis. This study provides an accurate description of SWC spatio-temporal variation at the hillslope scale, which is helpful for addressing eco-hydrological models and water management issues in the Earth's CZs.

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Section: Resultssupporting

confidence: 94%

Section: Resultssupporting

confidence: 81%

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Spatio‐temporal variability of multi‐layer soil water at a hillslope scale in the critical zone of the Chinese Loess Plateau

Wang

et al. 2020

Hydrological Processes

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“…Furthermore, deep learning techniques have produced promising results throughout the agricultural field, helping more farmers and food-producing workers, such as detection of plant disease [15], analysis of weeds [16], discovery of valuable seeds [17], insect detection [18], fruit processing [15], and so on, which has led to dealing with image analysis. Furthermore, a few implementations aimed to forecast future parameters such as crop production [19], climate conditions [20], and field soil water content [21]. We propose an integrated deep learning model, for automated citrus fruit disease detection, based on the tremendous results of CNN-based methods in image classification.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

et al. 2021

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Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. The experimental results indicate that the CNN Model outperforms the competitors on a number of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases.

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“…Although some studies have provided detailed assessments of multiple reanalysis SAT datasets on the QTP, most of these studies were conducted before the 21st century (Frauenfeld et al ., 2005; Ma et al ., 2009; You et al ., 2010). In addition, thawing and freezing indices can provide reference information for construction engineering on the QTP and can be utilized to assess their vulnerability to climate change (Steurer and Crandell, 1995; Shi et al ., 2019; Qin et al ., 2020; Zhu et al ., 2020). Until now, the majority of annual air thawing and freezing indices were calculated based on the mean daily SAT from several scattered meteorological observations; however, with this method, it was not possible to determine continuous spatial distributions of air thawing and freezing indices over the entire region (Luo et al ., 2014; Wu et al ., 2018; Wang et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal freeze–thaw variations over the Qinghai‐Tibet Plateau 1981–2017 from reanalysis

Yanhui

Zhang

et al. 2020

Intl Journal of Climatology

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The thawing and freezing conditions on the Qinghai‐Tibet Plateau (QTP) are considered effective indicators that are widely used in ecology, climate change, cold‐region engineering design, and permafrost mapping. The purpose of this study is to utilize reanalysis to detect spatial variations in freeze–thaw conditions across the QTP from 1981–2017. From a comparison of five recent reanalysis models (MERRA2: National Aeronautics and Space Administration Modern‐Era Reanalysis for Research and Applications, Version 2; ERA: European Center for Medium‐Range Weather Forecasts Interim Reanalysis; GLDAS: Global Land Data Assimilation System NOAH; CFS: National Centers for Environmental Prediction Climate Forecast System Reanalysis, Version 2; and CMFD: China Meteorological Forcing Dataset) against existing sparse observations of 2‐m surface air temperature (SAT), we find that NASA MERRA2 dataset is the most applicable to the QTP. Then, the MERRA2 SAT dataset was selected for this study and was corrected and validated by utilizing calibration models built from the observed data. The results revealed that the correlation coefficients between the corrected MERRA2 SAT and meteorological station observed data increased from 0.52 to 0.93, 0.49 to 0.90, 0.56 to 0.93, and 0.69 to 0.93 in spring, summer, autumn, and winter, respectively. The corrected parameters performed better in the southern and southeastern portions of the QTP. Finally, we utilize the corrected MERRA2 dataset to develop freeze–thaw indices and evaluate statistical trends. We find that relatively high air freezing indices are one of the important factors for the presence of permafrost in the central and northeastern portions of the QTP. Trends in the air thawing and freezing indices in the permafrost and seasonally frozen ground regions indicate that, from 1981 to 2017 in the permafrost regions, the warming was more significant in the summer than in the other seasons. However, the winter warming rates in permafrost and seasonally frozen ground regions are almost the same.

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Soil moisture temporal stability and spatio‐temporal variability about a typical subalpine ecosystem in northwestern China

Cited by 8 publications

References 77 publications

Spatio‐temporal variability of multi‐layer soil water at a hillslope scale in the critical zone of the Chinese Loess Plateau

Spatio‐temporal variability of multi‐layer soil water at a hillslope scale in the critical zone of the Chinese Loess Plateau

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

Spatiotemporal freeze–thaw variations over the Qinghai‐Tibet Plateau 1981–2017 from reanalysis

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