Impact of Meteorological Factors on Thermokarst Lake Changes in the Beilu River Basin, Qinghai-Tibet Plateau, China (2000–2016)

Ni, Zixuan; Lü, Xiang‐fei; Huang, Guanwen

doi:10.3390/w13111605

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…The research area is situated in the permafrost zone of the Beiluhe Basin on the central Tibetan Plateau. According to meteorological data, the Beiluhe Basin has an arid environment with an average yearly temperature in the range of −5.4°C to −3.6°C and mean annual precipitation of 369.8 mm, which is much less than the region’s mean annual evaporation of 1317 mm ( Peddle and Franklin, 1993 ; Ni et al, 2021 ). Stipa purpurea and Carex rigescens dominate the alpine steppe site ( Zhang et al, 2013 ).…”

Section: Methodsmentioning

confidence: 99%

Soil texture and microorganisms dominantly determine the subsoil carbonate content in the permafrost-affected area of the Tibetan Plateau

et al. 2023

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Under climate warming conditions, storage and conversion of soil inorganic carbon (SIC) play an important role in regulating soil carbon (C) dynamics and atmospheric CO2 content in arid and semi-arid areas. Carbonate formation in alkaline soil can fix a large amount of C in the form of inorganic C, resulting in soil C sink and potentially slowing global warming trends. Therefore, understanding the driving factors affecting carbonate mineral formation can help better predict future climate change. Till date, most studies have focused on abiotic drivers (climate and soil), whereas a few examined the effects of biotic drivers on carbonate formation and SIC stock. In this study, SIC, calcite content, and soil microbial communities were analyzed in three soil layers (0–5 cm, 20–30 cm, and 50–60 cm) on the Beiluhe Basin of Tibetan Plateau. Results revealed that in arid and semi-arid areas, SIC and soil calcite content did not exhibit significant differences among the three soil layers; however, the main factors affecting the calcite content in different soil layers are different. In the topsoil (0–5 cm), the most important predictor of calcite content was soil water content. In the subsoil layers 20–30 cm and 50–60 cm, the ratio of bacterial biomass to fungal biomass (B/F) and soil silt content, respectively, had larger contributions to the variation of calcite content than the other factors. Plagioclase provided a site for microbial colonization, whereas Ca2+ contributed in bacteria-mediated calcite formation. This study aims to highlight the importance of soil microorganisms in managing soil calcite content and reveals preliminary results on bacteria-mediated conversion of organic to inorganic C.

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Section: Methodsmentioning

confidence: 99%

Soil texture and microorganisms dominantly determine the subsoil carbonate content in the permafrost-affected area of the Tibetan Plateau

et al. 2023

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“…The study area is located in the permafrost region of the Beilu River Basin on the central Tibetan plateau. The mean annual temperature measured at a meteorological station varies from −5.4°C to −3.6°C ( Ni et al, 2021 ). The area has an arid climate with a mean annual precipitation of 369.8 mm, according to meteorological records, this is much smaller than the mean annual evaporation of 1,317 mm ( Peddle and Franklin, 1993 ).…”

Section: Methodsmentioning

confidence: 99%

Soil texture influences soil bacterial biomass in the permafrost-affected alpine desert of the Tibetan plateau

et al. 2022

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Under warm climate conditions, permafrost thawing results in the substantial release of carbon (C) into the atmosphere and potentially triggers strong positive feedback to global warming. Soil microorganisms play an important role in decomposing organic C in permafrost, thus potentially regulating the ecosystem C balance in permafrost-affected regions. Soil microbial community and biomass are mainly affected by soil organic carbon (SOC) content and soil texture. Most studies have focused on acidic permafrost soil (pH < 7), whereas few examined alkaline permafrost-affected soil (pH > 7). In this study, we analyzed soil microbial communities and biomass in the alpine desert and steppe on the Tibetan plateau, where the soil pH values were approximately 8.7 ± 0.2 and 8.5 ± 0.1, respectively. Our results revealed that microbial biomass was significantly associated with mean grain size (MGS) and SOC content in alkaline permafrost-affected soils (p < 0.05). In particular, bacterial and fungal biomasses were affected by SOC content in the alpine steppe, whereas bacterial and fungal biomasses were mainly affected by MGS and SOC content, respectively, in the alpine desert. Combined with the results of the structural equation model, those findings suggest that SOC content affects soil texture under high pH-value (pH 8–9) and that soil microbial biomass is indirectly affected. Soils in the alpine steppe and desert are dominated by plagioclase, which provides colonization sites for bacterial communities. This study aimed to highlight the importance of soil texture in managing soil microbial biomass and demonstrate the differential impacts of soil texture on fungal and bacterial communities in alkaline permafrost-affected regions.

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“…Reasonable selection of the region of interest (ROI) [29], which directly related to the quality of the extracted data. ROI is extracted by ENVI 5.3 [30], as shown in Figure 1c, and the spectral average of all pixels in this area calculate as the final spectral value of the sample, and the extracted spectral data corresponds to the physicochemical test data of mango, reducing the coarse error of the sample and effectively reflecting the sample information. Impacted by the fluorescent filter, the final collected spectral range is 475 nm to 1100 nm, with a total of 104 spectra channels, with a total of 125 bands (variables).…”

Section: Fluorescence Hyperspectral Data Extractionmentioning

confidence: 99%

Nondestructive Testing Model of Mango Dry Matter Based on Fluorescence Hyperspectral Imaging Technology

et al. 2022

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The dry matter test of mango has important practical significance for the quality classification of mango. Most of the common fruit and vegetable quality nondestructive testing methods based on fluorescence hyperspectral imaging technology use a single algorithm in algorithms such as Uninformative Variable Elimination (UVE), Random Frog (RF), Competitive Adaptive Reweighted Sampling (CARS) and Continuous Projection Algorithm (SPA) to extract feature spectral variables, and the use of these algorithms alone can easily lead to the insufficient stability of prediction results. In this regard, a nondestructive detection method for the dry matter of mango based on hyperspectral fluorescence imaging technology was carried out. Taking the ‘Keitt’ mango as the research object, the mango samples were numbered in sequence, and their fluorescence hyperspectral images in the wavelength range of 350–1100 nm were collected, and the average spectrum of the region of interest was used as the effective spectral information of the sample. Select SPXY algorithm to divide samples into a calibration set and prediction set, and select Orthogonal Signal Correction (OSC) as preprocessing method. For the preprocessed spectra, the primary dimensionality reduction (UVE, SPA, RF, CARS), the primary combined dimensionality reduction (UVE + RF, CARS + RF, CARS + SPA), and the secondary combined dimensionality reduction algorithm ((CARS + SPA)-SPA, (UVE + RF)-SPA) and other 12 algorithms were used to extract feature variables. Separately constructed predictive models for predicting the dry matter of mangoes, namely, Support Vector Regression (SVR), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN) model, were used; The results show that (CARS + RF)-SPA-BPNN has the best prediction performance for mango dry matter, its correlation coefficients were RC2 = 0.9710, RP2 = 0.9658, RMSEC = 0.1418, RMSEP = 0.1526, this method provides a reliable theoretical basis and technical support for the non-destructive detection, and precise and intelligent development of mango dry matter detection.

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Impact of Meteorological Factors on Thermokarst Lake Changes in the Beilu River Basin, Qinghai-Tibet Plateau, China (2000–2016)

Cited by 6 publications

References 38 publications

Soil texture and microorganisms dominantly determine the subsoil carbonate content in the permafrost-affected area of the Tibetan Plateau

Soil texture and microorganisms dominantly determine the subsoil carbonate content in the permafrost-affected area of the Tibetan Plateau

Soil texture influences soil bacterial biomass in the permafrost-affected alpine desert of the Tibetan plateau

Nondestructive Testing Model of Mango Dry Matter Based on Fluorescence Hyperspectral Imaging Technology

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