Spatio-temporal evolution of Allium L. in the Qinghai–Tibet-Plateau region: Immigration and in situ radiation

Permafrost degradation may induce soil carbon (C) loss, critical for global C cycling, and be mediated by microbes. Despite larger C stored within the active layer of permafrost regions, which are more affected by warming, and the critical roles of Qinghai-Tibet Plateau in C cycling, most previous studies focused on the permafrost layer and in high-latitude areas. We demonstrate in situ that permafrost degradation alters the diversity and potentially decreases the stability of active layer microbial communities. These changes are associated with soil C loss and potentially a positive C feedback. This study provides insights into microbial-mediated mechanisms responsible for C loss within the active layer in degraded permafrost, aiding in the modeling of C emission under future scenarios.

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Brief Communication: Evaluation and comparisons of permafrost map over Qinghai-Tibet Plateau based on inventory of in-situ evidence

10.5194/tc-2018-190 ◽

2018 ◽

Author(s):

Bin Cao ◽

Tingjun Zhang ◽

Qinghai Wu ◽

Yu Sheng ◽

Lin Zhao ◽

...

Keyword(s):

Controlling Factors ◽

In Situ Measurements ◽

Tibet Plateau ◽

Permafrost Region ◽

Improved Method ◽

Limited Evidence ◽

Wide Range ◽

Communication Evaluation ◽

Qinghai Tibet Plateau

Abstract. Many maps have been produced to estimate permafrost distribution over the Qinghai-Tibet Plateau, however, the evaluation and comparisons of them are poorly understood due to limited evidence. Using a large number data from various sources, we present the inventory of permafrost presence/absence with 1475 sites/plots over the QTP. Based on the in-situ measurements, our evaluation results showed a wide range of map performance with the overall accuracy of about 59–82 %, and the estimated permafrost region (1.42–1.84 × 106 km2) and area (0.76–1.25 × 106 km2) are extremely large. The low agreement in areas near permafrost boundary and fragile landscapes require improved method considering more controlling factors at both medium-large and local scales.

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SHORT REPORT: IDENTIFICATION OF ECHINOCOCCUS SPECIES FROM A YAK IN THE QINGHAI-TIBET PLATEAU REGION OF CHINA

American Journal of Tropical Medicine and Hygiene ◽

10.4269/ajtmh.2003.69.445 ◽

2003 ◽

Vol 69 (4) ◽

pp. 445-446 ◽

Cited By ~ 24

Author(s):

NING XIAO ◽

PHILIP S. CRAIG ◽

MINORU NAKAO ◽

JIAMIN QIU ◽

KAZUHIRO NAKAYA ◽

...

Keyword(s):

Tibet Plateau ◽

Short Report ◽

Plateau Region ◽

Qinghai Tibet Plateau

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WATER VAPOR CONVERSION FACTOR OVER QINGHAI-TIBET PLATEAU REGION CONSIDERING ELEVATION, LATITUDE AND FINE SEASONAL VARIATION IS CONSTRUCTED

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-271-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 271-277

Author(s):

H. Peng ◽

L. K. Huang ◽

C. Li ◽

L. L. Liu ◽

S. Wang ◽

...

Keyword(s):

Water Vapor ◽

High Precision ◽

Conversion Factor ◽

Conversion Coefficient ◽

Reference Value ◽

Radiosonde Data ◽

Tibet Plateau ◽

Grid Data ◽

Plateau Region ◽

Qinghai Tibet Plateau

Abstract. In this paper, the conversion factor K model of Qinghai-Tibet plateau region was established based on the QTm model which is established using high-precision the Global Geodetic Observing System (GGOS) Atmosphere grid data from 2007 to 2014. The model took into account the influence of elevation fluctuation and latitude change on the model, and analyzed the relevant characteristics with seasonal changes. The 2015 GGOS grid data and radiosonde data were used as the reference value for accuracy assess. The established QTm model was compared with GPT2w model in bias and RMS. Compared with GGOS grid data, the average annual bias and RMS of QTm model were -0.28K and 2.70k respectively. The RMS of GPT2w-5 and GPT2w-1 were 58.16% and 28.84% higher, respectively. Compared with radiosonde data, QTm model has 1.13k average annual bias and the RMS error of 2.92k. Compared with GPT2w-5 and GPT2w-1, the RMS value of QTm model was improved by 25.08% and 29.43%, respectively. The value of atmospheric water vapor conversion coefficient was calculated by the integral method calculated by radio sounding data in the Qinghai-Tibet region in 2015 was used as the reference value for assess the performance of conversion factor K, and compared and analyzed the conversion coefficient K which provided by QTm and GPT2w. The results show that the value of Tm provided by QTm model has the highest accuracy, which is 25.07% higher than that of GPT2w-5 and 29.42% higher than that of GPT2w-1. QTm models can achieve GPS-PWV retrieval precision of better than 2 mm. Which has potential application for high-precision real-time GNSS-PWV retrieving in Qinghai-Tibet region.

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Transition Characteristics of the Dry-Wet Regime and Vegetation Dynamic Responses over the Yarlung Zangbo River Basin, Southeast Qinghai-Tibet Plateau

Remote Sensing ◽

10.3390/rs11101254 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1254 ◽

Cited By ~ 6

Author(s):

Liu Liu ◽

Qiankun Niu ◽

Jingxia Heng ◽

Hao Li ◽

Zongxue Xu

Keyword(s):

Climate Change ◽

Water Content ◽

Dynamic Responses ◽

Tibet Plateau ◽

Vegetation Dynamic ◽

Reversal Phenomenon ◽

Yarlung Zangbo River ◽

Spatio Temporal ◽

Qinghai Tibet Plateau ◽

Basin Area

The dry-wet transition is of great importance for vegetation dynamics, however the response mechanism of vegetation variations is still unclear due to the complicated effects of climate change. As a critical ecologically fragile area located in the southeast Qinghai-Tibet Plateau, the Yarlung Zangbo River (YZR) basin, which was selected as the typical area in this study, is significantly sensitive and vulnerable to climate change. The standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) based on the GLDAS-NOAH products and the GIMMS-NDVI remote sensing data from 1982 to 2015 were employed to investigate the spatio-temporal characteristics of the dry-wet regime and the vegetation dynamic responses. The results showed that: (1) The spatio-temporal patterns of the precipitation and temperature simulated by the GLDAS-NOAH fitted well with those of the in-situ data. (2) During the period of 1982–2015, the whole YZR basin exhibited an overall wetting tendency. However, the spatio-temporal characteristics of the dry-wet regime exhibited a reversal phenomenon before and after 2000, which was jointly identified by the SPEI and runoff. That is, the YZR basin showed a wetting trend before 2000 and a drying trend after 2000; the arid areas in the basin showed a tendency of wetting whereas the humid areas exhibited a trend of drying. (3) The region where NDVI was positively correlated with SPEI accounted for approximately 70% of the basin area, demonstrating a similar spatio-temporal reversal phenomenon of the vegetation around 2000, indicating that the dry-wet condition is of great importance for the evolution of vegetation. (4) The SPEI showed a much more significant positive correlation with the soil water content which accounted for more than 95% of the basin area, implying that the soil water content was an important indicator to identify the dry-wet transition in the YZR basin.

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Retrieval snow depth by artificial neural network methodology from integrated AMSR-E and in-situ data—A case study in Qinghai-Tibet Plateau

Chinese Geographical Science ◽

10.1007/s11769-008-0356-2 ◽

2008 ◽

Vol 18 (4) ◽

pp. 356-360 ◽

Cited By ~ 8

Author(s):

Yungang Cao ◽

Xiuchun Yang ◽

Xiaohua Zhu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Snow Depth ◽

Tibet Plateau ◽

In Situ Data ◽

Artificial Neural ◽

Qinghai Tibet Plateau

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Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas

Remote Sensing ◽

10.3390/rs11070792 ◽

2019 ◽

Vol 11 (7) ◽

pp. 792 ◽

Cited By ~ 6

Author(s):

Jin Liu ◽

Linna Chai ◽

Zheng Lu ◽

Shaomin Liu ◽

Yuquan Qu ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Regional Scale ◽

Temporal Variations ◽

Comparison Method ◽

Tibet Plateau ◽

Quantitative Evidence ◽

Retrieval Algorithms ◽

Qinghai Tibet Plateau

High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.

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