Response of Vegetation to Drought in the Source Region of the Yangtze and Yellow Rivers Based on Causal Analysis

Lu, Jie; Qin, Tianling; Yan, Denghua; Lv, Xizhi; Yuan, Zhe; Wen, Jie; Xu, Shu; Yang, Yuhui; Feng, Jianming; Li, Wei

doi:10.3390/rs16040630

Cited by 4 publications

(1 citation statement)

References 63 publications

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“…With the intensification of human activities and the massive emission of greenhouse gases, climate warming is occurring. The warming rate of the Qinghai Tibet Plateau, known as the "Asian Water Tower", is 0.36 • C/10a [1], which is about twice the global warming rate [2].…”

Section: Introductionmentioning

confidence: 99%

A Remote Sensing Water Information Extraction Method Based on Unsupervised Form Using Probability Function to Describe the Frequency Histogram of NDWI: A Case Study of Qinghai Lake in China

Liu,

Qiu,

2024

Water

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With escalating human activities and the substantial emissions of greenhouse gases, global warming intensifies. This phenomenon has led to increased occurrences of various extreme hydrological events, precipitating significant changes in lakes and rivers across the Qinghai Tibet Plateau. Therefore, accurate information extraction about and delineation of water bodies are crucial for lake monitoring. This paper proposes a methodology based on the Normalized Difference Water Index (NDWI) and Gumbel distribution to determine optimal segmentation thresholds. Focusing on Qinghai Lake, this study utilizes multispectral characteristics from the US Landsat satellite for analysis. Comparative assessments with seven alternative methods are conducted to evaluate accuracy. Employing the proposed approach, information about water bodies in Qinghai Lake is extracted over 38 years, from 1986 to 2023, revealing trends in area variation. Analysis indicates a rising trend in Qinghai Lake’s area following a turning point in 2004. To investigate this phenomenon, Pearson correlation analysis of temperature and precipitation over the past 38 years is used and unveils the fact that slight precipitation impacts on area and that there is a positive correlation between temperature and area. In conclusion, this study employs remote sensing data and statistical analysis to comprehensively investigate mechanisms driving changes in Qinghai Lake’s water surface area, providing insights into ecological shifts in lake systems against the backdrop of global warming, thereby offering valuable references for understanding and addressing these changes.

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Section: Introductionmentioning

confidence: 99%

A Remote Sensing Water Information Extraction Method Based on Unsupervised Form Using Probability Function to Describe the Frequency Histogram of NDWI: A Case Study of Qinghai Lake in China

Liu,

Qiu,

2024

Water

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Causes of Increased Compound Temperature and Precipitation Extreme Events in the Arid Region of Northwest China from 1961 to 2100

Niu,

Sun,

Huai

et al. 2024

Remote Sensing

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Compound extreme events pose more grave threats to human health, the natural environment, and socioeconomic systems than do individual extreme events. However, the drivers and spatiotemporal change characteristics of compound extreme events under climate transition remain poorly understood, especially in the arid region of Northwest China. This study examined the spatiotemporal change characteristics and driving mechanisms of extreme temperature and precipitation compound events in Northwest China based on data from 86 national meteorological stations and 11 climate models of the Coupled Model Intercomparison Project, Phase 6. The results indicated that (1) the frequency values of heat extremity–dry (1.60/10a) and heat extremity–heavy precipitation (0.60/10a) events increased from 1961 to 2020, and showed a faster uptrend after 1990 than before. (2) Under four shared socioeconomic pathway scenarios, there is also the likelihood of an upward trend in heat extremity–dry and heat extremity–heavy precipitation events in Northwest China by the end of 21 century, especially under SSP585, with probability values of 1.70/10a and 1.00/10a, respectively. (3) A soil moisture deficit leads to decreased evaporation and increased sensible heat by reduction in the soil–atmosphere exchange; the non-adiabatic heating process leads to a higher frequency of hot days. This land–air interaction feedback mechanism is a significant driver of heat extremity–dry events in Northwest China. (4) In the Northwest China region, the warmer trend surpasses the wetter trend, contributing to increased specific humidity, and the vapor pressure deficit may lead to an increasing frequency of extreme precipitation, consequently increasing heat extremity–heavy precipitation events. These results provide new insights for the understanding of compound extreme events, in order to cope with their risks.

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Monitoring Spatial-Temporal Variability of Vegetation Coverage and Its Influencing Factors in the Yellow River Source Region from 2000 to 2020

Wang,

Si,

Jia

et al. 2024

Remote Sensing

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As a vital conservation area for water sources in the Yellow River Basin, understanding the spatial-temporal dynamics of vegetation coverage is crucial, along with the factors that affect it, to ensure ecological preservation and sustainable development of the Yellow River Source Region (YRSR). In this paper, we utilized Landsat surface reflectance data from 2000 to 2020 using de-clouding and masking methods implementing the Google Earth Engine (GEE) cloud platform. We investigated spatial-temporal changes in vegetation coverage by combining the maximum value composite (MVC), the dimidiate pixel model (DPM), the Theil–Sen median slope, and the Mann–Kendall test. The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. The outcomes suggested the following: (1) On the temporal scale, vegetation coverage exhibited a general upward trend between 2000 and 2020, with the YRSR showing a yearly growth rate of 0.23% (p < 0.001). In comparison to 2000, the area designated as having extremely high vegetation coverage increased by 19.3% in 2020. (2) Spatially, the central and southeast regions have higher values of vegetation coverage, whereas the northwest has lower values. In the study area, 75.5% of the region demonstrated a significant improvement trend, primarily in Xinghai County, Zeku County, and Dari County in the south and the northern portion of the YRSR; conversely, a notable tendency of degradation was identified in 11.8% of the area, mostly in the southeastern areas of Qumalai County, Chenduo County, Shiqu County, and scattered areas in the southeastern region. (3) With an explanatory power of exceeding 45%, the three influencing factors that had the biggest effects on vegetation coverage were mean annual temperature, elevation, and mean annual precipitation. Mean annual precipitation has been shown to have a major impact on vegetation covering; the interconnections involving these factors have increased the explanatory power of vegetation coverage’s regional distribution. (4) Predictions for 2030 show that the vegetation coverage is trending upward in the YRSR, with a notable recovery trend in the northwestern region. This study supplies a theoretical foundation to formulate strategies to promote sustainable development and ecological environmental preservation in the YRSR.

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Response of Vegetation to Drought in the Source Region of the Yangtze and Yellow Rivers Based on Causal Analysis

Cited by 4 publications

References 63 publications

A Remote Sensing Water Information Extraction Method Based on Unsupervised Form Using Probability Function to Describe the Frequency Histogram of NDWI: A Case Study of Qinghai Lake in China

A Remote Sensing Water Information Extraction Method Based on Unsupervised Form Using Probability Function to Describe the Frequency Histogram of NDWI: A Case Study of Qinghai Lake in China

Causes of Increased Compound Temperature and Precipitation Extreme Events in the Arid Region of Northwest China from 1961 to 2100

Monitoring Spatial-Temporal Variability of Vegetation Coverage and Its Influencing Factors in the Yellow River Source Region from 2000 to 2020

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