Differences and variations in the elevation-dependent climatic growing season of the northern and southern slopes of the Qinling Mountains of China from 1985 to 2015

Deng, Chenhui; Bai, Haiyan; Gao, Shan; Zhao, Tianyu; Ma, Xinping

doi:10.1007/s00704-018-2654-7

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…In Qinba Mountains, several studies showed the overall spatial distribution of the spring phenology has an advanced trend [15,16]. The spatial pattern was consistent with the local hydrothermal conditions and has strong topographic dependence in the area [17,18].…”

Section: Introductionmentioning

confidence: 74%

Change Analysis of Spring Vegetation Green-Up Date in Qinba Mountains under the Support of Spatiotemporal Data Cube

Feng

Yin

et al. 2020

Journal of Sensors

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In recent decades, global and local vegetation phenology has undergone significant changes due to the combination of climate change and human activities. Current researches have revealed the temporal and spatial distribution of vegetation phenology in large scale by using remote sensing data. However, researches on spatiotemporal differentiation of remote sensing phenology and its changes are limited which involves high-dimensional data processing and analysing. A new data model based on data cube technologies was proposed in the paper to efficiently organize remote sensing phenology and related reanalysis data in different scales. The multidimensional aggregation functions in the data cube promote the rapid discovery of the spatiotemporal differentiation of phenology. The exploratory analysis methods were extended to the data cube to mine the change characteristics of the long-term phenology and its influencing factors. Based on this method, the case study explored that the spring phenology of Qinba Mountains has a strong dependence on the topography, and the temperature plays a leading role in the vegetation green-up date distribution of the high-altitude areas while human activities dominate the low-altitude areas. The response of green-up trend slope seems to be the most sensitive at an altitude of about 2000 meters. This research provided a new approach for analysing phenology phenomena and its changes in Qinba Mountains that had the same reference value for other regional phenology studies.

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Section: Introductionmentioning

confidence: 74%

Change Analysis of Spring Vegetation Green-Up Date in Qinba Mountains under the Support of Spatiotemporal Data Cube

Feng

Yin

et al. 2020

Journal of Sensors

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“…The terrain clearly undulates, with an average annual precipitation of 700-1500 mm and an average annual temperature of 12-16 °C ( Zhang and Liang, 2020 ). As the area is located in the transitional zone between the warm temperate zone and northern subtropical zone, vegetation types are clearly differentiated along EG, and their response to climate change is more sensitive ( Deng et al., 2019 ; Qi et al., 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Important role of precipitation in controlling a more uniform spring phenology in the Qinba Mountains, China

Guan²,

Han³

et al. 2023

Front. Plant Sci.

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Under global warming, the gradual pattern of spring phenology along elevation gradients (EG) has significantly changed. However, current knowledge on the phenomenon of a more uniform spring phenology is mainly focused on the effect of temperature and neglected precipitation. This study aimed to determine whether a more uniform spring phenology occurs along EG in the Qinba Mountains (QB) and explore the effect of precipitation on this pattern. We used Savitzky-Golay (S-G) filtering to extract the start of season (SOS) of the forest from the MODIS Enhanced Vegetation Index (EVI) during 2001-2018 and determined the main drivers of the SOS patterns along EG by partial correlation analyses. The SOS showed a more uniform trend along EG in the QB with a rate of 0.26 ± 0.01 days 100 m-1 per decade during 2001-2018, but there were differences around 2011. A delayed SOS at low elevations was possibly due to the reduced spring precipitation (SP) and spring temperature (ST) between 2001 and 2011. Additionally, an advanced SOS at high elevations may have been caused by the increased SP and reduced winter temperature (WT). These divergent trends contributed to a significant uniform trend of SOS with a rate of 0.85 ± 0.02 days 100 m-1 per decade. Since 2011, significantly higher SP (especially at low elevations) and rising ST advanced the SOS, and the SOS at lower altitudes was more advanced than at higher altitudes, resulting in greater SOS differences along EG (0.54 ± 0.02 days 100 m-1 per decade). The SP determined the direction of the uniform trend in SOS by controlling the SOS patterns at low elevations. A more uniform SOS may have important effects on local ecosystem stability. Our findings could provide a theoretical basis for establishing ecological restoration measures in areas experiencing similar trends.

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“…The Qinling Mountains is also the geographical boundary between the north and south of China and a sensitive area for climate change, which is generally consistent with the 0 °C isotherm in January, the 800-mm annual equivalent precipitation line, and the 2,000-h sunshine hour line (Bai et al, 2012). The Qinling Mountains is the dividing line between five key elements: geography, climate, biology, water system, and soil (Deng et al, 2018a;Deng et al, 2019;Hu et al, 2020;Wang et al, 2020).…”

Section: Study Areamentioning

confidence: 99%

“…This paper uses the NDVI index of the growing season to measure the vegetation status. According to the climate and vegetation growth in the Qinling Mountains, the vegetationgrowing season is from March to October (Deng et al, 2019). NDVI data were obtained from the MOD13Q1 dataset of NASA (https://ladsweb.modaps.eosdis.nasa.gov/search/order/1/ MOD13Q1-6), with a spatial resolution of 250 m × 250 m, and a time resolution of 16 days.…”

Section: Data Sourcesmentioning

confidence: 99%

Quantitative Assessment of the Contributions of Climate Change and Human Activities to Vegetation Variation in the Qinling Mountains

Cheng

Song

et al. 2021

Front. Earth Sci.

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Quantitative assessment of the contributions of climate change and human activities to vegetation change is important for ecosystem planning and management. To reveal spatial differences in the driving mechanisms of vegetation change in the Qinling Mountains, the changing patterns of the normalized difference vegetation index (NDVI) in the Qinling Mountains during 2000–2019 were investigated through trend analysis and multiple regression residuals analysis. The relative contributions of climate change and human activities on vegetation NDVI change were also quantified. The NDVI shows a significant increasing trend (0.23/10a) from 2000 to 2019 in the Qinling Mountains. The percentage of areas with increasing and decreasing trends in NDVI is 87.96% and 12.04% of the study area, respectively. The vegetation change in the Qinling Mountains is caused by a combination of climate change and human activities. The Tongguan Shiquan line is a clear dividing line in the spatial distribution of drivers of vegetation change. Regarding the vegetation improvement, the contribution of climate change and human activities to NDVI increase is 51.75% and 48.25%, respectively. In the degraded vegetation area, the contributions of climate change and human activities to the decrease in NDVI were 22.11% and 77.89%, respectively. Thus, vegetation degradation is mainly caused by human activities. The implementation of policies, such as returning farmland to forest and grass, has an important role in vegetation protection. It is suggested that further attention should be paid to the role of human activities in vegetation degradation when formulating corresponding vegetation protection measures and policies.

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Differences and variations in the elevation-dependent climatic growing season of the northern and southern slopes of the Qinling Mountains of China from 1985 to 2015

Cited by 9 publications

References 20 publications

Change Analysis of Spring Vegetation Green-Up Date in Qinba Mountains under the Support of Spatiotemporal Data Cube

Change Analysis of Spring Vegetation Green-Up Date in Qinba Mountains under the Support of Spatiotemporal Data Cube

Important role of precipitation in controlling a more uniform spring phenology in the Qinba Mountains, China

Quantitative Assessment of the Contributions of Climate Change and Human Activities to Vegetation Variation in the Qinling Mountains

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