Marshland Loss Warms Local Land Surface Temperature in China

Shen, Xiangjin; Liu, Binhui; Jiang, Ming; Lü, Xianguo

doi:10.1029/2020gl087648

Cited by 128 publications

(73 citation statements)

References 46 publications

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“…At present, studies on vegetation response to climate change mostly focus on forest and grassland vegetation, and the research on the effect of climate change on marsh vegetation is relatively less (T. Chen et al., 2020; Erwin et al., 2009; Ji et al., 2020; Neubauer et al., 2013; Ross et al., 2013; D. Wei et al., 2017). The environmental conditions are different between marsh ecosystem and other ecosystems (Czapla et al., 2020; X. D. Wei et al., 2018), which may cause obviously different responses of vegetation to climate change (X. Shen et al., 2020a; Y. J. Wang et al., 2020). For a better understanding the correlation between vegetation and climate, it is of importance to explore the response of marsh vegetation to climate change.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Change of Marsh Vegetation and Its Response to Climate Change in China From 2000 to 2019

et al. 2021

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China has the third largest area of marshes in the world. Understanding the change of marsh, vegetation and its response to climate change in China is important for the protection of wetland ecosystem. Based on the climate and normalized difference vegetation index (NDVI) data, we investigated the spatiotemporal variation in vegetation and its response to climate change for different types of marshes in China. The results indicated that growing season NDVI increased significantly (0.02/decade, P < 0.01) over the whole marshes of China from 2000 to 2019. The increased precipitation and minimum temperature during the growing season could promote the growth of marsh vegetation over China. This study found for the first time that the increase of temperature during the day and at night had asymmetric effects on vegetation growth in marshes of China, and the positive effect of night warming was more significant. Spatially, increasing growing season precipitation was beneficial to the vegetation growth of herbaceous marshes, marsh meadows, inland salt marshes, and seasonal saltwater marshes in eastern Inner Mongolia. Increased growing season minimum temperature could greatly promote the vegetation growth of marsh meadows, herbaceous marshes, inland salt marshes, forest swamp, and bush swamp in cold and high‐altitude regions of Northern Northeast China and the Tibet Plateau. In the context of global climate change, more attention should be paid to the different responses of vegetation to climate change for different marsh types, especially when we use the model to simulate the impact of climate change on vegetation in different marsh regions of China.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Change of Marsh Vegetation and Its Response to Climate Change in China From 2000 to 2019

et al. 2021

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“…Therefore, DTR is considered as an important indicator and is receiving much more attention in regional and global scale in recent times [7][8][9][10]. Previous studies have reported that DTR changes were not uniform globally, which was attributed to complicated interactions of local climatic and anthropogenic factors [11,12]. In most parts of world, it is reported that DTR decreased with individual rate, such as in Bangladesh [13], Australia [14], and United States [15].…”

Section: Introductionmentioning

confidence: 99%

Elevation-Dependent Trend in Diurnal Temperature Range in the Northeast China during 1961–2015

2021

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The diurnal temperature range (DTR) is considered a signature of observed climate change, which is defined as the difference between the maximum (Tmax) and minimum temperatures (Tmin). It is well known that the warming rate of mean temperature is larger at high elevations than at low elevations in northeast China. However, it is still uncertain whether DTR trend is greater at high elevations. This study examined the spatiotemporal variation in DTR and its relationship with elevation in northeast China based on data from 68 meteorological stations from 1961 to 2015. The results show that there was a significant declining trend (0.252 °C/decade) in DTR from 1961 to 2015 due to the fact that Tmin increased at a faster rate than Tmax. Seasonally, DTR in northeast China showed a decreasing trend with the largest decrease rate in spring (−0.3167 °C/decade) and the smallest decrease rate in summer (−0.1725 °C/decade). The results of correlation analysis show that there was a significant positive correlation between the annual DTR trend and elevation in northeast China. This is due to the fact that increasing elevation has a significant warming effect on Tmax. Seasonally, there were significant positive correlations between the DTR trend and elevation in all seasons. The elevation gradient of DTR trend was the greatest in winter (0.392 °C/decade/km) and the lowest in autumn (0.209 °C/decade/km). In spring, summer, and autumn, increasing elevation has a significant warming effect on Tmax, leading to a significant increase of the DTR trend with increasing elevation. However, in winter, increasing elevation has a significant cooling effect on Tmin, resulting in a significant increase of the DTR trend with increasing elevation.

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“…The ecosystem formed by "crop-soil-fertilizer" seems to continue indefinitely, but in each cycle, there is more or less natural loss, which needs to be replenished and controlled by human factors to continue the cycle [1][2][3][4][5][6]. Since its introduction in the 1980s, precision fertilization has been a significant constraint on balancing the contradiction between land resources, ecology, and population growth and a key technology for maintaining sustainable development [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

et al. 2021

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Precision fertilization is a major constraint in consistently balancing the contradiction between land resources, ecological environment, and population increase. Even more, it is a popular technology used to maintain sustainable development. Nitrogen (N), phosphorus (P), and potassium (K) are the main sources of nutrient income on farmland. The traditional fertilizer effect function cannot meet the conditional agrochemical theory’s conditional extremes because the soil is influenced by various factors and statistical errors in harvest and yield. In order to find more accurate scientific ratios, it has been proposed a multi-strategy-based grey wolf optimization algorithm (SLEGWO) to solve the fertilizer effect function in this paper, using the “3414” experimental field design scheme, taking the experimental field in Nongan County, Jilin Province as the experimental site to obtain experimental data, and using the residuals of the ternary fertilizer effect function of Nitrogen, phosphorus, and potassium as the target function. The experimental results showed that the SLEGWO algorithm could improve the fitting degree of the fertilizer effect equation and then reasonably predict the accurate fertilizer application ratio and improve the yield. It is a more accurate precision fertilization modeling method. It provides a new means to solve the problem of precision fertilizer and soil testing and fertilization.

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Marshland Loss Warms Local Land Surface Temperature in China

Cited by 128 publications

References 46 publications

Spatiotemporal Change of Marsh Vegetation and Its Response to Climate Change in China From 2000 to 2019

Spatiotemporal Change of Marsh Vegetation and Its Response to Climate Change in China From 2000 to 2019

Elevation-Dependent Trend in Diurnal Temperature Range in the Northeast China during 1961–2015

Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

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