The timing of the end of the vegetation growing season (EOS) plays a key role in terrestrial ecosystem carbon and nutrient cycles. Autumn phenology is, however, still poorly understood, and previous studies generally focused on few species or were very limited in scale. In this study, we applied four methods to extract EOS dates from NDVI records between 1982 and 2011 for the Northern Hemisphere, and determined the temporal correlations between EOS and environmental factors (i.e., temperature, precipitation and insolation), as well as the correlation between spring and autumn phenology, using partial correlation analyses. Overall, we observed a trend toward later EOS in ~70% of the pixels in Northern Hemisphere, with a mean rate of 0.18 ± 0.38 days yr-1 . Warming preseason temperature was positively associated with the rate of EOS in most of our study area, except for arid/semi-arid regions, where the precipitation sum played a dominant positive role. Interestingly, increased preseason insolation sum might also lead to a later date of EOS. In addition to the climatic effects on EOS, we found an influence of spring vegetation green-up dates on EOS, albeit biome dependent. Our study, therefore, suggests that both environmental factors and spring phenology should be included in the modeling of EOS to improve the predictions of autumn phenology as well as our understanding of the global carbon and nutrient balances.

Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology

During the last decades, the effects of global warming have become apparent also in Europe, causing relevant impacts in many sectors. Under projected future global warming, such a tendency can be expected to persist until the end of this century and beyond. Identifying which climate‐related impacts are likely to increase, and by how much, is an important element of any effective strategy for managing future climate risks. This study investigates whether energy demand for cooling and heating buildings can be expected to increase or decrease under climate change. Two indicators of weather‐related energy consumption for heating and cooling buildings are considered: heating degree‐days (HDD) and cooling degree‐days (CDD). The evolution of these indicators has been analysed based on 11 high‐resolution bias‐adjusted EURO‐CORDEX simulations for two emission representative concentration pathways (RCP4.5 and RCP8.5). Both indicators have been validated over the period 1981–2010 using an independent data set that contains more than 4000 station data, showing very high correlation over most of Europe. Trends of HDD and CDD from 1981 to 2100, together with their uncertainties, are analysed. For both RCPs, all simulations project a significant decrease for HDD, especially over Scandinavia and European Russia, and an increase of CDD which peaks over the Mediterranean region and the Balkans. Overall, degree‐day trends do not show remarkable differences if population weighting is applied. If a constant population scenario is considered, the decrease in HDD will outbalance the increase in CDD in the 21st century over most of Europe. Thus the related energy demand (expressed as Energy Degree‐days, EDD) is expected to decrease. If, however, population projections over the 21st century are included in the calculations, it is shown that despite the persisting warming, EDD will increase over northern Europe, the Baltic countries, Great Britain, Ireland, Benelux, the Alps, Spain, and Cyprus, resulting in an overall increase in EDD over Europe.

Changes of heating and cooling degree‐days in Europe from 1981 to 2100

This dataset provides supplementary files for the trials and sites described in the 2011 paper in https://www.nature.com/nclimate/ available at https://dx.doi.org/10.1038/NCLIMATE1043

Replication Data for: Nonlinear heat effects on African maize as evidenced by historical yield trials

In the arid region of northwestern China (ARNC), water resources are the most critical factor restricting socioeconomic development and influencing the stability of the area’s ecological systems. The region’s complex water system and unique hydrological cycle show distinctive characteristics. Moreover, the intensified hydrological cycle and extreme climatic and hydrological events resulting from global warming have led to increased uncertainty around water resources as well as heightened conflict between water supply and water demand. All of these factors are exerting growing pressures on the socioeconomic development and vulnerable ecological environment in the region. This research evaluates the impacts of climate change on water resources, hydrological processes, agricultural system, and desert ecosystems in the ARNC, and addresses some associated risks and challenges specific to this area. The temperature is rising at a rate of 0.31 °C per decade during 1961–2017 and hydrological processes are being significantly influenced by changes in glaciers, snow cover, and precipitation form, especially in the rivers recharged primarily by melt water. Ecosystems are also largely influenced by climate change, with the Normalized Difference Vegetation Index (NDVI) of natural vegetation exhibited an increasing trend prior to 1998, and then reversed in Xinjiang while the Hexi Corridor of Gansu showed the opposite trends. Furthermore, the desert-oasis transition zone showed a reduction in area due to the warming trend and the recent rapid expansion of irrigated area. Both the warming and intensified drought are threatening agriculture security. The present study could shed light on sustainable development in this region under climate change and provides scientific basis to the construction of the “Silk Road Economic Belt”.

Potential risks and challenges of climate change in the arid region of northwestern China

Plant phenology is a key link for controlling interactions between climate change and biogeochemical cycles. Satellite-derived normalized difference vegetation index (NDVI) has been extensively used to detect plant phenology at regional scales. Here, we introduced a new vegetation index, plant senescence reflectance index (PSRI), and determined PSRI-derived start (SOS) and end (EOS) dates of the growing season using Moderate Resolution Imaging Spectroradiometer data from 2000 to 2011 in the Inner Mongolian Grassland. Then, we validated the reliability of PSRI-derived SOS and EOS dates using NDVI-derived SOS and EOS dates. Moreover, we conducted temporal and spatial correlation analyses between PSRI-derived SOS/EOS date and climatic factors and revealed spatiotemporal patterns of PSRI-derived SOS and EOS dates across the entire research region at pixel scales. Results show that PSRI has similar performance with NDVI in extracting SOS and EOS dates in the Inner Mongolian Grassland. Precipitation regime is the key climate driver of interannual variation of grassland phenology, while temperature and precipitation regimes are the crucial controlling factors of spatial differentiation of grassland phenology. Thus, PSRI-derived vegetation phenology can effectively reflect land surface vegetation dynamics and its response to climate change. Moreover, a significant linear trend of PSRI-derived SOS and EOS dates was detected only at small portions of pixels, which is consistent with that of greenup and brownoff dates of herbaceous plant species in the Inner Mongolian Grassland. Overall, PSRI is a useful and robust metric in addition to NDVI for monitoring land surface grassland phenology.

Assessing Plant Senescence Reflectance Index retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland

Contrasting drought impacts on the start of phenological growing season in Northern China during 1982-2015

Liquid superlubricity has attracted much attention, due to its ability to significantly reduce friction on the macroscale. However, the severe wear caused by the long running-in period is still one of the bottlenecks restricting the practical application of liquid superlubricating materials. In this work, the obtained polyethylene glycol-phytic acid (PEG-PA) composite liquid lubricants showed outstanding superlubricating properties (μ ≈ 0.006) for Si3N4/glass friction pairs with an ultrashort running-in period (∼1 s) under high Hertzian contact pressure of ∼758 MPa. More importantly, even after up to 12 h (∼700 m of travel), only about 100 nm deep wear scars were found on the surface of the glass sheet (wear rate = 2.51× 10-9 mm3 N-1 m-1). From the molecular point of view, the water molecules anchored between the two friction pairs have extremely low shear force during the friction process, and the strong hydrogen bond interaction between PEG and PA greatly improves the bearing capacity of the lubricant. This work addresses the challenge of liquid superlubricant simultaneously exhibiting low shear force and high load-carrying capacity and makes it possible to obtain liquid superlubrication performance with an extremely short running-in time.

Macroscale Superlubricity with Ultralow Wear and Ultrashort Running-In Period (∼1 s) through Phytic Acid-Based Complex Green Liquid Lubricants.

Drought risk has posed severe challenges to social development and human security in the Yellow River Basin (YRB), which is vulnerable to water scarcity. When dealing with drought, the insufficient coordination among various departments caused by the uncertainty of the comprehensive drought risk under climate change could lead to water conflict. Therefore, in this study, the drought risk changes in the YRB under shared socioeconomic pathway (SSP) scenario 5–8.5 were assessed by integrating the temporally dynamic hazard with the static vulnerability and exposure. Then, suggestions for drought risk management were developed according to the spatial differentiation of the drought risk characteristics. The results show that the drought hazard may increase as the standardized precipitation evapotranspiration index (SPEI) decreases from the baseline (1985–2014; −0.04 ± 0.26) to the mid-term period (2041–2070; −0.09 ± 0.21), and may decrease as the SPEI increases (0.14/10 yr) significantly (p < 0.01) during the long-term period (2071–2100). As a result, the drought risk in the upstream, midstream, and downstream areas may change by 0.26 ± 0.56, −0.05 ± 0.49, and −0.86 ± 0.38 levels in the long term, respectively, compared with the baseline. To reduce the drought risk, it is suggested that agricultural technology be further developed in the southwestern upstream, central upstream, and northern midstream areas; the water management be strengthened in the Hetao Plain and Ordos Plateau; the scale of exposure be controlled in the provincial capitals, southeastern midstream, and downstream areas; and the drought disaster research and early warning system be improved in the areas between 105°E and 110°E. Detailed suggestions for drought risk response were developed for each specific region. 

Future drought risks in the Yellow River Basin and suggestions for targeted response

A terrestrial surface is characterized by a set of elements such
as landform, climate, water, and soil and vegetation. The interaction
of such elements forms a series of systematic regions on a terrestrial
surface with a geographical zonation distribution; this is known as
a terrestrial pattern. Realization of the internal relationship between
the terrestrial elements and knowledge of their interaction and pattern
formation would be helpful to further understand the physical geographical
processes and the state of sustainable resource use and environmental
protection planning.

Amplitude and velocity of the shifts in the Chinese terrestrial surface regions from 1960 to 2011

Climate change will bring huge risks to human society and the economy. Regional climate change risk assessment is an important basic analysis for addressing climate change, which can be expressed as a regional system of comprehensive climate change risk. This study establishes regional systems of climate change risks under the proposed global warming targets. Results of this work are spatial patterns of climate change risks in China, indicated by the degree of climate change and the status of the risk receptors. Therefore, the risks show significant spatial differences. The high-risk regions are mainly distributed in East, South, and central China, while the medium-high risk regions are found in North and southwestern China. Under the 2°C warming target, more than 1/4 of China’s area would be at high and medium-high risk, which is more severe than under the 1.5°C warming target, and would extend to the western and northern regions. This work provides regional risk characteristics of climate change under different global warming targets as a foundation for dealing with climate change. 

http://www.geogsci.com/CN/article/downloadArticleFile.do?attachType=PDF&id=53554

Identification of regional pattern of climate change risk in China under different global warming targets

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Haoyu Deng

Author Tools

Chat about Author