Spatial heterogeneity of the relationship between vegetation dynamics and climate change and their driving forces at multiple time scales in Southwest China

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for ‘systems’ research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research.

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Rockfish response to low-frequency ocean climate change as revealed by the diet of a marine bird over multiple time scales

Marine Ecology Progress Series ◽

10.3354/meps281207 ◽

2004 ◽

Vol 281 ◽

pp. 207-216 ◽

Cited By ~ 49

Author(s):

AK Miller ◽

WJ Sydeman

Keyword(s):

Climate Change ◽

Time Scales ◽

Low Frequency ◽

Multiple Time Scales ◽

Multiple Time ◽

Marine Bird ◽

Ocean Climate

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Conceptual change and development on multiple time scales: From incremental evolution to origins

Sign Systems Studies ◽

10.12697/sss.2014.42.2-3.03 ◽

2014 ◽

Vol 42 (2/3) ◽

pp. 193-218 ◽

Cited By ~ 3

Author(s):

Joel Parthemore

Keyword(s):

Time Scales ◽

Conceptual Development ◽

Multiple Time Scales ◽

Multiple Time ◽

Human Species ◽

Individual Agent ◽

Incremental Change ◽

Form Part ◽

Four Levels ◽

The Relationship

In the context of the relationship between signs and concepts, this paper tackles some of the ongoing controversies over conceptual development and change – including the claim by some that concepts are not open to revision at all – taking the position that concepts pull apart from language and that concepts can be discussed on at least four levels: that of individual agent, community, society, and language. More controversially, it claims that concepts are not just inherently open to revision but that they, and the frameworks of which they form part, are in a state of continuous, if generally incremental, change: a position that derives directly from the enactive tradition in philosophy. Concepts, to be effective as concepts, must strike a careful balance between being stable enough to apply across suitably many contexts and flexible enough to adapt to each new context. The paper’s contribution is a comparison and contrast of conceptual development and change on four time scales: that of the day-to-day life of an individual conceptual agent, the day-to-day life of society, the lifetime of an individual agent, and the lifetime of society and the human species itself. It concludes that the relationship between concepts and experience (individual or collective) is one of circular and not linear causality.

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Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.142419 ◽

2021 ◽

Vol 755 ◽

pp. 142419 ◽

Cited By ~ 1

Author(s):

Shangyu Shi ◽

Jingjie Yu ◽

Fei Wang ◽

Ping Wang ◽

Yichi Zhang ◽

...

Keyword(s):

Climate Change ◽

Time Scales ◽

Loess Plateau ◽

Human Activities ◽

Multiple Time Scales ◽

Vegetation Changes ◽

Multiple Time ◽

The Loess Plateau ◽

Quantitative Contributions

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Climate change effects on drought severity

Advances in Geosciences ◽

10.5194/adgeo-17-23-2008 ◽

2008 ◽

Vol 17 ◽

pp. 23-29 ◽

Cited By ~ 60

Author(s):

A. Loukas ◽

L. Vasiliades ◽

J. Tzabiras

Keyword(s):

Climate Change ◽

Time Scales ◽

Standardized Precipitation Index ◽

Precipitation Index ◽

Geographical Information ◽

Multiple Time Scales ◽

Drought Severity ◽

Multiple Time ◽

Monthly Precipitation ◽

Climate Change Effects

Abstract. This paper evaluates climate change effects on drought severity in the region of Thessaly, Greece. The Standardized Precipitation Index (SPI) has been used for estimation of drought severity. A geographical information system is applied for the division of Thessaly region to twelve hydrological homogeneous areas based on their geomorphology. Mean monthly precipitation values from 50 precipitation stations of Thessaly for the hydrological period October 1960–September 1990 were used for the estimation of mean areal precipitation. These precipitation timeseries have been used for the estimation of Standardized Precipitation Index (SPI) for multiple time scales (1-, 3-, 6-, 9-, and 12-months) for each sub-basin or area. The outputs of Global Circulation Model CGCM2 were applied for two socioeconomic scenarios, namely, SRES A2 and SRES B2 for the assessment of climate change impact on droughts. The GCM outputs were downscaled to the region of Thessaly using a statistical methodology to estimate precipitation time series for two future periods 2020–2050 and 2070–2100. A method has been proposed for the estimation of annual cumulative drought severity-time scale-frequency curves. These curves integrate the drought severity and frequency for various types of drought. The SPI timeseries and annual weighted cumulative drought severity were estimated and compared with the respective timeseries and values of the historical period 1960–1990. The results showed that the annual drought severity is increased for all hydrological areas and SPI time scales, with the socioeconomic scenario SRES A2 being the most extreme.

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The Relationship between NDVI and Climate Factors at Different Monthly Time Scales: A Case Study of Grasslands in Inner Mongolia, China (1982–2015)

Sustainability ◽

10.3390/su11247243 ◽

2019 ◽

Vol 11 (24) ◽

pp. 7243 ◽

Cited By ~ 8

Author(s):

Zhifang Pei ◽

Shibo Fang ◽

Wunian Yang ◽

Lei Wang ◽

Mingyan Wu ◽

...

Keyword(s):

Time Scales ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Meteorological Data ◽

Pearson Correlation ◽

Growing Season ◽

Multiple Time Scales ◽

Multiple Time ◽

Climate Factors ◽

The Relationship

There are currently only two methods (the within-growing season method and the inter-growing season method) used to analyse the normalized difference vegetation index (NDVI)–climate relationship at the monthly time scale. What are the differences between the two methods, and why do they exist? Which method is more suitable for the analysis of the relationship between them? In this study, after obtaining NDVI values (GIMMS NDVI3g) near meteorological stations and meteorological data of Inner Mongolian grasslands from 1982 to 2015, we analysed temporal changes in NDVI and climate factors, and explored the difference in Pearson correlation coefficients (R) between them via the above two analysis methods and analysed the change in R between them at multiple time scales. The research results indicated that: (1) NDVI was affected by temperature and precipitation in the area, showing periodic changes, (2) NDVI had a high value of R with climate factors in the within-growing season, while the significant correlation between them was different in different months in the inter-growing season, (3) with the increase in time series, the value of R between NDVI and climate factors showed a trend of increase in the within-growing season, while the value of R between NDVI and precipitation decreased, but then tended toward stability in the inter-growing season, and (4) when exploring the NDVI–climate relationship, we should first analyse the types of climate in the region to avoid the impacts of rain and heat occurring during the same period, and the inter-growing season method is more suitable for the analysis of the relationship between them.

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