Using the multidimensional synthesis methods with non-parameter test, multiple time scales analysis to assess water quality trend and its characteristics over the past 25 years in the Fuxian Lake, China

2019 ◽  
Vol 655 ◽  
pp. 242-254 ◽  
Author(s):  
Junxu Chen ◽  
Yan Lyu ◽  
Zhifang Zhao ◽  
Hong Liu ◽  
Hongling Zhao ◽  
...  
Author(s):  
L. Gudmundsson ◽  
S. I. Seneviratne

Abstract. Recent climate projections suggest pronounced changes in European drought frequency. In the north, increased precipitation volumes are likely to reduce drought occurrence, whereas more frequent droughts are expected for southern Europe. To assess whether this pattern of changes in drought frequency can already be identified for the past decades, we analyse trends in a recently developed pan-European drought climatology that is based on the Standardized Precipitation Index (SPI). The index is derived on multiple time scales, ranging from 1 to 36 months, which allows the assessment of trends in both short term and multi-year droughts. Trends are quantified using the Theil-Sen trend estimator combined with an extension of the Mann–Kendal test (p < 0.05) that accounts for serial correlation. Field significance is assessed on the basis of techniques that control the false discovery rate in a multiple testing setting. The trend analysis indicates that changes in drought frequency are more pronounced on time scales of one year and longer. The analysis also reveals that there has been a tendency for decreased drought frequency in northern Europe in the past decades, whereas droughts have likely become more frequent in selected southern regions.


2018 ◽  
Author(s):  
Yan Liang ◽  
◽  
Daniele J. Cherniak ◽  
Chenguang Sun

2019 ◽  
Vol 11 (4) ◽  
pp. 1163 ◽  
Author(s):  
Melissa Bedinger ◽  
Lindsay Beevers ◽  
Lila Collet ◽  
Annie Visser

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.


2021 ◽  
Vol 40 (9) ◽  
pp. 2139-2154
Author(s):  
Caroline E. Weibull ◽  
Paul C. Lambert ◽  
Sandra Eloranta ◽  
Therese M. L. Andersson ◽  
Paul W. Dickman ◽  
...  

Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.


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