Nonparametric trend detection in river monitoring network data: a spatio-temporal approach

2009 ◽  
Vol 20 (3) ◽  
pp. 283-297 ◽  
Author(s):  
Lieven Clement ◽  
Olivier Thas
Keyword(s):  
Monitoring Network ◽  
Trend Detection ◽  
Network Data ◽  
River Monitoring ◽  
Spatio Temporal

scholarly journals The Murrumbidgee soil moisture monitoring network data set

2012 ◽  
Vol 48 (7) ◽  
Author(s):  
A. B. Smith ◽  
J. P. Walker ◽  
A. W. Western ◽  
R. I. Young ◽  
K. M. Ellett ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Monitoring Network ◽  
Network Data ◽  
Data Set ◽  
Soil Moisture Monitoring

Spatio-temporal variability of temperature and potential evapotranspiration over India

2016 ◽  
Vol 7 (4) ◽  
pp. 810-822 ◽  
Author(s):  
P. Sonali ◽  
D. Nagesh Kumar
Keyword(s):  
Hydrological Cycle ◽  
Potential Evapotranspiration ◽  
Grid Point ◽  
Rank Correlation ◽  
Trend Detection ◽  
Detection Analysis ◽  
Change Impact ◽  
Spatio Temporal ◽  
Grid Points ◽  
Significant Factors

Worldwide, major changes in the climate are expected due to global warming, which leads to temperature variations. To assess the climate change impact on the hydrological cycle, a spatio-temporal change detection study of potential evapotranspiration (PET) along with maximum and minimum temperatures (Tmax and Tmin) over India have been performed for the second half of the 20th century (1950–2005) both at monthly and seasonal scale. From the observed monthly climatology of PET over India, high values of PET are envisioned during the months of March, April, May and June. Temperature is one of the significant factors in explaining changes in PET. Hence seasonal correlations of PET with Tmax and Tmin were analyzed using Spearman rank correlation. Correlation of PET with Tmax was found to be higher compared to that with Tmin. Seasonal variability of trend at each grid point over India was studied for Tmax, Tmin and PET separately. Trend Free Pre-Whitening and Modified Mann Kendall approaches, which consider the effect of serial correlation, were employed for the trend detection analysis. A significant trend was observed in Tmin compared to Tmax and PET. Significant upward trends in Tmax, Tmin and PET were observed over most of the grid points in the interior peninsular region.

Detecting persistent change in the habitat of salmon-bearing streams in the Pacific Northwest

2004 ◽  
Vol 61 (2) ◽  
pp. 283-291 ◽  
Author(s):  
David P Larsen ◽  
Philip R Kaufmann ◽  
Thomas M Kincaid ◽  
N Scott Urquhart
Keyword(s):  
Pacific Northwest ◽  
Pacific Salmon ◽  
Monitoring Network ◽  
Habitat Characteristics ◽  
Detection Sensitivity ◽  
Trend Detection ◽  
Spatial And Temporal Variation ◽  
The Pacific ◽  
Two Factors ◽  
Consistent Change

In the northwestern United States, there is considerable interest in the recovery of Pacific salmon (Oncorhynchus spp.) populations listed as threatened or endangered. A critical component of any salmon recovery effort is the improvement of stream habitat that supports various life stages. Two factors in concert control our ability to detect consistent change in habitat conditions that could result from significant expenditures on habitat improvement: the magnitude of spatial and temporal variation and the design of the monitoring network. We summarize the important components of variation that affect trend detection and explain how well-designed networks of 30–50 sites monitored consistently over years can detect underlying changes of 1–2% per year in a variety of key habitat characteristics within 10–20 years, or sooner, if such trends are present. We emphasize the importance of the duration of surveys for trend detection sensitivity because the power to detect trends improves substantially with the passage of years.

scholarly journals The Functional Spatio-Temporal Statistical Model with Application to O3 Pollution in Beijing, China

2020 ◽  
Vol 17 (9) ◽  
pp. 3172
Author(s):  
Yaqiong Wang ◽  
Ke Xu ◽  
Shaomin Li
Keyword(s):  
Statistical Model ◽  
Diurnal Cycle ◽  
Monitoring Network ◽  
Temporal Correlation ◽  
Ground Level ◽  
Quality Data ◽  
Ozone Pollution ◽  
Spatial Correlation Structure ◽  
Spatio Temporal ◽  
Beijing China

In recent years, with rapid industrialization and massive energy consumption, ground-level ozone ( O 3 ) has become one of the most severe air pollutants. In this paper, we propose a functional spatio-temporal statistical model to analyze air quality data. Firstly, since the pollutant data from the monitoring network usually have a strong spatial and temporal correlation, the spatio-temporal statistical model is a reasonable method to reveal spatial correlation structure and temporal dynamic mechanism in data. Secondly, effects from the covariates are introduced to explore the formation mechanism of ozone pollution. Thirdly, considering the obvious diurnal pattern of ozone data, we explore the diurnal cycle of O 3 pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to O 3 pollution data of 36 stations in Beijing, China, we give explanations of the covariate effects on ozone pollution, such as other pollutants and meteorological variables, and meanwhile we discuss the diurnal cycle of ozone pollution.

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