Dynamic factor analysis of surface water management impacts on soil and bedrock water contents in Southern Florida Lowlands

Kisekka, Isaya; Migliaccio, Kati W.; Muñoz‐Carpena, Rafael; Schaffer, Bruce; Li, Y. C.

doi:10.1016/j.jhydrol.2013.02.035

Cited by 13 publications

(20 citation statements)

References 27 publications

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“…DFA describes a set of N observed time series and aims to keep M as small as possible while still producing an optimal model fit (Kisekka et al, 2013). Including explanatory variables partly reduces unexplained variability in the observed time series (Kisekka et al, 2013;Zuur et al, 2003b).…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…Including explanatory variables partly reduces unexplained variability in the observed time series (Kisekka et al, 2013;Zuur et al, 2003b).…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…(2) translates into Eq. (3) (Kisekka et al, 2013;Kuo et al, 2011;Ritter and Muñoz-Carpena, 2006;Zuur and Pierce, 2004;Zuur et al, 2007):…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…is a linear combination of common trends, in which α m (t) is the mth unknown common trend (with 1 ≤ m ≤ M) at time t and γ m,n is the factor loading that indicates the importance of each of the common trends to each response variable (Kisekka et al, 2013;Kuo et al, 2011). Factor loading (A cut-off point of 0.15) was applied to test which common trends are related to the macrofaunal time series (Ligas et al, 2010).…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

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Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Shojaei

Gutow

Dannheim

et al. 2016

Journal of Sea Research

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We examined long-term variability in the abundance of German Bight soft bottom macro-zoobenthos together with major environmental factors (sea surface temperature, winter NAO index, salinity, phosphate, nitrate and silicate) using one of the most comprehensive ecological long-term data sets in the North Sea . Two techniques, Min/Max Autocorrelation Factor Analysis (MAFA) and Dynamic Factor Analysis (DFA) were used to identify underlying common trends in the macrofaunal time series and the relationships between this series and environmental variables. These methods are particularly suitable for relatively short (N15-25 years), non-stationary multivariate data series. Both MAFA and DFA identify a common trend in German Bight macrofaunal abundance i.e. a slight decrease (1981-mid-1990s) followed by a sharp trough in the late 1990s. Subsequently, scores increased again towards 2011. Our analysis indicates that winter temperature and North Atlantic Oscillation were the predominant environmental drivers of temporal variation in German Bight macrofaunal abundance. The techniques applied here are suitable tools to describe temporal fluctuations in complex and noisy multiple time series data and can detect distinct shifts and trends within such time series.

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Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…Including explanatory variables partly reduces unexplained variability in the observed time series (Kisekka et al, 2013;Zuur et al, 2003b).…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

“…(2) translates into Eq. (3) (Kisekka et al, 2013;Kuo et al, 2011;Ritter and Muñoz-Carpena, 2006;Zuur and Pierce, 2004;Zuur et al, 2007):…”

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

Section: Dynamic Factor Analysis (Dfa)mentioning

confidence: 99%

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Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Shojaei

Gutow

Dannheim

et al. 2016

Journal of Sea Research

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“…(1, 2, 3, and 5) were maintained by University of Florida (UF), IFAS (Kisekka et al, 2013a). UF wells 114…”

Section: Introduction 38mentioning

confidence: 99%

Simulating water table response to proposed changes in surface water management in the C-111 agricultural basin of south Florida

Kisekka

Migliaccio

Muñoz‐Carpena

et al. 2014

Agricultural Water Management

Self Cite

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Detection and analysis of soil water content based on experimental reflectance spectrum data

Fan

2020

Asia-Pacific J Chem Eng

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In order to improve the yield of crops, the method of soil water content detection and analysis based on experimental reflectance spectrum data is adopted. From the four aspects of the farmland soil water detection based on reflection spectrum information, soil water content detection in farmland based on spectral absorption characteristics, remote sensing inversion of soil water content of farmland by reflectance spectra data, and soil water content detection in farmland based on near‐infrared spectroscopy, the soil water content of farmland can be analyzed. The absorption valley near 1,920 nm is the characteristic absorption valley of soil water in the black soil, which can be used to predict the water content of the black soil by the spectral characteristic parameters of the absorption valley; soil water will increase the absorption of radiation energy and reduce the overall reflectance spectra of soil. The quantitative monitoring of soil water content can be achieved by using the remote sensing model of soil water content in this paper. In the range of low soil water content, the reflectance decreases with the increase of soil water content in farmland, whereas in the range of high soil water content, it increases with the increase of soil water content in farmland. The correlation between soil water content and normalized spectral reflectance is positive and negative correlation and mainly negative correlation. The method can detect soil water content in farmland under extreme conditions of different texture, soil water, salinity, and organic matter. It is an effective method for soil water content detection.

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Dynamic factor analysis of surface water management impacts on soil and bedrock water contents in Southern Florida Lowlands

Cited by 13 publications

References 27 publications

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

Simulating water table response to proposed changes in surface water management in the C-111 agricultural basin of south Florida

Detection and analysis of soil water content based on experimental reflectance spectrum data

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