scholarly journals Defining the climatic signal in stream salinity trends using the Interdecadal Pacific Oscillation and its rate of change

2006 ◽  
Vol 3 (5) ◽  
pp. 2963-2990 ◽  
Author(s):  
V. H. McNeil ◽  
M. E. Cox
Keyword(s):  
Time Series ◽  
Climatic Variability ◽  
Geographical Area ◽  
Shallow Groundwater ◽  
Rate Of Change ◽  
Interdecadal Pacific Oscillation ◽  
New Approach ◽  
Decadal Scale ◽  
Hydrological Cycles ◽  
The Impact

Abstract. The impact of landuse on stream salinity is difficult to separate from decadal climatic variability, as the decadal scale climatic cycles in ground water and stream hydrology have similar wavelengths to the landuse pattern. These hydrological cycles determine the stream salinity through accumulation or release of salt in the landscape. The Interdecadal Pacific Oscillation (IPO) has been investigated before as an indicator of hydrological and related time series in the southern hemisphere. This study presents a new approach, which uses the rate of change in the IPO, rather than just its absolute value, to define an indicator for the climate component of ambient shallow groundwater tables and corresponding stream salinity. Representative time series of water table and stream salinity indicators are compiled, using an extensive but irregular database covering a very wide geographical area. These are modelled with respect to the IPO and its rate of change to derive climatic indicators. The effect of removing the decadal climatic influence from stream salinity trends is demonstrated.

scholarly journals Defining the climatic signal in stream salinity trends using the Interdecadal Pacific Oscillation and its rate of change

2007 ◽  
Vol 11 (4) ◽  
pp. 1295-1307 ◽  
Author(s):  
V. H. McNeil ◽  
M. E. Cox
Keyword(s):  
Time Series ◽  
Geographical Area ◽  
Shallow Groundwater ◽  
Rate Of Change ◽  
Interdecadal Pacific Oscillation ◽  
Groundwater Levels ◽  
Decadal Scale ◽  
Hydrological Cycles ◽  
The Impact ◽  
Related Time Series

Abstract. The impact of landuse on stream salinity is currently difficult to separate from the effect of climate, as the decadal scale climatic cycles in groundwater and stream hydrology have similar wavelengths to the landuse pattern. These hydrological cycles determine the stream salinity through accumulation or release of salt in the landscape. Widespread patterns apparent in stream salinity are discussed, and a link is demonstrated between stream salinity, groundwater levels and global climatic indicators. The Interdecadal Pacific Oscillation (IPO) has previously been investigated as a contributory climatic indicator for hydrological and related time series in the Southern Hemisphere. This study presents an approach which explores the rate of change in the IPO, in addition to its value, to define an indicator for the climate component of ambient shallow groundwater levels and corresponding stream salinity. Composite time series of groundwater level and stream salinity are compiled using an extensive but irregular database covering a wide geographical area. These are modelled with respect to the IPO and its rate of change to derive control time series. A example is given of how a stream salinity trend changes when the decadal climatic influence is removed.

scholarly journals Spatiotemporal analysis of some extreme rainfall indices over Iraq (1981–2017)

2021 ◽  
Vol 30 (2) ◽  
pp. 221-235
Author(s):  
Alaa Al-Lami ◽  
Hasanain Al-Shamarti ◽  
Yaseen Al-Timimi
Keyword(s):  
Time Series ◽  
Extreme Rainfall ◽  
Spatiotemporal Analysis ◽  
Rate Of Change ◽  
Mean Annual Precipitation ◽  
Spatiotemporal Pattern ◽  
Northwestern Part ◽  
Positive Trend ◽  
Human Environment ◽  
The Impact

Extreme rainfall is one of the environmental hazards with disastrous effects on the human environment. Water resources management is very vulnerable to any changes in rainfall intensities. A spatiotemporal analysis is essential for study the impact of climate change and variability on extreme rainfall. In this study, daily rainfall data for 36 meteorological stations in Iraq during 1981–2017 were used to investigate the spatiotemporal pattern of 10 extreme rainfall indices using RClimDex package. These indices were classified into two categories: rainfall total (PRCPTOT, SDII, R95p, R99p, RX1day, and RX5day) and rainfall days (CDD, CWD, R10, and R20). Depending on the mean annual precipitation data, the study area was divided into three climatic zones to examine the time series features of those 10 indices. Results showed a tendency to increase in precipitation toward the northwestern part of Iraq, and more than 70% of stations achieved a positive trend for most indices. The most frequent negative trend appeared in eight stations distributed in the western and southern parts of Iraq, namely (Heet, Haditha, Anah, Rutba, Qaim, Nukheb, Najaf, and Fao). A significant positive trend appeared obviously in PRCPTOT and R95p with a rate of 0.1–4.6 and 0.5–2.7 mm per year, respectively. Additionally, the least trend increasing appeared in all precipitation days indices specifically in R10 and R20. Time series analyses revealed a positive trend in all regions under study, except SDII in the southern region. The most significant rate of change was noticed in regions one and two (northern and middle parts of Iraq), particularly for PRCPTOT and R95p 3.26 and 2.45 mm per day, respectively. Only the northern and eastern regions of Iraq experienced a high probability of significant extreme rainfall.

scholarly journals A New Approach to Stochastically Generating Six-Monthly Rainfall Sequences Based on Empirical Mode Decomposition

2008 ◽  
Vol 9 (6) ◽  
pp. 1377-1389 ◽  
Author(s):  
Thomas A. McMahon ◽  
Anthony S. Kiem ◽  
Murray C. Peel ◽  
Phillip W. Jordan ◽  
Geoffrey G. S. Pegram
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Southern Oscillation ◽  
Monthly Rainfall ◽  
Anthropogenic Climate Change ◽  
New Approach ◽  
Mode Decomposition ◽  
Natural Climate ◽  
The Impact

Abstract This paper introduces a new approach to stochastically generating rainfall sequences that can take into account natural climate phenomena, such as the El Niño–Southern Oscillation and the interdecadal Pacific oscillation. The approach is also amenable to modeling projected affects of anthropogenic climate change. The method uses a relatively new technique, empirical mode decomposition (EMD), to decompose a historical rainfall series into several independent time series that have different average periods and amplitudes. These time series are then recombined to form an intradecadal time series and an interdecadal time series. After separate stochastic generation of these two series, because they are independent, they can be recombined by summation to form a replicate equivalent to the historical data. The approach was applied to generate 6-monthly rainfall totals for six rainfall stations located near Canberra, Australia. The cross correlations were preserved by carrying out the stochastic analysis using the Matalas multisite model. The results were compared with those obtained using a traditional autoregressive lag-one [AR(1)], and it was found that the new EMD stochastic model performed satisfactorily. The new approach is able to realistically reproduce multiyear–multidecadal dry and wet epochs that are characteristic of Australia’s climate and are not satisfactorily modeled using traditional stochastic rainfall generation methods. The method has two advantages over the traditional AR(1) approach, namely, that it can simulate nonstationarity characteristics in the historical time series, and it is easy to alter the decomposed time series components to examine the impact of anthropogenic climate change.

scholarly journals Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets

2018 ◽  
Vol 10 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Babatunde Adeniyi Osunmadewa ◽  
Worku Zewdie Gebrehiwot ◽  
Elmar Csaplovics ◽  
Olabinjo Clement Adeofun
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Anthropogenic Activities ◽  
Phase 1 ◽  
Climatic Variability ◽  
Annual Rainfall ◽  
Series Data ◽  
Vegetation Phenology ◽  
The Impact

Abstract Time series data are of great importance for monitoring vegetation phenology in the dry sub-humid regions where change in land cover has influence on biomass productivity. However few studies have inquired into examining the impact of rainfall and land cover change on vegetation phenology. This study explores Seasonal Trend Analysis (STA) approach in order to investigate overall greenness, peak of annual greenness and timing of annual greenness in the seasonal NDVI cycle. Phenological pattern for the start of season (SOS) and end of season (EOS) was also examined across different land cover types in four selected locations. A significant increase in overall greenness (amplitude 0) and a significant decrease in other greenness trend maps (amplitude 1 and phase 1) was observed over the study period. Moreover significant positive trends in overall annual rainfall (amplitude 0) was found which follows similar pattern with vegetation trend. Variation in the timing of peak of greenness (phase 1) was seen in the four selected locations, this indicate a change in phenological trend. Additionally, strong relationship was revealed by the result of the pixel-wise regression between NDVI and rainfall. Change in vegetation phenology in the study area is attributed to climatic variability than anthropogenic activities.

River-Basin Planning for Control of Nitrate Pollution

1982 ◽  
Vol 14 (4-5) ◽  
pp. 245-252 ◽  
Author(s):  
C S Sinnott ◽  
D G Jamieson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
River Basin ◽  
Water Resource ◽  
Potable Water ◽  
Potential Problem ◽  
Nitrate Pollution ◽  
Optimal Mix ◽  
Regional Basis ◽  
The Impact

The combination of increasing nitrate concentrations in the River Thames and the recent EEC Directive on the acceptable level in potable water is posing a potential problem. In assessing the impact of nitrates on water-resource systems, extensive use has been made of time-series analysis and simulation. These techniques are being used to define the optimal mix of alternatives for overcoming the problem on a regional basis.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

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