Regional analysis of trend and non‐stationarity of hydro‐climatic time series in the Southern Alborz Region, Iran

2019 ◽  
Vol 40 (4) ◽  
pp. 1979-1991 ◽  
Author(s):  
Mahsa Mirdashtvan ◽  
Ali Najafinejad ◽  
Arash Malekian ◽  
Amir Sa'doddin
Keyword(s):  
Time Series ◽  
Regional Analysis ◽  
Alborz Region ◽  
Climatic Time Series

scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

A Regional Analysis of the Dependency between Registered Unemployment and the Stocks and Flows of Notified Vacancies

1978 ◽  
Vol 10 (7) ◽  
pp. 853-866 ◽  
Author(s):  
G Hyman ◽  
D J Palmer
Keyword(s):  
Time Series ◽  
Regional Analysis ◽  
Leading Indicators ◽  
Labour Demand ◽  
Short Term ◽  
Quarterly Data ◽  
Rapid Changes ◽  
Duration Of Unemployment ◽  
Stocks And Flows ◽  
Regional Labour Markets

This paper presents the results of a time-series analysis of short-term changes in the conditions prevailing in regional labour markets. A set of alternative indicators of changes in these conditions are evaluated for each of the standard regions by use of quarterly data for a period that includes the rapid changes in the economy associated with the ‘Barber Boom’. Leading indicators of changes in labour demand are contrasted with lagging indicators and the findings for different regions compared. The results of the analysis show that in general the numbers of vacant jobs and the rates at which the jobs are being filled provide leading indicators of changes in the region's level of unemployment and of changes in the duration of unemployment in the region, and that there is no feedback from unemployment to change the demand for labour in the region. In consequence it would be justified to claim that changes in regional unemployment and its duration are caused by changes in the demand for labour in the region.

scholarly journals Revisiting Climate Region Definitions via Clustering

2009 ◽  
Vol 22 (7) ◽  
pp. 1787-1800 ◽  
Author(s):  
Robert Lund ◽  
Bo Li
Keyword(s):  
Time Series ◽  
Clustering Methods ◽  
Distance Metric ◽  
Climatic Zones ◽  
Time Series Clustering ◽  
Climate Region ◽  
Covariate Effects ◽  
Weather Stations ◽  
Climatic Time Series ◽  
Natural Way

Abstract This paper introduces a new distance metric that enables the clustering of general climatic time series. Clustering methods have been frequently used to partition a domain of interest into distinct climatic zones. However, previous techniques have neglected the time series (autocorrelation) component and have also handled seasonal features in a suboptimal way. The distance proposed here incorporates the seasonal mean and autocorrelation structures of the series in a natural way; moreover, trends and covariate effects can be considered. As an important by-product, the methods can be used to statistically assess whether two stations can serve as reference stations for one another. The methods are illustrated by partitioning 292 weather stations within the state of Colorado into six different zones.

scholarly journals Short-duration rainfall extremes in Sweden: a regional analysis

2019 ◽  
Vol 50 (3) ◽  
pp. 945-960 ◽  
Author(s):  
Jonas Olsson ◽  
Johan Södling ◽  
Peter Berg ◽  
Lennart Wern ◽  
Anna Eronn
Keyword(s):  
Time Series ◽  
Short Duration ◽  
Regional Analysis ◽  
Generalized Pareto Distribution ◽  
Extreme Value ◽  
Northern Region ◽  
Generalized Extreme Value Distribution ◽  
Return Periods ◽  
Station Coordinates ◽  
Rainfall Extremes

Abstract We investigate short-duration rainfall extremes in a 22-year long (1996–2017) time series of 15-min values from 128 stations in Sweden. Two different approaches are used to fit extreme value distributions (EVDs) to the data: generalized extreme value distribution with annual maximum values and generalized Pareto distribution with peak-over-threshold values. First, EVDs are fitted to the data from each station, and the extreme value parameters are used together with the station coordinates in a cluster analysis. Based on the clustering results, a division of Sweden into four regions is proposed. Following the station-year method, the time series from all stations in each cluster are pooled into regional series after which depth–duration–frequency (DDF) statistics are calculated. Averaged over durations and return periods, the highest DDF values are found in region south-west, being ∼25% higher than the lowest ones found in the northern region. A six-parameter DDF function is proposed which, together with a simple empirical expression for the associated confidence interval, allows for easy DDF estimation at arbitrary durations and return periods, including uncertainty. No tendencies of change in magnitude or frequency of the short-duration extremes are found during the period.

Chaos and periodicities in a climatic time series of the Iberian Margin

2020 ◽  
Vol 30 (6) ◽  
pp. 063126
Author(s):  
Berenice Rojo-Garibaldi ◽  
David Alberto Salas-de-León ◽  
María Adela Monreal-Gómez ◽  
Simone Giannerini ◽  
Julyan H. E. Cartwright
Keyword(s):  
Time Series ◽  
Climatic Time Series ◽  
Iberian Margin

Disaggregation Modeling Process for Climatic Time Series

1996 ◽  
Vol 122 (3) ◽  
pp. 205-212
Author(s):  
Susan E. Firor ◽  
Brad A. Finney ◽  
Robert Willis ◽  
John A. Dracup
Keyword(s):  
Time Series ◽  
Modeling Process ◽  
Climatic Time Series

Identifying Regional Differences in the Spanish Mortgage Market with Sheaf Methodology

2013 ◽  
Vol 44 (3) ◽  
Author(s):  
Magdalena Ferrán Aranaz ◽  
Juan Antonio Márquez García
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Financial Institutions ◽  
Regional Differences ◽  
Regional Analysis ◽  
Mortgage Market ◽  
Statistical Graphics ◽  
Mortgage Credit ◽  
Geographic Time Series ◽  
Strong Expansion

The strong expansion of mortgage credit in Spain during the second half of the nineties was due to a decline in interest rates (a reduction that was greater in Spain than elsewhere in Europe) and also to a tremendous competition between financial institutions to increase market share. This expansive phase lasted until 2006, followed by a sharp turnaround in 2007. The aim of this paper is to illustrate the evolution of the mortgage market in different Spanish provinces. We have performed a comparative analysis of different regional trajectories using statistics on the number of monthly housing loans for the period between late 1995 and early 2012, applying sheaf methodology for the visual comparison of geographic time series. We conclude that the provinces that have been hit the hardest by the recent recession are those that reached the higher peaks during the expansionary cycle. Keywords: Loan market, regional analysis, geographic time series, statistical graphics, interest rates, sheaf methodology.

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