Analysis of long-term fluctuations in stream flow time series: An application to Litani River, Lebanon

2013 ◽  
Vol 62 (1) ◽  
pp. 164-179 ◽  
Author(s):  
Amin Shaban ◽  
Luciano Telesca ◽  
Talal Darwich ◽  
Nabil Amacha
Keyword(s):  
Time Series ◽  
Stream Flow ◽  
Flow Time ◽  
Litani River

Analysis and prediction of chaos in rainfall and stream flow time series

1994 ◽  
Vol 153 (1-4) ◽  
pp. 23-52 ◽  
Author(s):  
A.W. Jayawardena ◽  
Feizhou Lai
Keyword(s):  
Time Series ◽  
Stream Flow ◽  
Flow Time

scholarly journals Changes in land cover and stream flows in Gilgel Abbay catchment, Upper Blue Nile basin – Ethiopia

2010 ◽  
Vol 7 (6) ◽  
pp. 9567-9598 ◽  
Author(s):  
T. H. M. Rientjes ◽  
A. T. Haile ◽  
C. M. M. Mannaerts ◽  
E. Kebede ◽  
E. Habib
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Stream Flow ◽  
Agricultural Land ◽  
Flow Time ◽  
Nile Basin ◽  
Classification Analysis ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Upper Blue Nile Basin

Abstract. We evaluated the land cover change in the Upper Gilgel Abbay catchment in the Upper Blue Nile basin through classification analysis of remote sensing based land cover data and through assessing the changes in the hydrological regime by statistical analysis of stream flow observations. Results of the land cover classification analysis indicated that 50.9% and 16.7% of the catchment area was covered by forest in 1973 and 2001, respectively. This significant decrease in forest cover is mainly due to expansion of agricultural land. A comparison of stream flow time series of the Upper Gilgel Abbay catchment to stream flow time series from two neighbouring catchments shows a different trend and a statistically significant change over time. In 1986–2001, the annual and the high flows of the catchment increased by 13% and 46%, respectively while the low flows decreased by 35%. Generally, the results indicate significant changes in land cover and the hydrological regimes of the Upper Gilgel Abbay catchment over the past 30 years.

scholarly journals Downscaling stream flow time series from monthly to daily scales using an auto-regressive stochastic algorithm: StreamFARM

2016 ◽  
Vol 537 ◽  
pp. 297-310 ◽  
Author(s):  
N. Rebora ◽  
F. Silvestro ◽  
R. Rudari ◽  
C. Herold ◽  
L. Ferraris
Keyword(s):  
Time Series ◽  
Stream Flow ◽  
Flow Time ◽  
Stochastic Algorithm ◽  
Auto Regressive

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

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