scholarly journals Trends in hydrologic time series

2009 ◽  
Author(s):  
M. M. Portela ◽  
J. F. Santos ◽  
A. C. Quintela ◽  
C. Vaz
Keyword(s):  
Time Series ◽  
Hydrologic Time Series

An Application of Elman Networks In Treatment and Prediction of Hydrologic Time Series

2011 ◽  
Vol 9 (3) ◽  
pp. 148-156
Author(s):  
Leonardo G. Tampelini ◽  
Clodis Boscarioli ◽  
Sarajane M. Peres ◽  
Silvio C. Sampaio
Keyword(s):  
Time Series ◽  
Hydrologic Time Series

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

Measuring nonlinear dependence in hydrologic time series

2008 ◽  
Vol 23 (7) ◽  
pp. 907-916 ◽  
Author(s):  
H. S. Kim ◽  
K. H. Lee ◽  
M. S. Kyoung ◽  
B. Sivakumar ◽  
E. T. Lee
Keyword(s):  
Time Series ◽  
Nonlinear Dependence ◽  
Hydrologic Time Series

Multivariate hydrologic time series analysis

1981 ◽  
Vol 4 (2) ◽  
pp. 83-95
Author(s):  
C.T. Hsu ◽  
K. Adamowski
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Hydrologic Time Series ◽  
Series Analysis

Matched block bootstrap for resampling multiseason hydrologic time series

2005 ◽  
Vol 19 (18) ◽  
pp. 3659-3682 ◽  
Author(s):  
V. V. Srinivas ◽  
K. Srinivasan
Keyword(s):  
Time Series ◽  
Block Bootstrap ◽  
Hydrologic Time Series

Assessment of aggregated hydrologic time series modelling

1994 ◽  
Vol 156 (1-4) ◽  
pp. 447-458 ◽  
Author(s):  
Gwo-Fong Lin ◽  
Fong-Chung Lee
Keyword(s):  
Time Series ◽  
Hydrologic Time Series ◽  
Time Series Modelling

On the variability of seasonal parameters in hydrologic time series

1974 ◽  
Vol 23 (3-4) ◽  
pp. 279-287
Author(s):  
Rafael G. Quimpo ◽  
Mow-Soung Cheng
Keyword(s):  
Time Series ◽  
Hydrologic Time Series
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