Current Status of Time Series Analysis in Hydrological Sciences - Hydrologic Time Series Analysis: Theory and Practice - page 119

Yurekli et al. (2004) analyzed daily maximum streamflow data of each
month from three gauge stations on Cekerek Stream in Turkey for simulation
using stochastic approaches. Initially non-parametric Mann-Kendall (MK)
test was used to identify the trend during study period. The two approaches of
stochastic modelling, ARIMA and Thomas-Fiering models, were used to
simulate monthly maximum data. The error estimates (RMSE and MAE) of
predictions from both approaches were compared to identify the most suitable
approach for reliable simulation. The MK test suggested no linear trend in
monthly maximum data sequences of each mentioned gauge station. The two
error estimates calculated for two approaches indicate that ARIMA model
appear to be slightly better than Thomas-Fiering. However, both approaches
were identified as appropriate method for simulating daily maximum
streamflow of Cekerek Stream.
Recent evidence of nonstationary trends in water resources time series as
a result of natural and/or anthropogenic climate variability and change, has
raised more interest in nonlinear dynamic system modelling methods. Coulibaly
and Baldwin (2005) investigated the effectiveness of dynamically driven
recurrent neural networks (RNN) for complex time-varying water resources
system modelling. An optimal dynamic RNN approach is proposed to directly
forecast different nonstationary hydrological time series. The proposed method
automatically selects the most optimally trained network in any case. The
simulation performance of the dynamic RNN-based model is compared with
the results obtained from optimal multivariate adaptive regression splines
(MARS) models. It is shown that the dynamically driven RNN model can be
a good alternative for the modelling of complex dynamics of a hydrological
system, performing better than the MARS model on the three selected
hydrological time series, namely the historical storage volumes of the Great
Salt Lake, the Saint-Lawrence River flows, and the Nile River flows.
Burn et al. (2008) performed trend analysis on streamflow data in terms
of spring and summer runoff volumes, peak flow rates and peak flow
occurrences, as well as an annual volume measure, for analysis periods of
1966-2005, 1971-2005, and 1976-2005 for 26 hydrometric gauging stations
in Canadian Prairies. The data were analyzed by using Mann-Kendall test.
The Mann-Kendall test for trend and bootstrap resampling were used to identify
the trends and to determine the field significance of the trends. Partial correlation
analysis was used to identify relationships between hydrological variables
that exhibit a significant trend and meteorological variables that exhibit a
significant trend. The results revealed decreasing trends in the spring snowmelt
runoff event volume and peak flow, decreasing trends (earlier occurrence) in
the spring snowmelt runoff event peak date and decreasing trends in the
seasonal (1 March-31 October) runoff volume. These trends were attributed to
a combination of reductions in snowfall and increases in temperatures during
the winter months.
Wu et al. (2008) detected spatial and temporal trends in streamflow
droughts in terms of frequency, duration and severity in Nebraska. The studies