Evaluation of the effects of climate change on meteorological and hydrological parameters using climatic models and Mann – Kendall test (case study: Urmia Lake)

J. Bio. & Env. Sci. 2014
Observed data <strong>of</strong> indexed synoptic stations in this
study belong to <strong>the</strong> period 1961-2001. In order to
investigate <strong>the</strong> effect <strong>of</strong> <strong>climate</strong> <strong>change</strong> on <strong>the</strong> rivers
run<strong>of</strong>fs <strong>of</strong> Urmia Lake watershed, <strong>the</strong> data <strong>of</strong> stations
in mountainous areas and on <strong>the</strong> main rivers were
used. Position <strong>of</strong> 8 hydrometric stations used in this
study has been shown in Table (2) and Figure (1). At
first, data homogeneity was investigated <strong>the</strong>n, Mann-
Kendall test was applied on <strong>the</strong> data and after that,
data <strong>of</strong> temperature and precipitation were
downscaled and predicted and ultimately, predicted
temperature and precipitation and neural network
MLP were used to model precipitation to run<strong>of</strong>f.
Considering long-term statistics and data continuity
<strong>of</strong> temperature, precipitation and evaporation as daily
in Urmia and Tabriz stations, statistics <strong>of</strong> <strong>the</strong>se
stations were used in this study. Position <strong>of</strong> <strong>the</strong>se
index synoptic stations has been shown in table (1).
Figure (1) shows <strong>the</strong> position <strong>of</strong> index synoptic and
hydrometric stations in <strong>the</strong> concerned area.
Fig. 1. Position <strong>of</strong> hydrometric and synoptic stations
in Urmia Lake watershed.
General Circulation Models (GCM)
These models are three-dimensional and are able to
simulate climatic system considering most <strong>of</strong>
processes in global or continental scale. The models
require calculation, saving and replication <strong>of</strong>
computations in each point <strong>of</strong> <strong>the</strong> network to
calculate each climatic variable (Shahabfar & Ghiami,
2001). These models are not applicable in practical
studies with smaller dimensions, for instance, most <strong>of</strong>
hydrological studies are faced with small-scale
processes and sub-basins which <strong>the</strong>ir scale is much
smaller than <strong>the</strong> scale given by general circulation
models. Global models should be downscaled to be
used in hydrological studies. (Wilby et al., 1997).
There are two approaches for downscaling and
obtaining <strong>the</strong> data in local or regional scale, from
global <strong>climate</strong> scenarios generated by general
circulation models, including dynamical and
statistical approaches. In <strong>the</strong> first approach, high
speed computers are needed but, in <strong>the</strong> second
approach, domestic computers can be used and is a
fast and low-cost approach. In statistical downscaling,
<strong>the</strong> maximum limitation is for recorded observations
(Benestad et al., 2004).
Multilayer Perceptron network (MLP)
This network consists <strong>of</strong> an input layer, one or a
number one or more hidden layers and an output
layer. For training this network, Back Propagation
(BP) algorithm is usually used. During <strong>the</strong> training <strong>of</strong>
MLP network using Back Propagation (BP) learning
algorithm, <strong>the</strong> computations start from <strong>the</strong> network
input toward <strong>the</strong> network output and <strong>the</strong>n, calculated
error values are propagated into <strong>the</strong> previous layers.
Output computations are conducted as layer by layer
and output <strong>of</strong> each layer is <strong>the</strong> input <strong>of</strong> <strong>the</strong> next layer.
In Back Propagation state, <strong>the</strong> output layer is
adjusted since, <strong>the</strong>re is a desired value for each output
layer neurons, and <strong>the</strong> weights can be adjusted with
<strong>the</strong>ir help and update rules. Despite Back Propagation
(BP) algorithm has presented much proper results in
resolving <strong>the</strong> problems, it acts weakly in some
problems which can be due to <strong>the</strong> learning time being
long or unclear, unsuitable selection <strong>of</strong> learning
coefficient or random distribution <strong>of</strong> initial weights.
In some case also, learning process is disturbed due to
existence <strong>of</strong> local minimum. Training stages using
this algorithm including (Dayh<strong>of</strong>f, 1990): A) assigning
random weight matrix to each connections. B)
selection <strong>of</strong> input vector and its consistent output. C)
calculation <strong>of</strong> neuron output in each layer and
consequently calculation <strong>of</strong> neuron output in output
layer. D) updating <strong>the</strong> weights by network error
propagation into <strong>the</strong> previous layers, that <strong>the</strong> error is
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