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Space-time kriging of precipitation variability in Turkey for the period 1976–2010


Abstract and Figures

The purpose of this study is to revaluate the changing spatial and temporal trends of precipitation in Turkey. Turkey is located in one of the regions at greatest risk from the potential effects of climate change. Since the 1970s, a decreasing trend in annual precipitation has been observed, in addition to an increasing number of precipitation-related natural hazards such as floods, extreme precipitation, and droughts. An understanding of the temporal and spatial characteristics of precipitation is therefore crucial to hazard management as well as planning and managing water resources, which depend heavily on precipitation. The ordinary kriging method was employed to interpolate precipitation estimates using precipitation records from 228 meteorological stations across the country for the period 1976–2010. A decreasing trend was observed across the Central Anatolian region, except for 1996–2000 which saw an increase in precipitation. However, this same period is identified as the driest year in Eastern and South Eastern Anatolia. The Eastern Black Sea region has the highest precipitation in the country; after 1996, an increase in annual precipitation in this region is observed. An overall reduction is also seen in southwest Turkey, with less variation in precipitation.
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Space-time kriging of precipitation variability in Turkey
for the period 19762010
Nussaïbah B. Raja
&Olgu Aydin
&Necla Türkoğlu
&Ihsan Çiçek
Received: 16 July 2015 /Accepted: 20 March 2016
#Springer-Verlag Wien 2016
Abstract The purpose of this study is to revaluate the
changing spatial and temporal trends of precipitation in
Turkey. Turkey is located in one of the regions at
greatest risk from the potential effects of climate change.
Since the 1970s, a decreasing trend in annual precipita-
ber of precipitation-related natural hazards such as
floods, extreme precipitation, and droughts. An under-
standing of the temporal and spatial characteristics of
precipitation is therefore crucial to hazard management
as well as planning and managing water resources, which
depend heavily on precipitation. The ordinary kriging
method was employed to interpolate precipitation esti-
mates using precipitation records from 228 meteorologi-
cal stations across the country for the period 19762010.
A decreasing trend was observed across the Central
Anatolian region, except for 19962000 which saw an
increase in precipitation. However, this same period is
identified as the driest year in Eastern and South
Eastern Anatolia. The Eastern Black Sea region has the
highest precipitation in the country; after 1996, an in-
crease in annual precipitation in this region is observed.
An overall reduction is also seen in southwest Turkey,
with less variation in precipitation.
1 Introduction
Precipitation-related natural hazards such as floods, extreme
precipitation, landslides, and drought have become a recurrent
phenomenon over the last decade in Turkey due to global
warming and climate change (Can et al. 2005; Sönmez et al.
2005; Yilmaz et al. 2012; Yucel and Onen 2014). In addition,
the decreasing trend in annual precipitation observed since the
1970s (Türkeşet al. 2009) has generated a need for more
agricultural irrigation, especially in the semiarid regions of
Turkey (Çiçek and Duman 2015). This may potentially lead
to ecologically problematic projects such as importing water
from different basins and extracting more groundwater.
Located between the mid-latitude temperate and subtropical
climate zones, Turkey is characterized by a Mediterranean
macro-climate with several climate regimes (Iyigun et al.
2013). Turkey is also located in one of the regions at greatest
risk from the potential effects of global warming and climate
change (Iglesias et al. 2010;IPCC2012;Ozturketal.2015).
Given the recent reports on climate change and its extreme
variability, an understanding of the temporal and spatial char-
acteristics of precipitation is not only crucial to hazard man-
agement but is also vital for the planning and management of
water resources, which depend heavily on precipitation
(Michaelides et al. 2009). It is therefore imperative to track
the changing patterns of precipitation in the region so the
effects of the changing climate can be monitored and
The Mediterranean region is one of the regions at most risk
from global climate change due to the increase in anthropo-
genic greenhouse gases and aerosol forcing as well as sea
surface forcing with desertification being the likely outcome
(Mariotti 2010;IPCC2012;Hoerlingetal.2012). There are
several studies of long-term precipitation variations in this
particular region. Mariotti et al. (2002) studied precipitation
*Olgu Aydin
Department of Geography, Faculty of Humanities, Ankara
University, Ankara, Turkey
Theor Appl Climatol
DOI 10.1007/s00704-016-1788-8
variability and the water budget in the Mediterranean Sea
using gauge-satellite merged products and atmospheric re-
analyses. It was observed that during the last 50 years of the
twentieth century, average winter precipitation decreased by
20 %, mostly during the late 1970s to the early 1990s.
Similarly, an investigation of the wet and dry periods in the
Mediterranean region by Xoplaki et al. (2004)showedthatthe
relative maxima of precipitation took place in the late 1970s to
the early 1980s and the late 1990s while the relative minima
occurred during the early 1970s and the early 1990s.
Goubanova and Li (2007)s modeling of extreme precipitation
and temperatures in the Mediterranean Basin predicted an
increase in extreme precipitation in all seasons except summer
but a decrease in total precipitation and a warmer climate.
Philandras et al. (2011)s study showed that most of the
Mediterranean region experienced a significant decrease in
annual precipitation, for the period 19012009, except for
northern Africa, southern Italy, and the western Iberian
peninsula, though the slightly positive trends were not
statistically significant. On the other hand, Longobardi and
Villani (2010) analyzed temporal trends in the precipitation
series in southern Italy and highlighted a predominantly
negative trend on the annual and seasonal scale except for
summer. Similarly, Brunetti et al. (2006) studied precipitation
variability in Italy in the last two centuries. They observed a
5 % decrease per century in annual precipitation, as a result of
adecreaseinspringprecipitation(9 %). Despite being a low
amount, this decrease is significant to the annual distribution
of precipitation. Gualdi et al. (2013) generated climate change
projections for the Mediterranean region and also noted a 5 %
decrease in the whole Mediterranean region for the scenario
period of 19512050.
Several studies have been conducted regarding the effects
of climate changeto precipitation patterns in Turkey (Koç and
Irdem 2007;Türkeşet al. 2009;Türkeş2011;Güçlü2014;
Ozturk et al. 2015).While there has been a decreasing trend in
Mediterranean precipitation since the 1970s, some regions of
Turkey have shown an increasing trend, especially in summer
precipitation, mostly in the continental Mediterranean and
Central Anatolia regions. The analysis of long-term variations
of precipitation shows that precipitation trends are character-
ized by successive dry and wet periods. Around the 1960s,
there was a regional shift from humid to dry or subhumid
conditions. However, this did not extend to the northern part
of continental Central Anatolia, which underwent a shift to
humid or semi-humid climatic conditions. Also, significant
spatial and temporal changes have been observed in precipi-
tation trends over the last 10 years, as indicated by atmospher-
ic oscillation indices such as the North Atlantic Oscillation
(NAO) and North-Sea-Caspian Pattern (NCP) (Türkeşand
Erlat 2003; Tatlı2006;Türkeşet al. 2009; Güçlü 2014).
This study reports on the changing spatial and temporal
trends of precipitation in Turkey. A range of geostatistical
interpolation techniques such as regression and kriging are
used in climate studies to study and predict climatic variables
such as precipitation (Hengl et al. 2012). Among these, spa-
tiotemporal kriging stands out as a method for producing
gridded datasets and improving spatial interpolation of
datasets with long temporal sequences as it produces data that
better incorporate changing patterns of precipitation over
space and time (Biondi 2013). Due to Turkeys multi-
climate regime and a heterogeneous and inadequate meteoro-
logical observation network, evaluating the precipitation dis-
tribution is challenging. Kriging interpolation methods, how-
ever, provide a means to overcome this (Ekstrom et al. 2007;
Li et al. 2009). The aim of this study is to determine and
evaluate the spatial and temporal variability of precipitation
in Turkey as well as provide some insights about factors lead-
ing to the changes in precipitation. For this purpose, kriging
methods are applied to interpolate and analyze precipitation
data obtained from 228 stations for the period 19762010 at 5-
year intervals.
2 Materials and methods
2.1 Study area
Spanning latitudes 3642° N and longitudes 2645° E, the
landscape of Turkey includes mountain ranges, plateaus with
deep river valleys, mountains of volcanic origins or old lake
and marine sediments, major river deltas, and tectonic basins
covered with alluvium (Koçman 1993). This landscape has a
range of altitudes, with an average elevation of about 1132 m
in mountainous areas, and with more than 55 % of the region
classified as high plains (Fig. 1). The North Anatolian
Mountains and Taurus Mountains are the mountain ranges
along the northern and southern coasts, respectively, which
prevent the coastal effect from reaching the Central
Anatolian region. The uplift between these mountain ranges
has created uneven terrain in the Eastern Anatolian region.
The Central Anatolia region, on the other hand, consists of
an area with large and high plains that extends toward the
Aegean and Marmara seas. These factors, among others, help
create the countrys multi-climatic regime. The slopes of the
mountain ranges overlooking the sea receive abundant, long-
term precipitation while the interior slopes receive less, with
annual increases in temperature differences, suggesting that
proximity to the coast represents the first and foremost effect
on continentality (Koçman 1993). More specifically, precipi-
tation on northwest-facing slopes is higher than on north-
facing slopes of the North Anatolian Mountains. Similarly,
precipitation is higher on southwest-facing slopes than on
the east- and southeast-facing slopes of the Taurus
Mountains. Other geographical factors such as slope
N.B. Raja et al.
characteristics and pressure also significantly affect the pre-
cipitation distribution in Turkey.
2.2 The data
A long-term period of at least 30 years is considered appro-
priate for estimating climate factors such as precipitation, ac-
cording to the World Meteorological Organization (WMO).
However, even periods of 1020 years may be enough to
identify changes in precipitation due to climate change
(Linacre 2003). This study spans 35 years, from 1976 to
2010, and includes precipitation data from 228 meteorological
stations from throughout Turkey (Fig. 1). A relatively dense
network is observed in the Aegean region as compared to
sparser networks in South East Anatolia, South Marmara,
and Central Anatolia.
A 5-year interval was chosen to explore the temporal trends
in the data so 5-year averages of total precipitation were cal-
culated using monthly averages for the following time inter-
vals: 19761980, 19811985, 19861990, 19911995, 1996
2000, 20012005, and 20062010. These intervals show a
decreasing temporal trend (Fig. 2) and spatial variability
(Fig. 3). A detailed analysis of the spatial distribution of an-
nual mean total precipitation is provided by Aydin and Çiçek
(2015). On average, Central Anatolia receives less precipita-
tion than anywhere else in Turkey while the Mediterranean
and Black Sea regions receive the most annual precipitation.
Although kriging techniques do not require a normal dis-
tribution, logarithmic transformationwasappliedtothe
precipitation values in order to bring the data as close as pos-
sible to a normal distribution as the data was severely skewed
to the right (Fig. 4).
2.3 Spatial interpolation methods
All interpolation computations were carried out using the R
3.1.3 (R Development Core Team 2015)usingthegstat
Fig. 1 Distribution network of meteorological stations across Turkey, with BVal u e ^referring to elevation (m)
Annual Mean Total Precipitation (mm)
Fig. 2 Time series of annual mean precipitation (mm) in Turkey for the
period 19762013 (solid red line represents decreasing trend of
Space-time kriging of precipitation variability in Turkey
(Pebesma 2004) and spacetime (Pebesma 2012) packages.
Precipitation data were spatially interpolated using ordinary
kriging (OK), one of several geostatistical kriging methods.
Kriging allows for the statistical generation of optimal spatial
predictions (Cressie 1993) using the weighted averages of the
ðÞ ð1Þ
where ns is the total number of observed points, Y
the interpolation value at location s
served values at locations s
to s
the weights generated from a model of the spatial corre-
lation structure of the data, usually a valid variogram
model fit.
2.3.1 Variogram modeling
The experimental semivariogram was first calculated as half
the squares of the difference between paired values to the
distance by which they were separated:
γhðÞ¼ 1
where N(h) is the number of pairs of the data locations sepa-
rated by a certain distance h(Ly et al. 2011). An isotropic
spatial pattern and hence a homogenous variability in all di-
rections was assumed. The average squared distances obtain-
ed for all pairs, separated by a range of distances, were plotted
against the average separation distance. This enables the
determination of the dependency rule that exists at each loca-
tion. A theoretical model was then fitted to the experimental
semivariogram, the coefficient of which was used for kriging
In this study, the semivariogram was first fitted for the
whole dataset without considering the temporal scale and
then fitted for each 5-year interval for the period 1976
2010. Several semivariogram models are commonly used
in geostatistics, e.g., exponential, spherical, gaussian, cir-
cular, and linear (Ly et al. 2011). The spherical model is
considered to be the best suited for precipitation datasets
as it follows the behavioral patterns of precipitation
(Holawe and Dutter 1999; Goovaerts 2000;Verwornand
Haberlandt 2011) so it was applied at both temporal and
spatial scales, as presented as follows:
for θ
and θ
The semivariogram increases steadily with distance h,and
when a certain range is reached, it then remains constant at a
certain sill, which provides the maximum value of the
Theoretical semivariograms were then fitted to the ex-
perimental semivariogram with various methods in order
to obtain the most accurate estimates for kriging. The
gstat package provides several fitting methods for spatio-
temporal kriging, two of which were selected for this
study: (i) the Bseparable^method, which uses a separate
variogram for the spatial and temporal analysis with a
Black Sea Region
Central Anatolia Region
Aegean Region
Marmara Region
Eastern Anatolia Region
Southeastern Anatolia Region
Mediterranean Region
Van Lak e
Fig. 3 Spatial distribution of total mean annual precipitation (mm) in Turkey for the period 19762010 interpolated using ordinary kriging, following
Aydin and Çiçek (2015)
N.B. Raja et al.
joint sill and (ii) the Bmetric^method, which uses a joint
variogram for both the spatial and temporal analysis using
a spatiotemporal anisotropy ratio to generate the results.
Due to the difference in the parameters used by the
models, the optimized root mean squared differences
(RMSD) between the two surfaces (the sample and the
model) serves as a numeric means of assessing the good-
ness of fit between the model and sample variogram
(Pebesma 2012). The RMSD is given as follows:
RMSD ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
where (x
) is the location of path iat time t,andMis
the sum of the time stamps (Sobek 2008). If two space-
time paths are identical, the expected RMSD value is 0,
the theoretical minimum. However, the greater the incon-
sistency in the spatial and temporal aspects of the sample
and the model the greater the RMSD value will be. There
is no upper limit to the values. The RMSD value is de-
pendent on the reference system used, so two different
systems will generate different values. Hence, it does
not indicate the magnitude of difference between the sam-
ple variogram and the modeled one. However, this meth-
od is suitable, along with the visual comparison, to create
a model that accurately reproduces the observed precipi-
tation values.
(a)(i) (a)(ii)
(b)(i) (b)(ii)
Precipitation (mm) Log Transformation of Precipitation
Theoretical Theoretical
Fig. 4 Precipitation data averaged into 5-year intervals. aHistograms of annual mean total precipitation (mm) and bQQ-plots of the (i) original data and
(ii) transformed logarithmic data
Space-time kriging of precipitation variability in Turkey
2.3.2 Kriging analysis
The OK analysis was carried out without any covariates
(Eq. 1) (Ly et al. 2011). Variations in the estimates were de-
rived from the semivariogram model. The variables were con-
sidered static and the average fixed. It created non-biased es-
timates as the average difference between the predicted and
observed values was assumed to be 0. The equations for the
calculation of the weights for OK are the following:
ðÞγijμ¼γi0 for j¼1;;ns
where γ
represents the semi-variances of observed values
between locations iand j,andμis the Lagrange parameter.
The weights w(s
) are inserted into Eq. 1for the kriging esti-
mation. The constraint of the sum of the weight is 1; hence, the
Lagrange parameter is required to ensure an unbiased
2.3.3 Performance analysis
One common method used to evaluate the performance of
geospatial interpolation is cross validation. Cross validation
uses the information available in the original data set to exam-
ine the relationship between the observed and the predicted
values (Isaaks and Srivasta 1989). The evaluation of the
kriging interpolation used in this study was carried out using
the Bleave one out^method. This involves temporarily remov-
ing data at one location point and then predicting its value
using the chosen semivariogram model. This process is then
repeated for all the remaining samples and the residual, i.e.,
the difference between the observed and predicted values at
each location is noted as the error value. Error-estimation
maps can then be produced by comparing the cross validation
results to the interpolation results.
BError metrics^can also be used to evaluate interpolation
performance and identify outliers in the model fit, namely
mean error (ME), mean absolute error (MAE), root mean
squared error (RMSE) and R
(Urquhart et al. 2013), which
are given as follows:ME,
ME ¼1
hi ð6Þ
MAE ¼1
RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
xy ¼1
in which;σ2
2and σ2
where y(s
) is the observed value at location i,Ŷ(s
) is the
interpolated value, and nrepresents the sample size (on the
spatial and temporal scales). σ
is the square of the sum of
errors occurred during the estimation, which states the rela-
tionship between x
and y
in the linear equation y=a+bx. σ
represents the variation of y.
Isaaks and Srivasta (1989) present a detailed explanation of
error metrics. ME is applied to determine the degree of error
regarding bias in the prediction, with negative values showing
that the estimated values are less than the observed values.
The only difference between the RMSE (Eq. 8)andthe
RMSD (Eq. 4) is that instead of the Euclidean distances be-
tween the plane coordinates used to generate the RMSD, the
error is used instead. An accurate algorithm will return an
RMSE value close to 0, that is, close to no error. However,
RMSE is sensitive to extreme values as it gives a higher
weight to large errors, which makes it an unreliable error met-
ric for the kriging estimate. On the other hand, MAE is not
affected by extreme values unlike RMSE, although they both
provide similar measurements and generate the average pre-
diction error. R
represents the correlation between the pre-
dicted values and the observed data and indicates the strength
of their linear relationship. It is expressed as the square of the
Pearson product-moment correlation coefficient and varies
between 0 and 1.
The OK technique was then carried out by first fitting the
chosen model to the experimental semivariogram for the ob-
servation points after which maps were generated for the in-
terpolated values as well as the cross validation results for
each 5-year interval between 1976 and 2010.
3 Results and analysis
3.1 Variograms
On the spatial scale, the lag distance between stations was
processed for various intervals to achieve the model that best
predicted observed precipitation values; 80 km was the best fit
for the experimental variogram (Fig. 5). The theoretical
N.B. Raja et al.
variogram of the spherical type was then fitted to the experi-
mental variogram (Figs. 6and 7).
On the temporal scale, the initial chosen time interval
of 5 years was retained. Table 1shows the parameters
generated by the Bseparable^and Bmetric^model fits,
respectively. These two variogram models were generated
for each 5-year interval, taking into consideration spatial
and temporal relationships derived from the precipitation
variable of the sample data. The RMSD values for the two
variogram models were 103.5 and 55.6, respectively.
According to the visual fit as well as the RMSD values,
the Bmetric^model was chosen as it provided a better fit
to the sample variogram.
3.2 Spatiotemporal distribution of precipitation in Turkey
The kriging results are presented in Fig. 8. The highest
fluctuations in precipitation are observed in Central and
Eastern Anatolia. A pronounced decrease is observed in
the region between the periods 19761990 and 2001
2005 across the Central Anatolian region while the wet-
test period is identified as 19962000 and a slight in-
crease in precipitation in this region noted for the period
20062010. A slight increase in precipitation is also
identified in the South East Mediterranean region. On
the other hand, the period 19962000 is identified as
the driest year in Eastern and South Eastern Anatolia
while the period 19861995 experienced an increase in
precipitation. This contrasts with the Aegean region,
which had the lowest precipitation values for this period.
An overall drying up is also seen in southwest Turkey,
with less variation than any other regions. The Eastern
Black Sea had the highest precipitation in the country,
which had a striking increase in annual precipitation after
3.3 Performance of the kriging interpolation technique
3.3.1 Cross validation
Cross validation results are shown in Fig. 9. Severe underes-
timations of precipitation values (by 10001500 mm) were
observed in the Eastern Black Sea and Mediterranean regions,
where there is usually heavy precipitation. There was a slight
overestimation (<500 mm) for inland regions.
3.3.2 Error metrics
The error metrics in Table 1are averages for the entire
study period. According to the R
value of 0.47, about
half of the precipitation can be explained by the model.
Hence spatial factors account for only half of the amount
of precipitation at a certain location in Turkey. The nega-
tive ME values indicate that the interpolated precipitation
values were underestimated. This may be due to the se-
vere underestimations discussed in Section 3.3.1.The
101.3 difference between the RMSE and MAE shows
the variance in the individual errors in the interpolated
values. From Fig. 10, it is observed that interpolated
values were more accurate for lower values. Conversely,
as values increased, simulated values were less accurate,
Distance (m)
2e+05 4e+05 6e+05
Fig. 6 Theoretical variogram (solid line) fitted to the experimental
variogram (data points) of annual mean total precipitation of Turkey for
Distance (m)
2e+05 4e+05 6e+05
Fig. 5 Experimental variogram of the annual mean total precipitation of
Turkey for the period 19762010
Space-time kriging of precipitation variability in Turkey
with many extreme values identified for values greater
than 2000 mm.
However, since it is especially difficult to provide an accu-
rate estimation for climate variables where spatial distribution
has particularly high variability, the error metrics are consid-
ered to be acceptable.
4 Discussion and conclusion
Turkey is in the group of countries at greatest risk from climate
change and global warming. The primary motivation for this
study was to understand the combined long-term spatial and
temporal variability of precipitation in Turkey, assumed to be
a result of a changing climate. Water resources depend mostly
on precipitation and therefore the potential effects of climate
change on precipitation will affect the water resources in
Turkey. Also, the occurrence of natural hazards such as land-
slides and floods has increased in recent years. The
Distance (km)
Distance (km)
Time lag (days)
Fig. 7 a Variogram map, bvariogram foreach time lag, andcwireframe plots for the sample and fitted space-time variograms of the annual meantotal
precipitation of Turkey
Tabl e 1 Error metrics
for the entire study
0.47 253.2 25.8 151.9
N.B. Raja et al.
precipitation distribution in Turkey, however, has a complex
structure. Therefore, accurately modeling precipitation will
aid in the investigation of the spatiotemporal trends and con-
sequently help to plan not only the maintenance and protec-
tion of resources but also environmental risk management.
The OK method was employed to interpolate precipitation
for all of Turkey using precipitation records from 228 meteoro-
logical stations across the country for the period 19762010.
Previous studies of spatiotemporal patterns of precipitation
employed the Bproduct sum^covariance model, which is seen
the most effective for such applications (Ekstrom et al. 2007;Li
et al. 2009). The Bmetric^model used in this study is able to
mimic Bproduct sum^variograms, preferred for precipitation
studies, by including a suitable spatiotemporal anisotropy pa-
rameter during the generation of the semivariogram model
(Graler et al. 2015) and is therefore an appropriate tool for
analyzing spatiotemporal precipitation trends. The interpolation
results showed an overall increase in precipitation in the Eastern
Black Sea. On the other hand, in several regions such as Central
and Eastern Anatolia and South Western Turkey, a gradual
desertification process was identified. The driest period in the
Eastern and Southern Anatolia, 19962000, can be linked to the
North Atlantic Oscillation (NAO) which led to drier than long-
term average conditions in the region as a result of a positive
NAO anomaly phase (Türkeş2003;Tatlı2006).
Overall, the results are consistent with previous studies on
the spatial and temporal precipitation distributions in Turkey
and the Mediterranean region in general (Kadioğlu 2000;
Türkeş2003; Türkeşand Erlat, 2003; Komuscu et al. 2003;
Sönmez et al. 2005; Türkeşet al. 2009; Mariotti 2010; Yavuz
and Erdoğan 2012; Barkhordarian et al. 2013). Significant
decreases in precipitation totals have been observed in the
Mediterranean as well as a general decrease in the annual
precipitation trend in Turkey in recent years. Barkhordarian
et al. (2013)s projections show an increase in a narrow band
in the northern part of the Mediterranean but a decrease in the
remaining area, especially in the Western Mediterranean in-
cluding Turkey. This contrast between lower precipitation in
the south and higher precipitation in the north was also report-
ed by a meridional contrast in precipitation with the drying
effect in the south and the increase in observed precipitation in
the north is further confirmed by Mariotti (2010). Extremely
wet phases in northeast Turkey and also dry and extremely dry
phases in the southeast were identified. Türkeşet al. (2009)
attributed the overall decreasing trend to decreasing winter
precipitation in Turkey, with an especially pronounced
Fig. 8 Spatial distribution of
annual mean total precipitation
(mm) across Turkey for the period
19762010, generated for each 5-
year interval
Space-time kriging of precipitation variability in Turkey
decrease in overall precipitation in South Western Turkey.
Güçlü (2014) argues that decreases in winter precipitation in
Turkey are the result of regional precipitation anomalies,
namely the NAO and NCP. These studies agree on an overall
increase in precipitation in the Eastern Black Sea region.
Fluctuations in the Central Anatolian region are explained as
a result of it being a transition region between the
Mediterranean and Black Sea climates. This region dominates
the central part of continental Turkey (Iyigun et al. 2013)and
represents one of regions at most risk for future droughts, as it
is dominated by dry forests and large steppe lands over large
plains and already has a dry-subhumid to semiarid climate.
The model remains incomplete as it only accounts for
about half of the spatiotemporal precipitation trends in
Turkey. This may be due to the limits of the interpolation
technique. OK does not include any covariates during in-
terpolation, of which there are several in Turkey such as
elevation, coastal proximity, temperature, humidity, and
especially seasonal variability, among others. Several stud-
ies make use of these variables for interpolating precipita-
tion data (Diadato 2005; Yin et al. 2008; Apaydin et al.
2011;Bostanetal.2012). They show that including these
variables generated more accurate models. Hence the
country-wide model could be drastically improved by in-
cluding such auxiliary variables, which would probably
reduce error metrics as well as increased R
One of the most significant variables to be considered in
future models is seasonal variability. Due to the location of
Estimation Type
Fig. 9 Error maps of annual
mean total precipitation variable
in Turkey obtained by cross
validation (one leave out)
methods: residuals (mm) from the
cross validation results. Red
represents overestimations, green
Observed Values (mm)
Simulated Values (mm)
500 1000 1500 2000 2500
Fig. 10 One to one model regression between actual and simulated
precipitation values for the annual mean total precipitation of Turkey,
red solid line shows linear regression line
N.B. Raja et al.
Turkey, more precipitation is received during the Bcold^sea-
son while a meteorological drought is observed during the
Bhot^season (Çiçek and Duman 2015). Winter is the wettest
season in Turkey, with 40 % of the annual precipitation, main-
ly in the form of snowfall at high latitudes, which is the result
of the Mediterranean coastal belt of Turkey being located in a
microclimate zone. According to Türkeşet al. (2009), the
contribution of winter precipitation to annual precipitation is
significant, except for the North Eastern Anatolia and Black
Sea regions. While there is a significant reduction in winter
precipitation in the Mediterranean region, there is an increase
in the Black Sea region (Türkeş2003;Türkeşet al. 2009).
Seasonal trends will help to isolate periods where extreme
precipitation may occur and therefore the periods for the oc-
currence of hazards such as floods and landslide events. This
will also result in the prediction of the overall desertification
process identified in Turkey as well as identifying the current
and future regions at risk to desertification. The investigation
of the spatiotemporal variation of precipitation is therefore the
key to preparing for the potential effects of a changing climate
which could lead to detrimental effects on the water resources
of Turkey as well as an increase in precipitation-related haz-
ards such as floods and landslides.
This research represents a stepping stone for climatological
studies in Turkey as it breaks away from the traditional statis-
tical techniques previously employed. The use of contempo-
rary and effective statistical techniques constitutes an impor-
tant contribution to understanding the hydro-climatological
systems and water resources in Turkey. An improved future
model would include auxiliary variables such as elevation,
temperature, and slope. Moreover, while in the current find-
ings of this paper, only the annual distribution of precipitation
in Turkey is considered, seasonal variability is significant to
understanding the effects of a changing climate on precipita-
tion and should therefore be taken into consideration.
Furthermore, this will allow for a more optimized model and
will better represent the precipitation of Turkey. As the scope
of this study was limited to precipitation variability for the
period 19762010, one of the unanswered questions left by
this study is the future spatial and temporal variability of pre-
cipitation in Turkey. The first step, however, to projecting
future climatic changes is to examine historical data for an
insight and a better understanding of the changing climatic
conditions, which in turn allows the understanding of the ef-
fects of these changes on water resources.
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N.B. Raja et al.
... In this study, the covariates were the postprocessed PERSIANN-CCS satellite precipitation data and geographical features. Spacetime kriging and STRK have been successfully applied to different datasets and rainfall networks to analyze precipitation variations in space and time [24][25][26][27][28][29][30][31][32][33][34]. These works endorse the applicability of the methodology on precipitation data. ...
... A spatiotemporal geostatistical analysis of precipitation measurements was performed using space-time regression or residual kriging (STRK). This methodology has been successfully applied in similar applications using annual precipitation data [28,31,33]. The geostatistical analysis using the proposed methods and tools was performed using the "gstat" package of R software [45]. ...
... Specifically, the proposed model improves the estimation error on average 40% and reduces the estimation uncertainty by 25%. In addition, the results of the best-performing method competes with those of recent similar applications that apply STRK [13,28,31,33]. All of these works provide similar estimation errors of around 10-15% in terms of the mean absolute relative error metric. ...
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The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space-time modeling and prediction of precipitation on Crete island in Greece. The proposed space-time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10-2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space-time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.
... We used spatiotemporal (ST) kriging as a tool to examine the ST distribution of extreme precipitation in Artvin. Kriging is a local estimation technique based on the best linear unbiased estimator (BLUE) that can be used to predict unknown values of ST variables across space and time (Raja et al. 2017). It makes several assumptions such as stochasticity, stationarity, and spatial ergodicity to compute a variogram based on the provided data. ...
... The experimental semivariogram and kriging analyses were carried out according to the method used in Raja et al. (2017). This entailed fitting a theoretical model to the experimental semivariogram, the coefficient of which was used for kriging purposes. ...
... Several other measures, namely the mean error (ME), mean absolute error (MAE), root mean squared error (RMSE), and R 2 were computed to evaluate the performance of the fitted model. These metrics are described in details in Raja et al. (2017). 3 Results and analysis ...
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The region of Artvin, located in the northeast of Turkey, usually experiences high-intensity precipitation events that occur within a short-time period. Along with other environmental impacts, these events also lead to flash floods in this region. The purpose of this study was to evaluate the changing spatial and temporal characteristics of precipitation on August 24, 2015 and November 11 to 12, 2015 that triggered flash floods and landslides in Artvin. We apply spatiotemporal (ST) kriging as a tool to investigate these ST patterns. The average hourly precipitation data were used, taking episodes of heavy rainfall into account. Our results show that precipitation reached the highest values of 30–50 mm between 8:00 and 9:00 pm in the Hopa district on August 24, 2015. On November 11, 2015, rainfall values reached ≥ 15 mm at 4:00 PM in the Borçka district. During both events occurred on November 12, 2015, it rained for 7 h continuously with precipitation values ranging between 5 and 10 mm. The duration of the precipitation lasted for 8 h. In addition, flood events also occurred in the districts of Arhavi and Murgul. This highlights that these districts are vulnerable in the Artvin region and paves the way for decision-makers to implement measures against flash floods in these at-risk districts. In conclusion, the analysis of spatiotemporal characteristics of heavy rainfall events represents an indispensable basis to ensure that necessary precautions regarding flash flood events in the city are taken.
... Among several commonly used semi-variance functions (such as the exponential model, spherical model, or Gaussian model), the spherical model is the most suitable for precipitation data. Therefore, Kriging interpolation based on the spherical model was used to analyze the spatial characteristics of extreme precipitation in the FRB (Raja et al., 2017;Kumar Adhikary et al., 2016). ...
... As for the method used, EEMD can conduct adaptive decomposition according to the characteristics of the signal, which is essentially superior to the wavelet change and tendency rate methods (Duan et al., 2019). Kriging interpolation accuracy is superior to inverse distance weight, and the spherical model is suitable for precipitation data (Raja et al., 2017). In 2022, the Ministry of Emergency Management of People's Republic of China released the top 10 natural disasters in the country, five of which were rain-related (https:// www. ...
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Analysis of the probability of extreme precipitation events leading to rainstorm and flood disasters can aid in disaster prevention policy development. Using daily precipitation data from 16 meteorological stations from 1960 to 2019, we calculated eight extreme precipitation indices to analyze the spatio-temporal characteristics of extreme precipitation in the Fen River Basin (FRB) through ensemble empirical mode decomposition and Kriging interpolation. Extreme precipitation events and disasters were defined based on a combination of the antecedent precipitation index (API) and extreme precipitation on the event day and classified; extreme precipitation and the API were ranked from small to large and classified into dry, wet, and moderate (mod) precipitation periods, respectively, yielding nine extreme precipitation event categories. The probability of disasters caused by different types of extreme precipitation events was calculated using a binomial distribution. The results are as follows: (1) between 1960 and 2019, except for extreme precipitation period length, which continuously increased, the extreme precipitation indices changed from a downward to an upward trend since the 1980s. All extreme precipitation indices showed similar interannual variation over short periods and different interdecadal variation over long periods. (2) The extreme precipitation indices showed latitudinal and zonal spatial divergence patterns, but different spatial characteristics were observed around the 1980s. (3) More than 70% of extreme precipitation events in the midstream and downstream fell into four categories: “dry-dry,” “dry-mod,” “mod-dry,” and “mod-mod.” (4) A single category VII (VIII) extreme precipitation event in the midstream (downstream) had a maximum probability of causing disaster of 14%. When more than four extreme precipitation events occurred in a year, the probability of one disaster was the highest and that of four or more disasters was < 0.1%. The probability of rainstorm and flood disasters increased gradually with increasing frequency of annual extreme precipitation events.
... From the year 1990 onwards, considerable literature analysing trends in Turkish precipitation has become available. Some examples of earlier studies on the subject are Toros (1993), Türkeş (1996), Partal and Kahya (2006), Unal et al. (2012), Yavuz and Erdoğan (2012), Çiçek and Duman (2015), Raja et al. (2017), and Hadi and Tombul (2018). More recently, Sezen and Partal (2020) used innovative trend analysis (ITA) as proposed by Şen (2012) to study annual and seasonal trends in precipitation records from the Euphrates-Tigris basin of Turkey. ...
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This study aimed to provide broad insight into the long-term trends and periodicity of monthly and seasonal precipitation in Turkey and to evaluate their implications for sectoral water availability. Overall, Turkey’s monthly precipitation declined over the last five decades. While decreasing precipitation trends were dominant in the wet season, consistently increasing trends prevailed in the dry season. The monthly monotonic trends were marked by larger downward trends, especially in early winter and spring. The trend magnitudes in annual and wet season precipitation decreased from north to south and west to east, reaching a maximum value of 7.5 mm/year increase in the eastern Black Sea. The magnitude of dry season trends was much smaller but consistent across the country, varying by 1–5 mm/year. Monthly changes in the trend magnitudes varied between − 2.2 and 3.4 mm, reflecting both decrease and increase, respectively. The magnitude of the downward precipitation trends was higher in inland regions than in other regions. Spatial patterns in the trends evidenced that wet season precipitation variability largely governs annual precipitation variability. The wavelet spectrum indicated a strong annual signal for the monthly precipitation. The inland regions experienced the periodicity of wet years at much longer durations. An average 8-year periodicity was dominant for the annual precipitation, underlining its inter-annual variability and coincided well with the NAO spectrum. Assuming the identified trends persist in the future, a further increase in the magnitude of precipitation trends in coastal areas can enhance the flood risk. However, the precipitation decreases during the wet season are bound to have adverse consequences on sectoral-dependent water availability.
... In Dutch, nature reserves using an exponential space-time variogram metric model (Hoogland et al., 2010). In addition, the space-time ordinary kriging was used to design precipitation networks and to analyze precipitation variations in space and time (Biondi, 2013;Raja et al., 2016) and was tested in a comparative study to estimate runoff time series at uncalibrated locations (Skøien and Blöschl, 2007). ...
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Groundwater is the world's biggest dispersed freshwater storage system, and it plays a vital role in ecosystem sustainability as well as human resilience to climatic change and fluctuation. The relevance of GIScience methodologies and new technology for geospatial analysis in the context of sustainable groundwater development is the subject of a recent chapter. Geostatistics and GIScience can help the community protect itself from changing climate settings, increase productivity, and make better use of scarce resources like water. The research also highlights the "data imbalance" effect, which is a crucial issue in the long-term examination of environmental evolution and geosphere-anthroposphere interconnections. This chapter emphasizes the role of existing and emerging geospatial technologies in the groundwater sector, which can aid water scientists in identifying gaps in long-term groundwater management. The challenges and potential outcomes of a conversation between GIScience and groundwater are discussed in this context.
... Experimental semivariograms, used in the geostatistics, improve accuracy and efficiency, with low costs, providing the most suitable model fit for spatial data dependence, including directional effects (i.e., anisotropy) on regionalized variables (Holdaway, 1996). The spherical model is particularly good for modeling spatial rainfall (Holawe & Dutter, 1999;Raja et al. 2017). ...
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PRCPTOT) increases in the coastal section and decreases in the upper basin sector. Consecutive dry days (CDD) and consecutive wet days (CWD) show a strong positive tendency in the lower basin section, where the metropolitan area is located, flooding risks increase in response to positive trends of intensive short-term rainfall events. These results support managers developing and planning sustainability strategies to assure water security and subsidize adaptative responses to extreme hydrological events.
... These data were strictly screened to eliminate the abnormal data, and then the univariate linear regression method was used to calculate the interpolation for the meteorological values of individual missing measurements. After a comparison with other interpolation methods such as ordinary kriging (OK) and empirical Bayesian kriging (EBK) [31][32][33], the precipitation data were gridded by inverse distance weighted (IDW) [34] with the spatial resolution of 1 km, which was more efficient and accurate in the cases of dealing a lot of data on the large region. ...
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Since the early 2000s, the vegetation cover of the Loess Plateau (LP) has increased significantly, which has been fully recorded. However, the effects on relevant eco-hydrological processes are still unclear. Here, we made an investigation on the changes of actual evapotranspiration (ETa) during 2000–2018 and connected them with vegetation greening and climate change in the LP, based on the remote sensing data with correlation and attribution analysis. Results identified that the average annual ETa on the LP exhibited an obvious increasing trend with the value of 9.11 mm yr−1, and the annual ETa trend was dominated by the changes of ETa in the third quarter (July, August, and September). The future trend of ETa was predicted by the Hurst exponent. Partial correlation analysis indicated that annual ETa variations in 87.8% regions of the LP were controlled by vegetation greening. Multiple regression analysis suggested that the relative contributions of potential evapotranspiration (ETp), precipitation, and normalized difference vegetation index (NDVI), to the trend of ETa were 5.7%, −26.3%, and 61.4%, separately. Vegetation greening has a close relationship with the Grain for Green (GFG) project and acts as an essential driver for the long-term development trend of water consumption on the LP. In this research, the potential conflicts of water demanding between the natural ecosystem and social-economic system in the LP were highlighted, which were caused by the fast vegetation expansion.
Precipitation, which is the main source of fresh water, is the main input of water potential calculations and water management studies. In the present study, annual mean basin-based precipitation potential of Turkey was analyzed. In order to ensure data frequency and homogeneity, rainfall data between 1965 and 2018 were used from 391 stations, 254 of which were operated by Turkish State Meteorological Service (TSMS) and 137 of which were operated by State Hydraulic Works (DSI). The TSMS stations located in urban areas and DSI stations in rural areas complement one another. Ordinary kriging (OK) and inverse distance weighted (IDW) methods were used as common techniques of analysis. The verification and comparison of precipitation models was carried out. The basins with the highest rate of precipitation are the East Black Sea (OK, 1013.7 mm; IDW, 1053.7 mm) and Antalya (OK, 939.2 mm; IDW, 895.4 mm). Konya (OK, 389.3 mm; IDW, 402.0 mm) is the basin with the lowest rate of precipitation. Euphrates-Tigris was found to be the basin with the highest precipitation volume (OK, 99.2 billion m3; IDW, 102.05 billion m3). This basin takes up roughly the 22% of Turkey’s total precipitation volume. On the other hand, the OK method indicated that the spatial mean annual total precipitation of Turkey is 587.7 mm and precipitation volume is 458.43 billion m3. With the IDW method, areal total precipitation was found to be 593.8 mm and volume 463.21 billion m3.
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Precipitation data are useful for the management of water resources as well as flood and drought events. However, precipitation monitoring is sparse and often unreliable in regions with complicated geomorphology. Subsequently, the spatial variability of the precipitation distribution is frequently represented incorrectly. Satellite precipitation data provide an attractive supplement to ground observations. However, satellite data involve errors due to the complexity of the retrieval algorithms and/or the presence of obstacles that affect the infrared observation capability. This work presents a methodology that combines satellite and ground observations leading to improved spatiotemporal mapping and analysis of precipitation. The applied methodology is based on space–time regression kriging. The case study refers to the island of Crete, Greece, for the time period of 2010–2018. Precipitation data from 53 stations are used in combination with satellite images for the reference period. This work introduces an improved spatiotemporal approach for precipitation mapping. HIGHLIGHTS Space-time precipitation trend model incorporating satellite measurements and elevation.; Application of 3D distance metric for spatiotemporal prediction.; Application of the non-separable Spartan space–time variogram in precipitation data.; Space–time residual kriging in geostatistical analysis of non-stationary data.;
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This paper analyses extreme precipitation characteristics of Turkey based on selected WMO climate change indices. The indices – monthly total rainy days ( RDays ); monthly maximum 1-day precipitation ( Rx1day ); simple precipitation intensity index ( SDII ); and monthly count of days when total precipitation (represented by PRCP) exceeds 10 mm ( R10mm ) – were calculated for 98 stations for the 38-year overlapping period (1975–2012). Cluster analysis was applied to evaluate the spatial characterisation of the annual precipitation extremes. Four extreme precipitation clusters were detected. Cluster 1 corresponds spatially to Central and Eastern Anatolia and is identified with the lowest values of the indices, except rainy days. Cluster 2 is concentrated mainly on the west and south of Anatolia, and especially the coastal zone, and can be characterised with the lowest rainy days, and high and moderate values of other indices. These two clusters are the most prominent classes throughout the country, and include a total of 82 stations. Cluster 3 is clearly located in the Black Sea coastal zone in the north, and has high and moderate index values. Two stations on the north-east coast of the Black Sea region are identified as Cluster 4, which exhibits the highest values among all indices. The overall results reveal that winter months and October have the highest proportion of precipitation extremes in Turkey. The north-east part of the Black Sea region and Mediterranean coastal area from the south-west to the south-east are prone to frequent extreme precipitation events.
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We present new spatio-temporal geostatistical modelling and interpolation capabilities of the R package gstat. Various spatio-temporal covariance models have been implemented, such as the separable, product-sum, metric and sum-metric models. In a real-world application we compare spatiotemporal interpolations using these models with a purely spatial kriging approach. The target variable of the application is the daily mean PM10 concentration measured at rural air quality monitoring stations across Germany in 2005. R code for variogram fitting and interpolation is presented in this paper to illustrate the workflow of spatio-temporal interpolation using gstat. We conclude that the system works properly and that the extension of gstat facilitates and eases spatio-temporal geostatistical modelling and prediction for R users.
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Hydrological modelling of floods relies on precipitation data with a high resolution in space and time. A reliable spatial representation of short time step rainfall is often difficult to achieve due to a low network density. In this study hourly precipitation was spatially interpolated with the multivariate geostatistical method kriging with external drift (KED) using additional information from topography, rainfall data from the denser daily networks and weather radar data. Investigations were carried out for several flood events in the time period between 2000 and 2005 caused by different meteorological conditions. The 125 km radius around the radar station Ummendorf in northern Germany covered the overall study region. One objective was to assess the effect of different approaches for estimation of semivariograms on the interpolation performance of short time step rainfall. Another objective was the refined application of the method kriging with external drift. Special attention was not only given to find the most relevant additional information, but also to combine the additional information in the best possible way. A multi-step interpolation procedure was applied to better consider sub-regions without rainfall. The impact of different semivariogram types on the interpolation performance was low. While it varied over the events, an averaged semivariogram was sufficient overall. Weather radar data were the most valuable additional information for KED for convective summer events. For interpolation of stratiform winter events using daily rainfall as additional information was sufficient. The application of the multi-step procedure significantly helped to improve the representation of fractional precipitation coverage.
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The Mediterranean Basin is a region where global climate change will be most evident. Located in the Eastern Mediterranean Basin, Turkey is a potential area of risk, too. Climate change will significantly affect the high precipitation variability in the Mediterranean Basin. This study used seasonal and annual precipitation data from 1975-2008 from 83 meteorology stations to identify the precipitation trends in Turkey. Seasonal and annual precipitation analysis is performed with two types of statistical analyses. First, the presence of a monotonic increasing or decreasing trend is tested with the nonparametric Mann-Kendall test and, secondly, the slope of a linear trend is estimated with the nonparametric Sen's method. These analyses revealed a decreasing trend in all seasonal and annual precipitation other than those in autumn, during which an increasing trend prevails throughout the country. While the northern coast of Turkey seems to have an increased precipitation trend other than in summer, a decrease can be observed in southern and central regions. The overall semi-arid qualities of these regions where precipitation rates fall call for a sustainable irrigation management.
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This document describes classes and methods designed to deal with different types of spatio-temporal data in R implemented in the R package spacetime, and provides examples for analyzing them. It builds upon the classes and methods for spatial data from package sp, and for time series data from package xts. The goal is to cover a number of useful representations for spatio-temporal sensor data, and results from predicting (spatial and/or temporal interpolation or smoothing), aggregating, or subsetting them, and to represent trajectories. The goals of this paper is to explore how spatio-temporal data can be sensibly represented in classes, and to find out which analysis and visualisation methods are useful and feasible. We discuss the time series convention of representing time intervals by their starting time only. This document is the main reference for the R package spacetime, and is available (in updated form) as a vignette in this package.
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Annual and seasonal precipitation series and annual aridity index series of Turkey were investigated with respect to spatial and temporal variations for the period 1930–1993. Analysis of normalized precipitation anomalies was also performed for the 1994–2000 period. Semi-arid and dry sub-humid climatic conditions are dominant over the continental interiors and continental Mediterranean region of Turkey. Normalized annual and winter precipitation series have tended to decrease over a considerable part of Turkey since the early 1970s. For the normalized annual and winter precipitation anomaly series, wet conditions generally occurred during the 1940s, 1960s, late 1970s, early 1980s and mid-late 1990s, whereas dry conditions generally dominated over the early-mid 1930s, early-mid 1970s, mid-late 1980s, early 1990s, and 1999/2000 in most of Turkey. Spring precipitation series generally indicated an upward trend from the mid 1940s to the late 1960s at many stations and to 1980s at some stations. This period was generally followed by a downward trend at many stations. Significant decreasing trends showed up in the annual precipitation series of 15 stations and in the winter precipitation series of 14 stations, mostly over the Mediterranean rainfall region. Summer rainfall series have tended to increase significantly at 7 stations. There has also been a general tendency from humid conditions of around the 1960s towards dry sub-humid climatic conditions in the aridity index values of many stations of Turkey. At some stations over the Aegean part of the Mediterranean region, there has been a significant change from humid conditions to dry sub-humid or semi-arid climatic conditions.
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The purpose of this study is to examine the annual and seasonal status of rainfall anomalies at the Mediterranean coast of Turkey during the period between 1950 and 2010. The rainfall anomalies at the Mediterranean coast of Turkey have shown significant annual and seasonal changes during the examined period. There are significant relations determined between the rainfall anomalies and the annual NAO and NCP; the NAO, NCP and the weather temperature during winter, the weather temperature during spring and the NAO during autumn. The rainfall anomaly values have presented a symmetric distribution in Fethiye and Anamur stations and an asymmetric distribution at the other stations in terms average deviation; and an asymmetric distribution in Fethiye, Alanya and Anamur stations and a symmetric distribution at the other stations in terms standard deviation. The rainfall anomalies have shown an increase tendency between Fethiye and Alanya and a decrease tendency between Alanya and Iskenderun during spring, a decrease tendency in Fethiye, Antalya and Anamur and an increase tendency at the other stations during summer whilst they have shown a decrease tendency at all stations during winter and an increase tendency at all stations during autumn.