1. bookVolume 19 (2020): Issue 1 (December 2020)
Journal Details
License
Format
Journal
First Published
01 Jun 2009
Publication timeframe
2 times per year
Languages
English
access type Open Access

The spatial pattern of selected extreme precipitation indices for Turkey (1975-2012)

Published Online: 04 Dec 2020
Page range: 19 - 30
Received: 04 Sep 2020
Accepted: 02 Nov 2020
Journal Details
License
Format
Journal
First Published
01 Jun 2009
Publication timeframe
2 times per year
Languages
English
Abstract

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.

Keywords

Introduction

During the second half of the 20th century, a significant proportion of the global land area was increasingly affected by a significant change in precipitation, which was characterised by more frequent heavy rainfall and significant increases in the number of extreme precipitation events (Frich et al. 2002). The hydrological cycle was intensified by global warming, leading to more evaporation and precipitation (Arnell 1999). However, at the regional scale, some parts of the world experienced wetter conditions than before (Alexander et al. 2006). For the Mediterranean basin, which is a transition area between subtropical and mid-latitude regions, precipitation patterns show quite a variable character, and it is thus even more important to identify the impact of climatic changes. Mediterranean countries are mainly dominated by regular dry conditions during the hot season and repeated periods of drought or extreme precipitation events during late autumn and winter (Oikonomou et al. 2008). According to Paxian et al. (2015), the Mediterranean basin will experience strong summer and winter drying over the northern and southern Mediterranean, respectively. However, precipitation extremes are tending to increase in even more Mediterranean areas, implying regions with decreasing totals but intensifying extremes, e.g. southern Europe and Turkey in winter and the Balkans in summer (Paxian et al. 2015). The results for different areas across the Mediterranean region show considerable regional differences in precipitation indices over the larger region (Nastos et al. 2013). Therefore, regional scale studies are becoming more and more important for Mediterranean countries in an area considered to be a hotspot of future climate crisis.

Due to its location between the subtropical and mid-latitude zones, the Azores (high) and the Iceland (low) strongly influence Turkey’s climate and precipitation regime. Turkey is affected by Polar and Tropical air masses in winter and summer, respectively. The cP (continental Polar) is a continental, cold, dry air mass that originates from Siberia and generates orographic rains if it becomes saturated while crossing the Black Sea (Akçar et al. 2007). The mP (marine Polar) air mass originates from the Atlantic Ocean and travels across Europe and the Balkans. It becomes unstable over Turkey and causes rainfall in coastal areas (Black Sea and Marmara), and snowfall at higher elevations and in the inner parts of the country. The transport of mP air into the Mediterranean basin creates the Mediterranean cyclogenesis and Mediterranean air mass, which also prevail in western and southern parts of Anatolia. In combination with the local orographic conditions, the Mediterranean air mass produces a considerable amount of precipitation. The Mediterranean trajectory of mP is more effective than the Atlantic trajectory in terms of generating rainfall (Tatli et al. 2004). Turkey’s precipitation regime character is shaped by: (1) large-scale atmospheric circulation during the winter months for coastal regions of Marmara, the Black Sea, the Aegean and Mediterranean regions; and (2) convectional rainfall for interior regions that experience a rainy spring. Turkey’s physiographic character has a major influence on its precipitation regimes. High relief and continentality play an important role in causing a rainfall deficit for interior regions; also, where mountains are located along the coast, there is high precipitation, particularly in winter (Sariş et al. 2010).

For Turkey, it has been demonstrated by several researchers that the monthly, seasonal and annual precipitation totals indicate significant changes both in space and time (Türkeş 1996 and 1998; Şen and Habib 2000; Kadioğlu et al. 1999; Tatli et al. 2004; Partal and Kahya 2006; Türkeş et al. 2008a; Sariş et al. 2010; Raja et al. 2017). Important decreasing trends in annual and winter precipitation totals are highlighted over western and southern coastal regions of Turkey. Yeşilırmak and Atatanır’s (2016) study on precipitation concentration over Turkey analysed daily precipitation data and found higher values of Daily Precipitation Concentration Index in north-western and southern parts, and lower values in western-central, central, eastern and north-eastern parts, and that the southern part was the most critical part of Turkey, with the highest values of precipitation concentration and annual total precipitation but the lowest number of rainy days. Regional studies over the western and southern part of Turkey has shown that dry periods are prolonged (Çelik 2019) while, on the other hand, heavy rainfall events tend to be more frequent. For example, Yılmaz (2015) stated that Antalya (southern Turkey) haves the potential to experience more intensive rainfalls in the future, which may lead to floods. Şensoy et al. (2013) have analysed extreme climate events over Turkey. For precipitation they revealed that numbers of heavy precipitation days have been increasing at most stations except the Aegean and south-eastern Anatolia, and usually cause extreme flood events. The maximum one-day precipitation has been increasing at most of the stations except in south-eastern Anatolia.

The observed trends in intensity and frequency of extreme precipitation events must be taken into account in order to improve the management of water resources. Therefore, it is very important to evaluate past changes in extreme precipitation events together with the current status in order to present perceptible and explicable spatiotemporal variation patterns of Turkey's precipitation climatology. This effort will lead to further regional-scale studies which are becoming critical for a country like Turkey with its vast regional discrepancies. This paper aims to present the variability patterns (spatial and intra-annual) in precipitation extremes by analysing changing conditions in the selected indices for Turkey. The following procedure is adopted to be able to clarify characteristics of extremes for the period 1975–2012. In particular, for each station and for each year: (i) (RDays) Monthly total rainy days, (Rx1day) Monthly maximum 1-day precipitation, (SDII) Simple precipitation intensity index, and (R10mm) Monthly count of days when PRCP≥10 mm were calculated; (ii) cluster analyses were applied to classify precipitation extremes throughout Turkey; and (iii) intra-annual variability of precipitation extremes was identified (box-and-whisker plots). Providing a contribution to understanding the extreme precipitation character and variation patterns over Turkey is the overall objective of this study.

Data and method

Daily precipitation totals for 98 Turkish State Meteorological Service stations were used; station selection was based upon record length and the aim to provide optimal spatial coverage across Turkey. A 38-year overlapping period for daily precipitation records from 1975 to 2012 was used. The metadata of the stations are listed in Table 1.

Basic information of the selected stations

Station NameStation NumberData LentghLatitude (N)Longitude (E)Altitude (m a.s.l.)
Adana173517337.0035.3327
Adiyaman172656537.7538.28672
Ağri170996539.7243.051632
Akhisar171846638.9227.8593
Aksaray178346438.3834.08965
Amasya170856740.6535.83412
Anamur173205936.0832.834
Ankara171307339.9532.88891
Antakya179846236.2036.17100
Ardahan176306541.1242.721829
Artvin170455741.1841.82628
Aydin172347237.8527.8556
Bafra176225041.5735.9220
Bandirma171145740.3527.9758
Bayburt176867340.2540.231584
Bilecik171227040.1529.98539
Bingöl172034338.8840.481177
Bodrum172906637.0527.4326
Burdur172386337.7230.28967
Ceyhan179607037.0335.8230
Cihanbeyli178005138.6532.93968
Çanakkale171126640.1526.426
Çankiri170805540.6033.62751
Çemişgezek177686439.0738.92953
Çorlu170546641.1727.8083
Çorum170847340.5534.95776
Denizli172375537.7829.08425
Dikili171806239.0726.883
Dinar178626538.0730.17864
Divriği177344839.3738.121225
Dörtyol179627336.8536.2228
Dursunbey177004639.5828.63639
Edirne170507341.6726.5751
Edremit176964039.6027.0221
Elaziğ172017238.6739.23990
Ereğli172485237.5034.051044
Fethiye172966436.6229.123
Gaziantep172616437.0737.38855
Geyve176627340.5230.301000
Giresun170347340.9238.4037
Gökçeada171106540.2025.9072
Gönen176745340.1027.6537
Gümüşhane170884540.4739.471219
Hadim179284536.9832.471552
Hakkari172855237.5843.731728
Hinis177406539.3741.701715
Hopa170424241.4041.4333
Isparta172407237.7730.55997
Inebolu170246041.9833.7764
Ipsala176324640.9326.4010
Iskenderun173706336.5836.174
Ispir176665040.4841.001222
Izmir172206538.4327.1725
Kahramanmaraş172554937.6036.93572
Karaman179326637.1833.221025
Kars170987340.6243.101775
Kastamonu170747341.3733.78800
Kayseri171966638.7335.481093
Kirklareli170527041.7327.23232
Kizilcahamam176644640.4732.651033
Kilis179787136.7237.12638
Kocaeli170666540.7829.9376
Kuşadasi172324537.8727.2522
Kütahya177257339.4229.97969
Malatya171997238.3538.32948
Malazgirt177804839.1542.531565
Manavgat179545736.7831.4338
Manisa171867338.6227.4371
Mardin172756437.3040.731050
Mersin173407336.8034.603
Merzifon170836840.8735.33755
Muğla172927337.2228.37646
Muş172045338.7341.481320
Niğde172506837.9734.681211
Ordu170335240.9837.904
Polatli177287339.5832.15886
Rize170407341.0340.529
Salihli177926338.4828.13111
Samsun170307341.2836.3044
Siirt172107237.9241.95896
Silifke173307336.3833.9315
Simav177484239.0828.98809
Sinop170267142.0235.1732
Sivas170907339.7537.021285
Sivrihisar177267339.4531.531070
Şanliurfa172706637.1338.77549
Şebinkarahisar176824240.3038.421300
Şile176106341.1829.3783
Tefenni178924937.3229.771142
Tokat170867040.3036.57608
Tosya176505141.0234.03870
Trabzon170376541.0039.7230
Uşak171887338.6829.40919
Van171725738.5043.381661
Yalova176604640.6529.274
Yozgat171404939.8234.801298
Zile176814340.3035.75700
Zonguldak170227241.4531.80137
Climate index calculations

The indices of the World Meteorological Organization–Commission for Climatology (WMO–CCL) and the Research Programme on Climate Variability and Predictability (CLIVAR) were adopted for identifying precipitation extreme characteristics (Peterson et al. 2001; Klein Tank and Können, 2003). Daily records were used in order to calculate time series of precipitation extremes at monthly and annual scales. The selected extreme precipitation indices are as follows:

RDays, Monthly total rainy days

Rx1day, Monthly maximum 1-day precipitation:

Let RR_j be the daily precipitation amount on day i in period j. The maximum 1-day value for period j is: Rx1dayj=max(RRij)Rx{\it 1}dayj = \max \,(RRij)

SDII, Simple precipitation intensity index:

Let RRwj be the daily precipitation amount on wet days, w (RR1mm) in period j. If W represents number of wet days in j, then: SDIIj=w=1WRRwjWSDII_{j} = {{\sum\nolimits_{w = 1}^W {RRwj}} \over W}

R10mm, Monthly count of days when PRCP≥10mm, where PRCP is the total precipitation measured as water equivalent:

Let RRij be the daily precipitation amount on day i in period j. Count the number of days where: RRij10 mmRRij \ge {\it10}mm

Cluster Analysis

Cluster analysis is a data reduction method based on grouping a set of data into object clusters. Cluster analysis is widely used in climatology research and in this study it was evaluated for classifying precipitation extremes by considering their magnitude. The classification procedure, based on magnitude characteristics, was developed by Hannah et al. (2000) and has been adopted in several studies (Harris et al. 2000; Bower et al. 2004; Kansakar et al. 2004; Hannah et al. 2005). For Turkey, Ünal et al. (2003), Sariş et al. (2010) and İyigün et al. (2013) have applied cluster analysis to precipitation data at the national scale. In this study, the classification procedure starts with the calculation of annual series of four indices (RDays, Rx1day, SDII, R10mm) for each station, regardless of their timing. Standardised z-score values of stations were preferred for inputting into the analysis in order control for differences in their relative values. The magnitude classification was performed by hierarchical, agglomerative cluster analysis using Ward’s method. Ward’s method typically outperforms other algorithms in terms of the separation to give relatively dense clusters with small within-group variance (Griffith and Amrhein 1997; Yarnal 1992). The dendrogram structure and agglomeration schedule (scree) plot that shows the breaks of slope was evaluated to determine the appropriate number of clusters for homogeneous classification. Thus, each of the 98 stations was grouped into indiscrete clusters and the classification of extreme precipitation of Turkey based on magnitude was provided.

Results

Monthly total rainy days (RDays), Monthly maximum 1-day precipitation (Rx1day), Monthly Simple precipitation intensity index (SDII) and Monthly count of days when PRCP≥10 mm (R10mm) were calculated for each year for the 98 stations. These indices were also calculated for annual series. Annual series were evaluated for spatially classifying extreme precipitation indices (RDays, Rx1Day, R10mm and SDII) for the 98 stations based on their magnitude characteristics. Monthly data sets were assessed for describing the seasonal character of extreme precipitation.

Cluster analysis was performed with annual data sets to elucidate the distinct spatial character of precipitation extremes. Figure 1 illustrates the spatial distribution of extreme precipitation clusters. According to the dendrograms and scree plots, four classes acceptably define the spatial variability for magnitude regimes of precipitation extremes across Turkey. Cluster 1 (48 stations) corresponds spatially to Central and Eastern Anatolia, but also predominates in the more inland parts of the Black Sea region. Cluster 2 (34 stations) is concentrated mainly in the west and south of Anatolia, and especially its coastal zone. These two clusters are the most prominent classes throughout the country. Cluster 3 (13 stations) is clearly located in the Black Sea coastal zone to the north. Two stations on the northeast coast of the Black Sea region are identified with Cluster 4 (2 stations).

Fig. 1

Spatial distribution of extreme precipitation clusters

Box-and-whisker plots show the magnitude characteristic of the defined clusters for each index. Regarding rainy days, the highest values were detected in Cluster 4. Cluster 3, which is also located in the northern part of the country, also has a high number of rainy days. The north of Turkey experiences a longer rainy season than other regions and this precipitation regime pattern can be explained by the frequent north-eastern Atlantic-originating depressions in autumn (Sariş et al. 2010). In terms of rainy days, Cluster 1 (Central and Eastern Anatolia) has higher values than Cluster two (the west and south coastal area). The reason that Cluster 2 has the lowest values of rainy days (RDays) can be explained by the seasonality of precipitation in this area arising from its Mediterranean climate character (Fig. 2a). For the monthly maximum of 1-day precipitation (R1Max), Cluster 4 again has the highest values; in fact, this cluster is characterised by the highest values of all indices. Cluster 1 has the lowest values of all indices except RDays. Cluster 2 and Cluster 3 show similar patterns to one another in monthly maximum of 1-day precipitation (Fig. 2b). For the other indices, simple precipitation intensity index (SDII) values are higher in Cluster 2 than in Cluster 3 (Fig. 2c) and for monthly count of days when precipitation exceeds 10 mm (R10mm), these two clusters show a resemblance (Fig. 2d).

Fig. 2

Box and Whiskers plots of annual precipitation extremes for each cluster

Based on the box-and-whisker plots, the determined clusters can be characterised as;

Cluster 1: concentrated in Central Anatolia with lowest index values of R1Max (34.8 mm), SDII (5.3 days), R10mm (14.1 days) and second lowest cluster for RDays (117.1 days).

Cluster 2: concentrated on western and southern coastal areas of Anatolia, namely the Aegean and Mediterranean, with high values of SDII (7.9 days) and R1Max (61.1 mm), moderate values in R10mm (22.9 days) and the lowest values of RDays (90.9 days).

Cluster 3: concentrated in northern coastal areas of Anatolia, namely the Black Sea, with the second-highest values of RDays (154.8 days) and R10mm (26.8 days) and moderate values in R1Max (57.8 mm) and SDII (5.6 days).

Cluster 4: localised in the north-east of the Black Sea region, with the highest values of RDays (181.9 days), R1Max (108.9 mm), SDII (12.4 days) and R10mm (67.5 days).

Figure 3 presents the intra-annual variability for each index. Figures a, b, c and d show the seasonality patterns for RDays, R1Max, SDII and R10mm, respectively. As stated before, Cluster 4 has remarkably extreme values among all indices. For R1Max, SDII and R10mm, all clusters exhibit clear seasonality – and, mainly, high values in autumn and winter as well. The seasonality of precipitation over Turkey is mainly dominated by the North Atlantic and Mediterranean depressions, which are influential in winter. However, the Black Sea coastal region of Turkey, and more significantly the north-east part of Turkey, are characterised by a remarkable October peak, which may be explained by prefrontal depression systems (Sariş et al. 2010).

Fig. 3

Intra-annual variability of (a) Rainy Days (b) R1Max (c) SDII and (d) R10 indices for each cluster.

For rainy days, Clusters 1, 2 and 3 show evident seasonality, with high rainy days values during autumn and winter (12–16 days per month) and the lowest values in summer. Cluster 4 has a distinctive character with an average of 15 rainy days per month during the year (Fig. 3a). Türkeş et al. (2008b) have studied the climatology of seasonal rainy days through Principal Component Analysis and explained spatial variability and relationships. Prior to analysis they had illustrated the spatial variability of seasonal rainy days. Although the temporal resolution of their studies is different, quite similar results were obtained.

For Cluster 2, the highest R1max values were seen in December. For Cluster 3 and Cluster 4 (Black Sea coastal region), the peak values of R1max occur in October. For Cluster 1, R1max values are high in the transitional seasons (Figure 3b). An earlier study by Tümertekin and Cöntürk (1958), which analysed the maximum daily precipitation totals per year for 223 stations, showed that the daily average maximum precipitation amount ranges from 20 to 150 mm. Similar results to Tümertekin’s were obtained here in terms of magnitude of daily maximum precipitation.

SDII values are highest in October for Clusters 4, 3 and 1 alike. However, Cluster 1 has very low values compared to the other clusters, and October–November values are very close to each other. Cluster 2 (Mediterranean) has a December peak in SDII (Fig. 3c). For R10mm (Fig. 3d) similar seasonality patterns were obtained for all clusters.

Figures 4 and 5 present the spatial variability of SDII and R10mm indices over Turkey, respectively. These two indices were specifically mapped since there has been no national-scale precipitation study on these indices. The simple daily precipitation index refers to rate of daily precipitation amount on wet days. The spatial variability of SDII index is greater in north-east Turkey and the Mediterranean region (from south-west to south-east along the Mediterranean coast), which corresponds to a significant proportion of annual SDII values. The SDII value is around 14 mm/day over these regions. Precipitation intensity is notable in particular areas of Turkey. The enhanced precipitation intensity over these areas refers to high amount of daily rainfall, which may lead to high overland (surface) flow and possibly flash floods based on land-use characteristics.

Fig. 4

Spatial variability of SDII index over Turkey

Fig. 5

Spatial variability of R10 index over Turkey

The R10mm index corresponds to the monthly count of days when daily precipitation exceeds 10 mm. In this map, annual series were evaluated. For R10mm indices, the north-east region still has an explicitly different pattern, along with other stations on the Black Sea coast. The yearly average value of the R10mm index over these areas is around 65 days. This average is around 30–40 days over the Mediterranean, where seasonality of precipitation is higher. Taking into account that the R1Max values concentrated between 30–120 mm for the coastal regions of Turkey, the risk of intensified surface flow might rise.

Conclusions

Daily precipitation data for 98 stations over Turkey were evaluated to elucidate the character of extreme precipitation in Turkey for selected indices at monthly and annual scale. The evaluated indices provide considerable insight into the understanding of the extreme character of precipitation; hence, the spatiotemporal variability of extreme precipitation was determined. In order to regionalise extreme precipitation variability, Cluster Analysis was employed, and four clusters were identified for the country. The overall analysis results suggest that the coastal regions have the highest values of precipitation extremes in Turkey. For each index, the results reveal that the maximum frequency of extreme precipitation events and the highest precipitation amount occurred in the Mediterranean and Black Sea coastal regimes. The peak time of extreme precipitation events is December in the Mediterranean coastal region and October in the Black Sea coastal region. For the inland regions, both the number of extreme precipitation events and the amount of precipitation for selected percentiles are distinctly low, except rainy days.

In terms of seasonality of extreme indices, October, December and January are significant months, as peak times for extreme precipitation events, which are observed mostly in the north-east region and the Mediterranean Coast. The coastal regime regions account for a considerable amount of the detected events. The seasonal pattern of extreme precipitation indices showed that the winter conditions over Turkey, which are characterised by large-scale circulation patterns originating from the North Atlantic and Mediterranean Basin, shape the spatiotemporal variability of extreme precipitation. Coastal (Mediterranean and Black Sea) regions are distinguished, along with some transitional regions. Precipitation extremes over Turkey are significantly related to an orographically enhanced frontal rainfall pattern for the coastal regions (Saris et al. 2010; Türkeş et al. (2008a).

The obtained results for precipitation extremes have some similarities with other Mediterranean-based extreme precipitation studies (Brunetti 2004; Zhang et al. 2005; Norrant and Douguédroit 2006; Nastos and Zerefos 2007; Bartholy and Pongrácz 2007). In particular, the results of precipitation intensity and precipitation amounts over the 10-mm threshold become significant. Considering the increased frequency and intensity of hydro-meteorological hazards over Turkey (Ceylan and Kömüşçü 2007), which implies a growing risk of hazardous events such as floods and landslides; the results of precipitation analyses must be evaluated as a matter of high priority. The findings of this study support the urgent need for integrated management of water resources with a wide perspective and multidisciplinary aspect. Coastal areas, and especially the Mediterranean region, are the most populated areas of the country, and are experiencing rapid land-cover changes. Therefore, this region is subject to a substantial risk of hydrometeorologically induced disasters due to the high proportion of the magnitude, frequency and duration of heavy precipitation events. Meanwhile, deforestation of upper basins for mining or hydropower investments is causing excessive overflow in the Black Sea region where flood-inducing rains are quite likely. Along with the changing pattern of precipitation amounts and timing, (precipitation intensity) the type of precipitation is also changing from snow to rain, which will restrain water availability in soil moisture and ground flow reservoirs.

To sum up, in terms of extreme precipitation variability, Turkey is facing two risks: water stress/scarcity and flood. The annual water amount per capita is 1,519 m3 in Turkey (http://www.dsi.gov.tr/toprak-ve-su-kaynaklari). According to the Falken-mark index, this value indicates that Turkey is close to experiencing water stress. Naturally, there are regional differences in terms of water availability, ranging from water surplus to water scarcity (Aydın et al. 2017). Nevertheless, water scarcity is already being felt in major cities of Turkey. In Istanbul, one such metropolis, the domestic water relies on (dammed) surface reservoirs. Since the local resources could not meet the need, water transfer between water-sheds is potentially required. Multi-faceted efforts should be made to secure future water availability in Turkey. Taking into account the changes in extreme precipitation and reducing the impact of extreme precipitation, especially by protecting forest areas, are just two of the actions required in order to be able to implement an efficient water-use strategy. Reducing the amount of surface runoff and increasing direct rainfall to the soil moisture and groundwater accumulation are very important in order to provide the necessary potential for both agricultural and drinking water in the future. Enhancing knowledge about extreme precipitation events will contribute to better management of both water and the disasters resulting from changes in the climate and environment.

Fig. 1

Spatial distribution of extreme precipitation clusters
Spatial distribution of extreme precipitation clusters

Fig. 2

Box and Whiskers plots of annual precipitation extremes for each cluster
Box and Whiskers plots of annual precipitation extremes for each cluster

Fig. 3

Intra-annual variability of (a) Rainy Days (b) R1Max (c) SDII and (d) R10 indices for each cluster.
Intra-annual variability of (a) Rainy Days (b) R1Max (c) SDII and (d) R10 indices for each cluster.

Fig. 4

Spatial variability of SDII index over Turkey
Spatial variability of SDII index over Turkey

Fig. 5

Spatial variability of R10 index over Turkey
Spatial variability of R10 index over Turkey

Basic information of the selected stations

Station NameStation NumberData LentghLatitude (N)Longitude (E)Altitude (m a.s.l.)
Adana173517337.0035.3327
Adiyaman172656537.7538.28672
Ağri170996539.7243.051632
Akhisar171846638.9227.8593
Aksaray178346438.3834.08965
Amasya170856740.6535.83412
Anamur173205936.0832.834
Ankara171307339.9532.88891
Antakya179846236.2036.17100
Ardahan176306541.1242.721829
Artvin170455741.1841.82628
Aydin172347237.8527.8556
Bafra176225041.5735.9220
Bandirma171145740.3527.9758
Bayburt176867340.2540.231584
Bilecik171227040.1529.98539
Bingöl172034338.8840.481177
Bodrum172906637.0527.4326
Burdur172386337.7230.28967
Ceyhan179607037.0335.8230
Cihanbeyli178005138.6532.93968
Çanakkale171126640.1526.426
Çankiri170805540.6033.62751
Çemişgezek177686439.0738.92953
Çorlu170546641.1727.8083
Çorum170847340.5534.95776
Denizli172375537.7829.08425
Dikili171806239.0726.883
Dinar178626538.0730.17864
Divriği177344839.3738.121225
Dörtyol179627336.8536.2228
Dursunbey177004639.5828.63639
Edirne170507341.6726.5751
Edremit176964039.6027.0221
Elaziğ172017238.6739.23990
Ereğli172485237.5034.051044
Fethiye172966436.6229.123
Gaziantep172616437.0737.38855
Geyve176627340.5230.301000
Giresun170347340.9238.4037
Gökçeada171106540.2025.9072
Gönen176745340.1027.6537
Gümüşhane170884540.4739.471219
Hadim179284536.9832.471552
Hakkari172855237.5843.731728
Hinis177406539.3741.701715
Hopa170424241.4041.4333
Isparta172407237.7730.55997
Inebolu170246041.9833.7764
Ipsala176324640.9326.4010
Iskenderun173706336.5836.174
Ispir176665040.4841.001222
Izmir172206538.4327.1725
Kahramanmaraş172554937.6036.93572
Karaman179326637.1833.221025
Kars170987340.6243.101775
Kastamonu170747341.3733.78800
Kayseri171966638.7335.481093
Kirklareli170527041.7327.23232
Kizilcahamam176644640.4732.651033
Kilis179787136.7237.12638
Kocaeli170666540.7829.9376
Kuşadasi172324537.8727.2522
Kütahya177257339.4229.97969
Malatya171997238.3538.32948
Malazgirt177804839.1542.531565
Manavgat179545736.7831.4338
Manisa171867338.6227.4371
Mardin172756437.3040.731050
Mersin173407336.8034.603
Merzifon170836840.8735.33755
Muğla172927337.2228.37646
Muş172045338.7341.481320
Niğde172506837.9734.681211
Ordu170335240.9837.904
Polatli177287339.5832.15886
Rize170407341.0340.529
Salihli177926338.4828.13111
Samsun170307341.2836.3044
Siirt172107237.9241.95896
Silifke173307336.3833.9315
Simav177484239.0828.98809
Sinop170267142.0235.1732
Sivas170907339.7537.021285
Sivrihisar177267339.4531.531070
Şanliurfa172706637.1338.77549
Şebinkarahisar176824240.3038.421300
Şile176106341.1829.3783
Tefenni178924937.3229.771142
Tokat170867040.3036.57608
Tosya176505141.0234.03870
Trabzon170376541.0039.7230
Uşak171887338.6829.40919
Van171725738.5043.381661
Yalova176604640.6529.274
Yozgat171404939.8234.801298
Zile176814340.3035.75700
Zonguldak170227241.4531.80137

AKÇAR N, YAVUZ V, IVY-OCHS S, KUBIK PW, VARDAR M and SCHLÜCHTER C, 2007, Palaeoglacial records from Kavron Valley, NE Turkey: Field and cosmogenic exposure dating evidence. Quaternary International 164–165: 170–183.AKÇARNYAVUZVIVY-OCHSSKUBIKPWVARDARMSCHLÜCHTERC2007Palaeoglacial records from Kavron Valley, NE Turkey: Field and cosmogenic exposure dating evidenceQuaternary International164–165170183Search in Google Scholar

ALEXANDER LV, ZHANG X, PETERSON TC, CAESAR J, GLEASON B, KLEIN TANK AMG, HAY-LOCK M, COLLINS D, TREWIN B, RAHIMZADEH F, TAGIPOUR A, RUPA KUMAR K, REVADEKAR J, GRIFFITHS G, VINCENT L, STEPHENSON DB, BURN J, AGUILAR E, BRUNET M, TAYLOR M, NEW M, ZHAI P, RUSTICUCCI M and VAZQUEZ-AGUIRRE JL, 2006, Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research 111: D05109, DOI: https://doi:10.1029/2005JD006290ALEXANDERLVZHANGXPETERSONTCCAESARJGLEASONBKLEIN TANKAMGHAY-LOCKMCOLLINSDTREWINBRAHIMZADEHFTAGIPOURARUPA KUMARKREVADEKARJGRIFFITHSGVINCENTLSTEPHENSONDBBURNJAGUILAREBRUNETMTAYLORMNEWMZHAIPRUSTICUCCIMVAZQUEZ-AGUIRREJL2006Global observed changes in daily climate extremes of temperature and precipitationJournal of Geophysical Research111D05109DOI: https://doi:10.1029/2005JD006290Search in Google Scholar

AYDIN O, ÜNALDI ÜE, DUMAN N, ÇIÇEK İ and TÜRKOĞLU N, 2017, Türkiye’de su kıtlığının mekânsal ölçekte değerlendirilmesi. Türk Coğrafya Dergisi 68: 11–18 (in Turkish).AYDINOÜNALDIÜEDUMANNÇIÇEKİTÜRKOĞLUN2017Türkiye’de su kıtlığının mekânsal ölçekte değerlendirilmesiTürk Coğrafya Dergisi681118(in Turkish).Search in Google Scholar

ARNELL N, 1999, Climate Change and Global Water Resources. Global Environmental Change 9: 31–49.ARNELLN1999Climate Change and Global Water ResourcesGlobal Environmental Change93149Search in Google Scholar

BARTHOLY J and PONGRÁCZ R, 2007, Regional analysis of extreme temperature and precipitation indices for from 1946 to 2001. Global and Planetary Change 57: 83–95.BARTHOLYJPONGRÁCZR2007Regional analysis of extreme temperature and precipitation indices for from 1946 to 2001Global and Planetary Change578395Search in Google Scholar

BOWER D, HANNAH DM and MCGREGOR GR, 2004, Techniques for assessing the climatic sensitivity of river flow regimes. Hydrological Processes 18: 2515–2543.BOWERDHANNAHDMMCGREGORGR2004Techniques for assessing the climatic sensitivity of river flow regimesHydrological Processes1825152543Search in Google Scholar

BRUNETTI M, 2004, Changes in daily precipitation frequency and distribution in Italy over the last 120 years. Journal of Geophysical Research 109: D05102, DOI: https://doi:10.1029/2003JD004296BRUNETTIM2004Changes in daily precipitation frequency and distribution in Italy over the last 120 yearsJournal of Geophysical Research109D05102DOI: https://doi:10.1029/2003JD004296Search in Google Scholar

CEYLAN A and KÖMÜŞÇÜ AÜ, 2007, Meteorolojik Karakterli Doğal Afetlerin Uzun Yıllar ve Mevsimsel Dağılımlari. I. Türkiye İklim Değişikliği Kongresi – Tikdek 2007. 11–13 April 2007, Istanbul Turkey, 93–104 (in Turkish).CEYLANAKÖMÜŞÇÜ2007Meteorolojik Karakterli Doğal Afetlerin Uzun Yıllar ve Mevsimsel Dağılımlari. I. Türkiye İklim Değişikliği Kongresi – Tikdek 200711–13April2007Istanbul Turkey93104(in Turkish).Search in Google Scholar

ÇELIK MA, 2019, Akdeniz Kıyılarında Ekstrem Nemli Ve Kurak Mevsimlerin Dağılımı (1967–2016). Academic Platform Journal of Engineering and Science 7–1: 56–66 (in Turkish).ÇELIKMA2019Akdeniz Kıyılarında Ekstrem Nemli Ve Kurak Mevsimlerin Dağılımı (1967–2016)Academic Platform Journal of Engineering and Science7–15666(in Turkish).Search in Google Scholar

FRICH P, ALEXANDER LV, DELLA-MARTA P, GLEASON B, HAYLOCK M, KLEIN TANK AMG and PETERSON T, 2002, Observed coherent changes in climatic extremes during 2nd half of the 20th century. Climate Research 19: 193–212.FRICHPALEXANDERLVDELLA-MARTAPGLEASONBHAYLOCKMKLEIN TANKAMGPETERSONT2002Observed coherent changes in climatic extremes during 2nd half of the 20th centuryClimate Research19193212Search in Google Scholar

GRIFFITH DA and AMRHEIN CG, 1997, Multivariate Statistics for Geographers. Prentice-Hall: New Jersey.GRIFFITHDAAMRHEINCG1997Multivariate Statistics for GeographersPrentice-HallNew JerseySearch in Google Scholar

HANNAH DM, KANSAKAR SR, GERRARD AJ and REES G, 2005, Flow regimes of Himalayan rivers of Nepal: their nature and spatial patterns. Journal of Hydrology 308: 18–32.HANNAHDMKANSAKARSRGERRARDAJREESG2005Flow regimes of Himalayan rivers of Nepal: their nature and spatial patternsJournal of Hydrology3081832Search in Google Scholar

HANNAH DM, SMITH BPG, GURNELL AM and MCGREGOR GR, 2000, An approach to hydrograph classification. Hydrological Processes 14: 317–338.HANNAHDMSMITHBPGGURNELLAMMCGREGORGR2000An approach to hydrograph classificationHydrological Processes14317338Search in Google Scholar

HARRIS NM, GURNELL AM, HANNAH DM and PETTS GE, 2000, Classification of river regimes: A context for hydroecology. Hydrological Processes 14: 2831–2848.HARRISNMGURNELLAMHANNAHDMPETTSGE2000Classification of river regimes: A context for hydroecologyHydrological Processes1428312848Search in Google Scholar

IYIGUN C, TÜRKEŞ M, BATMAZ İ, YOZGATLIGIL C, PURUTÇUOĞLU V, KARTAL KOÇ E and ÖZTÜRK MZ, 2013, Clustering current climate regions of Turkey by using a multivariate statistical method. Theoretical and Applied Climatology 114: 95–106. DOI: https://doi.org/10.1007/s00704-012-0823-7IYIGUNCTÜRKEŞMBATMAZİYOZGATLIGILCPURUTÇUOĞLUVKARTAL KOÇEÖZTÜRKMZ2013Clustering current climate regions of Turkey by using a multivariate statistical methodTheoretical and Applied Climatology11495106DOI: https://doi.org/10.1007/s00704-012-0823-7Search in Google Scholar

KADIOĞLU M, ÖZTÜRK N, ERDUN H and ŞEN Z, 1999, On the precipitation climatology of Turkey by Harmonic analysis. International Journal of Climatology 19: 1717–1728.KADIOĞLUMÖZTÜRKNERDUNHŞENZ1999On the precipitation climatology of Turkey by Harmonic analysisInternational Journal of Climatology1917171728Search in Google Scholar

KANSAKAR SR, HANNAH DM, GERRARD AJ and REES G, 2004, Spatial pattern in the precipitation regime of Nepal. International Journal of Climatology 24: 1645–1659.KANSAKARSRHANNAHDMGERRARDAJREESG2004Spatial pattern in the precipitation regime of NepalInternational Journal of Climatology2416451659Search in Google Scholar

KLEIN TANK AMG and KÖNNEN GP, 2003, Trends in Indices of Daily Temperature and Precipitation Extremes in Europe, 1946–1999. Journal of Climate 16: 3665–3680.KLEIN TANKAMGKÖNNENGP2003Trends in Indices of Daily Temperature and Precipitation Extremes in Europe, 1946–1999Journal of Climate1636653680Search in Google Scholar

NASTOS PT and ZEREFOS CS, 2007, On extreme daily precipitation totals at Athens, Greece. Advances in Geosciences 10: 59–66.NASTOSPTZEREFOSCS2007On extreme daily precipitation totals at Athens, GreeceAdvances in Geosciences105966Search in Google Scholar

NASTOS PT, KAPSOMENAKIS J and DOUVIS KC, 2013, Analysis of precipitation extremes based on satellite and high-resolution gridded data set over Mediterranean basin. Atmospheric Research, 131: 46–59.NASTOSPTKAPSOMENAKISJDOUVISKC2013Analysis of precipitation extremes based on satellite and high-resolution gridded data set over Mediterranean basinAtmospheric Research1314659Search in Google Scholar

NORRANT C and DOUGUÉDROIT A, 2006, Monthly and daily precipitation trends in the Mediterranean (1950–2000). Theoretical and Applied Climatology 83: 89–106.NORRANTCDOUGUÉDROITA2006Monthly and daily precipitation trends in the Mediterranean (1950–2000)Theoretical and Applied Climatology8389106Search in Google Scholar

OIKONOMOU C, FLOKAS HA, HATZAKI M, ASIMAKOPOULOS DN and GIANNAKOPOULOS C, 2008, Future changes in the occurrence of extreme precipitation events in Eastern Mediterranean, Global NEST Journal 10(2): 255–262.OIKONOMOUCFLOKASHAHATZAKIMASIMAKOPOULOSDNGIANNAKOPOULOSC2008Future changes in the occurrence of extreme precipitation events in Eastern MediterraneanGlobal NEST Journal102255262Search in Google Scholar

PARTAL T and KAHYA E, 2006, Trend analysis in Turkish precipitation data. Hydrological Processes 20: 2011–2026.PARTALTKAHYAE2006Trend analysis in Turkish precipitation dataHydrological Processes2020112026Search in Google Scholar

PAXIAN A, HERTIG E, SEUBERT S, VOGT G, JACOBEIT J and PAETH H, 2015, Presentday and future Mediterranean precipitation extremes assessed by different statistical approaches. Climate Dynamics 44: 845–860.PAXIANAHERTIGESEUBERTSVOGTGJACOBEITJPAETHH2015Presentday and future Mediterranean precipitation extremes assessed by different statistical approachesClimate Dynamics44845860Search in Google Scholar

PETERSON TC, FOLLAND C, GRUZA G, HOGG W, MOKSSIT A and PLUMMER N, 2001, Report on the activities of the Working Group on Climate Change Detection and Related Reporters 1998–2001. World Meteorological Organisation Rep. WCDMP-47, WMO-TD 1071, Geneva, Switzerland, 143.PETERSONTCFOLLANDCGRUZAGHOGGWMOKSSITAPLUMMERN2001Report on the activities of the Working Group on Climate Change Detection and Related Reporters 1998–2001World Meteorological Organisation Rep. WCDMP-47, WMO-TD 1071Geneva, Switzerland143Search in Google Scholar

RAJA NB, AYDIN O, TÜRKOĞLU N and ÇIÇEK İ, 2017, Space-time kriging of precipitation variability in Turkey for the period 1976–2010. Theoretical and Applied Climatology 129: 293–304. DOI: https://doi:10.1007/s00704-016-1788-8RAJANBAYDINOTÜRKOĞLUNÇIÇEKİ2017Space-time kriging of precipitation variability in Turkey for the period 1976–2010Theoretical and Applied Climatology129293304DOI: https://doi:10.1007/s00704-016-1788-8Search in Google Scholar

SARIŞ F, HANNAH DM and EASTWOOD WJ, 2010, Spatial variability of precipitation regimes over Turkey. Hydrological Sciences Journal 55(2): 234–249.SARIŞFHANNAHDMEASTWOODWJ2010Spatial variability of precipitation regimes over TurkeyHydrological Sciences Journal552234249Search in Google Scholar

ŞEN Z and HABIB Z 2000, Spatial analysis of monthly precipitation in Turkey. Theoretical and Applied Climatology 67: 81–96.ŞENZHABIBZ2000Spatial analysis of monthly precipitation in TurkeyTheoretical and Applied Climatology678196Search in Google Scholar

ŞENSOY S, TÜRKOĞLU N, AKÇAKAYA A, EKICI M, DEMIRCAN M, ULUPINAR Y, ATAY H, TÜVAN A and DEMIRBAŞ H, 2013, Trends in Turkey Climate Indices from 1960 to 2010. In 6th Atmospheric Science Symposium – ATMOS 2013 3 – 5 Haziran 2013, İstanbul.ŞENSOYSTÜRKOĞLUNAKÇAKAYAAEKICIMDEMIRCANMULUPINARYATAYHTÜVANADEMIRBAŞH2013Trends in Turkey Climate Indices from 1960 to 2010In 6th Atmospheric Science Symposium – ATMOS 2013 3 – 5 Haziran 2013, İstanbul.Search in Google Scholar

TATLI H, DALFES N and MENTEŞ S, 2004, A statistical downscaling method for monthly total precipitation over Turkey. International Journal of Climatology 24: 161–188.TATLIHDALFESNMENTEŞS2004A statistical downscaling method for monthly total precipitation over TurkeyInternational Journal of Climatology24161188Search in Google Scholar

TÜMERTEKIN E and CÖNTÜRK H, 1958, Türkiye’de günlük maksimum yağışlar. Coğrafya Enstitüsü Dergisi 9: 115–121 (Turkish).TÜMERTEKINECÖNTÜRKH1958Türkiye’de günlük maksimum yağışlarCoğrafya Enstitüsü Dergisi9115121(Turkish).Search in Google Scholar

TÜRKEŞ M, 1996, Spatial and temporal analysis of annual rainfall variations in Turkey. International Journal of Climatology 16: 1057–1076.TÜRKEŞM1996Spatial and temporal analysis of annual rainfall variations in TurkeyInternational Journal of Climatology1610571076Search in Google Scholar

TÜRKEŞ M, 1998, Influence of geopotential heights, cyclone frequency and southern oscillation on rainfall variations in Turkey. International Journal of Climatology 18: 649–680.TÜRKEŞM1998Influence of geopotential heights, cyclone frequency and southern oscillation on rainfall variations in TurkeyInternational Journal of Climatology18649680Search in Google Scholar

TÜRKEŞ M, KOÇ T and SARIŞ F, 2008a, Spatiotemporal variability of precipitation total series over Turkey. International Journal of Climatology 29(8): 1056–1074.TÜRKEŞMKOÇTSARIŞF2008aSpatiotemporal variability of precipitation total series over TurkeyInternational Journal of Climatology29810561074Search in Google Scholar

TÜRKEŞ M, KOÇ T, and SARIŞ F, 2008b, Türkiye’de yağışlı gün sayılarının klimatolojisi ve alansal ilişki desenleri, Atmosfer Bilimleri Sempozyumu IV, İstanbul, Türkiye, 25–28 Mart 2008: 500–511 (Turkish).TÜRKEŞMKOÇTSARIŞF2008bTürkiye’de yağışlı gün sayılarının klimatolojisi ve alansal ilişki desenleriAtmosfer Bilimleri Sempozyumu IVİstanbul, Türkiye25–28 Mart 2008500511(Turkish).Search in Google Scholar

ÜNAL Y, KINDAP T, KARACA M, 2003, Redefining the climate zones of Turkey using cluster analysis. International Journal of Climatology 23: 1045–1055.ÜNALYKINDAPTKARACAM2003Redefining the climate zones of Turkey using cluster analysisInternational Journal of Climatology2310451055Search in Google Scholar

YARNAL B, 1992, Synoptic Climatology in Environmental Analysis: A Primer. Belhaven Press: London.YARNALB1992Synoptic Climatology in Environmental Analysis: A PrimerBelhaven PressLondonSearch in Google Scholar

YEŞILIRMAK E and ATATANIR L, 2016, Spatiotemporal variability of precipitation concentration in western Turkey. Natural Hazards 81: 687–704. DOI: https://doi:10.1007/s11069-015-2102-2YEŞILIRMAKEATATANIRL2016Spatiotemporal variability of precipitation concentration in western TurkeyNatural Hazards81687704DOI: https://doi:10.1007/s11069-015-2102-2Search in Google Scholar

YILMAZ AG, 2015, The effects of climate change on historical and future extreme rainfall in Antalya, Turkey. Hydrological Sciences Journal 60(12): 2148–2162. DOI: https://doi:10.1080/02626667.2014.945455YILMAZAG2015The effects of climate change on historical and future extreme rainfall in Antalya, TurkeyHydrological Sciences Journal601221482162DOI: https://doi:10.1080/02626667.2014.945455Search in Google Scholar

ZHANG X, AGUILAR E, SENSOY S, MELKONYAN H, TAGIYEVA U, AHMED N, KUTALADZE N, RAHIMZADEH F, TAGHIPOUR A, HANTOSH TH, ALBERT P, SEMAWI M, ALI MK, AL-SHABIBI MHS, AL-OULAN Z, ZATARI T, KHELET IAD, HAMOUD S, SAGIR R, DEMIRCAN M, EKEN M, ADIGUZEL M, ALEXANDER L, PETERSON TC and WALLIS T, 2005, Trends in Middle East climate extreme indices from 1950 to 2003. Journal of Geophysical Research 110: D22104. DOI: https://doi:10.1029/2005JD006181ZHANGXAGUILARESENSOYSMELKONYANHTAGIYEVAUAHMEDNKUTALADZENRAHIMZADEHFTAGHIPOURAHANTOSHTHALBERTPSEMAWIMALIMKAL-SHABIBIMHSAL-OULANZZATARITKHELETIADHAMOUDSSAGIRRDEMIRCANMEKENMADIGUZELMALEXANDERLPETERSONTCWALLIST2005Trends in Middle East climate extreme indices from 1950 to 2003Journal of Geophysical Research110D22104DOI: https://doi:10.1029/2005JD006181Search in Google Scholar

TURKEY’S GENERAL DIRECTORATE OF STATE HYDRAULIC WORKS, http://www.dsi.gov.tr/toprak-ve-su-kaynaklariTURKEY’S GENERAL DIRECTORATE OF STATE HYDRAULIC WORKShttp://www.dsi.gov.tr/toprak-ve-su-kaynaklariSearch in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo