Trends in total precipitation and magnitude–frequency of extreme precipitation in Iran,...

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. (2015)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.4465

Trends in total precipitation and magnitude–frequency ofextreme precipitation in Iran, 1969–2009

Mohammad Reza Najafia* and Saber Moazamiba Pacific Climate Impacts Consortium, University of Victoria, Canada

b Department of Civil Engineering, Faculty of Engineering, Islamshahr Branch, Islamic Azad University, Iran

ABSTRACT: Spatiotemporal changes in total precipitation as well as the magnitude and frequency of extreme precipitationevents in Iran are assessed using 187 gauging stations with at least 41 years of records until 2009. The spatial distributionof extreme precipitation is evaluated based on the generalized extreme value (GEV) distribution fitted to the annual/seasonalmaximum daily series at each location. Temporal trends of the magnitude of extreme precipitation events are also analysedusing the annual/seasonal maximum daily series, while temporal trends of the frequency of extremes are assessed based onrecords exceeding the 99th percentile threshold. Results show an overall declining trend of the annual precipitation in particularin regions located on the north, west and northwest of Iran. Seasonal analysis shows the largest contribution of winter to thisdeclining trend. In addition, precipitation has significantly decreased in the northwest during spring. Although the changesin the magnitudes of extreme precipitation events are insignificant, with increasing trends in 50% of the stations, the overallfrequencies show significant declines in particular during winter. The magnitude and frequency of extreme precipitation havealso significantly declined in the northwest region during spring.

KEY WORDS spatiotemporal trend; extreme precipitation; magnitude; frequency; generalized extreme value distribution;Poisson distribution; return level; Iran

Received 24 March 2015; Revised 29 June 2015; Accepted 7 July 2015

1. Introduction

There is strong evidence that anthropogenic forcing sig-nals, including greenhouse gases, are changing the vari-ability and trend of the hydroclimate quantities (Min et al.,2011; Trenberth, 2011; Wan et al., 2014; Najafi et al.,2015). Precipitation is the key driver of the hydrologiccycle, and changes in its frequency and magnitude canhave serious consequences on the societal, agricultural andeconomic developments. According to the Fifth Assess-ment Report of the Intergovernmental Panel on ClimateChange (IPCC), the globally averaged observed precipita-tion data sets exhibit overall increasing trends from 1901to 2008. The zonal average precipitation over the latitudeband of 30∘–60∘N shows statistically significant increases,while there is no significant trend in tropical land precipi-tation (30∘S–30∘N) (Hartmann et al., 2014).

On regional scales, large variations in precipitationtrends are seen in particular over Middle East. Evans(2010) found decreases in precipitation over the east-ern Mediterranean and Turkey but overall increases insouthwest Caspian Sea and Zagros mountains. Lelieveldet al. (2012) showed projected decreases in the annualprecipitation in the southern Europe-Turkey as well aseastern Mediterranean, and increases in the Persian Gulf

* Correspondence to: M. R. Najafi, Pacific Climate Impacts Consortium,University House 1, University of Victoria, PO Box 1700 STN CSC,Victoria, BC, V8W 2Y2, Canada. E-mail: [email protected]

area. Philandras et al. (2011) found statistically significantdeclining trends in the annual precipitation totals in themajority of the Mediterranean regions. These decliningtrends are partly responsible for the recent drought con-ditions and ground water depletion in this region (Vosset al., 2013).

Iran, because of its complex topography, experienceslarge spatiotemporal variability of precipitation withan overall decline in recent decades (Modarres and daSilva, 2007; Ghahraman and Taghvaeian, 2010; Kousariand Zarch, 2011; Ahani et al., 2012; Delju et al., 2013).Some’e et al. (2012) analysed the annual and seasonalprecipitation data from 28 synoptic stations during1967–2006 and found overall negative trends in theannual precipitation. Based on data from 41 stations overthe period of 1966–2005, Tabari and Talaee (2011) alsofound decreasing trends in the annual precipitation atabout 60% of the stations, with general decreases duringwinter and spring. They detected significant negativetrends in the northwest of Iran. Soltani et al. (2012) anal-ysed 33 synoptic stations over 1951–2005 to investigatethe annual and monthly trends of the rainfall amounts,number of rainy days and 24-h maximum precipitation.Results showed that in April, May and February, recordsfrom 29, 21 and 21 stations had negative trends, respec-tively, while in December and July records from 26 and 24stations had positive trends. Using precipitation and soilmoisture data from MERRA-Land, Golian et al. (2014)showed significant drying trends in northern, northwestern

© 2015 Royal Meteorological Society

M. R. NAJAFI AND S. MOAZAMI

and central parts of Iran and insignificant trends in theeastern and northeastern regions.

Understanding the temporal trends of the regionalextreme precipitation events is crucial for infrastructuredesign as well as proper attribution of changes to humaninfluence (Min et al., 2011; Najafi et al., 2011; Kharinet al., 2013; Najafi and Moradkhani, 2014). Accordingto IPCC, while there is substantial regional variationin extreme precipitation, it is likely that the number ofheavy precipitation events (above 95th percentile) haveincreased significantly since 1951 in more regions thanthey have significantly decreased (Hartmann et al., 2014).Westra et al. (2013) also found significant increasingtrends in the global annual maximum daily precipitation.Using precipitation data from MERRA-Land, Tabari et al.(2014) showed significant changes in extreme conditionsof the northwest and southeast parts of Iran. Modarresand Sarhadi (2009) studied the trends of the annual and24-h maximum rainfall events from 145 rain gaugesover 1951–2000. Their results indicated that the annualrainfall decreased at 67% of the stations; however, the24-h maximum rainfall increased at 50% of the stations.By studying 27 synoptic stations in Iran over 1951–2003,Rahimzadeh et al. (2009) showed overall negative trendsof very wet days defined as the fraction of annual totalprecipitation that exceeds the 95th percentile (except forthe central regions) and the 99th percentile. They alsofound negative trends in the maximum precipitation in the

southern regions of the country, and mixed positive andnegative trends in the northern parts.

Understanding the changes in both magnitude and fre-quency of extreme precipitation is essential for futureflood risk assessment and water resources management(Beguería et al., 2011; Seneviratne et al., 2012; Hirsch andArchfield, 2015; Mallakpour and Villarini, 2015). Recentstudy by Mallakpour and Villarini (2015) over the cen-tral United States showed that there is limited evidencefor the increases in the magnitudes of flood events, whilestrong evidence suggests that the frequency of floodinghas increased. Over Iran, only few studies have investi-gated changes in the magnitudes of extreme precipitationevents, and to the knowledge of the authors, there hasbeen no study on the frequencies of extreme events in thisregion. In this study, a comprehensive assessment of theannual/seasonal trends of total and extreme precipitationis performed during the extended period of 1969–2009.The spatial distribution of extreme precipitation is assessedfollowed by the analysis of the spatiotemporal changes inthe magnitude and frequency of extremes based on recordsfrom 187 gauging stations.

2. Study area and data

Iran is located between 25∘–40∘N in latitude and44∘–64∘E in longitude and has a total area of

Persian Gulf

Caspian Sea

65°0'0"E

65°0'0"E

60°0'0"E

60°0'0"E

55°0'0"E

55°0'0"E

50°0'0"E

50°0'0"E

45°0'0"E

45°0'0"E

40°0'0"N 40°0'0"N

35°0'0"N 35°0'0"N

30°0'0"N 30°0'0"N

25°0'0"N 25°0'0"N

Gauging stations

Elevation

High:

558

7 m

Low: –

30 m

Figure 1. Study area.

© 2015 Royal Meteorological Society Int. J. Climatol. (2015)

TREND OF MEAN AND EXTREME PRECIPITATION IN IRAN

1 648 195 km2 (Figure 1). Sixty percent of the areaconsists of mountainous regions of Alborz and Zagros onthe north, west and southwest, respectively. Central partsof the country include the dry deserts of Dasht-e-Kavir andDasht-e-Lut. The complex topography of the region has asubstantial role in its climate regime resulting in signifi-cant spatial variations in the rainfall pattern. The averageannual rainfall is approximately 250 mm, which rangesfrom 50 mm in the deserts to 1600 mm in northern areasclose to the Caspian Sea. Iran’s climate is categorized asarid and semi-arid, except for the coastal regions close tothe Caspian Sea as well as parts of the west (Soltani et al.,2012). Iran suffers from extreme climate events such asdroughts and floods. The most intensive droughts haveoccurred in the last few decades especially in southeasternand central parts of the country (Abarghouei et al., 2011;Tabari and Aghajanloo, 2013). Several parts of Iran arealso prone to flood events including regions in the north,northwest and southwest, therefore with the ongoingregional and global temperature increases, it is importantto evaluate the changes of extremes over this region.

In this study, daily precipitation records from 187 sta-tions are obtained from the meteorological organization ofIran. All stations have at least 41 years of records until2009. The temporal trends of the annual precipitation aswell as the magnitude and frequency of extreme precipita-tion are studied for all stations over the common periodof 1969–2009. Majority of the stations have less than5% missing data. Out of 187 stations, only 7 stations aremissing maximum of 3 years of precipitation records; 25stations are missing 2 years, 80 stations are missing only1 year, and the rest of the stations have complete coverage.Annual and seasonal data have consistent temporal cov-erages. While in 1969, 22 stations (12% of the total) andin 1982 and 2006 about 5.5% of the stations have missingrecords, for the rest of the analysis period less than 4% ofthe stations have missing data. Consequently, the missingdata sets will have minimal impact on the overall results.

3. Methodology

The annual maximum daily series are considered to evalu-ate the trends of the magnitude of extreme precipitationevents. Additionally, to characterize the contribution ofeach season to the annual trend, seasonal analyses are per-formed. The trends of the total annual precipitation as wellas the magnitudes of extreme events are assessed using theMann–Kendall test (Mann, 1945; Kendall, 1975) whichis not bound by any assumptions on the distribution ofthe data sets and is robust to the influence of the outliers.The non-parametric rank-based test requires the data to beindependent. This method has been widely used to detecttemporal trends in the hydroclimate variables (Helsel andHirsch, 1992).

The null hypothesis for the trend of the random vari-able x is Pr(xj > xi)= 0.5, if j> i, indicating that the datavalues are independent and identically distributed (iid)with no upward or downward trends. Alternatively, this

hypothesis is rejected for Pr(xj > xi)≠ 0.5, if j> i in a twosided test.

The Mann–Kendall test statistic S is computed as:

S =n−1∑i=1

n∑j=i+1

sgn(xj − xi

)(1)

where n is the length of the data set, xi and xj are theobservations in years i and j (j> i), and the sign functionsgn is defined as:

sgn(xj − xi

)=⎧⎪⎨⎪⎩+1, if xj − xi > 0

0, if xj − xi = 0

−1, if xj − xi < 0

(2)

In addition to the magnitude of extreme precipitation,the trends in the frequency of extreme precipitation areassessed based on the peaks over threshold approach. The99th percentile of the daily records at each station isconsidered as the threshold. Poisson regression is a formof generalized linear model (GLM) which assumes a lineartrend in the logarithm of the rate parameter 𝜆t:

𝜆t = exp(𝛽0 + 𝛽1t

)(3)

The spatial variation of the extreme precipitation, con-sidering return periods of 50 years (i.e. 2% chance ofoccurrence in a given year), is assessed based on theextreme value theory which is commonly used to char-acterize the tails of the hydroclimate variable distribu-tions (Coles et al., 2001; Halmstad et al., 2013; Najafi andMoradkhani, 2013). In this study, the generalized extremevalue (GEV) distribution is considered:

F (x |𝜇, 𝜎, 𝜅 ) = exp

{−[1 + 𝜅

(x − 𝜇

𝜎

)]−1∕𝜅}(4)

where 𝜇, 𝜎 and 𝜅 are the location, scale and shapeparameters of the GEV distribution, respectively, and1+ 𝜅(x−𝜇)/𝜎 > 0. Depending on 𝜅, the GEV distributionis divided into three classes: for 𝜅 > 0, the upper tail of thedistribution decreases slowly with a power function andnever reaches zero (i.e. Frechet distribution), for 𝜅 < 0, thedistribution has a bounded upper tail (i.e. Weibull distribu-tion) and for 𝜅 = 0, it has an exponentially decreasing tail(i.e. Gumbel distribution). GEV is fitted to the annual andseasonal maximum rainfall records and the model param-eters are estimated using the L-moments approach.

The extreme events with 𝜏-year return levels are obtainedfrom:

z𝜏 = 𝜇 − 𝜎

𝜅

[1 − (− log (1 − 1∕𝜏))−𝜅

](5)

4. Results

4.1. Trends in the annual precipitation

The mean annual precipitation during 1969–2009 isshown for the 187 gauging stations in Figure 2 (left).Highest precipitation values between 550 and 1550 mmare recorded in regions close to the Caspian Sea and the



Annual rainfall trendMean annual rainfall

85 – 200 mm 200 – 300 mm 300 – 400 mm

400 – 550 mm 550 – 1550 mm

Negative (significant)

Negative (insignificant) Positive (insignificant)

Positive (significant)

Figure 2. Mean annual rainfall amount at each station (left) and the annual rainfall trend (right) estimated based on the Mann–Kendall test consideringa 5% significance level.

Alborz Range on the north as well as the Zagros Rangeon the west. Minimum precipitation values are recordedin the central parts, east of Zagros, as well as southeasternregions. Areas located on the west of the Zagros andnorthwest of the Persian Gulf experience low precipitationbetween 200 and 300 mm. Stations that are located on thenorthwest and northeast of Iran commonly show low tomedium mean annual precipitation values ranging from200 to 550 mm per year.

Trend analysis using the Mann–Kendall test shows thatthe majority of stations experience declines in the annualrainfall events for the extended period of 1969–2009consistent with previous studies (Modarres and Sarhadi,2009; Tabari and Talaee, 2011; Some’e et al., 2012). Of187 gauging stations, 120 stations (64%) show decreas-ing trends, in which 19 stations show significant decreasesconsidering 5% significant level (Figure 2, right). Signif-icant decreases occur in the southeast, west and north-west of Iran as well as the southwest of the Alborz Rangeand southeast of the Caspian Sea. Because of the complextopography of the country, there is spatial heterogeneity inprecipitation trends. For example, although several stationsexhibit significant decreases on the northern and north-western parts of the country, two stations also show sig-nificant increases in these regions. Few stations also showsignificant increasing trends in the annual precipitation ofthe northeast.

4.2. Trends in the seasonal precipitation

Similar to the total annual precipitation, highest seasonalprecipitation occurs in the regions close to the Caspian Seaand the Alborz Range on the north as well as the ZagrosRange on the west. Minimum precipitation values arerecorded by stations in the central parts, east of the ZagrosRange, as well as southeast. The seasonal results show thatmost of the precipitation occurs in winter which reducesduring spring and summer and increases in fall (Figure 3,

left). Nevertheless, some stations on the northwest of Iranshow higher values in spring compared to winter. Mostof the stations in the northeast and southeast and closeto the Zagros Range, in particular on its southeasternregions, show low precipitation values varying between0 and 100 mm in all seasons except winter. The northernparts near the Caspian Sea generally experience relativelylarge events in all seasons including summer with valuesranging from 225 to 750 mm.

In the analysis performed by Tabari and Talaee (2011),decreases were found in the winter and spring precipitationduring 1966–2005 over the northwest regions based on 41station records. In this study, similar trends are detectedusing the larger set of observations for the extended periodof 1969–2009. Overall, winter has the largest contributionto the annual precipitation trend in Iran. It has a majorcontribution to the total annual precipitation and showsthe largest overall decline compared to the other seasons(Figure 3, right). Based on the Mann–Kendall test, 83%of the stations experience decreasing trends in this seasonwith 37 stations (20% of the total) showing significantdecreases. Of 187 stations, only one shows significantincreasing trend in the northeast. Significant decreases areseen over the regions located in the southeast, west andnorthwest of Iran as well as southwest of the Alborz Rangeand southeast of the Caspian Sea. Precipitation declineduring winter, which is in the form of snowfall in higherelevations of Alborz and Zagros ranges, can result in watershortages in the following months. Spring precipitationhas significantly declined in the northwest of Iran, andno significant changes are detected in other parts of thecountry in this season.

The negative precipitation trend reverses during summerwhen 118 of 187 stations show positive trends with 27statistically significant increases. Nevertheless, the overallcontribution of summer to the annual precipitation is smallbecause of the low rainfall amounts in this season which



Fall Fall

Mean seasonal rainfall(winter)

Seasonal rainfall trend(winter)

Spring Spring

Summer Summer

0 – 100 mm 100 – 125 mm 125 – 175 mm

175 – 225 mm 225 – 750 mm

Negative (significant)

Negative (insignificant) Positive (insignificant)


Figure 3. Mean seasonal rainfall amount at each station (left) and the seasonal rainfall trend (right) estimated based on the Mann–Kendall testconsidering a 5% significance level.



50-year return level rainfall (stationary)

A

B

C

A

B

C

50-year return level rainfall (nonstationary)

0 – 35 mm 35 – 50 mm 50 – 60 mm 60 – 80 mm 80 – 110 mm 110 – 300 mm

Figure 4. Extreme precipitation with 2% chance of occurrence in a given year based on the stationary (left) and non-stationary (right) GEVdistributions fitted to the annual maximum daily series at each station.

vary between 0 and 100 mm except in the northern regions.In this season, because the precipitation records containmany zero values at most stations, the significant trendsare not reliable. The observational data sets do not revealsignificant precipitation trends in fall.

4.3. Spatial distribution of extreme precipitation

The spatial distribution of the annual extreme precipitationwith 2% probability of occurrence in a given year (i.e.return period of 50 years) is shown in Figure 4. Resultsare based on the stationary (left) and non-stationary(right) GEV distributions fitted to the annual maximumdaily rainfall series at the individual stations. In thenon-stationary analysis, the location parameter is timevariant (𝜇(t)=𝜇1t+𝜇0), and all the parameters are esti-mated using the maximum likelihood approach. Resultsare based on the medians of the time varying returnlevels. Although the extreme values vary between thetwo approaches for few stations, the spatial distributionpatterns are consistent. We focus on the stationary GEVdistribution with parameters estimated based on theL-moments approach as it is more robust, considering thesmall sample sizes, than the maximum likelihood method(Hosking, 1990). It should be noted that this assumptionmight not hold for the design and risk assessment pur-poses at the individual sites because of the non-stationaryclimate (Cheng et al., 2014).

The largest extreme events with values between 110 mmand 300 mm per day occur close to the Caspian Sea andthe Alborz Range on the north, west and southwest as wellas the southeast of the Zagros Range. The lowest extremesoccur in the central regions, east of the Zagros Range andsouthwest of the Alborz Range. The extreme precipitationin the northwestern and northeastern regions commonlyvaries between 35 and 80 mm per day.

Figure 5 shows plots of the return levels versus returnperiods up to 100 years for three stations (shown by A,

B and C in Figure 4). The 95% confidence intervals ofthe return levels, shown by grey shadings, indicate thatthe uncertainties of the estimated return levels increase forlonger return periods. These stations show different trendsin extreme precipitation which is discussed in Section 4.4.

Similar to the annual extreme precipitation, the highestseasonal extreme events occur in the regions that arelocated close to the Caspian Sea and the Alborz Range onthe north, west and southwest as well as southeast of theZagros Range. Overall, the largest events occur in winterwhich then reduce in spring and summer and increase infall (Figure 6). Regions on the northwest and northeast ofIran show higher rates of extreme precipitation in springcompared to winter. The stations located on the north ofthe Alborz Range close to the Caspian Sea show slightreductions in the extreme events from winter to spring,which then increase during summer and fall.

4.4. Trends in the magnitude and frequency of theannual extreme precipitation

Changes in the magnitude and frequency of the extremeprecipitation events over Iran are assessed using theMann–Kendall test and the Poisson regression, respec-tively. Although majority of the stations show decreasedtotal annual precipitation (64% with 19 stations havingsignificant declines), 50% of the stations show that themagnitude of the extreme precipitation increased during1969–2009 (Figure 7). Modarres and Sarhadi (2009) alsoshowed increasing trends in the 24-h maximum precipi-tation in half of their studied stations over the period of1951–2000. However, only few stations show significantincreases in the magnitude of extreme precipitation.In fact, there is a mixture of significant increasing anddecreasing trends in the magnitude of the annual extremeprecipitation over Iran in recent decades.

Although there is no dominant trend in the magnitude,substantial declining trends are detected in the frequency



(a)

Return period (year)

Ret

urn

leve

l (m

m)

2 10 25 50 10030

40

50

60

70

80

90 (b)

Return period (year)2 10 25 50 100

20

30

40

50

(c)

Return period (year)2 10 25 50 100

40

50

60

70

80

90

110

Figure 5. Annual maximum daily precipitation return level versus return period for three gauging stations shown in Figure 4.

Winter Spring

Summer Fall

0 – 35 mm 35 – 50 mm 50 – 60 mm 60 – 80 mm 80 – 110 mm 110 – 300 mm

Figure 6. Extreme precipitation with 2% chance of occurrence in a given year obtained based on the GEV distribution fitted to the seasonal maximumdaily series at each station.

of extreme precipitation over the region. Thirty stationsshow significant declines in the frequency of extremes inparticular in the southeast, west, north and northwest ofIran. Few stations show significant increasing trends overthe Zagros region.

4.5. Trend in the magnitude and frequency of theseasonal extreme precipitation

During winter, there is a mixture of significant increas-ing and decreasing trends in the magnitude of extreme

precipitation, with most of the stations showing insignif-icant trends (Figure 8). In this season, 5 stations showsignificant positive trends in the magnitude of extremesand 13 stations show significant declines. Nonetheless, thefrequency of extreme precipitation has declined substan-tially over the region with 24 stations showing significantdecreases in the southeast, north and northwest of Iran.

There are fewer stations with declining trends in themagnitude and frequency of extremes in other seasons.During spring, both magnitude and frequency of extreme



Maximum rainfall Frequency of extreme rainfall

A

B

C

A

B

C

Negative (significant)Negative (insignificant) Positive (insignificant)


Figure 7. The trends of the magnitudes (left) and frequencies (right) of the annual extreme rainfall events obtained using the annual maximum dailyrainfall events and the number of exceedances over the 99th percentile threshold, respectively.

precipitation have significantly declined in the northwestregion. Other parts of the country do not show significantchanges in these components in this season. Forty-fourpercent of the stations show that the magnitude of theextreme precipitation increased and 42% show increasedfrequency, however trends are generally insignificant.

The summer season shows significant increases in themagnitude of the extreme events rather than their frequen-cies. Sixty-two percent of the stations show that the mag-nitude of the seasonal extreme precipitation increased and56% show that its frequency increased. In this season,22 stations show significant positive trends in the magni-tude and only 1 station shows significant positive trend inthe frequency of extremes. In addition, only three stationsshow significant declines in the magnitude of extreme pre-cipitation and three stations show significant declines inits frequency. Considering that precipitation records frommost of the stations contain many zero values in summer,the significant trends are not reliable.

During fall, 55% of the stations show that the magnitudeof the seasonal extreme precipitation has increased and58% show that its frequency has increased, neverthelessfew stations show significant trends.

5. Conclusions

Trends of the annual/seasonal total as well as the maximumdaily precipitation events in Iran are assessed using theMann–Kendall test. The trends of the extreme rainfallfrequencies are also evaluated using the Poisson regressionapplied to the daily exceedances over the 99th percentilethreshold.

Results show an overall decline in the total annual pre-cipitation particularly on the southeast, west and northwestas well as southwest of the Alborz Range and southeast ofthe Caspian Sea. The seasonal analyses show that winterprecipitation has the major contribution to this decline

with 83% of the stations showing decreasing trends, 20%of which are significant. Spring precipitation has signif-icantly declined in the northwest regions, however it hasnot changed considerably in other parts of the country.

An overall declining trend is detected in the frequen-cies of the annual extreme precipitation over Iran, whilethere is a mixture of increasing and decreasing trends in themagnitudes with the majority of stations showing insignif-icant trends. Winter has the largest contribution to theoverall decline in the frequency of extremes. Majority ofthe stations that show declines in the frequency of winterextreme precipitation events also exhibit decreasing pre-cipitation totals in this season. During spring, both magni-tude and frequency of extreme precipitation have substan-tially declined in the northwest region.

Changes in precipitation events over Iran are partly asso-ciated with the teleconnection signals such as the El NiñoSouthern Oscillation (ENSO) and the Southern Oscilla-tion Index (SOI) (Nazemosadat and Cordery, 2000; Naze-mosadat et al., 2006). Additionally, the anthropogenic sig-nals can influence the precipitation trends over this region,however attributing the changes in the annual/seasonal pre-cipitation to human influence, taking into account the mag-nitude and frequency of extremes, is challenging usingonly the observational records. The multi-model ensemblesimulations from the general circulation models (GCMs)provide the opportunity to understand the causes of changein the precipitation mean and extreme and to predict theprojected events (Najafi et al., 2010; Najafi and Morad-khani, 2015b).

In this study, a large number of gauging stations wereused to assess the precipitation trend in Iran; howevermost of the stations were located on the north, westand south of the country. Satellite records as well as thereanalysis data sets, which assimilate in situ and remotelysensed observations into the numerical models, can beused as additional data sources to provide more detailed



Fall Fall

Maximum rainfall(winter)

Frequency of extreme rainfall(winter)

Spring Spring

Summer Summer

Negative (significant)Negative (insignificant) Positive (insignificant)


Figure 8. The trends of the magnitudes (left) and frequencies (right) of the seasonal extreme rainfall events obtained using the seasonal maximumdaily rainfall events and the number of exceedances over the 99th percentile threshold, respectively.

assessments of precipitation trends in regions located onthe east and central parts of Iran.

Although the frequency of the annual extreme pre-cipitation has generally declined in Iran, due to itscomplex topography, several stations show increasing

trends in the magnitude/frequency of extremes. Thenonstationarity of extreme precipitation challenges theconventional design approaches which assume stationaryintensity-duration-frequency (IDF) curves, as it mightlead to higher failure risks in the infrastructure systems



(Cheng and AghaKouchak, 2014). In addition, hydrologicforecast methods are required to take the nonstationarityof the hydroclimate system into account as the conven-tional approaches, which merely rely on the historicalrealizations, are incapable of accurately predicting thefuture extreme events (Najafi et al., 2012; Najafi andMoradkhani, 2015a).

References

Abarghouei HB, Zarch MAA, Dastorani MT, Kousari MR, Zarch MS.2011. The survey of climatic drought trend in Iran. Stoch. Environ.Res. Risk Assess. 25(6): 851–863.

Ahani H, Kherad M, Kousari MR, Rezaeian-Zadeh M, Karampour MA,Ejraee F, Kamali S. 2012. An investigation of trends in precipitationvolume for the last three decades in different regions of Fars province,Iran. Theor. Appl. Clim. 109(3–4): 361–382.

Beguería S, Angulo-Martínez M, Vicente-Serrano SM, López-MorenoJI, El-Kenawy A. 2011. Assessing trends in extreme precip-itation events intensity and magnitude using non-stationarypeaks-over-threshold analysis: a case study in northeast Spainfrom 1930 to 2006. Int. J. Climatol. 31(14): 2102–2114.

Cheng L, AghaKouchak A. 2014. Nonstationary precipitationintensity-duration-frequency curves for infrastructure design ina changing climate. Sci. Rep. 4, 7093, doi:10.1038/srep07093.

Cheng L, AghaKouchak A, Gilleland E, Katz RW. 2014. Non-stationaryextreme value analysis in a changing climate. Clim. Change 127(2):353–369.

Coles S, Bawa J, Trenner L, Dorazio P. 2001. An Introduction toStatistical Modeling of Extreme Values. Springer, London, 208 pp.

Delju A, Ceylan A, Piguet E, Rebetez M. 2013. Observed climate vari-ability and change in Urmia Lake Basin, Iran. Theor. Appl. Climatol.111(1–2): 285–296.

Evans JP. 2010. Global warming impact on the dominant precipita-tion processes in the Middle East. Theor. Appl. Climatol. 99(3–4):389–402.

Ghahraman B, Taghvaeian S. 2010. Investigation of annual rainfalltrends in Iran. J. Agric. Sci. Technol. 10: 93–97.

Golian S, Mazdiyasni O, AghaKouchak A. 2014. Trends in meteorologi-cal and agricultural droughts in Iran. Theor. Appl. Climatol. 119(3–4):679–688.

Halmstad A, Najafi MR, Moradkhani H. 2013. Analysis of precip-itation extremes with the assessment of regional climate modelsover the Willamette River Basin, USA. Hydrol. Process. 27(18):2579–2590.

Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Brön-nimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR,Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2014. Observa-tions: Atmosphere and Surface: In Climate Change 2013: The Physi-cal Science Basis Contribution of Working Group I to the Fifth Assess-ment Report of the Intergovernmental Panel on Climate Change. Cam-bridge University Press: Cambridge, UK.

Helsel DR, Hirsch RM. 1992. Statistical Methods in Water Resources.Elsevier: New York, NY, 49 pp.

Hirsch RM, Archfield SA. 2015. Flood trends: not higher but more often.Nat. Clim. Change 5(3): 198–199.

Hosking JRM. 1990. L-moments: analysis and estimation of distribu-tions using linear combinations of order statistics. J. R. Stat. Soc. Ser.B 52: 105–124.

Kendall M. 1975. Rank Correlation Methods, 4th edn. Charles Griffin:San Francisco, CA, 8.

Kharin VV, Zwiers F, Zhang X, Wehner M. 2013. Changes in tempera-ture and precipitation extremes in the CMIP5 ensemble. Clim. Change119(2): 345–357.

Kousari MR, Zarch MAA. 2011. Minimum, maximum, and mean annualtemperatures, relative humidity, and precipitation trends in arid andsemi-arid regions of Iran. Arab. J. Geosci. 4(5–6): 907–914.

Lelieveld J, Hadjinicolaou P, Kostopoulou E, Chenoweth J, El MaayarM, Giannakopoulos C, Hannides C, Lange M, Tanarhte M, Tyrlis E.2012. Climate change and impacts in the Eastern Mediterranean andthe Middle East. Clim. Change 114(3–4): 667–687.

Mallakpour I, Villarini G. 2015. The changing nature of flood-ing across the central United States. Nat. Clim. Change 5(3):250–254.

Mann HB. 1945. Nonparametric tests against trend. Econometrica 13(3):245–259.

Min S-K, Zhang X, Zwiers FW, Hegerl GC. 2011. Human contributionto more-intense precipitation extremes. Nature 470(7334): 378–381.

Modarres R, da Silva VdPR. 2007. Rainfall trends in arid and semi-aridregions of Iran. J. Arid Environ. 70(2): 344–355.

Modarres R, Sarhadi A. 2009. Rainfall trends analysis of Iran in thelast half of the twentieth century. J. Geophys. Res. 114: D03101, doi:10.1029/2008JD010707.

Najafi MR, Moradkhani H. 2013. Analysis of runoff extremes usingspatial hierarchical Bayesian modeling. Water Resour. Res. 49(10):6656–6670.

Najafi MR, Moradkhani H. 2014. A hierarchical Bayesian approach forthe analysis of climate change impact on runoff extremes. Hydrol.Process. 28(26): 6292–6308.

Najafi M, Moradkhani H. 2015a. Ensemble combination of seasonalstreamflow forecasts. J. Hydrol. Eng., doi: 10.1061/(ASCE)HE.1943-5584.0001250, 04015043.

Najafi M, Moradkhani H. 2015b. Multi-model ensemble analysis ofrunoff extremes for climate change impact assessments. J. Hydrol.525: 352–361.

Najafi MR, Moradkhani H, Wherry SA. 2010. Statistical downscaling ofprecipitation using machine learning with optimal predictor selection.J. Hydrol. Eng. 16(8): 650–664.

Najafi M, Moradkhani H, Jung I. 2011. Assessing the uncertainties ofhydrologic model selection in climate change impact studies. Hydrol.Process. 25(18): 2814–2826.

Najafi MR, Moradkhani H, Piechota TC. 2012. Ensemble streamflowprediction: climate signal weighting methods vs climate forecast sys-tem reanalysis. J. Hydrol. 442: 105–116.

Najafi MR, Zwiers FP, Gillett NP. 2015. Attribution of Arctic temper-ature change to greenhouse-gas and aerosol influences. Nat. Clim.Change. 5: 246–249, doi:10.1038/nclimate2524.

Nazemosadat M, Cordery I. 2000. On the relationships between ENSOand autumn rainfall in Iran. Int. J. Climatol. 20(1): 47–61.

Nazemosadat M, Samani N, Barry D. 2006. ENSO forcing on climatechange in Iran: precipitation analysis. Iranian J. Sci. Technol. Trans.B: Eng. 30(ECOL-ARTICLE-2007-006): 555–565.

Philandras C, Nastos P, Kapsomenakis J, Douvis K, Tselioudis G,Zerefos C. 2011. Long term precipitation trends and variabilitywithin the Mediterranean region. Nat. Hazard Earth Syst. Sci. 11(12):3235–3250.

Rahimzadeh F, Asgari A, Fattahi E. 2009. Variability of extreme temper-ature and precipitation in Iran during recent decades. Int. J. Climatol.29(3): 329–343.

Seneviratne SI, Nicholls N, Easterling D, Goodess CM, Kanae S, KossinJ, Luo Y, Marengo J, McInnes K, Rahimi M. 2012. Changes in climateextremes and their impacts on the natural physical environment. InManaging the Risks of Extreme Events and Disasters to AdvanceClimate Change Adaptation. A Special Report of Working Groups Iand II of the Intergovernmental Panel on Climate Change, Field CB,Barros V, Stocker TF, Dahe Q, Dokken DJ, Ebi KL, MastrandreaMD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds.).Cambridge University Press: Cambridge, UK, 109–230.

Soltani S, Saboohi R, Yaghmaei L. 2012. Rainfall and rainy days trendin Iran. Clim. Change 110(1–2): 187–213.

Some’e BS, Ezani A, Tabari H. 2012. Spatiotemporal trends and changepoint of precipitation in Iran. Atmos. Res. 113: 1–12.

Tabari H, Aghajanloo MB. 2013. Temporal pattern of aridity index inIran with considering precipitation and evapotranspiration trends. Int.J. Climatol. 33(2): 396–409.

Tabari H, Talaee PH. 2011. Temporal variability of precipitation overIran: 1966–2005. J. Hydrol. 396(3): 313–320.

Tabari H, AghaKouchak A, Willems P. 2014. A perturbation approachfor assessing trends in precipitation extremes across Iran. J. Hydrol.519: 1420–1427.

Trenberth KE. 2011. Changes in precipitation with climate change. Clim.Res. 47(1): 123.

Voss KA, Famiglietti JS, Lo M, Linage C, Rodell M, Swenson SC.2013. Groundwater depletion in the Middle East from GRACEwith implications for transboundary water management in theTigris-Euphrates-Western Iran region. Water Resour. Res. 49(2):904–914.

Wan H, Zhang X, Zwiers F, Min S-K. 2014. Attributing northernhigh-latitude precipitation change over the period 1966–2005 tohuman influence. Clim. Dyn., doi: 10.1007/s00382-014-2423-y.

Westra S, Alexander LV, Zwiers FW. 2013. Global increasing trends inannual maximum daily precipitation. J. Clim. 26(11): 3904–3918.


Trends in total precipitation and magnitude–frequency of extreme precipitation in Iran,...

Documents

Transcript of Trends in total precipitation and magnitude–frequency of extreme precipitation in Iran,...