Technical Note

Confidence Interval Assessment to Estimate Dry and WetSpells under Climate Change in Shahrekord Station, Iran

Masoomeh Fakhri1; Mohammad Reza Farzaneh2; Saeid Eslamian3; and Mohammad Javad Khordadi4

Abstract: Global warming and its resulting climate change will affect different elements, such as water resources, in the future. One effectis that rainfall becomes very difficult to predict, as it is under the influence of several different elements. In this study, which considersShahrekord synoptic station in Iran, various sources of uncertainty in rainfall prediction in the future and its effect on dry and wet spellsare investigated. In the present research, CCSIRO, CGCM, ECHO-G, HADCM3, ECHAM, and PCM Atmospheric and Ocean GeneralCirculation Model (AOGCM) models and A1, A2, B1, and B2 emission scenarios under three downscaling methods are examined.The results indicate a significant impact of the various downscaling methods on increasing the uncertainty band in rainfall estimationfor the future. The AOGCM models in all of the scenarios except A2 are in agreement. The results of wet and dry spells estimation displaya long-duration drought at the beginning of the upcoming 30-year period, followed by a long-duration wet spell. DOI: 10.1061/(ASCE)HE.1943-5584.0000688. © 2013 American Society of Civil Engineers.

CE Database subject headings: Uncertainty principles; Emissions; Iran; Climate change; Droughts; Floods.

Author keywords: Uncertainty; LARS-WG; SDSM; Emission scenarios; Downscaling; Dry and wet spells; Shahrekord.

Introduction

The development of a rainfall occurrence model is increasinglyin demand, not only for data-generation purposes, but also to providesome useful information in various applications, including waterresource management and the hydrological and agricultural sectors.Identifying the appropriatemodel of rainfall occurrence, particularlyfor the distribution of dry (wet) spells, is very important, as almostall of the climate variables are dependent on rainfall events.

Increasing greenhouse gases and the resulting global warmingare the main reasons for climate change in the future. This eventwill affect different elements. One of the most important restrictingelements in recent decades is water resources, which have signifi-cant relationships with climate change. The main factor in the hy-drology cycle that limits water resources in the future is rainfall.

Climate change has highly affected urban floods in recent years.As local weather characteristics are influenced by climate change,high variations in rainfall, temperature, and runoff result. This phe-nomenon is intensified in urban areas because of their specialcharacteristics (Karamouz et al. 2011).

Determining an appropriate model for describing the distribu-tion of the rainfall is important, particularly for the purpose of waterresource management in the hydrological and agricultural sectors.To decide on the best model among several competing models torepresent the data distribution, the model with the least number ofparameters is preferred. The development of the rainfall occurrencemodel is very important because the climate variables are depen-dent on rainfall events. Therefore, identifying the appropriate prob-ability models to represent the distribution of wet and dry spells isimportant and requires a comprehensive study.

The study of the effects of climate variability and change onhydrologic response is complex because the effects of the large-scaleforcing that drives climate change are coupled nonlinearly with localand regional forcing, and therefore those impacts cannot be readilyassessed (Kang and Ramirez 2007). Global climate models (GCM)may capture large-scale circulation patterns and correctly modelsmoothly varying fields, such as surface pressure, but it is unlikelythat these models properly reproduce nonsmooth fields, such asprecipitation (Hughes and Guttorp 1994). Additionally, the spatialscale on which a GCM can operate is quite coarse for hydrologicapplications (Prudhomme et al. 2003). Therefore, GCM simulationsof local climate at individual grid points are often poor, especiallywhen the area has a complex topography (Schubert 1998). However,in most climate change impact studies, such as hydrologic impacts ofclimate change, models are usually required to simulate subgrid scalephenomena and, therefore, require input data (e.g., precipitation,temperature) at similar subgrid scales. To overcome this problem,downscaling is necessary tomodel hydrologic variables (e.g., precipi-tation) at a smaller scale from larger-scale GCM outputs.

Various methods have been used to predict the hydro-logical parameters, but it seems the methods that bear differentemission scenarios of greenhouse gases are more practical. TheAtmospheric and Ocean General Circulation Model (AOGCM)models these scenarios, estimating varied climatic parameters. Oneof these parameters is rainfall predicted for the diverse periods ofthe future, but computed cells precision in these kinds of modelsis not enough for hydrologic research.

1Postgraduate, Dept. of Hydraulic Structure, Faculty of Water ScienceEngineering, Shahid Chamran Univ., 61357-831351 Iran. E-mail:masoomeh.fakhri@gmail.com

2Ph.D. Student, Dept. of Water Engineering, College of Agriculture,Tarbiat Modares Univ., Tehran, 14115-111 Iran (corresponding author).E-mail: m.farzaneh@modares.ac.ir

3Associate Professor, Dept. of Water Engineering, College. of Agricul-ture, Isfahan Univ. of Technology, Isfahan 84156-83111, Iran. E-mail:saeid@cc.iut.ac.ir

4Ph.D. Student, Dept. of Water Engineering, College of Agriculture,Ferdowsi Univ. of Mashhad, 91779-48974 Iran. E-mail: khordadi@ag.ut.ac.ir

Note. This manuscript was submitted on August 18, 2011; approved onJuly 6, 2012; published online on August 7, 2012. Discussion periodopen until December 1, 2013; separate discussions must be submitted forindividual papers. This technical note is part of the Journal of HydrologicEngineering, Vol. 18, No. 7, July 1, 2013. © ASCE, ISSN 1084-0699/2013/7-911-918/$25.00.

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For this reason, it is necessary to downscale the outputs. Variousmanners of downscaling, different models of AOGCM, and theirvarious emission scenarios are sources of uncertainty in rainfallestimation for the future. Several studies were done in this field;some of them are noted here.

The downscaling models based on different methods thathave an important role on climate change studied are SDSM,LARS-WG, and high resolution data (HRD) models. The lastone is proposed by the Climatic Research Unit (CRU) and usedthroughout the world for local and downscaled assessments. TheCRU is a component of the University of East Anglia and is oneof the leading institutions concerned with the study of natural andanthropogenic climate change. With a staff of some 30 researchscientists and students, the CRU has contributed to the developmentof a number of the data sets widely used in climate research, in-cluding one of the global temperature records used to monitorthe state of the climate system, as well as statistical software pack-ages and climate models. In this paper, the data from CRU is calledHRD data.

Farzaneh et al. (2012), using the generated data by CRU, com-pared emission scenarios of CSIRO, CGCM2, CCSR-NIES, andHADCM3 models in Behesht-Abad basin, Iran, and displayedmodel differences in estimation of temperature and rainfall param-eters. They concluded HadCM3 had the most similarity withmeasured data.

Busuioc et al. (2001) used the statistical downscaling modelsto validate AOGCM and estimate regional rainfalls in Sweden.In a study in England by Wilby et al. (2002a), downscaling of sea-sonal rainfall variables was investigated using conditioned weathergenerator parameters (CWGP). Wilby et al. (2003), to simulaterainfalls for some stations, used three models in east Englandand Scotland. Hulme and Brown (1998) extracted the observedannual rainfall and temperature for the 1896–1996 period fromCRU to study climate change effects. Then the anomalous situa-tion of temperature and rainfall was investigated simultaneously.The result indicated an increasing trend in temperature and a de-creasing trend in rainfall for historical data. The climatic changesin the region were calculated by the HadCM2 general circulationmodel analyzing 1,000 years of data. Their results showed that onlyin the last years – 1987 to 1996 – temperature and rainfall in thestudied region were out of critical limits of climatic variabilitywhich significantly indicated climate change. To assign variouslimits of uncertainty in the simulation of temperature and rainfallas climatic parameters in future periods, the IS92 scenario was con-sidered under all AOGCM models by MAGICC software, and useof pattern scaling simulated the parameters for the 2040–2069period. It is observed that temperature and rainfall changes infuture will be attributable to climate change caused by increasinggreenhouse gases and are not categorized as typical climaticchanges.

Crawford et al. (2007) studied daily rainfall north of Ireland us-ing a statistical-regression downscaling method. Statistical down-scaling model (SDSM) software was used to establish a linearregression relation. The conclusions showed the predictor outputswere weak for summer and spring seasons.

Semenev (2007) investigated climate change scenarios in theUK with the UKCIPO2 program. In the study, LARS-WS stochas-tic weather generator was used to create daily climatic scenarios.Also, climate change impact on growth of two varieties of wheat(Avalon and Mercia) was assessed until the year 2080. The study oftwo parameters—dry stress coefficient and high temperature—make clear that the two varieties are resistant to increasing temper-ature and decreasing rainfall in summer, and their growth will notbe stopped.

Dibike and Coulibaly (2005) investigated the uncertainty relat-ing to downscaling of GCM models using SDSM and LARS-WGin a basin located in northern Canada. Mean daily rainfall wasmodeled appropriately by both of the models. LARS-WG modeleddry and wet spells better, but SDSM underestimated wet spells.For minimum and maximum temperatures, both models had goodresults. SDSM displayed a continuous bias to low temperatures.In future scenarios, the models had different results, including anincreasing trend in mean rainfall for the SDSM model. Sajad-Khanet al. (2006) in research on Chute-du-Diable subbasin in Quebecprovince, Canada, with 9,700 km2 area, assessed the uncertainty ofthree downscaling models, including ANN, SDSM, and LARS-WG by the bootstrap method. They concluded that the SDSMmodel had better results than the LARS-WG model, and theLARS-WG model had better results than the ANN method.

Schuls and Abbaspur (2007) investigated the data generatedin the CRU database with two methods. Then SWAT model wasrun with the data. In this study, simulation of monthly and yearlyriver discharge for a 25-year period using daily measured data isperformed in some stations.

In the research, the role of diverse uncertainty sources includingAOGCM models, emission scenarios, and downscaling methodsis assessed.

Materials and Methods

Study Area

Behesht-Abad subbasin is one of the most important hydrologicsubbasins in Iran. The subbasin is one of the water supply resourcesin the North-Karoon basin and is also used for water conveyance toother arid basins (interbasin water transfer). Behesht-Abad subba-sin is located north and northeast of North-Karoon basin with geo-graphic coordinates as 50°23′ to 51°25′ E and 32°1′ to 32°34′ N.This subbasin’s area is about 3,860 km2 and encompasses about27% of the North-Karoon River basin. It is expected that futureclimate change will affect the rainfall patterns and quantity. Utiliz-ing quality control and existing duration of data for various stationsin this subbasin, Shahrekord synoptic station, at 50°51′ E and32°20′ N, was chosen for this study (Fig. 1). Also, the chart relatingto various uncertainty sources in dry and wet spells estimation ispresented in Fig. 1.

To perform the studies of climate change, two types of data arerequired. The first type is the observed data, including observedstation data and large-scale observed data [National Centers forEnvironmental Prediction (NCEP) predictors]. The second typeis the large-scaled data from AOGCM models divided into two cat-egories: simulated climatic parameters and simulated predictors.

In this paper, the five data groups below are collected:• Observed daily rainfall in Shahrekord station;• NCEP large-scale observed parameters;• Predictors relating to A2 emission scenario of the HADCM3

model;• Large-scaled rainfall from the ECHO-G model; and• High-resolution downscaled rainfall data for HADCM3,

CCSIRO, and CGCM2 models and A1, A2, B1, and B2scenarios (Hijmans et. al 2005).Using the observed rainfall, NCEP and HADCM3 predictors

(A2 scenario), and SDSM downscaling model rainfall, the bestpredictors in the region are chosen. After uncertainty analysis,the rainfall for the 2020s (2010–2039) is predicted.

Using the observed rainfalls and large-scaled data from theECHO-G model under the effect of the A1 emission scenario

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and LARS-WG, downscaled rainfalls for the historical and 2020periods are assessed.

In the next step—comparing the estimated rainfalls in two re-cent stages with the rainfall values relating to equivalent scenario ineach status in the Tyndell Climate Center downscaling method—the uncertainty from downscaling manner, AOGCM model, andemission scenario of predicted rainfall is estimated.

In the final stage—considering total annual rainfalls in the2020s from the effect of all of the above statuses—the uncertaintyband of total annual rainfall using the Bootstrap method isachieved. Then, uncertainty band at 95% for that is used in the5-year moving average to introduce dry and wet spells in the bestand worst conditions.

Downscaling Methods

In general, important downscaling methods can be categorizedinto two main approaches. The first approach relates to statisticaltechniques and the second approach is dynamic downscaling. Twomodels used in this study—SDSM and LARS-WG—are the stat-istical downscaling types.

SDSM is a model using a stochastic-deterministic method, andit also has the large-scaled observed parameters that downscalesthe data. SDSM is the first tool of its type offered to the broaderclimate change community. Most statistical downscaling modelsare generally restricted in their use to specialist researchers

and/or research establishments. Other software, although moreaccessible, produces relatively coarse regional scenarios of climatechange (both spatially and temporally). For instance, SCENGEN(Hulme et al. 1995) blends and rescales user-defined combinationsof GCM experiments and then interpolates monthly climate changescenarios onto a 5° latitude × 5° latitude global grid. “Weather gen-erators” such as WGEN (Richardson 1981), LARS-WG (Semenovand Barrow 1997), or CLIGEN (Nicks et al. 1995) are widely usedin the hydrological and agricultural research communities, but donot directly employ GCM output in the scenario construction proc-esses (Wilks 1992).

SDSM facilitates the rapid development of multiple, low-cost,single-site scenarios of daily surface weather variables under cur-rent and future regional climate forcing. Additionally, the softwareperforms ancillary tasks of predictor variable prescreening, modelcalibration, basic diagnostic testing, statistical analyses, and graph-ing of climate data (Wilby et al. 2002b).

Samadi et al. (2011) used SDSM to downscale GCM data in anumeric model and improved it to develop future forecasts of mini-mum and maximum temperature and precipitation in KhorasanProvince, Iran. The software reduces the task of statistically down-scaling daily weather series into five discrete processes (denoted inFig. 2 by the heavy boxes): (1) screening of predictor variables;(2) model calibration; (3) synthesis of observed data; (4) generationof climate change scenarios; and (5) diagnostic testing and statis-tical analyses.

CCSIRO CGCM HADCM3 ECHAM PCM ECHO-G CCSIRO CGCM HADCM3 ECHAM PCMB1 B1 B1 B1 B1 A1 A1 A1 A1 A1 A1

LARS-WG

CCSIRO CGCM HADCM3 ECHAM PCM HADCM3 CCSIRO CGCM HADCM3 ECHAM PCMB2 B2 B2 B2 B2 A2 A2 A2 A2 A2 A2

SDSM

HADCM3 ECHO-G CCSIRO CGCM HADCM3 ECHAM PCMA2 A1

SDSM LARS-WG

Assessment of climate change Impact on Dry and Wet Spell in 95% Uncertainty Band

Uncertainty of AOGCM Models on B2 ES Uncertainty of Downscaling Method & AOGCM models in A2 ES

A1, A2, B1 and B2Tyndall Climate Change Center

Uncertainty of Downscaling, Emission Scenarios (ES) and AOGCM models

Prediction of annual rainfall for 2010 to 2039 period

High Resolution Data (HRD) High Resolution Data (HRD)Uncertainty of AOGCM Models oin B1 ES Uncertainty of Downscaling Method & AOGCM models in A1 ES

High Resolution Data (HRD) High Resolution Data (HRD)

Fig. 1. Study area and the performed steps

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Weather generators are primarily in two groups: Richardsonand serial. LARS-WG used in this paper is from the serial modelsgroup. A preliminary version of LARS-WG was presented as a partof agricultural risk assessment projects in 1990 in Budapest,Hungary.

LARS-WG is a stochastic weather generator based on the seriesapproach (Racsko et al. 1991), with a detailed description given inSemenov (2007). LARS-WG produces a synthetic daily time seriesof maximum and minimum temperatures, precipitation, and solarradiation. The WG uses observed daily weather for a given siteto compute a set of parameters for probability distributions ofweather variables as well as correlations between them. This setof parameters is used to generate a synthetic weather time seriesof arbitrary length by randomly selecting values from the appropri-ate distributions. By perturbing parameters of distributions for asite with the predicted changes of climate derived from globalor regional climate models, a daily climate scenario for this sitecould be generated and used in conjunction with a process-basedimpact model for assessment of impacts. LARS-WG has beentested in diverse climates and has demonstrated a good perfor-mance in reproducing various weather statistics, including extremeweather events (www.rothamsted.bbsrc.ac.uk/mas-models/larswg.php; Semenov et al. 1998).

LARS-WG uses a semiempirical distribution (SED) to approxi-mate probability distributions of dry and wet series, daily precipi-tation, minimum and maximum temperatures, and solar radiation.A semiempirical distribution is defined as the cumulative probabil-ity distribution function (PDF). The number of intervals (n) usedin SED is 23, which offers a more accurate representation of theobserved distribution compared with the 10 used in the previousversion.

As the large-scaled data is extended throughout the world bythe AOGCM models, institutions in every part of the world nowdo downscaling process to create regional data. One of these insti-tutions is Tyndall Climate Change Center that whose method issimilar to the proposed method by New et al. (2000).

Emission Scenarios

Uncertainty in future population, land use, CO2 density in atmos-phere, and other effective parameters that affect the earth’s climatemakes the parameter prediction for future periods difficult. Theemission scenarios regarding the different elements of human-effected environment allow future climate research for the mostoptimistic situation to the most pessimistic one.

Various assumptions of these scenarios and their created uncer-tainty require simultaneous use of them to increase the study ac-curacy. The work presented in this paper uses four main types ofthe emission scenarios, including A1, A2, B1, and B2.

Dry and Wet Spells Analysis

Wet and dry spells are one of practical components of rainfall.The various methods were extended to estimate the componentsfor which, in the present research, annual time scale is considered.To combine the achieved results of AOGCM models, emissionscenarios, and downscaling methods, the total yearly rainfalls infuture periods are resampled under the effects of different condi-tions separately by the bootstrap method.

Bootstrap Method

The uncertainty existing in climate change studies has many effectson water resources simulation in a basin so that it is necessary toconsider this uncertainty in related calculations. Bootstrapping is asimple technique to estimate the required values in a specific stat-istical pattern. The general structure in confidence-interval findingin the majority of usual cases is to obtain a function from the re-quired parameter having independent distribution from that param-eter. Many times, the function finding is not easy; for overcomingthis problem. The bootstrap method could be used. The furtherinformation about bootstrap is available in Efron and Tibshirani(1993) and DiCiccio and Efron (1996).

Discussion

As noted in the previous section, the study aim is the investigationof different uncertainty sources in dry and wet spell estimation fornear-future periods. To do so, the first of the three downscalingmethods is considered. The first method was proposed by Hijmanset. al (2005) used for the CCIRO, CGCM, ECHAM, HADCM3,and PCM AOGCM models. In this method, the A1, A2, B1, andB2 emission scenarios are considered. The HRD-predicted rain-falls in future periods are shown in Fig. 3. The second methodis the use of statistical downscaling, and the SDSM model withthe A2 scenario from the HADCM3 model is used. The estimatedresults are displayed in Fig. 4. In the third method, using statisticaldownscaling and LARS-WG, precipitation is predicted underthe A1 scenario and ECHO-G impact. The validation results arepresented in Fig. 5.

Comparison of the results of the AOGCM models indicateagreement among all of the models in total annual rainfall predic-tions under all of the scenarios except the A2 scenario. As presentedin Fig. 3, the HADCM3 model has an underestimation in propor-tion to the rest of the models.

The results of the SDSM and LARS-WG statistical down-scaling method validations are displayed in Figs. 4 and 5, and theyindicate very accurate estimation of the models in all months ofthe year.

Fig. 2. Steps of the creation procedure of required scenarios in the paper using SDSM

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Annual Rainfall Changes Investigated under the Effectof Different Emission Scenarios

In this section, to consider the bootstrap method to estimate uncer-tainty band in a 95% confidence interval, three conditions are stud-ied. Total annual rainfall for the 2010–2039 period (2020s) ispredicted in which these three different source impacts of uncer-tainty are considered separately.

In the first condition, disregarding downscaling method im-pacts, the uncertainty band made by the different models isresearched. For this aim, B1 and B2 emission scenarios andHRD data for all of the models are used. The results are shownin Figs. 6(c and d). The narrow band of uncertainty made byvarious AOGCM models shows two agreement points among themodels in yearly rainfall estimation for 2020s period. Of course,with passing time and toward the end of the period, the widthof this band increased. This indicates more impacts of differentsources of uncertainty made by AOGCMmodels with passing time.

In the second condition, to investigate the uncertainty made bythe downscaling methods and AOGCM models using A1 and A2

scenarios, respectively, the impacts of SDSM and LARS-WGdownscaling methods will be added to the first condition. Theresults of A1 scenario uncertainty made by LARS–WG statisticaldownscaling model and the HRD data are displayed in Fig. 6(a).Fig. 6(b) shows adding the SDSM statistical downscaling mannerimpact to the first condition. The results of this stage indicate anincreasing model uncertainty band with the addition of the differentdownscaling methods.

In the third condition, to add the difference made by the variousemission scenarios to the previous stages, the results of all the mod-els and downscaling methods are combined and the uncertaintyband of all conditions is considered at a 95% confidence interval.As seen in Fig. 7, the uncertainty band has a significant declinecompared with the second condition. The decline could be attrib-utable to the increasing effects of HRD.

Fig. 3. Variation of HRD-predicted precipitation (rainfall) under the effects of different emission scenarios and models

Fig. 4. Downscaling using SDSM model

Fig. 5. Downscaling using LARS-WG model

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In the final stage of the study, considering minimum and maxi-mum annual rainfalls predicted in the previous section, wet and dryspells are estimated separately by a 5-year moving average methodfor all of the future studied years.

As presented in Figs. 8 and 9, in two conditions, a long-durationdry spell is identified before 2016 and also a long-duration wetspell after that time. In the following years, occurrence probabilityof wet and dry spells with shorter duration is predicted. The resultssignify the rainfall average changes from 141 mm in the worst con-dition to 387 mm in the best condition.

Annual rainfall change in the future period is assessed as verysevere, probably because of the increasing severity of extremeevents such as drought and flood. In general, total annual rainfallhas an increasing trend that indicate that, if the floods were man-aged in the region, at first the severe flood damages would beabated and in the end, existing water resources in these floods couldbe used in the next dry spell.

As is shown in Fig. 10, dry and wet spells are estimatedunder two states on the basis of mean minimum and maximumof annual rainfall for all future scenarios. If it is needed to studyclimate change impact on dry and wet spells, considering theannual rainfall in historical periods can help estimate wet anddry spells. In Fig. 10, the three states noted previously are dis-played as a future band and historical average. As total rainfallvalues under the future scenarios are different, the investigation

of uncertainty related to these scenarios is also considered as afuture average.

In Fig. 10, the results of the emission scenarios in AOGCMmodels and various methods of downscaling are shown. In the

Fig. 6. Effects of different sources of uncertainty on total annual rainfalls for near-future period

Fig. 7. Total effect of all uncertainty sources on estimation of totalannual rainfall in Shahrekord for the 2020s

Fig. 8. Investigation of wet and dry spells changes in the future periodunder the effect of the least predicted rainfall values for the differentconditions

Fig. 9. Investigation of wet and dry spells changes in the future periodunder the effect of the most predicted rainfall values for the differentconditions

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SDSM and LARS methods, climate change effect on future wetyear is significant. In all the scenarios except B2, the predictedvalues based on historical average are less than that of the futureaverage. Generally, application of moving average method for es-timation of wet and dry spells is highly sensitive to the selection ofthreshold value. Moreover, the uncertainty initiated by selectionof a scenario is added to other sources of uncertainty in predictionof future time series to achieve the threshold value.

Conclusions

Uncertainty sources are key topics in climate change science forwhich their effect estimation and finding methods to decrease theseeffects are very important and substantial. The study concludes thatthe sources will be increased by adding different downscalingmanners. Therefore in climate change research, use of one methodis recommended.

Another source is the uncertainty from the various models ofAOGCM; it is proposed that the effect of these models and theiragreement or disagreement size in the studied parameters estima-tion is considered. Another source of uncertainty is related to theemission scenarios.

It is suggested in the studies at first the impacts of all emissionscenarios on parameter prediction are investigated. Of course,regarding uncertainty in emission scenarios efficiency on climatechange in the future period, combination investigation of the emis-sion scenarios can be practical and effective in decision making.

The conclusions display an increasing uncertainty band madeby all of the noted uncertainty sources with the passing of time.

Approaching the near-future period (2040 to 2069), the effectsbecome more pronounced. Because of that, management scenarioinvestigation for the near-future period (2020s) will have morecertainty.

The results of wet and dry spells study indicate the occurrenceof a long-duration dry spell at the beginning of the 2020s periodand, after that, a long-duration wet spell will occur. Total yearlyrainfall changes are very severe; in some years it will reach606 mm per year and in other years, 83 mm per year.

Very severe changes in annual rainfall values show the increas-ing extreme events from the impact of climate change. For this rea-son, presentation of manageable solutions to abate flood damagesand use the excess water for the required water supply is needed.

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Fig. 10. Accumulated effects of uncertainty caused by selection of different emission scenario in AOGCM and downscaling methods and uncertaintyinitiated by selection of threshold on future wet years

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