The Impact of Climate Change on Climate Variables and Meteorological Drought Using the climate change Toolkit (CCT) in the Karkheh River Basin, Iran

Drought appears as an environmentally integral part of climate change. This study was conducted to 8 investigate the impact of climate change on climate variables, meteorological drought and pattern recognition 9 for severe weather conditions in the Karkheh River Basin in the near future (2043-2071) and the distant future 10 (2072-2100). The outputs of GFDL-ESM2, HadGEM2-ES, IPSL-CM5A-LR, MIROC and NoerESM1-M 11 models were downscaled under the RCP 2.6 and RCP8.5 scenarios using the Climate Change Toolkit (CCT) at 12 17 meteorological stations. Then the SPEI index was calculated for the base and future periods and compared 13 with each other. The results showed that the basin annual precipitation will likely increase in both future periods, 14 especially in the near future. The annual maximum and minimum temperatures may also increase especially in 15 the distant future. The rise in the maximum temperature will be possibly greater than the minimum temperature. 16 Seasonal changes in maximum and minimum temperatures and precipitation indicate that the greatest increase 17 in temperature and decrease in precipitation may occur in summer. Hence meteorological drought was also found 18 to increase in the distant future . The application of the CCT model in the region showed that at least once a wet 19 period similar to the flood conditions of 2019 will be observed for the near future. There will also be at least one 20 similar drought in 2014 for the distant future in the region. However, in previous climate studies, future events 21 have not been calculated based on identifying the pattern of those events in the past.

components of the hydrological cycle and their interactions; 2-Drought spatial and temporal characteristics 32 using integrated methods; And 3-Future changes in the components under climate change scenarios. The 33 existing literature mainly looks at one of the mentioned dimensions. Despite the importance of this perspective 34 for the effective management of regional drought, there are limited attempts to consider different aspects of 35 drought using a standard method (Peters 2006). Drought is classified into meteorological, agricultural, 36 hydrological and socio-economic categories. The first three types of drought reflect the physical characteristics 37 of a drought phenomenon (ie, physical drought). Economic drought is related to water scarcity, the impact of 38 which is manifested through socio-economic systems. Although droughts are caused by a lack of rainfall, 39 hydrological drought is usually followed by a meteorological drought because it takes some time for the lack 40 of rainfall to appear in various subsurface components of the hydrological system, such as soil moisture,

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showed that there is a tendency for frequent and severe winter-spring drought in the study area and by the end 61 of the 21st century, more than half of the wheat belt is exposed to winter-spring drought. Kang

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In this work, the Climate Change Toolkit (CCT) was used, which was developed with several objectives in 78 mind. These were i) handling of big data, as it is required by climate change analyses, especially at large scales 79 and long time periods, ii) easy and seamless calculation of necessary steps in climate change studies, such as 80 data reformatting, data interpolation, downscaling and bias correction, and iii) projection of historical extreme 81 events into the future by pattern recognition of past events. CCT is developed to provide an easy-to-use platform 82 for climate change studies. As management of big databases, bias correction and downscaling, and interpolation 83 of climate data to finer resolution are essential processes in climate change studies; Ashraf Vaghefi et al. (2017) 84 developed the software to consider all these vital tasks in one package. In this study, changes in precipitation, 85 minimum temperature, maximum temperature and meteorological drought were investigated in the near future 86 (2043 to 2071) and distant future (2072 to 2100) compared to the base period (1991 to 2019) using the SPEI 87 drought index. Then, using the CCT model capability to calculate critical periods (Critical Consecutive Day 88 Analyzer), dry and wet periods in the future were obtained. Using the flood and drought conditions of previous 89 periods as a critical limit, it is possible to identify the frequency and frequency of floods and drought for the 90 future and apply the right management. In this case, the damage caused by floods and droughts can be reduced.

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Therefore, in climate studies of the world's major watersheds, such as the Karkheh Basin, the CCT model is a 92 good option for performing micro-scaling processes.

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Karkheh River is the third high-water river in Iran, whose water is controlled by the largest reservoir dam 96 ever built in Iran and the Middle East (Faiznia 2008

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Climate is defined as the average, or more precisely, the statistical description of the surface variables of a 118 climatic system, such as precipitation and temperature, over time. Climate change refers to the change in the 119 climatic condition of a region, including change in the mean or other statistical features of surface variables 120 over time (10 years or more). Drought is a recurring, temporary meteorological event that results from a lack 121 of rainfall relative to normal or the expected climatic value that can occur in any climate, but its characteristics

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The Climate Change Toolkit (CCT) is an effective tool for extraction, interpolation and bias-correction of 146 data obtained from global General Circulation Models (GCM). This model is also used to analyze extreme 147 events such as drought and flood. This model is connected to five global databases of ISI-MIP. It also uses four 148 RCP scenarios. Table 3 Where

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According to this method, the models obtaining a high weight in the past modeling are expected to achieve Where W i is the weight of each model in the studied month, ∆F i is the long-term mean deviation of the 179 simulated parameter by each model in the base period from the mean actual or observed data and n is the number

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As mentioned, in the CCT model, the length of wet and dry periods can be examined by defining the 189 threshold for wet and dry days. Table 4 is used to define the threshold.

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SPEI is a climatic drought index that shows the degree of drought and wetness and is calculated using equations 195 5 to 8.

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Where P and PET are the precipitation and potential evapotranspiration, respectively, D is their difference,

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and i is the month number. There are several equations for calculating PET with no limitation in using SPEI.

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After calculating the log-logistic cumulative distribution function according to Equation 6 and its conversion 199 to the standard normal distribution, the SPEI index is computed using Equation 7 (Allen 1998 203 W = √−2 ln(P) For P ≤ 0 (8) If P> 0.5, then P is replaced with 1 − P in Equation. 204 This index can be used to monitor dry and wet periods. Drought begins when the index values reach -1e 205 and ends when it becomes positive. The classification of this index is shown in Where SPEI ̅̅̅̅̅̅ is the weighted mean of the standardized precipitation-evapotranspiration index, SPEI i is

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In all months, especially in the three summer months, HadGEM2-ES had the highest weight in estimating the 245 maximum temperature under both scenarios. The minimum temperature results show that GCMs weigh less 246 than 0.45, and the MIROC model, with a slight difference, ranked as the best predictor of minimum temperature.

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In general, changes in the weight of GCM models showed that these models differed significantly in 248 estimating maximum temperature and precipitation than minimum temperature. In other words, GCM models 249 have a more similar potency in estimating minimum temperature than maximum temperature and precipitation.

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Precipitation, maximum temperature and minimum temperature produced using the best-performed models and 251 scenarios were analyzed to investigate the future effect of climate change on temperature and precipitation.

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The maximum temperature will also increase under both scenarios as well as in both future periods from March 284 to October. The highest minimum temperature increase of 0.92 ° C will occur in July of the distant future period 285 under the RCP2.6 scenario. This increase in July of the near future is expected to be 0.91° C. The highest 286 minimum temperature increase of 1.1 ° C was found to be in July of the distant future under the RCP8.5 287 emission scenario. This value July of the near future is projected to be 0.95 ° C. Generally, the minimum 288 temperature increase is projected to occur in April to October of both future periods compared to the base 289 period. Both minimum and maximum temperatures will increase from early spring to early autumn and decrease 290 from late autumn to late winter in both future periods.

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The results of seasonal changes indicate that the highest maximum temperature increase of 1.55 and 1.57 ° 292 C in the near future and 1.53 and 1.68 ° C in the distant future, under the RCP2.6 and RCP8.5 scenarios, 293 respectively, will occur in summer. Moreover, winter will experience the highest maximum temperature drop

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The results of using the critical period calculation operator in the CCT model showed that in the near future,

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6 and in terms of meteorology, the conditions will be opposite in which the basin will be drier than the base 332 period. In Figures 5 and 6, concentric circular spots may represent the hypersensitivity of the interpolation 333 method to low-distributed data.

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shows that the severity of the drought in the near future under the RCP2.6 scenario will be the same as the 343 baseline period, except that the probability of its occurrence is almost doubled. However, in this time period 344 under RCP8.5, the probability density function is shifted to the right, meaning that the drought intensity will be 345 less than the base period, but the probability of its occurrence is less than doubled. As shown in Fig. 7(B), the 346 severity of drought in the distant future under RCP2.6 would be the same as the base period, except that the 347 probability of its occurrence is almost doubled. Under RCP8.5, the probability density function is shifted to the 348 left, meaning that the intensity of the drought will be higher than the base period, but the probability of its 349 occurrence is less than double.

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To determine the frequency and duration of droughts, drought was considered to occur when the SPEI index 358 is projected to decrease to 8 and 6 times in the near future (Fig. 8(A)) and increase to 14 and 17 times in the 359 distant future (Fig. 8(B)) under the RCP2.6 and RCP8.5 scenarios, respectively. Changes in the duration of

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Drought is classified as a natural disaster that has major effects on parts of an ecosystem. Although it is not 370 possible to prevent its occurrence, but its measures can reduce the negative effects. Since the study area has the 371 largest reservoir dam in the Middle East on the Karkheh River, so the study of various droughts, including 372 meteorological drought in the basin is very important.

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This study was designed to investigate the effect of climate change on climate variables and meteorological    operator in the CCT model showed that in the near future, the average cumulative frequency of wet periods is 404 higher than dry periods, which may increase the risk of flooding. This result is similar to the study by Ashraf 405 Vaghefi et al. (2017) in California. In the distant future, the situation will be quite the opposite of the near 406 future, and the average cumulative frequency of dry periods is higher than wet periods. Therefore, the need to 407 pay more attention to water resources management in Karkheh basin based on climate scenarios in the future 408 should be on the agenda of managers and researchers. One of the limitations of the CCT program is the use of 409 only 5 climate models. It is suggested that other types of droughts, climate models and scenarios be considered

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The data that support the findings of this study are available from the corresponding author (Afshin 413 Honarbakhsh, afshin.honarbakhsh@gmail.com), upon reasonable request.    Weights of different GCMs to predict precipitation Changes in the average monthly rainfall in future periods for the RCP2.6 and RCP8.5 scenarios compared to the base period