Drought trends projection under future climate change scenarios for Iran region

compared to other scenarios, which is in line with the current investigation. Although in some investigations like [21], an increased precipitation under the B1 scenario compared to other scenarios has been reported, it should be mentioned that the time scale is different in this study. Moreover, since the changes of both precipitation and PET are taken into account for calculating SPEI, the interpretation of SPEI can be distinct from precipitation. In parallel with results of the B1 and A1B scenarios, the most frequency of dry months is observed in Shiraz with 22.63percent, while this value for the B1 and A1B scenarios are 19.70 and 21.16 percent (Tables 4 and 5). 1.23percent of months in Bandarabbas expected to experi- ence extreme droughts, which can be labeled as the driest station throughout the next years. This result matches with the survey of [37]. Due to the proportion of dry months, it can be inferred that approximately over 16.42 percent of months of each station will be experiencing droughts during the next years, which is alarming for Iran. Although the number of months with extreme droughts is much more than other categories, it should be declared that losses and damages of extreme intensity droughts are dramatically more destructive than others. In other words, a short-time drought period with extreme intensity can have long-term repercussions. The highest figure of humid months under the A2 scenario belongs to Ghazvin with 19.93percent, which is similar to the results of the A1B scenario. It is prominent that for all the

stations, the number of months with moderate wet is significantly more than the cumulative amount of extreme and severe wets. The highest number of months with extreme wet is related to Bosheher with 2.30 percent. For examining the changes in the trend of SPEI over the studied period from 2011 to 2112, the results of Mann-Kendall test (Z) are provided in Fig 4. The aim of this test is to statistically evaluate if there is a monotonic increase or decrease trend of the studied variables over the time. It is noticeable that Z�|1.96| refers to the significance level 0.05, while Z�|2.58| implies to the significance level 0.01. Fig 4 indicates that there is a downward trend in the SPEI changes in all the stations, except Esfahan under the B1 scenario. In other words, the value of SPEI over time will decrease Fig 4. The result of Mann-Kendall test on SPEI trend over the whole period. https://doi.org/10.1371/journal.pone.0290698.g004 PLOS ONE Drought trends projection under future climate change scenarios for Iran region PLOS ONE | https://doi.org/10.1371/journal.pone.0290698 November 9, 2023 18 / 24

significantly. So that, for most of the stations, this trend is significant in the level of 0.01. The highest downward trend for a large number of stations is observed in the A2 scenario. Thus, it can be declared that the amount of SPEI will drop sharply over time and this decrease under the A2 scenario will be the most intensive. As a consequence, it can be inferred that the A2 scenario predicts the most serious drought in comparison to other scenarios. Moreover, since the survey of [36] demonstrated that the certainty of the predicted PET values under the A2 scenario is higher than the other scenarios, the results of the A2 scenario were examined for extracting the characteristics of SPEI based on the Run theory,. Findings are provided in Table 7 for each station for the next 102 years. As an overall trend, comparing the number of dry periods for all the stations indicate that the proportion of dry periods in humid and cold regions is more than warm climate. For instance, 288 dry months are expected for Gorgan, in comparison to 218 dry months for Zahe- dan. However, in regard to long dry periods, the longest dry periods are expected to be observed in warmer stations; 8 in Gorgan compared to 25 in Zahedan. The maximum deficit can also be seen in warm climates such as Zahedan, with 27.36. This consequence can be seen Table 7. Characteristics of dry and wet periods based on SPEI under the A2 scenario. Station Dry period Wet period Number of periods Longest period Maximum deficit Total deficit Number of periods Longest period Maximum surplus Total surplus

Ahvaz 210 24 22.73 501.43 211 15 14.55 508.67 Arak 246 20 24.43 506.77 252 14 12.28 512.99 Ardebil 271 15 16.1 497.68 268 8 11. 504.91 Bandar abas 266 16 17.7 488.75 263 19 16.4 503.50 Birjand 250 18 17.26 500.57 251 15 14.81 508.13 Bojnoord 269 16 14.84 504.92 264 8 9.8 510.39 Boshehr 281 14 12.06 494.97 275 17 19.36 505.15 Esfahan 217 21 21.01 506.28 222 15 16.99 512.71 Ghazvin 216 21 19.53 514.54 223 12 13.14 519.73 Ghom 232 22 22.69 503.45 240 16 16.86 509.59 Gorgan 286 8 8.50 504.00 285 8 9.67 510.38 Hamedan 253 22 21.58 507.41 252 12 14.37 512.47 Ilam 233 17 19.34 504.62 231 20 20.41 510.20 Karaj 258 10 11.12 507.75 260 12 12.78 512.45 Kerman 216 19 22.02 505.28 224 31 30.31 510.99 Kermanshah 234 16 17.41 506.19 237 10 11.51 511.46 Khoramabad 231 19 16.38 511.63 241 18 20.71 514.76 Mashhad 231 25 21.52 503.20 244 14 13.11 508.52 Oroomie 253 19 16.03 500.67 246 18 14.98 507.71 Rasht 303 19 17.24 502.56 309 6 6.77 509.73 Sanandaj 225 14 14.72 506.03 228 15 11.82 513.01 Semnan 228 14 13.71 504.03 232 20 20.01 510.36 Shahrekord 232 24 13.87 508.38 245 23 21.94 514.23 Shiraz 229 18 19.18 512.50 225 26 24.86 515.82 Tabriz 274 10 12.07 510.63 265 12 10.53 516.59 Tehran 242 20 24.93 ( 505.32 247 10 11.3 510.95 Yasooj 242 12 12.12 507.48 240 19 16.07 515.50 Yazd 225 22 23.04 504.55 227 22 21.71 508.28 Zahedan 218 25 27.36 507.67 216 20 16.97 513.33

Zanjan 241 21 23.60 501.38 242 11 11.70 506.53 https://doi.org/10.1371/journal.pone.0290698.t007 PLOS ONE Drought trends projection under future climate change scenarios for Iran region PLOS ONE | https://doi.org/10.1371/journal.pone.0290698 November 9, 2023 19 / 24

in other stations such as Ghom with a hot climate and Tabriz with a cold climate. In regard to wet periods, the results are the same, i.e. the highest frequency of wet periods is related to cold climates, whereas the lowest belongs to warm climates. However, the duration of dry periods in warm climates is longer than that of cold climates. The information given for Rasht and Gorgan in comparison with Zahedan and Ahvaz can confirm these declarations. Sobhan et al. (2019) also reported that the southern stations of Iran are more exposed to drought than others. It should be mentioned that uncertainty in the prediction of future events is inevitable, it is significantly affected by the type of data used in terms of quality and quantity aspects, and also the structure of downscaling model [36, 49]. Regarding the data, using dataset with no missing data will reduce uncertainty, which was done in this study. Apart from that, the SPEI index assimilates more factors to quantify drought characteristics which affects less uncertainty [25]. From aspect of model, considering the calibration results, it can be claimed that the present model could discover connections of weather parameters with high accuracy, thus the perfor- mance of the model is acceptable. Moreover, different scenarios provided a chance to consider different assumptions in prediction which was compared. There is no hesitation that removal of uncertainty in modelling is impossible, nevertheless, the above-mentioned reasons can con- firm reliability of the findings in this study.

For more understanding, the monthly changes of PET and precipitation predicted under the A2 scenario for each station are provided in Fig 5. Fig 5 presents the area charts of PET and precipitation in a monthly time scale for the next 102 years. It seems to be crystal clear that for the vast majority of stations, the area of PET is signifi- cantly more than the area of precipitation. The most difference can be seen in the middle months of the year (e.g., in warm months), which is a logical and rational consequence. These findings are consistent with the report of [50], who concluded that the magnitudes of PET in Iran increases from January to July, then drop from August to December, and all stations reach their maximum levels in July. Since the highest temperature takes place in warm months, subsequently the amount of PET would increase as warming accelerates. However, the proba- bility of precipitation occurrence would drop. These findings can be observed in regions with hot climates such as Semnan, Yazd, Zahedan, and Kerman which are located in the southern part of Iran. [37] also declared that the southern regions of Iran are more severely affected by drought. These results refer to the importance of suitable water management in warm months and especially preserving and harvesting precipitation during the months with high precipita- tion. Contrary to these regions, the amount of precipitation (110.80 mm/month) in Rasht is approximately 26 percent more than the PET (87.51 mm/month). These consequences are in line with the findings of [51].

In general, it can be concluded that water shortage in warm climates would be much more serious than in other climates. Despite insufficient precipitation being the principal cause of water scarcity and drought, non-meteorological drivers such as rapid population growth, poor water resource management, and unsuitable cropping patterns also play critical roles [51]. 4. Conclusion Evaluating the amount of precipitation and PET for the base period and the next 102 years depicted that the highest PET and the lowest precipitation are related to the southeast of the studied area. Prediction of drought by SPEI under climate change scenarios indicated that the vast majority of stations will be involved in drought events. However, regions with hotter cli- mates will experience more intensive drought. Comparing forecasted drought under the A2, A1B and B1 scenarios showed that 19.23% of months over the next 102 years under the A2 PLOS ONE Drought trends projection under future climate change scenarios for Iran region PLOS ONE | https://doi.org/10.1371/journal.pone.0290698 November 9, 2023 20 / 24

scenario will struggle with water shortage and dry periods, which is more than other scenarios and the base time scale. The study highlights these findings: the number of dry periods in cold and cool terrains would be more than hot climates, whereas their intensity would be less than hot and warm climates. Furthermore, the temporal distribution of PET and precipitation revealed that for most of the stations, the precipitation was considerably less than the PET, especially in the middle months of the year. This situation in the southern regions is likely to be more significant. As a consequence, the severity and number of future droughts are expected to deteriorate particularly in warmer climates in terms of both meteorological and hydrological aspects, which highlights the importance of current water resources management for transmitting these natural resources to the next generations. According to the findings, this study broadens the knowledge on the future drought condition over Iran with acute water shortage crisis. Based on the observation, different regions will encounter distinct drought Fig 5. The temporal distribution of PET and precipitation for the next 102 years. https://doi.org/10.1371/journal.pone.0290698.g005 PLOS ONE Drought trends projection under future climate change scenarios for Iran region PLOS ONE | https://doi.org/10.1371/journal.pone.0290698 November 9, 2023 21 / 24

circumstances in the future which necessitates implementing specific plans to address water deficit in each area. Author Contributions Conceptualization: Maryam Bayatavrkeshi, Monzur Alam Imteaz, Ozgur Kisi, Mohammad Ghabaei, Bassim Mohammed Hashim, Baqer Al-Ramadan, Zaher Mundher Yaseen. Data curation: Maryam Bayatavrkeshi, Mohammad Farahani, Mohammad Ghabaei, Ahmed Mohammed Sami Al-Janabi. Formal analysis: Maryam Bayatavrkeshi, Ozgur Kisi, Mohammad Farahani, Mohammad Ghabaei, Ahmed Mohammed Sami Al-Janabi, Bassim Mohammed Hashim, Baqer Al-Ramadan, Zaher Mundher Yaseen. Funding acquisition: Baqer Al-Ramadan. Investigation: Maryam Bayatavrkeshi, Monzur Alam Imteaz, Ozgur Kisi, Mohammad Farahani, Ahmed Mohammed Sami Al-Janabi, Bassim Mohammed Hashim, Baqer Al-Ramadan, Zaher Mundher Yaseen. Methodology: Maryam Bayatavrkeshi, Bassim Mohammed Hashim. Project administration: Zaher Mundher Yaseen. Resources: Maryam Bayatavrkeshi. Software: Maryam Bayatavrkeshi. Supervision: Monzur Alam Imteaz, Ozgur Kisi, Baqer Al-Ramadan, Zaher Mundher Yaseen. Validation: Maryam Bayatavrkeshi, Monzur Alam Imteaz, Ozgur Kisi, Mohammad Farahani, Baqer Al-Ramadan, Zaher Mundher Yaseen. Visualization: Maryam Bayatavrkeshi, Monzur Alam Imteaz, Ozgur Kisi, Mohammad Farahani, Bassim Mohammed Hashim, Baqer Al-Ramadan, Zaher Mundher Yaseen. Writing – original draft: Maryam Bayatavrkeshi, Monzur Alam Imteaz, Ozgur Kisi, Mohammad Farahani, Mohammad Ghabaei, Ahmed Mohammed Sami Al-Janabi, Bassim Mohammed Hashim, Baqer Al-Ramadan, Zaher Mundher Yaseen.

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