مقایسه الگوهای زمانی-مکانی و همبستگی مقیاسی شاخص‌های خشکسالی در جنوب‌شرق ایران با تأکید بر وضعیت دما و بارشِ سال‌های اخیر

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران.

2 گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

چکیده

خشکسالی به‌عنوان یکی از مخرب‌ترین پدیده‌های اقلیمی، تهدیدی جدی برای امنیت آبی و غذایی مناطق خشک و نیمه‌خشک محسوب می‌شود. پژوهش حاضر با هدف تحلیل الگوهای زمانی-مکانی خشکسالی هواشناسی در استان سیستان و بلوچستان طی دوره ۱۹۹۰ تا ۲۰۲۴ با استفاده از داده‌های شش ایستگاه سینوپتیک انجام شد. شاخص‌های SPI و SPEI در مقیاس‌های زمانی ۱، ۳، ۶ و ۱۲ماهه محاسبه و روند آن‌ها با آزمون من-کندال با روش پیش‌سفید کردن و شیب سن ارزیابی گردید. نتایج نشان داد که دمای منطقه روند افزایشی معناداری داشته و آزمون پتیت تغییر رژیم اقلیمی در سال‌های ۱۹۹۸ تا ۲۰۰۱ را تأیید کرد. تبخیرتعرق در ایستگاه زابل با میانگین 65/8 میلی‌متر بر روز در تابستان بیشترین مقدار را نشان داد. اگرچه در کلیه ایستگاه‌ها روند نزولی شاخص‌ها مشهود است، اما معنی‌داری آماری آن عمدتاً در مقیاس‌های بلندمدت (۱۲ماهه) و به‌ویژه در ایستگاه زابل ثبت شد. با این حال، شاخص SPEI با نشان دادن روند نزولی در ایستگاه‌هایی که SPI فاقد روند معنادار است، شواهدی حاکی از ماهیت انرژی‌محور خشکسالی و غلبه اثرات گرمایش و تبخیرتعرق بر شرایط رطوبتی منطقه ارائه می‌دهد. همبستگی میان SPI و SPEI با افزایش مقیاس زمانی از ۲۱/۰ به ۷۳/۰ افزایش یافت که نشان‌دهنده غلبه نقش بارش در مقیاس‌های بلندمدت و تأثیرپذیری SPEI از نوسانات دمایی در مقیاس‌های کوتاه‌مدت است. ایستگاه چابهار نیز به‌دلیل موقعیت ساحلی، کمترین همبستگی را نشان داد. بر اساس نتایج، مدیریت خشکسالی در این منطقه باید بر کاهش تقاضای تبخیری و افزایش تاب‌آوری در برابر گرمایش متمرکز شود. 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Spatiotemporal Patterns and Scale-dependent Correlations of Drought Indices in Southeastern Iran with Emphasis on Recent Temperature and Precipitation Conditions

نویسندگان [English]

  • Haniye Mohammadi 1
  • Javad Bazrafshan 1
  • Arezoo Nazi Ghameshlou 1
  • Shahin Rafiee 2
1 Department of Irrigation and Reclamation Engineering, College of Agricultural and Natural Resources, University of Tehran, Karaj, Iran.
2 Department of Biosystems Mechanical Engineering, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

Drought, as one of the most destructive climatic phenomena, poses a serious threat to water and food security in arid and semi‑arid regions. The present study aimed to analyze the spatio‑temporal patterns of meteorological drought in Sistan and Baluchestan Province during the period 1990–2024 using data from six synoptic stations. The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were calculated at 1‑, 3‑, 6‑, and 12‑month timescales, and their trends were evaluated using the Mann–Kendall test with pre‑whitening and Sen’s slope estimator. The results indicated a significant increasing trend in regional temperature, and the Pettitt test confirmed a climatic regime shift during 1998–2001. Evapotranspiration at Zabol station showed the highest values, with an average of 65.8 mm day⁻¹ in summer. Although negative trends in the indices were observed across all stations, statistical significance was mainly detected at the long‑term timescale (12 months), particularly at Zabol. Notably, SPEI exhibited negative trends even in stations where SPI showed no significant trend, providing evidence for the energy‑driven nature of drought and the stronger influence of warming and evapotranspiration on regional moisture conditions. The correlation between SPI and SPEI increased from 0.21 to 0.73 with increasing timescale, suggesting the dominant role of precipitation at longer timescales and the sensitivity of SPEI to temperature variability at shorter timescales. Chabahar station, due to its coastal location, exhibited the lowest correlation. Based on these findings, drought management in the region should focus on reducing evaporative demand and enhancing resilience to warming.

کلیدواژه‌ها [English]

  • Mann–Kendall test
  • Meteorological drought
  • SPEI
  • SPI
  • Sistan and Baluchestan

Introduction

Drought is one of the most destructive natural hazards, causing far‑reaching environmental, economic, and social consequences. It disrupts regional water balances through reduced precipitation, increased temperatures, and intensified evapotranspiration, leading to secondary crises such as food insecurity, groundwater depletion, and ecosystem degradation. In Iran, Sistan and Baluchestan province — characterised by an arid to hyper‑arid climate, extreme precipitation variability, and a local economy heavily dependent on water‑sensitive sectors (agriculture, livestock, and wetlands) — exhibits high vulnerability to drought. Previous studies in this region have predominantly relied on univariate indices such as the Standardised Precipitation Index (SPI) and have generally covered statistical periods ending around 2018 or 2020. Consequently, the influence of the unprecedented warming observed during the last decade (2014–2024) on drought intensification remains underexplored. Moreover, a comparative assessment of SPI and the Standardised Precipitation Evapotranspiration Index (SPEI) across multiple time scales (1, 3, 6, and 12 months) — and under the specific thermodynamic conditions of southeastern Iran — is still lacking. Against this background, the present study has four main objectives: (1) to characterise meteorological drought in Sistan and Baluchestan over the period 1990–2024 using SPI and SPEI; (2) to detect temporal trends and abrupt change points in drought severity; (3) to compare the behaviour of the two indices across different accumulation scales; and (4) to evaluate the degree of convergence/divergence between SPI and SPEI as a function of climate type (coastal vs. interior arid).

Method

This applied research follows a quantitative‑analytical design. The study area is Sistan and Baluchestan province (≈180,726 km²), located in southeastern Iran, bordering Afghanistan and Pakistan. Daily meteorological data — precipitation, minimum and maximum temperature, relative humidity, wind speed, and sunshine hours — were obtained from six synoptic stations (Zabol, Zahedan, Iranshahr, Khash, Saravan, and Chabahar) for the period 1990–2024 from the Iran Meteorological Organization. Missing values were reconstructed using linear interpolation and correlation‑based infilling. Homogeneity of the time series was verified using the SNHT (Standard Normal Homogeneity Test) at α = 0.05. The FAO Penman‑Monteith method, which accounts for radiation, humidity, and wind, was used to estimate reference evapotranspiration (ET₀) for SPEI calculation. SPI and SPEI were computed at four-time scales (1, 3, 6, and 12 months) following the widely accepted gamma and log‑logistic distribution fits, respectively.

To ensure robust trend analysis, the pre‑whitening procedure was applied to remove the effect of serial correlation. The non‑parametric Mann–Kendall test was then used to detect monotonic trends, and Sen’s slope estimator quantified the magnitude of change. The Pettitt test (α = 0.05 and 0.01) identified potential abrupt change points in mean annual temperature and total precipitation. Pearson’s correlation coefficient was calculated between SPI and SPEI at each station and time scale to examine behavioural convergence.

Results and Discussion

Mean annual temperature exhibited a significant increasing trend (p < 0.01) at all six stations. The Pettitt test detected a coherent breakpoint around 1998–2001 at Zabol, Zahedan, Khash, Saravan, and Iranshahr, while Chabahar (coastal) showed a later shift in 2007. In contrast, annual precipitation displayed no significant structural trend; only Zabol had a detectable breakpoint (1998). The lack of a precipitation trend coexists with a marked rise in potential evapotranspiration (PET). The highest PET values were recorded at Zabol (summer mean 8.65 mm day⁻¹, with daily extremes exceeding 21 mm day⁻¹), followed by the interior arid stations (Iranshahr, Zahedan, Khash, Saravan ≈5.7–6.7 mm day⁻¹). Chabahar showed the lowest PET and the smallest seasonal range due to maritime moderation.

The SPI‑12 series revealed that long‑term drought episodes dominate the study period; pluvial events, although occasionally intense, were short‑lived and insufficient to offset the cumulative moisture deficit. SPEI‑12 indicated consistently more severe and prolonged droughts than SPI, especially after 2000, highlighting the exacerbating role of increased evaporative demand. The Mann–Kendall trend analysis on the 12‑month scale showed a significant decreasing trend for both SPI and SPEI only at Zabol (Z = –2.165 for SPI, and –2.149 for SPEI; p < 0.05). At Zahedan, Khash, Saravan, and Iranshahr, SPEI exhibited negative Sen’s slopes (e.g., –0.0041 year⁻¹ at Zahedan) that were not statistically significant, but the contrast with SPI (which had near‑zero slopes) suggests a detectable thermal‑evaporative signal. No significant trends were found at short scales (1‑ and 3‑month) after pre‑whitening. Interestingly, at Chabahar, a significant positive trend was observed for SPI‑6 (Z = 2.168, p < 0.05), indicating a wetting tendency in mid‑scale precipitation, but this was cancelled in SPEI‑6 (non‑significant negative slope), implying that increased temperature and PET have offset the rainfall gain.

Pearson correlation between SPI and SPEI increased systematically from short to long accumulation scales: from ≈0.21–0.54 at 1–3 months to ≈0.66–0.73 at 12 months. The highest 12‑month correlation was found at Khash (0.732) and Zahedan (0.729), and the lowest at Chabahar and Zabol at short scales. This scale‑dependent convergence confirms that at longer time scales, precipitation becomes the dominant driver of both indices, whereas at short scales, surface energy fluxes control SPEI more strongly.

Conclusions

Drought in Sistan and Baluchestan province results from the interaction of three key processes: (i) structural temperature increase and thermal regime shift (with a breakpoint in the late 1990s), (ii) sustained growth of potential evapotranspiration (especially in Zabol and interior areas), and (iii) reduced precipitation efficiency in restoring long-term moisture. These factors have led to an energy-amplified drought. SPEI provides a more realistic picture of drought severity because it accounts for temperature. Significant decreasing trends were observed only at Zabol (at long scales), indicating a structural aridity effect in the northern part of the province. At other stations, although trends are non-significant, the difference between SPI and SPEI reveals the decisive role of temperature. The increasing correlation between the two indices at long scales indicates that precipitation dominates over long periods, but divergence at short scales suggests that surface energy controls drought in its early stages. Overall, water resource management in this province should focus on reducing water demand, curbing evapotranspiration, and enhancing resilience to warming, because precipitation alone cannot compensate for the moisture deficit. It is recommended to establish an early warning and monitoring system based on SPEI (prioritizing Zabol station), revise cropping patterns, and adopt modern irrigation technologies.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors’ contributions

  1. Mohammadi: Student, Data curation, Software, Formal analysis, Investigation (including statistical analysis, interpretation of data and results), Writing – original draft, Writing-Reviewing and Editing.
  2. Bazrafshan: Supervision, Conceptualization, Methodology, Validation, Writing-Reviewing and Editing, Project administration, Finalization of the manuscript.
  3. Nazi Ghameshlou: Supervision, Conceptualization, Methodology, Validation, Writing-Reviewing and Editing, Project administration, Finalization of the manuscript.
  4. Rafiee: Thesis Advisor, Participation in research design, Supervision of research, Study and revision of the manuscript.

All authors have read and agreed to the published version of the manuscript. All authors contributed equally to the conceptualization of the article and the writing of the original and subsequent drafts.

Declaration of Generative AI and AI-assisted technologies in the writing process

The authors did not use any artificial intelligence tools in preparing this manuscript.

Data Availability Statement

Data available on request from the authors.

Acknowledgements

This research was supported by the University of Tehran. The authors express their special thanks to the Vice Chancellor for Research Affairs of the University of Tehran. They are also sincerely grateful to the Iran Meteorological Organization for providing the meteorological data required for this study.

Ethical considerations

The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

All authors declare that they have no conflict of interest.

Afshar, M.H., Bulut, B., Duzenli, E., Amjad, M., & Yilmaz, M. (2022). Global spatiotemporal consistency between meteorological and soil moisture drought indices. Agric. For. Meteorol. 316, 108848.
Ahmed, K, Shahid, S., & Nawaz, N. (2018). Impacts of climate variability and change on seasonal drought characteristics of Pakistan. Atmospheric Research, 214, 364–374. 10.1016/j.atmosres.2018.08.020. 
Alexandersson, H. (1986) A homogeneity test applied to precipitation data. Journal of Climatology, 6(6), 661–675.
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
Bahrami, M., & Mahmoudi, M.R. (2020). Rainfall modelling using backward generalized estimating equations: a case study for Fasa plain, Iran. Meteorol Atmos Phys. 132, 771-779.
Bahrami, M., Bazrkar, S., & Zarei, A.R. (2019). Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index. J Water Clim Change, 10(1), 181-196.
Barahooie, D., Hamidianpour, M., & Shoja, F. (2025). Identification of Spatiotemporal Drought Patterns in Southeastern Iran Using a Graphical Trend Analysis Approach. Physical Geography Research Quarterly, 57 (2), 77-98. http://doi.org/10.22059/jphgr.2025.398145.1007893  (In Persian)
Bazrafshan, J. (2017). Effect of air temperature on historical trend of long-term droughts in different climates of Iran. Water Resources Management, 31(14), 4683-4698.
Bickici Arikan, B., & Kahya, E. (2019). Homogeneity revisited: analysis of updated precipitation series in Turkey. Theoretical and Applied Climatology, 135(1), 211-220.
Bozorgzadeh, M., jahantigh, H., Rigi, M. & mohammadi, M. (2024). comprehensive assessment of drought severity with multi-indicator approach in saravan city-sistan and baluchistan province. ournal of Climate Change Research, 5(20), 19-32. (In Persian)
Dai, A. (2011). Drought under global warming: a review.Wiley Interdiscip. Rev Clim Chang. 2, 45–65. https://doi.org/10.1002/wcc.81  
Darroudi, H., Khosroshahi, M., & Shahabi, M. (2022). Investigating variations in climatic factors and drought trends in Sistan and Baluchestan Province. Desert Ecosystem Engineering, 10(32), 15-30. doi: 10.22052/deej.2021.10.32.11. (In Persian)
Dashtpagerdi, M. M., Kousari, M. R., Vagharfard, H., Ghonchepour, D., Hosseini, M. E., & Ahani, H. (2015). An investigation of drought magnitude trend during 1975–2005 in arid and semi-arid regions of Iran. Environmental earth sciences, 73(3), 1231-1244.
de Medeiros, F.J., Gomes, R.d.S., Coutinho, M.D.L., & Lima, K.C. (2022). Meteorological droughts and water resources: Historical and future perspectives for Rio Grande do Norte state, Northeast Brazil. Int. J. Climatol, 42, 6976–6995.
Deldarzehi, Z., Mahmoudi, P. & Khosravi, M. (2024). Arabian Sea’s Moisture Transfer Mechanisms in Pervasive Dry and Wet Periods of Iran. Geography and Environmental Planning35(1), 45-72. (In Persian)
Du, W., & Wang, G. (2013). Intra-event spatial correlations for cumulative absolute velocity, arias intensity, and spectral accelerations based on regional site conditions. Bull. Seismol. Soc. Am, 103, 1117–1129.
Fawen, L., Manjing, Z., Yong, Z., & Rengui, J. (2023). Influence of irrigation and groundwater on the propagation of meteorological drought to agricultural drought. Agric. Water Manag, 277, 108099.
Firoozi, F., Mahmoudi, P., Jahanshahi, S.M.A., Tavousi, T., Liu., Y., & Liang, Zh. (2020).  Modeling changes trend of time series of land surface temperature (LST) using satellite remote sensing productions (case study: Sistan plain in east of Iran). Arab J Geosci, 13, 367. https://doi.org/10.1007/s12517-020-05314-w
Golian, S., Mazdiyasni, O., & AghaKouchak, A. (2015). Trends in meteorological and agricultural droughts in Iran. Theoretical and Applied Climatology, 119(3), 679–688.
Guhathakurta, P., Menon, P., Mazumdar, A. B., & Sreejith, O. P. (2010). Changes in extreme rainfall events and flood risk in India during the last century. National Climatic Centre, Research Report, 3, 1-20.
Gurrapu, S., Chipanshi, A., Sauchyn, D., & Howard, A. (2014). Comparison of the SPI and SPEI on predicting drought conditions and streamflow in the Canadian prairies. In: 28th Conference on Hydrology and the 26th Conference on Climate Variability and Change. American Metereological Society, Georgia, p7.
Hoover, D.L., Hajek, O.L., Smith, M.D., Wilkins, K., Slette, I.J., & Knapp, A.K. (2022). Compound hydroclimatic extremes in a semi-arid grassland: Drought, deluge, and the carbon cycle. Glob. Chang. Biol, 28, 2611–2621.
IPCC. (2013). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Special Report of the Intergovernmental Panel on Climate Change (Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignor, M. & Midgley, P. M., eds), Cambridge University Press, Cambridge, UK and New York, NY, USA.
Karimi, M., Khoshakhlagh, F., shamsi por, A. A. and noruzi, F. (2019). Arabian subtropical High Pressure circulation patterns in the middle troposphere and its relationship with Iran's Precipitation. Journal of Geography and Planning, 23(69), 233-255. (In Persian)
Kendall, M.G. (1975). Rank Correlation Methods, 4th edition, Charles Griffin, London.
Keshavarz, A. (2025). Trends in Meteorological Drought in Iran Using the SPI Index and Mann-Kendall Test: A Comprehensive Review. Journal of Asian Geography, 4 (2), 79-83.
Kheyruri, Y., Nikaein, E., & Sharafati, A. (2023). Spatial monitoring of meteorological drought characteristics based on the NASA POWER precipitation product over various regions of Iran. Environ. Sci. Pollut. Res, 30, 43619–43640.
Kousari, M. R., Dastorani, M. T., Niazi, Y., Soheili, E., Hayatzadeh, M., & Chezgi, J. (2014). Trend detection of drought in arid and semi-arid regions of Iran based on implementation of reconnaissance drought index (RDI) and application of non-parametrical statistical method. Water resources management28(7), 1857-1872.
Liu, L., Liao, J., Chen, X., Zhou, G., Su, Y., Xiang, Z., ... & Shao, H. (2017). The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010). Remote Sensing of Environment, 199, 302-320.
Liu, Y., & Chen, J. (2021). Socioeconomic risk of droughts under a 2.0 C warmer climate: Assessment of population and GDP exposures to droughts in China. Int. J. Climatol. 41, E380–E391.
Lotfinasab Asal, S., Dost, G, A., & Khosroshahi, M. (2018). Assessment and application of geostatistics in identifying and analyzing drought characteristics of Jazmourian watershed. Watershed Manage Res. 1(18), 12-25.
Lotfirad, M., Esmaeili-Gisavandani, H., & Adib, A. (2022). Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran. Journal of Water and Climate Change13(2), 383-406.
Mahmoudi, P., Rigi, A., & Miri Kamak, M. (2019). A comparative study of precipitation-based drought indices with the aim of selecting the best index for drought monitoring in Iran: P. Mahmoudi et al. Theoretical and Applied Climatology, 137(3), 3123-3138.
Mahmoudi, P., Shirazi, S.A., Firoozi, F., Jahanshahi, S.M.A., & Mazhar, N. (2020). Detection of land cover changes in Balouchestan (shared between Iran, Pakistan, and Afghanistan) using the MODIS Land Cover Product. Arab. J. Geosci, 13, 1-14.
Mann, H.B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 245-259.
McKee, T.B., Doesken, N.J., & Kleist J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology: American Meteorological Society, 17(22), 179-183.
Mehdizadeh, S, Ahmadi, F, Mehr, AD, & Safari, MJS. (2020). Drought modeling using classic time series and hybrid wavelet-gene expression programming models. Journal of Hydrology, 587, Article 125017.10.1016/j.jhydrol.2020.125017.
Mirzavand, M., & Bagheri, R. (2020). The water crisis in Iran: development or destruction? World Water Policy, 6(1), 89-97.
Nouri, M., & Homaee, M. (2020). Drought trend, frequency and extremity across a wide range of climates over Iran. Meteorological Applications, 27(2), e1899.
Omidvar, K., Nabavizadeh, M., Rousta, I., & Olafsson, H. (2024). Remote sensing-based drought monitoring in Iran’s sistan and balouchestan province. Atmosphere15(10), 1211.
Pearson, K. (1897). Mathematical contributions to the theory of evolution. on a form of spurious correlation which may arise when indices are used in the measurement of organs. Proceedings of the royal society of London, 60 (359-367), 489-498.
Pettitt, A. N. (1979). A Non-Parametric Approach to the Change-Point Problem Journal of the Royal Statistical Society Series C (Applied Statistics), 28, 126–135. https://doi.org/10.2307/2346729  
Pourasghar, F., Ghaemi, H., Jahanbakhsh, S. & Sarisarraf, B. (2017). Variability of Precipitation in Southern Part of Iran and Linkage to Indian Ocean Sea Surface Temperature. Geography and Environmental Planning28(2), 145-166. (In Persian)
Qutbudin, I, Shiru, MS, Sharafati, A, Ahmed, K, Al-Ansari, N, Yaseen, ZM, Shahid, S, & Wang, X. (2019). Seasonal drought pattern changes due to climate variability: Case study in Afghanistan. Water, 11 (5), 1096.10.3390/w11051096. 
Raza, A., Mubarik, M.S., Sharif, R., Habib, M., Jabeen, W., Zhang, C., Chen, H., Chen, Z.H., Siddique, K.H., & Zhuang, W. (2023). Developing drought-smart, ready-to-grow future crops. Plant Genome. 16, e20279.
Saeidipou, M., Radmanesh, F., & Eslamian, S. (2019). Metreological drought monitoring using the multivariate index of SPEI (case study: Karun Basin). AUT J Civ Eng, 3, 85–92. https://doi.org/10.22060/ajce.2018.14740.5494
Saemian, P., Tourian, M.J., AghaKouchak, A., Madani, K., & Sneeuw, N. (2022). How much water did Iran lose over the last two decades? J Hydrology: Reg Stud, 41, 101095,
Salimi, H., Asadi, E., & Darbandi, S. (2021). Meteorological and hydrological drought monitoring using several drought indices. Applied Water Science, 11(2), 1-10.
Sen, P.K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379-1389.
Sharafi, S., & Ghaleni, M. M. (2022). Spatial assessment of drought features over different climates and seasons across Iran. Theoretical and Applied Climatology, 147(3), 941-957.
Siasar, H. & Salari, A. (2023). Predicting the probability of droughts using SPI drought index based on Markov chain model (Case study: Villages of Sistan and Baluchistan province). Rural Development Strategies, 10(3), 387-402. (In Persian)
Siasar, H., Salari, A., Bahrami, M., & Hamidifar, H. (2025). Integrating remote sensing and meteorological analysis for monitoring drought conditions in arid regions: a case study from Sistan and Baluchestan province, Iran. Theoretical and Applied Climatology, 156(5), 291.
So¨nmez, F.K., Koemuescue, A.U., Erkan, A., & Turgu, E. (2005). An analysis of spatial and temporal dimension of drought vulnerability in Turkey using the standardized precipitation index. Natural Hazards, 35, 243–264.
Svoboda, M., & Fuchs, B. (2017). Handbook of Drought Indicators and Indices Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev, 38, 55. https://doi.org/10.2307/210739
Tabari, H., Abghari, H., & Hosseinzadeh Talaee, P. J. H. P. (2012). Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran. Hydrological processes26(22), 3351-3361.
Talebi, M. (2023). Water crisis in Iran and its security consequences. J Hydraulic Struct, 8(4), 17-28.
Theil, H. (1950). A rank invariant method of linear and Polynomial regression analysis, Part3. Netherlands Akademic van Wettenschappen, Proceedings, 53, 1379-1412.
Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical review, 38(1), 55-94.
Torabinezhad, N., Zarrin, A. & Dadashi-Roudbari, A. (2023). Analysis of Different Types of Droughts and Their Characteristics in Iran Using the Standardized Precipitation Evapotranspiration Index (SPEI). Water and Soil, 37(3), 473-486. doi: 10.22067/jsw.2023.81322.1257 (In Persian)
Trenberth, KE., Dai, A., van der Schrier, G., Jones, PD., Barichivich, J., Briffa, KR., & Sheffield, J. (2014). Global warming and changes in drought. Nat Clim Chang, 4, 17–22. https://doi.org/10.1038/nclimate2067
Tsakiris, G., Pangalou, D., & Vangelis, H. (2007). Regional drought assessment based on the reconnaissance drought index (RDI). J Water Resour Manage, 21, 821–833. https://doi.org/10.1007/s11269- 006-9105-4
Ullah, I., Yuanjie, Z., Ali. S., & Rahman, G. (2020). Rainfall and drought variability in spatial and temporal context in Lop Nor region, South Xinjiang, China, during 1981–2018. Arabian Journal of Geosciences, 13, 1–13.
Valenzuela-Morales, G., Hernández-Téllez, M., Fonseca-Ortiz, C., Gómez-Albores, M., Esquivel-Ocadiz, A., Arévalo-Mejía, R., Mejía-Olivares, A., & Mastachi-Loza, C. (2023). Climatic and socioeconomic regionalization of the meteorological drought in Mexico using a predictive algorithm. Nat. Hazards, 117, 1381–1403.
Vicente-Serrano, S. M., Van der Schrier, G., Beguería, S., Azorin-Molina, C., & Lopez-Moreno, J. I. (2015). Contribution of precipitation and reference evapotranspiration to drought indices under different climates. Journal of Hydrology, 526, 42-54.
Vicente-Serrano, SM., Lopez-Moreno, J-I., Beguería, S., Lorenzo-Lacruz, J., Sanchez-Lorenzo, A., García-Ruiz, JM., Azorin-Molina, C., Morán- Tejeda, E., Revuelto, J., Trigo, R., Coelho, F., & Espejo, F. (2014). Evidence of increasing drought severity caused by temperature rise in southern Europe. Environ Res, Lett 9, 044001. https://doi.org/10.1088/1748-9326/9/4/044001
Visente Serrano, S.M., López-Moreno, J.I., Drummond, A., Gimeno, L., Nieto, R., Morán-Tejeda, E., & Zabalza, J. (2011). Effects of warming processes on droughts and water resources in the NW Iberian Peninsula, (1930-2006). Climate Research, 48, pp. 203-212.
Von Storch, H. (1999). Misuses of statistical analysis in climate research. In: Analysis of climate variability. Springer, pp 11-26.
Wang, T., Tu, X., Singh, V.P.; Chen, X., Lin, K., Zhou, Z., & Tan, Y.  (2023). Assessment of future socioeconomic drought based on CMIP6: Evolution, driving factors and propagation. J. Hydrol. 617, 129009.
Wu, H., Svoboda, M. D., Hayes, M. J., Wilhite, D. A., & Wen, F. (2007). Appropriate application of the standardized precipitation index in arid locations and dry seasons.
Xu, J., Zhou, G., Su, S., Cao, Q., & Tian, Z. (2022). The development of a rigorous model for bathymetric mapping from multispectral satellite-images. Remote Sens, 14, 2495.
Zarch, M. A. A., Sivakumar, B., & Sharma, A. (2015). Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI). Journal of hydrology526, 183-195.
Zhang, F., Cui, N., Guo, S., Yue, Q., Jiang, S., Zhu, B., & Yu, X. (2023). Irrigation strategy optimization in irrigation districts with seasonal agricultural drought in southwest China: A copula-based stochastic multi objective approach. Agric. Water Manag, 282, 108293.
Zhou, G., Lin, G., Liu, Z., Zhou, X., Li, W., Li, X., & Deng, R.  (2023a). An optical system for suppression of laser echo energy from the water surface on single-band bathymetric LiDAR. Opt. Lasers Eng, 163, 107468.
Zhou, G., Zhang, H., Xu, C., Zhou, X., Liu, Z., Zhao, D., Lin, J., & Wu, G. (2023b). A real-time data acquisition system for single-band bathymetric LiDAR. IEEE Trans. Geosci. Remote Sens, 61, 1–21.