Abstract
Every year, droughts cause a lot of damage in arid and semi-arid regions. Based on drought indicators, the study aimed to determine and predict drought in southern Iran. In this study, we used Reconnaissance Drought Index (RDI), Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Temperature Vegetation Dryness Index (TVDI) drought indices prepared from MODIS image and Standardized Precipitation Index (SPI), and RDI prepared from 18 meteorological stations from 2000 to 2020 (March to September) in time scale 1, 3, 6, and 9, 12-months. In addition to Principal component analysis (PCA), the most relevant remote sensing drought indices are also selected using meteorological drought indices. The drought situation for the region is also forecast using the Markov and CA-Markov methods. Further, the multilayer perceptron (MLP) method is used to predict drought and replace meteorological drought indices with remote sensing drought indices. It also examined how drought conditions relate to landforms in the study area. the results of indices from 2000 to 2020 showed that currently, the drought situation is more severe in the eastern half of the study area. According to PCA results in the study area, SPI and TVDI are the most relevant drought and meteorological indices to investigate the drought. SPI drought index can also be predicted using the MLP method using TVDI with an accuracy of about 0.8. Based on the CA-Markov the study area will suffer from drought in 2040. So that 34% of the area is classified as arid in 2040. According to the results of the relationship between landforms and drought indices, ridges and peaks have the least drought compared to other landforms. So, it is clear that the region is at risk of drought in the future, but if management measures are taken, damage should be prevented.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
In response to reasonable requests, the corresponding author will provide the data used in this research.
References
Aghelpour P, Varshavian V (2021) Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA). Complexity 2021. https://doi.org/10.1155/2021/6610228
Ali Z, Hussain I, Nazeer A, et al (2020) Measuring and restructuring the risk in forecasting drought classes: an application of weighted Markov chain based model for standardised precipitation evapotranspiration index (SPEI) at one-month time scale. 72:1–10. https://doi.org/10.1080/16000870.2020.1840209
Bai JJ, Yu Y, Di L (2017) Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain, China. J Integr Agric 16:389–397. https://doi.org/10.1016/S2095-3119(15)61302-8
Balzter H (2000) Markov chain models for vegetation dynamics. Ecol Modell 126:139–154. https://doi.org/10.1016/S0304-3800(00)00262-3
Brown DG, Pijanowski BC, Duh JD (2000) Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. J Environ Manag 59:247–263. https://doi.org/10.1006/JEMA.2000.0369
Chang H, He G, Wang Q et al (2021) Use of sustainability index and cellular automata-Markov model to determine and predict long-term spatio-temporal variation of drought in China. J Hydrol 598:126248. https://doi.org/10.1016/J.JHYDROL.2021.126248
Chen X, Zhao P, Ouyang L et al (2020) Whole-plant water hydraulic integrity to predict drought-induced Eucalyptus urophylla mortality under drought stress. For Ecol Manage 468:118179. https://doi.org/10.1016/J.FORECO.2020.118179
Chopra P (2006) Drought risk assessment using remote sensing and GIS: a case study of Gujarat. Enschede, The Netherlands: ITC, pp 81 (chopra-libre.pdf (d1wqtxts1xzle7.cloudfront.net))
Dikshit A, Pradhan B, Alamri AM (2020) Temporal Hydrological Drought Index Forecasting for New South Wales Australia Using Machine Learning Approaches. Atmos 11:585. https://doi.org/10.3390/ATMOS11060585
Estakhr A, Ranjbar G (2021) The Preliminary Study of Camelina Compatibility as a New Oil Crop in the Temperate Region of Fars Province. Agrotechniques Ind Crop 1:77–84. https://doi.org/10.22126/ATIC.2021.6525.1017
Fadhil RM, Unami K (2021) A multi-state Markov chain model to assess drought risks in rainfed agriculture: a case study in the Nineveh Plains of Northern Iraq. Stoch Environ Res Risk Assess 35:1931–1951. https://doi.org/10.1007/S00477-021-01991-5/FIGURES/17
Gao Z, Gao W, Bin CN (2011) Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int J Appl Earth Obs Geoinf 13:495–503. https://doi.org/10.1016/J.JAG.2010.10.005
Ghasemi MM, Pakparvar M, Mokarram M (2021) Preparation of landforms using geomorphon method and its relationship with drought in the east and south of Fars province. Quant Geomorphol Res 10. https://doi.org/10.22034/GMPJ.2021.279116.1262
Heim RR (2002) A Review of Twentieth-Century Drought Indices Used in the United States. Bull Am Meteorol Soc 83:1149–1166. https://doi.org/10.1175/1520-0477-83.8.1149
Henríquez C, Azócar G, Romero H (2006) Monitoring and modeling the urban growth of two mid-sized Chilean cities. Habitat Int 30:945–964. https://doi.org/10.1016/J.HABITATINT.2005.05.002
Heydari Alamdarloo E, Khosravi H, Nasabpour S, Gholami A (2021) Assessment of drought hazard, vulnerability and risk in Iran using GIS techniques. J Arid L 126(12):984–1000. https://doi.org/10.1007/S40333-020-0096-4
Huete AR, Liu HQ, van Leeuwen WJD (1997) Use of vegetation indices in forested regions: Issues of linearity and saturation. Int Geosci Remote Sens Symp 4:1966–1968. https://doi.org/10.1109/IGARSS.1997.609169
Kamusoko C, Aniya M, Adi B, Manjoro M (2009) Rural sustainability under threat in Zimbabwe - Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Appl Geogr 29:435–447. https://doi.org/10.1016/J.APGEOG.2008.10.002
Karul C, Soyupak S (2003) A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation. Ecol Inform 249–263. https://doi.org/10.1007/978-3-662-05150-4_13
Khalili N, Arshad M, Kächele H et al (2021) Drought shocks and farm household consumption behaviour: Insights from Fars province of Iran. Int J Disaster Risk Reduct 66:102625. https://doi.org/10.1016/J.IJDRR.2021.102625
Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Sp Res 15:91–100. https://doi.org/10.1016/0273-1177(95)00079-T
Krak T (2021) An Introduction to Imprecise Markov Chains. Optim Under Uncertain with Appl to Aerosp Eng 141–179. https://doi.org/10.1007/978-3-030-60166-9_5
Kukunuri ANJ, Murugan D, Singh D (2020) Variance based fusion of VCI and TCI for efficient classification of agriculture drought using MODIS data. https://doi.org/10.1080/10106049.2020.1837256
Labudova L, Schefczyk L, Heinemann G, et al. (2014) The comparison of the SPI and the SPEI using COSMO model data in two selected Slovakian river basins. EGUGA 16:4357. https://ui.adsabs.harvard.edu/abs/2014EGUGA..16.4357L/abstract
Li J, Zhang S, Huang L et al (2020a) Drought prediction models driven by meteorological and remote sensing data in Guanzhong Area, China. Hydrol Res 51:942–958. https://doi.org/10.2166/NH.2020.184
Li L, She D, Zheng H et al (2020b) Elucidating Diverse Drought Characteristics from Two Meteorological Drought Indices (SPI and SPEI) in China. J Hydrometeorol 21:1513–1530. https://doi.org/10.1175/JHM-D-19-0290.1
Liu Q, Zhang G, Ali S et al (2019) SPI-based drought simulation and prediction using ARMA-GARCH model. Appl Math Comput 355:96–107. https://doi.org/10.1016/J.AMC.2019.02.058
Liu X, Zhu X, Zhang Q et al (2020) A remote sensing and artificial neural network-based integrated agricultural drought index: Index development and applications. CATENA 186:104394. https://doi.org/10.1016/J.CATENA.2019.104394
Lu D, Mausel P, Brondízio E, Moran E (2010) Change detection techniques. 25:2365–2401. https://doi.org/10.1080/0143116031000139863
Malik A, Kumar A (2020) Meteorological drought prediction using heuristic approaches based on effective drought index: a case study in Uttarakhand. Arab J Geosci 13:1–17. https://doi.org/10.1007/S12517-020-5239-6/FIGURES/7
McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology 17(22):179–183
Mokarram M, Hojati M (2018) Using sub-pixel attractions model as a new model to extract shore lines. Desert Ecosyst Eng J 6:57–68. https://doi.org/10.22052/6.17.57
Mokarram M, Pham TM (2022) CA-Markov model application to predict crop yield using remote sensing indices. Ecol Indic 139:108952. https://doi.org/10.1016/J.ECOLIND.2022.108952
Mokarram M, Zarei AR (2021) Determining prone areas to gully erosion and the impact of land use change on it by using multiple-criteria decision-making algorithm in arid and semi-arid regions. Geoderma 403:115379. https://doi.org/10.1016/J.GEODERMA.2021.115379
Mokarram M, Mokarram MJ, Gitizadeh M et al (2020a) A novel optimal placing of solar farms utilizing multi-criteria decision-making (MCDA) and feature selection. J Clean Prod 261:121098. https://doi.org/10.1016/J.JCLEPRO.2020.121098
Mokarram M, Mokarram MJ, Khosravi MR et al (2020b) Determination of the optimal location for constructing solar photovoltaic farms based on multi-criteria decision system and Dempster-Shafer theory. Sci Rep 2020 101(10):1–17. https://doi.org/10.1038/s41598-020-65165-z
Mokarram M, Pourghasemi HR, Hu M, Zhang H (2021) Determining and forecasting drought susceptibility in southwestern Iran using multi-criteria decision-making (MCDM) coupled with CA-Markov model. Sci Total Environ 781:146703. https://doi.org/10.1016/J.SCITOTENV.2021.146703
Mokarram M, Zarei AR, Etedali HR (2021b) Optimal location of yield with the cheapest water footprint of the crop using multiple regression and artificial neural network models in GIS. Theor Appl Climatol 143:701–712. https://doi.org/10.1007/S00704-020-03413-Y/TABLES/5
Mokarram M, Pourghasemi HR, Huang K, Zhang H (2022) Investigation of water quality and its spatial distribution in the Kor River basin, Fars province, Iran. Environ Res 204:112294. https://doi.org/10.1016/J.ENVRES.2021.112294
Mokarrama M, Hojati M (2018) Landform classification using a sub-pixel spatial attraction model to increase spatial resolution of digital elevation model (DEM). Egypt J Remote Sens Sp Sci 21:111–120. https://doi.org/10.1016/J.EJRS.2016.11.005
Moradi HR, Rajabi M, Faragzadeh M (2011) Investigation of meteorological drought characteristics in Fars province, Iran. CATENA 84:35–46. https://doi.org/10.1016/J.CATENA.2010.08.016
Moreira EE, Paulo AA, Pereira LS, Mexia JT (2006) Analysis of SPI drought class transitions using loglinear models. J Hydrol 331:349–359. https://doi.org/10.1016/J.JHYDROL.2006.05.022
Nafarzadegan AR, Rezaeian Zadeh M, Kherad M et al (2012) Drought area monitoring during the past three decades in Fars province, Iran. Quat Int 250:27–36. https://doi.org/10.1016/J.QUAINT.2010.12.009
Naresh Kumar M, Murthy CS, Sesha Sai MV, Roy PS (2009) On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Meteorological Applications: A journal of forecasting, practical applications, training techniques and modelling. 16(3):381–9
Niaz R, Zhang X, Ali Z, et al (2021) A new propagation-based framework to enhance competency in regional drought monitoring. 73:1–13. https://doi.org/10.1080/16000870.2021.1975404
Nielsen-Gammon J, McRoberts B (2009) An assessment of the meteorological severity of the 2008-09 Texas drought through July 2009 12
Pattanayak S, Loha C, Hauchhum L, Sailo L (2020) Application of MLP-ANN models for estimating the higher heating value of bamboo biomass. Biomass Convers Biorefin 1–10. https://doi.org/10.1007/S13399-020-00685-2/TABLES/4
Phan VH, Dinh VT, Su Z (2020) Trends in Long-Term Drought Changes in the Mekong River Delta of Vietnam. Remote Sens 12:2974. https://doi.org/10.3390/RS12182974
Pontius RG, Malanson J (2005) Erratum: Comparison of the structure and accuracy of two land change models (International Journal of Geographical Information Science (2005) Vol. 19 (6) (745–748)). Int J Geogr Inf Sci 19:745–748. https://doi.org/10.1080/13658810512331389445
Price JC (1990) U sing Spatial Context in Satellite Data to Infer Regional Scale Evapotranspiration. IEEE Trans Geosci Remote Sens 28:940–948. https://doi.org/10.1109/36.58983
Quiring SM, Ganesh S (2010) Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agric for Meteorol 150:330–339. https://doi.org/10.1016/J.AGRFORMET.2009.11.015
Samani N, Jamshidi Z (2017) Climate Change Trend in Fars Province, Iran and Its Effect on Groundwater Crisis. In: Proceedings of the International Conference of Recent Trends in Environmental Science and Engineering (RTESE’17) Toronto. Canada–August pp 23–25
Sandholt I, Rasmussen K, Andersen J (2002) A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens Environ 79:213–224. https://doi.org/10.1016/S0034-4257(01)00274-7
Sang L, Zhang C, Yang J et al (2011) Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math Comput Model 54:938–943. https://doi.org/10.1016/J.MCM.2010.11.019
Savari M, Eskandari Damaneh H, Eskandari Damaneh H (2022) Drought vulnerability assessment: Solution for risk alleviation and drought management among Iranian farmers. Int J Disaster Risk Reduct 67:102654. https://doi.org/10.1016/J.IJDRR.2021.102654
Sayadi A, Taleb Beydokhti N, Najarchi M, NajafiZadeh MM (2020) Climatic change impact assessment on temperature, precipitation, and runoff of Droudzan catchment area of Fars province using multi-model ensemble mean approach. Water Resour Eng 12:115–130
Shamsnia SA, Khodadadi Dehkordi D (2020) Agricultural Drought Monitoring Using Reconnaissance Drought Index (RDI) and Kriging Geostatistical Model (Case Study: Central and Southern Regions of Fars Province). Environ Water Eng 6:444–458. https://doi.org/10.22034/JEWE.2020.242605.1400
Stagge JH, Kohn I, Tallaksen LM, Stahl K (2015) Modeling drought impact occurrence based on meteorological drought indices in Europe. J Hydr 1;530:37–50
Tsakiris G, Pangalou D, Vangelis H (2006) Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI). Water Resour Manag 215(21):821–833. https://doi.org/10.1007/S11269-006-9105-4
TerrSet (2020) https://clarklabs.org/terrset/
Tsakiris G, Vangelis H (2005) Establishing a Drought Index Incorporating Evapotranspiration. European water 10;9(10):3–11
Törnros T, Menzel L (2014) Addressing drought conditions under current and future climates in the Jordan River region. HESS 23;18(1):305–18
Tucker CJ, Pinzon JE, Brown ME, et al (2010) An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. 26:4485–4498. https://doi.org/10.1080/01431160500168686
Van Loon AF, Van Lanen HAJ (2012) A process-based typology of hydrological drought. Hydrol Earth Syst Sci 16:1915–1946. https://doi.org/10.5194/HESS-16-1915-2012
Vicente-Serrano SM, Beguería S (2016) Comment on ‘Candidate distributions for climatological drought indices (SPI and SPEI)’ by James H. Stagge et al. Int J Climatol 36:2120–2131. https://doi.org/10.1002/JOC.4474
Vicente-Serrano SM, López-Moreno JI, Drumond A et al (2011) Effects of warming processes on droughts and water resources in the NW Iberian Peninsula (1930–2006). Clim Res 48:203–212. https://doi.org/10.3354/CR01002
Wu L, Fan Z, Guo L, Li Y, Chen ZL, Qu LJ (2005) Over-expression of the bacterial nhaA gene in rice enhances salt and drought tolerance. Plant Sci 1;168(2):297–302
Xie F, Fan H (2021) Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary? Int J Appl Earth Obs Geoinf 101:102352. https://doi.org/10.1016/J.JAG.2021.102352
Zhou L, Dang X, Sun Q, Wang S (2020) Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustain Cities Soc 55:102045. https://doi.org/10.1016/J.SCS.2020.102045
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Contributions
The participation of Faride Taripanah and Marzieh mokarram includes the data collection, analyzing the results, and writing the article.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Responsible Editor: Amjad Kallel
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mokarram, M., Taripanah, F. Prediction drought using CA–Markov model and neural networks and its relationship to landforms. Arab J Geosci 16, 342 (2023). https://doi.org/10.1007/s12517-023-11325-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-023-11325-0