Spatial Analysis of Environmental Hazards

Spatial Analysis of Environmental Hazards

Explaining the spatial patterns of drought intensities in Iran

Authors
Sousan Heidari
Mostafa karimi
Ghasem Azizi
AliAkbar Shamsipour
Department of Physical Geography, Faculty of Geography, University of Tehran
Abstract
Explaining the spatial patterns of drought intensities in Iran



Abstract

Recognition of spatial patterns of drought plays an important role in monitoring, predicting, confronting, reducing vulnerability, and increasing adaptation to this hazard. This study aims to identify the spatial distribution and analyze the spatial patterns of annual, seasonal, and monthly drought intensities in Iran. For this purpose, the European center Medium-Range Weather Forecast (ECMWF) data for the period 1979-2021 and the ZSI index were used to extract the drought intensities. To achieve the research goal and explain the spatial pattern of the frequency of drought intensities (Extreme, severe, moderate, and weak), spatial statistical methods such as global Moran’s I, Anselin local Moran’s Index, and hot spots were used. The results of the global Moran’s I showed that with increasing intensity, the spatial distribution of drought events has become clustered. The spatial distribution of the local Moran’s Index and hot spots also confirms this. Very clear contrast was observed in the local clusters of high (low) occurrence as well as hot (cold) spots of severe (Extreme) yearly droughts in the south, southeast, and east. In autumn, weak to Extreme droughts show a southeast-northwest pattern. But in spring and winter, the spatial pattern of drought is very strong as opposed to severe and moderate drought. Despite the relatively high variability of maximum positive spatial Autocorrelation of severe and Extreme monthly droughts, their spatial pattern is almost similar. The spatial clusters of severe and very severe droughts in the northwest, northeast, and especially on the Caspian coast, are a serious warning for the management of water resources, especially for precipitation-based activities, such as agriculture.

Introduction

Drought or lack of precipitation over some time is the most widespread natural hazard on the earth compared to its long-term average. This risk negatively affects various sectors such as hydropower generation, health, industry, tourism, agriculture, livestock, environment, and economy. To reduce these negative or destructive effects, it must be determined how often drought occurs during the period and in which areas it is most severe. Doing so requires determining the characteristics of the drought. These characteristics include area, intensity, duration, and frequency of drought. Discovering the geographical focus, recognizing the pattern governing the frequency of occurrence and temporal-spatial distribution as well as changes in the dynamics of this hazard facilitate an important role in drought monitoring, early warning, forecasting, and dealing with these potential hazards; this information can be used to create a drought plan by providing analysts and decision-makers with ideas about drought, helping to reduce the negative and vulnerable effects and ultimately make it easier to protect or replace for greater adaptation. Many researchers have been led by these approaches to the use of statistical analysis. Numerous studies have been conducted in the study of climatic phenomena such as drought with space statistics techniques in various regions, including China, India, South Korea, and even Iran. Part of the domestic research on spatial patterns of drought is without the use of spatial statistics and a limited number of others who have used these analyzes have only studied the overall intensity of drought and have not studied the spatial patterns of different drought intensities. The main purpose of this study is to identify the distribution and spatial patterns of drought intensities in Iran using spatial analysis functions of spatial statistics based on the frequency of drought intensities (Extreme, severe, moderate, and weak) with yearly, seasonal and monthly multi-scale approach. Therefore, this study will answer the questions: a) What is the spatial distribution of drought intensity data in Iran? And b) What is the variability of spatial patterns of Iranian droughts at different time scales?

Material &Method

ERA5 monthly precipitation data for a period of 43 years from 1979 to 2021 were used for this study. an array of dimensions of 78×59×504 of data were formed in MATLAB software in which 78×59 is the number of nodes with a spatial resolution of 0.25 degrees and 504 represents the month. After creating the database, the ZSI index was used to calculate the severity of drought in annual, seasonal, and monthly comparisons. Finally, to achieve the research goal and explain the spatial pattern governing the frequency of drought intensities (Extreme, severe, moderate, and weak), spatial statistical methods such as global Moran’s I, Anselin local Moran I and hot spots was used.

Discussion of Results

Due to its ecological conditions, geographical location, and location in an arid and semi-arid region of the world, Iran is among the most vulnerable countries due to natural hazards, including drought. It has experienced many severe droughts in the last century. The occurrence of drought and its effects is one of the major challenges of water resources management in this century. The results of the Global Moran’s Index for all three annual, seasonal, and monthly scales showed a highly clustered pattern of drought events in the country. Spatial clustering of the occurrence of severe and Extreme yearly droughts in the eastern, southeastern, and southern regions is also an interesting result. These conditions are due to low precipitation and high spatial variation coefficient in these areas. This contrast of spatial clusters of drought intensities indicates the relationship between drought and temporal-spatial anomalies of precipitation so that with increasing precipitation, spatial variability of precipitation decreases, and consequently spatial homogeneity of precipitation increases. severe and moderate-intensity spots in the south-southeast in autumn and spring can be affected by fluctuations in the beginning and end of the monsoon season in South Asia due to the high variability of atmospheric circulation at the beginning and end of precipitation in these areas. Some studies have also shown the relationship between precipitation in these areas and the monsoon behavior of South Asia. Extreme drought events in winter and spring have had a positive spatial correlation pattern in the southwest, west, and northwest. However, precipitation at this time of year is concentrated in these areas. Warm clusters or concentrations of very severe drought events in the northern strip of the country, especially in the Caspian region, can be due to the high variability of precipitation at the beginning of the annual precipitation season (late summer and early autumn). Observations of these conditions in the northern strip indicate that an event with a high frequency of severe droughts, even in rainy areas, should not be unexpected. Spatial clusters of Extreme, severe, moderate, and weak drought every month using both local Moran and hot spots statistics show the fact that in Iran, the most severe droughts have occurred in the western, northwestern, and coastal areas of the Caspian Sea. However, the absence of severe droughts or spatial clusters has been the occurrence of low drought in the southeast and to some extent in the south. On a yearly scale, the south, southeast, and east have played a significant role in the spatial cluster of severe and extreme droughts. So that these areas of the country have had positive spatial solidarity. However, in these areas, negative spatial correlation prevailed in the autumn for severe drought. This may indicate an anomaly and a tendency to concentrate more precipitation in Iran, as well as many changes in seasonal and local precipitation regimes. According to the research results, a high incidence of severe and extreme drought on all three scales (monthly, seasonal and annual) even in the wettest climate of the country (northern Iran, especially the southern shores of the Caspian Sea) shows that High-intensity droughts can occur in all parts of the country, regardless of the weather conditions.

Keywords: Natural hazards, spatial patterns, Moran statistics, spatial autocorrelation, hot spots




Keywords
Natural hazards
spatial patterns
Moran statistics
Spatial Autocorrelation
hot spots
Álvarez-Berríos, N.L.; S. Soto-Bayó, E. Holupchinski, S.J. Fain, and W.A. Gould. 2018. Correlating drought conservation practices and drought vulnerability in a tropical agricultural system. Renewable Agriculture and Food Systems, 3: 279-291. DOI: https://doi.org/10.1017/S174217051800011X.
Anselin, L. 1995. Local indicators of spatial association—LISA. Geographical analysis, 2: 93-115. doi.org/10.1111/j.1538-4632.1995.tb00338.x.
Ekwezuo, C.S.; and J.C. Madu, 2020. Evaluation of Different Rainfall-based Drought Indices Detection of Meteorological Drought Events in Imo State, Nigeria. Journal of Applied Sciences and Environmental Management, 4: 713-717. DOI: 10.4314/jasem.v24i4.25.
Emadodin, I.; T. Reinsch, and F. Taube. 2019. Drought and desertification in Iran. Hydrology, 3: 66. doi.org/10.3390/hydrology6030066.
Fang, W.; S, Huang, Q. Huang, G. Huang, H. Wang, G. Leng, L. Wang, and Y. Guo. 2019. Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China. Remote Sensing of Environment, 232: 111290. doi.org/10.1016/j.rse.2019.111290.
Ghalhari, G.F.; A.D. Roudbari, and M. Asadi. 2016. Identifying the spatial and temporal distribution characteristics of precipitation in Iran. Arabian Journal of Geosciences, 12: 1-12. doi.org/10.1007/s12517-016-2606-4.
Gümüş, V. 2017. Hydrological drought analysis of Asi River Basin with streamflow drought index. Gazi Univ Fen Blm Derg, 1: 65-73. doi.org/10.1016/j.jhydrol.2018.07.081.
Guo, H.; A. Bao, F. Ndayisaba, T. Liu, G. Jiapaer, A.M. El-Tantawi, and P. De Maeyer. 2018. Space-time characterization of drought events and their impacts on vegetation in Central Asia. Journal of Hydrology, 564: 1165-1178.
Guo, Y.; S. Huang, Q. Huang, H. Wang, W. Fang, Y. Yang, and L. Wang. 2019. Assessing socioeconomic drought based on an improved Multivariate Standardized Reliability and Resilience Index. Journal of Hydrology, 568: 904-918. doi.org/10.1016/j.jhydrol.2018.11.055
Hadi Pour, S.; A.K. Abd Wahab, and S. Shahid. 2020. Spatiotemporal changes in precipitation indicators related to bioclimate in Iran. Theoretical and Applied Climatology, 1: 99-115. doi.org/10.1007/s00704-020-03192-6.
Han, Z.; S. Huang, Q. Huang, G. Leng, H. Wang, L. He, W. Fang, and P. Li. 2019. Assessing GRACE-based terrestrial water storage anomalies dynamics at multi-timescales and their correlations with teleconnection factors in Yunnan Province, China. Journal of Hydrology, 574: 836-850. doi.org/10.1016/j.jhydrol.2019.04.093.
Haylock, M.R.; N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones, and M. New. 2008. A European daily high‐resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres, 113(D20). doi.org/10.1029/2008JD010201.
Hersbach, H.; B. Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz‐Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, and A. Simmons. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 730: 1999-2049.doi.org/10.1002/qj.3803.
Hosseini, A.; Y. Ghavidel, A. M. Khorshiddoust, and M. Farajzadeh. 2021. Spatio-temporal analysis of dry and wet periods in Iran by using Global Precipitation Climatology Center-Drought Index (GPCC-DI). Theoretical and Applied Climatology, 3: 1035-1045. doi.org/10.1007/s00704-020-03463-2.
Huang, S.; L. Wang, H. Wang, Huang, Q., Leng, G., Fang, W. and Zhang, Y., 2019. Spatio-temporal characteristics of drought structure across China using an integrated drought index. Agricultural Water Management, 218: 182-192. doi.org/10.1016/j.agwat.2019.03.053.
Illian, J.; A. Penttinen, H. Stoyan, and D. Stoyan, 2008. Statistical analysis and modelling of spatial point patterns (Vol. 70). John Wiley & Sons.
Jiang, Q.; W. Li, Z. Fan, X. He, W. Sun, S. Chen, J. Wen, J. Gao, and J. Wang. 2021. Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland. Journal of Hydrology, 595: 125660. doi.org/10.1016/j.jhydrol.2020.125660
Lee, J.H.; S.Y. Park, J.S. Kim, C. Sur, and J. Chen. 2018. Extreme drought hotspot analysis for adaptation to a changing climate: Assessment of applicability to the five major river basins of the Korean Peninsula. International Journal of Climatology, 10: 4025-4032. doi.org/10.1002/joc.5532.
Liu, Y.; J. Chen, and T. Pan. 2021. Spatial and temporal patterns of drought hazard for China under different RCP scenarios in the 21st century. International Journal of Disaster Risk Reduction, 52: 101948. doi.org/10.1016/j.ijdrr.2020.101948.
Lloyd‐Hughes, B.; and M.A. Saunders. 2002. A drought climatology for Europe. International Journal of Climatology: A Journal of the Royal Meteorological Society, 13: 1571-1592. doi.org/10.1002/joc.846.
Mahmoudi, P.; A. Rigi, and M.M. Kamak. 2019. A comparative study of precipitation-based drought indices with the aim of selecting the best index for drought monitoring in Iran. Theoretical and Applied Climatology, 3: 3123-3138. doi.org/10.1007/s00704-019-02778-z.
Mashari Eshghabad, S.; E. Omidvar, and K. Solaimani. 2014. Efficiency of some meteorological drought indices in different time scales (case study: Tajan Basin, Iran). Ecopersia, 1: 441-453. doi.org/20.1001.1.23222700.2014.2.1.3.0.
Mastrangelo, A.M.; E. Mazzucotelli, D. Guerra, P. De Vita, and L. Cattivelli. 2012. Improvement of drought resistance in crops: from conventional breeding to genomic selection. In Crop stress and its management: Perspectives and strategies (pp. 225-259). Springer, Dordrecht. doi.org/10.1007/978-94-007-2220-0_7.
Mishra, S.S.; and R. Nagarajan. 2011. Spatio-temporal drought assessment in Tel river basin using Standardized Precipitation Index (SPI) and GIS. Geomatics, Natural Hazards and Risk, 1: 79-93. doi.org/10.1080/19475705.2010.533703.
Morid, S.; V. Smakhtin, and K. Bagherzadeh. 2007. Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology: A Journal of the Royal Meteorological Society, 15: 2103-2111. doi.org/10.1002/joc.1498.
Ord, J.K; and A. Getis. 1995. Local spatial autocorrelation statistics: distributional issues and an application. Geographical analysis, 4: 286-306. doi.org/10.1111/j.1538-4632.1995.tb00912.x
Quang-Tuong, V.; S. Jae-Min, and B. Deg-Hyo. 2020. An Integrated Framework for Extreme Drought Assessments Using the Natural Drought Index, Copula and Gi* Statistic. Water Resources Management, 4: 1353-1368. doi.org/10.1007/s11269-020-02506-7
Rakhmatova, N.; M. Arushanov, L. Shardakova, B. Nishonov, R. Taryannikova, V. Rakhmatova, and D.A. Belikov. 2021. Evaluation of the Perspective of ERA-Interim and ERA5 Reanalyses for Calculation of Drought Indicators for Uzbekistan. Atmosphere, 5: 527. doi.org/10.3390/atmos12050527
Raziei, T.; B. Saghafian, A.A. Paulo, L.S. Pereira, and I. Bordi. 2009. Spatial patterns and temporal variability of drought in western Iran. Water resources management, 3: 439-455. doi.org/10.1007/s11269-008-9282-4
Salehnia, N.; A. Alizadeh, H. Sanaeinejad, Bannayan, M., Zarrin, A. and Hoogenboom, G., 2017. Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. Journal of arid land, 6: 797-809. doi.org/10.1007/s40333-017-0070-y
Samantaray, A.K.; G. Singh, M. Ramadas, and R.K. Panda. 2019. Drought hotspot analysis and risk assessment using probabilistic drought monitoring and severity–duration–frequency analysis. Hydrological Processes, 3: 432-449. doi.org/10.1002/hyp.13337
Taghizadeh, E.; F. Ahmadi-Givi, L. Brocca, and E. Sharifi. 2021. Evaluation of satellite/reanalysis precipitation products over Iran. International Journal of Remote Sensing, 9: 3474-3497. doi.org/10.1080/01431161.2021.1875508
Tobler, W.R.; 1979 Cellular geography. In S. Gale and G. Olsson (Eds.), Philosophy in Geography: 379-86 (Dordrecht, Reidel). doi.org/10.1007/978-94-009-9394-5_18
Wang, F.; H. Yang, Z. Wang, Z. Zhang, and Z. Li. 2019. Drought evaluation with CMORPH satellite precipitation data in the Yellow River Basin by using gridded standardized precipitation evapotranspiration index. Remote Sensing, 5: 485. doi.org/10.3390/rs11050485
Wang, Q.; Y.Y. Liu, Y.Z. Zhang, L.J. Tong, X. Li, J.L. Li, and Z. Sun. 2019. Assessment of spatial agglomeration of agricultural drought disaster in China from 1978 to 2016. Scientific reports, 1: 1-8. doi.org/10.1038/s41598-019-51042-x
Wang, R.; J. Zhang, E. Guo, S. Alu, D. Li, S. Ha, and Z. Dong. 2019. Integrated drought risk assessment of multi-hazard-affected bodies based on copulas in the Taoerhe Basin, China. Theoretical and Applied Climatology, 1: 577-592. doi.org/10.1007/s00704-018-2374-z
Wilhite, D.A; and M.D. Svoboda. 2000. Drought early warning systems in the context of drought preparedness and mitigation. Early warning systems for drought preparedness and drought management, 1-21.
Wu, X.; Z. Hao, F. Hao, C. Li, and X. Zhang. 2019. Spatial and temporal variations of compound droughts and hot extremes in China. Atmosphere, 2: 95. doi.org/10.3390/atmos10020095.