Investigating the Relationship Between Topography and Drought in Southwestern Iran Using Remote Sensing

Document Type : Full length article

Authors

Department of Geography, shiraz university

Abstract

Extended Abstract
Introduction
Human beings have faced one of the most important problems in recent years: the water crisis and the occurrence of drought. Due to this, it is important to study the drought situation when managing water resources. In any climate, drought is caused by a lack of rainfall. However, unfortunately, defining drought and how it relates to hydrological phenomena is very difficult. First of all, drought may not affect all components of the hydrological system simultaneously. The second point is that drought does not refer to an absolute lack of moisture, but to a relative one. As a result of climate change and reduced rainfall and increased evapotranspiration in recent years, drought has become a major problem in the world, in general in arid and semi-arid regions such as Iran. Therefore, drought monitoring and management are essential. Most traditional methods rely on observations from meteorological stations and emphasize droughts. However, researchers and experts have considered the use of remote sensing techniques and satellite images as a useful tool for spatial and temporal monitoring of agricultural drought. A variety of meteorological and remote sensing indicators influence the study of droughts. Standard Index-Evapotranspiration Index (SPEI) and RDI Drought Index  are two such methods.
Due to the importance of the topic of this study, the study examined drought status in the study area using remote sensing indicators (EVI, NDVI), drought prediction using Markov and Kumarkov chains, and the connection between them and drought conditions.
 
Materials and methods
EVI index
Hewitt and Liu introduced the EVI in 1994. As defined below, improved vegetation indices minimize atmospheric effects and differences in blue and red reflections.
                                                                                  (1)
NIR, RED, Blue are amount of reflections in the blue, red and infrared bands.   L is the aerosol penetration coefficient and soil separation parameter which was considered equal to 1 in this equation. The coefficients C1, C2, G were considered equal to 6, 7.5, 2.5, respectively.
NDVI index
Normalized difference vegetation index (NDVI) is an indirect measure of photosynthetic activity. The range of this index is -1 for the minimum and +1 for the maximum amount of photosynthetic activity. NDVI is defined as follows (Tucker et al., 2010):
                                                                                                                (2)
Pnear and Pred are the reflectance values of the near infrared and red wavelengths, respectively, for pixel i during the months j and year k, respectively.
 
Results and Discussion
According to the results, southern regions have lower values for all vegetation indices, which indicates a lack of vegetation in these regions and the existence of drought. There is no drought in the northern areas and parts of the east of the region. Also, the results show that the region often falls into the middle class of drought based on EVI and NDVI indices. The results indicate that in 2000, 96% and 78% of the region were in the middle classes of drought, and in 2020, 98% and 93% of the region were in the middle classes of drought.
The results for the EVI index showed that 47% class 1 to class 3, 20% class 2 to class 4, 12% class 4 to class 5, 29% class 5 to class 6, 27% class 6 to class 7, 21% class 7 to Class 8, 14% to Class 8 to Class 9, and about 8% to Class 9 to Class 10, indicating an increase in drought in the area. NDVI index 49% Class 1 to Class 7, 32% Class 4 to Class 5, 19, Class 5 to Class 6, 14, Class 6 to Class 7, 11% Class 7 to Class 8 and 27% Class 8 to Class 9 has done.
 
Conclusion
Then, in order to extract the landform map of the study area, the topographic position index (TPI) was used. The results showed that high areas such as ridges and hills, near zero codes indicate flat areas or areas with low slope changes and negative codes indicate low areas such as valleys and waterways. The results showed that the highest landform in the middle drainage and plain area (about 14) percent and the lowest area is related to narrow valleys (about 1 percent). The results showed that the NDVI and EVI index in low altitude areas is lower (dry condition) and the index in high altitude areas is the highest (wet condition). Thus, the topographic situation can be used to predict the drought situation.

Keywords

Main Subjects


  1.  صوفی، م و علیجانی، ب. (1391). تغییر اقلیم در ناهمواری‌های زاگرس. فصلنامه سرزمین، 9(34)، 66-47.
  2.  مجرد، ف و مرادی فر، ح. (1382). مدل‌سازی رابطه بارش با ارتفاع در منطقه زاگرس. مدرس علوم انسانی ، 7(23)، صص 182-163.
  1. Brown, D.G., Pijanowski, B.C., & Duh, J.D., (2000). Modeling the relationships between land use and land cover on private lands in the Upper Midwest, J. Environ. Manage. 59, 247–263.
  2. Chang, H., He, G., Wang, Q., Li, H., Zhai, J., Dong, Y., Zhao, Y., Zhao, J., (2021). Use of sustainability index and cellular automata-Markov model to determine and predict long-term spatio-temporal variation of drought in China. Hydrol. 598, 126248.
  3. Choubin, B., Soleimani, F., Pirnia, A., Sajedi-Hosseini, F., Alilou, H., Rahmati, O., Melesse, A.M., Singh, V.P., & Shahabi, H., (2019). Effects of drought on vegetative cover changes: Investigating spatiotemporal patterns. Extrem. Hydrol. Clim. Var. Monit. Model. Adapt. Mitig. 213–222.
  4. Chung, K.L., (1960). Markov Chains with Stationary Transition Probabilities. Markov Chain. with Station. Transit. Probab. 1–130.
  5. Ding, Y., Gong, X., Xing, Z., Cai, H., Zhou, Z., Zhang, D., Sun, P., & Shi, H., (2021). Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China. Water Manag, 255, 106996.
  6. EarthExplorer [WWW Document], 2021. URL https://earthexplorer.usgs.gov/ (accessed 11.11.21).
  7. Ebrahimi-Khusfi, Z., Mirakbari, M., Ebrahimi-Khusfi, M., Taghizadeh-Mehrjardi, R., (2020). Impacts of vegetation anomalies and agricultural drought on wind erosion over Iran from 2000 to 2018. Appl. Geogr, 125, 102330.
  8. Fadhil, R.M., &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.
  9. Fars Meteorological Bureau [WWW Document], 2021. URL https://www.farsmet.ir/ (accessed 11.11.21).
  10. Ghasemi, M.M., 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, 1-12.
  11. Jahantigh, M., & Jahantigh, M., (2021). Monitoring Changes in Erosion areas Using Remote sensing Data in Three years of Wet, Normal and Drought (Case study: Nimroz Region of Sistan). Eros. Res. J, 11, 1–26.
  12. Javed, T., Li, Y., Feng, K., Ayantobo, O.O., Ahmad, S., Chen, X., Rashid, S., Suon, S., (2021). Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China. Sci. Pollut. Res, 28, 3644–3659.
  13. Jiao, W., Wang, L., & McCabe, M.F., (2021). Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future. Remote Sens. Environ, 256, 112313.
  14. Karimi, H., Raeisi, E., & Rezaei, A., (2018). Determination of karst aquifer characteristics using physicochemical parameters (A case study from west of Iran). Geopersia, 8, 293–305.
  15. Kędzior, M., & Zawadzki, J., (2017). SMOS data as a source of the agricultural drought information: Case study of the Vistula catchment, Poland. Geoderma, 306, 167–182.
  16. Kiem, A.S., Austin, E.K., (2013). Drought and the future of rural communities: Opportunities and challenges for climate change adaptation in regional Victoria, Australia. Glob. Environ. Chang, 23, 1307–1316.
  17. Li, L., She, D., Zheng, H., Lin, P., & Yang, Z.-L., (2020). Elucidating Diverse Drought Characteristics from Two Meteorological Drought Indices (SPI and SPEI) in China. Hydrometeorol, 21, 1513–1530.
  18. Li, P., Zhu, D., Wang, Y., & Liu, D., (2020). Elevation dependence of drought legacy effects on vegetation greenness over the Tibetan Plateau. Agric. Meteorol, 295, 108190.
  19. Liu, X., Zhu, X., Zhang, Q., Yang, T., Pan, Y., & Sun, P., (2020). A remote sensing and artificial neural network-based integrated agricultural drought index: Index development and applications. CATENA, 186, 104394.
  20. Mansouri Daneshvar, M.R., Ebrahimi, M., & Nejadsoleymani, H., (2019). An overview of climate change in Iran: facts and statistics. Syst. Res. 81(8), 1–10.
  21. Mokarram, M., Pourghasemi, H.R., Hu, M., & Zhang, H., (2021). Determining and forecasting drought susceptibility in southwestern Iran using multi-criteria decision-making (MCDM) coupled with CA-Markov model. Total Environ, 781, 146703.
  22. Mokarram, M., & Sathyamoorthy, D., (2016). Investigation of the relationship between drinking water quality based on content of inorganic components and landform classes using fuzzy AHP (case study: South of Firozabad, west of Fars province, Iran). Water Eng. Sci, 9, 57–67.
  23. Mokarrama, M., & Hojati, M., (2018). Landform classification using a sub-pixel spatial attraction model to increase spatial resolution of digital elevation model (DEM). J. Remote Sens. Sp. Sci. 21, 111–120.
  24. Nafarzadegan, A.R., Rezaeian Zadeh, M., Kherad, M., Ahani, H., Gharehkhani, A., Karampoor, M.A., & Kousari, M.R., (2012). Drought area monitoring during the past three decades in Fars province, Quat. Int. 250, 27–36.
  25. Pei, Z., Fang, S., Wang, L., & Yang, W., (2020). Comparative Analysis of Drought Indicated by the SPI and SPEI at Various Timescales in Inner Mongolia, China. Water, 12, 1912-1925.
  26. Rabbi, S.M.F., Tighe, M.K., Warren, C.R., Zhou, Y., Denton, M.D., Barbour, M.M., & Young, I.M., (2021). High water availability in drought tolerant crops is driven by root engineering of the soil micro-habitat. Geoderma, 383, 114738.
  27. Rabiner, L.R., & Juang, B.H., (1986). An Introduction to Hidden Markov Models. IEEE ASSP Mag, 3, 4–16.
  28. Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W., (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math. Comput. Model, 54, 938–943.
  29. Sharafati, A., Nabaei, S., & Shahid, S., (2020). Spatial assessment of meteorological drought features over different climate regions in Iran. J. Climatol. 40, 1864–1884.
  30. Tanda, A.S., (2021). Native Bees Are Important and Need Immediate Conservation Measures: A Review † 1–15.
  31. Tsakiris, G., & Vangelis, H., (2005). Establishing a Drought Index Incorporating Evapotranspiration.
  32. Tucker, C.J., Pinzon, J.E., Brown, M.E., Slayback, D.A., Pak, E.W., Mahoney, R., Vermote, E.F., & El & Saleous, N., (2010). An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26, 4485–4498.
  33. Vali, A., Ranjbar, A., Mokarram, M., & Taripanah, F., (2020). Investigating the topographic and climatic effects on vegetation using remote sensing and GIS: a case study of Kharestan region, Fars Province, Iran. Theor. Climatol. 140, 37–54.
  34. Van Loon, A.F., & Van Lanen, H.A.J., (2012). A process-based typology of hydrological drought. Hydrol. Earth Syst. Sci, 16, 1915–1946.
  35. Vicente-Serrano, S.M., & Beguería, S., (2016). Comment on ‘Candidate distributions for climatological drought indices (SPI and SPEI)’ by James H. Stagge et al. J. Climatol. 36, 2120–2131.
  36. Xie, F., Fan, H., 2021. Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary?, J. Appl. Earth Obs. Geoinf, 101, 102352.
  37. Zhou, K., Li, J., Zhang, T., & Kang, A., (2021). The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China. Agric. Water Manag, 243, 106479.