Anthropogenic Factors Affecting the Vegetation Dynamics in the Arid Middle East

[1] Anderson R. G., Canadell J. G., Randerson J. T., Jackson R. B., Hungate B. A., Baldocchi D. D., Ban-Weiss G. A., Bonan G. B., Caldeira K., Cao L. Biophysical considerations in forestry for climate protection. Front. Ecol. Environ. 2011:9(3):174–182. https://doi.org/10.1890/090179 Search in Google Scholar

[2] Cramer W., Bondeau A., Woodward F. I., Prentice I. C., Betts R. A., Brovkin V., Cox P. M., Fisher V., Foley J. A., Friend A. D. Global response of terrestrial ecosystem structure and function to CO₂ and climate change: results from six dynamic global vegetation models. Glob. Chang. Biol. 2001:7(4):357–373. https://doi.org/10.1046/j.1365-2486.2001.00383.x Search in Google Scholar

[3] Gaston K. J. Global patterns in biodiversity. Nature 2000:405:220–227. https://doi.org/10.1038/3501222810821282 Search in Google Scholar

[4] Guo W., Ni X., Jing D., Li S. Spatial-temporal patterns of vegetation dynamics and their relationships to climate variations in Qinghai Lake Basin using MODIS time-series data. J. Geogr. Sci. 2014:24:1009–1021. https://doi.org/10.1007/s11442-014-1134-y Search in Google Scholar

[5] Jackson R. B., Randerson J. T., Canadell J. G., Anderson R. G., Avissar R., Baldocchi D. D., Bonan G. B., Caldeira K., Diffenbaugh N. S., Field C. B. Protecting climate with forests. Environ. Res. Lett. 2008:3(4):044006. https://doi.org/10.1088/1748-9326/3/4/044006 Search in Google Scholar

[6] Theurillat J.-P., Guisan A. Potential impact of climate change on vegetation in the European Alps: a review. Clim. Change 2001:50:77–109. https://doi.org/10.1023/A:1010632015572 Search in Google Scholar

[7] Verbesselt J., Hyndman R., Newnham G., Culvenor D. Detecting trend and seasonal changes in satellite image time series. Remote Sens. Environ. 2010:114(1):106–115. https://doi.org/10.1016/j.rse.2009.08.014 Search in Google Scholar

[8] Wolters V., Silver W. L., Bignell D. E., Coleman D. C., Lavelle P., Van Der Putten W. H., De Ruiter P., Rusek J., Wall D. H., Wardle D. A. Effects of Global Changes on Above-and Belowground Biodiversity in Terrestrial Ecosystems: Implications for Ecosystem Functioning: We identify the basic types of interaction between vascular plants and soil biota; describe the sensitivity of each type to changes in species composition; and, within this framework, evaluate the potential consequences of global change drivers on ecosystem processes. Bioscience 2000:50(12):1089–1098. https://doi.org/10.1641/0006-3568(2000)050[1089:EOGCOA]2.0.CO;210.1641/0006-3568(2000)050[1089:EOGCOA]2.0.CO;2 Search in Google Scholar

[9] Chuai X., Huang X., Wang W., Bao G. NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998–2007 in Inner Mongolia, China. Int. J. Climatol. 2013:33(7):1696–1706. https://doi.org/10.1002/joc.3543 Search in Google Scholar

[10] Wang J., Rich P. M., Price K. P. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. Int. J. Remote Sens. 2003:24(11):2345–2364. https://doi.org/10.1080/01431160210154812 Search in Google Scholar

[11] Badreldin N., Goossens R. Monitoring land use/land cover change using multi-temporal Landsat satellite images in an arid environment: a case study of El-Arish, Egypt. Arab. J. Geosci. 2014:7:1671–1681. https://doi.org/10.1007/s12517-013-0916-3 Search in Google Scholar

[12] Bagherzadeh A., Hoseini A. V., Totmaj L. H. The effects of climate change on normalized difference vegetation index (NDVI) in the Northeast of Iran. Model. Earth Syst. Environ. 2020:6:671–683. https://doi.org/10.1007/s40808-020-00724-x Search in Google Scholar

[13] Cui L., Shi J. Temporal and spatial response of vegetation NDVI to temperature and precipitation in eastern China. J. Geogr. Sci. 2010:20:163–176. https://doi.org/10.1007/s11442-010-0163-4 Search in Google Scholar

[14] Schultz P., Halpert M. S. Global correlation of temperature, NDVI and precipitation. Adv. Space Res. 1993:13(5):277–280. https://doi.org/10.1016/0273-1177(93)90559-T Search in Google Scholar

[15] Schultz P., Halpert M. S. Global analysis of the relationships among a vegetation index, precipitation and land surface temperature. Int. J. Remote Sens. 1995:16(15):2755–2777. https://doi.org/10.1080/01431169508954590 Search in Google Scholar

[16] Zhang G., Xu X., Zhou C., Zhang H., Ouyang H. Responses of grassland vegetation to climatic variations on different temporal scales in Hulun Buir Grassland in the past 30 years. J. Geogr. Sci. 2011:21:634–650. https://doi.org/10.1007/s11442-011-0869-y Search in Google Scholar

[17] Li X., Jia X., Dong G. Influence of desertification on vegetation pattern variations in the cold semi-arid grasslands of Qinghai-Tibet Plateau, North-west China. J. Arid Environ. 2006:64(3):505–522. https://doi.org/10.1016/j.jaridenv.2005.06.011 Search in Google Scholar

[18] Lucas R., Rowlands A., Brown A., Keyworth S., Bunting P. Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping. ISPRS J. Photogramm. Remote Sens. 2007:62(3):165–185. https://doi.org/10.1016/j.isprsjprs.2007.03.003 Search in Google Scholar

[19] Tong X., Wang K., Yue Y., Brandt M., Liu B., Zhang C., Liao C., Fensholt R. Quantifying the effectiveness of ecological restoration projects on long-term vegetation dynamics in the karst regions of Southwest China. Int. J. Appl. Earth Obs. Geoinf. 2017:54:105–113. https://doi.org/10.1016/j.jag.2016.09.013 Search in Google Scholar

[20] Xiao X., Wang Y., Jiang S., Ojima D. S., Bonham C. D. Interannual variation in the climate and above-ground biomass of Leymus chinense steppe and Stipa grandis steppe in the Xilin river basin, Inner Mongolia, China. J. Arid Environ. 1995:31(3):283–299. https://doi.org/10.1016/S0140-1963(05)80033-3 Search in Google Scholar

[21] Bradley B. A., Mustard J. F. Comparison of phenology trends by land cover class: a case study in the Great Basin, USA. Glob. Chang. Biol. 2008:14(2):334–346. https://doi.org/10.1111/j.1365-2486.2007.01479.x Search in Google Scholar

[22] Frolking S., Palace M. W., Clark D., Chambers J. Q., Shugart H., Hurtt G. C. Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J. Geophys. Res. Biogeosci. 2009:114(G2). https://doi.org/10.1029/2008JG000911 Search in Google Scholar

[23] Fu B., Burgher I. Riparian vegetation NDVI dynamics and its relationship with climate, surface water and groundwater. J. Arid Environ. 2015:113:59–68. https://doi.org/10.1016/j.jaridenv.2014.09.010 Search in Google Scholar

[24] Ghafarian Malamiri H. R., Rousta I., Olafsson H., Zare H., Zhang H. Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA). Atmosphere 2018:9(9):334. https://doi.org/10.3390/atmos9090334 Search in Google Scholar

[25] Guillevic P., Koster R., Suarez M., Bounoua L., Collatz G., Los S., Mahanama S. Influence of the interannual variability of vegetation on the surface energy balance—A global sensitivity study. J. Hydrometeorol. 2002:3:617–629. https://doi.org/10.1175/1525-7541(2002)003<0617:IOTIVO>2.0.CO;210.1175/1525-7541(2002)003<0617:IOTIVO>2.0.CO;2 Search in Google Scholar

[26] Gupta A., Moniruzzaman M., Hande A., Rousta I., Olafsson H., Mondal K. K. Estimation of particulate matter (PM_2.5, PM₁₀) concentration and its variation over urban sites in Bangladesh. SN Appl. Sci. 2020:2(1993):1–15. https://doi.org/10.1007/s42452-020-03829-1 Search in Google Scholar

[27] Höpfner C., Scherer D. Analysis of vegetation and land cover dynamics in north-western Morocco during the last decade using MODIS NDVI time series data. Biogeosciences 2011:8(11):3359–3373. https://doi.org/10.5194/bg-8-3359-2011 Search in Google Scholar

[28] Ludwig J. A., Bastin G. N., Chewings V. H., Eager R. W., Liedloff A. C. Leakiness: a new index for monitoring the health of arid and semiarid landscapes using remotely sensed vegetation cover and elevation data. Ecol. Indic. 2007:7(2):442–454. https://doi.org/10.1016/j.ecolind.2006.05.001 Search in Google Scholar

[29] Lunetta R. S., Knight J. F., Ediriwickrema J., Lyon J. G., Worthy L. D. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens. Environ. 2006:105(2):142–154. https://doi.org/10.1016/j.rse.2006.06.018 Search in Google Scholar

[30] Moniruzzaman M., Roy A., Bhatt C. M., Gupta A., An N. T. T., Hassan M. R. Impact Analysis of Urbanization on Land Use Land Cover Change for Khulna City, Bangladesh Using Temporal Landsat Imagery. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2018:XLII-5:757–760. https://doi.org/10.5194/isprs-archives-XLII-5-757-2018 Search in Google Scholar

[31] Mushore T. D., Dube T., Manjowe M., Gumindoga W., Chemura A., Rousta I., Odindi J., Mutanga O. Remotely sensed retrieval of Local Climate Zones and their linkages to land surface temperature in Harare metropolitan city Zimbabwe. Urban Clim. 2019:27:259–271. https://doi.org/10.1016/j.uclim.2018.12.006 Search in Google Scholar

[32] Tucker C. J., Slayback D. A., Pinzon J. E., Los S. O., Myneni R. B., Taylor M. G. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. Int. J. Biometeorol. 2001:45:184–190. https://doi.org/10.1007/s00484-001-0109-811769318 Search in Google Scholar

[33] White A. B., Kumar P., Tcheng D. A data mining approach for understanding topographic control on climate-induced inter-annual vegetation variability over the United States. Remote Sens. Environ. 2005:98:1–20. https://doi.org/10.1016/j.rse.2005.05.017 Search in Google Scholar

[34] Zhao B., Yan Y., Guo H., He M., Gu Y., Li B. Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators: an application in the Yangtze River Delta area. Ecol. Indic. 2009:9(2):346–356. https://doi.org/10.1016/j.ecolind.2008.05.009 Search in Google Scholar

[35] Justice C. O., Vermote E., Townshend J. R., Defries R., Roy D. P., Hall D. K., Salomonson V. V., Privette J. L., Riggs G., Strahler A. The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Trans. Geosci. Remote Sens. 1998:36:1228–1249. https://doi.org/10.1109/36.701075 Search in Google Scholar

[36] Dineshkumar C., Nitheshnirmal S., Bhardwaj A., Priyadarshini K. N. Phenological Monitoring of Paddy Crop Using Time Series MODIS Data. Proceedings 2019:24(1):06205. https://doi.org/10.3390/IECG2019-06205 Search in Google Scholar

[37] Rousta I., Olafsson H., Moniruzzaman M., Zhang H., Liou Y.-A., Mushore T. D., Gupta A. Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan. Remote Sens. 2020:12(15):2433. https://doi.org/10.3390/rs12152433 Search in Google Scholar

[38] Fensholt R., Sandholt I., Rasmussen M. S. Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sens. Environ. 2004:91(3–4):490–507. https://doi.org/10.1016/j.rse.2004.04.009 Search in Google Scholar

[39] Ichii K., Kawabata A., Yamaguchi Y. Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982-1990. Int. J. Remote Sens. 2002:23(18):3873–3878. https://doi.org/10.1080/01431160110119416 Search in Google Scholar

[40] Pettorelli N., Vik J. O., Mysterud A., Gaillard J.-M., Tucker C. J., Stenseth N. C. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 2005:20(9):503–510. https://doi.org/10.1016/j.tree.2005.05.01116701427 Search in Google Scholar

[41] Reed B. C., Brown J. F., VanderZee D., Loveland T.R., Merchant J.W., Ohlen D.O. Measuring phenological variability from satellite imagery. J. Veg. Sci. 1994:5(5):703–714. https://doi.org/10.2307/3235884 Search in Google Scholar

[42] Faisal B., Rahman H., Sharifee N. H., Sultana N., Islam M. I., Ahammad T. Remotely Sensed Boro Rice Production Forecasting Using MODIS-NDVI: A Bangladesh Perspective. AgriEngineering 2019:1(3):356–375. https://doi.org/10.3390/agriengineering1030027 Search in Google Scholar

[43] Rousta I., Olafsson H., Moniruzzaman M., Ardö J., Zhang H., Mushore T. D., Shahin S., Azim S. The 2000–2017 drought risk assessment of the western and southwestern basins in Iran. Model. Earth Syst. Environ. 2020:6:1201–1221. https://doi.org/10.1007/s40808-020-00751-8 Search in Google Scholar

[44] Schnur M. T., Xie H., Wang X. Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA. Ecol. Inform. 2010:5(5):400–409. https://doi.org/10.1016/j.ecoinf.2010.05.001 Search in Google Scholar

[45] Busetto L., Meroni M., Colombo R. Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series. Remote Sens. Environ. 2008:112:118–131. https://doi.org/10.1016/j.rse.2007.04.004 Search in Google Scholar

[46] Sims N. C., Colloff M. J. Remote sensing of vegetation responses to flooding of a semi-arid floodplain: Implications for monitoring ecological effects of environmental flows. Ecol. Indic. 2012:18:387–391. https://doi.org/10.1016/j.ecolind.2011.12.007 Search in Google Scholar

[47] Cho M. A., Skidmore A., Corsi F., Van Wieren S. E., Sobhan I. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int. J. Appl. Earth Obs. Geoinf. 2007:9(4):414–424. https://doi.org/10.1016/j.jag.2007.02.001 Search in Google Scholar

[48] Rouse J., Haas R., Schell J., Deering D. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication 1974:351:309. Search in Google Scholar

[49] Goward S. N., Dye D. G. Evaluating North American net primary productivity with satellite observations. Adv. Space Res. 1987:7(11):165–174. https://doi.org/10.1016/0273-1177(87)90308-5 Search in Google Scholar

[50] Nielsen T. T., Adriansen H. J. L. D. Government policies and land degradation in the Middle East. Land Degrad. Dev. 2005:16(2):151–161. https://doi.org/10.1002/ldr.677 Search in Google Scholar

[51] Barbosa H., Huete A., Baethgen W. A 20-year study of NDVI variability over the Northeast Region of Brazil. J. Arid Environ. 2006:67(2):288–307. https://doi.org/10.1016/j.jaridenv.2006.02.022 Search in Google Scholar

[52] Gaughan A. E., Stevens F. R., Gibbes C., Southworth J., Binford M. W. Linking vegetation response to seasonal precipitation in the Okavango–Kwando–Zambezi catchment of southern Africa. Int. J. Remote Sens. 2012:33(21):6783–6804. https://doi.org/10.1080/01431161.2012.692831 Search in Google Scholar

[53] Ji L., Peters A. J. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens. Environ. 2003:87(1):85–98. https://doi.org/10.1016/S0034-4257(03)00174-3 Search in Google Scholar

[54] Zaitchik B. F., Evans J. P., Geerken R. A., Smith R. B. Climate and vegetation in the Middle East: Interannual variability and drought feedbacks. J. Climate 2007:20:3924–3941. https://doi.org/10.1175/JCLI4223.1 Search in Google Scholar

[55] Archer E. R. Beyond the “climate versus grazing” impasse: using remote sensing to investigate the effects of grazing system choice on v egetation cover in the eastern Karoo. J. Arid Environ. 2004:57(3):381–408. https://doi.org/10.1016/S0140-1963(03)00107-1 Search in Google Scholar

[56] Herrmann S. M., Anyamba A., Tucker C. J. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Glob. Environ. Change 2005:15(4):394–404. https://doi.org/10.1016/j.gloenvcha.2005.08.004 Search in Google Scholar

[57] Pinzon J. E., Tucker C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 2014:6(8):6929–6960. https://doi.org/10.3390/rs6086929 Search in Google Scholar

[58] Running S. W., Nemani R. R. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates. Remote Sens. Environ. 1988:24(2):347–367. https://doi.org/10.1016/0034-4257(88)90034-X Search in Google Scholar

[59] Cai H., Yang X., Wang K., Xiao L. Is forest restoration in the southwest China Karst promoted mainly by climate change or human-induced factors? Remote Sens. 2014:6(10):9895–9910. https://doi.org/10.3390/rs6109895 Search in Google Scholar

[60] Wang J., Meng J., Cai Y. Assessing vegetation dynamics impacted by climate change in the southwestern karst region of China with AVHRR NDVI and AVHRR NPP time -series. Environ. Geol. 2008:54:1185–1195. https://doi.org/10.1007/s00254-007-0901-9 Search in Google Scholar

[61] Evans J., Geerken R. Discrimination between climate and human-induced dryland degradation. J. Arid Environ. 2004:57(4):535–554. https://doi.org/10.1016/S0140-1963(03)00121-6 Search in Google Scholar

[62] Quaye-Ballard J. A., Okrah T. M., Andam-Akorful S. A., Awotwi A., Osei-Wusu W., Antwi T., Tang X. Assessment of vegetation dynamics in Upper East Region of Ghana based on wavelet multi-resolution analysis. Model. Earth Syst. Environ. 2020:6:1783–1793. https://doi.org/10.1007/s40808-020-00789-8 Search in Google Scholar

[63] Zhang W., Wang L., Xiang F., Qin W., Jiang W. Vegetation dynamics and the relations with climate change at multiple time scales in the Yangtze River and Yellow River Basin, China. Ecol. Indic. 2020:110:105892. https://doi.org/10.1016/j.ecolind.2019.105892 Search in Google Scholar

[64] Jiang H., Xu X., Guan M., Wang L., Huang Y., Jiang Y. Determining the contributions of climate change and human activities to vegetation dynamics in agro-pastural transitional zone of northern China from 2000 to 2015. Sci. Total Environ. 2020:718:134871. https://doi.org/10.1016/j.scitotenv.2019.13487131839307 Search in Google Scholar

[65] Hameed M., Ahmadalipour A., Moradkhani H. Drought and food security in the middle east: An analytical framework. Agric. For. Meteorol. 2020:281:107816. https://doi.org/10.1016/j.agrformet.2019.107816 Search in Google Scholar

[66] Lelieveld J., Hadjinicolaou P., Kostopoulou E., Chenoweth J., El Maayar M., Giannakopoulos C., Hannides C., Lange M. A., Tanarhte M., Tyrlis E., Xoplaki E. Climate change and impacts in the Eastern Mediterranean and the Middle East. Clim. Change 2012:114:667–687. https://doi.org/10.1007/s10584-012-0418-4437277625834296 Search in Google Scholar

[67] Ahmadalipour A., Moradkhani H., Escalating heat-stress mortality risk due to global warming in the Middle East and North Africa (MENA). Environ. Int. 2018:117:215–225. https://doi.org/10.1016/j.envint.2018.05.01429763817 Search in Google Scholar

[68] Amiraslani F, Dragovich D. Combating desertification in Iran over the last 50 years: an overview of changing approaches. J. Environ. Manag. 2011:92(1):1–13. https://doi.org/10.1016/j.jenvman.2010.08.01220855149 Search in Google Scholar

[69] Hameed M., Moradkhani H., Ahmadalipour A., Moftakhari H., Abbaszadeh P., Alipour A. A review of the 21^st century challenges in the food-energy-water security in the Middle East. Water 2019:11(4):682. https://doi.org/10.3390/w11040682 Search in Google Scholar

[70] Badreldin N., Goossens R. A satellite-based disturbance index algorithm for monitoring mitigation strategies effects on desertification change in an arid environment. Mitig. Adapt. Strateg. Glob. Change 2015:20:263–276. https://doi.org/10.1007/s11027-013-9490-y Search in Google Scholar

[71] Fisher L. Presidential war power, 3^rd ed. University Press of Kansas, 201310.2307/jj.2990348 Search in Google Scholar

[72] Budhwar P., Mellahi K. Introduction: human resource management in the Middle East. Int. J. Hum. Resour. Manag. 2007:18(1):2–10. https://doi.org/10.1080/09585190601068227 Search in Google Scholar

[73] Budhwar P., Mellahi K. HRM in the Middle East. In Handbook of Research on Comparative Human Resource Management, 2^nd ed. Publisher: Edward Elgar Publishing, 2018.10.4337/9781784711139.00034 Search in Google Scholar

[74] Didan K., Munoz A. B., Solano R., Huete A. MODIS vegetation index user’s guide (MOD13 series) version 3.00 (Collection 6). University of Arizona: Vegetation Index and Phenology Lab 2015. Search in Google Scholar

[75] Loveland T. R., Zhu Z., Ohlen D. O., Brown J. F., Reed B. C., Yang L. An analysis of the IGBP global land-cover characterization process. Photogramm. Eng. Remote Sensing 1999:65:1021–1032. Search in Google Scholar

[76] Huffman G., Bolvin D., Braithwaite D., Hsu K., Joyce R., Xie P. GPM Integrated Multi-Satellite Retrievals for GPM (IMERG) Algorithm Theoretical Basis Document (ATBD) Version 4.4, NASA/GSFC, 2014:1–30. [Online]. [Accessed: 15.01.2022]. Available: http://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.4.pdf Search in Google Scholar

[77] Huffman G., Stocker E., Bolvin D., Nelkin E., Jackson T. GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), 2019. Search in Google Scholar

[78] Rodell M., Houser P. R., Jambor U., Gottschalck J., Mitchell K., Meng C.-J., Arsenault K., Cosgrove B., Radakovich J., Bosilovich M., Entin J. K., Walker J. P., Lohmann D., Toll D. The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc. 2004:85(3):381–394. https://doi.org/10.1175/BAMS-85-3-381 Search in Google Scholar

[79] Zandbergen P. Applications of shuttle radar topography mission elevation data. Geogr. Compass 2008:2(5):1404–1431. https://doi.org/10.1111/j.1749-8198.2008.00154.x Search in Google Scholar

[80] Wickland D. E. Mission to Planet Earth: The Ecological Perspective. Ecology 1991:72(6):1923–1933. https://doi.org/10.2307/1941547 Search in Google Scholar

[81] Dutta D., Kundu A., Patel N., Saha S., Siddiqui A. Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). Egypt. J. Remote. Sens. Space Sci 2015:18(1):53–63. https://doi.org/10.1016/j.ejrs.2015.03.006 Search in Google Scholar

[82] Gitelson A. A., Viña A., Arkebauer T. J., Rundquist D. C., Keydan G., Leavitt B. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett. 2003:30:1248. https://doi.org/10.1029/2002GL016450 Search in Google Scholar

[83] Tarpley J., Schneider S., Money R. Global vegetation indices from the NOAA-7 meteorological satellite. J. Clim. Appl. Meteorol. 1984:23(3):491–494. https://doi.org/10.1175/1520-0450(1984)023<0491:GVIFTN>2.0.CO;210.1175/1520-0450(1984)023<0491:GVIFTN>2.0.CO;2 Search in Google Scholar

[84] Thenkabail P. S., Gamage M. The use of remote sensing data for drought assessment and monitoring in Southwest Asia. International Water Management Institute, 2004. Search in Google Scholar

[85] Geerken R., Zaitchik B., Evans J. Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity. Int. J. Remote Sens. 2005:26(24):5535–5554. https://doi.org/10.1080/01431160500300297 Search in Google Scholar

[86] Martínez B., Gilabert M. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens. Environ. 2009:113(9):1823–1842. https://doi.org/10.1016/j.rse.2009.04.016 Search in Google Scholar

[87] Moulin S., Kergoat L., Viovy N., Dedieu G. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. J. Climate 1997:10(6):1154–1170. https://doi.org/10.1175/1520-0442(1997)010<1154:GSAOVP>2.0.CO;210.1175/1520-0442(1997)010<1154:GSAOVP>2.0.CO;2 Search in Google Scholar

[88] Running S. W., Loveland T. R., Pierce L. L., Nemani R. R., Hunt Jr E. A remote sensing based vegetation classification logic for global land cover analysis. Remote Sens. Environ. 1995:51(1):39–48. https://doi.org/10.1016/0034-4257(94)00063-S Search in Google Scholar

[89] Townshend J. R., Justice C. Analysis of the dynamics of African vegetation using the normalized difference vegetation index. Int. J. Remote Sens. 1986:7(11):1435–1445. https://doi.org/10.1080/01431168608948946 Search in Google Scholar

[90] Bhandari A., Kumar A., Singh G. Feature extraction using Normalized Difference Vegetation Index (NDVI): A case study of Jabalpur city. Proc. Technol. 2012:6:612–621. https://doi.org/10.1016/j.protcy.2012.10.074 Search in Google Scholar

[91] Cai D., Fraedrich K., Sielmann F., Guan Y., Guo S., Zhang L., Zhu X. Climate and vegetation: An ERA-interim and GIMMS NDVI analysis. J. Climate 2014:27(13):5111–5118. https://doi.org/10.1175/JCLI-D-13-00674.1 Search in Google Scholar

[92] Chuvieco E., Cocero D., Riano D., Martin P., Martınez-Vega J., de la Riva J., Pérez F. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens. Environ. 2004:92(3):322–331. https://doi.org/10.1016/j.rse.2004.01.019 Search in Google Scholar

[93] Gandhi G. M., Parthiban S., Thummalu N., Christy A. NDVI: Vegetation change detection using remote sensing and gis–A case study of Vellore District. Procedia Comput. Sci. 2015:57:1199–1210. https://doi.org/10.1016/j.procs.2015.07.415 Search in Google Scholar

[94] Goward S. N., Markham B., Dye D. G., Dulaney W., Yang J. Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer. Remote Sens. Environ. 1991:35(2–3):257–277. https://doi.org/10.1016/0034-4257(91)90017-Z Search in Google Scholar

[95] Didan K. MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250m SIN grid V006. NASA EOSDIS Land Processes DAAC, 2015. Search in Google Scholar

[96] Weier, J., Herring D. Measuring vegetation (NDVI & EVI). NASA Earth Observatory 2000:20. Search in Google Scholar

[97] Wang R., Cherkauer K., Bowling L. Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series. Remote Sens. 2016:8(4):269. https://doi.org/10.3390/rs8040269 Search in Google Scholar

[98] Deng G., Zhang H., Guo X., Ying H. Assessment of Drought in Democratic People’s Republic of Korea in 2017 Using TRMM Data. In: Proceedings of 2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Xi’an. 2018. https://doi.org/10.1109/EORSA.2018.8598557 Search in Google Scholar

[99] Duan Z., Bastiaanssen W. First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling–calibration procedure. Remote Sens. Environ. 2013:131:1–13. https://doi.org/10.1016/j.rse.2012.12.002 Search in Google Scholar

[100] Mossad A., Alazba A. Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems. Arab. J. Geosci. 2018:11(132):1–16. https://doi.org/10.1007/s12517-018-3487-5 Search in Google Scholar

[101] Nastos P., Kapsomenakis J., Philandras K. Evaluation of the TRMM 3B43 gridded precipitation estimates over Greece. Atmos. Res. 2016:169:497–514. https://doi.org/10.1016/j.atmosres.2015.08.008 Search in Google Scholar

[102] Skofronick-Jackson G., Kirschbaum D., Petersen W., Huffman G., Kidd C., Stocker E., Kakar R. The Global Precipitation Measurement (GPM) mission’s scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations. Q. J. R. Meteorol. Soc. 2018:144(S1):27–48. https://doi.org/10.1002/qj.3313658145831213729 Search in Google Scholar

[103] Almazroui M. Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009. Atmos. Res. 2011:99(3–4):400–414. https://doi.org/10.1016/j.atmosres.2010.11.006 Search in Google Scholar

[104] Kummerow C., Barnes W., Kozu T., Shiue J., Simpson J. The tropical rainfall measuring mission (TRMM) sensor package. J. Atmos. Oceanic Tech. 1998:15(3):809–817. https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;210.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2 Search in Google Scholar

[105] Patel N., Chopra P., Dadhwal V. Analyzing spatial patterns of meteorological drought using standardized precipitation index. Met. Apps 2007:14:329–336. https://doi.org/10.1002/met.33 Search in Google Scholar

[106] Stevens-Rumann C. S., Kemp K. B., Higuera P. E., Harvey B. J., Rother M. T., Donato D. C., Morgan P., Veblen T. T. Evidence for declining forest resilience to wildfires under climate change. Ecol. Lett. 2018:21(2):243–252. https://doi.org/10.1111/ele.12889 Search in Google Scholar

[107] Raja R., Nayak A., Panda B., Lal B., Tripathi R., Shahid M., Kumar A., Mohanty S., Samal P., Gautam P. Monitoring of meteorological drought and its impact on rice (Oryza sativa L.) productivity in Odisha using standardized precipitation index. Arch. Agron. Soil Sci. 2014:60(12):1701–1715. https://doi.org/10.1080/03650340.2014.912033 Search in Google Scholar

[108] Dahiru T. P-value, a true test of statistical significance? A cautionary note. Ann. Ib. Postgrad. Med. 2008:6:21–26. https://doi.org/10.4314/aipm.v6i1.64038 Search in Google Scholar

[109] Eberly L. E. Correlation and simple linear regression. Methods Mol. Biol. 2007:404:143–164. https://doi.org/10.1007/978-1-59745-530-5_8 Search in Google Scholar

[110] Zou K. H., Tuncali K., Silverman S. G. Correlation and simple linear regression. Radiology 2003:227(3):617–628. https://doi.org/10.1148/radiol.2273011499 Search in Google Scholar

[111] Allan J. A. Fortunately there are substitutes for water otherwise our hydropolitical futures would be impossible. In: Proceedings of the conference on Priorities for Water Resources Allocation and Management; Overseas Development Administration (ODA), London, UK. 1993. Search in Google Scholar

[112] Sofroniou A., Bishop S. Water Scarcity in Cyprus: A Review and Call for Integrated Policy. Water 2014:6(10):2898–2928. https://doi.org/10.3390/w6102898 Search in Google Scholar

[113] Li J., Chou J. Dynamical analysis on splitting of subtropical high-pressure zone. Chin. Sci. Bull. 1998:43:1285–1289. https://doi.org/10.1007/BF02884143 Search in Google Scholar

[114] Najafi M. S., Sarraf B., Zarrin A., Rasouli A. Climatology of atmospheric circulation patterns of Arabian dust in western Iran. Environ. Monit. Assess. 2017:189:473. https://doi.org/10.1007/s10661-017-6196-828849292 Search in Google Scholar

[115] Rousta I., Doostkamian M., Haghighi E., Mirzakhani B. Statistical-synoptic analysis of the atmosphere thickness pattern of Iran’s pervasive frosts. Climate 2016:4(3):41. https://doi.org/10.3390/cli4030041 Search in Google Scholar

[116] Rousta I., Karampour M., Doostkamian M., Olafsson H., Zhang H., Mushore T.D., Karimvandi A.S., Vargas E. R. M. Synoptic-dynamic analysis of extreme precipitation in Karoun River Basin, Iran. Arab. J. Geosci. 2020:13:1–16. https://doi.org/10.1007/s12517-020-5101-x Search in Google Scholar

[117] Bolin B. On the influence of the earth’s orography on the general character of the westerlies. Tellus 1950:2(3):184–195. https://doi.org/10.3402/tellusa.v2i3.8547 Search in Google Scholar

[118] Toggweiler J. Shifting westerlies. Science 2009:323:1434–1435. https://doi.org/10.1126/science.116982319286540 Search in Google Scholar

[119] Rousta I., Nasserzadeh M.H., Jalali M., Haghighi E., Ólafsson H., Ashrafi S., Doostkamian M., Ghasemi A. Decadal spatial-temporal variations in the spatial pattern of anomalies of extreme precipitation thresholds (Case Study: Northwest Iran). Atmosphere 2017:8(8):135. https://doi.org/10.3390/atmos8080135 Search in Google Scholar

[120] Rousta I., Javadizadeh F., Dargahian F., Olafsson H., Shiri-Karimvandi A., Vahedinejad S.H., Doostkamian M., Monroy Vargas E. R., Asadolahi A. Investigation of vorticity during prevalent winter precipitation in Iran. Adv. Meteorol. 2018:ID6941501:1–13. https://doi.org/10.1155/2018/6941501 Search in Google Scholar

[121] Rousta I., Doostkamian M., Ólafsson H., Zhang H., Vahedinejad S. H., Sarif M. O., Monroy Vargas E. R. Analyzing the fluctuations of atmospheric precipitable water in Iran during various periods based on the retrieving technique of NCEP/NCAR. Open Atmospheric Sci. J. 2018:12:48–57. https://doi.org/10.2174/1874282301812010048 Search in Google Scholar

[122] Rousta I., Doostkamian M., Olafsson H., Ghafarian-Malamiri H., Zhang H., Taherian A., Sarif M., Gupta R., Monroy-Vargas E. On the relationship between the 500 hPa height fluctuations and the atmosphere thickness over Iran and the Middle East. Tethys 2019:16:3–14. Search in Google Scholar

[123] Elbana T. A., Bakr N., Elbana M. Reuse of treated wastewater in Egypt: challenges and opportunities. In: Unconventional Water Resources and Agriculture in Egypt. The Handbook of Environmental Chemistry, Negm, A. (eds). Springer, Cham. 2017:75:429–453. https://doi.org/10.1007/698_2017_46 Search in Google Scholar

[124] Abdel-Shafy H. I., Mansour M. S. Overview on water reuse in Egypt: present and future. Sustainable Sanitation Practice 2013:14:17–25. Search in Google Scholar

[125] Loutfy N. M. Reuse of Wastewater in Mediterranean Region, Egyptian Experience. In: Waste Water Treatment and Reuse in the Mediterranean Region, Barceló D., Petrovic M. (eds). Springer Berlin Heidelberg, 2011:183–213. https://doi.org/10.1007/698_2010_76 Search in Google Scholar

[126] Ewaid, S. H., Abed S. A., Al-Ansari N. Water Footprint of Wheat in Iraq. Water 2019:11(3):535. https://doi.org/10.3390/w11030535 Search in Google Scholar

[127] Bilgen A. The Southeastern Anatolia Project (GAP) revisited: The evolution of GAP over forty years. New Perspect. Turk. 2018:58:125–154. https://doi.org/10.1017/npt.2018.8 Search in Google Scholar

[128] Özcan O., Bookhagen B., Musaoğlu N. Impact of the Atatürk dam lake on agro-meteorological aspects of the southeastern Anatolia region, Turkey. J. Indian Soc. Remote. Sens. 2018:46:471–481. https://doi.org/10.1007/s12524-017-0703-9 Search in Google Scholar

[129] Al-Madhhachi A.-S. T., Rahi K. A., Leabi W. K. Hydrological Impact of Ilisu Dam on Mosul Dam; the River Tigris. Geosciences 2020:10(4):120. https://doi.org/10.3390/geosciences10040120 Search in Google Scholar

[130] Kankal M., Nacar S., Uzlu E. Status of hydropower and water resources in the Southeastern Anatolia Project (GAP) of Turkey. Energy Rep. 2016:2:123–128. https://doi.org/10.1016/j.egyr.2016.05.003 Search in Google Scholar

[131] Frenken K. Legislative and institutional framework of water management. In: Irrigation in the middle east region in figures. AQUASTAT survey. FAO Water Reports 2008:34:55–56. Search in Google Scholar

[132] Abdel-Satar A. M., Al-Khabbas M. H., Alahmad W. R., Yousef W. M., Alsomadi R. H., Iqbal T. Quality assessment of groundwater and agricultural soil in Hail region Saudi Arabia. Egypt. J. Aquat. Res. 2017:43(1):55–64. https://doi.org/10.1016/j.ejar.2016.12.004 Search in Google Scholar

[133] Fiaz S., Noor M. A., Aldosri F.O. Achieving food security in the Kingdom of Saudi Arabia through innovation: Potential role of agricultural extension. J. Saudi Soc. Agric. Sci. 2018:17(4):365–375. https://doi.org/10.1016/j.jssas.2016.09.001 Search in Google Scholar