
Drought is one of the most natural hazards that cause damage to ecosystems, agricultural production, and water resources. This study has analyzed seasonal and annual rainfall trends using monthly data series of 33 years (1983–2015) in Addis Ababa city over three stations namely; Sendafa, Bole, and Observation. Here, we examined the occurrence of historical drought trends in the study jurisdiction. The Reconnaissance Drought Index (RDI) and the Standardized Precipitation Index (SPI) were employed to find long-term drought trends as well as to examine the occurrence of drought history at a longer duration. The analysis indicated that severe drought conditions were observed for SPI and RDI indices in the year 2013 for Bole station, while medium droughts were recorded for the years 1991 and 2002 for all stations. Similarly, the RDI indices for 1996 was recorded as severe drought for the Observatory station. On the other hand, higher variability (coefficient of variation) of rainfall during winter seasons were 95.8%, 95.9%, and 77.9% for Sendafa, Bole, and Observatory stations respectively. However, the lower coefficient of variation during annual rainfall was 15.59% for Sendafa, 14.38% for Bole, and 13.98% for the Observatory station. Furthermore, the drought severity classification for the long-term drought analysis of annual precipitation shows that 3% of severe drought, 12% of moderate drought, and 85% of the normal condition were recorded in Bole station. The severe and moderate drought indices due to the reduction of rainfall, temperature change, and other factors can cause a shortage of urban water supply. Thus, the results of this study will help the water sector professionals in forecasting weather variations and for better management of urban water resources.
Citation: Zinabu A. Alemu, Emmanuel C. Dioha, Michael O. Dioha. Hydro-meteorological drought in Addis Ababa: A characterization study[J]. AIMS Environmental Science, 2021, 8(2): 148-168. doi: 10.3934/environsci.2021011
[1] | Charles D Smith, Linda J Van Eldik, Gregory A Jicha, Frederick A Schmitt, Peter T Nelson, Erin L Abner, Richard J Kryscio, Ronan R Murphy, Anders H Andersen . Brain structure changes over time in normal and mildly impaired aged persons. AIMS Neuroscience, 2020, 7(2): 120-135. doi: 10.3934/Neuroscience.2020009 |
[2] | Zygmunt Siedlecki, Sebastian Grzyb, Danuta Rość, Maciej Śniegocki . Plasma HGF concentration in patients with brain tumors. AIMS Neuroscience, 2020, 7(2): 107-119. doi: 10.3934/Neuroscience.2020008 |
[3] | Seidu A. Richard . Elucidating the novel biomarker and therapeutic potentials of High-mobility group box 1 in Subarachnoid hemorrhage: A review. AIMS Neuroscience, 2019, 6(4): 316-332. doi: 10.3934/Neuroscience.2019.4.316 |
[4] | Daniel Chimuanya Ugwuanyi, Tochukwu Florence Sibeudu, Chidmma Precious Irole, Michael Promise Ogolodom, Chukwudi Thaddeus Nwagbara, Adaobi Maryann Ibekwe, Awajimijan Nathaniel Mbaba . Evaluation of common findings in brain computerized tomography (CT) scan: A single center study. AIMS Neuroscience, 2020, 7(3): 311-318. doi: 10.3934/Neuroscience.2020017 |
[5] | Naeimeh Akbari-Gharalari, Maryam Ghahremani-Nasab, Roya Naderi, Leila Chodari, Farshad Nezhadshahmohammad . The potential of exosomal biomarkers: Revolutionizing Parkinson's disease: How do they influence pathogenesis, diagnosis, and therapeutic strategies?. AIMS Neuroscience, 2024, 11(3): 374-397. doi: 10.3934/Neuroscience.2024023 |
[6] | Shigeru Obayashi . Cognitive and linguistic dysfunction after thalamic stroke and recovery process: possible mechanism. AIMS Neuroscience, 2022, 9(1): 1-11. doi: 10.3934/Neuroscience.2022001 |
[7] | Brian P. Leung, Kevin R. Doty, Terrence Town . Cerebral Innate Immunity in Drosophila Melanogaster. AIMS Neuroscience, 2015, 2(1): 35-51. doi: 10.3934/Neuroscience.2015.1.35 |
[8] | Kevin Pierre, Vanessa Molina, Shil Shukla, Anthony Avila, Nicholas Fong, Jessica Nguyen, Brandon Lucke-Wold . Chronic traumatic encephalopathy: Diagnostic updates and advances. AIMS Neuroscience, 2022, 9(4): 519-535. doi: 10.3934/Neuroscience.2022030 |
[9] | Nisha Syed Nasser, Krish Sharma, Parv Mahendra Mehta, Vidur Mahajan, Harsh Mahajan, Vasantha Kumar Venugopal . Estimation of white matter hyperintensities with synthetic MRI myelin volume fraction in patients with multiple sclerosis and non-multiple-sclerosis white matter hyperintensities: A pilot study among the Indian population. AIMS Neuroscience, 2023, 10(2): 144-153. doi: 10.3934/Neuroscience.2023011 |
[10] | Arosh S. Perera Molligoda Arachchige . The blue brain project: pioneering the frontier of brain simulation. AIMS Neuroscience, 2023, 10(4): 315-318. doi: 10.3934/Neuroscience.2023024 |
Drought is one of the most natural hazards that cause damage to ecosystems, agricultural production, and water resources. This study has analyzed seasonal and annual rainfall trends using monthly data series of 33 years (1983–2015) in Addis Ababa city over three stations namely; Sendafa, Bole, and Observation. Here, we examined the occurrence of historical drought trends in the study jurisdiction. The Reconnaissance Drought Index (RDI) and the Standardized Precipitation Index (SPI) were employed to find long-term drought trends as well as to examine the occurrence of drought history at a longer duration. The analysis indicated that severe drought conditions were observed for SPI and RDI indices in the year 2013 for Bole station, while medium droughts were recorded for the years 1991 and 2002 for all stations. Similarly, the RDI indices for 1996 was recorded as severe drought for the Observatory station. On the other hand, higher variability (coefficient of variation) of rainfall during winter seasons were 95.8%, 95.9%, and 77.9% for Sendafa, Bole, and Observatory stations respectively. However, the lower coefficient of variation during annual rainfall was 15.59% for Sendafa, 14.38% for Bole, and 13.98% for the Observatory station. Furthermore, the drought severity classification for the long-term drought analysis of annual precipitation shows that 3% of severe drought, 12% of moderate drought, and 85% of the normal condition were recorded in Bole station. The severe and moderate drought indices due to the reduction of rainfall, temperature change, and other factors can cause a shortage of urban water supply. Thus, the results of this study will help the water sector professionals in forecasting weather variations and for better management of urban water resources.
Intracranial dermoid cysts are rare, and benign cysts are composed of both epidermal and dermal structures. These can include the abnormal proliferation of fat cells, squamous epithelium, hair follicles, and teeth, as well as sweat, apocrine, and sebaceous glands [1]. Dermoid cysts are benign congenital inclusion cysts that form from ectodermal tissue during the neural tube closure between the third and fifth week of embryogenesis [2]. As such, they typically present in patients younger than 30 years of age. Intracranial dermoid cysts are typically found in the midline, most often in the suprasellar region, though they have also been reported in locations such as the parasellar, frontonasal, and posterior fossa [2],[3]. While most are asymptomatic, some cysts may either compress the surrounding structures and cause headaches, seizures, and focal neurologic defects or develop malignant features including the transformation into a squamous cell carcinoma [3].
However, the feared complication of intracranial dermoid cysts is a rupture. The overproduction of oils from the abnormal growth of glands can cause an increasing pressure and the eventual rupture of the cyst wall. Another cause of a cyst rupture is trauma that results in direct damage to the cyst and the leakage of its contents [4]. The rupture of a dermoid cyst, either spontaneously or by trauma, will result in fat droplets in the brain CSF spaces. Fat droplets in the CSF may provoke acute meningeal inflammation, with symptoms varying from headaches and focal neurological signs to frank chemical meningitis that may present with nuchal rigidity, fever, and an altered mental status. Additionally, the rupture of cysts located in the posterior fossa can cause an obstructive hydrocephalus that manifests as headaches, an altered mental status, papilledema, and a coma [1].
The patient is a 73-year-old, right-handed female with a past medical history of hyperlipidemia and anxiety. The patient presented early in the morning to the Emergency Department with visual disturbances and left-hand numbness. The patient's symptoms started in the evening after cleaning her trailer predominantly with her right hand with a resolution upon waking up the next morning. The visual disturbances were as if she was seeing through blue prisms. The patient denied headaches and speech or language disturbances. The patient denied weakness of the left hand, trauma to the left hand, or a prolonged pressure on the left elbow. An early stroke alert on arrival was canceled due to the patient's resolved symptoms. The vitals obtained were a blood pressure of 134/79, a heart rate of 84 beats per minute, a respiratory rate of 22, and an oxygen saturation of 98%. The physical examinations, including a neurologic exam, were normal. Her NIH Stroke Scale Score was 0. Her electroencephalogram (EEG) and routine labs were normal.
Computed tomography (CT) and magnetic resonance imaging (MRI) (Figure 1) of the head were performed. Her CT head revealed hypodense “lesions” in the lateral ventricles and basal cisterns, which may represent either air or fat. The CT Hounsfield unit of the lesions were between −41 to −83 Hu, which are compatible with fat and not air. The T1 weighted and FLAIR MRI revealed hyperintense lesions “floating” on top of the CSF in the lateral ventricles, which are typical for fat droplets, presumably caused by a ruptured dermoid cyst. Imaging showed no evidence of a residual cyst or hydrocephalus, and the patient was discharged with a recommendation to establish outpatient neurosurgical care. No intervention was deemed necessary.
Dermoid cysts are rare, intracranial masses that contain a heterogeneous mixture of epidermoid and dermoid cells. Although they are benign, dermoid cysts can rupture secondary to trauma, iatrogenic complications, or the overproduction of oils within the cyst [1],[5]. A rupture of the cystic contents can cause a wide range of symptoms including focal neurologic deficits, headaches, and rarely chemical meningitis [1]. MRI imaging of a ruptured dermoid cyst often shows small, scattered foci of T1 hyperintensities with fat-fluid levels in the subarachnoid and intraventricular spaces [5]. Given the non-specific nature of the symptoms, it is imperative that a thorough examination of the neuroimaging is performed to distinguish a ruptured dermoid cyst from other pathologies that can appear similarly.
The CT scans of this patient showed well-demarcated areas of hypodense foci in the lateral ventricles and subarachnoid space, which is indicative of either air or fat. It is imperative to differentiate between the two. On initial, visual review of the CT imaging, these foci can appear to be composed of air floating above the relatively hyperdense cerebrospinal fluid. This may indicate pneumocephalus, which is defined as the presence of air in the epidural, subdural, or subarachnoid space, as well as the brain parenchyma and ventricles [6]. 75% of cases result from a neurotrauma, while the remainder of the cases can be caused by tumors, infections, and neurosurgery [6]. Pneumocephalus, while often asymptomatic, can share many clinical symptoms with a dermoid cyst rupture including headaches, confusion, dizziness, seizures, and focal neurological deficits [6]. As air and fat can appear similar on initial visual inspection of the CT neuroimaging, it is critical to further analyze the densities of the lesions to differentiate between the two.
A careful measurement of the density of our patient's lesions revealed a variable HU of −40.5 to −97.25, which is more similar to that of fat. The typical density of fat on CT imaging is roughly −50 to −100 HU; by contrast, air would have a density closer to −1000 HU [7]. The heterogeneous density of the fat droplets may be attributed to the varying lipid content of a dermoid cyst, which includes sebum (mainly fatty acids, cholesterol, and esters), keratin debris, and epidermal cells [8]–[10]. Furthermore, the foci on our patient's MRI imaging appeared hyperintense in both the T1 and T2FLAIR sequences, which is consistent with fat. Dermoid cysts can present with a variable intensity on T1 and T2-weighted images [8],[11]. Conversely, air would appear dark on an MRI.
It is also important to rule out other lesions that may appear hyperintense on both T1 and T2FLAIR imaging. Other fat-rich masses including lipomas, teratomas, and some subtypes of meningiomas and ependymomas can appear similar to dermoid cysts on imaging [2],[5]. During the late subacute phase of an intracranial hemorrhage, roughly 7–14 days post-bleed, lesions may also appear hyperintense in both the T1 and T2FLAIR sequences due to erythrocyte degradation and the presence of extracellular methemoglobin [5].
Should symptoms prove chronic or severe, determining the correct diagnosis will affect which definitive treatment should be given. For non-severe or resolving symptoms (<1 week), the treatment is similar for a ruptured dermoid cyst or pneumocephalus: observation, rest, raising the bed to 30 degrees, and analgesics [12],[13]. However, in cases of prolonged symptoms from a ruptured dermoid cyst, surgical micro-resection is necessary, especially if the components of the cyst remain attached to the dura [12],[14]. In contrast, severe symptoms from pneumocephalus would require a needle aspiration [13]. In cases where debris can obstruct the flow of the cerebral spinal fluid (CSF), a diversion with ventriculoperitoneal shunts or external ventricular devices may be used [15]. A definitive differentiation between the two conditions is necessary to accurately inform future treatment decisions and can drastically change the patient outcomes.
Our case illustrated the importance of a diligent review of neuroimaging when a dermoid cyst rupture is suspected. A careful examination of the CT Hounsfield Units and MRI should be performed as pneumocephalus and a dermoid cyst rupture can present very similarly on CT imaging.
Mark Reed: preparation and drafting of the whole manuscript and of the figure. Chris Miller: preparation and drafting of the whole manuscript and of the figure. Cortney Connor: preparation and drafting of the whole manuscript and of the figure. Jason Chang: provision, management and discussions about clinical cases and review of the manuscript. Forshing Lui: preparation and drafting of the whole manuscript and of the figure.
Consent was obtained from the fact for publication of this case report.
[1] |
Najihah TS, Ibrahim M H, Zain NAM, et al. (2020) Activity of the oil palm seedlings exposed to a different rate of potassium fertilizer under water stress condition. AIMS Environ Sci 7: 46-68. doi: 10.3934/environsci.2020004
![]() |
[2] |
Sönmez FK, Kömüscü AÜ, Erkan A, et al. (2005) An analysis of spatial and temporal dimension of drought vulnerability in Turkey using the standardized precipitation index. Nat Hazards 35: 243-264. doi: 10.1007/s11069-004-5704-7
![]() |
[3] |
Manjowe M, Mushore TD, Gwenzi J, et al. (2018) Circulation mechanisms responsible for wet or dry summers over Zimbabwe. AIMS Environ Sci 5: 154-172. doi: 10.3934/environsci.2018.3.154
![]() |
[4] |
Smakhtin VU, Hughes DA, (2007) Automated estimation and analyses of meteorological drought characteristics from monthly rainfall data. Environ Modell Softw 22: 880-890. doi: 10.1016/j.envsoft.2006.05.013
![]() |
[5] |
Christy JR (2019) Examination of extreme rainfall events in two regions of the United States since the 19th century. AIMS Environ Sci 6: 109-126. doi: 10.3934/environsci.2019.2.109
![]() |
[6] | Yahaya I, Adamu SJ, Muhammed BB, (2017) The use of Standardized Precipitation Index (SPI) for Drought Intensity Assessment in North-Eastern Nigeria. Researchjournal's J Geo 4:1-13 |
[7] |
Feyissa G, Zeleke G, Bewket W, et al. (2018) Downscaling of future temperature and precipitation extremes in Addis Ababa under climate change. Climate 6: 58. doi: 10.3390/cli6030058
![]() |
[8] | Engida M (1999) Annual rainfall and evapotranspiration in Ethiopia. Ethiopian Journal of Natural Resources 1: 137-154. |
[9] | IPCC (2007) Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group Ⅱ to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by Parry, M., Canziani, O., Palutikof, J., Linden, P.vd., Hanson, C., Cambridge University Press 32 Avenue of the Americas, New York, USA, 10013-2473. |
[10] | Alemu ZA, Dioha MO, (2020a) Climate change and trend analysis of temperature: the case of Addis Ababa, Ethiopia. Environ Syst Res 9: 1-15. |
[11] | FDRE, Ethiopian Government Portal, 2018. Available from: http://www.ethiopia.gov.et/addis-ababa-city-administration. |
[12] | Niemeyer S, New drought indices. European Commission, DG Joint Research Centre, Institute for Environment and Sustainability, T.P. 261, JRC-IES, I-21020 Ispra (VA), Italy, 2020 Availablefrom: https://om.ciheam.org/om/pdf/a80/00800451.pdf |
[13] | Gebreyesus M, Cherint A, Ashine T, et al. Drought Analysis Using Reconnaissance Drought Index (RDI): In the case of Awash River Basin, Ethiopia2020. Available from: https://www.researchgate.net/publication/344440865_Drought_analysis_using_reconnaissance_drought_index_RDI_In_the_case_of_Awash_River_Basin_Ethiopia |
[14] | Tigkas D, Vangelis H, Tsakiris G (2013) The RDI as a composite climatic index. E.W. Publications. European Water 41: 17-22. |
[15] | McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration of time scales. Eighth Conference on Applied Climatology, American Meteorological Society, Anaheim CA. |
[16] | Aksoy H, Onoz B, Cetin M, et al. (2018) SPI-based Drought Severity-Duration-Frequency Analysis. 13th International Congress on Advances in Civil Engineering, Izmir/Turkey. |
[17] | Pashiardis S, Michaelides S (2008) Implementation of the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) for Regional Drought Assessment: A case study for Cyprus. E.W. Publications. Eur Water 23/24: 57-65. |
[18] |
Asadi-Zarch MA, Malekinezhad H, Mobin MH, et al. (2011) Drought Monitoring by Reconnaissance Drought Index (RDI) in Iran. Water Resour Manag 25: 3485-3504. doi: 10.1007/s11269-011-9867-1
![]() |
[19] | Ansarifard S, Shamsnia SA (2018) Monitoring drought by Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI) using DrinC software. Water Utility J 20: 29-35. |
[20] |
Hayes MJ, Svoboda MD, Wilhite DA, et al. (1999) Monitoring the 1996 Drought Using the Standardized Precipitation Index. B Am Meteorol Soc 80: 429-438. doi: 10.1175/1520-0477(1999)080<0429:MTDUTS>2.0.CO;2
![]() |
[21] |
Zarei AR, Moghimi MM, Mahmoudi MR (2016) Analysis of Changes in Spatial Pattern of Drought Using RDI Index in south of Iran. Water Resour Manag 30: 3723-3743. doi: 10.1007/s11269-016-1380-0
![]() |
[22] | Pramudya Y, Onishi T, (2018) Assessment of the Standardized Precipitation Index (SPI) in Tegal City, Central Java, Indonesia. IOP Conf. Series: Earth Environ Sci 129: 012-019. |
[23] | Maroua BA, Nouiri I, (2018) Study of trends in historical variation and mapping of drought events in Tunisia: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI) for the period 1973-2016. 3rd International Conference on Integrated Environmental Management for Sustainable Development. ISSN 1737-3638. |
[24] |
Vangelis H, Tigkas D, Tsakiris G, (2012) The effect of PET method on Reconnaissance Drought Index (RDI) calculation. J Arid Environ 88: 130-140. doi: 10.1016/j.jaridenv.2012.07.020
![]() |
[25] |
Tsakiris G, Vangelis H, Pangalou D, (2007) Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI). Water Resour Manage 21: 821-833. doi: 10.1007/s11269-006-9105-4
![]() |
[26] | Fitsume Y, (2014) Precipitation Extremes and their Pattern in the Central Highlands of Ethiopia: SPI Based Analysis. J Nat Sci Res 4: 92-97. |
[27] |
Gidey E, Dikinya O, Sebego R, et al. (2018) Modeling the Spatio-Temporal Meteorological Drought Characteristics Using the Standardized Precipitation Index (SPI) in Raya and Its Environs, Northern Ethiopia. Earth Sys Environ 2: 281-292. doi: 10.1007/s41748-018-0057-7
![]() |
[28] |
Spinoni J, Naumann G, Carrao H, et al. (2013) World drought frequency, duration, and severity for 1951-2010. Int J Climatol 34: 2792-2804. doi: 10.1002/joc.3875
![]() |
[29] |
Tigkas D, Vangelis H, Tsakiris G (2015) DrinC: a software for drought analysis based on drought indices. Earth Sci Inform 8: 697-709. doi: 10.1007/s12145-014-0178-y
![]() |
[30] | Climate-Ethiopia, Climates to travel world climate guide, 2021. Available from: https://www.climatestotravel.com/climate/ethiopia. |
[31] | Alemu ZA, Dioha MO (2020b) Modelling scenarios for sustainable water supply and demand in Addis Ababa city, Ethiopia. Environ Syst Res 9: 1-14. |
[32] | FDRE, City Map of Addis Ababa City Administration, Ethiopia, 2020. Available from: http://www.addisababa.gov.et/de/web/guest/city-map |
[33] | Rossi G, Bonaccorso B, Vega T (2007) Methods and tools for drought analysis and management, Springer Science and Business Media, Berlin. vol 62. ISBN 978-1-4020-5923-0 |
[34] | Tsakiris G, Nalbantis I, Pangalou D, et al. (2008) Drought meteorological monitoring network design for the reconnaissance drought index (RDI). In: Franco Lopez A. (Ed.), Proceedings of the 1st International Conference "Drought Management: scientific and technological innovations". Zaragoza, Spain: Option Méditerranéennes, Series A, 80: 57-62. |
[35] |
Vangelis H, Tigkas D, Tsakiris G (2013) The effect of PET method on Reconnaissance Drought Index (RDI) calculation. J Arid Environ 88: 130-140. doi: 10.1016/j.jaridenv.2012.07.020
![]() |
[36] | Allen RG, Pereira LS, Raes D, et al. (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO irrigation and drainage paper 56, 1st edition. Rome, Italy. |
[37] |
Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1: 96-99. doi: 10.13031/2013.26773
![]() |
[38] | Tigkas D (2008) Drought Characterization and Monitoring in Regions of Greece. Eur Water 23/24: 29-39. |
[39] |
Khanmohammadi N, Rezaie H, Montaseri M, et al. (2017) The Effect of Temperature Adjustment on Reference Evapotranspiration and Reconnaissance Drought Index (RDI) in Iran. Water Resour Manag 31: 5001-5017. doi: 10.1007/s11269-017-1793-4
![]() |
[40] | Gupta SP (2007) Statistical Methods. Seventh Revised and Enlarged Edition ed. Sultan Chand and Sons, Educational Publisher. New Delhi. |
[41] | Helsel DR, Hirsch RM (2002) Statistical methods in water resources. Techniques of water-resources investigations of the United States geological survey, book 4, hydrologic analysis and interpretation. U. S. Geological survey |
[42] | Philip S, Kew SF, Oldenborgh GJ, et al. (2018) Attribution Analysis of the Ethiopian Drought of 2015. American Meteorological Society 2465-2486. |
[43] | Jamshidi H, Khalili D, Zadeh MR, et al. (2011) Assessment and comparison of SPI and RDI meteorological drought indices in selected synoptic stations of Iran. In World Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability, 1161-1173. |
[44] |
Haied N, Foufou A, Chaab S, et al. (2017) Drought assessment and monitoring using meteorological indices in a semi-arid region. Energy Procedia 119: 518-529. doi: 10.1016/j.egypro.2017.07.064
![]() |
[45] |
Shah R, Bharadiya N, Manekar V (2015) Drought index computation using Standardized Precipitation Index (SPI) method for Surat district, Gujarat. J Aquat Proced 4: 1243-1249. doi: 10.1016/j.aqpro.2015.02.162
![]() |
[46] |
Thilakarathne M, Sridhar V (2017) Characterization of future drought conditions in the Lower Mekong River Basin. Weather Climate Extremes 17: 47-58. doi: 10.1016/j.wace.2017.07.004
![]() |
[47] |
Khatiwada KR, Pandey VP (2019) Characterization of hydro-meteorological drought in Nepal Himalaya: A case of Karnali River Basin. Weather Climate Extremes 26: 100239. doi: 10.1016/j.wace.2019.100239
![]() |
[48] |
Abdelmalek MB, Nouiri I (2020) Study of trends and mapping of drought events in Tunisia and their impacts on agricultural production. Sci Total Environ 734: 139311. doi: 10.1016/j.scitotenv.2020.139311
![]() |
[49] | Almedeij J, (2014) Drought analysis for kuwait using standardized precipitation index. The Sc World J. 2014 |
[50] | NMSA, (1996) Climatic and agro climatic resources of Ethiopia. National Meteorology Service Agency of Ethiopia, Addis Ababa. 1: 137. |
[51] |
Temam D, Uddameri V, Mohammadi G, et al. (2019) Long-Term Drought Trends in Ethiopia with Implications for Dryland Agriculture. MDPI-Water 11: 2571. doi: 10.3390/w11122571
![]() |
[52] |
Cermak V, Bodri L, Safanda J, et al. (2019) Variability trends in the daily air temperatures series Running head: Variability trends prague. AIMS Environ Sci 6: 167-185. doi: 10.3934/environsci.2019.3.167
![]() |