博碩士論文 105690602 詳細資訊



以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：31 、訪客IP：3.145.8.141
姓名 葛太丘(Getachew Mehabie Mulualem)  查詢紙本館藏   畢業系所 國際研究生博士學位學程
論文名稱 利用遙感技術監測衣索比亞乾旱的時空變化
(Spatiotemporal Variability of Droughts in Ethiopia)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2024-12-31以後開放)
摘要(中) 最近在衣索比亞幾個地區發生的乾旱與大氣和海洋環流模式的變化有關。全球暖化的空前影響加劇了乾旱的發生。了解這些大規模現象與植被生產力息息相關在衣索比亞是很重要的。有關乾旱的時空分佈及其趨勢的知識對於風險管理和制定緩解的策略至關重要。 本研究應用數種技術與資料庫來分析研究從2001年至2018年植被對氣候變化的時空變化。本研究應用中解析度成像光譜 (MODIS)、地/水常態化差異植被指數(NDVI)、地表溫度(LST)、氣候災害小組的紅外光與地面測站(CHIRPS)整合的日降水，以及飢荒預警系統網絡(FEWS NET)的陸地資料同化系統(FLDAS)的土壤水分數據資料庫，並且使用以像素為基礎的曼肯德爾(Mann-Kendall)趨勢分析與植被狀況指數(VCI)來估計作物季節的乾旱模式。
此外，我們開發了七個人工神經網絡(ANN)預測模型，這些模型結合了水文氣象、氣候、海表溫度和地形屬性，以預測1986年至2015年衣索比亞上藍尼羅河流域（UBN）七個站的標準降水蒸發散指數（SPEI）；主要目的是分析引發乾旱的輸入參數的敏感性，並將預測值與觀測值進行比較來衡量其預測能力。結果指出，衣索比亞的中部高地和西北部的土地被農田覆蓋，但降水量和常態化差異植被指數卻下降了。約52.8％的像素顯示降水減少的趨勢，其中明顯的減少趨勢集中在中部和低陸地區。另外，在衣索比亞西北部地區，有41.67％的像素顯示出常態化差異植被指數下降的趨勢。 根據趨勢測試和植被狀況指數分析，在2009年和2015年的聖嬰現象期間，全國發生了嚴重的乾旱。此外，相關係數分析證明，常態化差異植被指數低主要與有限的降水和土壤中水的有效利用有關。
本研究提供了寶貴的資訊，可用於確定可能引起乾旱的地點，並立即規劃救濟之措施。這項研究提出了首次嘗試使用最近開發的常態化差異潛熱指數（NDLI）來監測乾旱狀況的結果，其結果顯示常態化差異潛熱指數與態化差異植被指數（NDVI）(r=0.96)、降水（r = 0.81）、土壤水分（r = 0.73）和地表溫度LST（r = -0.67）有高度相關。常態化差異潛熱指數(NDLI)成功地捕獲了歷史乾旱，並且與氣候變量有著顯著的相關性。分析顯示，利用綠色、紅色和SWIR的光譜，可以開發出具有適度準確性的簡化作物監測模型。
從不同的人工神經網絡模型的統計比較顯示，在預測標準降水蒸發散指數值時可以得到準確的結果，而SPEI值可以通過包含大規模氣候指數來實現。發現最佳模式的決定係數和均方根誤差範圍分別為0.820至0.949及0.263至0.428。本研究所使用的人工神經網絡提供了一個預測標準降水蒸發散指數乾旱指數的替代框架。
關鍵詞：乾旱； 常態化差異植被指數； 植被狀況指數； 時間序列分析； 人工神經網絡; 乾旱預測； 標準化降水蒸發散指數
摘要(英) The recent droughts that occurred in different parts of Ethiopia have been generally linked to changes in patterns in atmospheric and ocean circulation. The occurrence of drought has intensified with the unprecedented impacts of climate change. Understanding these large-scale phenomena that play a crucial role in vegetation productivity in Ethiopia is essential. Knowledge about the spatiotemporal distribution of droughts and trends is vital for risk management and developing adaptation and mitigation strategies.
In this study, several techniques and datasets were analyzed to study the Spatio-temporal variability of vegetation in response to a changing climate. Eighteen years (2001-2018) of Moderate Resolution Imaging Spectroscopy (MODIS) Terra/Aqua Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) daily precipitation, and the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) soil moisture datasets were processed. Pixel-based Mann-Kendall trend analysis and Vegetation Condition Index (VCI) was also used to assess drought patterns during the cropping season.
Moreover, we have developed seven Artificial Neural Network (ANN) predictive models that incorporate hydro-meteorological, climate, sea surface temperatures and topographic attributes to forecast the Standardized Precipitation Evapotranspiration Index (SPEI) for seven stations in the Upper Blue Nile basin (UBN) of Ethiopia, from 1986 to 2015. The main aim was to analyze the sensitivity of input parameters that trigger droughts and measure their predictive ability by comparing the predicted with the observed values.
The results indicate that the central highlands and northwestern areas of Ethiopia, which have land cover dominated by cropland, had experienced decreasing precipitation and NDVI. About 52.8 % of the pixels showed a decreasing precipitation trend, of which the significant decreasing trends focused on the central and low land areas. Also, 41.67% of the pixels showed a decrease in NDVI, especially in the northwestern region of Ethiopia. Based on the trend test and VCI analysis, significant countrywide droughts occurred during the 2009 and 2015 El Niño years. Further, correlation coefficient analysis shows that the low NDVI was mainly related to the limited precipitation and reduced water availability in the soils.
This study provides valuable information in identifying the locations with the potential concern of drought and planning for immediate action of relief measures. This study presents the results of the first attempt to apply a recently developed index, Normalized Difference Latent Heat Index (NDLI), to monitor drought conditions. The results showed that NDLI was highly correlated with NDVI (r=0.96), precipitation (r=0.81), Soil moisture (r=0.73) and LST ( r= -0.67). NDLI successfully captured historical droughts and has a notable correlation with the climatic variables. The analysis showed that using the Green, Red, and SWIR, a simplified crop monitoring model with satisfying accuracy and easiness can be developed.
The statistical comparisons of the different ANN models showed accurate results in predicting SPEI values that can be achieved by including large-scale climate indices. It was found that the coefficient of determination and root-mean-square error of the best-fit models ranged from 0.820-0.949, 0.263-0.428, respectively. The ANN models used here offer an alternative framework for forecasting the SPEI drought index.
關鍵字(中) ★ 常態化差異植被指數
★ 植被狀況指數
★ 時間序列分析
★ 人工神經網絡
★ 乾旱預測
★ 標準化降水蒸發散指數		 關鍵字(英) ★ Drought
★ Normalized Difference Vegetation Index
★ Standardized Precipitation Evapotranspiration Index
★ Artificial Neural Network
★ Vegetation Condition Index
★ time series analysis
論文目次 Chapter 1: Introduction 1
1.1 Motivation: Droughts in Ethiopia 1
1.2 Research objectives 4
1.3 Dissertation outline 5
Chapter 2: Theoretical background 6
2.1 Drought 6
2.2 Types of drought 7
2.3 Impacts of droughts 9
2.4 Global Drivers 11
2.4.1 Intertropical Convergence Zone (ITCZ) 12
2.4.2 El Niño-Southern Oscillation (ENSO) 13
2.4.3 Indian Ocean Dipole (IOD) 18
2.5 The role of remote sensing for drought monitoring 20
2.6 Drought Indices 21
2.6.1 Metrological drought indices 22
2.6.2 Agricultural drought indices 23
2.6.3 Hydrological drought indices 24
2.6.4 Remote sensing-based drought indices 25
2.7 Drought forecasting 28
Chapter 3: Study area and data used 31
3.1 Study area 31
3.1.1 Climate 34
3.1.2 Land cover 39
3.2 Data sets 39
3.2.1 Meteorological data 39
3.2.2 Remote sensing data 40
3.2.3 Climate indices 41
Chapter 4: Methodology 43
4.1 Derivation of drought indices 43
4.1.1 Standardized Precipitation Index (SPI) 43
4.1.2 Standardized Precipitation Evapotranspiration Index (SPEI) 47
4.1.3 Vegetation Condition Index (VCI) 50
4.2 Trend analysis 52
4.2.1 Mann - Kendall trend analysis 52
4.3 Multiple- linear regression 54
4.4 Artificial Neural Networks (ANNs) 55
4.4.1 Structure of Artificial Neural Networks (ANNs) 55
4.4.2 ANN model development 57
4.4.3 Statistical performance measures 59
Chapter 5: Spatiotemporal assessment of drought 61
5.1 Drought occurrence 61
5.2 Drought episodes 69
5.3 Teleconnection over Ethiopia 75
5.4 Spatial and temporal trends 80
5.5 Standardized Anomaly Index 84
5.6 Vegetation based drought analysis 86
5.7 Multi-linear regression and correlation statistics 88
Chapter 6: ANN forecasting model 93
6.1 Upper Blue Nile Basin (UBN) 93
6.2 Data preparation 96
6.3 Cross-correlation between predictor variables with SPEI 99
6.4 SPEI forecasts 101
6.4.1 Comparison of different models 107
Chapter 7: Conclusions and future works 109
7.1 Summary and conclusions 109
7.1.1 Evaluation of existing drought indices 109
7.1.2 Development of ANN forecasting model 110
7.2 Recommendations for future research 111
REFERENCES 112
參考文獻 1. Shiferaw, B.; Prasanna, B.M.; Hellin, J.; Bänziger, M. Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur. 2011, 3, 307.
2. Tschakert, P. Views from the vulnerable: understanding climatic and other stressors in the Sahel. Glob. Environ. Chang. 2007, 17, 381–396.
3. Gommes, R.A.; Petrassi, F. Rainfall variability and drought in Sub-Saharan Africa since 1960 - Google Books. 1994.
4. Ayalew, D.; Tesfaye, K.; Mamo, G.; Yitaferu, B.; Bayu, W. Variability of rainfall and its current trend in Amhara region, Ethiopia. African J. Agric. Res. 2012, 7, 1475–1486, doi:10.5897/AJAR11.698.
5. Haile, T. Causes and Characteristics of Drought in Ethiopia. Ethiop. J. Agric. Sci. 1988.
6. Simane, B.; Beyene, H.; Deressa, W.; Kumie, A.; Berhane, K.; Samet, J. Review of climate change and health in Ethiopia: Status and gap analysis. Ethiop. J. Heal. Dev. 2016, 30, 28–41.
7. Tsegay Wolde-Georgis. El Niño and Drought Early Warning in Ethiopia. Int. J. African Stud. 1997, 10.
8. Goyol, K.B.; Girma, A.A. One WASH national program (OWNP) Ethiopia: a SWAp with a comprehensive management structure. In Proceedings of the Loughborough University; Loughborough University, 2015.
9. Schmidt, W.; Peter Uhe, A.; Kimutai, J.; Otto, F.; Cullen, H. Climate and Development Knowledge Network and World Weather Attribution Initiative Raising Risk Awareness. R. Netherlands Meteorol. Inst. 2017, 2016–2017.
10. Barrientos, M.; Soria, C. IndexMundi - Country Facts Available online: https://www.indexmundi.com/ (accessed on May 15, 2020).
11. Kumar, B.G. Ethiopian famines 1973–1985: A case-study. Polit. Econ. Hunger 1990, 2, 173–216.
12. Gore, T.; Hillier, D. Climate Change and Future Impacts on Food Security. Oxfam Policy Pract. Agric. Food L. 2011, 11, 57–62.
13. Yusuf, A.A.; Francisco, H. Climate Change Vulnerability Mapping for Southeast Asia Vulnerability Mapping for Southeast Asia. East 2009, 181, 1–19, doi:10.1158/1541-7786.MCR-07-0267.
14. IPCC(AR4) Available online: http://www.ipcc.ch/publications_and_data/ar4/syr/en/contents.html (accessed on Jul 7, 2017).
15. Philip, S.; Kew, S.F.; Jan van Oldenborgh, G.; Otto, F.; O’Keefe, S.; Haustein, K.; King, A.; Zegeye, A.; Eshetu, Z.; Hailemariam, K.; et al. Attribution Analysis of the Ethiopian Drought of 2015. J. Clim. 2018, 31, 2465–2486, doi:10.1175/JCLI-D-17-0274.1.
16. USAID El niño in ethiopia, A Real-Time Review of Impacts and Responses 2015-2016; 2016;
17. Camberlin, P. Rainfall anomalies in the source region of the Nile and their connection with the Indian summer monsoon. J. Clim. 1997, 10, 1380–1392.
18. Korecha, D.; Sorteberg, A. Validation of operational seasonal rainfall forecast in Ethiopia. Water Resour. Res. 2013, 49, 7681–7697.
19. Author, E.; Keller, E.J. Drought, War, and the Politics of Famine in; 1992; Vol. 30;.
20. Gebrehiwot, T.; van der Veen, A.; Maathuis, B. Spatial and temporal assessment of drought in the Northern highlands of Ethiopia. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 309–321, doi:10.1016/j.jag.2010.12.002.
21. Wolde-Georgis, T. El Niño and Drought Early Warning in Ethiopia. Internet J. African Stud. 1997, 2, 10.
22. Webb, P.; Braun, J. von Famine and food security in Ethiopia: lessons for Africa.; John Wiley & Sons Ltd, 1994; ISBN 0471948217.
23. Nicholls, N. What are the potential contributions of El Niño-Southern Oscillation Research to Early Warning of Potential Acute Food-deficit Situations? Internet J. African Stud. 1997.
24. Quinn, W.H.; Neal, V.T.; De Mayolo, S.E.A. El Niño occurrences over the past four and a half centuries. J. Geophys. Res. Ocean. 1987, 92, 14449–14461.
25. Mohammed, Y.; Yimer, F.; Tadesse, M.; Tesfaye, K. Meteorological drought assessment in north east highlands of Ethiopia. Int. J. Clim. Chang. Strateg. Manag. 2018.
26. Liou, Y.-A.; Mulualem, G.M. Spatio–temporal Assessment of Drought in Ethiopia and the Impact of Recent Intense Droughts. Remote Sens. 2019, 11, 1828, doi:10.3390/rs11151828.
27. Mishra, S.S.; Nagarajan, R. Forecasting drought in Tel River Basin using feedforward recursive neural network. Int. Conf. Environ. Biomed. Biotechnol. 2012, 41, 122–126.
28. Wilhite, D.A.; Glantz, M.H. Understanding: the Drought Phenomenon: The Role of Definitions. Water Int. 1985, 10, 111–120, doi:10.1080/02508068508686328.
29. Cheng, C.-H.; Nnadi, F.; Liou, Y.-A. Energy Budget on Various Land Use Areas Using Reanalysis Data in Florida. Adv. Meteorol. 2014, 2014, 1–13, doi:10.1155/2014/232457.
30. Dorjsuren, M.; Liou, Y.-A.; Cheng, C.-H. Time Series MODIS and in Situ Data Analysis for Mongolia Drought. Remote Sens. 2016, 8, 509, doi:10.3390/rs8060509.
31. Hao, Z.; Singh, V.P.; Xia, Y. Seasonal Drought Prediction: Advances, Challenges, and Future Prospects. Rev. Geophys. 2018, 56, 108–141, doi:10.1002/2016RG000549.
32. Hayes, M.J.; Svoboda, M.D.; Wardlow, B.D.; Anderson, M.C.; Kogan, F. Drought monitoring: Historical and current perspectives. In Remote Sensing of Drought: Innovative Monitoring Approaches; 2012; pp. 1–19 ISBN 9781439835609.
33. Du, L.; Song, N.; Liu, K.; Hou, J.; Hu, Y.; Zhu, Y.; Wang, X.; Wang, L.; Guo, Y. Comparison of Two Simulation Methods of the Temperature Vegetation Dryness Index (TVDI) for Drought Monitoring in Semi-Arid Regions of China. Remote Sens. 2017, 9, 177, doi:10.3390/rs9020177.
34. Adnan, S.; Ullah, K.; Shuanglin, L.; Gao, S.; Khan, A.H.; Mahmood, R. Comparison of various drought indices to monitor drought status in Pakistan. Clim. Dyn. 2018, 51, 1885–1899, doi:10.1007/s00382-017-3987-0.
35. Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Chang. 2013, 3, 52–58, doi:10.1038/nclimate1633.
36. Sheffield, J.; Wood, E.F. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim. Dyn. 2008, 31, 79–105, doi:10.1007/s00382-007-0340-z.
37. Carrão, H.; Russo, S.; Sepulcre-Canto, G.; Barbosa, P. An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data. Int. J. Appl. Earth Obs. Geoinf. 2016, 48, 74–84, doi:10.1016/j.jag.2015.06.011.
38. Mishra, A.K.; Singh, V.P. A review of drought concepts. J. Hydrol. 2010, 391, 202–216.
39. Few, R. Health and climatic hazards: framing social research on vulnerability, response and adaptation. Glob. Environ. Chang. 2007, 17, 281–295.
40. Yanda, P.Z.; Mubaya, C.P. Managing a changing climate in Africa: Local level vulnerabilities and adaptation experiences; African Books Collective, 2011; ISBN 9987080898.
41. XIE, S.; Philander, S.G.H. A coupled ocean‐atmosphere model of relevance to the ITCZ in the eastern Pacific. Tellus A 1994, 46, 340–350.
42. Robinson, P.J.; Henderson-Sellers, A. Contemporary climatology; Routledge, 2014; ISBN 131788955X.
43. Glantz, M.H.; Katz, R.W.; Nicholls, N. Teleconnections linking worldwide climate anomalies; Cambridge University Press Cambridge, 1991; Vol. 535;.
44. Glantz, M.H. Currents of change: impacts of El Niño and La Niña on climate and society; Cambridge University Press, 2001; ISBN 052178672X.
45. Hoerling, M.P.; Kumar, A.; Zhong, M. El Niño, La Niña, and the nonlinearity of their teleconnections. J. Clim. 1997, 10, 1769–1786.
46. Wolter, K.; Timlin, M.S. El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). Int. J. Climatol. 2011, 31, 1074–1087, doi:10.1002/joc.2336.
47. Mazzarella, A.; Giuliacci, A.; Scafetta, N. Quantifying the Multivariate ENSO Index (MEI) coupling to CO2 concentration and to the length of day variations. Theor. Appl. Climatol. 2013, 111, 601–607, doi:10.1007/s00704-012-0696-9.
48. Golden Gate Weather Services El Niño and La Niña Years and Intensities Available online: https://ggweather.com/enso/oni.htm (accessed on Jun 9, 2020).
49. Ashok, K.; Guan, Z.; Yamagata, T. A look at the relationship between the ENSO and the Indian Ocean dipole. J. Meteorol. Soc. Japan. Ser. II 2003, 81, 41–56.
50. Black, E. The relationship between Indian Ocean sea–surface temperature and East African rainfall. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2005, 363, 43–47.
51. Conway, D.; Hanson, C.E.; Doherty, R.; Persechino, A. GCM simulations of the Indian Ocean dipole influence on East African rainfall: Present and future. Geophys. Res. Lett. 2007, 34, L03705, doi:10.1029/2006GL027597.
52. Behera, S.K.; Luo, J.-J.; Masson, S.; Delecluse, P.; Gualdi, S.; Navarra, A.; Yamagata, T. Paramount impact of the Indian Ocean dipole on the East African short rains: A CGCM study. J. Clim. 2005, 18, 4514–4530.
53. Saji, N.H.; Goswami, B.N.; Vinayachandran, P.N.; Yamagata, T. A dipole mode in the tropical Indian Ocean. Nature 1999, 401, 360–363.
54. Kogan, F. Global drought detection and impact assessment from space. Drought a Glob. Assess. 2000.
55. Kuri, F.; Murwira, A.; Murwira, K.S.; Masocha, M. Predicting maize yield in Zimbabwe using dry dekads derived from remotely sensed Vegetation Condition Index. Int. J. Appl. Earth Obs. Geoinf. 2014, 33, 39–46.
56. Jiao, W.; Zhang, L.; Chang, Q.; Fu, D.; Cen, Y.; Tong, Q. Evaluating an enhanced vegetation condition index (VCI) based on VIUPD for drought monitoring in the continental United States. Remote Sens. 2016, 8, 224.
57. Townshend, J.R.G.; Justice, C.O. Analysis of the dynamics of African vegetation using the normalized difference vegetation index. Int. J. Remote Sens. 1986, 7, 1435–1445.
58. Kogan, F.N. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull. Am. Meteorol. Soc. 1995, 76, 655–668.
59. Tadesse, T.; Demisse, G.B.; Zaitchik, B.; Dinku, T. Satellite‐based hybrid drought monitoring tool for prediction of vegetation condition in Eastern Africa: A case study for Ethiopia. Water Resour. Res. 2014, 50, 2176–2190.
60. Dastorani, M.T.; Massah Bavani, A.R.; Poormohammadi, S.; Rahimian D A Associate, M.H. Assessment of potential climate change impacts on drought indicators (Case study: Yazd station, Central Iran). DESERT 2011, 16, 159–167.
61. Cheng, C.-H.; Nnadi, F.; Liou, Y.-A. A regional land use drought index for Florida. Remote Sens. 2015, 7, 17149–17167, doi:10.3390/rs71215879.
62. Ntale, H.K.; Gan, T.Y. Drought indices and their application to East Africa. Int. J. Climatol. 2003, 23, 1335–1357.
63. Mckee, T.B.; Doesken, N.J.; Kleist, J. The relationship of drought frequency and duration to time scales. Eighth Conf. Appl. Climatol. 17-22 January 1993, Anaheim, Calif. 1993, 179, 17–22.
64. Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 2010, 23, 1696–1718, doi:10.1175/2009JCLI2909.1.
65. Rouse, J. W., R. H. Haas, J. A. Schell, and D.W.D. Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium. Nasa Sp-351 I, 1973, 309–317.
66. Gebrehiwot, T.; Van der Veen, A.; Maathuis, B. Governing agricultural drought: Monitoring using the vegetation condition index. Ethiop. J. Environ. Stud. Manag. 2016, 9, 354, doi:10.4314/ejesm.v9i3.9.
67. Liu, W.T.; Kogan, F.N. Monitoring regional drought using the Vegetation Condition Index. Int. J. Remote Sens. 1996, 17, 2761–2782, doi:10.1080/01431169608949106.
68. Nagarajan, R. Drought assessment.; Springer, 2014; ISBN 9789400789920.
69. Winkler, K.; Gessner, U.; Hochschild, V. Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO. Remote Sens. 2017, 9, 831, doi:10.3390/rs9080831.
70. Van Rooy, M.P. A RAINFALL ANOMALLY INDEX INDEPENDENT OF TIME AND SPACE, NOTOS. 1965.
71. Palmer, W.C. Meteorological drought. U.S. Weather Bur. Res. Pap. 1965, 45.
72. Byun, H.-R.; Wilhite, D.A. Objective quantification of drought severity and duration. J. Clim. 1999, 12, 2747–2756.
73. Tsakiris, G.; Pangalou, D.; Vangelis, H. Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resour. Manag. 2007, 21, 821–833, doi:10.1007/s11269-006-9105-4.
74. Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I.; Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. J. Clim. 2010, 23, 1696–1718, doi:10.1175/2009JCLI2909.1.
75. Narasimhan, B.; Srinivasan, R. Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. In Proceedings of the Agricultural and Forest Meteorology; Elsevier, 2005; Vol. 133, pp. 69–88.
76. Palmer, W.C. Meteorological drought; US Department of Commerce, Weather Bureau, 1965; Vol. 30;.
77. Palmer, W.C. Keeping Track of Crop Moisture Conditions, Nationwide: The New Crop Moisture Index. Weatherwise 1968, 21, 156–161, doi:10.1080/00431672.1968.9932814.
78. Garen, D.C. Revised Surface‐Water Supply Index for Western United States. J. Water Resour. Plan. Manag. 1993, 119, 437–454, doi:10.1061/(ASCE)0733-9496(1993)119:4(437).
79. Nalbantis, I.; Tsakiris, G. Assessment of hydrological drought revisited. Water Resour. Manag. 2009, 23, 881–897, doi:10.1007/s11269-008-9305-1.
80. KOGAN, F.N. Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int. J. Remote Sens. 1990, 11, 1405–1419, doi:10.1080/01431169008955102.
81. Parameters | MCST Available online: https://mcst.gsfc.nasa.gov/calibration/parameters (accessed on Jun 9, 2020).
82. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ. 1974, 351, 309.
83. Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. 1978.
84. ZHANG, F.; ZHANG, L.; WANG, X.; HUNG, J. Detecting Agro-Droughts in Southwest of China Using MODIS Satellite Data. J. Integr. Agric. 2013, 12, 159–168, doi:10.1016/S2095-3119(13)60216-6.
85. Liu, H.Q.; Huete, A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens. 1995, 33, 457–465.
86. Gitelson, A.A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 2004, 161, 165–173.
87. Huete, A.R.; Liu, H.Q.; Batchily, K. V; Van Leeuwen, W. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 1997, 59, 440–451.
88. Zhang, Q.; Liu, L.; Wei, X. Improved algorithm for image encryption based on DNA encoding and multi-chaotic maps. AEU - Int. J. Electron. Commun. 2014, 68, 186–192, doi:10.1016/j.aeue.2013.08.007.
89. Gao, B.C. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266, doi:10.1016/S0034-4257(96)00067-3.
90. World Droughts in the New Millennium from AVHRR-based Vegetation Health Indices Principles of a New Algorithm. Eos EOS Trans. 2002, 83.
91. S. Pulwarty, R.; Sivakumar, M.V.K. Information systems in a changing climate: Early warnings and drought risk management. Weather Clim. Extrem. 2014, 3, 14–21, doi:10.1016/J.WACE.2014.03.005.
92. Khashei, M.; Bijari, M. An artificial neural network (p, d, q) model for timeseries forecasting. Expert Syst. Appl. 2009, 37, 479–489, doi:10.1016/j.eswa.2009.05.044.
93. Hao, Z.; Singh, V.P.; Xia, Y. Seasonal Drought Prediction: Advances, Challenges, and Future Prospects. Rev. Geophys. 2018, 56, 108–141, doi:10.1002/2016RG000549.
94. Barua, S.; Ng, A.W.M.; Perera, B.J.C. Artificial Neural Network–Based Drought Forecasting Using a Nonlinear Aggregated Drought Index. J. Hydrol. Eng. 2012, 17, 1408–1413, doi:10.1061/(ASCE)HE.1943-5584.0000574.
95. Mislan; Haviluddin; Hardwinarto, S.; Sumaryono; Aipassa, M. Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia. Procedia Comput. Sci. 2015, 59, 142–151, doi:10.1016/J.PROCS.2015.07.528.
96. Morid, S.; Smakhtin, V.; Bagherzadeh, K. Drought forecasting using artificial neural networks and time series of drought indices. Int. J. Climatol. Int. J. Clim. 2007, 27, 2103–2111, doi:10.1002/joc.1498.
97. Wu, X.; Hongxing, C.; Flitma, A. Forecasting monsoon precipitation using artificial neural networks. Adv. Atmos. Sci. 2011, 14, 123–123, doi:10.1007/s00376-997-0014-0.
98. Liou, Y.A.; Liu, S.F.; Wang, W.J. Retrieving soil moisture from simulated brightness temperatures by a neural network. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1662–1672, doi:10.1109/36.942544.
99. Masinde, M. Artificial neural networks models for predicting effective drought index: Factoring effects of rainfall variability. Mitig. Adapt. Strateg. Glob. Chang. 2014, 19, 1139–1162, doi:10.1007/s11027-013-9464-0.
100. Belayneh, A.; Adamowski, J.; Khalil, B. Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustain. Water Resour. Manag. 2016, 2, 87–101, doi:10.1007/s40899-015-0040-5.
101. Deo, R.C.; Şahin, M. Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia. Atmos. Res. 2015, 161–162, 65–81, doi:10.1016/j.atmosres.2015.03.018.
102. Le, M.H.; Perez, G.C.; Solomatine, D.; Nguyen, L.B. Meteorological Drought Forecasting Based on Climate Signals Using Artificial Neural Network – A Case Study in Khanhhoa Province Vietnam. Procedia Eng. 2016, 154, 1169–1175, doi:10.1016/J.PROENG.2016.07.528.
103. Schubert, S.D.; Stewart, R.E.; Wang, H.; Barlow, M.; Berbery, E.H.; Cai, W.; Hoerling, M.P.; Kanikicharla, K.K.; Koster, R.D.; Lyon, B.; et al. Global Meteorological Drought: A Synthesis of Current Understanding with a Focus on SST Drivers of Precipitation Deficits. J. Clim. 2016, 29, 3989–4019, doi:10.1175/JCLI-D-15-0452.1.
104. Roundy, J.K.; Wood, E.F.; Roundy, J.K.; Wood, E.F. The Attribution of Land–Atmosphere Interactions on the Seasonal Predictability of Drought. J. Hydrometeorol. 2015, 16, 793–810, doi:10.1175/JHM-D-14-0121.1.
105. Yang, W.; Seager, R.; Cane, M.A.; Lyon, B.; Yang, W.; Seager, R.; Cane, M.A.; Lyon, B. The East African Long Rains in Observations and Models. J. Clim. 2014, 27, 7185–7202, doi:10.1175/JCLI-D-13-00447.1.
106. Lyon, B. Seasonal Drought in the Greater Horn of Africa and Its Recent Increase during the March–May Long Rains. J. Clim. 2014, 27, 7953–7975, doi:10.1175/JCLI-D-13-00459.1.
107. Hoell, A.; Funk, C.; Hoell, A.; Funk, C. The ENSO-Related West Pacific Sea Surface Temperature Gradient. J. Clim. 2013, 26, 9545–9562, doi:10.1175/JCLI-D-12-00344.1.
108. Ethiopia Population (2020) - Worldometer Available online: https://www.worldometers.info/world-population/ethiopia-population/ (accessed on Jun 10, 2020).
109. Worqlul, A.W.; Jeong, J.; Dile, Y.T.; Osorio, J.; Schmitter, P.; Gerik, T.; Srinivasan, R.; Clark, N. Assessing potential land suitable for surface irrigation using groundwater in Ethiopia. Appl. Geogr. 2017, 85, 1–13, doi:10.1016/j.apgeog.2017.05.010.
110. Devereux, S. Food insecurity in Ethiopia: A discussion paper for DFID (Department for International Development). IDS (Institute Dev. Stud. Sussex, UK 2000.
111. Billi, P. Geomorphological landscapes of Ethiopia. In Landscapes and landforms of Ethiopia; Springer, 2015; pp. 3–32.
112. Awulachew, S.; Yilma, A.; Loulseged, M.; Willibad, L.; Ayana, M.; Alamirew, T. Water Resources and Irrigation Development in Ethiopia; Working pa.; International Water Management Institute (IWMI): Colombo, Sri Lanka, 2007;
113. Ambelu, B.A. Biological monitoring based on macroinvertebrates for decision support of water management in Ethiopia. Ghent Univ. Ghent 2009.
114. Terefe, T.; Mengistu, G. Spatial and temporal variability of summer rainfall over Ethiopia from observations and a regional climate model experiment climate model experiments. Theor. Appl. Climatol. 2012, 111.
115. Viste, E.; Korecha, D.; Sorteberg, A. Recent drought and precipitation tendencies in Ethiopia. Theor. Appl. Climatol. 2013, 112, 535–551, doi:10.1007/s00704-012-0746-3.
116. Diro, G.T.; Grimes, D.I.F.; Black, E. Teleconnections between Ethiopian summer rainfall and sea surface temperature: part I—observation and modelling. Clim. Dyn. 2011, 37, 103–119.
117. Chen, D.; Chen, H.W. Using the Köppen classification to quantify climate variation and change: An example for 1901-2010. Environ. Dev. 2013, 6, 69–79, doi:10.1016/j.envdev.2013.03.007.
118. Gonfa, L. Climate classifications of Ethiopia; National Meteorological Services Agency, 1996;
119. Birhane, E.; Ashfare, H.; Fenta, A.A.; Hishe, H.; Gebremedhin, M.A.; Solomon, N. Land use land cover changes along topographic gradients in Hugumburda national forest priority area, Northern Ethiopia. Remote Sens. Appl. Soc. Environ. 2019, 13, 61–68.
120. Liou, Y.-A.; Le, M.S.; Chien, H. Normalized Difference Latent Heat Index for Remote Sensing of Land Surface Energy Fluxes. IEEE Trans. Geosci. Remote Sens. 2019, 57, 1423–1433, doi:10.1109/TGRS.2018.2866555.
121. Larbi, I.; Hountondji, F.; Annor, T.; Agyare, W.; Mwangi Gathenya, J.; Amuzu, J.; Larbi, I.; Hountondji, F.C.C.; Annor, T.; Agyare, W.A.; et al. Spatio-Temporal Trend Analysis of Rainfall and Temperature Extremes in the Vea Catchment, Ghana. Climate 2018, 6, 87, doi:10.3390/cli6040087.
122. Muthoni, F.K.; Odongo, V.O.; Ochieng, J.; Mugalavai, E.M.; Mourice, S.K.; Hoesche-Zeledon, I.; Mwila, M.; Bekunda, M. Long-term spatial-temporal trends and variability of rainfall over Eastern and Southern Africa. Theor. Appl. Climatol. 2018, 1–14, doi:10.1007/s00704-018-2712-1.
123. McNally, A.; Arsenault, K.; Kumar, S.; Shukla, S.; Peterson, P.; Wang, S.; Funk, C.; Peters-Lidard, C.D.; Verdin, J.P. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci. data 2017, 4, 170012.
124. Saji, N.H.; Goswami, B.N.; Vinayachandran, P.N.; Yamagata, T. A dipole mode in the tropical Indian Ocean. Nature 1999, 401, 360–363, doi:10.1038/43854.
125. Qin, Y.; Yang, D.; Lei, H.; Xu, K.; Xu, X. Comparative analysis of drought based on precipitation and soil moisture indices in Haihe basin of North China during the period of 1960–2010. J. Hydrol. 2015, 526, 55–67.
126. Gallo, K.; Ji, L.; Reed, B.; Dwyer, J.; Eidenshink, J. Comparison of MODIS and AVHRR 16-day normalized difference vegetation index composite data. Geophys. Res. Lett. 2004, 31, n/a-n/a, doi:10.1029/2003GL019385.
127. Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I.; Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. J. Clim. 2010, 23, 1696–1718, doi:10.1175/2009JCLI2909.1.
128. Yu, C.; Li, C.; Xin, Q.; Chen, H.; Zhang, J.; Zhang, F.; Li, X.; Clinton, N.; Huang, X.; Yue, Y.; et al. Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions. Environ. Model. Softw. 2014, 62, 454–464, doi:10.1016/J.ENVSOFT.2014.08.004.
129. Meza, F.J. Recent trends and ENSO influence on droughts in Northern Chile: An application of the Standardized Precipitation Evapotranspiration Index. Weather Clim. Extrem. 2013, 1, 51–58, doi:10.1016/J.WACE.2013.07.002.
130. Thornthwaite, C.W. An Approach toward a Rational Classification of Climate. Geogr. Rev. 1948, 38, 55, doi:10.2307/210739.
131. Barton, D.E.; Abramovitz, M.; Stegun, I.A. Handbook of Mathematical Functions with Formulas, Graphs and Mathematical Tables. J. R. Stat. Soc. Ser. A 1965, 128, 593, doi:10.2307/2343473.
132. Baniya, B.; Tang, Q.; Xu, X.; Haile, G.G.; Chhipi-Shrestha, G. Spatial and Temporal Variation of Drought Based on Satellite Derived Vegetation Condition Index in Nepal from 1982−2015. Sensors (Basel). 2019, 19, doi:10.3390/s19020430.
133. Measho, S.; Chen, B.; Trisurat, Y.; Pellikka, P.; Guo, L.; Arunyawat, S.; Tuankrua, V.; Ogbazghi, W.; Yemane, T. Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea. Remote Sens. 2019, 11, 724, doi:10.3390/rs11060724.
134. Hamed, K.H.; Ramachandra Rao, A. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 1998, 204, 182–196, doi:10.1016/S0022-1694(97)00125-X.
135. Baniya, B.; Tang, Q.; Xu, X.; Haile, G.G.; Chhipi-Shrestha, G. Spatial and Temporal Variation of Drought Based on Satellite Derived Vegetation Condition Index in Nepal from 1982−2015. Sensors (Basel). 2019, 19, doi:10.3390/s19020430.
136. de Jong, R.; de Bruin, S.; de Wit, A.; Schaepman, M.E.; Dent, D.L. Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sens. Environ. 2011, 115, 692–702, doi:10.1016/J.RSE.2010.10.011.
137. Sobrino, J.A.; Julien, Y. Global trends in NDVI-derived parameters obtained from GIMMS data. Int. J. Remote Sens. 2011, 32, 4267–4279, doi:10.1080/01431161.2010.486414.
138. Julien, Y.; Sobrino, J.A.; Mattar, C.; Ruescas, A.B.; Jiménez-Muñoz, J.C.; Sòria, G.; Hidalgo, V.; Atitar, M.; Franch, B.; Cuenca, J. Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian land cover between 1981 and 2001. Int. J. Remote Sens. 2011, 32, 2057–2068, doi:10.1080/01431161003762363.
139. Sen, P.K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389, doi:10.1080/01621459.1968.10480934.
140. Tian, F.; Wang, Y.; Fensholt, R.; Wang, K.; Zhang, L.; Huang, Y. remote sensing Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau. Remote Sens 2000, 5, 4255–4279, doi:10.3390/rs5094255.
141. Behrangi, A.; Nguyen, H.; Granger, S. Probabilistic Seasonal Prediction of Meteorological Drought Using the Bootstrap and Multivariate Information. J. Appl. Meteorol. Climatol. 2015, 54, 1510–1522, doi:10.1175/JAMC-D-14-0162.1.
142. Seo, Y.; Kim, S. River Stage Forecasting Using Wavelet Packet Decomposition and Data-driven Models. Procedia Eng. 2016, 154, 1225–1230, doi:10.1016/J.PROENG.2016.07.439.
143. Schuman, C.D.; Birdwell, J.D. Dynamic Artificial Neural Networks with Affective Systems. PLoS One 2013, 8, e80455, doi:10.1371/journal.pone.0080455.
144. Poulton, M.M. Chapter 3 Multi-layer perceptrons and back-propagation learning; Pergamon, 2001; Vol. 30; ISBN 9780080439860.
145. Riedmiller, M.; Riedmiller, M.; Braun, H. A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. IEEE Int. Conf. NEURAL NETWORKS 1993, 16, 586--591.
146. Legates, D.R.; McCabe, G.J. Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 1999, 35, 233–241, doi:10.1029/1998WR900018.
147. Nash, J.E.; Sutcliffe, J. V River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 1970, 10, 282–290.
148. Krause, P.; Boyle, D.P.; Bäse, F. Comparison of different efficiency criteria for hydrological model assessment. Adv. Geosci. 2005, 5, 89–97.
149. Willmott, C.J. On the validation of models. Phys. Geogr. 1981, 2, 184–194, doi:10.1080/02723646.1981.10642213.
150. Bayissa, Y.; Tadesse, T.; Demisse, G.; Shiferaw, A.; Bayissa, Y.; Tadesse, T.; Demisse, G.; Shiferaw, A. Evaluation of Satellite-Based Rainfall Estimates and Application to Monitor Meteorological Drought for the Upper Blue Nile Basin, Ethiopia. Remote Sens. 2017, 9, 669, doi:10.3390/rs9070669.
151. Stojanovic, M.; Liberato, M.L.R.; Sorí, R.; Vázquez, M.; Phan-Van, T.; Duongvan, H.; Hoang Cong, T.; Nguyen, P.N.B.; Nieto, R.; Gimeno, L. Trends and extremes of drought episodes in Vietnam sub-regions during 1980–2017 at different timescales. Water 2020, 12, 813.
152. Edossa, D.C.; Babel, M.S.; Gupta, A. Das Drought analysis in the Awash River Basin, Ethiopia. Water Resour. Manag. 2010, 24, 1441–1460, doi:10.1007/s11269-009-9508-0.
153. Bayissa, Y.; Maskey, S.; Tadesse, T.; Van Andel, S.J.; Moges, S.; Van Griensven, A.; Solomatine, D. Comparison of the performance of six drought indices in characterizing historical drought for the upper Blue Nile basin, Ethiopia. Geosciences 2018, 8, 81.
154. Bayissa, Y.; Tadesse, T.; Demisse, G.; Shiferaw, A. Evaluation of Satellite-Based Rainfall Estimates and Application to Monitor Meteorological Drought for the Upper Blue Nile Basin, Ethiopia. Remote Sens. 2017, 9, 669, doi:10.3390/rs9070669.
155. Degefu, M.A.; Rowell, D.P.; Bewket, W. Teleconnections between Ethiopian rainfall variability and global SSTs: observations and methods for model evaluation. Meteorol. Atmos. Phys. 2017, 129, 173–186, doi:10.1007/s00703-016-0466-9.
156. Kassahun, B. Weather systems over Ethiopia. In Proceedings of the Proceedings of First Technical conference on meteorological research in Eastern and Southern Africa. Kenya Meteorological Department, Nairobi, Kenya; 1987; pp. 53–57.
157. Karoly, D.J. Southern hemisphere circulation features associated with El Niño-Southern Oscillation events. J. Clim. 1989, 2, 1239–1252.
158. Alemu, A.; Korecha, D.; Mohamod, M. Impacts of Various ENSO Phases on Cereal Crop Productivity in the Upper Awash Basin, Central High Land of Ethiopia. In Proceedings of the Proceedings of the International Conference on Impact of El Niño on Biodiversity, Agriculture, and Food Security, 23–24 February 2017, Harayama University, Ethiopia; 2017; pp. 3–18.
159. Workie, T.G.; Debella, H.J. Climate change and its effects on vegetation phenology across ecoregions of Ethiopia. Glob. Ecol. Conserv. 2018, 13, e00366, doi:10.1016/J.GECCO.2017.E00366.
160. Ravindrababu, S.; Ratnam, M.; Basha, G.; Liou, Y.-A.; Reddy, N. Large Anomalies in the Tropical Upper Troposphere Lower Stratosphere (UTLS) Trace Gases Observed during the Extreme 2015–16 El Niño Event by Using Satellite Measurements. Remote Sens. 2019, 11, 687, doi:10.3390/rs11060687.
161. Anyamba, A.; Glennie, E.; Small, J.; Anyamba, A.; Glennie, E.; Small, J. Teleconnections and Interannual Transitions as Observed in African Vegetation: 2015–2017. Remote Sens. 2018, 10, 1038, doi:10.3390/rs10071038.
162. Cochrane, L.; Bekele, Y.W. Average crop yield (2001–2017) in Ethiopia: Trends at national, regional and zonal levels. Data Br. 2018, 16, 1025–1033, doi:10.1016/j.dib.2017.12.039.
163. Korecha, D.; Barnston, A.G.; Korecha, D.; Barnston, A.G. Predictability of June–September Rainfall in Ethiopia. Mon. Weather Rev. 2007, 135, 628–650, doi:10.1175/MWR3304.1.
164. Yan, D.; Xu, T.; Girma, A.; Yuan, Z.; Weng, B.; Qin, T.; Do, P.; Yuan, Y.; Yan, D.; Xu, T.; et al. Regional Correlation between Precipitation and Vegetation in the Huang-Huai-Hai River Basin, China. Water 2017, 9, 557, doi:10.3390/w9080557.
165. Zhao, W.; Zhao, X.; Zhou, T.; Wu, D.; Tang, B.; Wei, H. Climatic factors driving vegetation declines in the 2005 and 2010 Amazon droughts. PLoS One 2017, 12, e0175379, doi:10.1371/journal.pone.0175379.
166. Greenland, S.; Senn, S.J.; Rothman, K.J.; Carlin, J.B.; Poole, C.; Goodman, S.N.; Altman, D.G. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur. J. Epidemiol. 2016, 31, 337–50, doi:10.1007/s10654-016-0149-3.
167. Wu, D.; Zhao, X.; Liang, S.; Zhou, T.; Huang, K.; Tang, B.; Zhao, W. Time-lag effects of global vegetation responses to climate change. Glob. Chang. Biol. 2015, 21, 3520–3531, doi:10.1111/gcb.12945.
168. Allam, M.M.; Jain Figueroa, A.; McLaughlin, D.B.; Eltahir, E.A.B. Estimation of evaporation over the upper Blue Nile basin by combining observations from satellites and river flow gauges. Water Resour. Res. 2016, 52, 644–659, doi:10.1002/2015WR017251.
169. Tekleab, S.; Mohamed, Y.; Uhlenbrook, S. Hydro-climatic trends in the Abay/Upper Blue Nile basin, Ethiopia. Phys. Chem. Earth, Parts A/B/C 2013, 61–62, 32–42, doi:10.1016/J.PCE.2013.04.017.
170. Samy, A.; Ibrahim, M.G.; Mahmod, W.E.; Fujii, M.; Eltawil, A.; Daoud, W. Statistical Assessment of Rainfall Characteristics in Upper Blue Nile Basin over the Period from 1953 to 2014. Water 2019, 11, 468, doi:10.3390/w11030468.
171. Broman, D.; Rajagopalan, B.; Hopson, T.; Gebremichael, M. Spatial and temporal variability of East African Kiremt season precipitation and large‐scale teleconnections. Int. J. Climatol. 2020, 40, 1241–1254, doi:10.1002/joc.6268.
172. Giannini, A.; Biasutti, M.; Held, I.M.; Sobel, A.H. A global perspective on African climate. Clim. Change 2008, 90, 359–383.
173. Siam, M.S.; Wang, G.; Demory, M.E.; Eltahir, E.A.B. Role of the Indian Ocean sea surface temperature in shaping the natural variability in the flow of Nile River. Clim. Dyn. 2014, 43, 1011–1023, doi:10.1007/s00382-014-2132-6.
174. Diro, G.T.; Grimes, D.I.F.; Black, E. Teleconnections between Ethiopian summer rainfall and sea surface temperature: Part II. Seasonal forecasting. Clim. Dyn. 2011, 37, 121–131, doi:10.1007/s00382-010-0896-x.
175. Alhamshry, A.; Fenta, A.A.; Yasuda, H.; Shimizu, K.; Kawai, T. Prediction of summer rainfall over the source region of the Blue Nile by using teleconnections based on sea surface temperatures. Theor. Appl. Climatol. 2019, 137, 3077–3087, doi:10.1007/s00704-019-02796-x.
176. Segele, Z.T.; Lamb, P.J.; Leslie, L.M. Seasonal-to-Interannual Variability of Ethiopia/Horn of Africa Monsoon. Part I: Associations of Wavelet-Filtered Large-Scale Atmospheric Circulation and Global Sea Surface Temperature. J. Clim. 2009, 22, 3396–3421, doi:10.1175/2008JCLI2859.1.
177. Berhane, F.; Zaitchik, B.; Dezfuli, A. Subseasonal analysis of precipitation variability in the Blue Nile River Basin. J. Clim. 2014, 27, 325–344, doi:10.1175/JCLI-D-13-00094.1.
178. Gebremicael, T.G.; Mohamed, Y.A.; Betrie, G.D.; van der Zaag, P.; Teferi, E. Trend analysis of runoff and sediment fluxes in the Upper Blue Nile basin: A combined analysis of statistical tests, physically-based models and landuse maps. J. Hydrol. 2013, 482, 57–68, doi:10.1016/j.jhydrol.2012.12.023.
179. Coffel, E.D.; Keith, B.; Lesk, C.; Horton, R.M.; Bower, E.; Lee, J.; Mankin, J.S. Future Hot and Dry Years Worsen Nile Basin Water Scarcity Despite Projected Precipitation Increases. Earth’s Futur. 2019, 7, 967–977, doi:10.1029/2019EF001247.
180. DeSA, U.N. World population prospects: the 2012 revision. Popul. Div. Dep. Econ. Soc. Aff. United Nations Secr. New York 2013, 18.
181. Conway, D. The climate and hydrology of the Upper Blue Nile river. Geogr. J. 2000, 166, 49–62, doi:10.1111/j.1475-4959.2000.tb00006.x.
182. Wagesho, N.; Goel, N.K.; Jain, M.K. Temporal and spatial variability of annual and seasonal rainfall over Ethiopia. Hydrol. Sci. J. 2013, 58, 354–373, doi:10.1080/02626667.2012.754543.
183. Mellander, P.-E.; Gebrehiwot, S.G.; Gärdenäs, A.I.; Bewket, W.; Bishop, K. Summer Rains and Dry Seasons in the Upper Blue Nile Basin: The Predictability of Half a Century of Past and Future Spatiotemporal Patterns. PLoS One 2013, 8, e68461, doi:10.1371/journal.pone.0068461.
184. Günther, F.; Fritsch, S. neuralnet: Training of neural networks. R J. 2010, 2, 30–38.
185. Remesan, R.; Mathew, J. Hydrological data driven modelling: A case study approach; 2015; ISBN 9783319092355.
186. Sheela, K.G.; Deepa, S.N. Review on methods to fix number of hidden neurons in neural networks. Math. Probl. Eng. 2013, 2013, doi:10.1155/2013/425740.
187. Stathakis, D. How many hidden layers and nodes? Int. J. Remote Sens. 2009, 30, 2133–2147, doi:10.1080/01431160802549278.
188. Beck, M.W. NeuralNetTools: Visualization and analysis tools for neural networks. J. Stat. Softw. 2018, 85, 1, doi:10.18637/jss.v085.i11.
189. Garson, G.D. A Comparison of Neural Network and Expert Systems Algorithms with Common Multivariate Procedures for Analysis of Social Science Data. Soc. Sci. Comput. Rev. 1991, 9, 399–434, doi:10.1177/089443939100900304.
指導教授 劉說安博士(Yuei-An Liou) 審核日期 2020-7-20
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題，請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407，或E-mail聯絡  - 隱私權政策聲明