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研究生(外文):Getachew Mehabie Mulualem
論文名稱(外文):Spatiotemporal Variability of Droughts in Ethiopia
指導教授(外文):Yuei-An Liou
外文關鍵詞:DroughtNormalized Difference Vegetation IndexStandardized Precipitation Evapotranspiration IndexArtificial Neural NetworkVegetation Condition Indextime series analysis
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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。
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
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.
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
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