Land-use/land-cover change is a major issue of global environmental change. This study was aimed to investigate LULCC, driving forces, and their implications on climate variability in the case of Kereba Sub-Catchment of Awash Basin, Eastern Ethiopia from 1999 to 2019.
Satellite image data were downloaded from the USGS websites. Gridded temperature and rainfall data were obtained from the NMSA of Ethiopia. Also, FGD, KII, and field observation were used to address drivers of LULCC. Google Earth and Global Positioning System were employed for ground verification. The maximum likelihood supervised classification method was used to classify LU/LC types, NDVI and LST using ERDAS imagine 2015 and ArcGIS 10.3 software.
Coefficient of Variation, Precipitation Concentration Index, and the moving average was used to analyze temperature and rainfall data. Regression analysis/Correlation coefficient was used to signify the association of LULCC and NDVI with climate variables. Landsat image of the study area was classified into agricultural land, forest land, grazing land, settlement, and shrub land.
TABLE OF CONTENTS
DEDICATION
BIOGRAPHICAL SKETCH
ACKNOWLEDGMENTS
ACRONYMS AND ABBREVIATIONS
TABLE OF CONTENTS
LISTS OF TABLES
LISTS OF FIGURES
LIST OF TABLES IN THE APPENDIX
LIST OF FIGURES IN THE APPENDIX
ABSTRACT
1. INTRODUCTION
1.1. Background of the Study
1.2. Statement of the Problem
1.3. Objectives of the Study
1.3.1. General objective
1.3.2. Specific objectives
1.4. Research Questions
1.5. Significance of the Study
1.6. Scope of the Study
1.7. Definition of Key Terms
1.8. Limitations of the Study
1.9. Organization of the Thesis
2. LITERATURE REVIEW
2.1. Concepts of Land Use/Cover Changes and Climate Variability
2.1.1. Concepts of land use/cover changes
2.1.2. Concepts of climate variability
2.2. Driving Forces of Land Use/Land Cover Changes
2.3. Land-Use/Land-Cover Changes in Global Perspectives
2.4. Land-Use/Land-Cover Changes in Ethiopia
2.5. Climate Variability in Global and African Context
2.6. Climate Variability in Ethiopia Context
2.7. Land Surface Temperature
2.8. Normalized Difference Vegetation Index
2.9. Empirical Evidence of LULCC and Climate Variability
2.9.1. Evidence of land-use/cover changes
2.9.2. Evidence of climate variability
2.10. Role of GIS and Remote Sensing in LULCC and Climate Variability
2.10.1. Role of GIS and RS in land-use/land-cove changes
2.10.2. Role of GIS and RS in climate variability assessment
2.11. Implications of LULCC on Climate Variability
2.12. Conceptual Framework
3. RESEARCH METHODOLOGY
3.1. Descriptions of the Study Area
3.1.1. Location and size
3.1.2. Topography and soil
3.1.3. Climate and drainage
3.1.4. Vegetation and wildlife
3.1.5. Population characteristics and socio-economic setting
3.2. Research Design
3.3. Data Types and Sources
3.3.1. Satellite image data and acquisition techniques
3.3.2. Meteorological data
3.4. Sample Size and Sampling Techniques
3.5. Instruments of Data Collection
3.5.1. Key Informant’s Interviews
3.5.2. Focus Group Discussions
3.5.3. Field Observation
3.6. Method of Data Analysis
3.6.1. Remote sensing data analysis procedures
3.6.1.1. Landsat image pre-processing
3.6.1.2. Image enhancement
3.6.1.3. Image classification
3.6.1.4. Classification accuracy assessment
3.6.1.5. Land-use and land-cover thematic layer
3.6.1.6. Land-use/land-cover change detection
3.6.1.7. Computation of NDVI and land surface temperature
3.6.2. Rainfall and temperature variability and trend analysis
3.6.3. Geo-statistical methods
3.6.4. Descriptive statistics
3.7. Ethical Consideration
4. RESULTS AND DISCUSSION
4.1. Land-Use/Land-Cover in 1999, 2009, and 2019 in the Study Area
4.1.1. Land Use Land Cover Class Mapping
4.1.2. Accuracy assessment of LU/LC classification
4.1.3. Rate or magnitude of LU/LC class change
4.1.4. Land-use/cover change detection
4.1.4.1. Trends of LULCC from 1999 to 2009
4.1.4.2. Trends of LULCC from 2009 to 2019
4.1.4.3. Trends of LULCC from 1999 to 2019
4.2. Normalized Difference Vegetation Index Results
4.3. Spatial Pattern of Land Surface Temperature
4.4. The Major Driving Forces of LULCC in Kereba Sub-catchment
4.4.1. Proximate (direct) drivers
4.4.2. Underlying (indirect) drivers
4.4.3. Biophysical driving forces
4.5. Trends of Temperature and Rainfall
4.5.1. Temperature pattern and variability
4.5.1.1. Annual temperature
4.5.1.2. Seasonal temperature pattern and variability
4.5.2. Patterns of rainfall distribution and trends of rainfall variability
4.5.2.1. Patterns of annual rainfall distribution
4.5.2.2. Patterns of seasonal and monthly rainfall
4.6. Association of LULCC and NDVI with Temperature and Rainfall
4.6.1. Association of LU/LC changes with temperature
4.6.2. Association of LU/LC changes and NDVI with rainfall
4.7. Verification of the Result for Land Surface Temperature
4.8. Implications of LULCC on Climate Variability
5. CONCLUSIONS AND RECOMMENDATIONS
5.1. Conclusions
5.2. Recommendations
6. REFERENCES
7. APPENDICES
DEDICATION
This thesis is heartily dedicated to my beloved wife Ashrefa Adem Musa, and my children Hayu and Sebaif for their moral support and encouragement during my work at Haramaya University.
BIOGRAPHICAL SKETCH
The author, Juhar Mohammed Abdula, was born on 12 April 1989 in Bekalcha Biftu Village, Doba District, West Hararghe Zone, and Oromia National Regional State, Ethiopia. He attended primary education at Uta and Sodoma Elementary school, Secondary and preparatory education at Hirna secondary and preparatory school from 1996-2008.
Following the compilation of my preparatory school education, he joined Haramaya University in October 2008 to pursue tertiary education and graduated with the degree of Bachelor of Science (BSc) in Rural Development and Agricultural Extension (RDAE) in July 2010. Upon graduation, he worked for two years as an agricultural development expert in the agricultural office and three years as advisor of communication office of Doba District West Hararghe Zone, Oromia National Regional State, Ethiopia.
Moreover, under the structure of the Oromo Democratic Organization Office of the west Hararghe zone, he worked for three years as advisor of the financial and development office of the zone. After this, he joined Postgraduate Studies of Haramaya University in 2018 to attend an M.A degree in the School of Geography and Environmental Studies specializing in Climate Change and Disaster risk Management.
ACKNOWLEDGMENTS
It is difficult to acknowledge all people and institutions that have supported and encouraged me from the beginning of the class up to the end during this M.A study at Haramaya University, but the following ought to have a special mention.
First and foremost I am greatly indebted to Solomon Tekalign (PhD), my major advisor, for his professional support and due concerns from the start of designing the research proposal up to the thesis write-up. My genuine gratitude also goes to my co-advisor Solomon Asfaw (PhD) for his invaluable support and critical comments on the accomplishment of this thesis. Frankly speaking, without them many improvements in this research work could not have been possible. Besides, I would like to thank the member of the school of Geography and Environmental Studies at Haramaya University for their support and cooperation.
Similarly, the assistance from Mr. Sultan Mohammed was so much that without his support, the GIS-related works could not have completed on time. He devoted much of his invaluable time to help me to finalize this work. Much so, my acknowledgment goes to the Oromia National Regional State for providing the sponsorship and granting a research fund for my research work. Moreover, it would be a mistake not to mention the role of the sample farmers in this study who kindly spared their time and effort and responded tirelessly to the lengthy process of the discussion and interview.
Last but not the least, my great appreciation goes to my beloved wife Ashrefa Adem, my entire family, and all my intimate friends specifically Hilina Meka, Abdurehman Ubeybi, and Amadin Kemal. They have a special place in my mind for their endless moral support and cooperation to accomplish this course of MA study at Haramaya University.
ACRONYMS AND ABBREVIATIONS
Abbildung in dieser Leseprobe nicht enthalten
LISTS OF TABLES
1. Descriptions of Satellite Image and Toposheet Used in Study
2. Detail information of Gridded Meteorological Stations
3. Parameters Used for LU/LC Classification Accuracy Assessment
4. Land-Use Land-Cover Classes and their Description
5. ETM+ and TM Thermal Band Calibration Constants
6. The Metadata of Landsat 8-TIR
7. LU/LC Classes and their Area Coverage in 1999, 2009, and 2019
8. Confusion Matrix for Classification Accuracy Assessment of LULC Map 1999
9. Confusion Matrix for Classification Accuracy Assessment of LULC Map 2009
10. Confusion Matrix for Classification Accuracy Assessment of LULC Map 2019
11. Rate of LU/LC Class Changed in (km[2] and %) from 1999-2019 in the Study Area.
12. Change Detection Matrix for 1999 and 2009
13. Change Detection Matrix for 2009 and 2019
14. Change Detection Matrix for 1999 and 2019
15. Normalized Difference Vegetation Index Results in 1999, 2009, and 2019
16.Variation in the Maximum, Minimum, and Mean Annual Temperature of the Study Area
17. Leaner trend equations of Annual mean Tmin, Tmax, and mean Temperature
18. Normal Mean, Moving Average, and Seasonal Temperature Variations
19. Leaner Equations and Regression Values of Seasonal Rainfall of the Study Area
20. Mean Monthly Rainfall and Rainfall Concentration (P and RC are in mm)
21. Average of LST (oC) and Change Corresponding to Different Land Use Types
LISTS OF FIGURES
1. Conceptual Framework
2. Location Map of the Study Area
3. Digital Elevation Model (Left) and Soil Types (Right) of Kereba sub-catchment
4. Agroecology Map of the Study Area
5.Temperature and Rainfall Distribution of the Study Area records from 1999 to 2019
6. Procedures Used to Analyze LULCC and Their Implications on Climate Variability
7. Temporal LU/LC Changes in 1999, 2009, and 2019
8. LU/LC Map of Kereba Sub-Catchment in 1999
9 . LU/LC Map of Kereba Sub-Catchment in 2009
10. LU/LC Map of Kereba Sub-Catchment in 2019
11. Rate of LU/LC Class Changed in Percentage from 1999 to 2019 in the Study Area
12 . Normalized Difference Vegetation Index map: (a) 1999 (b) 2009, and (c) 2019.
13. Spatial Patterns of LST of Kereba Sub-Catchment in 1999, 2009, and 2019.
14. The total population pressure of Doba District in 2014, 2015, 2016, and 2017.
15. Spatial Pattern of Seasonal Temperature at the Study Area (1999-2018)
16. Patterns and Trend line of Mean Annual Rainfall Distribution (1999-2019 )
17. Percentage of Seasonal Rainfall Distribution of Kereba sub-catchment
18.Patterns of Mean Monthly Rainfall of Kereba sub-catchment (1999-2019)
19: Comparisons of Mean LST in Different LU/LC Classes during the Study Period
20. Rainfall and NDVI Correlation for the Years 1999, 2009, and 2019
21. Interpolated Temperature map of Kereba sub-catchment from 1999-2019
LIST OF TABLES IN THE APPENDIX
1. Annual Rainfall (mm), Annual Mean Tmax (oC), and Tmin (oC) of the Study area
2. Monthly Rainfall (mm), Monthly Tmax (oC), and Tmin (oC) of Kereba sub-catchment
3. Moving Average and Variation in Tmax, Tmin, and Annual Mean Temperature
4. Normal Mean, Moving Average, and Seasonal Temperature Variations
LIST OF FIGURES IN THE APPENDIX
1. Annual Average Maximum, Minimum, and Mean Temperature
2. Spatial Distribution of Annual Rainfall of Kereba sub-catchment (1999-2019)
3. Patterns of Trend Line of Seasonal Rainfall of Kereba Sub-Catchment (1999-2018)
4. Variations of mean LST for different LU/LC types from 1999-2019 over the Study Area
Land Use/Land Cover Changes, Driving Forces and Their Implications on Climate Variability: The Case of Kereba Sub-Catchment of Awash Basin, Eastern Ethiopia
ABSTRACT
Land-use/land-cover change is a major issue of global environmental change. This study was aimed to investigate LULCC, driving forces, and their implications on climate variability in the case of Kereba Sub-Catchment of Awash Basin, Eastern Ethiopia from 1999 to 2019. Satellite image data were downloaded from the USGS websites. Gridded temperature and rainfall data were obtained from the NMSA of Ethiopia. Also, FGD, KII, and field observation were used to address drivers of LULCC. Google Earth and Global Positioning System were employed for ground verification. The maximum likelihood supervised classification method was used to classify LU/LC types, NDVI and LST using ERDAS imagine 2015 and ArcGIS 10.3 software. Coefficient of Variation, Precipitation Concentration Index, and the moving average was used to analyze temperature and rainfall data. Regression analysis/Correlation coefficient was used to signify the association of LULCC and NDVI with climate variables. Landsat image of the study area was classified into agricultural land, forest land, grazing land, settlement, and shrub land. The result showed a dramatic increment of agricultural land and settlement from (43.13 and 3.75%) in 1999 to (58.27 and 5.64%) in 2019 with an annual expansion rate (35.11 and 50.12%) per annum, respectively. Contrary, forest cover, grazing land, and shrub land were declined from (5.16, 20.72, and 27.23%) in 1999 to (3.80, 14.15, and 18.14%) in 2019 with an annual decreasing rate (-26.42,-31.71, and -33.38%) per annum, respectively. Expansions of agriculture, demand for fuelwood and construction material, overgrazing, and unplanned expansion of settlement were identified as the major proximate drivers of these changes. Moreover, population pressure, changes in policy and institution, and biophysical factors such as recurrent drought, topography, and landslides were claimed as the prominent underlying drivers of LULCC. Climate variability analysis result revealed that average annual maximum, minimum and mean temperature have positive trends while annual rainfall showed an insignificant increase with R[2] values 0.405 and 0.046 in the spring and winter season, respectively, and decreased trends with R[2] values 0.068 and 0.022 in the summer and autumn season, respectively. These alterations of climate conditions were mainly associated with LULCC mostly changes in vegetation cover. During the last two decades average LST showed a rise of 12oC in the settlement area, 11.2oC in agricultural land, 9.3oC in grazing land, and a rise of 8.8oC, 7.7oC in shrub and forest land, respectively. Likewise, there were strong positive correlations between decadal rainfall and NDVI with R[2] values 96.75%, 93.85%, and 86.30% for 1999, 2009, and 2019, respectively. In general, the study concluded that LULCC particularly increasing trend in agricultural land, settlement, and vice versa in forest cover, shrub land, and grazing land can be contributed to climate variability at Kereba sub-catchment over the last two decades. Hence, land-based adaptation and mitigation measure critically needed in the Kereba sub-catchment to reverse the negative effects of LULCC on climate variability.
Keywords: Climate Variability, Kereba, Land-use Land-cover, LST, NDVI, Sub-catchment
1. INTRODUCTION
1.1. Background of the Study
The land is the most basic natural resource and material basis for human survival and development (Foley et al., 2005). Human activities have profoundly changed the natural geographical environment, and directly change surface cover through land use. The pace, magnitude, and spatial reach of human alteration of the Earth’s land surface are unprecedented (Lambin et al., 2001), and have exacerbated land degradation, biodiversity loss, environmental pollution, land shortage, urban and rural land imbalance, and other global issues. LULCC has been identified as one of the prime determinants of global change as well as an important issue affecting the sustainable development of nature, humans, and society (Lambin et al., 2001).
Land-use/land-cover Changes caused by natural and human processes have played a major role in global as well as regional-scale patterns of the climate and other aspects of the earth (Ramachandra et al., 2012). Anthropogenic-related drivers such as population growth (Meshesha et al., 2014), urbanization (Wang et al., 2016; Yirsaw et al., 2017), agricultural expansion (Mustard et al., 2012), pasturing, and global market forces (Lambin and Meyfroidt, 2011) are among the known drivers of LULCC. Cultural factors such as habits of logging vegetation cover, exposure of soil for erosion, and degrading land may also trigger the LULCC (Lambin et al., 2003). Besides, institutional causes like political, legal, economic, and traditional factors and their interaction with individual decision-making are important for LULCC and management (Lambin et al., 2003).
According to FAO (2012), 4 billion hectares (31%) of the world's land surface is under forest cover. Deforestation is one of the key causes of land cover change and it is the most challenging factor in developing countries, particularly in tropical rain forests, which covers some 550 million ha of the globe, with an annual harvesting rate of over 2%. So that the forest cover of the world is declining continuously and has global environmental implications. Being a less developed society, the adhered dynamics of the land are rapid in Africa than in developed continents (Belete, 2017). In most Eastern African countries, LU/LC has been happening due to the increase of both human and livestock populations (Pomeroy et al., 2003).
Land-use/land-cover Changes in Ethiopia, particularly in the highland areas caused by a combination of various factors though it depends on the conditions of the area (Mohammed, 2011; Yeshaneh et al., 2013; Hassen et al., 2015). Most studies conducted in Ethiopia indicated that LULCC were mainly the conversion of natural vegetation to agricultural lands and grazing lands due to high demand for agricultural food production and livestock grazing lands (Bewket and Abebe, 2013; Kibret et al., 2016). Moreover, Yeshaneh et al. ( 2013) showed that significant declines in natural vegetation cover at the expense of open grassland and cultivated lands could exacerbate the problem of land degradation.
Land use/land cover changes can potentially affect regional and global climates by emitting or sequestering carbon and by altering the overall reflectance properties of the earth’s surface (Houghton and Hackler, 2006). Land surface temperature is one of the land surface properties that is affected by LULCC ( Eunice, 2013). Deforestation and forest degradation contributed about 17% of global GHG emissions (Bruno et al., 2011). Pielke (2001) suggested that deforestation can affect moisture convergence, atmospheric stability, and changes in rainfall.
According to IPCC (2009), rising temperature is caused by deforestation, forest degradation, illegal logging, and settlements and encroachments into the forests. Furthermore, Robust results have shown that albedo changes due to an increase in croplands and pastures leading to a decrease in surface temperature tend to dominate over the mid-latitudes, whereas the decrease in evapotranspiration, roughness length, and cloudiness play a primary role in increasing surface warming in the tropics (Lawrence and Chase, 2010; Pitman et al., 2012).
In Ethiopia, LULCC plays a crucial role in climate change and variability by increased droughts, and changes in rainfall patterns, causing floods and reducing the sizes of lakes (Tsegaye et al., 2010; Meshesha et al., 2012; Molla, 2014). There is a high correlation between rainfall and NDVI which proved that vegetation cover change can give an accurate indication of rainfall change (Batool et al., 2015). The depletion of forest resources contributes significantly to the climatic and physical changes of the environment (Fekadu, 2015).
Kereba sub-catchment is also one of the most vulnerable catchments affected by LULCC induced problems resulted in climate variability. Hence, this study was intended to investigate LULCC, driving forces, and their implications on climate variability in the case of the Kereba sub-catchment of Awash Basin, Eastern Ethiopia.
1.2. Statement of the Problem
Land-use/land-cover changes pose significant economic and environmental risks worldwide (FAO, 2013) and Changes in LU/LC affect the overall functioning of the earth system at local, regional, and global levels (Lambin and Meyfroidt, 2011). This in turn brings global change (Kueppers and Snyder, 2012), particularly temperature (Wang et al., 2013) and rainfall (Woldemichael et al., 2012). In Africa, land-use change accounts for a conversion of 75 million hectares of forest to agriculture and pasture between the years 1990 and 2010 (FAO, 2010). FAO (2012) stated that the slow development of agricultural technology in Africa is attributed to becoming the commonplaceness of slash-and-burn cultivation that resulted in harvesting forests for fuelwood and charcoal to reply to the growth of cities, and due to population growth, the fallow period became shorter. More importantly, in East Africa, nearly 13 million hectares of original forest were lost over the same 20 year period and the remaining forest is fragmented and continued to be under threat (FAO, 2010).
In most eastern African Country, Ethiopia has a fragile highland ecosystem that is currently under stress due to increasing population pressure, severe deforestation, and loss of biodiversity and ecosystem services, land degradation, pollution of land, air, and water (Kim and Kaluarachchi, 2009) and traditional agricultural practices like cultivating on steep slopes without protective measures, climate and land use and cover change (EPA, 2012). Consequently, these would cause the loss of forest cover and greater hydrological variability (Kim and Kaluarachchi, 2009; EPA, 2012).
In Ethiopia, the problem of LULCC is more severe in the highlands (Eshetu and Hogberg, 2000). Several studies conducted on LULCC at the national and local level of Ethiopia indicated that under Ethiopian situations, shifting of lands to agricultural land is the major driving force of LULCC. For example, a study conducted by Temesgen et al. (2017) in the Andassa watershed, Northwestern highlands of Ethiopia showed that agricultural land expanded from 62.7% in 1985 to 76.8% in 2015. The same study conducted by Berhan et al. (2014) in the Modjo watershed Central highland of Ethiopia revealed that agricultural land changed from 55% in 1973 to 74.9% in 2007. The study conducted by Wondwosen et al. (2018) in the Wanka watershed, Northern western highland of Ethiopia, showed that shifting of lands to agricultural land grow from 61.5% in 1957 to 74% in 2017.
Similarly, other scholars such as Wakjira et al. (2020) in Finchaa Catchment, Northwestern Ethiopia; Afera et al. (2019) in Gumara watershed of Amhara region; Abel (2018) in Bahir Dar town and its surrounding; Worku (2018) in North Gondar zone of Amhara region; Morka (2018) in Arsi Zone of Oromia region; Megersa and Degefa (2019) in Adei watershed of west Shoa zone Oromia region; Abebe (2018) in Dendi district of Oromia Region; Tulu (2017) in Ameya district of Oromia Region; Birhan and Assefa (2017) in Gelana sub-watershed of Amhara region; Tesfa et al. (2016) in Beressa Watershed of Basona district north Shoa zone of Amhara region and Aklile (2014) in Denki River catchment of Ankober district of Amhara region have been conducted the study on LULCC.
However, many of these scholars gave little attention to the implications of LULCC on climate variability. On the other hand, those scholars’ attempts to conduct the study on LULCC connected with climate variability also focused only on quantifying trends of LULCC using remote sensing images and neglect comparing the observed LULCC with the perception of different stakeholders. Furthermore, there is an inconsistency of ideas among scholars based on the model they have been used in their study and findings. Despite that, there are no reliable studies purposely conducted in Kereba sub-catchment related to LULCC, driving forces, and their implications on climate variability. Thus, this initiates the investigator to research LULCC, driving forces, and their implications on climate variability in the case of Kereba Sub-Catchment of Awash Basin, Eastern Ethiopia.
1.3. Objectives of the Study
1.3.1. General objective
The general objective of this study is to investigate LULCC, driving forces, and their implications on climate variability from 1999-2019 in the Kereba sub-catchment.
1.3.2. Specific objectives
- To analyze the spatial and temporal variations of LULCC, NDVI, and LST.
- To identify the major driving forces of LULCC in the study area.
- To examine the trends and variability of temperature and rainfall.
- To evaluate the associations of LULCC and NDVI with temperature and rainfall
- To investigate the implications of LULCC on climate variability
1.4. Research Questions
1. What are the spatial and temporal variations of LULCC, NDVI and LST in the study area?
2. What are the major driving forces that cause LULCC in the study area?
3. What trends do actual temperature and rainfall data reveal in the study area?
4. What are the associations of LULCC and NDVI with temperature and rainfall?
5. What are the implications of LULCC on climate variability?
1.5. Significance of the Study
The change in LU/LC leads to environmental changes. Extensive LULCC is due to the rise in the demand for land for settlement and agriculture and other intervention in human activities. Rapid LULCC induces considerable LST variations that in turn affect rainfall. The variation in land use and climate could threaten the social-economic development of human beings worldwide. Many African countries including Ethiopia are experiencing several floods and droughts in recent years due to the effects of LULCC and climate changes.
This study was designed to assess the degree and extent of LULCC that induced adverse effects on climate variability and attempts to provide insight on the possible direction(s) to address the problems. The results of this study would provide information that is important to maintain the agro-climatic condition, assure sustainability in resource utilization, and proper land use planning and decision making for policymakers, agricultural experts, non-governmental organizations, planners, and land administrators. Besides, the study will provide firsthand information for the other investigators who have an interest to conduct further study related to LULCC, driving forces, and their implications on climate variability from remote sensing and meteorological data.
1.6. Scope of the Study
Geographically, the study was conducted in the Kereba sub-catchment of Awash Basin, eastern Ethiopia. The spatial scope of this study is to investigate the LULCC, driving forces, and their implications on climate variability. Whereas the temporal scope is to analyze LULCC, NDVI, and LST changes; to identify the major driving forces of LULCC; to examine trends and variability of temperature and rainfall, and to evaluate the implications of LULCC on climate variability across the study catchment from 1999 to 2019. Therefore, for future study research should consider a better way to measure these areas.
1.7. Definition of Key Terms
Relative to this investigation, definitions of the following terms were provided to clarify each in the context of the topic.
Catchment : is an area in which water falling on or flowing across the land surface drains into a particular stream or river and flows ultimately through a single point or outlet (Daly, 2013).
Climate variability : refers to the variations in the mean state and other climate statistics (such as standard deviations, the occurrence of extremes, etc.) on all temporal and spatial scales beyond those of individual weather events. Variability may result from natural internal processes within the climate system (internal variability) or from variations in natural or anthropogenic external forces (external variability) (IPCC, 2007a).
Land Cover : refers to the visible biophysical features and elements on the earth’s surface and immediate surface (Prakasam, 2010). It includes vegetation, water (surface, groundwater), desert, ice, soil, relief, and anthropogenic structures like mining and settlement (Di Gregerio, 2005).
Land use : is the modification of the land carrying various activities and using different inputs that convert the land cover kind to produce, change or preserve the land (Di Gregerio, 2005).
Land use and land cover change : is a general term for the human modification of Earth's terrestrial surface (Meyer and Turner, 1994).
1.8. Limitations of the Study
The investigation work cannot free from constraints. To this end, some of the limitations were observed in this study. The first and unforgettable challenge was the Coronavirus (COVID-19)-induced problems. For instance, high stress resulted in fearfulness among the participants of FGD and the investigator during data collection; restricting the investigator to contact face-to-face with an advisor, and delaying the time to submit the final thesis were the major coronavirus-induced problems in this study.
Moreover, unavailable of meteorological stations in the study area, the dysfunction of internet connection, shortages of document-recorded reports, books, and lack of updated related literature were other limitations encountered in this study.
1.9. Organization of the Thesis
This thesis has been divided into five chapters. The first chapter is an introduction that presents an overview of the thesis: the background of the study, statement of the problem, objectives of the study, basic questions of the study, significance of the study, the scope of the study, and definitions of operational terms. The second dealt with the literature review and the third chapter addressing the research methodology. The fourth chapter mostly focused on presentation, discussion, and interpretation of data. Finally, the fifth chapter presents the main conclusions and recommendations. The appendices are attached at the end of the thesis next to the later chapter.
2. LITERATURE REVIEW
This chapter dealt with the review literature. In the first, this chapter presents the concepts of LULCC and Climate Variability, driving forces of LU/LC changes, LULCC and Climate variability and change in Global and Ethiopian context. Furthermore, LST, NDVI, empirical evidence of LULCC and climate variability, the role of GIS and RS in LULCC, and Climate variability as well as implications of LULCC on climate variability and conceptual framework of the study were also described under this chapter.
2.1. Concepts of Land Use/Cover Changes and Climate Variability
2.1.1. Concepts of land use/cover changes
Land-use/land-cover changes are fundamental processes on the earth’s surface and have significant impacts on human society, climate, biodiversity, hydrological cycles, ecosystems, and many other processes (Lin et al., 2018). Changes in land cover have become key components of global environmental change and represent the impact of human activity (Zhao et al., 2017). LULCC is as old as human activity (Turner et al., 1993 as cited in Alemenesh et al., 2020). However, the recent rate of change is different from earlier changes because of major changes in societal development and population growth (Gebreslassie, 2014).
The speed, degree, and intensity of LULCC are now faster compared to the past (Lambin and Meyfroidt, 2011; Lin et al., 2018). For instance, during the past three centuries, the extent of earth cultivated land has grown by more than 45% increasing from 2.65 million km² to 15 million km² and at the same time, other natural resources such as forest have been shrinking due to agricultural land expansion and urbanization (Santa, 2011).
Land-use changes modify land cover due to human intrusions, such as cultivation, settlement, transportation, infrastructure, manufacture, recreation, mining, and fishing. In contrast, land cover change converts land cover from one type to another and/or the modification of conditions within a category (Pellikka et al., 2013). These changes alter the accessibility of various biophysical resources including water, vegetation, soil, animal feed, and others (Alemenesh et al., 2020).
Generally, LULCC is driven by natural factors and anthropogenic activities (Bennett and Saunders, 2010) causing the alteration of ecological process and the loss of native biodiversity across different ecosystems (Msofe et al., 2019). LU/LC is constantly changing in response to the dynamic interaction between drivers of change and proximate causes (Leh et al., 2013).
2.1.2. Concepts of climate variability
Climatic variabilities are the types of changes (temperature, rainfall) in magnitude and rate of climate change that causes the impacts on the area of public health, agriculture, food security, biodiversity, and forest and water resources (Amogne, 2013). Climate variability will increase almost everywhere (Belay and Getaneh, 2016). Northern latitudes will experience more rainfall; many subtropical regions will see less (IPCC, 2014).
Africa is one of the most vulnerable continents to climate change and climate variability, a situation aggravated by the interaction of ‘multiple stresses’, occurring at various levels, and low adaptive capacity (Belay and Getaneh, 2016). Several studies on precipitation and temperature change indicated that the African continent is now warmer than it was 100 years ago and the rainfall exhibited higher inter-annual and intra-seasonal variability (Cooper et al., 2009; Cooper and Coe, 2011; Rosell, 2011).
Climate variability over the last three decades of the 20th century resulted in droughts and famine in several countries of Africa (Conway and Schipper, 2011; Dixit et al., 2011). Among eastern African countries, Ethiopia has been identified as one of the most vulnerable countries to climate variability and change and is frequently faced with climate-related hazards, commonly drought and floods (Temesgen et al., 2014).
The local Ethiopian communities have commonly perceived an increase in temperature and a decrease in annual total rainfall (Bewket, 2013). Collected meteorological data shows that rainfall has declined in southern Ethiopia during both the rainy seasons of February–May, and June–September, and also that spring droughts have occurred more frequently in all parts of Ethiopia during the last 10–15 years (Viste et al., 2013).
2.2. Driving Forces of Land Use/Land Cover Changes
Land-use/land-cover changes occurred due to natural and anthropogenic factors (Geist and Lambin, 2002). As EPA (1999) states there were three main driving causes for LULCC. These are (1) natural processes, such as wildfire, climate, and atmospheric changes, and pest infestation; (2) direct effects of human activity such as deforestation, soil erosion, and reduction of biodiversity which brings land degradation, and construction like road and buildings; and (3) indirect effects of human activities, like depletion or lowering of the water table and contamination of groundwater.
Anthropogenic factors are the major driving forces of LULCC (Niamir-Fuller et al., 2012; Brink et al., 2014) even though there is also a contribution from the natural processes. LULCC is a very complex process due to its causes and impacts are very closely related; for example, land degradation (Abate, 2011). Currently, the human-related causes of LULCC are very serious (Agarwal et al., 2002). For instance, expansion of agricultural land (Bongers and Tennigkeit, 2010; Brink et al., 2014) due to rapid population growth responsible for the massive collapse of natural vegetation, loss of biodiversity, and land degradation (Donato et al., 2016; Sandra et al., 2017). Extreme transformation of forests, grassland, and shrub land brings a reduction of plant species diversity and continuously shrinking of natural wildlife. Intensification of agriculture such as crop and pastoral land towards the natural ecosystem which is related to population growth also contributes to extreme changes of LU/LC and environment (Lambin and Meyfroidt, 2011).
Moreover, rapid population growth reduces forest areas and woodlands (Sandra et al., 2017). This destruction affects biological diversities and functions ecosystems (Peter, 1994) and causes climate change which raises the risk for wildfire (Donato et al., 2016). Yadvinder et al. (2008) also agreed that population expansion causes deforestation and creates pressure on forest resilience. This extreme destruction of forestland is a root for climate change at the local, regional and global levels (Peter, 1994). Deforestation also affects the process of atmosphere and thermodynamics at the earth-atmosphere interface and water storage capacity and soil hydraulic conductivity (Shukla et al., 1990). Besides, changes in LU/LC affect hydrological cycles and their parameters (Zhenglei et al., 2009).
Desertification is also another effect of extreme LU/LC that increases the concentration of carbon-di-oxide in the atmosphere and brings wildfire. Therefore, incidences of forest fire-related to LU/LC degradation/change increase the emission of toxic gasses such as carbon monoxide and nitric oxide which alter the chemistry of the atmosphere causing air pollution, affecting energy balance and climate, and global warming (Peter, 1994).
Land-use/land-cover change has impacts on hydrology and changes the quality of water and water flows, causing surface water pollution, depletion of groundwater aquifers (Rosell, 2011). Further, LULCC due to urban expansion causes urban heat island (Jianga and Tiana, 2010) that has adverse social, economic and environmental effects both at the local, regional and global scale (Zheng et al., 2014). Thus, higher urban temperatures increase the demand for air conditioning, change urban thermal environments and ultimately lead to thermal discomforts and incidence of heat-related illnesses (Qijiao and Zhixiang, 2015).
2.3. Land-Use/Land-Cover Changes in Global Perspectives
Globally, land-use change through the conversion of the world’s forest land to other uses continues on an increasing scale due to the unprecedented growth of the human population which increases the demand for food and land (FAO, 2015). Many studies have confirmed that, several decades ago, forests covered a vast portion of the land area on earth. The world’s forest decline was estimated at 129 million ha between 1990 and 2015, approximately the size of South Africa’s landmass, representing an annual rate of loss of 1.3% (Global Forest Resources Assessment, 2015).
Deforestation, forest degradation, and their associated environmental problems are the focus of discussion at several environmental fora deliberating on the measures required to mitigate the impact of these global phenomena (Goll et al., 2014). The historical linkage between forest cover alterations and economic growth have been outlined by sociologists and forestry scientists about three decades ago (Rudel et al., 1989 as cited in Jamal, 2020). The industrial development and the accelerated pace and intensity of human activities have greatly altered forest cover with spatiotemporal heterogeneity among nations (Wang et al., 2016).
Global cropland showed a fivefold increase from 1770 up to 1990 and pastureland also increased by above six-fold from 1700 to 1990. This increase of cropland and pastureland was at the expense of forest, natural grassland, and savannas (Temesgen, 2014). For example, forest cover was decreased from 5000-6200 million hectares in 1700 to 4300- 5300 million hectares in 1990 (Lambin et al., 2003).
More rapidly than the aforementioned periods, more woody vegetation cover was converted to cropland between 1950 and 1980. But the direction of land use and land cover change is not similar for all parts of the world. In the last two decades, the area of temperate forest was increasing by almost 3 million hectares, while the tropical forest was decreasing by 12 million hectares per year (Millennium Ecosystem Assessment, 2005).
2.4. Land-Use/Land-Cover Changes in Ethiopia
Land-use dynamics is one of the major environmental problems in Ethiopia (Berhan, 2010). Land use and land-cover changes in Ethiopia, particularly in the highland areas caused by a combination of various factors though it depends on the conditions of the area (Mohammed, 2011; Yeshaneh et al., 2013; Hassen et al., 2015). Even though the causes of LULCC vary from place to place human-induced factors are the main driving forces, and create a complex system and troubles (Verheye, 2007). This complexity ranges from biophysical attributes to social and economic drivers of changes (Veldkamp and Lambin, 2001).
These driving forces change the landscape patterns and land-cover types, for instance, change and modification of topography, climate, vegetation, and soil characteristics. Cultural factors such as habits of logging vegetation cover, exposure of soil for erosion, and degrading land may also trigger LULCC (Lambin et al., 2003). Besides these, institutional causes like political, legal, economic, and traditional factors and their interaction with individual decision-making are important for LULCC and management (Lambin et al., 2003).
In Ethiopia, vegetation cover is decreasing due to the expansion of cultivated land (Abate, 2011). Estimates of deforestation in Ethiopia, which is mainly for the expansion of rain-fed agriculture that varies from 80,000 to 200,000 ha per annum (EPA, 1997). As EFAP (1993) stated that the extent of the forest was much higher from 40% at the beginning of the twentieth century, 16% in the 1950s, 3.1% by 1982, only 2–3% in the 1990s, and 3.56% in 2004 (Wubalem, 2012). Accordingly, some remnant stands of natural forests are mainly restricted to religious sites, along rivers and streams, and on peaks of hills where crop cultivation is difficult in the highlands of Ethiopia (Warra et al., 2013). This is due to the rapid expansion of agriculture and rural and urban settlement.
Therefore, the rapid increase in deforestation as well as poor practices of managing farmlands accelerating soil erosion and land degradation in the Ethiopian highlands (Hassen et al., 2015), principally common where high populations exist and their livelihoods directly depend on the exploitation of natural resources. Such fast rates of vegetation conversion, unsustainable agricultural land-use, and severe soil erosion are the effects of LULCC and land degradation in the highlands of Ethiopia. Because of population pressures, economic factors, and policy issues, settlements, farmland, and degraded lands have been expanding while grasslands and forest areas have been diminishing largely (Getachew et al., 2011; Eleni et al., 2013; Tsehaye and Mohammed, 2013; Alemu et al., 2015; Hassen et al., 2015). Moreover, LULCC is associated with deforestation, biodiversity loss, and land degradation (Maitima et al., 2009). Thus, LULCC is a serious problem in changing the environment (Abate, 2011).
2.5. Climate Variability in Global and African Context
Changes in temperature and rainfall patterns are widely observed in many parts of the developing world that are likely to become even hotter and dryer with time (Collier et al., 2008). Recent studies showed that the warmest temperature extremes, particularly those derived from minimum temperature, have significantly increased over the 20th century and will continue to increase throughout the 21st century (Tagel and Anne, 2013). Evidence suggests that globally, there have been more flood/drought-inducing events, which are set to escalate in frequency and intensity in the future (Tebaldi et al., 2006).
The average temperature rise in Africa is faster than the global average and is likely to persist in the future (Tagel and Anne, 2013). This warming occurred at the rate of about 0.5ºC per decade with slightly larger warming in crops that are grown close to the thermal tolerance limits (Collier et al., 2008). Climate variability over the last three decades of the 20th century resulted in droughts and famine in several countries of Africa (Conway and Schipper, 2011).
The Third Assessment Report of the International Panel on Climate Change (IPCC) stated that developing countries are expected to suffer most from the negative impacts of climate change and climate variability (IPCC, 2001). This is due to the economic importance of climate-sensitive sectors for these countries and to their limited human, institutional and financial capacity to anticipate and respond to the direct and indirect effects of climate change and variability. This is particularly true in Africa where there is a high direct dependence on the natural environment for livelihood and high levels of poverty (Dixon et al ., 2001 as cited in Tagel and Anne, 2013). Particularly, Africa’s agriculture is negatively affected by climate change and climate variability (Conway and Schipper, 2011). In most developing countries and many African countries, agricultural production including access to food is projected to be severely compromised by climate change and variability (IPCC, 2007).
Hellmuth et al. (2007) reported that alterations in the patterns of extreme events, a setback in the development process of African countries for decades and threaten the lives and livelihoods of the resource-poor more than other social groups. The occurrence of a single climate disaster is capable of stagnating or even-reversing the economic growth achieved over a decade as the main source of economic growth of African countries is climate-related sectors. This is attributed to the fact that climate change and variability are expected to affect the two most important direct agricultural production inputs: precipitation and temperature. These inputs are crucial for livelihoods in Africa, where the majority of the population relies on local supply systems sensitive to climate variation (Deschênes and Greenstone, 2007). As the adverse impacts become more frequent and severe, the already fragile socioeconomic activity of the continent is more likely to exacerbate (Collier et al., 2008).
Furthermore, the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) stated that in many regions, climate variability manifest in an increase in the number of heavy rainfall, extreme increase in temperature, high sea levels, and decrease in cold temperature (IPCC, 2014). The increase in population size, economic activity, lifestyle, energy use, land use patterns, technology, and climate policy had been the main drivers of anthropogenic emissions of greenhouse (IPCC, 2014).
It is widely accepted that the increase in heat stress due to increased temperatures will have larger negative impacts in the tropical areas than in the temperate latitudes (Rao et al., 2007). Heat waves, droughts, floods, cyclones, and wildfires are recent climate-related extremes impacts that reveal significant vulnerability and exposure of some ecosystems and many human systems to current climate variability and change (IPCC, 2014).
2.6. Climate Variability in Ethiopia Context
Ethiopia is characterized by diverse climatic conditions ranging from humid to semi-arid environments (Belay, 2014). Its climate system is largely determined by the seasonal migration of the Inter-tropical Convergence Zone (ITCZ) and a complex topography (NMA, 2001). Mean annual rainfall distribution ranges from a maximum of more than 2,000 mm over the south-western highlands to a minimum of less than 300 mm over the south-eastern and north-western lowlands. The south-west and western areas of the country are characterized by a uni-modal pattern whereas the remaining parts exhibit a bi-modal rainfall pattern (World Bank, 2006). The mean annual temperature varies widely, from lower than 15°C in the highlands (>1500 m.a.s.l.) to more than 25°C in the lowlands (< 1500 m.a.s.l.).
Climate variability, particularly rainfall variability and associated droughts have been caused for food insecurity in Ethiopia (Rosell, 2011). For instance, every year, approximately three million Ethiopians are affected by crop production shortfalls adding to the 7.6 million supported every year by the Productive Safety Net Program, a social safety net supporting some of Ethiopia’s poorest and most food-insecure families (GOE, 2015) because of extreme drought. Very recently, the 2015 El Niño-induced drought has caused food insecurity among 10.2 million people, one of the highest on the record (FAO, 2016).
Various studies indicate that future climate change in Ethiopia will lead to an increase in climate variability. For instance, Van de Steeg et al. (2008) indicated that the growing season in some parts of Ethiopia could be 20% shorter by 2050s relative to the current baseline period (1960-1990) which would have negative repercussions on food production. Likewise, the UNDP (2008) suggested that climate change in Ethiopia could lead to extreme temperatures and rainfall events, as well as more heavy and extended droughts and floods.
Furthermore, the future temperature projections of the IPCC mid-range scenario show that the mean annual temperature will increase in the range of 0.9 to 1.1ºC by 2030, in the range of 1.7 to 2.1ºC by 2050, and in the range of 2.7 to 3.4ºC by 2080 in Ethiopia compared to 1961 to 1990 (Emerta, 2013). Therefore, understanding the climate variability, particularly trends and variability of rainfall and temperature has paramount importance for planning and designing appropriate interventions.
2.7. Land Surface Temperature
Land surface temperature is the fundamental climatic parameter in determining the surface radiation and the energy exchange (He et al., 2019). It denotes the skin temperature of the earth's surface phenomena (Kayet et al., 2016). LST also an important variable in land-atmosphere interactions and a climate change indicator that varies over space and time as a function of vegetation cover, surface moisture, soil types, and topography (Eunice, 2013).
Land-use change alters the thermal environment; the LST is a proper change indicator to show the thermal changes concerning land-use changes (Youneszadeh, 2015). The changes of LST is related to many factors, including changes in land use, land surface parameters, seasonal variation, climatic condition, and economic development (Worku, 2018). In other words, the change of land use is the important reason leading to an increase in LST (Jing and Guangjin, 2010).
The concept of LST has been widely used by many researchers across the globe for unpredictable rainfall, temperature fluctuations, and vegetation patterns are aspects that alter the LU/LC in a region (Owojori and Hongjie, 2015). The shifting of this land use/land cover is attributed to anthropogenic activities that alter the physical characteristics of the land surface and abrupt changes in temperature in a particular region (Srivanit et al., 2012). It is well documented that as land surface cover changes, the surface temperature of that particular area also changes (Buyadi et al.; 2013; Hua and Ping, 2018).
2.8. Normalized Difference Vegetation Index
Normalized Difference Vegetation Index is the difference between near-infrared and visible red reflectance values normalized over reflectance and calculated from reflectance measurements in the near-infrared (NIR) and red portion of the spectrum (Burgan and Hartford, 1993). To calculate the Normalized Difference Vegetation Index, subtracting the red band from near-infrared and then dividing to the near-infrared plus red band. The value is ranging from -1 to1, the negative values are indicative of water, snow, clouds, non-reflective surface, and other non-vegetated, while the positive value expresses reflective surfaces such as vegetated area (Burgan and Hartford 1993). Vegetation has a direct match/correspondence with thermal, moisture, and radiative properties of the earth’s surface that determine LST (Weng, 2004). The index is expressed as NDMI= (NIR-IR)/ (NIR+IR), it evaluates the different content of humidity from the landscape elements, especially in soils, rocks, and vegetations and it is an excellent indicator of dryness. Values greater than 0.1 are symbolized light colors and they signal high humidity levels, whereas values close to -1 symbolized by dark colors represent low-level humidity levels (Mihai, 2012).
2.9. Empirical Evidence of LULCC and Climate Variability
2.9.1. Evidence of land-use/cover changes
Land-use change is one of the major environmental problems in Ethiopia (Berhan, 2010). Abate (2011) and Belay et al. (2014) indicated that vegetation cover is decreasing due to the expansion of cultivated land. Estimates of deforestation in Ethiopia, which is mainly for the expansion of rain-fed agriculture that varies from 80,000 to 200,000 ha per annum (EPA, 1997). As EFAP (1993) stated that the extent of the forest was much higher from 40% at the beginning of the twentieth century, 16% in the 1950s, 3.1% by 1982, only 2–3% in the 1990s, and 3.56% in 2004 (Wubalem, 2012).
Abel (2018) in his study showed that land-cover such as grasslands, wetland vegetation, forest, water bodies were depleted. For example, shrub land reduced from 18.06 km[2] in 1987 to 9.39 km[2] in 2017. Wetland vegetation was 21.27 km[2] in 1987 but decreased to 17.05 km[2] in 2017. Grasslands also decreased from 19.17 km[2] (7.89%) in 1987 to 13.22 km[2] (5.44%) in 2017. This land-cover depletion resulted from agricultural land expansion (mainly plantation) due to rapid population growth. The increased settlement, service and industrial areas, recreation, and paved surface also caused the depletion of grasslands, wetlands, and shrublands.
Birhane and Zemenu (2018) researched impacts of resettlement on land use land cover changes and natural vegetation conservation practices of resettlers in Abobo District, Gambella, Ethiopia showed that trends of LULCC over the period were fluctuating. Trends observed from 1987 to 2002 were decreased in forestland by a rate of 0.52ha, grassland at the rate of 1.47ha, and woodland at a rate of 2.84ha per year. On the contrary, an increase was observed in farmland with a rate of 37.3ha and bare land with a rate of 9.50ha per year. Between 2002 and 2009, the bare land and farmland were continued to increase with a mean annual rate of (859.43ha) and (9.88ha) respectively. Nevertheless, the overall land cover of forest and grassland trend over the specified study period was declining.
Alem-meta and K. N. (2017) indicated that the expansion of farm lands and degraded lands in the sub-watershed was at the expense of grass lands, shrub lands, and forest lands. These changes are caused by the expansion of agricultural lands through unplanned and inappropriate land management practices to meet the food demand of the growing population. Furthermore, population growth, crop land expansion, landlessness, overgrazing, climate change, land degradation, drought, and shortage of rainfall are mentioned as drivers of LULCC.
Asmamaw (2013) concluded that the LU/LC of the Gilgel Abbay watershed for the period of 1986 to 2001 showed significant change. Cultivated land was drastically changed from 9 % in 1986 to 55 % in 2001 in the expenses of the other classes. The expansion of agricultural land and rural settlement has an impact on the decrement of forest land.
2.9.2. Evidence of climate variability
Climate change is probably the greatest long-term challenge facing the human race. Humanity everywhere in the world is being haunted by multitudes of problems and challenges; and in effect, climate change is one of humanity's most devastating problems. Different researches had been conducted to assess the spatial and temporal patterns of climate in different parts of the country. Particularly a few studies have been conducted on the issue of climate variability both in space and time. The study made by Getnet (2010) can be mentioned focusing on Climate Variability and Impacts in Central Rift Valley and Adjacent Arsi Highlands Using GIS and Remote Sensing, the finding of the study denoted that temperature is getting increased by 0.37oC in the rift valley and 0.48oC in highland and by 0.4oC in whole areas per 12 years and rain is constant and shows an insignificant rise.
Besides, another recent study carried out by Mulugojjam and Ferede (2012) emphasizing Spatio-temporal Variability and Trend of Rainfall and Temperature in Western Amhara: Ethiopia: A GIS Approach. The study has used 5 meteorological stations with 30 years of daily rainfall, maximum and minimum temperatures data were used (1979-2008). The study revealed that the contribution of kiremit rainfall to the annual total rainfall was very high in all study stations. Variations of minimum and maximum temperatures were found every month. The long-term recorded rainfall data indicated an increasing trend during 1995-2008 and decreasing trend during 1979-1994, except, inter-annual fluctuation. Further, late-onset and early cessation of rainfall were noted in all study stations in recent years. The spatiotemporal variability of rainfall, minimum temperature, and maximum temperatures observed in the study stations are alarming.
On the other hand, Dereje et al. (2012) studied the variability of rainfall and its current trend in the Amhara region, Ethiopia. For this purpose, 10 meteorological stations with 30 years of daily rainfall data have been used. Variations of rainfall were found every month in all stations. The spatial distribution of annual rainfall was varied from 850 to 1485 mm. Belg (“small rainfall” in March-May) rain makes a considerable contribution to the annual total in the central and eastern stations of the region. Annual rainfall has shown negative and positive anomalies for much of the 1980s and 1990s, respectively.
2.10. Role of GIS and Remote Sensing in LULCC and Climate Variability
2.10.1. Role of GIS and RS in land-use/land-cove changes
Geographic information system and Remote sensing techniques have been widely used over the world for the study of historical changes in LU/LC and LST analysis Remote sensing has been used to identify vegetation cover, air pollution, LST, and other surface characteristics (Zha, 2012). Furthermore, understanding the correlation between LST and LU/LC is important to manage the land. It provides a large variety and amount of data about the earth’s surface for detailed analysis, changes detection with the help of various airborne, and space-born. With the availability of historical remote sensing data, the reduction in data cost, and increased resolution from satellite platforms, remote sensing technology appears ready to make an even greater impact on monitoring land-cover change (Zha, 2012).
Land-use/land-cover change can be analyzed over a period using Landsat sensors such as MSS data and TM data by image classification techniques (Gumindoga, 2010). Since 1972, Landsat satellites have provided repetitive, synoptic, global coverage of high-resolution multispectral imageries. Their long history and reliability have made them a popular source for documenting changes in LU/LC over time (Turner et al., 2003) and their evolution is further marked by the launch of Landsat7 (Enhanced Thematic Mapper Plus sensors) by the United State in 1999.
According to Macleod and Congation (1998), the following are four LULCC detections, which are important when monitoring natural resources: distinguishing the nature of the change; detection/finding of the changes that have occurred; measuring the area extent of the change and assessing and investigating the spatial pattern of the change. The basis of using remote sensing data for change detection is that changes in land-cover result in changes in radiance values, which can be remotely sensed.
2.10.2. Role of GIS and RS in climate variability assessment
The issue of climate change is currently an important concept that attracts the attention of several scholars. The problem of climate change is broadly studied from various angles that emanate due to the seriousness of the problem. Among those, the most recent is the application of satellite images which is used to analyze through Remote Sensing and GIS techniques that are developed with the development of Earth Observing Satellites.
Reducing the risks caused by climate change is an immense challenge. Scientists, policymakers, developers, engineers, and many others have used geographic information system (GIS) technology to better understand a complex situation and offer some tangible solutions (Esubalew,2014). A GIS-based framework helps us to gain a scientific understanding of earth systems at a truly global scale and leads to more thoughtful, informed decision making. GIS users represent a vast reservoir of knowledge, expertise, and best practices in applying this cornerstone technology to the science of climate change and understanding its impact on natural and human systems (ESRI, 2008).
The same source further explained that the key to understanding our dynamic climate is creating a framework to take many different pieces of past and future data from a variety of sources and merge them into a single system. GIS creates a new framework for studying global climate change by allowing users to inventory and display large, complex spatial data sets. They can also analyze the potential interplay between various factors, getting us closer to a true understanding of how our dynamic climate may change in the coming decades and centuries (ESRI, 2008). Applications of scientific principles such as GIS and Remote Sensing equipped with GPS in detecting climatic variation are crucial in the world today both in developed and developing countries. Epidemiological studies also possible by integrating spatial data with malaria as attribute data (Cracknel, 2001).
2.11. Implications of LULCC on Climate Variability
The influence of LULCC on climate and weather is complex, and specific effects depend on the type of change, the scale of the assessment (local, regional, or global), the size of the area under consideration, the aspect of climate and weather being evaluated (such as temperature, precipitation, or seasonal trends), and the region where the change occurs (Pielke et al., 2011; Pitman et al., 2011).
Recent studies suggest that forests tend to be cooler than herbaceous croplands throughout much of the temperate region (Anderson et al., 2011; Wickham et al., 2012; Wickham et al., 2013; Zhao and Jackson, 2014). These studies suggest that reforestation in the temperate forest region would promote cooling, with the magnitude of cooling decreasing with increasing latitude (Anderson et al., 2011; Wickham et al., 2012).
Biogeophysical (albedo, surface roughness, and transpiration) changes arising from land-cover change tend to result in more localized changes. Fires in forests, grasslands, shrub lands, and agricultural lands affect climate in two ways: 1) transporting carbon from the land to the atmosphere in the form of carbon dioxide and other greenhouse gases, and 2) increasing the concentration of small particles (aerosols) in the atmosphere that tends to reduce the amount of solar energy reaching the surface of Earth by increasing (although often temporarily) the reflectivity of the atmosphere (Sommers et al., 2014).
Urbanization has a small effect on global temperatures, with more dramatic effects evident regionally where urbanization is extensive (Zhou et al., 2005). Although the evidence is not conclusive, urbanization may also increase downwind precipitation (Argüeso et al., 2014). Further, climate change may act synergistically with future urbanization (that is, an increase in impervious cover), resulting in increased likelihoods and magnitudes of flood events (Hamdi et al., 2011; Huong and Pathirana, 2013).
Water transport and application to cropland also impact climate (Loveland et al., 2018). Globally, the amount of water transported to the atmosphere through irrigated agriculture is roughly equivalent to the amount of water not transported to the atmosphere from deforestation (Gordon et al., 2005). Studies have shown reductions in surface air temperatures in the vicinity of irrigation due to both evaporation effects and increases in downwind precipitation as a result of increased atmospheric moisture (Sacks et al., 2009).
2.12. Conceptual Framework
The conceptual framework of the study is a metaphor designed to indicate the systematic interplay between components and variables of the study, the processes that take place, and the outcomes of the interactions. This conceptual framework incorporated the drivers of LULCC such as proximate drivers, underlying drivers, and biophysical factors, as well as implications of LULCC on climate variability (Figure 1). The purpose of this framework is to guides the investigator to understand LULCC, driving force, and their implications on climate variability.
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Figure 1. Conceptual Framework Source: Modified from Tegegne (2014) and Tesfa et al. (2016)
3. RESEARCH METHODOLOGY
This chapter has paramount importance comparing with other chapters. Because it focused on the methodological framework used to achieve the objectives of the study. In the first of this chapter, the descriptions of the study area (i.e. location and size, topography and soil, climate and drainage, vegetation and wildlife, population characteristics, and socio-economic setting) were briefly introduced. Research design, data types and sources, sampling techniques, instruments of data collection, and data analyzing methods were also explained in this chapter.
3.1. Descriptions of the Study Area
3.1.1. Location and size
The study was conducted at the Kereba sub-catchment of Awash Basin, eastern Ethiopia (Figure 2). Based on the division of governmental administration, this catchment is found in Doba district, west Hararghe zone of Oromia National Regional State of Ethiopia. It shares a border with Boroda town in the southeast, in the southwest by the administrative town of Doba district, in the northeast and northwest by Bike and Afdem town, respectively. Geographically, the Kereba sub-catchment is located between 09016'N to 09028'N latitude and 4106’E to 41015' E longitude with a total area of 218 km[2].
For copyright reasons, this illustration is not included in the publication.
Figure 2. Location Map of the Study Area Source: Ethio-GIS data, 2019
3.1.2. Topography and soil
The elevation of the Kereba sub-catchment varies from 1153 at the mouth of the River to 2725 m.a.s.l at its highest point on the hades mountain (Figure 3). The catchment is characterized by undulating topography with hills, mountains, plains, and river valleys. According to the data obtained from the ministry of agriculture, the major soil types found in the study catchment area Eutric Leptosols and Eutric Cambisols. Eutric Leptosols are the major soil types and cover about 59.90% of the study area. Eutric Cambisols are the second dominant soil types that covered 40.10% of the catchment area (Figure 3).
For copyright reasons, this illustration is not included in the publication.
Figure 3. Digital Elevation Model (Left) and Soil Types (Right) of Kereba sub-catchment Source: USGS and MOA (2019)
3.1.3. Climate and drainage
The agro-climatic zones of Kereba sub-catchment by their altitudinal ranges are highland which ranges from 2300-2725 m accounts for about 4.2.% (9.21 km[2]), Midland ranging from 1500- 2300 m and sharing 67.52% (147.50 km[2]), Lowland that ranges from 1153--1500 m shares 28.25% (61.72 km[2]) of the catchment area (Figure 4). The majority of the catchment is lies within the midland and is characterized by distinct dry and wet seasons. The dry seasons occur between November and May and the wet season between June and October; small rains occur sporadically during March, April, and May. Annual mean minimum, maximum and mean temperature ranges from 11.7oC-14.1oC, 25.0oC-27.1oC and 18.4oC-20.6oC, respectively (Figure 5). The precipitation of the study area is characterized by bi-modal distribution with a mean annual rainfall of 960.10 mm . Kereba River is one of the major uses of the river which drains the east pact of the Kereba sub-catchment forming a tributary to Awash.
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Figure 4.Agro ecology Map of the Study Area
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Figure 5.Temperature and Rainfall Distribution of the Study Area records from 1999 to 2019 Source: NMSA (2019)
3.1.4. Vegetation and wildlife
The type of natural vegetation found in the Kereba sub-catchment includes shrubs in the lower part of the catchment and thick and scattered forests in the middle and upper parts of the catchment of the area. The commonly observed vegetations (free species) are cooperies, hygienic podocarpus, tid, weira and others are well known. The known wild animal in the catchment area includes a monkey, rabbit, hyena, tiger, pig, etc. There is no reserved area (Natural Park, Game Reserves, and sanctuaries) in the Kereba sub-catchment.
3.1.5. Population characteristics and socio-economic setting
According to the Central Statistical Agency (CSA, 2014), the total population living in the Kereba sub-catchment is 55,519 of which 51,392 live in rural areas while 4,127 live in urban areas. This indicates about 92.6% of the population lives in rural areas and the rest in urban areas. The average family size is estimated to be 6 and 4 per household in rural and urban areas, respectively. The population density in the upper catchment is extremely higher than the population density in the middle and lower catchment.
The economy is based mainly on subsistence agriculture. The farming system is characterized by mixed farming. The average farm size is less than 0.25 ha per household. Farmers mainly use their land to produce cereal crops like wheat and barley as well as chat, coffee, vegetables, and fruits. Sorghum and maize inter-cropping with haricot beans is the dominant crop combination.
3.2. Research Design
To achieve the specified objectives of this study the investigator employed the mixed research design. The purpose of mixed-method design is to collect data from different sources and applied the triangulation method to enhance and improve the quality of the data during the analysis and interpretation (Creswell, 2005). This approach is preferred over others due to its merits to substantiate, cross-validate, or confirm findings within a single study as the research under consideration is complex and needs to be examined from various angles (Creswell,2005). Furthermore, this strategy enables the investigator to collect data in a short time (Gay et al., 2009).
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- Citation du texte
- Juhar Mohammed (Auteur), 2021, Land Use/Land Cover Changes, Driving Forces and Their Implications on Climate Variability. The Case of Kereba Sub-Catchment of Awash Basin, Eastern Ethiopia, Munich, GRIN Verlag, https://www.grin.com/document/1297654
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