Gis and Remote Techniques Application on the Spatio-Temporal Climate Variability Analysis: The Case of Ziway Dugda and Dodota Woreda, Arsi Zone, Oromia Region, Ethiopia
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Date
2014-11
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Addis Ababa University
Abstract
Climate change is the most complex and cross-cutting environmental problem facing the world today. Ethiopia has historically suffered from climate variability and extremes. The impact of climate variability is high in Ethiopia. The problem of climate change is broadly studied from various angles that emanates from solemnity of the problem. In this regard, the application of GIS and Remote Sensing technologies in analyzing climatic data is crucial. The study aimed at
assessing GIS and Remote Sensing Techniques Application on the Spatio-Temporal Climate Variability Analysis: The Case of Ziway Dugda and Dodota woreda of Arsi Zone, Oromia Region, Ethiopia using GIS and Remote Sensing Techniques. In this study, gridded time series data of both rainfall and temperature ranging from 1983-2012 were employed as a principal data source. Auxiliary data from NASA's LANDSAT satellite images of three sets of time 1984TM, 1995TM and 2013ETM+ with row 54, 55 and path 168 have been used. In addition, Normalized
Difference Vegetation Index [NDVI] data from National Aeronautics and Space Administration [NASA] of Moderate Resolution Imaging Spectrordadiometer [MODIS] terra sensor also utilized. Moreover, a survey was also carried out in the study site. This was made in order to complement the analysis output obtained from gridded data with the survey result. Due to the homogenous nature of the population a total of 40 sample respondents and four key informants: two agricultural extension officers and two Disaster Prevention Preparedness Commission
officials, one from each ‘woreda’ were involved in the study. Both the sample respondents and
key informants were carefully selected on the basis of purposive sampling technique. Direct
narration and Simple descriptive statistics were used to analyze the data obtained from sample
respondents. The temporal and spatial distribution of temperature, precipitation, land use land
cover and NDVI have been analyzed in detailed and presented in annual and seasonal basis for
different periods using the data obtained from 50 gridded meteorological stations, satellite
images and MODIS data for the study site. The study revealed that long term recorded rainfall
data showed an increasing trend during the observation period between 1983 and 2012, with an
overall mounting rate of 78.8 mm, except inter annual fluctuation and the mean maximum total
rainfall analysis output of the same period lacks consistent pattern. Furthermore, late onset and
early cessation of rainfall has also characterized the study area. On the other hand, it is found
that in the period from 1981 to 2010, the analysis result has shown that the maximum
temperature increased by 0.230C per decade with an overall rise of 0.70C in thirty years period.
While the minimum temperature is in a constant rate it did not show significant change in the
observation time. Investigations indicate that climate variability is persistent particularly in the
small rainy season ‘belg’, there was a decline in the amount of rainfall and affected vegetation
condition and crop production. Statistical correlation analyses shows that there is moderate
positive correlation between NDVI and mean annual rainfall in most cases and ‘meher’ season whereas a strong correlation found between rainfall and NDVI in ‘belg’ season. On the opposite,
negative correlation was found between temperature and NDVI. Lastly, pertaining with the land
use land cover classification the result denoted that the proportion of forest coverage is
significantly decreasing from time to time. The forest coverage has been reduced to 12258.4 ha (7.5%) by 2013 from that of 1984 having a total forest coverage of 19140.4ha (11.7%). Generally, the time series analysis result reflected that rainfall, minimum temperature and maximum temperature observed in the study area have shown a clear spatial and temporal variation which contributed for the present climate dynamics in the locality.
Keywords: Climate Variability, GIS, Remote Sensing, Spatial, Temporal.
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Keywords
Climate Variability, GIS, Remote Sensing, Spatial, Temporal