Spatial-temporal analyses of climate elements, vegetation characteristics and sea surface temperature anomaly. A Case study in Gojam, Ethiopia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Agriculture is the backbone of Gojjam economy as it depends on seasonal characteristics of rainfall. This study analyses the components of regional climate variability, especially La Niña or El Niño Southern Oscillation (ENSO) events and their impact on rainfall variability and the growing season normalized difference vegetation index. The temporal and spatial distribution of temperature, precipitation and vegetation cover have been investigated statistically in two agricultural productive seasons for a period of 9 years (2000–2008), using data from 11 meteorological station and MODIS satellite data in Gojam, Ethiopia. The normalized difference vegetation index (NDVI) is widely accepted as a good indicator for providing vegetation properties and associated changes for large scale geographic regions. Investigations indicate that climate variability is persistent particularly in the small rainy season Belg and continues to affect vegetation condition and thus Belg crop production. Statistical correlation analyses shows strong positive correlation between NDVI and rainfall in most years, and negative relationship between temperature and NDVI in both seasons. Although El Niño and La Niña events vary in magnitude in time, ENSO analyses shows that two strong La Niña years and one strong El Niño years. ENSO analyses result shows that its impact to the region rainfall variability is mostly noticeable but it is inconsistent and difficult to predict all the time. The NDVI anomaly patterns approximately agree with the main documented precipitation and temperature anomaly patterns associated with ENSO, but also show additional patterns not related to ENSO. The spatial and temporal analyses of climate elements and NDVI values for the growing season shows that NDVI and rainfall are very unstable and variable during the 9 years period. ENSO /El Niño and La Niña events analyses shows an increase of vegetation coverage during El Niño episodes contrasting to La Niña episodes. Moreover, El Niño years are good for Belg crop production

Assessing Impact of Climate Change on Hydrology of Melka Kuntrie Subbasin, Ethiopia with Ar4 and Ar5 Projections

Assessing future challenges in water resources management is crucial to the Melka Kuntrie (MK) subbasin suffering water shortage. Impact assessments are evaluated by the HBV hydrological model with six scenarios, including two GCMs of AR4-A2 and two GCMs of AR5-RCP4.5 and RCP8.5, for the time periods 2021–2050 and 2071–2100. Evapotranspiration is expected to increase under all scenarios—due to rising temperature—and induce more water stress on rainfed agriculture of the area. However, the increase in the monthly minimum temperature is beneficial to crops against chilling damages. Five out of six projections show significant increases of rainfall and streamflow in both annual and major rainy seasons, except ECHAM-A2. Annual rainfall (streamflow) is expected to increase by 38% (23%) and 57% (49%) during 2021–2050 and 2071–2100, respectively, under RCP8.5 scenarios. Greater flashflood risk is a concern because of the projected increase in streamflow. The projection of decreased streamflow with ECHAM-A2 will exacerbate the existing water shortage, especially in the minor rainy season. Water harvesting during the major rainy season would be vital to enhance water management capacities and reduce flashflood risks. Lacking sufficient hydraulic and irrigation infrastructures, the MK subbasin will be more vulnerable to the impacts of climate change

Evaluating the impact of land use land cover changes on the values of ecosystem services in the Chacha Watershed, Ethiopia's central highland

Land use/land cover (LULC) changes are the foremost drivers that enormously modified ecosystem services in Ethiopia. Evaluating LULC changes impacts on ecosystem services values (ESV) is vital to show the susceptibility of ecosystem services and also have an indispensable role in land resource management. This study was conducted to (1) determine the tempo-spatial LULC changes; and (2) estimate the ESV in response to LULC changes in the Chacha Watershed, central highlands of Ethiopia. A supervised image classification technique using the maximum likelihood classifier was employed to analyze the trends of LULC changes over the past 24 years, i.e., 1997–2021 periods. The ESV of the identified LULC types was estimated using ecosystem service value coefficients. The findings revealed that the largest area of the Chacha Watershed was covered by cultivated land, followed by open grassland and forest land. The cultivated land decreased by 16.30% whose rate is about 5.09 km2 yr−1. In contrast, the forest land increased by 49.52% with an annual increasing rate of 1.17 km2. Moreover, the built-up area showed a 905.06% increase in size during the analysis periods. The estimated overall ESV of each analysis period ranged from US$32.7 million in 1997 to US$ 35.56 million in 2006. The largest ESV was recorded for the cultivated land, followed by the open grassland and the forest land. Annually, there is a net positive gain of US$ 80 thousand of change of ESV in the study watershed. This indicates that the small positive change in the size of the forest land has significantly increased the ESV. The promising positive change of the ESV for the forest land, the open grassland, and the riverbeds is an indication that the total ESV can be maximized by increasing the land coverage for these three LULC types in the study watershed. The resulting scientific insights and knowledge are essential to highlight existing concerns on the tempo-spatial changes of LULC and their associated impacts on the ESV. This may in turn assist policy- and decision-makers and land-use planners to make an appropriate resource allocation decision in order to achieve sustainable management of ecosystems and their key attributes