Urban impact on ecological integrity of nearby rivers in developing countries: the Borkena River in highland Ethiopia

Accelerated pollution and eutrophication of rivers and streams because of human activity are a concern throughout the world and severe in Africa where Ethiopia is case in point. The objective of this study was to assess the urban impact on the ecological integrity of the Borkena River at the eastern escarpment of the central Ethiopian highlands. The water quality status and macroinvertebrate distribution and diversity of the river were assessed during the dry and wet seasons. Diversity indices revealed that a severe decline in the ecological integrity of the Borkena River downstream of Dessie and within Kombolcha towns in terms of macroinvertebrate abundance and composition. Clustering and ordination analysis clearly separated reference sites from urban impacted sites. At the urban-impacted sites, dissolved oxygen was also depleted to 0.5 mg/l and BOD5 values were reached to a level of above 1,000 mg/l, with extremely low biological diversity of pollution-sensitive taxa. These patterns are the result of a combination of rampant dumping of untreated wastes exacerbated by geologic, topographic, climatic and land use factors.     

Landscape-based upstream-downstream prevalence of land-use/cover change drivers in southeastern rift escarpment of Ethiopia

Characterized by high population density on a rugged topography, the Gedeo-Abaya landscape dominantly contains a multi-strata traditional agroforests showing the insight of Gedeo farmers on natural resource management practices. Currently, this area has been losing its resilience and is becoming unable to sustain its inhabitants. Based on both RS-derived and GIS-computed land-use/cover changes (LUCC) as well as socioeconomic validations, this article explored the LUCC and agroecological-based driver patterns in Gedeo-Abaya landscape from 1986 to 2015. A combination of geo-spatial technology and cross-sectional survey design were employed to detect the drivers behind these changes. The article discussed that LUCC and the prevalence of drivers are highly diverse and vary throughout agroecological zones. Except for the population, most downstream top drivers are perceived as insignificant in the upstream region and vice versa. In the downstream, land-use/cover (LUC) classes are more dynamic, diverse, and challenged by nearly all anticipated drivers than are upstream ones. Agroforestry LUC has been increasing (by 25% of its initial cover) and is becoming the predominant cover type, although socioeconomic analysis and related findings show its rapid LUC modification. A rapid reduction of woodland/shrubland (63%) occurred in the downstream, while wetland/marshy land increased threefold (158%), from 1986 to 2015 with annual change rates of -?3.7 and +?6%, respectively. Land degradation induced by changes in land use is a serious problem in Africa, especially in the densely populated sub-Saharan regions such as Ethiopia (FAO 2015). Throughout the landscape, LUCC is prominently affecting land-use system of the study landscape due to population pressure in the upstream region and drought/rainfall variability, agribusiness investment, and charcoaling in the downstream that necessitate urgent action.     

Spatial assessment of groundwater quality in Guna Tana landscape

This study focuses on investigating the quality of groundwater for irrigation and drinking water purposes. Spatial distribution of physicochemical and microbiological parameters was assessed from samples collected from springs, hand‐dug wells, and boreholes found the Guna Tana landscape. A total of 70 samples were considered for physical, chemical, and bacteriological water quality determination. The results revealed that most of the groundwater quality index (WQI) values lie between good and excellent. The maximum, minimum, mean, and standard deviation of each water quality parameter were prepared for evaluating groundwater quality. According to the WQI values, more than 83% of the water samples were classified as excellent water for drinking. More than 92% of the water samples showed low sodium hazards for irrigation and about 48% and 46% of the water samples were classified as within the excellent and good water classes for irrigation based on their electrical conductance levels. Therefore, the groundwater that is found in the Guna Tana landscape could be used for drinking and irrigation purposes without any advanced treatment.     

Building future scenarios and uncovering persisting challenges of participatory forest management in Chilimo Forest, Central Ethiopia

We examined the changes in forest status and people's livelihoods through building future scenarios for Chilimo Forest in Central Ethiopia where participatory forest management (PFM) is being implemented. Participatory methods were employed to collect data, and a dynamic modeling technique was applied to explore trends over time. By integrating the more quantitative model outputs with qualitative insights, information on forests and livelihoods was summarized and returned to users, both to inform them and get feedback. A scenario of open access without PFM provides higher income benefits in the short term but not over the longer term, as compared to a scenario with PFM. Follow up meetings were organized with national decision makers to explore the possibility of new provisions in the national forest proclamation related to joint community-state ownership of forests. Project implementers must constantly work towards improving short term incentives from PFM, as these may be insufficient to garner support for PFM. Other necessary elements for PFM to succeed include: ensuring active participation of the communities in the process; and, clarifying and harmonizing the rules and regulations at different levels.     

Statistical Modelling of Extreme Rainfall Indices using Multivariate Extreme Value Distributions

Environmental Modeling & Assessment - Multivariate extreme value models are used to investigate the combined behaviour of several weather variables. To investigate joint dependence of extreme...     

Analysis of observed and perceived climate change and variability in Arsi Negele District,Ethiopia

Climate change and variability has been detected in Ethiopia. Smallholder and subsistence farmers, pastoralists and forest-dependent households are the most hit by climate-related hazards. They have to have perception of climate change in order to respond it through making coping and/or adaptation strategies. Local perceptions and coping strategies provide a crucial foundation for community-based climate change adaptation measures. This study was specifically designed to (1) assess households’ perception and knowledge in climate change and/or variability, and (2) establish the observed changes in climate parameters with community perceptions and climate anomalies. Purposive stratified random sampling method has been used to gather information from 355 sample households for individual interviews supplemented by group discussion and key informants interviews. The analysis of observed and satellite climate data for the study district showed that mean maximum and minimum temperature for the period 1983–2014 has increased by 0.047 and 0.028 °C/year, respectively. However, the total rainfall has declined by 10.16 mm per annum. Seasonally, the rainfall has declined by 2.198, 4.541, 1.814 and 1.608 mm per annum for Ethiopian summer, spring, autumn and winter seasons, respectively. Similarly, the mean maximum temperature of the study area had showed an increment of 0.035, 0.049, 0.044 and 0.065 °C per year for spring, winter, autumn and summer seasons, respectively. The observed climate variation has been confirmed by people’s perception. Considering what had been the existed situations before 30 years ago as normal, an increase in temperature, an increase in drought frequency, a decrease in total rainfall, erratic nature of its distribution and the tardiness of its onset had been perceived by 88, 70, 97, 80 and 94% of the respondents, respectively, at current time—2015. Deforestation as a casual factor of climate change and variability had been perceived by 99.7% of the respondents. This had been also confirmed by scientific studies as it emits carbon dioxide and is the main driver of climate change and variability. Indigenous knowledge, including climate predictions, has been used by people to implement their day-to-day agricultural activities. Therefore, science should be integrated with the perception and indigenous knowledge of people to come up with concrete solution for climate change and variability impacts on human livelihoods.     

The impact of traditional coffee processing on river water quality in Ethiopia and the urgency of adopting sound environmental practices

Although waste from coffee processing is a valuable resource to make biogas, compost, and nutrient-rich animal food, it is usually dumped into nearby water courses. We carried out water quality assessment at 44 sampling sites along 18 rivers that receive untreated waste from 23 coffee pulping and processing plants in Jimma Zone, Ethiopia. Twenty upstream sampling sites free from coffee waste impact served as control, and 24 downstream sampling sites affected by coffee waste were selected for comparison. Physicochemical and biological results revealed a significant river water quality deterioration as a result of disposing untreated coffee waste into running water courses. During coffee-processing (wet) season, the highest organic load (1,900?mg/l), measured as biochemical oxygen demand, depleted dissolved oxygen (DO) to a level less than 0.01?mg/l, and thus curtailed nitrification. During off season, oxygen started to recuperate and augmented nitrification. The shift from significantly elevated organic load and reduced DO in the wet season to increased nitrate in the off season was found to be the determining factor for the difference in macroinvertebrate community structure as verified by ordination analysis. Macroinvertebrate diversity was significantly reduced in impacted sites during the wet season contrary to the off season. However, there was a significant difference in the ratio of sensitive to pollution-tolerant taxa in the off season, which remained depreciated in the longer term. This study highlights the urgency of research exploring on the feasibility of adopting appropriate pollution abatement technologies to implement ecologically sound coffee-processing systems in coffee-growing regions of Ethiopia.     

Modelling dependence structures of extreme wind speed using bivariate distribution: a Bayesian approach

When investigating extremes of weather variables, it is seldom that a single weather station determines the damage, and extremes may be caused from the combined behaviour of several weather stations. To investigate joint dependence of extreme wind speed, a bivariate generalised extreme value distribution (BGEVD) was considered from frequentist and Bayesian approaches to analyse the extremes of component-wise monthly maximum wind speed at selected weather stations in South Africa. In the frequentist approach, the parameters of extreme value distributions (EVDs) were estimated with maximum likelihood, whereas in the Bayesian approach the Markov Chain Monte Carlo (MCMC) technique was used with the Metropolis–Hastings algorithm. The results showed that when fitted to component-wise maxima of extreme weather variables, the BGEVD provided apparent benefits over the univariate method, which allowed information to be pooled across stations and resulted in improved precision of the estimates for the parameters and return levels of the distributions. The paper also discusses a method to construct informative priors empirically using historical data of the underlying process from weather characteristics of four pairs of surrounding weather stations at various distances. The results from the Bayesian analysis showed that posterior inference might be affected by the choice of priors that were used to formulate the informative priors. From the results, it could be inferred that the Bayesian approach provides a satisfactory estimation strategy in terms of precision, compared with the frequentist approach, because it accounts for uncertainty in parameters and return level estimations.     

Bayesian modelling of extreme wind speed at Cape Town,South Africa

In the framework of generalized extreme value (GEV) distribution, the frequentist and Bayesian methods have been used to analyse the extremes of annual maxima wind speed recorded by automatic weather stations in Cape Town, Western Cape, South Africa. In the frequentist approach, the GEV distribution parameters were estimated using maximum likelihood, whereas in the Bayesian method the Markov Chain Monte Carlo technique with the Metropolis–Hastings algorithm was used. The results show that the GEV model with trend in the location parameter appears to be a better model for annual maxima data. The paper also discusses a method to construct informative priors empirically using historical data of the underlying process from other weather stations. The results from the Bayesian analysis show that posterior inference might be affected by the choice of priors and hence by the distance between a weather station used to formulate the priors and the point of interest.