Yihun Taddele Dile

While the availability of “big data” on biophysical parameters through citizen science and/or from public/private sources is expected to help in addressing data scarcity issues, there is little understanding of whether and/or how such data will improve watershed simulations. This research aimed to evaluate whether improvements in resolutions of Digital Elevation Model (DEM) and soil data will enhance streamflow and sediment yield simulations and thereby improve soil and water management decisions. The study was conducted in two different-sized watersheds (Anjeni and Gilgel Abay with ~ 1 km² and ~ 1655 km² area, respectively) in the Upper Blue Nile basin in Ethiopia. Effects of DEM and soil data resolutions on streamflow and sediment yield were evaluated using the Soil and Water Assessment Tool (SWAT). The results showed that the effect of DEM and soil data resolution on streamflow and sediment yield simulation was scale dependent finer resolution DEM and soil datasets improved streamflow and sediment yield simulations in the smaller Anjeni watershed, whereas DEM resolution had no effect in the bigger Gilgel Abay watershed. Small watersheds are often used to understand watershed processes, and thus the use of finer-resolution spatial data for watershed simulations could result in better results. Findings from the smaller Anjeni watershed suggested that the combined use of finer resolution DEM and soil data could potentially improve sediment yield simulations although the lack of observed sediment yield data did not allow verification of this at the larger Gilgel Abay watershed.

This study evaluates the effect of the statistical bias correction techniques of distribution mapping and linear scaling on climate change signals and extreme rainfall indices under different climate change scenarios in the Jemma sub-basin of the Upper Blue Nile Basin. The mean, cumulative distribution function (CDF), mean absolute error (MAE), probability of wet days (Pr_wet (%)), and 90th percentile (X₉₀ (mm)) of observed rainfall and the regional climate model (RCM) simulations of rainfall with and without statistical bias correction were compared with the historical climate (1981–2005). For future (2071–2100) climate scenarios, the change in climate signal and extreme rainfall indices in the RCM simulations with and without bias correction were also evaluated using different statistical metrics. The result showed that the statistical bias correction techniques effectively adjusted the mean annual and monthly RCM simulations of rainfall to the observed rainfall. However, distribution mapping is effective and better than linear scaling for adjusting the probability of wet days and the 90th percentile of RCM simulations. In future climate scenarios, RCM simulations showed an increase in rainfall. However, the statistically bias-adjusted RCM outputs revealed a decrease in rainfall, which indicated that the statistical bias correction techniques triggered a change in climate signal. Statistical bias correction methods also result in changes in the extreme rainfall indices, such as frequency of wet days (R1mm), number of heavy precipitation days (R10mm), number of very heavy rainfall days (R20mm), and other intensity and frequency indices.

Climate extreme events have been observed more frequently since the 1970s throughout Ethiopia, which adversely affects the socio-economic development of the country, as its economy depends on agriculture, which, in turn, relies heavily on annual and seasonal rainfall. Climate extremes studies conducted in Ethiopia are mainly limited to a specific location or watershed, making it difficult to have insights at the national level. The present study thus aims to examine the observed climate extreme events in Ethiopia at both station and agro-ecological zone (AEZ) levels. Daily rainfall and temperature data for 47 and 37 stations, respectively (1986 up to 2020), were obtained from the National Meteorology Agency (NMA). The Modified Mann–Kendall (MMK) trend test and the Theil–Sen slope estimator were employed to estimate the trends in rainfall and temperature extremes. This study examines trends of 13 temperature and 10 rainfall extreme indices using RClimDex in R software. The results revealed that most of the extreme rainfall indices showed a positive trend in the majority of the climate stations. For example, an increase in consecutive dry days (CDD), very heavy rainfall days (R20), number of heavy rainfall days (R10) and consecutive wet days (CWD) were exhibited in most climate stations. In relation to AEZs, the greater number of extreme rainfall indices illustrated an upward trend in cool and sub-humid, cool and humid, and cool and moist AEZs, a declining trend in hot arid AEZ, and equal proportions of increasing and decreasing trends in warm semi-arid AEZs. Concerning extreme temperature indices, the result indicated an increasing trend of warm temperature extreme indices and a downward trend of cold temperature extreme indices in most of the climate stations, indicating the overall warming and dryness trends in the country. With reference to AEZs, an overall warming was exhibited in all AEZs, except in the hot arid AEZ. The observed trends in the rainfall and temperature extremes will have tremendous direct and indirect impacts on agriculture, water resources, health, and other sectors in the country. Therefore, the findings suggest the need for identifying and developing climate change adaptation strategies to minimize the ill effects of these extreme climate events on the social, economic, and developmental sectors.

The availability of sufficient water resources is critical for sustainable social and economic development globally. However, recurrent drought has been a precursor to inadequate water supply in the case of Borkena Catchment, Awash River Basin, Ethiopia. To support the conjunctive use and management of surface water and groundwater in Borkena Catchment, an integrated model was developed using the SWAT–MODFLOW model. The model was designed to operate on a monthly time scale. The change in the water balance obtained from the SWAT–MODFLOW model provides a quantitative means to assess the effect of the climate variability and changes, as well as the impact of human activities, on water resources. To advance the understanding at the regional and local scales, surface water–groundwater interactions in the Borkena Catchment geochemical information and piezometer maps were integrated. The results show that the groundwater recharge in the study area is approximately 122 mm/a. The surface water–groundwater interaction results show that the areas around Harbu and Dessie are characterized as losing rivers, while the areas around Kemisse-Chefa and the highlands of Kutaber, where the Borkena River originates, are characterized as gaining rivers. A geochemical analysis indicated that there is an inter-basin groundwater transfer from the Abbay to the Awash basin. The integrated model generated key temporal and spatial information that is useful for the sustainable conjunctive management of surface and groundwater in Borkena Catchment for climate resilience in the face of climate variability and increasing demand.

Ethiopia has the largest livestock population in Africa with 35 million tropical livestock units. The livestock system relies on natural open grazing which is affected by frequent droughts. However, little research exists that studies the suitability of the biophysical environment for fodder production and the risks due to climate change. The main objectives of the study are to evaluate the potential effects of climate change on land suitability for alfalfa production in Ethiopia and to assess the extent of irrigation requirements for alfalfa growing under the adverse climate change projections. The impact of climate change on land suitability for alfalfa was evaluated using projected changes in rainfall and temperature based on three global circulation models (CCSM4, HadGEM2-AO, and MIROC5). A multi-criteria evaluation in GIS that uses biophysical, climatic and topography factors was applied to identify the suitable land. The highly suitable area under current climate scenarios covered ~472,000 km², while moderately suitable and marginally suitable covered ~397,000 km² and ~16,200 km², respectively. The projected climate alters the suitable land for fodder production across Ethiopia. Expansion of suitable land occurred in the highlands where climate scenarios predict an increase in temperature and precipitation. Dryland regions showed a rainfall deficit for the three model projections. The research provides guidelines for growing alfalfa in Ethiopia considering ecological and climatic variability.

The effects of soil data sources on the performance of hydrologic model simulations remain poorly understood compared to the effects of other data inputs. This paper investigated the effects of different soil datasets in simulating streamflow and sediment yield using the Soil and Water Assessment Tool (SWAT). Furthermore, potential improvements in watershed simulations were evaluated by integrating field measured soil parameters (user soil) with global soil datasets. Five soil datasets, namely user soil, AfSIS (Africa Soil Information Service), Food and Agriculture Organization (FAO), and two integrated soils (User-AfSIS and User-FAO) produced by assimilating the user soil with the latter two, were evaluated. The benefits of the user soil in improving streamflow simulations to better replicate observed flow were greater at daily time steps than monthly. Compared to the individual AfSIS and FAO soils, their integration with the user soil improved the daily Nash-Sutcliffe Efficiency (NSE) by 0.19 and 0.17 during model calibration, respectively. Overall, all soils performed relatively similar with monthly sediment yield simulations, which were improved when it was integrated with the user soil. Based on selected rainfall events, the watershed response time was less than 1 h, which suggests that the watershed has a quick runoff response time. This paper showed that streamflow and sediment yield simulation performances of freely available global soil datasets can be improved through integration with locally measured soil information. This study demonstrated that the availability of local soil information is critical for daily hydrologic model simulations, which is critical for planning effective soil and water management practices at plot and field scales.

The largest freshwater lake in Ethiopia, Lake Tana, has faced ecological disaster due to water hyacinth (Eichhornia crassipes) infestation. The water hyacinth is a threat not only to the ecology but also to the socioeconomic development of the region and cultural value of the lake, which is registered as a UNESCO reserve. This study aims to map the spatiotemporal dynamics of the water hyacinth using high-resolution PlanetScope satellite images and assesses the major environmental variables that relate to the weed spatial coverage dynamics for the period August 2017 to July 2018. The plausible environmental factors studied affecting the weed dynamics include lake level, water and air temperature, and turbidity. Water temperature and turbidity were estimated from the moderate resolution imaging spectroradiometer (MODIS) satellite image and the water level was estimated using Jason-1 altimetry data while the air temperature was obtained from the nearby meteorological station at Bahir Dar station. The results indicated that water hyacinth coverage was increasing at a rate of 14 ha/day from August to November of 2017. On the other hand, the coverage reduced at a rate of 6 ha/day from December 2017 to June 2018. However, the length of shoreline infestation increased significantly from 4.3 km in August 2017 to 23.4 km in April 2018. Lake level and night-time water temperatures were strongly correlated with water hyacinth spatial coverage (p < 0.05). A drop in the lake water level resulted in a considerable reduction of the infested area, which is also related to decreasing nutrient levels in the water. The water hyacinth expansion dynamics could be altered by treating the nutrient-rich runoff with best management practices along the wetland and in the lake watershed landscape.