Your search found 2 records
1 Abera, W.; Tamene, L.; Tibebe, D.; Adimassu, Zenebe; Kassa, H.; Hailu, H.; Mekonnen, K.; Desta, G.; Sommer, R.; Verchot, L. 2020. Characterizing and evaluating the impacts of national land restoration initiatives on ecosystem services in Ethiopia. Land Degradation and Development, 31(1):37-52. [doi: https://doi.org/10.1002/ldr.3424]
Land restoration ; Ecosystem services ; Land degradation ; Landscape conservation ; Impact assessment ; Sustainable land management ; Projects ; Agroecological zones ; Soil erosion ; Highlands ; Case studies / Ethiopia / Tigray / Amhara
(Location: IWMI HQ Call no: e-copy only Record No: H049428)
(9.02 MB)
Land restoration is considered to be the remedy for 21st century global challenges of land degradation. As a result, various land restoration and conservation efforts are underway at different scales. Ethiopia is one of the countries with huge investments in land restoration. Tremendous land management practices have been implemented across the country since the 1970s. However, the spatial distribution of the interventions has not been documented, and there is no systematic, quantitative evidence on whether land restoration efforts have achieved the restoration of desired ecosystem services. Therefore, we carried out a meta-analysis of peer-reviewed scientific literature related to land restoration efforts and their impacts in Ethiopia. Results show that most of the large-scale projects have been implemented in the highlands, specifically in Tigray and Amhara regions covering about 24 agroecological zones, and land restoration impact studies are mostly focused in the highlands but restricted in about 11 agroecological zones. The highest mean effect on agricultural productivity is obtained from the combination of bunds and biological interventions followed by conservation agriculture practices with 170% and 18% increases, respectively. However, bunds alone, biological intervention alone, and terracing (fanya juu) reveal negative effects on productivity. The mean effect of all land restoration interventions on soil organic carbon is positive, the highest effect being from “bunds + biological” (139%) followed by exclosure (90%). Reduced soil erosion and runoff are the dominant impacts of all interventions. The results can be used to improve existing guidelines to better match land restoration options with specific desired ecosystem functions and services. Although the focus of this study was on the evaluation of the impacts of land restoration efforts on selected ecosystem services, impacts on livelihood and national socioeconomy have not been examined. Thus, strengthening socioeconomic studies at national scale to assess the sustainability of land restoration initiatives is an essential next step.
2 Berhanu, D.; Alamirew, T.; Taye, Meron Teferi; Tibebe, D.; Gebrehiwot, S.; Zeleke, G. 2023. Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia. Journal of Water and Climate Change, 14(8):2583-2605. [doi: https://doi.org/10.2166/wcc.2023.502]
Climate models ; Performance assessment ; Evaluation ; Rainfall patterns ; Spatial distribution ; Trends ; Precipitation ; Seasonal variation ; Datasets ; Climate change / Ethiopia
(Location: IWMI HQ Call no: e-copy only Record No: H052162)
https://iwaponline.com/jwcc/article-pdf/14/8/2583/1277280/jwc0142583.pdf
https://vlibrary.iwmi.org/pdf/H052162.pdf
https://vlibrary.iwmi.org/pdf/H052162.pdf
(1.79 MB) (1.79 MB)
Ethiopia is highly susceptible to the effects of climate change and variability. This study evaluated the performances of 37 CMIP6 models against a gridded rainfall product of Ethiopia known as Enhancing National Climate Services (ENACTS) in simulating the observed rainfall from 1981 to 2014. Taylor Skill Score was used for ranking the performance of individual models for mean monthly, June–September, and February–May seasonal rainfall. Comprehensive rating metrics (RM) were used to derive the overall ranks of the models. Results show that the performances of the models were not consistent in reproducing rainfall distributions at different statistical metrics and timeframes. More than 20 models simulated the largest dry bias on high topographic and rainfall-receiving areas of the country during the June–September season. The RM-based overall ranks of CMIP6 models showed that GFDL-CM4 is the best-performing model followed by GFDL-ESM4, NorESM2-MM, and CESM2 in simulating rainfall over Ethiopia. The ensemble of these four Global Climate Models showed the best performance in representing the spatiotemporal patterns of the observed rainfall relative to the ensembles of all models. Generally, this study highlighted the existence of dry bias in climate model projections for Ethiopia, which requires bias adjustment of the models, for impact assessment.
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