Climate change posed agricultural drought and potential of rainy season for effective agricultural water management, Kesem sub-basin, Awash Basin, Ethiopia

Yadeta, Dame; Kebede, Asfaw; Tessema, Negash

doi:10.1007/s00704-020-03113-7

Cited by 20 publications

(14 citation statements)

References 33 publications

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“…This result implies that wet condition are likely the prevailing weather of the future in the district (Kim & Yu 2012;Weller & Cai 2013). This finding is in harmony with the results of (Kim et al 2008;IPCC 2013;Gebremeskel & Kebede 2017;Osima 2018;Yadeta et al 2020aYadeta et al , 2020bTessema et al 2020).…”

Section: Resultssupporting

confidence: 86%

“…Whereas, agricultural productivity in such areas is vitally dependent on water availability during the crop production season (Wang et al 2016). Severe droughts and significant losses of yield in the arid and semi-arid zones are due to the rising temperature (Andreadis & Letten Maier 2006;Yadeta et al 2020a). Similarly, in the middle Awash River basin of Ethiopia, the impacts of climate change are worsening crop production via increasing evapotranspiration losses (Yadeta et al 2020b).…”

Section: Introductionmentioning

confidence: 99%

“…To do so, promising measurements and predictions have to be accessible to safeguard water resources and agricultural sectors in drylands (Gan 2000). Ethiopia in general and the middle Awash basin, in particular, are susceptible to the impacts of climate change and variability (Tessema et al 2020;Yadeta et al 2020a). The higher temperature resulting from climate change will induce higher evapotranspiration (Yadeta et al 2020b), which in turn affects the hydrological systems and agriculture.…”

Section: Introductionmentioning

confidence: 99%

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Modeling climate change impacts on crop water demand, middle Awash River basin, case study of Berehet woreda

Tessema

Kebede

Geleta

2021

Water Practice and Technology

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Climate change mainly affects crops via impacting evapotranspiration. This study quantifies climate change impacts on Evapotranspiration, crop water requirement, and irrigation water demand. 17 GCMs from the MarkSim-GCM were used for RCP 4.5 and 8.5 scenarios for future projection. A soil sample was collected from 15 points from the maize production area. Based on USDA soil textural classification, the soil is classified as silt loam (higher class), clay loam (middle class), and clay loam (lower class). The crop growing season onset and offset were determined using the Markov chain model and compared with the farmer's indigenous experience. The main rainy season (Kiremt) starts during the 1st meteorological decade of June for baseline period and 2nd decade to 3rd decade of June for both RCP 4.5 and RCP 8.5 of near (2020s) and mid (2050s) future period. The offset date is in the range of 270 (base period), RCP 4.5 (278, 284), and RCP 8.5 (281, 274) DOY for baseline, near, and mid future. The rainfall and temperature change show an increasing pattern from the base period under both scenarios. Furthermore, the reference evapotranspiration (ETo) estimating model was developed using multiple variable regression and used for a future period in this study. In the base period, ETo increases from 33.4 mm/dec in the 1st decade of July to the peak value of 52.1 mm/dec in the 3rd decade of May. Under RCP8 .5, the 2nd decade of August ETO is minimal (44.3 mm/dec) while in 1st decade of April ETO was maximum (75.3 mm/dec) and raise from 44.3 mm/dec in the 2nd decade of August to the peak value of 75.3 mm/dec in the 1st decade of April. Under RCP 4.5, ETO raises from 33 mm/dec in the 1st decade of Dec to the peak value of 48 mm/dec in the 3rd decade of May. ETo shows an increasing trend from the base period under both scenarios. During the base period, maize variety with a growing period of 110 days required 403.2 mm depth of water, while 67 mm is required as supplementary irrigation. Crop water and irrigation requirements of maize variety with a growing period of 110 days are predicted to be 436.1 and 445.1 mm water during the 2020 and 2050 s for RCP 4.5, while 101.8 to 63.7 mm depth of water as supplementary irrigation respectively and 441.3 and 447.3 mm of water during 2020 and 2050 s of the future period for RCP 8.5, while 142.9 to 134.0 mm required as supplementary irrigation for both periods of RCP 8.5 scenarios. Crop water need will increase by 8.16 and 10.39% for RCP 4.5 and by 9.45 and 10.94% for RCP 8.5 of the 2020 and 2050 s respectively. In this study, a new ETO model is developed using a multiple variable linear regression model and its degree of the fitting is statistically tested and Kc is adjusted for the local climate, hence, can be used in future irrigation and related studies. Generally, decision-makers, farmers, Irrigation engineers, and other stakeholders can use the results of this study in irrigation design, monitoring, scheduling, and other related activities. Highlight In this study, a new ETO model is developed and Kc is adjusted for the local climate, hence, can be used in future irrigation and related studies. Generally, decision-makers, farmers, Irrigation engineers, and other stakeholders can use the results of this study in irrigation design, monitoring, scheduling, and other related activities.

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Section: Resultssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling climate change impacts on crop water demand, middle Awash River basin, case study of Berehet woreda

Tessema

Kebede

Geleta

2021

Water Practice and Technology

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“…Livestock and on-farm-income-based and marginal and off-farm-income-based farm typologies are highly sensitive to drought than the intensive-irrigation-farming-based counterparts. The rainfall variability in space and time in the Awash Basin makes it difficult to predict where it falls, and it is this spatiotemporal variation in rainfall distribution that causes serious problems such as crop failure [92]. In terms of crop, animal, and human diseases, lowland AEZs are highly sensitive, with a weighted index of 0.216, followed by midlands, having an index value of 0.176.…”

Section: Sensitivity: Crop Failure Diseases and Crisismentioning

confidence: 99%

Analysis of Smallholders’ Livelihood Vulnerability to Drought across Agroecology and Farm Typology in the Upper Awash Sub-Basin, Ethiopia

et al. 2021

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Background: Assessing the magnitude of smallholder farmers’ livelihood vulnerability to drought is an initial step in identifying the causal factors and proposing interventions that mitigate the impacts of drought. This study aimed to assess smallholders’ livelihood vulnerability to the drought in the upper Awash sub-basin, Ethiopia. Household (HH) and climate data were used for indicators related to sensitivity, exposure, and adaptive capacity that define vulnerability to drought. The vulnerability of farmers’ livelihood to drought was compared among the studies agroecological zone (AEZ) and farm typologies. Results: The result illustrated a diverse magnitude of vulnerability index (VI) ranging from −1.956 to −4.253 for AEZ. The highest magnitude of VI was estimated for livelihood in the lowland AEZ, while the lowest magnitude of VI was estimated in midland AEZ. This could be accounted for by the fact that lowland farmers shown the highest exposure (0.432) and sensitivity (0.420) and the lowest adaptive capacity (0.288). A closer look at farmers’ livelihood typology, in each of the AEZ, showed substantial diversity of farmers’ livelihood vulnerability to drought, implying potential aggregations at AEZ. Accordingly, the vulnerability index for livestock and on-farm-income-based livelihood and marginal and off-farm-income-based livelihood typologies were higher than the intensive-irrigation-farming-based smallholders’ livelihood typology. Conclusions: Based on the result, we concluded that procedures for smallholders’ livelihood resilience-building efforts should better target AEZ to prioritize the focus region and farmers’ livelihood typology to tailor technologies to farms. Although the result emphasizes the importance of irrigation-based livelihood strategy, the overall enhancement of farmers adaptive capacity needs to focus on action areas such as reducing the sensitivity and exposure of the households, improving farmers usage of technologies, diversify farmers’ livelihood options, and, hence, long-term wealth accumulation to strengthen farmers’ adaptive capacity toward drought impacts.

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“…A recent study of long-term precipitation analysis in the study area indicated high variability trend (CV> 35%) especially for short growing season even though the amount of rainfall in the main season shows an increasing trend (Dame et al, 2020). Precipitation is the main source of agricultural water in the sub-basin.…”

Section: Causes Of Land Use/cover Dynamics and Community Perceptions mentioning

confidence: 79%

Modeling land use dynamics in the Kesem sub-basin, Awash River basin, Ethiopia

Tessema

Kebede

Yadeta

2020

Cogent Environmental Science

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Nowadays, the land use and land cover change and its impacts are revealing on different natural resource and man-made systems. This study attempted to examine the land use and land cover (LULC) dynamics on this using multi-spectral satellite imageries in Geographical Information System (GIS) in the Kesem sub-basin of the Awash River Basin, Ethiopia. It also attempted to assess the community perceptions on this using field observation. Three LULC maps were produced by analyzing remotely sensed images of Landsat satellite imageries within three periods of time from 1986 to 2013. Supervised image classification was used to map LULC classes and explain the drivers, and causes were linked to the changes. The result from LULC maps of 2013 showed nine major land use and land cover types. From 1986 to 2000, there were, among others, a dramatic expansion of agricultural land, massive settlement, and widespread eucalyptus plantation, all of which contributed to a dramatic decrement of shrub land and acacia in the sub-basin. The major driving ABOUT THE AUTHORS

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Climate change posed agricultural drought and potential of rainy season for effective agricultural water management, Kesem sub-basin, Awash Basin, Ethiopia

Cited by 20 publications

References 33 publications

Modeling climate change impacts on crop water demand, middle Awash River basin, case study of Berehet woreda

Modeling climate change impacts on crop water demand, middle Awash River basin, case study of Berehet woreda

Analysis of Smallholders’ Livelihood Vulnerability to Drought across Agroecology and Farm Typology in the Upper Awash Sub-Basin, Ethiopia

Modeling land use dynamics in the Kesem sub-basin, Awash River basin, Ethiopia

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