Effect of climate change on water availability in Bilate catchment, Southern Ethiopia

Edamo, Muluneh Legesse; Bushira, Kedir Mohammed; Ukumo, Tigistu Yisihak; Ayele, Mesfin Amaru; Alaro, Matusal Arja; Borko, Habtamu Bogale

doi:10.1016/j.watcyc.2022.06.001

Cited by 15 publications

(15 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dynamics of modeling watershed characterization could be highly influenced by selection of Agro-metrological observation station (Table 4) is important (Arsenault & Brissette, 2014;Condom et al, 2020;Remesan & Holman, 2015). However most of the scholars who adapted hydrological research in Bilate watershed has not described how the observation station for climate data observation stations was selected (Edamo et al, 2022;Negash, 2014;Orke & Li, 2021). Since long-term average values of areal rainfall distribution has a greater role in modeling agricultural watershed characterization, rainfall proportionality for model input is supposed the range of uncertainty based on selected gauged observation (Remesan & Holman, 2015).…”

Section: Metrological Stationsmentioning

confidence: 99%

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination index from cropland N-fertilizer in Bilate downstream

Assa

Bhowmick

Elias

2023

Preprint

View full text Add to dashboard Cite

Crop canopy water content and crop root zone soil water content have been predicted and observed for estimation of crop water balance for the rainy season nitrate runoff and leaching index of the Bilate watershed cropland. For the calibration of earth data observation, the watershed rain gauge station estimated the pattern of rainfall for the main cropping season of crop growth. Cropping season (Apr, May and Jun) monthly mean rainfall between (125mm/month to 165 mm/month) of agro metrological station has predicted time series crop canopy water in the analysis for crop land nitrate-nitrogen leaching/runoff index for the quantification of groundwater and surface runoff nitrate from the Bilate watershed farming zone. May and June were the months in which crops grew in the cropland of the study area, as indicated by the crop biomass statistics from MODIS 006 MOD13Q1-EVI of (Mine 0.19 and Max, 0.57) which is the average crop coefficient Kc for crop growth curve analyzed for model crop parameter. Hyper-spectral indices derived from enhanced vegetation indices (EVI) have been calculated for the analysis of crop zonal biomass statistics (kc) and integrated into the prediction model. The objective of the current research was to predict crop canopy water content in order to determine crop water balance for farmland Nitrogen Nitrate (NO− 3-N) runoff\leaching index for surface and groundwater pollution in the Bilate downstream. The overall predicted result of crop canopy water content has been validated with a regression coefficient (R2) with observed crop root zone soil water content. And the crop land nitrogen balance has been used to confirm the nitrate-nitrogen leaching and runoff index for the study area by comparing the current result with the crop land nitrogen balance.

show abstract

Section: Metrological Stationsmentioning

confidence: 99%

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination index from cropland N-fertilizer in Bilate downstream

Assa

Bhowmick

Elias

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The basin is divided into three geomorphologic zones: rift, escarpment, and highland (Haji et al, 2018). Due to diverse topographical features ranging from 1116 m in the lower reach of the base to 3355 m above mean sea level in the northern Gurage highlands (Figure 1), the mean annual rainfall varies substantially (Ayele et al, 2022;Edamo et al, 2022;Ukumo et al, 2022;Nannawo et al, 2022). The basin is divided into three sub-basins in terms of rainfall distribution, with yearly rainfall of high, moderate and low.…”

Section: Study Area Descriptionsmentioning

confidence: 99%

“…Figure 4 shows the use of climate scenario data to compare observed data to monthly average rainfall . The monthly ensemble means of predicted rainfall at the climatological stations at Hulbarag, Hossana, Alaba-Kulito, Boditi, and Bilate-Tena were computed using RCP4.5 and RCP8.5 greenhouse gas emission scenarios (Figure 4a-e) (Hughes et al, 2021;Tigabu et al, 2021;Edamo et al, 2022;Ukumo et al, 2022). The observed data from the same was compared to the historic model data from 1986 to 2015.…”

Section: Bias Correctionsmentioning

confidence: 99%

See 1 more Smart Citation

Envisaging the actual evapotranspiration and elucidating its effects under climate change scenarios on agrarian lands of bilate river basin in Ethiopia

Nannawo

Lohani

Eshete

2022

Heliyon

View full text Add to dashboard Cite

“…It participates in a range of biological and environmental processes (Hintz & Relyea, 2019;Xia et al, 2021), emphasizing human welfare and health (Vogeler et al, 2022). However, climate change, economic development, trade, and population increase have endangered this vital resource in terms of quality and quantity (Edamo et al, 2022;Jemmali & Sullivan, 2021). It is currently acknowledged as one of the most swiftly disappearing resources and contaminated (Akhtar et al, 2021;Hairom et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Estimating the Water Poverty in Semi-Arid Districts of Punjab, Pakistan

Shahid

Abedullah

2022

JASS

View full text Add to dashboard Cite

An increase in population and water demand, climate change, and changes in land-use patterns are severe problems in Pakistan. Water management in Pakistan has been a provincial matter after the 18th amendment. Each province in Pakistan measures the water on its own intention and indicators. The current study aims to map the water scarcity status through the water poverty index (WPI) using the Multiple Indiactor Cluster Survey (2018) at the semi-arid districts in Punjab, Pakistan. WPI was estimated by using the components named “Resources,” “Access”, “Capacity”, “Use”, and “Environment”. Each component score was calculated using ifferent indicators, assuming equal weights for all components. Layyah (57.6) and RajanPur (48.03) had the best and worst situations among all districts in 2018. The findings showed that the absence of physical water resources in these districts is not the only factor of domestic water shortage but environment and capacity also plays a significant role in achieving the domestic water need. Due to their socioeconomic ineptitude, lack of infrastructure, and restricted access to water, the region is experiencing severe to high levels of household water poverty. This study's results showed that WPI is an operative tool to measure water scarcity and could be used to make an important priority for water management.

show abstract

Effect of climate change on water availability in Bilate catchment, Southern Ethiopia

Cited by 15 publications

References 45 publications

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination index from cropland N-fertilizer in Bilate downstream

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination index from cropland N-fertilizer in Bilate downstream

Envisaging the actual evapotranspiration and elucidating its effects under climate change scenarios on agrarian lands of bilate river basin in Ethiopia

Estimating the Water Poverty in Semi-Arid Districts of Punjab, Pakistan

Contact Info

Product

Resources

About