Evaluation of static and dynamic land use data for watershed hydrologic process simulation: A case study in Gummara watershed, Ethiopia

Teklay, Achenafi; Dile, Yihun T.; Setegn, Shimelis Gebriye; Demissie, Solomon S.; Asfaw, Dereje H.

doi:10.1016/j.catena.2018.08.013

Cited by 52 publications

(33 citation statements)

References 35 publications

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“…The results of the study suggest that both LULC change and climate variability had a positive contribution to the average annual water supply variation in the Ribb–Gummara watershed from 2003 to 2017 despite spatial differences (Figures a and a). These results are consistent with the findings of previous studies (Andualem & Gebremariam, ; Ayele et al, ; Jemberie, Gebrie, & Gebremariam, ; Teklaya et al, ). However, the impact of both LULC change and climate variability on average annual water supply before and after 2010 was different.…”

Section: Discussionsupporting

confidence: 93%

“…From 2003 to 2017, nearly two‐thirds (2,257 km 2 ) of the watershed remained stable, while one‐third (1,016 km 2 ) of it changed. During this period, the most notable land cover transition was the persistent increment of shrubland in contrast to many previous findings in the Northern Highlands of Ethiopia (Moges & Bhat, ; Teklaya et al, ; Wubie, Assen, & Nicolau, ). In the same period, about 311 km 2 of cropland, 27 km 2 of grassland, and 45 km 2 of forestland were converted to shrubland (Table ).…”

Section: Resultscontrasting

confidence: 74%

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Partitioning the impacts of land use/land cover change and climate variability on water supply over the source region of the Blue Nile Basin

et al. 2020

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Water plays a vital role in sustaining the natural functioning of the entire ecosystem that supports life on Earth. It plays key roles in the well‐being of society in numerous ways. However, climate variability and land use land cover (LULC) change have caused spatiotemporal water supply variation. Disentangling the effects of climate variability from LULC change on water supply is crucial for sustainable water resource management. The main purpose of this study is, therefore, to disentangle the relative contribution of LULC change and climate variability to the overall average annual water supply variation. Residual trends analysis combined with Integrated Valuation of Environmental Services and Tradeoffs (InVEST) annual water yield model was adopted to perform simulations and disentangle the relative impacts of climate variability and LULC change. Ground and satellite data were used in this study. The study area has experienced a significant increasing wetness trend and significant LULC dynamics between 2003 and 2017. As a result, an increasing water supply was observed due to the joint effects of climate variability and LULC change in the watershed (203 mm). The contribution of climate variability was 94%, whereas LULC contributes only 6% from 2003 to 2017. Climate variability negatively led to water supply variation while LULC change contributed positively from 2010 to 2017. Although the ongoing soil and water conservation (SWC) practices improved vegetation cover and water retention of the watershed, climate variability is the main driver of water supply variation. Therefore, SWC practices should incorporate ecosystem‐based climate change adaptation strategies and scale up to community‐based integrated watershed management to sustain water supply.

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Section: Discussionsupporting

confidence: 93%

Section: Resultscontrasting

confidence: 74%

Partitioning the impacts of land use/land cover change and climate variability on water supply over the source region of the Blue Nile Basin

et al. 2020

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“…Overall, model simulation results demonstrated that the calibrated and validated model replicated observed streamflow very well in all study watersheds (Moriasi et al 2007;Arnold et al 2012). Our findings are in agreement with previous studies in the region (Dile et al 2016;Halefom et al 2018;Teklay et al 2018). Therefore, it is safe to conclude that the SWAT model is reliable to simulate the watershed hydrological processes in the Ethiopian highlands.…”

Section: Swat Model Calibration and Validationsupporting

confidence: 93%

Modeling the Impact of Climate Change on Hydrological Responses in the Lake Tana Basin, Ethiopia

Teklay

Dile

Asfaw

et al. 2020

Preprint

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BackgroundHydrologic systems have been changing due to the impact of climate change and climate variability. The impacts of climate change are set to increase in the future due to the rise of global warming. Quantifying the impact of climate change on the spatial and temporal hydrological processes is important for integrated water resource management. The Lake Tana basin, which is the source of the Upper Blue Nile, is vulnerable to climate change and variability. This study was carried out in the four major tributary watersheds of the Lake Tana basin: Gilgel Abay, Gumara, Ribb, and Megech. The climate model and hydrological model was used to (i) to evaluate the performance of the Soil and Water Assessment Tool for study watershed, (ii) to assess the future rainfall and temperature variability and change in the study watershed, and (iii) to examine the impact of climate change on future watershed hydrology. The study used dynamically downscaled climate data for the baseline (2010–2015) and future period (2046–2051) under two Representative Concentration Pathways (RCP4.5 and RCP8.5). The climate scenarios were simulated using the Weather Research and Forecasting (WRF) model, with a 4-km horizontal resolution. A linear scaling method was applied to minimize model biases. The SWAT model was used to estimate the baseline and future hydrology using the bias-corrected climate data. ResultsThe performance of the SWAT model was ‘good’ to ‘very good’ for both the calibration and validation periods, with the Nash–Sutcliffe efficiency values between 0.71 to 0.92. The projected changes in rainfall vary with seasons and watershed under both scenarios. On average, annual rainfall may increase by 9.8% and 21.2% under RCP4.5 and RCP8.5 scenarios, respectively. Minimum temperature may rise by 1.68 °C and 2.26 °C while maximum temperature may increase by 1.65 °C and 2.75 °C under RCP4.5 and RCP8.5 scenarios, respectively. The changes in climate may cause an increase in surface runoff by 20.9% and 46.5% under RCP4.5 and RCP8.5 scenarios, respectively, while the evapotranspiration increase by 4.7% and 12.2% under RCP4.5 and RCP8.5, respectively. ConclusionThe findings provide valuable insights to implement appropriate water management strategies to mitigate and adapt to the negative impacts of climate change and variability on the Lake Tana basin, and other regions which have similar agro-ecology.

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“…Temperature, Rain fall and Land cover and use dynamics [62] Supervised classification and Statistical method (Mann Kendall test) Land cover change, Rain fall and Stream flow [71] Soil and water assessment tool (SWAT), Statistical analysis (static land use (SLU) and dynamic land use (DLU)) models LULC change, Surface runoff, Evapo-transpiration and peak flow (hydrological response) [80] Parameter Efficient Distributed (PED) model Rain fall, Temperature and suspended sediment concentration [94] Analytical Hierarchy process (AHP) approach Litho logy, lineaments, drainage density, rainfall, slop, LULC and soils [51] Mass balance approach (modular three dimensional finite difference groundwater flow model and penmans, Meyers and Thornwaites methods)…”

Section: Models / Algorithms Parameters Used References Supervised CLmentioning

confidence: 99%

“…Land use land cover maps of the Gummara watershed: land uses for a) 1985, b) 1995, c) 2005, and d) 2015[80].…”

mentioning

confidence: 99%

The Application of Satellite Imagery in Surface Water/Lake Modelling: A Review of Previous Studies on Lake Tana and Its Basin

Teshome¹,

Tsidu²,

Kifle³

2020

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Satellite images give a synoptic view of target areas, measure target surface changes and provide the information needed for hydrological studies, river or Lake Basin management, water disaster prevention, and water management. Lake Tana is located at an altitude of 1830 m and latitude longitude of 11.27°N and 37.10°E. The lake is the source of the Blue Nile River and it is the largest lake in Ethiopia with a surface area of 3,150 km 2 , a maximum length and width of 78 and 68 km respectively. In the past, several studies have been published on Lake Tana and its basin in a scattered manner. This necessitates state of the art review that highlights achievements, models, algorithms, and identify gaps in knowledge. Different types of hydrological models have been applied. The majority of the recent studies utilized simple conceptual and statistical approaches for trend analysis and water balance estimations, mainly using rainfall, temperature and evapo-transpiration data. To a greater extent, recent studies have used advanced semi-physically or physically based distributed hydrological models driven by high resolution temporal and spatial data for diverse applications. A review of the methods used and the role of satellite remote sensing in this regard to understand the hydrology of Lake Tana and its basin are presented.

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Evaluation of static and dynamic land use data for watershed hydrologic process simulation: A case study in Gummara watershed, Ethiopia

Cited by 52 publications

References 35 publications

Partitioning the impacts of land use/land cover change and climate variability on water supply over the source region of the Blue Nile Basin

Partitioning the impacts of land use/land cover change and climate variability on water supply over the source region of the Blue Nile Basin

Modeling the Impact of Climate Change on Hydrological Responses in the Lake Tana Basin, Ethiopia

The Application of Satellite Imagery in Surface Water/Lake Modelling: A Review of Previous Studies on Lake Tana and Its Basin

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