Analysis of planform changes of the lower reach of Gilgel Abay River Upper Blue Nile Basin, Ethiopia, using remote sensing and GIS

2021 ◽  
Author(s):  
Bogale Atile Abebe ◽  
Asegdew Gashaw Mulat
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Lower Reach ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Upper Blue Nile Basin

scholarly journals Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

2019 ◽  
Vol 11 (2) ◽  
pp. 125 ◽  
Author(s):  
Getachew Ayehu ◽  
Tsegaye Tadesse ◽  
Berhan Gessesse ◽  
Yibeltal Yigrem
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Prediction Model ◽  
Residual Soil ◽  
Nile Basin ◽  
Blue Nile ◽  
Volumetric Soil Moisture ◽  
Blue Nile Basin ◽  
Sar Data ◽  
Upper Blue Nile Basin

In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.

scholarly journals The use of remote sensing to quantify wetland loss in the Choke Mountain range, Upper Blue Nile basin, Ethiopia

2010 ◽  
Vol 7 (4) ◽  
pp. 6243-6284 ◽  
Author(s):  
E. Teferi ◽  
S. Uhlenbrook ◽  
W. Bewket ◽  
J. Wenninger ◽  
B. Simane
Keyword(s):  
Remote Sensing ◽  
Regional Scale ◽  
Atmospheric Correction ◽  
Wetland Loss ◽  
Climatic Conditions ◽  
Mountain Range ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Upper Blue Nile Basin

Abstract. Wetlands provide multiple ecosystem services such as storing and regulating water flows and water quality, providing unique habitats to flora and fauna, and regulating micro-climatic conditions. Conversion of wetlands for agricultural use is a widespread practice in Ethiopia, particularly in the southwestern part where wetlands cover large areas. Although there are many studies on land cover and land use changes in this region, comprehensive studies on wetlands are still missing. Hence, extent and rate of wetland loss at regional scale is unknown. The objective of this paper is to quantify wetland dynamics and estimate wetland loss in the Choke Mountain range (area covering 17 443 km2) in the Upper Blue Nile basin, a key headwater region of the river Nile. Therefore, satellite remote sensing images of the period 1986–2005 were considered. To create images of surface reflectance that are radiometrically consistent, a combination of cross-calibration and atmospheric correction (Vogelman-DOS3) methods was used. A hybrid supervised/unsupervised classification approach was used to classify the images. Overall accuracies of 94.1% and 93.5% and Kappa Coefficients of 0.908 and 0.913 for the 1986 and 2005 imageries, respectively were obtained. The results showed that 607 km2 of seasonal wetland with low moisture and 22.4 km2 of open water are lost in the study area during the period 1986 to 2005. The current situation in the wetlands of Choke Mountain is characterized by further degradation which calls for wetland conservation and rehabilitation efforts through incorporating wetlands into watershed management plans.

scholarly journals The use of remote sensing to quantify wetland loss in the Choke Mountain range, Upper Blue Nile basin, Ethiopia

2010 ◽  
Vol 14 (12) ◽  
pp. 2415-2428 ◽  
Author(s):  
E. Teferi ◽  
S. Uhlenbrook ◽  
W. Bewket ◽  
J. Wenninger ◽  
B. Simane
Keyword(s):  
Remote Sensing ◽  
Land Use Changes ◽  
Atmospheric Correction ◽  
Wetland Loss ◽  
Climatic Conditions ◽  
Mountain Range ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Upper Blue Nile Basin

Abstract. Wetlands provide multiple ecosystem services such as storing and regulating water flows and water quality, providing unique habitats to flora and fauna, and regulating micro-climatic conditions. Conversion of wetlands for agricultural use is a widespread practice in Ethiopia, particularly in the southwestern part where wetlands cover large areas. Although there are many studies on land cover and land use changes in this region, comprehensive studies on wetlands are still missing. Hence, extent and rate of wetland loss at regional scales is unknown. The objective of this paper is to quantify wetland dynamics and estimate wetland loss in the Choke Mountain range (area covering 17 443 km2) in the Upper Blue Nile basin, a key headwater region of the river Nile. Therefore, satellite remote sensing imagery of the period 1986–2005 were considered. To create images of surface reflectance that are radiometrically consistent, a combination of cross-calibration and atmospheric correction (Vogelman-DOS3) methods was used. A hybrid supervised/unsupervised classification approach was used to classify the images. Overall accuracies of 94.1% and 93.5% and Kappa Coefficients of 0.908 and 0.913 for the 1986 and 2005 imageries, respectively were obtained. The results showed that 607 km2 of seasonal wetland with low moisture and 22.4 km2 of open water are lost in the study area during the period 1986 to 2005. The current situation in the wetlands of Choke Mountain is characterized by further degradation which calls for wetland conservation and rehabilitation efforts through incorporating wetlands into watershed management plans.

scholarly journals Multi-Dimensional Drought Assessment in Abbay/Upper Blue Nile Basin: The Importance of Shared Management and Regional Coordination Efforts for Mitigation

2021 ◽  
Vol 13 (9) ◽  
pp. 1835
Author(s):  
Yared Bayissa ◽  
Semu Moges ◽  
Assefa Melesse ◽  
Tsegaye Tadesse ◽  
Anteneh Z. Abiy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Earth Observation ◽  
Drought Indices ◽  
Severe Drought ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Small Streams ◽  
Upper Blue Nile Basin

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective of this study is to demonstrate and characterize the different forms of droughts and to assess the multidimensional nature of drought in the Abbay/ Upper Blue Nile River (UBN) basin and its national and regional scale implications. In this study, multiple drought indices derived from in situ and earth observation-based hydro-climatic variables were used. The meteorological drought was characterized using the Standardized Precipitation Index (SPI) computed from the earth observation-based gridded CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall data. Agricultural and hydrological droughts were characterized by using the Soil Moisture Deficit Index (SMDI) and Standardized Runoff-discharge Index (SRI), respectively. The monthly time series of SMDI was derived from model-based gridded soil moisture and SRI from observed streamflow data from 1982 to 2019. The preliminary result illustrates the good performance of the drought indices in capturing the historic severe drought events (e.g., 1984 and 2002) and the spatial extents across the basin. The results further indicated that all forms of droughts (i.e., meteorological, agricultural, and hydrological) occurred concurrently in Abbay/Upper Blue Nile basin with a Pearson correlation coefficient ranges from 0.5 to 0.85 both Kiremt and annual aggregate periods. The concurrent nature of drought is leading to a multi-dimensional socio-economic crisis as indicated by rainfall, and soil moisture deficits, and drying of small streams. Multi-dimensional drought mitigation necessitates regional cooperation and watershed management to protect both the common water sources of the Abbay/Upper Blue Nile basin and the socio-economic activities of the society in the basin. This study also underlines the need for multi-scale drought monitoring and management practices in the basin.

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