Sediment deposition (1940–2017) in a historically pristine lake in a rapidly developing tropical highland region in Ethiopia

Kebedew, Mebrahtom G.; Tilahun, Seifu A.; Belete, Mulugeta A.; Zimale, Fasikaw A.; Steenhuis, Tammo S.

doi:10.1002/esp.5118

Cited by 8 publications

(7 citation statements)

References 87 publications

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“…Moreover, as the rainy season continues, the soil becomes more cohesive and base flow begins to contribute to streamflow, diluting the sediment [33,34]. e runoff water then spreads over the river delta dropping the sediment load and contributes to delta development along the lakeshore [3,8,25,35].…”

Section: Generating Ssc Time Seriesmentioning

confidence: 99%

Estimation of Suspended Sediment Concentration from Remote Sensing and In Situ Measurement over Lake Tana, Ethiopia

Womber¹,

Zimale²,

Kebedew³

et al. 2021

Advances in Civil Engineering

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Discharge from basins joining a lake is the main factor determining the lake volume and sediment inflow to the lake. Suspended sediment is an important parameter for describing the water quality of aquatic ecosystems. Lake Tana is an important and the largest lake in Ethiopia for the local ecological system. However, environmental change and anthropogenic activities in the area threaten its water quality. The conventional methods of suspended sediment concentration (SSC) observation are unable to determine and compare spatial and temporal SSC patterns for the lake over a period of years. Remote sensing methods have made it possible to map SSC. The objective of this study is to characterize the spatial and temporal distribution of suspended sediment of Lake Tana using in situ measurement and remote sensing applications and specifically to develop a relationship between in situ and remote sensing observation to retrieve suspended sediment concentration and map the spatal distribution of SSC. This study used MODIS-Terra and in situ data to characterize the spatial and temporal distribution of SSC in the rainy season. Four sampling campaigns (20 samples per campaign) were carried out on Lake Tana, and the first three sampled campaigns on May 11–13, 2018, June 08–10, 2018, and July 15–17, 2018, were used for calibration of regression models. MODIS-Terra reflectance in NIR was found best related to in situ water quality data and varies linearly with SSC (r2 = 0.81) and turbidity (r2 = 0.85). Secchi disc depth (SDD) found the best fit for a power relation with NIR band reflectance (r2 = 0.74). The MODIS-Terra reflectance in red was found to be poorly related to in situ measurements. The relation in NIR reflectance was validated using the LOOCV (leave-one-out-cross-validation) technique and the fourth sampled data set collected on August 12–14, 2018. Developed models are validated with RMSE of 42.96 mg/l, 14.6 NTU, and 0.17 m, ARE of 23.3%, 27.6%, and 12.4%, and RRMSE of 25.1%, 44.5%, and 29.6% for SSC, turbidity, and SDD, respectively, using LOOCV. The equation was also validated using August 2018 collected data sets with RMSE of 87.6 mg/l, 11.7 NTU, 0.08 m, ARE of 20.8%, 25.9%, and 28.8%, and RRMSE of 17.8%, 20.5%, and 27.9% for SSC, turbidity, and SDD, respectively. Applying the developed regression model, a 10-year time series of SSC from 2008–2017 for May-August was estimated and the trend was tested using the Mann–Kendall trend test. It was found that an increasing trend was observed from the period 2008 to 2017. The result shows that satellite data like the MODIS-Terra imagery could be used to monitor and obtain past records of SSC with the developed equation. The increasing SSC can be reduced by implementing selected management practices in the surrounding watersheds of the lake to reduce nutrient and sediment inflow.

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Section: Generating Ssc Time Seriesmentioning

confidence: 99%

Estimation of Suspended Sediment Concentration from Remote Sensing and In Situ Measurement over Lake Tana, Ethiopia

Womber¹,

Zimale²,

Kebedew³

et al. 2021

Advances in Civil Engineering

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“…This may be attributed to the decrease in the SDT value of the lake during the wet season. As indicated in Kebedew et al (2021), the SDT value decreases in the wet season due to the sedimentation that takes place in this season and so does the nutrient enrichment from the upper catchment as indicated in Cai et al (2023). According to the result of this study, the phosphate and nitrate loading into Lake Tana is higher in the wet season (season 2) compared to the dry season (season 4) (See section 3.4 for the details).…”

Section: The Estimated Trophic State Index Of Lake Tanamentioning

confidence: 64%

Estimation of Nutrient Loads and Trophic Status of Lake Tana, the Upper Blue Nile Basin, Ethiopia

Ayele

2024

Preprint

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Lakes and water reservoirs encircled by a catchment with a clear influence of anthropogenic pressure are generally characterized by low water quality and high trophic status. In this study, 20 samples from major inflow rivers into Lake Tana were taken to estimate the phosphate and nitrate loading using a Vollendier Model for the four seasons. 67 samples were taken from three different portions of the Lake, and physicochemical water quality parameters in these samples were measured by the standard procedure in APHA, 2005 to estimate the trophic status using the Carlson trophic state model for the dry and wet seasons. The result showed that 0.369 to 106, 1.882 to 2.91, 1.564 to 2.51, and 0.67 to 2.16 kg of nitrate from Megech and Ribb watershed and 0.321 (Megech) to 0.370 (Gumara), 0.578 (Megech) to 0.728 (Gilgel Abbay), 0.418 (Megech) to 0.476 (Ribb) and 0.339(Megech) to 0.378 kg of phosphate from Ribb is discharged in second into the Lake in season 1, 2, 3 and 4 respectively. Ribb River which lies in the most extended flood plain of the Fogera Werda has a relatively high concentration of nitrate and phosphate; as a result, it discharges a higher amount of these nutrients in kg per second than the other tributary rivers in all seasons into the lake. Moreover, the rivers carried more nutrients in the wet season than in the dry season. As a result, the river mouth portions are more prone to nutrient enrichment than the central and Bahir Dar Gulf portions of the lake. Due to these, these parts of the lake have relatively average low Secchi Depth Transparency (0.54,0.36,0.50& 0.47m; 0.61,0.42&0.58m) and high average total phosphorus concentration (1.26,1.43,1.21& 1.23 mg/l; 0.80, 1.09,0.67 & 0.69mg/l) in the wet and dry season respectively. So, 50% of the river mouth portions have an STI value of 38 < TSI < 49 which falls in a mesotrophic status index compared to the central and Bahir Dar Gulf portions with an STI value < 38 which falls in an oligotrophic status index especially in the wet compared to the dry season. Preventive measures have to be practiced in the Lake Tana basin to reduce nutrient loading and improve the trophic status of the lake.

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“…The explanation for this is that the amount of cultivated land in the Lake Tana area has increased by 20% and covers 68% of the basin in the last 30 years [63]. The soil erosion from these areas ranges from 5 tons per hectare per year to 50 tons per hectare per year, indicating a doubling of the sediment transport to the lake from 1980s to 2020s [64]. With the expansion of irrigated land from 540 km 2 in 1980s to 1200 km 2 in 2020s, agrochemical uses are expanded.…”

Section: Significance Of ML In Lake Water Quality Managementmentioning

confidence: 99%

Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia

et al. 2023

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Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making.

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Sediment deposition (1940–2017) in a historically pristine lake in a rapidly developing tropical highland region in Ethiopia

Cited by 8 publications

References 87 publications

Estimation of Suspended Sediment Concentration from Remote Sensing and In Situ Measurement over Lake Tana, Ethiopia

Estimation of Suspended Sediment Concentration from Remote Sensing and In Situ Measurement over Lake Tana, Ethiopia

Estimation of Nutrient Loads and Trophic Status of Lake Tana, the Upper Blue Nile Basin, Ethiopia

Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia

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