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Water quality is deteriorating in the world's freshwater bodies, and Lake Tana in Ethiopia is becoming unpleasant to biodiversity. The objective of this study is to retrieve non-optical water quality data, specifically total nitrogen (TN) and total phosphorus (TP) concentrations, in Lake Tana using Machine Learning (ML) techniques applied to Landsat 8OLI imagery. TheMLmethods employed include Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regression (RF), XGBoost Regression (XGB), AdaBoost Regression (AB), and Gradient Boosting Regression (GB). The XGB algorithm provided the best result for TN retrieval, with determination coefficient (R²), mean absolute error (MARE), relative mean square error (RMSE) and Nash Sutcliff (NS) values of 0.80, 0.043, 0.52, and 0.81 mg/L, respectively. The RF algorithm was most effective for TP retrieval, with R² of 0.73, MARE of 0.076, RMSE of 0.17 mg/L, and NS index of 0.74. These methods accurately predicted TN and TP spatial concentrations, identifying hotspots along river inlets and northeasters. The temporal patterns of TN, TP, and their ratios were also accurately represented by combining in-situ, RS and ML-based models. Our findings suggest that this approach can significantly improve the accuracy of water quality retrieval in large inland lakes and lead to the development of potential water quality digital services. [ABSTRACT FROM AUTHOR]

Understanding the spatial and temporal patterns of sediment loading in water bodies is crucial for effective water quality management. Remote sensing (RS) has emerged as a valuable and reliable tool for monitoring turbidity, which can provide insights into sediment dynamics in water bodies. In this study, we investigate the potential of turbidity data derived from RS to explain simulated spatial and temporal sediment loading patterns in the Lake Tana basin, Ethiopia. Utilizing existing RS lake turbidity data from Copernicus Global Land Service (CGLS) and simulated seasonal and multiyear trends of river sediment loadings into Lake Tana from the Soil and Water Assessment Tool (SWAT + model), we estimate correlations at different river inlets into Lake Tana. The results reveal a strong positive correlation (R2 > 0.66) between the multiyear monthly average sediment load from inflow rivers and RS lake turbidity at most river inlets. This indicates that the simulated river sediment loads and lake turbidity at river inlets exhibit similar seasonal patterns. Notably, higher turbidity levels are observed at the river inlet with the highest sediment load export. These findings highlight the potential of RS turbidity products in characterizing temporal and spatial patterns of sediment loadings, particularly in data-scarce regions, contributing to a better understanding of water quality dynamics in such areas. [ABSTRACT FROM AUTHOR]

Water quality is deteriorating in the world's freshwater bodies, and Lake Tana in Ethiopia is becoming unpleasant to biodiversity. The objective of this study is to retrieve non-optical water quality data, specifically total nitrogen (TN) and total phosphorus (TP) concentrations, in Lake Tana using Machine Learning (ML) techniques applied to Landsat 8 OLI imagery. The ML methods employed include Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regression (RF), XGBoost Regression (XGB), AdaBoost Regression (AB), and Gradient Boosting Regression (GB). The XGB algorithm provided the best result for TN retrieval, with determination coefficient (R2), mean absolute error (MARE), relative mean square error (RMSE) and Nash Sutcliff (NS) values of 0.80, 0.043, 0.52, and 0.81 mg/L, respectively. The RF algorithm was most effective for TP retrieval, with R2 of 0.73, MARE of 0.076, RMSE of 0.17 mg/L, and NS index of 0.74. These methods accurately predicted TN and TP spatial concentrations, identifying hotspots along river inlets and northeasters. The temporal patterns of TN, TP, and their ratios were also accurately represented by combining in-situ, RS and ML-based models. Our findings suggest that this approach can significantly improve the accuracy of water quality retrieval in large inland lakes and lead to the development of potential water quality digital services.

Excess sediment and nutrient losses from intensifying agriculture degrade water quality and boost plant growth. The relationship between circulation patterns, spatial water quality degradation, and water hyacinth infestation is not adequately studied. The objective of this study is, therefore, to investigate the effect of lake circulation patterns on sediment and nutrient distribution and its implication on the spread of water hyacinth in a tropical lake. This study was carried out in Lake Tana, the largest freshwater lake in Ethiopia, where sediment and nutrient concentrations are increasing, and water hyacinths have become a challenge since 2011. The lake circulation pattern was simulated by the Delft3D model based on a bathymetry survey, discharge, and meteorological forcings. To predict the transport path of sediments and dissolved nutrients, an inert tracer was released in the four main river inlets of the lake. Observed lake water level measurements were used to validate the model. Our results show that the lake circulation pattern could explain the transport path of sediment and nutrients and the location of the water hyacinths found in the northeast of the lake. Sediments and nutrients from the largest river, Gilgel Abay, in the southeast of Lake Tana, flow through the two outlets nearby with little sediment deposition due to the relatively short retention time. The phosphorus-rich sediments of the 24 h at 105 °C remaining three main rivers joining the lake at the north and east are transported to the northeast. Thus, the management and control of water hyacinths should focus on the northern and eastern catchment areas of Lake Tana. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Advancing non-optical water quality monitoring in Lake Tana, Ethiopia: insights from machine learning and remote sensing techniques." The corrigendum addresses errors in Figure 9 and Figure 10 of the published article, where the captions did not correctly describe the part labels. The authors apologize for the error and state that it does not change the scientific conclusions of the article. The original article has been updated. [Extracted from the article]

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.

Predictive modelling corroborates decision-making in the development of a standard habitat assessment protocol. In this study, we modelled environmental requirements of benthic macroinvertebrates. Classification and regression tree models (CART) and ordination analysis were performed to identify important variables affecting macroinvertebrate community pattern in the Lake Tana Watershed. A dataset of 95 samples was collected from eight wetlands. Among the modelled taxa, Coenagrionidae and Libellulidae had substantial predictive performance based on Kappa statistic (ĸ > 0.6) whereas Baetidae, Physidae, Tipulidae and Hydrophilidae had moderate predictive model performance (ĸ ≥ 0.4). Vegetation cover, leather tanning, vegetation clearance and nitrate ion were the topmost selected environmental variables influencing the occurrence of macroinvertebrate taxa. The conditional analysis depicted that the abundance of Coenagrionidae and Libellulidae increased with the increasing in vegetation cover. Overall, macroinvertebrate taxa have a clear habitat requirement within the habitat gradient studied and hence, could be a potential candidate for biomonitoring and provide valuable information in the development of a standard wetland assessment protocol. [ABSTRACT FROM AUTHOR]

Modeling of suspended sediment emission into freshwater lakes is challenging due to data gaps in developing countries. Existing models simulate sediment concentration at a gauging station upstream and none of these studies had modeled total suspended solids (TSS) emissions by inflowing rivers to freshwater lakes as there are no TSS measurements at the river mouth in the upper Blue Nile basin. In this study a 10 year TSS time series data generated from remotely sensed MODIS/Terra images using established empirical relationship is applied to calibrate and validate a hydrology model for Lake Tana in Upper Blue Nile Basin. The result showed that at a monthly time scale TSS at the river mouth can be replicated with Nash–Sutcliffe efficiency (NS) of 0.34 for calibration and 0.21 for validation periods. Percent bias (PBIAS) and ratio of the root-mean-square error to the standard deviation of measured data (RSR) are all within range. Given the inaccessibility and costliness to measure TSS at river mouths to a lake the results found here are considered useful for suspended sediment budget studies in water bodies of the basin. [ABSTRACT FROM AUTHOR]

There is an increasing need globally to establish relationships among flow, ecology, and livelihoods to make informed decisions about environmental flows. This paper aimed to establish the ecological foundation for a holistic environmental flow assessment method in the Gumara River that flows into Lake Tana in Ethiopia and the Blue Nile River. First, the ecological conditions (fish, macro-invertebrate, riparian vegetation, and physicochemical) of the river system were characterized, followed by determining the hydrological condition and finally linking the ecological and hydrological components. The ecological data were collected at 30 sites along the Gumara River on March 2016 and 2020. River hydrology was estimated using the SWAT model and showed that the low flow decreased over time. Both physico-chemical and macroinvertebrate scores showed that water quality was moderate in most locations. The highest fish diversity index was in the lower reach at Wanzaye. Macroinvertebrate diversity was observed to decrease downstream. Both the fish and macroinvertebrate diversity indices were less than the expected maximum, being 3.29 and 4.5, respectively. The normalized difference vegetation index (NDVI) for 30 m and 60 m buffer distances from the river decreased during the dry season (March–May). Hence, flow conditions, water quality, and land-use change substantially influenced the abundance and diversity of fish, vegetation, and macroinvertebrate species. The pressure on the ecology is expected to increase because the construction of the proposed dam is expected to alter the flow regime. Thus, as demand for human water consumption grows, measures are needed, including quantification of environmental flow requirements and regulating river water uses to conserve the ecological status of the Gumara River and Lake Tana sub-basin. [ABSTRACT FROM AUTHOR]

Lakes hold most of the world's fresh surface water resources. Safeguarding these resources from water quality degradation requires knowledge of the relationship between lake morphometry and water quality. The 3046-km2 Lake Tana in Ethiopia is one of the water resources in which the water quality is decreasing and water hyacinths have invaded. The objective of this study is to understand the interaction between the lake morphometry and water quality and specifically the phosphorus dynamics and their effect on the water hyacinths. A bathymetric survey was conducted in late 2017. Various morphometric parameters were derived, and both these parameters and sediment available phosphorus were regressed with the dissolved phosphorus. The results show that, with a wave base depth that is nearly equal to a maximum depth of 14.8 m, the bottom sediments were continuously suspended in the water column. As a result of the resuspension mixing, we found that the dissolved phosphorus in the water column decreased with lake depth and increased with sediment available phosphorus (R2 = 0.84) in the northern half of the lake. This relationship is not as strong in the south due to a large flow of Gilgel Abay to the outlets. Water hyacinths were found where the lake was shallow and the available phosphorus was elevated. The large reservoir of sediment phosphorus will hamper any remedial efforts in removing the water hyacinths. [ABSTRACT FROM AUTHOR]