Analysis of Suspended Material in Lake Mead Using Remote Sensing Indices

Adjovu, Godson Ebenezer; Ahmad, Sajjad; Stephen, Haroon

doi:10.1061/9780784483466.069

Cited by 8 publications

(10 citation statements)

References 36 publications

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“…Some of the commonly used indices include the Normalized Suspended Material Index (NSMI), the Normalized Difference Suspended Sediment Index (NDSSI), and the Band Ratio (BR) used in the analysis of suspended sediments [190,[198][199][200]. Normalized Difference Salinity Index (NDSI), salinity indices, and TDS indices are used for the monitoring of TDS or salinity [127,189,201].…”

Section: Rs Spectral Indices Used In Estimating Tds and Tssmentioning

confidence: 99%

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

et al. 2023

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This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal–noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.

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Section: Rs Spectral Indices Used In Estimating Tds and Tssmentioning

confidence: 99%

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

et al. 2023

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“…Lake Mead is the largest reservoir in terms of water capacity, with a maximum capacity of 35 million km 3 within the United States, and it was formed after Hoover Dam was constructed in the 1930s. The Las Vegas Valley (LVV) sources about 90% of its water supply from the lake [17,72]. Lake Mead is a source of water for more than 25 million in the Southwestern US for their domestic, municipal, industrial, and agricultural needs [17,18,70,73].…”

Section: Study Areamentioning

confidence: 99%

“…Lake Mead is made of four connected basins, namely, the Gregg, Temple, Virgin, and Boulder Basins. Flow into the lake comes from the Colorado River, the Las Vegas Wash (LVW), the Muddy River, and the Virgin River, as shown in Figure 1 [17,72,75]. Almost all the inflows (98%) into Lake Mead are said to originate from the Upper Colorado River Basin (UCRB), which is a conduit of a significant amount of possibly dissolved salts and sediment transport from the UCRB.…”

Section: Study Areamentioning

confidence: 99%

Spatial and Temporal Dynamics of Key Water Quality Parameters in a Thermal Stratified Lake Ecosystem: The Case Study of Lake Mead

Adjovu

Stephen

Ahmad

2023

Earth

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Lake Mead located in the Arizona–Nevada region of the Mohave Dessert is a unique and complex water system whose flow follows that of a warm monomictic lake. Although monomictic lakes experience thermal stratification for almost the entire year with a period of complete mixing, the lake on occasion deviates from this phenomenon, undergoing incomplete turnovers categorized with light stratifications every other year. The prolonged drought and growing anthropogenic activities have the potential to considerably impact the quality of the lake. Lake Mead and by extension the Boulder Basin receive cooler flow from the Colorado River and flow with varying temperatures from the Las Vegas Wash (LVW), which impacts its stratification and complete turnovers. This study analyzes four key water quality parameters (WQPs), namely, total dissolved solids (TDS), total suspended solids (TSS), temperature, and dissolved oxygen (DO), using statistical and spatial analyses to understand their variations in light of the lake stratifications and turnovers to further maintain its overall quality and sustainability. The study also evaluates the impacts of hydrological variables including in and out flows, storage, evaporation, and water surface elevation on the WQPs. The results produced from the analysis show significant levels of TDS, TSS, and temperature from the LVW and Las Vegas Bay regions compared with the Boulder Basin. LVW is the main channel for conveying effluents from several wastewater treatment facilities into the lake. We observed an increase in the levels of TDS, TSS, and temperature water quality in the epilimnion compared with the other layers of the lake. The metalimnion and the hypolimnion layer, however, showed reduced DO due to depletion by algal blooms. We observed statistically significant differences in the WQPs throughout various months, but not in the case for season and year, an indication of relatively consistent variability throughout each season and year. We also observed a no clear trend of influence of outflows and inflows on TDS, temperature, and DO. TSS concentrations in the lake, however, remained constant, irrespective of the inflows and outflows, possibly due to the settling of the sediments and the reservoir capacity.

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“…Lake Mead was formed after Hoover Dam was constructed in the 1930s and it is the largest reservoir in terms of water capacity within the United States. It is the source of water for about 90% of the Las Vegas Valley water supply [50,51]. Lake Mead is one of the water bodies currently under intense water stress due to climate change and the effects of rapid population growth.…”

Section: Study Area Descriptionsmentioning

confidence: 99%

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Adjovu

Stephen

Ahmad

2023

Water

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Total dissolved solids (TDS) concentration determination in water bodies is sophisticated, time-consuming, and involves expensive field sampling and laboratory processes. TDS concentration has, however, been linked to electrical conductivity (EC) and temperature. Compared to monitoring TDS concentrations, monitoring EC and temperature is simpler, inexpensive, and takes less time. This study, therefore, applied several machine learning (ML) approaches to estimate TDS concentration in Lake Mead using EC and temperature data. Standalone models including the support vector machine (SVM), linear regressors (LR), K-nearest neighbor model (KNN), the artificial neural network (ANN), and ensemble models such as bagging, gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), and extra trees (ET) models were used in this study. The models’ performance were evaluated using several performance metrics aimed at providing a holistic assessment of each model. Metrics used include the coefficient of determination (R2), mean absolute error (MAE), percent mean absolute relative error (PMARE), root mean square error (RMSE), the scatter index (SI), Nash–Sutcliffe model efficiency (NSE) coefficient, and percent bias (PBIAS). Results obtained showed varying model performance at the training, testing, and external validation stage of the models, with obtained R2 of 0.77–1.00, RMSE of 2.28–37.68 mg/L, an MAE of 0.14–22.67 mg/L, a PMARE of 0.02–3.42%, SI of 0.00–0.06, NSE of 0.77–1.00, and a PBIAS of 0.30–0.97 across all models for the three datasets. We utilized performance rankings to assess the model performance and found the LR to be the best-performing model on the external validation datasets among all the models (R2 of 0.82 and RMSE of 33.09 mg/L), possibly due to the established existence of a relationship between TDS and EC, although this may not always be linear. Similarly, we found the XGBoost to be the best-performing ensemble model based on the external validation with R2 of 0.81 and RMSE of 34.19 mg/L. Assessing the overall performance of the models across all the datasets, however, revealed GBM to produce a superior performance based on the ranks, possibly due to its ability to reduce overfitting and improve generalizations. The findings from this study could be employed in assisting water resources managers and stakeholders in effective monitoring and management of water resources to ensure their sustainability.

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Analysis of Suspended Material in Lake Mead Using Remote Sensing Indices

Cited by 8 publications

References 36 publications

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

Spatial and Temporal Dynamics of Key Water Quality Parameters in a Thermal Stratified Lake Ecosystem: The Case Study of Lake Mead

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

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