Reconstructing Turbidity in a Glacially Influenced Lake Using the Landsat TM and ETM+ Surface Reflectance Climate Data Record Archive, Lake Clark, Alaska

Determination of caffeine in treated wastewater discharged in the Nile River with emphasis on the effect of zinc and physicochemical factors

The present study aimed to investigate the occurrence of caffeine residues in the Nile River according to drainage of treated wastewater at Assiut, Egypt, and the effects of physicochemical parameters and zinc on its concentration. Four different sites were selected to perform the study: S, wastewater treatment plant (WWTP) canal (source site); J, a junction site between WWTP canal and the Nile; R, a reference site in the Nile before J site; and A, a site located after J site in the Nile. Water and sediment samples were collected in Summer 2022 and Winter 2023. Caffeine and Zn concentrations and physicochemical parameters were measured in the collected samples. The caffeine concentrations in water samples ranged from 5.73 to 53.85 μg L-1 at S in winter and summer, respectively, while those in sediment ranged from 0.14 mg kg-1 at R in winter to 1.54 mg kg-1 at S in summer. Caffeine and Zn concentrations were higher in summer samples. The Water Quality Index (WQI) of the collected samples recorded the lowest values in winter season at S and J sites. The study found that caffeine and zinc concentrations are positively correlated with water temperature and conductivity, while negatively correlated with pH. The association between caffeine and Zn highlights the environmental impact of heavy metals and pharmaceutical residues, and stresses the need for future research on these interactions.

Decline of suspended particulate matter concentrations in Lake Taihu from 1984 to 2020: observations from Landsat TM and OLI

Suspended particulate matter (SPM) affects the optical properties of water, which can be used as a marker of water quality. The water quality of Lake Taihu has changed immensely since the 1980's. However, despite the link between water quality and SPM, long-term systematic studies on SPM have not been conducted in this lake. Here, we used Landsat-5 TM and Landsat-8 OLI data to model changes in the SPM concentration of Lake Taihu from 1984 to 2020. Various models were generated, calibrated, and finally validated using in situ SPM, remote sensing reflectance (R_rs) data, and synchronous satellite data. After comparing various commonly used models, it was found that the exponential model based on band combination [R_rs(red) + R_rs(NIR)/R_rs(green)] had the highest accuracy, with an average unbiased relative error greater than 35%. Subsequently, the SPM products of Lake Taihu during 1984-2020 were generated. Overall, the SPM concentration showed a downward trend over the study period, which might be primarily attributable to a decline in wind speed. These findings may assist in the conservation of Lake Taihu and its associated water resources.

Retrieving Water Turbidity in Araucanian Lakes (South-Central Chile) Based on Multispectral Landsat Imagery

Remote sensing was used as an early alert tool for water clarity changes in five Araucanian Lakes in South-Central Chile. Turbidity records are scarce or unavailable over large and remote areas needed to fully understand the factors associated with turbidity, and their spatial-temporal representation remains a limitation. This work aimed to develop and validate empirical models to estimate values of turbidity from Landsat images and determine the spatial distribution of estimated turbidity in the selected Araucanian Lakes. Secchi disk depth measurements were linked with turbidity measurements to obtain a turbidity dataset. This in turn was used to develop and validate a set of empirical models to predict turbidity based on four single bands and 16 combination bands from 15 multispectral Landsat images. The best empirical models predicted turbidity over the range of 0.3–12.3 NTUs with RMSE values around 0.31–1.03 NTU, R2 (Index of Agreement IA) around 0.93–0.99 (0.85–0.97) and mean bias error (MBE) around (−0.36–0.44 NTU). Estimation maps to analyze the temporal-spatial turbidity variation in the lakes were constructed. Finally, it was found that the meteorological conditions may affect the variation of turbidity, mainly precipitation and wind speed. The data indicate that the turbidity has slightly increased in winter–spring. These models will be used in the future to reconstruct large datasets that allow analyzing transparency trends in those lakes.

Understanding the factors associated with long-term reconstructed turbidity in Lake Diefenbaker from Landsat-imagery

Turbidity affects a variety of aquatic ecosystem processes. Turbidity events are dominated by suspended sediment in many systems. High levels of suspended sediment in lakes can occur during periods of high inflows from turbid tributaries or suspension of sediment from lake beds. This study reconstructed historic turbidity levels using Landsat-imagery on Lake Diefenbaker (LD), a large river-reservoir constructed in the late 1960's on the naturally turbid South Saskatchewan River (SSR). We examined the factors that were associated with it. Reconstructed turbidity levels, from Landsat-images, were similar to actual turbidity. The SSR flow and wind speed explained 64%, 54% and 69% of the variability in estimated turbidity levels at the riverine zone, the transition zone and the entire reservoir, respectively. The decrease in estimated turbidity from June to October and down the length of the reservoir is likely associated with the decline in the SSR flow and the settling of suspended sediments. The relationship between estimated turbidity and wind speed may be associated with the re-suspension of bottom sediment at the upper reach of LD. Wind speed and direction were related to estimated turbidity at the lacustrine zone (r²_adj = 0.19, P < 0.05), which may be attributed to the persistence of sediments. We observed high turbidity in 2002 that exceeded other estimates of turbidity. Since 2002 was preceded by a prolonged drought, the high estimate turbidity may be related to an increase in sediment loads from the SSR flow and an increase in shoreline erosion from a rise in LD's water level. Hence, extreme events (drought and flooding) are associated with high turbidity in LD. As the Canadian Prairies continues to undergo climate change, lakes located in this region are predicted to experience more frequent extreme events. These extreme events will cause further deterioration of water quality.

Retrieval of Turbidity on a Spatio-Temporal Scale Using Landsat 8 SR: A Case Study of the Ramganga River in the Ganges Basin, India

Nowadays, space-borne imaging spectro-radiometers are exploited for many environmental applications, including water quality monitoring. Turbidity is a standout amongst the essential parameters of water quality that affect productivity. The current study aims to utilize Landsat 8 surface reflectance (L8SR) to retrieve turbidity in the Ramganga River, a tributary of the Ganges River. Samples of river water were collected from 16 different locations on 13 March and 27 November 2014. L8SR images from 6 March and 17 November 2014 were downloaded from the United States Geological Survey (USGS) website. The algorithm to retrieve turbidity is based on the correlation between L8SR reflectance (single and ratio bands) and insitu data. The b2/b4 and b2/b3 bands ratio are proven to be the best predictors of turbidity, with R2 = 0.560 (p < 0.05) and R2 = 0.726 (p < 0.05) for March and November, respectively. Selected models are validated by comparing the concentrations of predicted and measured turbidity. The results showed that L8SR is a promising tool for monitoring surface water from space, even in relatively narrow river channels, such as the Ramganga River.

Long-Term High-Resolution Sediment and Sea Surface Temperature Spatial Patterns in Arctic Nearshore Waters Retrieved Using 30-Year Landsat Archive Imagery

The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems, the subsistence economy of the local population, and the climate because of the transformation of organic matter into greenhouse gases. Yet, the patterns of sediment dispersal in the nearshore zone are not well known, because ships do not often reach shallow waters and satellite remote sensing is traditionally focused on less dynamic environments. The goal of this study is to use the extensive Landsat archive to investigate sediment dispersal patterns specifically on an exemplary Arctic nearshore environment, where field measurements are often scarce. Multiple Landsat scenes were combined to calculate means of sediment dispersal and sea surface temperature under changing seasonal wind conditions in the nearshore zone of Herschel Island Qikiqtaruk in the western Canadian Arctic since 1982. We use observations in the Landsat red and thermal wavebands, as well as a recently published water turbidity algorithm to relate archive wind data to turbidity and sea surface temperature. We map the spatial patterns of turbidity and water temperature at high spatial resolution in order to resolve transport pathways of water and sediment at the water surface. Our results show that these pathways are clearly related to the prevailing wind conditions, being ESE and NW. During easterly wind conditions, both turbidity and water temperature are significantly higher in the nearshore area. The extent of the Mackenzie River plume and coastal erosion are the main explanatory variables for sediment dispersal and sea surface temperature distributions in the study area. During northwesterly wind conditions, the influence of the Mackenzie River plume is negligible. Our results highlight the potential of high spatial resolution Landsat imagery to detect small-scale hydrodynamic processes, but also show the need to specifically tune optical models for Arctic nearshore environments.

Climate change leads to a doubling of turbidity in a rapidly expanding Tibetan lake

Recent climate change is causing most lakes on the Tibetan Plateau to grow at an unprecedented rate. Changes in the physical properties and water storage of the lakes are now relatively well documented. Yet the impacts on their water quality remain poorly understood. Turbidity is a well-established optical water-quality indicator related to suspended particulate matter concentration which can affect vertical light attenuation and ecosystem functioning. Here, we use remotely sensed data to assess the seasonal and long-term variations in turbidity in Siling Lake, one of the fastest growing lakes on the Tibetan Plateau, and to identify potential driving mechanisms of this change. The lake experiences two distinct peaks of turbidity during the year: one in August (warm season) caused by the seasonal influx of sediments from the Zagya Zangbo River, and one in December (cold season) caused by the wind-driven resuspension of sediments along the lakes' shorelines. The analysis further revealed a persistent increasing trend that doubled the average lake turbidity between 2000 and 2017. Evidence suggests this rise in turbidity results from a climate-driven increase in sediment supply from the Zagya Zangbo River, and from sediment resuspension associated with the erosion of shorelines recently submerged during the rapid expansion of the lake (paleoshorelines). Our results highlight the vulnerability of the Tibetan Lakes' water quality to climate change.

Hindcasting eutrophication and changes in temperature and storage volume in a semi-arid reservoir: a multi-decadal Landsat-based assessment

In situ monitoring of freshwater systems is often constrained by cost and accessibility, particularly in developing countries and in remote areas. Satellite remote sensing is therefore increasingly being integrated with existing in situ water quality monitoring programs. In this study, we use the Landsat TM/ETM+ image record collected between 1984 and 2015 to track temporal changes in trophic status, chlorophyll-a levels, algal bloom incidences, water clarity, water temperature, and reservoir water volume in a poorly monitored hypereutrophic semi-arid reservoir. Historical reservoir water quality data are inferred from calibrated Landsat-based empirical algorithms. The results show that, although the reservoir has existed in a eutrophic to hypereutrophic state over the past 30 years, its water quality has significantly deteriorated in the most recent decade. Mean summer chlorophyll-a concentrations were found to have increased by around 163% between 1984 and 2015, while water clarity dropped by more than 58% over the same period. Statistically significant changes in surface water temperatures were also apparent for the month of August, with a cumulative increase of 1.24 °C over the 31-year study period. The rise in temperature appears to correlate with the incidence of Microcystis blooms observed in the reservoir over the past decade. On the other hand, the water volume in the reservoir was found to have been fairly stable over time, likely as a result of adaptive reservoir management. This study demonstrates the strength of using Landsat data to hindcast and quantify changes in water quality and quantity in poorly monitored freshwater systems.