Spatio-Temporal Analysis of Water Surface Temperature in a Reservoir and its Relation with Water Quality in a Climate Change Context (original) (raw)
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Journal of Applied Remote Sensing, 2019
The importance of lake water surface temperature has long been highlighted for ecological and hydrological studies as well as for water quality management. In the absence of regular field observations, satellite remote sensing has been recognized as a cost-effective way to monitor water surface temperature on large spatial and temporal scales. The thermal infrared sensors (TIRS) onboard of Landsat satellites (since 1984) are adequate tools for monitoring surface temperature of small to medium sized lakes with a biweekly frequency, as well as for performing retrospective analysis. Nonetheless, the satellite data have to deal with effects due to the atmosphere so that several approaches to correct for atmospheric contributions have been proposed. Among these are: (i) the radiative transfer equation (RTE); (ii) a single-channel algorithm that depends on water vapor content and emissivity (SC1); (iii) its improved version including air temperature (SC2); and (iv) a monowindow (MW) algorithm that requires emissivity, atmospheric transmissivity, and effective mean atmospheric temperature. We aim to evaluate these four approaches in a river dammed reservoir with a size of 12 km 2 using data gathered from the band 10 of the TIRS onboard of Landsat 8. Satellite-derived temperatures were then compared to in situ data acquired from thermistors at the time of Landsat 8 overpasses. All approaches showed a good performance, with the SC1 algorithm yielding the lowest root mean square error (0.73 K), followed by the SC2 method (0.89 K), the RTE (0.94 K), and then the MW algorithm (1.23 K). Based on the validation results, we then applied the SC1 algorithm to Landsat 4, 5, and 8 thermal data (1984 to 2018) to extend data series to past years. These data do not reveal any warming trend of the reservoir surface temperature. The results of this study also confirm how the 100-m spatial resolution of TIRS is valuable as an additional source of data to field-based monitoring.
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Most applications using remote sensing tend to assess fresh water quality via regression models between in situ data and spectral bands. Suspended Sediment (Turbidity), Dissolved Oxygen and Temperature are common parameters derived from RS and recurrently used in WQI and/or TDML indicators. In this study a series of ETM+ Landsat images, thermal band, in combination with in situ measurement over 7 years, 2001 to 2007, were used over the northern part of Lake Nasser (Egypt) to develop a regression model linking thermal band to water surface temperature. Relationship between Water Surface Temperature and Dissolved Oxygen was then extracted statistically at surface and at 80% depth of water column. A second series of eighteen Landsat ETM+ thermal band images was tested then to produce temporal and spatial pattern changes in the above mentioned parameters over various months of 2001 to 2003 and proposed to be implemented as shown in four dates of 2012/2013. The results showed a good resp...
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Modeling the water quality of rivers and assessing the effects of changing conditions is often hindered by a lack of in situ measurements for calibration. Here, we use a combination of satellite measurements, statistical models, and numerical modeling with CE-QUAL-W2 to overcome in situ data limitations and evaluate the effect of changing hydrologic and climate conditions on water temperature (Tw) in the Tigris River, one of the largest rivers in the Middle East. Because few in situ estimates of Tw were available, remotely-sensed estimates of Tw were obtained from Landsat satellite images at roughly 2 week intervals for the year 2009 at the upstream model boundary (Mosul Dam) and two locations further downstream, Baeji and Baghdad. A regression was then developed between air temperature and Landsat Tw in order to estimate daily Tw. These daily Tw were then used for the upstream model boundary condition and for model calibration downstream. Modeled Tw at downstream locations agreed w...
Remote Sensing of Environment, 2015
The application of remote sensing technology to water quality monitoring has special significance for lake management at regional scales. Many studies have proposed algorithms between Landsat data and in-situ water quality parameters using classical regression models. The novelty of this paper is that we developed algorithms to determine log-transformed chlorophyll-a concentration (Chl-a) and Secchi disk transparency (SDT) in Río Tercero reservoir using Landsat TM and ETM+ imagery, ancillary environmental factors and linear mixed models (LMM), obtaining an increase in the accuracy of the estimates. The validation results showed that LMM with spatial correlation structure that take into account water surface temperature (WST) and rainfall were the most suitable method for estimating these parameters. WST derived from the Landsat thermal band was also validated. The algorithms were used to generate quantitative maps providing spatially and temporally rich information on patterns of water quality throughout the reservoir. Water quality features related to the hydrogeomorphology of the reservoir, typical seasonality and influx from the cooling system of a local nuclear reactor were identified in the time series maps.
Reservoir Water-Quality Projections under Climate-Change Conditions
Water Resources Management, 2018
Reservoirs are key components of water infrastructure that serve many functions (water supply, hydropower generation, flood control, recreation, ecosystem services, etc.). Climate change affects the hydrology of the tributary areas to reservoirs, which may profoundly impact their operation and possibly the reservoirs' water quality, among which the temperature gradient and the total dissolved solids (TDS) are key qualitative characteristics of reservoirs, especially those with irrigation function. This study examines water-quality changes in the Aidoghmoush reservoir (East Azerbaijan-Iran) under climate-change conditions in the period 2026-2039. The temperature and precipitation climate variables are calculated by the HadCM3 model driven by emission scenario A2 in the baseline period (1987-2000), and these variables are then projected over the future period (2026-2039). The average annual runoff under climate-change conditions is simulated by the IHACRES model. The results show the future average annual runoff would decrease by about 1% compared to the baseline conditions. The CE-QUAL-W2 model is employed to simulate the reservoir water quality. It is predicted the surface air temperature would increase by 1.3°C under the climate-change scenario compared to the baseline condition, and the temperatures of the reservoir's surfaceand bottom-waters would increase by 1.19 and 1.24°C, respectively. The average TDS near the reservoir surface would increase by 44.5 g/m 3 (4.3%) relative to baseline TDS. The TDS near the reservoir surface are projected to be highest in autumn and winter for baseline and future conditions. This research shows that changes due to climate change are potentially severe, and presents a methodology that could assist managers and planners to find optimal strategies of reservoir operation to cope with changes in thermal stratification and TDS. This paper results identify the reservoir levels from which to withdraw water with the best waterquality characteristics.
Scientific Reports, 2024
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1ST SAMARRA INTERNATIONAL CONFERENCE FOR PURE AND APPLIED SCIENCES (SICPS2021): SICPS2021
Empirical models have been developed in this study to predict water quality parameters, with the intention of demonstrating both benefits and the viability of using Landsat 8 spectral images to estimate water quality parameters in one main of the Iraqi Rivers, it is the Tigris River. The study area was along the part of the Tigris River which is located in Baghdad (capital) and Babylon governorate, in the middle part of Iraq. Water quality data archive for the of total dissolved solids (TDS), phosphates (PO 4), Sulfate (SO 4), and total hardness (TH) of the water Tigris river during four seasons (winter, spring, summer, and autumn) distributed along four years (2013, 2014, 2015 and 2017), was collected from sixteen ground stations along the part of Tigris river. The objective of this study to establish seasonal empirical mathematical models can use them every year during the season time without the need for calibration every time. For this purpose, has been following the different approach when compared with all of the studies which are carried before by collecting data for the same season and for different years. Prior to the establishment of the models, both preprocessing of data as radiometric and atmospheric corrections were applied to the optical data. Through multiple regression analysis between measured water quality parameters of the ground stations and the reflectance of the pixels corresponding to the sampling stations. Statistical models with determination coefficients between 0.89-0.95 were generated. Results indicate this novelty approach has generated mathematical models for the open time for any year but in the special season. Another indicator that from a small number of measured parameters can generate reliable models to estimate the concentration of TDS, PO 4 , SO 4 , and TH. So models generated from Landsat 8 can be used as a tool to facilitate the environmental, economic, and social management of the surface waters bodies like a Tigris river.
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International Journal of Remote Sensing, 2016
Landsat 8 is the first Earth observation satellite with sufficient radiometric and spatial resolution to allow global mapping of lake CDOM and DOC (coloured dissolved organic matter and dissolved organic carbon, respectively) content. Landsat 8 is a multispectral sensor however, the number of potentially usable band ratios, or more sophisticated indices, is limited. In order to test the suitability of the ratio most commonly used in lake carbon content mapping, the green-red band ratio, we carried out fieldwork in Estonian and Brazilian lakes. Several atmospheric correction methods were also tested in order to use image data where the image-to-image variability due to illumination conditions would be minimal. None of the four atmospheric correction methods tested, produced reflectance spectra that matched well with in situ measured reflectance. Nevertheless, the green-red band ratio calculated from the reflectance data was in correlation with measured CDOM values. In situ data show that there is a strong correlation between CDOM and DOC concentrations in Estonian and Brazilian lakes. Thus, mapping the global CDOM and DOC content from Landsat 8 is plausible but more data from different parts of the world are needed before decisions can be made about the accuracy of such global estimation.
Studying the Dynamics of Lake Sevan Water Surface Temperature Using Landsat8 Sateliite Imagery
Annals of Valahia University of Targoviste, Geographical Series, 2018
Lake Sevan being Armenia's largest freshwater reservoir has a vital economic, recreational and cultural importance to both the catchment area and the nation as a whole. At present the Sevan which has seen the dramatic-some 20m drop-in water level entailing grave ecological consequences to the whole of its ecosystem, is at the stage of recovery. Hence, it is very important to study basic parameters describing the ecological status of the lake, and their annual and seasonal dynamics. The Sevan water surface temperature (WST) is a key parameter which influences all ecological processes that occur in the Lake. Declining lake level has brought to reduction of water volume and consequently to earlier warming of lake water in spring and its earlier cooling in the fall. Besides, more frequent becomes the complete surface freezing of Lake Sevan. Remotely sensed imagery makes it possible to get immediate information on a regular basis about WST across the entire surface of lakes. The purpose of this particular research was to study the space and time dynamics of Lake Sevan WST using Landsat 8 satellite imagery. The advantage of Landsat8 images is a regular frequency of capturing and availability of another thermal band that helps reduce the atmospheric refraction-induced errors/deviations. This research involved Landsat imagery for 2000-2018. The images underwent preprocessing steps (radiometric calibration, atmospheric correction, normalization etc) and then Lake Sevan WSTs and their monthly and annual changes over the mentioned periods were derived using both thermal bands (b10, b11). The research confirmed the fact, that Lake Sevan surface completely or partly freezing with periodicity of 2-3 years, whereas before the water drop the periodicity was 15-20 years. The study of spatial distribution of WST data derived from remote sensing shows that the temperature data corresponds to the overall general picture of temperature for Lake Sevan. This research has indicated that remotely sensed images and Landsat 8 imagery in particular allow derive both WST data on a regular basis and retrospective data (since 2013).