نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشیار گروه مهندسی آب دانشکده آب و خاک دانشگاه علوم کشاورزی و منابع طبیعی گرگان
2 گروه مهندسی آب، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Continuous monitoring of water quality in dam reservoirs, especially under the influence of climate change and anthropogenic pressures, is vital for sustainable water resource management. This study utilized Landsat 8 satellite imagery to model and map two key water quality parameters chlorophyll-a and turbidity in the Choke Canyon Reservoir, Texas, USA. A multivariate linearlinear regression model was developed based on in-situ data collected from 19 points across the reservoir surface. The results showed that the developed models performed well at the reference time, with R² and RMSE values of 0.96 and 0.09 for chlorophyll-a, and 0.84 and 0.10 for turbidity, respectively. To address temporal changes and environmental variability, a Spectral Correction Parameter (SCP) was introduced, which improved model accuracy for future time predictions. The RMSE for chlorophyll-a estimation decreased from 0.15 to 0.09, and for turbidity from 0.14 to 0.05 after applying SCP. Spatial analysis revealed that higher chlorophyll-a concentrations were mainly found in the western, shallow, and nearshore areas of the reservoir, while higher turbidity levels were concentrated in the central and deeper zones. These spatial patterns were closely related to environmental factors such as water depth, inflow currents, and reservoir topography. Overall, the integration of remote sensing data, statistical modeling, and spectral correction techniques proved effective for low-cost, large-scale monitoring of water quality in reservoirs.
کلیدواژهها [English]
Monitoring of water quality in dam reservoirs is of significant importance for the sustainable management of water resources, especially under conditions of increased urbanization, agricultural expansion, and climate change. Traditional field-based monitoring methods are often time-consuming, costly, and spatially limited. In recent years, remote sensing has emerged as an effective alternative for the estimation of key water quality parameters over large spatial and temporal scales. This study aims to estimate and analyze the spatial distribution of two important optical water quality parameters chlorophyll-a (Chl-a) and turbidity in the Choke Canyon Reservoir, located in Texas, USA, using Landsat 8 satellite imagery.
In-situ water quality data, including chlorophyll-a concentration and turbidity (NTU), were collected on January 15, 2023, from 19 sampling points across the reservoir. Satellite data corresponding to the same date were acquired from the Landsat 8 OLI sensor. A Modified Normalized Difference Water Index (MNDWI) was applied to extract the water body extent. Then, multivariate linearlinear regression models were developed using spectral reflectance values (mainly Bands 4 and 5) as independent variables to estimate chlorophyll-a and turbidity. To address the atmospheric and environmental variability in satellite reflectance, a Specific Correction Parameter (SCP) was introduced and applied to Landsat imagery from a second date (July 18, 2023) for model validation and temporal comparison.
The regression models achieved strong predictive performance during the reference period, with R² values of 0.96 for chlorophyll-a and 0.84 for turbidity, and corresponding RMSE values of 0.09 mg/m³ and 0.10 NTU, respectively. After applying the SCP correction for July 18, 2023, the model performance improved significantly, with RMSE reduced to 0.09 for Chl-a and 0.05 for turbidity, and R² values around 0.93 for both parameters. Spatial mapping revealed that high chlorophyll-a concentrations were mostly found in the northern shallow regions of the reservoir, likely due to nutrient accumulation and algal growth. In contrast, high turbidity levels were observed in the southern and central areas, possibly caused by sediment resuspension and inflow disturbances. These findings align well with similar studies using remote sensing for water quality monitoring and demonstrate the importance of incorporating atmospheric correction in multi-temporal analyses.
This study demonstrated that the integration of medium-resolution satellite imagery, linearlinear regression modeling, and specific spectral correction can provide an efficient, low-cost, and reliable approach for estimating and mapping key water quality parameters in reservoir systems. The methodology is particularly valuable for regions with limited field data and can be generalized for use in other similar environments. The use of SCP enhanced the comparability of multi-temporal satellite observations, making it a practical tool for dynamic water quality assessment and decision support in water resource management.
Abdolreza Zahiri: Supervision, review and editing, project administration, funding acquisition.
Hamed Feizabady: Conceptualization, methodology, investigation, data curation, analysis, visualization, writing—original draft.
All authors have read and approved the final manuscript.
The data supporting the findings of this study are available from the corresponding author, Hamed Feizabady, upon reasonable request, and are also accessible through the database referenced in the manuscript.
The authors would like to thank Gorgan University of Agricultural Sciences and Natural Resources for their support, and the reviewers for their valuable comments and suggestions.
The authors confirm that the study was conducted in accordance with ethical principles, and no data fabrication, falsification, plagiarism, or misconduct occurred.
The authors declare no conflict of interest.
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