Trophic state index for heavily impacted watersheds: modeling the influence of diffuse pollution in water bodies

2020 ◽  
Vol 65 (15) ◽  
pp. 2548-2560
Author(s):  
L. Carneiro ◽  
A. Ostroski ◽  
E. G. F. Mercuri
Keyword(s):  
Trophic State ◽  
Diffuse Pollution ◽  
Water Bodies ◽  
Trophic State Index ◽  
State Index

Trophic State Index derivation through the remote sensing of Case-2 water bodies in the Mediterranean region

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Christiana Papoutsa ◽  
Evangelos Akylas ◽  
Diofantos Hadjimitsis
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Regression Models ◽  
Mediterranean Region ◽  
Trophic State ◽  
Landsat Tm ◽  
Water Bodies ◽  
Trophic State Index ◽  
State Index ◽  
The Mediterranean

AbstractThe main goal of this study is the derivation of Carlson’s Trophic State Index (TSI) through the remote sensing of four different Case-2 waters in the Mediterranean region such as Cyprus and Greece. TSISD is derived through extensive field ground campaign of Secchi Disk Depth measurements for the Asprokremmos Dam, located in Paphos District in Cyprus; Alyki Salt Lake, located in Larnaca District in Cyprus; and in Karla Lake, located in Volos District in Greece; and finally to three coastal water areas in the Limassol coastal area. Several regression models have been applied in order to develop the best regression model between the TSISD and in-band reflectance values for Landsat TM/ETM derived from spectroradiometric measurements using a GER-1500 field spectroradiometer over the main case study area in Asprokremmos Dam in Cyprus. Finally, we apply several regression models for Asprokremmos Dam for retrieving the suitable Landsat TM/ETM band or band combinations (obtained from field spectroradiometric measurements) in which TSISD can be determined. Indeed, the best regression model has been obtained by correlating ‘TSI Versus Band2/Band3’, with R2=0.89. All field TSISD and in-band reflectance values from the four different water bodies have been used to develop the best fitted model for the established TSI SD Versus Band2/Band3 model. We find that the exponential regression model provides the best fitted equation over the four different water bodies.

Trophic Status of Lake Phewa and Kulekhani Reservoir, Nepal

2021 ◽  
Vol 18 (3) ◽  
pp. 49-57
Author(s):  
Smriti Gurung ◽  
Babi Kumar Kafle ◽  
Bed Mani Dahal ◽  
Milina Sthapit ◽  
Nani Raut ◽  
...  
Keyword(s):  
Trophic State ◽  
Trophic Status ◽  
Secchi Depth ◽  
Sustainable Use ◽  
Water Bodies ◽  
Limiting Factor ◽  
Trophic State Index ◽  
Post Monsoon ◽  
State Index ◽  
Lake Phewa

Eutrophication is one of the growing environmental concerns and is affecting and compromising freshwater bodies across the world making the trophic status assessment of water bodies crucial for their restoration and sustainable use. This paper describes the trophic status of Lake Phewa and Kulekhani Reservoir from Nepal. Sampling was conducted during October 2017 (post-monsoon), April 2018 (Pre-monsoon), July 2018 (Monsoon) and February 2019 (Winter). Trophic State Index (TSI) as given by Carlson (1977) and Trophic State Index Deviation given by Carlson (1991) were estimated to assess trophic status and deviations between the Trophic State Indices. One-way analysis of variance showed significant seasonal variation (p < 0.05) in Secchi depth, total phosphorus (TP), TSI in both the water bodies. Both the water bodies were classified as eutrophic during pre-monsoon and post-monsoon, and hypereutrophic during the monsoon indicating the increased flow of allochthonous inputs from their respective catchments. Non-algal turbidity was found to be the limiting factor for productivity. There is a need for sustainable watershed management in order to reduce the nutrients runoff and accumulation in the water bodies.

APLIKASI CITRA LANDSAT 8 OLI UNTUK IDENTIFIKASI STATUS TROFIK WADUK GAJAH MUNGKUR WONOGIRI, JAWA TENGAH

2017 ◽  
Vol 19 (2) ◽  
pp. 113
Author(s):  
Kusuma Wardani Laksitaningrum ◽  
Wirastuti Widyatmanti
Keyword(s):  
Chlorophyll A ◽  
Total Phosphorus ◽  
Trophic State ◽  
Water Clarity ◽  
Coefficient Of Determination ◽  
Physical Factors ◽  
Landsat 8 ◽  
Trophic State Index ◽  
Landsat 8 Oli ◽  
State Index

<p align="center"><strong>ABSTRAK</strong></p><p class="abstrak">Waduk Gajah Mungkur (WGM) adalah bendungan buatan yang memiliki luas genangan maksimum 8800 ha, terletak di Desa Pokoh Kidul, Kecamatan Wonogiri, Kabupaten Wonogiri. Kondisi perairan WGM dipengaruhi oleh faktor klimatologis, fisik, dan aktivitas manusia yang dapat menyumbang nutrisi sehingga mempengaruhi status trofiknya. Tujuan dari penelitian ini adalah mengkaji kemampuan citra Landsat 8 OLI untuk memperoleh parameter-parameter yang digunakan untuk menilai status trofik, menentukan dan memetakan status trofik yang diperoleh dari citra Landsat 8 OLI, dan mengevaluasi hasil pemetaan dan manfaat citra penginderaan jauh untuk identifikasi status trofik WGM. Identifikasi status trofik dilakukan berdasarkan metode <em>Trophic State Index</em> (TSI) Carlson (1997) menggunakan tiga parameter yaitu kejernihan air, total fosfor, dan klorofil-a. Model yang diperoleh berdasar pada rumus empiris dari hasil uji regresi antara pengukuran di lapangan dan nilai piksel di citra Landsat 8 OLI. Model dipilih berdasarkan nilai koefisien determinasi (R<sup>2</sup>) tertinggi. Hasil penelitian merepresentasikan bahwa nilai R<sup>2</sup> kejernihan air sebesar 0,813, total fosfor sebesar 0,268, dan klorofil-a sebesar 0,584. Apabila nilai R<sup>2 </sup>mendekati 1, maka semakin baik model regresi dapat menjelaskan suatu parameter status trofik. Berdasarkan hasil kalkulasi diperoleh distribusi yang terdiri dari kelas eutrofik ringan, eutrofik sedang, dan eutrofik berat yaitu pada rentang nilai indeks 50,051 – 80,180. Distribusi terbesar adalah eutrofik sedang. Hal tersebut menunjukkan tingkat kesuburan perairan yang tinggi dan dapat membahayakan makhluk hidup lain.</p><p><strong>Kata kunci: </strong>Waduk Gajah Mungkur, citra Landsat 8 OLI, regresi, TSI, status trofik</p><p class="judulABS"><strong>ABSTRACT</strong></p><p class="Abstrakeng">Gajah Mungkur Reservoir is an artificial dam that has a maximum inundated areas of 8800 ha, located in Pokoh Kidul Village, Wonogiri Regency. The reservoir’s water conditions are affected by climatological and physical factors, as well as human activities that can contribute to nutrients that affect its trophic state. This study aimed to assess the Landsat 8 OLI capabilities to obtain parameters that are used to determine its trophic state, identifying and mapping the trophic state based on parameters derived from Landsat 8 OLI, and evaluating the results of the mapping and the benefits of remote sensing imagery for identification of its trophic state. Identification of trophic state is based on Trophic State Index (TSI) Carlson (1997), which uses three parameters there are water clarity, total phosphorus, and chlorophyll-a. The model is based on an empirical formula of regression between measurements in the field and the pixel values in Landsat 8 OLI. Model is selected on the highest value towards coefficient of determination (R<sup>2</sup>). The results represented that R<sup>2</sup> of water clarity is 0.813, total phosphorus is 0.268, and chlorophyll-a is 0.584. If R<sup>2</sup> close to 1, regression model will describe the parameters of the trophic state better. Based on the calculation the distribution consists of mild eutrophic, moderate eutrophic, and heavy eutrophic that has index values from 50.051 to 80.18. The most distribution is moderate eutrophication, and it showed the high level of trophic state and may harm other living beings.</p><p><strong><em>Keywords: </em></strong><em>Gajah Mungkur Reservoir, </em><em>L</em><em>andsat 8 OLI satellite imagery, regression, TSI, trophic state</em></p>

scholarly journals Assessment of Trophic Responses of a Reservoir to Seasonal and Annual Variations in Monsoon

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2117
Author(s):  
Su-mi Kim ◽  
Hyun-su Kim
Keyword(s):  
Water Quality ◽  
Trophic State ◽  
Monsoon Season ◽  
Phytoplankton Growth ◽  
Phosphorus Loading ◽  
Nutrient Concentrations ◽  
Trophic State Index ◽  
Post Monsoon ◽  
Post Monsoon Season ◽  
State Index

The variations in water quality parameters and trophic status of a multipurpose reservoir in response to changing intensity of monsoon rain was investigated by applying a trophic state index deviation (TSID) analysis and an empirical regression model to the data collected in two periods from 2014 to 2017. The reservoir in general maintained mesotrophic conditions, and Carlson’s trophic state index (TSIc) was affected most by TSITP. Nutrient concentrations, particularly phosphorus, did not show strong correlations with precipitation, particularly in the period with weak monsoon, and a significant increase in total phosphorus (TP) was observed in Spring 2015, indicating the possibility of internal phosphorus loading under decreased depth and stability of water body due to a lack of precipitation. TSIChl was higher than TSISD in most data in period 1 when a negligible increase in precipitation was observed in the monsoon season while a significant fraction in period 2 showed the opposite trend. Phytoplankton growth was not limited by nutrient limitation although nutrient ratios (N/P) of most samples were significantly higher than 20, indicating phosphorus-limited condition. TSID and regression analysis indicated that phytoplankton growth was limited by zooplankton grazing in the Spring, and that cell concentrations and community structure in the monsoon and post-monsoon season were controlled by the changing intensity of the monsoon, as evidenced by the positive and negative relationships between community size and cyanobacterial population with the amount of precipitation in the Summer, respectively. The possibility of contribution from internal loading and an increase in cyanobacterial population associated with weak monsoon, in addition to potential for nutrient enrichment in the post-monsoon season, implies a need for the application of more stringent water quality management in the reservoir that can handle all potential scenarios of eutrophication.

scholarly journals Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1988
Author(s):  
Minqi Hu ◽  
Ronghua Ma ◽  
Zhigang Cao ◽  
Junfeng Xiong ◽  
Kun Xue
Keyword(s):  
Yangtze River ◽  
Trophic State ◽  
Inland Waters ◽  
Landsat 8 ◽  
Trophic State Index ◽  
Type I ◽  
Water Type ◽  
Type Ii ◽  
Turbid Water ◽  
State Index

Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R2 = 0.62, N = 282) and Type II water (R2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km2) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.

scholarly journals A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors

2021 ◽  
Vol 13 (13) ◽  
pp. 2498
Author(s):  
Shijie Zhu ◽  
Jingqiao Mao
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Water Temperature ◽  
Trophic State ◽  
Trophic Levels ◽  
Water Bodies ◽  
Trophic State Index ◽  
Urban Water ◽  
Estimation Model ◽  
Input Variables

To improve the accuracy of remotely sensed estimates of the trophic state index (TSI) of inland urban water bodies, key environmental factors (water temperature and wind field) were considered during the modelling process. Such environmental factors can be easily measured and display a strong correlation with TSI. Then, a backpropagation neural network (BP-NN) was applied to develop the TSI estimation model using remote sensing and environmental factors. The model was trained and validated using the TSI quantified by five water trophic indicators obtained for the period between 2018 and 2019, and then we selected the most appropriate combination of input variables according to the performance of the BP-NN. Our results demonstrate that the optimal performance can be obtained by combining the water temperature and single-band reflection values of Sentinel-2 satellite imagery as input variables (R2 = 0.922, RMSE = 3.256, MAPE = 2.494%, and classification accuracy rate = 86.364%). Finally, the spatial and temporal distribution of the aquatic trophic state over four months with different trophic levels was mapped in Gongqingcheng City using the TSI estimation model. In general, the predictive maps based on our proposed model show significant seasonal changes and spatial characteristics in the water trophic state, indicating the possibility of performing cost-effective, RS-based TSI estimation studies on complex urban water bodies elsewhere.

scholarly journals Phytoplanktonic associations: a tool to understanding dominance events in a tropical Brazilian reservoir

2007 ◽  
Vol 21 (3) ◽  
pp. 641-648 ◽  
Author(s):  
Ariadne do Nascimento Moura ◽  
Maria do Carmo Bittencourt-Oliveira ◽  
Ênio Wocyli Dantas ◽  
João Dias de Toledo Arruda Neto
Keyword(s):  
Dissolved Oxygen ◽  
Trophic State ◽  
Nitrogen Concentration ◽  
Trophic State Index ◽  
Dry Period ◽  
Tropical Reservoir ◽  
Phytoplankton Assemblages ◽  
Reservoir Water ◽  
Rainy Period ◽  
State Index

The aim of this study was to characterize phytoplankton associations, as well as discuss controlling factors determining algal dominance in a eutrophic tropical reservoir, Mundaú, Pernambuco, Brazil. Water samples were collected during the dry period (January/2005) and the rainy period (June/2005). The samples were collected from both limnetic and littoral regions, and the phytoplankton assemblages identified from current literature after preservation in formaldehyde 4%. At the same time as sampling was done, in situ measurements of water temperature, transparency, dissolved oxygen, and pH were also taken. Total phosphorus, total nitrogen concentration and the Trophic State Index were subsequently determined in the laboratory. Phytoplankton density (ind. L-1) was estimated using an inverted Zeiss microscope. Grouping of the phytoplankton associations was carried out using the Reynolds phytosociological classification. During the dry period, reservoir water showed low dissolved oxygen concentrations, alkaline pH and was relatively turbid compared to the situation during the rainy season. Reservoir water is limited by nitrogen during both seasonal periods. The Trophic State Index is classified as determining eutrophic conditions. Phytoplankton was represented by 70 infrageneric taxa grouped in 16 functional associations, with the majority typical of eutrophic systems. This fact is supported by quantitative analysis, which shows the dominance of S associations comprising exclusively R-strategist cyanobacteria.

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