scholarly journals Example application of a continuous lake trophic state index on lakes with limited data

2019 ◽  
Author(s):  
Farnaz Nojavan ◽  
Betty J Kreakie ◽  
Jeffrey W Hollister ◽  
Song Qian
Keyword(s):  
Multilevel Model ◽  
Trophic State ◽  
The United States ◽  
Nutrient Concentrations ◽  
Trophic State Index ◽  
Gis Data ◽  
Lake Trophic State ◽  
State Index ◽  
Multiple Metrics ◽  
Over Time

Lake trophic state indices have long been used to provide a measure of the trophic state of lakes. Over time it has been determined that these indices perform better when they utilize multiple metrics and provide a continuous measurement of trophic state. We utilize such a method for trophic state that is based upon a Proportional Odds Logistic Regression (POLR) model and extend this model with a Bayesian multilevel model that predicts nutrient concentrations from universally available GIS data. This Bayesian multilevel model provides relatively accurate measures of trophic state and has an overall accuracy of 60%. The approach illustrates a method for estimating a continuous, mutli-metric trophic state index for any lake in the United States. Future improvements to the model will focus on improving overall accuracy and use variables that are more sensitive to change over time.

scholarly journals Example application of a continuous lake trophic state index on lakes with limited data

2019 ◽  
Author(s):  
Farnaz Nojavan ◽  
Betty J Kreakie ◽  
Jeffrey W Hollister ◽  
Song Qian
Keyword(s):  
Multilevel Model ◽  
Trophic State ◽  
The United States ◽  
Nutrient Concentrations ◽  
Trophic State Index ◽  
Gis Data ◽  
Lake Trophic State ◽  
State Index ◽  
Multiple Metrics ◽  
Over Time

Lake trophic state indices have long been used to provide a measure of the trophic state of lakes. Over time it has been determined that these indices perform better when they utilize multiple metrics and provide a continuous measurement of trophic state. We utilize such a method for trophic state that is based upon a Proportional Odds Logistic Regression (POLR) model and extend this model with a Bayesian multilevel model that predicts nutrient concentrations from universally available GIS data. This Bayesian multilevel model provides relatively accurate measures of trophic state and has an overall accuracy of 60%. The approach illustrates a method for estimating a continuous, mutli-metric trophic state index for any lake in the United States. Future improvements to the model will focus on improving overall accuracy and use variables that are more sensitive to change over time.

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 Rethinking the lake Trophic State Index

2019 ◽  
Author(s):  
Farnaz Nojavan A. ◽  
Betty J Kreakie ◽  
Jeffrey W Hollister ◽  
Song S Qian
Keyword(s):  
Trophic State ◽  
Nitrogen Concentration ◽  
Current Approach ◽  
Classification Model ◽  
Phosphorus Concentration ◽  
Trophic State Index ◽  
Cyanobacteria Blooms ◽  
Lake Trophic State ◽  
State Index ◽  
Ordered Categorical

Lake trophic state classifications provide information about the condition of lentic ecosystems and are indicative of both ecosystem services (e.g., clean water, recreational opportunities, and aesthetics) and disservices (e.g., cyanobacteria blooms). The current classification schemes have been criticized for developing indices that are single-variable based (vs. a complex aggregate of multi-variables), discrete (vs. a continuum), and/or deterministic (vs. an inherent randomness). We present an updated lake trophic classification model using a Bayesian multilevel ordered categorical regression. The model consists of a proportional odds logistic regression (POLR) that models ordered, categorical, lake trophic state using Secchi disk depth, elevation, nitrogen concentration (N), and phosphorus concentration (P). The overall accuracy, when compared to existing classifications of trophic state index (TSI), for the POLR model was 0.68 and the balanced accuracy ranged between 0.72 and 0.93. This work delivers an index that is multi-variable based, continuous, and classifies lakes in probabilistic terms. While our model addresses all the limitations of the current approach to lake trophic classification, the addition of uncertainty quantification is important, because the trophic state response to predictors varies among lakes. Our model successfully addresses concerns with the current approach and performs well across trophic states in a large spatial extent.

scholarly journals Rethinking the lake Trophic State Index

2019 ◽  
Author(s):  
Farnaz Nojavan A. ◽  
Betty J Kreakie ◽  
Jeffrey W Hollister ◽  
Song S Qian
Keyword(s):  
Trophic State ◽  
Nitrogen Concentration ◽  
Current Approach ◽  
Classification Model ◽  
Phosphorus Concentration ◽  
Trophic State Index ◽  
Cyanobacteria Blooms ◽  
Lake Trophic State ◽  
State Index ◽  
Ordered Categorical

Lake trophic state classifications provide information about the condition of lentic ecosystems and are indicative of both ecosystem services (e.g., clean water, recreational opportunities, and aesthetics) and disservices (e.g., cyanobacteria blooms). The current classification schemes have been criticized for developing indices that are single-variable based (vs. a complex aggregate of multi-variables), discrete (vs. a continuum), and/or deterministic (vs. an inherent randomness). We present an updated lake trophic classification model using a Bayesian multilevel ordered categorical regression. The model consists of a proportional odds logistic regression (POLR) that models ordered, categorical, lake trophic state using Secchi disk depth, elevation, nitrogen concentration (N), and phosphorus concentration (P). The overall accuracy, when compared to existing classifications of trophic state index (TSI), for the POLR model was 0.68 and the balanced accuracy ranged between 0.72 and 0.93. This work delivers an index that is multi-variable based, continuous, and classifies lakes in probabilistic terms. While our model addresses all the limitations of the current approach to lake trophic classification, the addition of uncertainty quantification is important, because the trophic state response to predictors varies among lakes. Our model successfully addresses concerns with the current approach and performs well across trophic states in a large spatial extent.

scholarly journals Rethinking the lake trophic state index

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7936 ◽  
Author(s):  
Farnaz Nojavan A. ◽  
Betty J. Kreakie ◽  
Jeffrey W. Hollister ◽  
Song S. Qian
Keyword(s):  
Trophic State ◽  
Nitrogen Concentration ◽  
Current Approach ◽  
Classification Model ◽  
Phosphorus Concentration ◽  
Trophic State Index ◽  
Cyanobacteria Blooms ◽  
Lake Trophic State ◽  
State Index ◽  
Ordered Categorical

Lake trophic state classifications provide information about the condition of lentic ecosystems and are indicative of both ecosystem services (e.g., clean water, recreational opportunities, and aesthetics) and disservices (e.g., cyanobacteria blooms). The current classification schemes have been criticized for developing indices that are single-variable based (vs. a complex aggregate of multi-variables), discrete (vs. a continuous), and/or deterministic (vs. an inherently random). We present an updated lake trophic classification model using a Bayesian multilevel ordered categorical regression. The model consists of a proportional odds logistic regression (POLR) that models ordered, categorical, lake trophic state using Secchi disk depth, elevation, nitrogen concentration (N), and phosphorus concentration (P). The overall accuracy, when compared to existing classifications of trophic state index (TSI), for the POLR model was 0.68 and the balanced accuracy ranged between 0.72 and 0.93. This work delivers an index that is multi-variable based, continuous, and classifies lakes in probabilistic terms. While our model addresses aforementioned limitations of the current approach to lake trophic classification, the addition of uncertainty quantification is important, because the trophic state response to predictors varies among lakes. Our model successfully addresses concerns with the current approach and performs well across trophic states in a large spatial extent.

scholarly journals Nutrient concentrations and trophic state of three Andean lakes from Junín, Perú

2020 ◽  
Vol 15 (4) ◽  
pp. 1
Author(s):  
Fernán Chanamé-Zapata ◽  
María Custodio ◽  
Christian Poma-Chávez ◽  
Alex Huamán-De La Cruz
Keyword(s):  
Surface Water ◽  
Water Samples ◽  
Trophic State ◽  
Nutrient Concentrations ◽  
Trophic State Index ◽  
Fish Farms ◽  
Chl A ◽  
State Index ◽  
June July ◽  
Surface Water Samples

The study assessed the trophic state of three lakes used as fish farms in the region of Junín-Peru, under different hydrological conditions. Surface water samples were collected from three points at each lake in 2018 during the rainy (March-April) and dry (June-July) seasons. Total phosphorus, turbidity, and chlorophyll-a (Chl-a) were measured. Trophic indexes (TSI Chl-a, and TSI TP) were also computed. The water trophic state categorization was performed by adapting and calculating the Trophic State Index (TSI). The TSI (TP) classified the three lakes in both seasons (rainy and dry) as mesotrophic (30 < TSI ≤ 60). Pomacocha and Tipicocha were classified as eutropic (60 < TSI ≤ 90) in the two seasons according to TSI (Chl a), while Tranca Grande was classified as mesotrophic (also two seasons). The results for TSI showed a predominance of eutrophic and mesotrophic conditions in all lakes used as fish farms.

Sign in / Sign up

Export Citation Format

Share Document