The Town Lake Report, Volumes I and II

This report makes brief references to sediment and other trends seen in Waller Creek.EXECUTIVE SUMMARY: Town Lake’s importance as a natural resource is growing in tandem with Austin’s rapid population. The lake is a source of drinking water for the City, and its greenbelt and open waters are widely used for recreation and as a focal-point for public events. In 1992, under the Clean Lakes program, a comprehensive report entitled the “Town Lake Study” (COA 1992a; COA 1992b; COA 1992c) was prepared. It examined the condition of the lake (Volume I), water quality control alternatives (Volume II) and a feasibility study (Volume III). This report updates the diagnostic study, Volume I (COA 1992a), including the current status of water quality with data analyzed through the year 2000. It also includes a summary of measures taken to reduce pollution from urban runoff since 1990.Waller Creek Working Grou

Water Quality Modeling of Freshwater Diversions in the Pontchartrain Estuary

A 1-D tidal, salinity and water quality model that analyzes the general effects freshwater diversions have on the water quality of the Pontchartrain Estuary over a 17-year period is presented here. Using the modeled live algae concentrations in conjunction with the algal bloom probability model results produces an accurate prediction of algal bloom occurrences between 1990 and 2006. The model predicts that the addition of freshwater diversions into Maurepas swamp and increases to flow in the Bonnet Carré Spillway may cause more intense and frequent algal blooms to occur around the Pontchartrain Estuary. The model also predicts that high nutrient input events that occur earlier in the year (January/February) will not likely have algal blooms associated with them. When nutrient input events (even small events) occur in the late spring or early summer, algal blooms have a high probability of occurring when the salinity, temperature and light levels are sufficient

Decades of Delay: EPA Leadership Still Lacking in Protecting America's Great River

This report demonstrates the continuing failure of EPA's voluntary approach and the continuing and growing threats of unregulated nitrogen and phosphorus pollution. EPA has the power and the duty to act to require reasonable, common-sense regulations to address the growing scourge of nutrient pollution, and it should do so. Once again, MRC calls upon EPA to remedy this state of affairs, specifically recommending that EPA:Develop numeric phosphorus criteria for each of the eight states that have yet to adopt them, and numeric nitrogen criteria for all 10 states.Require states to assess their waters for nitrogen and phosphorus pollution and to prioritize TMDL development and implementation planning accordingly.Increase oversight of the state NPDES programs to ensure that both narrative and numeric nutrient criteria are implemented through limits in permits, including the use of Water Quality Based Effluent Limits (WQBELs) where appropriate.Disapprove TMDLs that lacking reasonable assurance that nonpoint source reductions are likely to occur and lack monitoring and timelines to ensure that planned reductions actually take place. Further, EPA needs to provide oversight to ensure consistency among EPA Regions in TMDL review and approval (especially in Regions 4 and 6.)Ensure that states' Nutrient Reduction Strategies contain implementation plans detailing point and nonpoint source reductions needed, responsible parties, funding mechanisms, milestones, measurement metrics, and reasonable timelines.Require states under Section 319 of the Clean Water Act to identify programs and practices for controlling nonpoint sources of pollution to the maximum extent possible

Forecasting Harmful Algal Blooms for Western Lake Erie Using Data Driven Machine Learning Techniques

Harmful algal blooms (HAB) have been documented for more than a century occurring all over the world. The western Lake Erie has suffered from Cyanobacteria blooms for many decades. There are currently two widely available HAB forecasting models for Lake Erie. The first forecasting model gives yearly peak bloom forecast while the second provides weekly short-term forecasting and offers size as well as location. This study focuses on bridging the gap of these two models and improve HAB forecast accuracy in western Lake Erie by letting historical observations tell the behavior of HABs. This study tests two machine learning techniques, artificial neural network (ANN) and classification and regression tree (CART), to forecast monthly HAB indicators in western Lake Erie for July to October. ANN and CART models were created with two methods of selecting input variables and two training periods: 2002 to 2011 and 2002 to 2013. First a nutrient loading period method which considers all nutrient contributing variables averaged from March to June and second a Spearman rank correlation to choose separate input sets for each month considering 224 different combinations of averaging and lag periods. The ANN models showed a correlation coefficient increase from 0.70 to 0.77 for the loading method and 0.79 to 0.83 for the Spearman method when extending the training period. The CART models followed a similar trend increasing overall precision from 85.5% to 92.9% for the loading method and 82.1% to 91% for the Spearman method. Both selection methods had similar variable importance with river discharge and phosphorus mass showing high importance across all methods. The major limitation for ANN is the time required for each forecast to be complete while the CART forecasts earlier is only able to produce a class forecast. In future work, the ANN model accuracy can be improved and use new sets of variables to allow earlier HAB forecasts. The final form of ANN and CART models will be coded in a user interface system to forecast HABs. The monthly forecasting system developed allows watershed planners and decision-makers to timely manage HABs in western Lake Erie

Florida Bay Science Program: a synthesis of research on Florida Bay

This report documents the progress made toward the objectives established in the Strategic Plan revised in 1997 for the agencies cooperating in the program. These objectives are expressed as five questions that organized the research on the Florida Bay ecosystem: Ecosystem History What was the Florida Bay ecosystem like 50, 100, and 150 years ago? Question 1—Physical Processes How and at what rates do storms, changing freshwater flows, sea level rise, and local evaporation and precipitation influence circulation and salinity patterns within Florida Bay and exchange between the bay and adjacent waters? Question 2—Nutrient Dynamics What is the relative importance of the influx of external nutrients and of internal nutrient cycling in determining the nutrient budget for Florida Bay? What mechanisms control the sources and sinks of the bay’s nutrients? Question 3—Plankton Blooms What regulates the onset, persistence, and fate of planktonic algal blooms in Florida Bay? Question 4—Seagrass Ecology What are the causes and mechanisms for the observed changes in the seagrass community of Florida Bay? What is the effect of changing salinity, light, and nutrient regimes on these communities? Question 5—Higher Trophic Levels What is the relationship between environmental and habitat change and the recruitment, growth, and survivorship of animals in Florida Bay? Each question examines different characteristics of the Florida Bay ecosystem and the relation of these to the geomorphological setting of the bay and to processes linking the bay with adjacent systems and driving change.This report also examines the additional question of what changes have occurred in Florida Bay over the past 150 years

Climate Change and Eutrophication: A Short Review

Water resources are vital not only for human beings but essentially all ecosystems. Human health is at risk if clean drinking water becomes contaminated. Water is also essential for agriculture, manufacturing, energy production and other diverse uses. Therefore, a changing climate and its potential effects put more pressure on water resources. Climate change may cause increased water demand as a result of rising temperatures and evaporation while decreasing water availability. On the other hand, extreme events as a result of climate change can increase surface runoff and flooding, deteriorating water quality as well. One effect is water eutrophication, which occurs when high concentrations of nutrients, such as nitrogen and phosphorus, are present in the water. Nutrients come from different sources including agriculture, wastewater, stormwater, and fossil fuel combustion. Algal blooms can cause many problems, such as deoxygenation and water toxicity, ultimately disrupting normal ecosystem functioning. In this paper, we investigate the potential impacts of climatic factors affecting water eutrophication, how these factors are projected to change in the future, and what their projected potential impacts will be

A Multiscale Analysis of the Factors Controlling Nutrient Dynamics and Cyanobacteria Blooms in Lake Champlain

Cyanobacteria blooms have increased in Lake Champlain due to excessive nutrient loading, resulting in negative impacts on the local economy and environmental health. While climate warming is expected to promote increasingly severe cyanobacteria blooms globally, predicting the impacts of complex climate changes on individual lakes is complicated by the many physical, chemical, and biological processes which mediate nutrient dynamics and cyanobacteria growth across time and space. Furthermore, processes influencing bloom development operate on a variety of temporal scales (hourly, daily, seasonal, decadal, episodic), making it difficult to identify important factors controlling bloom development using traditional methods or coarse temporal resolution datasets. To resolve these inherent problems of scale, I use 4 years of high-frequency biological, hydrodynamic, and biogeochemical data from Missisquoi Bay, Lake Champlain; 23 years of lake-wide monitoring data; and integrated process-based climate-watershed-lake models driven by regional climate projections to answer the following research questions: 1) To what extent do external nutrient inputs or internal nutrient processing control nutrient concentrations and cyanobacteria blooms in Lake Champlain; 2) how do internal and external nutrient inputs interact with meteorological drivers to promote or suppress bloom development; and 3) how is climate change likely to impact these drivers and the risk of cyanobacteria blooms in the future? I find that cyanobacteria blooms are driven by specific combinations of meteorological and biogeochemical conditions in different areas of the lake, and that in the absence of strong management actions cyanobacteria blooms are likely to become more severe in the future due to climate change

Global solutions to regional problems: Collecting global expertise to address the problem of harmful cyanobacterial blooms. A Lake Erie case study

In early August 2014, the municipality of Toledo, OH (USA) issued a ‘do not drink’ advisory on their water supply directly affecting over 400,000 residential customers and hundreds of businesses (Wilson, 2014). This order was attributable to levels of microcystin, a potent liver toxin, which rose to 2.5 mg L1 in finished drinking water. The Toledo crisis afforded an opportunity to bring together scientists from around the world to share ideas regarding factors that contribute to bloom formation and toxigenicity, bloom and toxin detection as well as prevention and remediation of bloom events. These discussions took place at an NSF- and NOAA-sponsored workshop at Bowling Green State University on April 13 and 14, 2015. In all, more than 100 attendees from six countries and 15 US states gathered together to share their perspectives. The purpose of this review is to present the consensus summary of these issues that emerged from discussions at the Workshop. As additional reports in this special issue provide detailed reviews on many major CHAB species, this paper focuses on the general themes common to all blooms, such as bloom detection, modeling, nutrient loading, and strategies to reduce nutrients

Quantifying physical transport and local proliferation of phytoplankton downstream of an eutrophicated lake

Eutrophication in a freshwater system has mainly been studied in lakes and their upstream rivers, which are responsible to bring pollutants into the lakes. However, the influence of lakes on downstream rivers suffered massive algae from upstream lakes has not been fully studied. Our study area is Liangxi river, downstream of Taihu Lake, which is highly eutrophicated. The algae in Liangxi river has two origins: the physical transport from Taihu Lake and the in-situ proliferation. This paper aims to apply numerical model to quantify these two processes. The model is calibrated against the measured data in 2018. This computational condition that includes both algal processes is termed as Scheme A. Then, we regarded phytoplankton as a conservative substance by turning off the phytoplankton biological process and calculated term it as Scheme E. We selected the chl-a concentration in Hongqiao (LX2) section to represent the amount of algae in Liangxi river. The average chl-a difference in this section between Schemes A and E, Delta, can be used to quantify the magnitude of in-situ proliferation. The Delta varies seasonally, and the annual average is 7.22 mg/m³, which is 44.7% of the amount attributed to the physical transport. Liangxi river lies in an urban area which might encounter extreme events which to facilitate the in-situ proliferation, such as increased temperature and or excessive nutrient load. To quantify the level of algae under extreme situations, we design Schemes B, C and D which eliminated the limitation on algal growth by temperature, nitrogen and phosphorus respectively. Compared with the Scheme A, Schemes B, C and D oberve 21.8%, 65.7%and 61.2% respectively, increase in the average algal concentration. In the vertical direction, the chl-a concentration varies between 0.8mg/m³ and 2 mg/m³ in Scheme A, while the vertical concentration variances of chl-a in schemes B, C and D are found to be 5.56 mg/m³, 12.11 mg/m³ and 3.30 mg/m³, respectively.National Natural Science Foundation of China (No.51779075); Priority Academic Program Development of Jiangsu Higher Education Institutions; Major Science and Technology Program for Water Pollution Control and Treatment of China (2017ZX07203002-01)