Evaluating management options to reduce Lake Erie algal blooms using an ensemble of watershed models

Martin, Jay F.; Kalcic, Margaret; Aloysius, Noel; Apostel, Anna; Brooker, Michael R.; Evenson, Grey R.; Kast, Jeffrey; Kujawa, Haley; Murumkar, Asmita; Becker, R. H.; Boles, Chelsie; Confesor, Remegio; Dagnew, Awoke; Guo, Ten-Huei; Long, Christopher; Muenich, Rebecca Logsdon; Scavia, Donald; Redder, Todd; Robertson, Dale M.; Wang, Yu Chen

doi:10.1016/j.jenvman.2020.111710

Cited by 29 publications

(22 citation statements)

References 49 publications

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“…The MRW is a highly productive and intensively managed agricultural region of Indiana, Michigan and Ohio, where about 80% of the land is in corn and soybean production (Figure 1). Intensive management practices associated with corn and soybean production [25,26] in this region have been found to be the primary source of nitrogen and phosphorous loads to the Maumee River [27][28][29], which drains to Lake Erie. The increase in nutrient concentrations has contributed to increasing occurrences of harmful algal blooms in Lake Erie, posing a serious threat to aquatic and human health.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

Zhao

Romanko

et al. 2021

Remote Sensing

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Cover cropping is a conservation practice that helps to alleviate soil health problems and reduce nutrient losses. Understanding the spatial variability in historic and current adoption of cover cropping practices and their impacts on soil, water, and nutrient dynamics at a landscape scale is an important step in determining and prioritizing areas in a watershed to effectively utilize this practice. However, such data are lacking. Our objective was to develop a spatial and temporal inventory of winter cover cropping practices in the Maumee River watershed using images collected by Landsat satellites (Landsat 5, 7 and 8) from 2008 to 2019 in Google Earth Engine (GEE) platform. Each year, satellite images collected during cover crop growing season (i.e., between October and April) were converted into two seasonal composites based on cover crop phenology. Using these composites, various image-based covariates were extracted for 628 ground-truth (field) data. By integrating ground-truth and image-based covariates, a cover crop classification model based on a random forest (RF) algorithm was developed, trained and validated in GEE platform. Our classification scheme differentiated four cover crop categories: Winter Hardy, Winter Kill, Spring Emergent, and No Cover. The overall classification accuracy was 75%, with a kappa coefficient of 0.63. The results showed that more than 50% of the corn-soybean areas in the Maumee River watershed were without winter crops during 2008–2019 period. It was found that 2019/2020 and 2009/2010 were the years with the largest and lowest cover crop areas, with 34% and 10% in the watershed, respectively. The total cover cropping area was then assessed in relation to fall precipitation and cumulative growing degree days (GDD). There was no apparent increasing trend in cover crop areas between 2008 and 2019, but the variability in cover crops areas was found to be related to higher accumulated GDD and fall precipitation. A detailed understanding of the spatial and temporal distribution of cover crops using GEE could help in promoting site-specific management practices to enhance their environmental benefits. This also has significance to policy makers and funding agencies as they could use the information to localize areas in need of interventions for supporting adoption of cover cropping practice.

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Section: Methodsmentioning

confidence: 99%

Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

Zhao

Romanko

et al. 2021

Remote Sensing

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“…Some of the more widely discussed conservation practices for reducing P loss at the edge of field that are being recommended and/or adopted in the Lake Erie watershed include nutrient management, no till or conservation tillage, and erosion control using vegetation (e.g., cover crops, vegetated buffers). Although this is not an exhaustive list, these are the practices that are most widely discussed throughout the watershed regarding P and are thought to have the greatest potential for success (Martin et al, 2021). A recent study by Martin et al (2021) applied an ensemble of watershed models to test various conservation practices and demonstrated significant uncertainty in the efficacy of different management scenarios.…”

Section: Regional Conservation Practice Strategiesmentioning

confidence: 99%

“…Although this is not an exhaustive list, these are the practices that are most widely discussed throughout the watershed regarding P and are thought to have the greatest potential for success (Martin et al, 2021). A recent study by Martin et al (2021) applied an ensemble of watershed models to test various conservation practices and demonstrated significant uncertainty in the efficacy of different management scenarios. Indeed, although all of these practices are directionally correct, their relative efficacy across the different geographic regions of the Lake Erie watershed may differ, and they may, in fact, have trade-offs or unintended consequences in some regions.…”

Section: Regional Conservation Practice Strategiesmentioning

confidence: 99%

One size does not fit all: Toward regional conservation practice guidance to reduce phosphorus loss risk in the Lake Erie watershed

et al. 2021

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Agricultural phosphorus (P) losses to surface water bodies remain a global eutrophication concern, despite the application of conservation practices on farm fields.Although it is generally agreed upon that the use of multiple conservation practices ("stacking") will lead to greater improvements to water quality, this may not be cost effective to farmers, reducing the likelihood of adoption. At present, wholesale recommendations of conservation practices are given; however, the application of specific conservation practices in certain environments (e.g., no-till with surface application, cover crops) may not be effective and can even lead to unintended consequences. In this paper, we present the Lake Erie watershed as a case study. The Lake Erie watershed contains regions with unique physical geographies that include differences in climate, soil, topography, and land use, which have implications for both P transport from agricultural fields and the efficacy of conservation practices in mitigating P losses. We define major regions within the Lake Erie watershed where common strategies for conservation practice implementation are appropriate, and we propose a five-step plan for bringing regionally tailored, adaptive, and cost-conscious conservation practice into watershed planning. Although this paper is specific to the Lake Erie watershed, our framework can be transferred across broader geographic regions to provide guidance for watershed planning.

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“…In order to meet the national water quality goals, Martin et al [9] developed five different models in SWAT to simulate the impacts of 18 different management practices on the nutrient transport from the Maumee river basin to Lake Erie over a time frame of 10 years. The five models agreed on practices that would reduce the quantity and concentration of phosphorus transport with some degree of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Impact of Seasonal Variation in Climate on Water Quality of Old Woman Creek Watershed Ohio Using SWAT

Olaoye

Confesor

Ortiz

2021

Climate

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The effect of the projected 21st century climate change on water quality in Old Woman Creek (OWC) watershed was evaluated using the Soil and Water Assessment Tool (SWAT) and the precipitation and temperature projections from three best Global Climate Circulation Model (GCM)l ensemble downloaded from the Coupled Model Intercomparison Project Phase 5 (CMIP5). These three best GCMs (GFDL-ESM2M, MPI-ESM-MR, EC-EARTH) were identified as those closest to the multivariate ensemble average of twenty different GCM-driven SWAT simulations. Seasonal analysis was undertaken in historical (1985–2014), current to near future (2018–2045), mid-century (2046–2075), and late-century (2076–2100) climate windows. The hydrological model calibration was carried out using a multi-objective evolutionary algorithm and pareto optimization. Simulations were made for stream flow and nine water quality variables (sediment, organic nitrogen, organic phosphorus, mineral phosphorus, chlorophyll a, carbonaceous biochemical oxygen demand, dissolved oxygen, total nitrogen, and total phosphorus) of interest. The average of twenty different CMIP5-driven SWAT simulation results showed good correlation for all the 10 variables with the PRISM-driven SWAT simulation results in the historical climate window (1985–2014). For the historical period, the result shows an over-estimation of flow, sediment, and organic nitrogen from January to March in simulations with CMIP5 inputs, relative to simulations with PRISM input. For the other climate windows, the simulation results show a progressive increase in stream flow with peak flow month shifting from April to March. The expected seasonal changes in each water quality variable have implications for the OWC estuary and Lake Erie water quality.

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Evaluating management options to reduce Lake Erie algal blooms using an ensemble of watershed models

Cited by 29 publications

References 49 publications

Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

One size does not fit all: Toward regional conservation practice guidance to reduce phosphorus loss risk in the Lake Erie watershed

Impact of Seasonal Variation in Climate on Water Quality of Old Woman Creek Watershed Ohio Using SWAT

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