Geology topics

S. Conor Keitzer

Publications and source records attributed to S. Conor Keitzer.

3 recordsLinked to original sources

USGS2023

Agricultural conservation practices could help offset climate change impacts on cyanobacterial harmful algal blooms in Lake Erie

Harmful algal blooms (HABs) are a recurring problem in many temperate large lake and coastal marine ecosystems, caused mainly by anthropogenic eutrophication. Implementation of agricultural conservation practices (ACPs) offers a means to reduce non-point source nutrient runoff and mitigate HABs. However, the effectiveness of ACPs in a changing climate remains uncertain. We used an integrated biophysical modeling approach to predict how Lake Erie cyanobacterial HAB severity (bloom biomass) may change under several climate and ACP implementation scenarios, using western Lake Erie and its largely agricultural watershed as our study system. An ensemble of general circulation model projections was used to drive spatially explicit land use and hydrology models of the Maumee River watershed, the output of which informed a predictive model of Lake Erie HAB severity. Results show that, in the absence of changes in ACPs, the frequency of severe HABs is projected to increase during coming decades, owing to increased inputs of nutrients from the watershed. These anticipated increases are due to increased total precipitation and more frequent higher-magnitude rainfall events. While further implementation of ACPs appears capable of reducing severe HAB events, widespread implementation would be necessary to reduce HAB severity below current management targets. This study highlights how continued climate change will only exacerbate the need for land management practices that can reduce nutrient runoff in agriculturally dominated ecosystems, such as Lake Erie. It also shows how interdisciplinary, biophysical modeling approaches can help identify strategies to mitigate HABs in the face of anthropogenic stressors.

Lake Erie↗

USGS2020

Forecasting the combined effects of anticipated climate change and agricultural conservation practices on fish recruitment dynamics in Lake Erie

Many aquatic ecosystems are experiencing multiple anthropogenic stressors that threaten their ability to support ecologically and economically important fish species. Two of the most ubiquitous stressors are climate change and non‐point source nutrient pollution. Agricultural conservation practices (ACPs, i.e. farming practices that reduce runoff, prevent erosion, and curb excessive nutrient loading) offer a potential means to mitigate the negative effects of non‐point source pollution on fish populations. However, our understanding of how ACP implementation amidst a changing climate will affect fish production in large ecosystems that receive substantial upstream sediment and nutrient inputs remains incomplete. Towards this end, we explored how anticipated climate change and the implementation of realistic ACPs might alter the recruitment dynamics of three fish populations (native walleye Sander vitreus and yellow perch Perca flavescens and invasive white perch Morone americana ) in the highly productive, dynamic west basin of Lake Erie. We projected future (2020–2065) recruitment under different combinations of anticipated climate change ( n = 2 levels) and ACP implementation ( n = 4 levels) in the western Lake Erie catchment using predictive biological models driven by forecasted winter severity, spring warming rate, and Maumee River total phosphorus loads that were generated from linked climate, catchment‐hydrology, and agricultural‐practice‐simulation models. In general, our models projected reduced walleye and yellow perch recruitment whereas invasive white perch recruitment was projected to remain stable or increase relative to the recent past. Our modelling also suggests the potential for trade‐offs, as ACP implementation was projected to reduce yellow perch recruitment with anticipated climate change. Overall, our study presents a useful modelling framework to forecast fish recruitment in Lake Erie and elsewhere, as well as offering projections and new avenues of research that could help resource management agencies and policy‐makers develop adaptive and resilient management strategies in the face of anticipated climate and land‐management change.

Michigan, Ohio, Indiana↗

USGS2016

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios

Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT2012) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the model's predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation.

Lake Erie Basin↗