Tile Drainage Increases Total Runoff and Phosphorus Export During Wet Years in the Western Lake Erie Basin

Artificial subsurface (tile) drainage is used in many agricultural areas where soils have naturally poor drainage to increase crop yield and field trafficability. Studies at the field scale indicate that tile drains disproportionately export large soluble reactive phosphorus (SRP) and nitrate loads to downstream waterbodies relative to other surface and subsurface runoff pathways, but knowledge gaps remain understanding the impact of tile drainage to nutrient export at watershed scales. The Western Lake Erie Basin is susceptible to summertime eutrophic conditions driven by non-point source nutrient pollution due to a shallow mean water depth and land use dominated by agriculture. The purpose of this study is to analyze the impact of tile drainage on downstream discharge, nutrient concentrations, and nutrient loads for 16 watersheds that drain to the Western Lake Erie Basin. Daily discharge and nutrient concentrations were summarized annually and during the main nutrient loading period (March–July) for 2 years representing normal nutrient loading period precipitation (2018) and above normal precipitation (2019). Results indicate positive correlations between watershed tile drainage percentage and runoff metrics during 2019, but no relationship during 2018. Additionally, SRP concentration and load were positively correlated to watershed tile drainage percentage in 2019, but not in 2018. Watershed tile drainage percentage was correlated with nitrate concentration and load for both years. The SRP concentration-discharge relationships suggested relatively weak, chemodynamic behavior, implying a slight enriching effect where SRP concentrations were greater at higher stream discharge conditions during both years. In contrast, nitrate concentration-discharge relationships suggested strong, enriching chemodynamic behavior during 2018, but chemostatic behavior during 2019. The difference in SRP and nitrate export patterns in the 2 years analyzed highlights the importance of implementing appropriate best management practices that target specific nutrients and treat primary delivery pathways to effectively improve downstream aquatic health conditions.

An evaluation of soil phosphorus storage capacity (SPSC) at proposed wetland restoration locations in the western Lake Erie Basin

Historical loss of wetlands coupled with excess phosphorus (P) loading at watershed scales have degraded water quality in portions of the western Lake Erie Basin (WLEB). In response, efforts are underway to restore wetlands and decrease P loading to surface waters. Because wetlands have a finite capacity to retain P, researchers have developed techniques to determine whether wetlands function as P sources or sinks. The following technical report evaluates the soil P storage capacity (SPSC) at locations under consideration for wetland restoration in collaboration with the Great Lakes Restoration Initiative (GLRI) and the H2Ohio initiative. Results indicate that the examined soils display a range of P retention capacities, reflecting historic land-use patterns and management regimes. However, the majority of study locations exhibited some capacity to sequester additional P. The analysis supports development of rankings and comparative analyses of areas within a specific land parcel, informing management through design, avoidance, removal, or remediation of potential legacy P sources. Additionally, the approaches described herein support relative comparisons between multiple potential wetland development properties. These results, in conjunction with other data sources, can be used to target, prioritize, justify, and improve decision-making for wetland management activities in the WLEB.

A Predictive Model of Chlorophyll a in Western Lake Erie Based on Artificial Neural Network

The reoccurrence of algal blooms in western Lake Erie (WLE) since the mid-1990s, under increased system stress from climate change and excessive nutrients, has shown the need for developing management tools to predict water quality. In this study, process-based model GLM-AED (General Lake Model-Aquatic Ecosystem Dynamics) and statistical model ANN (artificial neural network) were developed with meteorological forcing derived from surface buoys, airports, and land-based stations and historical monitoring nutrients, to predict water quality in WLE from 2002 to 2015. GLM-AED was calibrated with observed water temperature and chlorophyll a (Chl-a) from 2002 to 2015. For ANN, during the training period (2002–2010), the inputs included meteorological forcing and nutrient concentrations, and the target was Chl-a simulated by calibrated GLM-AED due to the lack of continuously daily measured Chl-a concentrations. During the testing period (2011–2015), the predicted Chl-a concentrations were compared with the observations. The results showed that the ANN model has higher accuracy with lower Chl-a RMSE and MAE values than GLM-AED during 2011 and 2015. Lastly, we applied the established ANN model to predict the future 10-year water quality of WLE, which showed that the probability of adverse health effects would be moderate, so more intense water resources management should be implemented.

Multi-Year Simulation of Western Lake Erie Hydrodynamics and Biogeochemistry to Evaluate Nutrient Management Scenarios

During the 1970s, harmful cyanobacteria (HFCB) were common occurrences in western Lake Erie. Remediation strategies reduced total P loads and bloom frequency; however, HFCB have reoccurred since the mid-1990s under increased system stress from climate change. Given these concurrent changes in nutrient loading and climate forcing, there is a need to develop management tools to investigate historical changes in the lake and predict future water quality. Herein, we applied coupled one-dimensional hydrodynamic and biogeochemical models (GLM–AED) to reproduce water quality conditions of western Lake Erie from 1979 through 2015, thereby removing the obstacle of setting and scaling initial conditions in management scenarios. The physical forcing was derived from surface buoys, airports, and land-based stations. Nutrient loads were reconstructed from historical monitoring data. The root-mean-square errors between simulations and observations for water levels (0.36 m), surface water temperature (2.5 °C), and concentrations of total P (0.01 mg L−1), PO4 (0.01 mg L−1), NH4 (0.03 mg L−1), NO3 (0.68 mg L−1), total chlorophyll a (18.74 μg L−1), chlorophytes (3.94 μg L−1), cyanobacteria (12.44 μg L−1), diatoms (3.17 μg L−1), and cryptophytes (3.18 μg L−1) were minimized using model-independent parameter estimation, and were within literature ranges from single year three-dimensional simulations. A sensitivity analysis shows that 40% reductions of total P and dissolved reactive P loads would have been necessary to bring blooms under the mild threshold (9600 MTA cyanobacteria biomass) during recent years (2005–2015), consistent with the Annex 4 recommendation. However, these would not likely be achieved by applying best management practices in the Maumee River watershed.

A System Dynamic Model of Water-Land-Food-Energy-Ecosystem-Environment-Economic-Social Nexus for Western Lake Erie Basin-USA

The concept of water-food-energy nexus has been widely studied in the past decade. In this paper we expand on this concept to Water-Food-Land-Energy-Ecosystem-Environment Nexus with economic and social aspects based on the life cycle assessment thinking. Set of Environment Footprint Assessment (EFA), Life Cycle Assessment (LCA), and Socio-Economic Assessment (SEA) indicators are proposed to apply this approach. Decision Support System for Water-Land-Food-Energy-Ecosystem-Environment-Economic-Social nexus (SD-WLF4ES-Nexus) applying system dynamic model approach for simulating this tackle is utilized. SD-WLF4ES-Nexus is applied to predict the WLF4ES nexus of one of the main crop, corn crop, in twenty counties located in Western Lake Erie Basin (WLEB) in USA for the period 2016-2030. The prediction is based on scenarios for population, planted and harvested land, and yield, crop production use by segments, and crop production costs and returns. A matrix for WLF4ES nexus of corn crop in WLEB is developed. This matrix can help in developing polices and strategies for managing the nexus in the basin.