Watershed

Oral

395380 - Predictive Data Analysis for a Large-Scale Landscape Restoration Experiment

Thursday, June 7
8:30 AM - 10:00 AM
Location: Greenway IJ

Adaptive management is increasingly used in natural resources management. It promotes flexible decision-making, as the uncertainties present in the initial stages of a project become better understood through learning-by-doing. Monitoring the project implementation through data collection and analysis is the essence of adaptive management practice. However, data analysis becomes increasingly difficult with the complexity of the environmental process that is being studied. As a part of a landscape-scale adaptive management experiment, Decompartmentalization Physical Model Project (DPM), a multi-agency team of scientist and engineers study the hydro-ecological uncertainties associated with quantifying the benefits of restoring sheetflow in water conservation areas. DPM, the second largest adaptive management program in the United States history, utilizes a set of culverts to deliver experimental flows in to the test area, to evaluate hydrologic and biogeochemical responses to flow events. The primary objective of the presented analysis is to identify key environmental variables explaining variations in the phosphorus concentration in the water column so that environmental triggers can be established to guide operations of the model, while ensuring low phosphorus concentrations in the downstream areas. Lessons learned from the previous four years of operations were used to identify the main contributing factors in observed phosphorus concentrations. The seasonal and long-term trends were identified and a multivariate analysis was employed to describe the dependencies through a statistical model. As the field experiment continues, it is expected that uncertainties in our understanding of the ecological process will be reduced and the predictive power of the model be improved as larger sample sizes become available.

Ceyda Polatel, Ph.D., P.E.

Hydraulic Engineer
U.S. Army Corps of Engineers

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395380 - Predictive Data Analysis for a Large-Scale Landscape Restoration Experiment



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