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College of Natural Resources

Center for Geospatial Analytics

Dan uses geospatial analytics to study complex environmental systems; his primary focus is on water quality dynamics in streams, lakes and coastal areas. He develops spatial and spatiotemporal geostatistical models to study hypoxia, harmful algal blooms and other aquatic phenomena. These models help characterize the extent of water quality impairments while also assessing key drivers of spatial and temporal variability. Other interests include environmental forecasting using mechanistic and empirical models with rigorous uncertainty quantification, so that policy makers and the public can be presented with the ranges of likely outcomes associated with different future scenarios, allowing for more informed decision-making.

Dan’s water quality research aims to reduce model uncertainty by more effectively leveraging available information such as field monitoring data, satellite imagery, and the results of previous experiments.

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Publications

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