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Coupling Models and Remote Sensing to Understand Wetland and Stream Dynamics
Citation:
Christensen, J., H. Golden, C. Lane, G. Evenson, L. Alexander, Q. Wu, AND M. Vanderhoof. Coupling Models and Remote Sensing to Understand Wetland and Stream Dynamics. 2020 Virtual Geospatial Water Technology Conference: Complex Systems, Virtual, August 04 - 13, 2020.
Impact/Purpose:
Stream and wetland systems are dynamic yet maps portray static conditions. We work in two regions in the US using models, remote sensing and field work to determine and map stream and wetland expansion. This is in partnership with USGS and benefits EPA programs to meet regulatory needs that center on the dynamic characteristics of streams and wetlands
Description:
Estimations of longitudinal and lateral extents of streams and wetlands are needed at broad spatial scales to support land managers in understanding, protecting, or restoring these aquatic systems. Yet static maps often fail to account for varied spatial and temporal dynamics of stream and wetland systems. In response to climate or weather conditions, wetlands expand, contract, merge, or spill. Likewise, stream systems vary in longitudinal and lateral extent, flow condition, and permanence classifications. Remote sensing can help map these landscapes at a moment in time, but our ability to explain surface hydrological dynamics is limited to available images and sensor resolutions. Similarly, hydrologic models can provide detailed time steps of stream dynamics, but may have high degrees of uncertainty, especially in headwaters and with increased distances from gages. To characterize these systems at the broad spatial scales necessary for land managers, we present examples that couple multiple approaches (remote sensing, process-based hydrological modeling, and field data) that converge on evidence to map the dynamics of stream and wetland systems. Two case study regions illustrate the variability of spatial and temporal stream and wetland dynamics: the Prairie Pothole Region (PPR) in North Dakota and the Delmarva Peninsula (DP) in Maryland and Delaware. The PPR experiences decadal drought to deluge cycles that dramatically expand and contract surface water storage (e.g., lakes, wetlands) and subsequently moderate downstream hydrological conditions (e.g., streamflow). Forested Delmarva Bays in the DP and the human-altered stream network that flows to the Chesapeake Bay have a high degree of seasonal and interannual hydrological variability. We present work in both the PPR and DP where the coupling of remote sensing and modeling has improved our understanding and mapping of temporal wetland dynamics. We further describe our current approach of coupling multiple models and remote sensing to provide improved understanding and mapping of streamflows and the classification of streamflow permanence.