Science Serving Maryland's Coasts

E/E-21e

Riparian Buffer Indicators of Eco-Hydraulic Function for Improved Watershed Management and Monitoring

Principal Investigator: 

Matthew Baker

Start/End Year: 

2013 to 2014

Institution: 

University of Maryland, Baltimore County

Strategic focus area: 

Resilient ecosystem processes and responses

Description: 

OBJECTIVES: The objectives of this study are two-fold: 1. Identify key functional traits that are most strongly and reliably related to flood frequency, intensity and duration. 2. Validate these relationships in three watersheds across the state of Maryland to develop regional indicators of local floodplain hydraulics.

METHODOLOGY: This study will be completed in two phases. Phase 1 will involve retrospective analysis of a unique, spatially extensive inventory of 94 overstory riparian plant communities spanning a broad range of hydrogeomorphic conditions. Species abundance data will be combined with a species-trait matrix to identify the most meaningful relationships among plant functional traits and environmental variables. Multi-gradient, single trait and single-gradient, multi-trait analysis will be used to develop a typology of trait assemblages and to assess which environmental cues are most associated with each type. Phase 2 includes a field validation of trait-environment relationships in a stratified sample of hydraulic conditions in Maryland watersheds. Within watersheds exhibiting distinct hydrologic regimes, 1-D simulation models will identify river reaches of contrasting flood frequencies, intensities, and duration. In each reach, vegetation and edaphic conditions will be sampled along belt transects perpendicular to flow. IButton loggers will be used to detect flood extent and depth via variation in diurnal temperature flux. Analyses will test observed trait distributions relative to those expected across the range of riparian hydroperiods.

RATIONALE: Restoration and conservation of riparian forests are strategic priorities for meeting nutrient and sediment reduction targets set for Chesapeake Bay, but many projects proceed without guidance about tree species best suited for any given site and lack efficient long-term monitoring. High rates of replanted tree mortality can be partially attributed to inadequate environmental conditions for growth. An understanding of plant- environment relationships is needed in order to select optimal tree species for long-term survival in reforested riparian zones. This study will identify riparian indicator species that respond strongly and reliably to local hydraulic conditions and will support cost-effective buffer restoration and monitoring. Reforestation projects guided by this information may improve sapling survival rates, thus reducing replanting expenditures. Additionally, this research will identify native Maryland species useful in monitoring floodplain eco-hydraulic function. Changes in the relative abundance or occurrence of these species may signal hydraulic alterations with implications for ecosystem function.

Impact/Outcome: 

This section describes how this project has advanced scientific knowledge and/or made a difference for coastal residents, communities, and environments. Maryland Sea Grant has reported these details to the National Oceanic and Atmospheric Administration (NOAA), one of our funding sponsors.

Summary:  Scientists developed a predictive model to help improve the survival of trees planted to restore and conserve riparian forests. This conservation method is being used to improve water quality in the Chesapeake Bay. The model can be used to identify species suited to survive in floodplains under distinct hydrological conditions in Maryland.

Relevance: Riparian (streamside) forests are considered effective at reducing nutrient and sediment loads to the Chesapeake Bay, but many such forests within the watershed have been degraded or removed by human activities. Thus, restoring and conserving riparian forests are priorities for meeting targets for improving water quality in the estuary. Organizations have planted trees to restore these forest areas without guidance about which species can best survive in chosen locations. One result can be high mortality of the replanted trees because they are poorly adapted to tolerate patterns of prolonged or intense inundation. Increasing survival rates could reduce the need for replanting and reduce the cost of restoration projects.

Response: Matthew Baker of the University of Maryland-Baltimore County and Molly Van Appledorn, a graduate fellow supported by Maryland Sea Grant, constructed a model that can be used to characterize which tree species are best suited to plant in riparian zones representing a variety of hydrological conditions in Maryland. Planting trees in areas to which they are well adapted can improve their survival. After completing a detailed analysis of stream gauge records relative to plant distributions in Maryland and in Michigan, the researchers conducted field research in four stream basins in Maryland’s Piedmont region. They performed statistical analyses that describe associations among the species’ functional traits; flood frequency, intensity and duration; and other hydrologic and landscape characteristics within the study areas.

Results: Study findings demonstrated the potential for this analytical approach to inform riparian restoration projects and improve understanding of forest floodplain ecology. The approach offers restoration managers a means to tailor restoration efforts to local and regional environmental conditions. The researchers’ hydrodynamic modeling departed from the methodologies used in most ecological studies of floodplain forests. This study appears to be the first to quantify regional patterns of riparian plant trait distributions, account for local variations in floodplain hydrology, and demonstrate that this knowledge can be applied to restoration decisions across different geographic regions. Van Appledorn plans to conduct follow-up work to adapt the predictive model for wider use across the United States.