1. An enhanced hydro-ecological model (RHESSys) to
explore climate change interactions between
precipitation patterns, topography and forests in a
New York City water supply watershed
Antoine L. Randolph1,3, Lawrence E. Band1, Christina L.
Tague2, Matthew B. Dickinson4, Elliot M. Schneiderman5
1 University of North Carolina Chapel Hill, Department of Geography
2 University of California Santa Barbara, Bren School of Environmental Science
3 CUNY Institute for Sustainable Cities, Hunter College
4 U.S Forest Service Northeast Research Station, Delaware OH
5 New York City Environmental Protection, Mapping and Modeling
Watershed/Tifft Science and Technical Symposium, 18-19 September 2013, West Point New York
2. 2
Presentation Outline
• Hydro-ecological models as management tools
• A brief overview of RHESSys
• Enhancements to the baseline version of RHESSys
• Implementation of RHESSys for Biscuit Brook
• Examples of Biscuit Brook model output
• Future research and development
3. Hydro-ecological models as management tools
tools
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• Can be used to forecast the potential impacts of climate
change on forest structure and composition:
– changes in the frequency of extreme weather events
• wind damage, ice damage, flooding
– canopy damage
– change in precipitation patterns
• increased stress
– greater susceptibility to insects and pathogens
– decrease in the viability of seed
– changes in the distribution of tree species
» habitat loss
» loss of commercially important tree species
• Changes in forest cover can affect the quantity and quality
of stream flow
– increased erosion, turbidity and nutrient loading
– changes in the spatial and temporal availability of water
8. Vegetation modeling
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Tree dimensions define zones of influence for each species, within
which the characteristics of the tree modify local microclimate. Canopy
dominant trees function as “Keystone species.”
9. RHESSys modeling enhancements (highlights)
• explicit modeling of tree species
– leaf C/N ratio, specific leaf area, environmental tolerances,
dynamic leaf phenology
• explicit modeling of tree growth and dimensions
– trunk diameter, height growth curve, rooting depth, bark
thickness, crown base height, stem counts, basal area
• addition of a litter layer structure
– L, F and H litter layer depths and moisture
– transpiration from the H layer and mineral soil
• implementation of fire modeling
– fire spread based on Rothermel’s mathematical model
– fire mortality based on bark thickness
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12. Overview of RHESSys worldfile creation
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Time series for minimum temperature, maximum temperature and daily
precipitation are the minimum required climatic inputs. Soil and vegetation
characteristics are specified via parameter definition (*.def) files. The GIS-
based preprocessing step allows broad flexibility in partitioning the
landscape (i.e., basins, hillslopes, micro-climatic zones, landscape
patches).
15. Model Calibration
• hydrological calibration
– modeled stream flow vs. actual stream flow data
– modeled soil moisture vs. actual soil moisture data
– modeled evapo-trans vs. actual evapo-trans data
• vegetation calibration
– modeled height or DBH growth vs. actual growth
– modeled leaf area index vs. actual leaf area index
– modeled basal area per hectare vs. actual basal area
– modeled biomass accumulation vs. actual accumulation
• snow pack calibration (under development)
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22. Terrain analysis example 1
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The western portion of the ridgeline and upslope region of Biscuit Brook
with 2m contours. Steep outcrops surrounding relatively flatter terrain is
prominent at this location, as indicated by the outlined areas.
23. Terrain analysis example 2
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Overlay of wetness index and the derived stream network (blue lines) suggests that
low lying upslope areas (i.e., shelves) are often at the source of 1st order streams.
Alternating soggy and dry conditions could lead to nutrient loading in 1st order
streams, depending on vegetation type and status.
25. Modeled Snow Pack SWE example 1
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March 1999: spatial variability in
mean monthly snow pack SWE
is high but the mean monthly
SWE quantity is low.
snow water equivalent (SWE) mm
26. Modeled Snow Pack SWE example 2
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Jan 1999: spatial variability in
mean monthly snow pack SWE
is low but the mean monthly
SWE quantity is high.
snow water equivalent (SWE) mm
27. Summary/Conclusions
• Take Home Messages
– Simulations are sensitive to species
– Simulations are sensitive to precipitation pattern
– Spatially adjusted snow modeling output
• individual components of snow pack loss
• Future Work
– Add additional local tree species
– Model calibration
– Expand scale of modeling (e.g., Neversink basin)
– Use the calibrated model to investigate the effects of
climate change on Catskill forests
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28. Acknowledgements
Thanks to my fellow CUNY postdocs for their
encouragement and expertise.
Thanks to DEP Modeling Group staff for their
feedback and help finding necessary data.
Special thanks to Larry Band for his continued
support and guidance
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Editor's Notes
Good morning. My name is Antoine Randolph. I am CUNY research associate working with the DEP Modeling unit in Kingston. My focus is hydro-ecological modeling of forested watersheds. Today I’ll be presenting an overview of my research and it’s application to DEP water management in the context of climate change.
Here is the outline of what I’ll be covering today.
One of the many unknowns cornering the potential impacts of climate change is how forests will respond. Climate, landscape vegetation interactions are very complex. Hydro-ecological models such as RHESSys can help us to identify potential consequences of climate change and to assess the broader impacts.
RHESSys was developed in the early 1990s by adding a spatially explicit landscape and the MT-CLIM micro-climate model to the existing FOREST-BGC framework. RHESSys is a model of intermediate complexity that utilizes a daily time step and models forests on a multi-decadal temporal scale.
