Presentation on theme: "An enhanced hydro-ecological model (RHESSys) to explore climate change interactions between precipitation patterns, topography and forests in a New York."— Presentation transcript:
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. Randolph 1,3, Lawrence E. Band 1, Christina L. Tague 2, Matthew B. Dickinson 4, Elliot M. Schneiderman 5 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 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
Hydro-ecological models as management tools tools 3 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
Vegetation modeling 8 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.”
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 9
Overview of RHESSys worldfile creation 12 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).
Detail of Biscuit Brook Hillslope Derivation 14
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) 15
(un-calibrated modeling results) Examples of RHESSys model output for Biscuit Brook 16
Terrain analysis example 1 22 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.
Terrain analysis example 2 23 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 1 st order streams. Alternating soggy and dry conditions could lead to nutrient loading in 1 st order streams, depending on vegetation type and status.
Modeled Snow Pack SWE example 1 25 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
Modeled Snow Pack SWE example 2 26 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
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 27
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 28