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ESA 2012-Osborne-Gowey-Modeled-Hydrology-Comparison-poster

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Osborne-Gowey, Jeremiah; Bachelet, Dominique; Mauger, Guillaume; Garcia, Elizabeth; Tague, Christina; Ferschweiler, Ken. 2012. Assessing the skill of hydrology models at simulaing the water cycle in …

Osborne-Gowey, Jeremiah; Bachelet, Dominique; Mauger, Guillaume; Garcia, Elizabeth; Tague, Christina; Ferschweiler, Ken. 2012. Assessing the skill of hydrology models at simulaing the water cycle in the HJ Andrews LTER: Assumptions, strengths, and weaknesses. Poster presentation at the 2012 Ecological Society of America annual meeting, Portland, Oregon. Short Abstract: Simulated impacts of climate on hydrology can vary greatly as a function of the scale of the input data, model assumptions, and model structure. We chose three models that have been used to simulate current and future streamflow and to estimate the impacts of climate change on the water cycle in the Pacific Northwest, USA (PNW): the MC1 Dynamic Global Vegetation Model, the Regional Hydro-Ecologic Simulation System (RHESSys) model and the Variable Infiltration Capacity (VIC) model. To better understand the differences between the models representations of hydrological dynamics, we compared results between these three models and observed streamflow data for the HJ Andrews Experimental Forest (HJA) experimental forest in the Oregon’s western Cascades. To better characterize the hydrology and make comparisons between models, we calculated runoff and Nash-Sutcliffe model efficiency coefficients.

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  • 1. Assessing the skill of hydrology models at simulating the water cycle in the HJ Andrews LTER: assumptions, strengths and weaknesses Jeremiah Osborne-Gowey, Dominique Bachelet, Guillaume Mauger, Elizabeth Garcia, Christina Tague, Ken Ferschweiler,ESA #39203, PS 86-225 Contact Information: jeremiahosbornegowey@gmail.com, 838 NW 28th Street, Corvallis, OR, 97330 USA MC1, 1949-2009 Actual observed runoff ratio = 0.75 INTRODUCTION 1,000 1,000 Runoff ratio = 0.75 y = 0.8928x + 12.863 Observed Simulated impacts of climate on HJA Lookout Creek Basin (64 km2) NS coefficient = 0.76 Simulated streamflow (mm) R² = 0.77 Monthly streamflow (mm) 800 800 MC1-B57 hydrology can vary greatly as a 600 600 function of the scale of the input 400 400 data, model assumptions, and 200 model structure. To better 200 understand differences in models 0 0 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 0 200 400 600 800 1,000 representations of water dynamics Observed streamflow (mm) at the watershed scale, we RHESSys, 1958-2006 Year 1,000 1,000 Runoff ratio = 0.73 y = 0.8235x + 19.433 compare simulated results from Observed NS coefficient = 0.73 Simulated streamflow (mm) Monthly streamflow (mm) R² = 0.77 800 800 RHESSys three commonly used models 600 600 among each other and with 400 400 observed streamflow data from 200 the HJ Andrews Long Term Cell-to-cell Map created on www.DataBasin.org 200 communi- 0 Ecological Research (LTER) site. Model Base Attributes Timestep cation? Inputs Outputs 0 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 MC1 – MAPSS- large-scale, monthly No temperature (min, max, mean), carbon pools, soil moisture, vegetation lifeforms and CENTURY-MCFIRE dynamic precipitation, vapor pressure or distribution, biomass, nutrient fluxes, streamflow, 0 200 400 600 800 1,000 hybrid vegetation model mean dew point, DEM, soil soil water storage, evapotranspiration Year linked with texture (X3), soil depths (X3), VIC, 1949-2006 Observed streamflow (mm) METHODS biogeochemical and fire models climate time series 1,000 1,000 Runoff ratio = 0.85 y = 0.8747x + 30.387 •Used existing modeled data from: RHESSys – Regional watershed scale, daily Yes topography (elevation, slope, water fluxes, evaporation, transpiration, snow Observed NS coefficient = 0.84 R² = 0.87 Monthly streamflow (mm) Simulated streamflow (mm) Hydro-Ecologic hydro-ecological aspect), air temperature, dynamics, soil water, carbon, photosynthesis, 800 800 VIC - MC1 dynamic global vegetation Simulation System modeling framework, precipitation, vegetation, drainage network, soil texture, respiration, decomposition, net primary productivity, nitrogen, litterfall, mineralization, landscape soil depth (X2), radiation, photosynthesis 600 600 (MC1) model 1 represented humidity, biome type, leaf area hierarchically, free index, climate time series, 400 - Regional Hydro-Ecologic from grid-based constraints disturbance history, water holding capacity 400 Simulation System (RHESSys) VIC – Variable large-scale, grid- sub-daily Infiltration Capacity based, semi- to daily No landcover, soil moisture, soil streamflow (needs to be routed), runoff, baseflow, texture, soil depth, precipitation, energy fluxes, soil moisture/infiltration, canopy 200 200 2 distributed water temperature (min, max, mean), precipitation interception, evaporation, model and energy DEM (optional), windspeed, evapotranspiration, relative humidity, air 0 balance model lakes/wetlands (optional), plant temperature, snow, snow-water-equivalent, snow 0 - Variable Infiltration Capacity (VIC) 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 with variable root depth depth, snow interception, snow temperature, snow infiltration, and melt, snow sublimation 0 200 400 600 800 1,000 model 3 non-linear base Year Observed streamflow (mm) flow •All models run at 800 meter resolution RESULTS CONCLUSIONS CITATIONS 1. MC1 model particulars: http://bit.ly/vIsAeB •Observed discharge at Lookout Creek gage 4 •Models reasonably approximate streamflow: •All models produce reasonable results 2. RHESSys model particulars: http://bit.ly/ujUNYT 3. VIC model particulars: http://bit.ly/tldXDt •Streamflows for RHESSys and VIC in daily - timing, magnitude, duration •Arrived at flows based on dissimilar inputs 4. Lookout Creek stream gage data http://bit.ly/sqEj2V 5. Nash, J. E. and J. V. Sutcliffe (1970). River flow increments, aggregated at monthly time steps •MC1 overestimates some high flows •VIC best model fit (NS = 0.84) forecasting through conceptual models part I — A •Grid cell streamflow values for MC1 spatially •RHESSys underestimates low flows •Model selection dependent on questions discussion of principles, Journal of Hydrology, 10 (3), 282–290. aggregated •Slight lag in RHESSys spring flows of interest and scale of study area ACKNOWLEDGEMENTS •MC1 and VIC overestimate low flows •Modeled low flows need adjusting • Dr. Barb Bond, HJ Andrews LTER, Oregon State University •Calculated runoff ratios and Nash-Sutcliffe model • Dr. David Conklin, Conservation Biology Institute, •Models could benefit from calibration efficiency (NS) coefficients5 •All three models had good fit (NS = 0.73-0.84) Corvallis, OR