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.

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)
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MC1-B57
hydrology can vary greatly as a 600 600
function of the scale of the input
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data, model assumptions, and
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model structure. To better 200
understand differences in models 0 0
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representations of water dynamics Observed streamflow (mm)
at the watershed scale, we RHESSys, 1958-2006 Year
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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
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RHESSys
three commonly used models 600 600
among each other and with 400
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observed streamflow data from
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the HJ Andrews Long Term Cell-to-cell Map created on www.DataBasin.org
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communi- 0
Ecological Research (LTER) site. Model Base Attributes Timestep cation? Inputs Outputs 0
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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,
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- Regional Hydro-Ecologic from grid-based
constraints
disturbance history, water
holding capacity
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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
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2 distributed water temperature (min, max, mean), precipitation interception, evaporation,
model and energy DEM (optional), windspeed, evapotranspiration, relative humidity, air
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balance model lakes/wetlands (optional), plant temperature, snow, snow-water-equivalent, snow 0
- Variable Infiltration Capacity (VIC)
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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