esa 2012-osborne-gowey-modeled-hydrology-comparison-poster
DESCRIPTION
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.TRANSCRIPT
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, Contact Information: [email protected], 838 NW 28th Street, Corvallis, OR, 97330 USA
RESULTS•Models reasonably approximate streamflow:-timing, magnitude, duration•MC1 overestimates some high flows•RHESSys underestimates low flows•Slight lag in RHESSys spring flows•MC1 and VIC overestimate low flows•All three models had good fit (NS = 0.73-0.84)
INTRODUCTIONSimulated impacts of climate on hydrology can vary greatly as a function of the scale of the input data, model assumptions, and model structure. To better understand differences in models representations of water dynamics at the watershed scale, we compare simulated results from three commonly used models among each other and with observed streamflow data from the HJ Andrews Long Term Ecological Research (LTER) site.
CITATIONS1. MC1 model particulars: http://bit.ly/vIsAeB 2. RHESSys model particulars: http://bit.ly/ujUNYT 3. VIC model particulars: http://bit.ly/tldXDt 4. Lookout Creek stream gage data http://bit.ly/sqEj2V5. Nash, J. E. and J. V. Sutcliffe (1970). River flow
forecasting through conceptual models part I — A discussion of principles, Journal of Hydrology, 10 (3), 282–290.
ACKNOWLEDGEMENTS• Dr. Barb Bond, HJ Andrews LTER, Oregon State University• Dr. David Conklin, Conservation Biology Institute,
Corvallis, OR
CONCLUSIONS•All models produce reasonable results•Arrived at flows based on dissimilar inputs•VIC best model fit (NS = 0.84)•Model selection dependent on questions of interest and scale of study area•Modeled low flows need adjusting•Models could benefit from calibration
•All models run at 800 meter resolution•Observed discharge at Lookout Creek gage4
•Streamflows for RHESSys and VIC in daily increments, aggregated at monthly time steps•Grid cell streamflow values for MC1 spatially aggregated•Calculated runoff ratios and Nash-Sutcliffe model efficiency (NS) coefficients5
METHODS•Used existing modeled data from:
- MC1 dynamic global vegetation (MC1) model 1
- Regional Hydro-Ecologic Simulation System (RHESSys)
model2 - Variable Infiltration Capacity (VIC)
model3
1949
1951
1953
1955
1957
1959
1962
1964
1966
1968
1970
1972
1975
1977
1979
1981
1983
1985
1988
1990
1992
1994
1996
1998
2001
2003
2005
2007
2009
0
200
400
600
800
1,000
Observed
MC1-B57
Year
Mon
thly
str
eam
flow
(mm
)
MC1, 1949-2009
0 200 400 600 800 1,0000
200
400
600
800
1,000
f(x) = 0.892832720119444 x + 12.863493756605R² = 0.770296316669301
Observed streamflow (mm)
Sim
ulat
ed s
trea
mflo
w (m
m)
Runoff ratio = 0.75NS coefficient = 0.76
1958
1960
1962
1963
1965
1967
1968
1970
1972
1973
1975
1977
1978
1980
1982
1983
1985
1987
1988
1990
1992
1993
1995
1997
1998
2000
2002
2003
2005
0
200
400
600
800
1,000
Observed
RHESSys
Year
Mon
thly
str
eam
flow
(mm
)
RHESSys, 1958-2006
0 200 400 600 800 1,0000
200
400
600
800
1,000
f(x) = 0.823453928486588 x + 19.4334289913485R² = 0.765479368357904
Observed streamflow (mm)
Sim
ulat
ed s
trea
mflo
w (m
m)
Runoff ratio = 0.73NS coefficient = 0.73
1949
1951
1953
1955
1957
1959
1962
1964
1966
1968
1970
1972
1975
1977
1979
1981
1983
1985
1988
1990
1992
1994
1996
1998
2001
2003
2005
2007
2009
0
200
400
600
800
1,000
Observed
VIC
Year
Mon
thly
str
eam
flow
(mm
)
VIC, 1949-2006
0 200 400 600 800 1,0000
200
400
600
800
1,000
f(x) = 0.874735896392027 x + 30.3871788980676R² = 0.864980671747796
Observed streamflow (mm)
Sim
ulat
ed s
trea
mflo
w (m
m)
Runoff ratio = 0.85NS coefficient = 0.84
Actual observed runoff ratio = 0.75
HJA Lookout Creek Basin (64 km2)
Model Base Attributes Timestep
Cell-to-cell communi-
cation? Inputs OutputsMC1 – MAPSS-CENTURY-MCFIRE hybrid
large-scale, dynamic vegetation model linked with biogeochemical and fire models
monthly No temperature (min, max, mean), precipitation, vapor pressure or mean dew point, DEM, soil texture (X3), soil depths (X3), climate time series
carbon pools, soil moisture, vegetation lifeforms and distribution, biomass, nutrient fluxes, streamflow, soil water storage, evapotranspiration
RHESSys – Regional Hydro-Ecologic Simulation System
watershed scale, hydro-ecological modeling framework, landscape represented hierarchically, free from grid-based constraints
daily Yes topography (elevation, slope, aspect), air temperature, precipitation, vegetation, drainage network, soil texture, soil depth (X2), radiation, humidity, biome type, leaf area index, climate time series, disturbance history, water holding capacity
water fluxes, evaporation, transpiration, snow dynamics, soil water, carbon, photosynthesis, respiration, decomposition, net primary productivity, nitrogen, litterfall, mineralization, photosynthesis
VIC – Variable Infiltration Capacity
large-scale, grid-based, semi-distributed water and energy balance model with variable infiltration, and non-linear base flow
sub-daily to daily
No landcover, soil moisture, soil texture, soil depth, precipitation, temperature (min, max, mean), DEM (optional), windspeed, lakes/wetlands (optional), plant root depth
streamflow (needs to be routed), runoff, baseflow, energy fluxes, soil moisture/infiltration, canopy precipitation interception, evaporation, evapotranspiration, relative humidity, air temperature, snow, snow-water-equivalent, snow depth, snow interception, snow temperature, snow melt, snow sublimation
ESA #39203, PS 86-225
Map created on www.DataBasin.org