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: jeremiahosbornegowey@gmail.com, 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

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MC1-B57

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thly

str

eam

flow

(mm

)

MC1, 1949-2009

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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

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RHESSys

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thly

str

eam

flow

(mm

)

RHESSys, 1958-2006

0 200 400 600 800 1,0000

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f(x) = 0.823453928486588 x + 19.4334289913485R² = 0.765479368357904

Observed streamflow (mm)

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ulat

ed s

trea

mflo

w (m

m)

Runoff ratio = 0.73NS coefficient = 0.73

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VIC

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thly

str

eam

flow

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VIC, 1949-2006

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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