1. Upper Rio Grande R Basin
Assessing the importance of hydrologic initial conditions versus climate forecasts for seasonal hydrologic prediction
Andrew W. Wood and Dennis P. Lettenmaier
Civil and Environmental Engineering
APPROACH
RESULTS
Summary
Models / Data
Seasonal streamflow forecast uncertainty arises mainly from errors in characterizing forecast initial conditions and in predicting atmospheric forcings (primarily precipitation, but also temperature and other surface variables) during the forecast period. By contrasting the influence of perturbations in initial conditions versus forcings, the relative contribution of uncertainty in each to forecasts errors can be estimated. For hydrologic forecasting, initial conditions are mainly the moisture states (snowpack and soil moisture), while the forcings are time series of climate variables such as precipitation and temperature. We present a framework for determining the relative importance of the two sources of error.
Via retrospective analysis of six month forecasts (of snowpack, soil moisture, and streamflow) in the western U.S., we estimated the relative contributions of uncertainty in these two sources to forecast uncertainty at different lead times, and for different forecast initiation months.
The approach is based on the comparison of the results of Ensemble Streamflow Prediction (ESP) forecasts with those of a "reverse-ESP" approach (illustrated below).
The results, which show considerable variation for streamflow locations across the domain, indicate when and where improvements in initial condition estimation versus in climate forecasts will most improve hydrologic forecasts, and ultimately, the forecast end users.
grid-based, semi-distributed macroscale model (Liang et al.., 1994)
solves surface water and energy balance
subgrid parameterizations for vegetation and soil properties and dynamics (e.g., infiltration)
non-linear baseflow response
for this application, VIC was run at daily timestep, and applied at 1/8 and 1/4 degree, varying by domain; and
outputs averaged by sub-basin and month for analysis
Kootenai R.
Daily precipitation, minimum and maximum temperature and windspeed from , described in Maurer, et al. (2002)
Columbia R.
climate forecast skill more important
initial conditions more important
Details
Snake R. 1 2 3 4
A retrospective 21 year period was chosen for the analysis,
Using the VIC model driven by observed precipitation, temperature and wind speed, 2 types of forecast ensemble were examined, ESP and “Reverse-ESP”, depicted by the figures below.
Ensemble forecasts were initialized at four points in the year: day 25 of January, April, July and October. Forecast results are reported beginning in the subsequent month, up to a 6 month lead time. A1 year spin-up simulation was used to define initial conditions.
Average RMSE (error) was calculated for monthly average flow at in each forecast month, for each location. The results for 20 locations are shown at right, using plots such as the one at left.
Columbia R.
PNW / Columbia R Basin
ICs
Spin-up
Forecast
observed
RMSE
perfect retrospective
met data to generate
perfect ICs
ensemble of met data
to generate ensemble
forecast
ESP forecast
hydrologic
state
ICs
Spin-up
Forecast
observed
RMSE
ensemble of met data
to generate ensemble
of ICs
perfect retrospective
met forecast
“Reverse-ESP” forecast
hydrologic
state
Bear R.
shows effects of initial condition uncertainty
Humboldt R.
Sacramento R.
Weber R.
Feather R.
Carson R.
Green R.
Gunnison R.
American R.
shows effects of climate forecast uncertainty
Great Basin
Rio Grande R.
San Juan R.
Rio Chama R.
San Joaquin R.
Conclusions
Generally:
uncertainty in initial conditions (ICs) dominate forecast error in spring, and summer, respectively, for lead times of 3-5 months; in autumn, climate forecast uncertainty dominates forecast error; in winter, the two uncertainty sources are more closely balanced;
not surprisingly, climate forecast uncertainty dominates forecast error at longer lead times.
Variations by river basin and location were notable, e.g.:
In the Columbia R. basin and California, the April IC’s had the longest/strongest influence on streamflow, due to generally earlier melt in other basins;
In some locations (Carson, Bear, Weber, Green Rivers), January climate forecast error dominated EXCEPT during peak runoff months, when IC-based errors were larger.
Implications for forecasting:
In spring, early summer, and to a lesser extent, late winter, improvements in initial condition estimates will greatly reduce error in 1-5 month lead forecasts of streamflow, varying by location;
at other times of the year, particularly autumn, it is much less important to estimate ICs accurately than to forecast boundary conditions (e.g., climate) correctly.
California
Rio Grande R.
Colorado R Basin
Colorado R.
Upper Rio Grande R Basin
Maurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen, 2002, A Long-Term Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous United States, J. Climate 15,
Twedt, T.M., J.C. Shaake, Jr., and E.L. Peck, 1977, National Weather Service Extended Streamflow Prediction, Proc. 45th Western Snow Conference, Albuquerque, pp , April.
Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994, A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415-14,428.