Download
1 / 24
240 likes | 317 Views
Experimental Real-time Seasonal Hydrologic Forecasting. Andrew Wood Dennis P. Lettenmaier University of Washington presented: AMS Conference on Applied Climatology, 2002 Portland, OR May 2002. Project Overview. Research Objective:
E N D
Experimental Real-time Seasonal Hydrologic Forecasting Andrew Wood Dennis P. Lettenmaier University of Washington presented: AMS Conference on Applied Climatology, 2002 Portland, OR May 2002
Project Overview • Research Objective: • To produce monthly to seasonal snowpack, streamflow, runoff & soil moisture forecasts for continental scale river basins • Underlying rationale/motivation: • Global numerical weather prediction / climate models (e.g. GSM) take advantage of SST – atmosphere teleconnections • Hydrologic models add soil-moisture – streamflow influence (persistence)
Topics • Approach • Columbia River basin (summer 2001) results • Ongoing Work • Comments
General Approach • climate model forecast • meteorological outputs • ~1.9 degree resolution (T62) • monthly total P, avg T • Use 3 steps: 1) statistical bias correction • 2) downscaling and disaggregation • 3) hydrologic simulation hydrologic model inputs • streamflow, soil moisture, snowpack, • runoff • 1/8-1/4 degree resolution • daily P, Tmin, Tmax
Models: 1. Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC • forecast ensembles available near beginning of each month, extend 6 months beginning in following month • each month: • 210 ensemble members define GSM climatology for monthly Ptot & Tavg • 20 ensemble members define GSM forecast
Flow Routing Network domain slide
TOBS a. b. c. TGSM One Way Coupling of GSM and VIC models a) bias correction: climate model climatology observed climatology b) spatial interpolation: GSM (1.8-1.9 deg.) VIC (1/8 deg) c) temporal disaggregation (via resampling of observed patterns): monthly daily
Bias Example:JFM precipitation from Parallel Climate Model (DOE)climate model vs. “observed” distributions at climate model scale (T42)
Dealing with bias using a climatology-based correction Note: we apply correction to both forecast ensemble and climatology ensemble itself (to use as a baseline)
monthly GSM anomaly (T62) interpolated to VIC scale VIC-scale monthly forecast observed mean fields (1/8-1/4 degree) note: month m, m = 1-6 ens e, e = 1-20 Downscaling: add spatial VIC-scale variability
Lastly, temporal disaggregation… for each VIC-scale monthly forecast value, e.g.:
1-2 years back VIC forecast ensemble VIC model spin-up VIC climatology ensemble NCDC met. station obs. up to 2-4 months from current LDAS/other met. forcings for remaining spin-up climate forecast information (from GSM) data sources Forecast Products streamflow soil moisture runoff snowpack Simulations start of month 0 end of month 6
forecast observed May climate forecast forecast medians
May snowpack forecast hindcast “observed” forecast forecast medians
forecast May runoff & soil moisture forecast hindcast “observed” forecast medians
Tercile Prediction “Hit Rate”e.g.,GSM Ensemble “Forecast” Average, January(based on retrospectiveperfect-SST ensemble forecasts)Masked for local significance
Summary Comments • climate-hydrology model forecasting method has potential • hydrologic persistence was most important in the CRB example • bias-correction of climate model outputs (using a climate model hindcast climatology) is critical • access to quality met data for hydrologic model initialization is also essential
