1. Sources of Skill and Error in Long Range Columbia River Streamflow Forecasts: A Comparison of the Role of Hydrologic State Variables and Winter Climate Forecasts JP1.2 JISAO/SMA Climate Impacts Group and the Department of Civil and Environmental Engineering, University of Washington Alan F. Hamlet, Andy Wood, Seethu Babu, Dennis P. Lettenmaier Overview In the Columbia River Basin (CRB) in the Pacific Northwest (PNW) skillful winter climate forecasts based on the El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) are available with lead times of up to about six months (i.e. June 1). Using a hydrologic model and near real time estimates of hydrologic state variables such as soil moisture and snowpack, probabilistic forecasts of streamflow at various basin locations can be produced starting in June (preceding the water year) and proceeding through the snow accumulation season. A useful standard of comparison for these experimental forecasts is a January 1 Ensemble Streamflow Prediction (ESP) forecast, which is comparable in skill and reliability to the statistical streamflow forecasts relating winter snow accumulation to spring and summer streamflow that are used operationally for water management throughout much of the western US. In order to understand the role of the various sources of hydrologic predictability, we generate probabilistic forecasts retrospectively for 50 years using both climate forecasts and estimates of hydrologic state variables on Oct 1, Nov 1, and Dec 1, and compare them, using a quantitative skill metric, to Jan 1 ESP forecasts based solely on hydrologic state variables. Tools and Forecasting Methods The figure below shows a schematic diagram of the forecasting method. Initial snow and soil moisture conditions are assumed to be “perfectly” known at each forecast date. A perfect categorical (warm, neutral, cool) ENSO forecast is also assumed. The VIC hydrologic model (version 4.0.3) was implemented over the Columbia River basin at ¼ degree resolution for these experiments. A schematic of the hydrologic model is shown above. Simulated and observed streamflow at The Dalles, OR (an important checkpoint for water resources management) were used in assessing the skill of the forecasts. Skill Metric Skill metrics that evaluate only the central tendency of a forecast ensemble are generally inadequate for our purposes. Instead we use a more sophisticated metric that both rewards accuracy and punishes “spread” in the forecasts. Note that in this case both the forecast and the climatology are treated as an ensembles as opposed to a single deterministic trace. Skill is defined as: Skill = 1 - [ Σ (forecast - obs)2/N / Σ (climatology - obs)2/M ] where N is the number of forecast ensemble members and M is the number of climatological observations. A skill of 0, using this metric is equivalent to the climatology, positive values between 0 and 1 are superior to the climatology, and negative values between 0 and -1 are inferior to the climatology. ENSO PDO Run Initialized Hydrologic Model Ensemble Streamflow Forecast Select Temperature and Precipitation Data from Historic Record Associated with Forecast Climate Category Climate Forecast Schematic for Forecasting Experiments Using Resampled Observed Data Elevation (m) The Dalles Role of Oct 1 Initial Soil Moisture Streamflow simulations based on a fixed water year (wet, normal, dry) paired with 50 different initial soil conditions have shown that the initial soil state can effect summer streamflow from April- September by as much as +- 8%. This effect has about the same magnitude in normal or dry years, and is somewhat less in wet years. In dry years, the peak streamflow timing in summer is also shown to be significantly affected by the initial soil state in fall. Normal Year Range =16% of ensemble summer mean Dry Year Streamflow peak is earlier when soils are wet, later when soils are dry OctNov Dec Jan ESP OctNov Dec Jan ESP OctNov DecJan ESP Results Long-range forecast is more skillful than Jan 1 ESP forecast Long-range forecast is comparable to Jan 1 ESP forecast Long-range forecast is inferior to Jan 1 ESP forecast A few examples from particular water years to illustrate the nature of the experiments and the performance of the skill metric: Warm ENSO Years ENSO Neutral YearsCool ENSO Years Changes in Jan 1 ESP Forecast Skill Associated with Climate Information (positive means improved skill) 32 of 48 improved or unchanged 26 of 48 improved or unchanged 35 of 48 improved or unchanged 33 of 48 improved or unchanged Conclusions PDO/ENSO forecasts are estimated to be worth more than $100 million per year in the Columbia basin in terms of hydropower revenue alone (value of forecast), but an objective comparison with a Jan 1 ESP forecasts is useful in understanding the skill and error characteristics of the forecasts in the context of water management. Forecast skill metrics that reward accuracy and punish spread are more appropriate than simple metrics based on the central tendency of the forecasts when the forecasts have different variability than the climatology and there are relatively small shifts in the central tendency of the forecast from year to year. Fall and early winter ENSO-based long-lead forecasts for the Columbia basin (based on resampling methods) typically have a lower skill than January 1 ESP forecasts based on persistence of hydrologic state (soil moisture and snow), but frequently have higher skill than climatology. Prior to December 1 the ENSO based forecasts are considerably less robust than the Jan 1 ESP forecasts, and the climate forecasts play a dominant role in the skill of the forecasts. For ENSO neutral years the skill metrics are questionable due to the distribution of flows within the ensemble, but the skill is generally lower than in warm and cool ENSO years Adding ENSO, ENSO transitions, and interannual PDO climate information to January ESP forecasts improves or leaves the forecast skill unchanged about 70% of the time. An interannual formulation of PDO appears to be preferable to an epochal formulation. Wet Year Range =13% of ensemble summer mean. Summary Results