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Long-term Trends in Water Supply Forecast Skill

Long-term Trends in Water Supply Forecast Skill. Tom.Pagano@por.usda.gov. Are there any long-term trends in April 1 st water supply forecast skill? If so, where, when and why?. What skill means here…. sum(forecast – observed) 2 sum(average – observed) 2. Skill = 1 -. = 1 - MSE/VAR.

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Long-term Trends in Water Supply Forecast Skill

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  1. Long-term Trends in Water Supply Forecast Skill Tom.Pagano@por.usda.gov

  2. Are there any long-term trends in April 1st water supply forecast skill? If so, where, when and why?

  3. What skill means here… sum(forecast – observed)2 sum(average – observed)2 Skill = 1 - = 1 - MSE/VAR +1.0 +0.5 0.0 -0.5 Perfect Could be better No better than guessing average Need to find a different job

  4. 29 Study basins Long history of forecasts Still active Unregulated Also made “synthetic” hindcasts using snow and precip data in 35-km buffer Analysis also done on 140 unregulated HCDN basins

  5. -.25 0.0 .25 .50 .75 1.0 January April Westwide Official skill for period of record Not Good Not Bad Perfect

  6. Good Bad Average skill of all sites over 20-yr moving window For the West, as a whole, skill peaked in ~1965-1985 then slumped afterwards

  7. Good Bad Average skill of all sites over 20-yr moving window For the West, as a whole, skill peaked in ~1965-1985 then slumped afterwards Good Bad “Synthetic” hindcasts reproduce this feature almost exactly Therefore it’s not the human forecaster

  8. Recent skill anomalies Where has skill been going down? Mostly in Oregon, Colorado, AZ/NM Doing fine in CA/NV/UT Better than usual +.15 0.0 -.15 -.30 Worse than usual

  9. Conventional wisdom: Extreme years are harder to forecast. If streamflow variability goes up, so should error. So, has streamflow become “Wilder”?

  10. Varieties of Year-to-Year Seasonal Streamflow Changes Less Variable More Variable years years

  11. Varieties of Year-to-Year Seasonal Streamflow Changes Less Variable More Variable Less Persistent More Persistent years years

  12. Of 137 stations around the western US Apr-Sept flow volumes: becoming increasingly variable* Variance End Year 20-yr moving window * at 10% significance level

  13. Of 137 stations around the western US Apr-Sept flow volumes: becoming increasingly variable* year-to-year persistence also high Variance Persistence Recently, most like: End Year 20-yr moving window * at 10% significance level

  14. Lake Powell Water Year Natural Inflow 1922-2004 1000’s acre-feet % of average

  15. Can see this in soil moisture too… University of Washington VIC Model Simulated Soil Moisture Columbia above the Dalles Drier ---- Wetter

  16. Whoa, hold on here a minute! In recent years, the streamflow persistence has gone up, driven by precipitation persistence.

  17. Whoa, hold on here a minute! In recent years, the streamflow persistence has gone up, driven by precipitation persistence. But, classically, we assume all persistence/autocorrelation is “soil moisture”

  18. Blacksmith Fork, Utah 1983-2002 Apr-Sep Flow this year r=0.59 Apr-Sep Flow last year

  19. Blacksmith Fork, Utah 1963-1982 1983-2002 Apr-Sep Flow this year r=0.01 r=0.59 Apr-Sep Flow last year

  20. Since ~1980 western streamflows are the most variable and persistent in modern history However… Where variability is going up skill isn’t necessarily going down.

  21. Variance ‘83-02 Colorado: Variability up Skill down Oregon: Variability down Skill down CA/NV: Variability up Skill up Much less variable than usual Much more variable than usual Anti- persistent persistent

  22. Variance ‘83-02 Persistence ’83-02 Colorado: Variability up Skill down Oregon: Variability down Skill down CA/NV: Variability up Skill up Much less variable than usual Much more variable than usual Anti- persistent persistent

  23. Another possibility for rise in error… Biggest source of April 1st forecast error: Extreme (wet or dry) spring precipitation Perhaps spring precipitation is becoming more extreme?

  24. “Spring Precip Irregularity” defined as… Apr 1 to end of season precip Z-score for each station, averaged across a basin. Index is squared and averaged over 20-year periods. Similar to variance but better Low variability, high irregularity

  25. 20-year moving window Spring precipitation “irregularity” More than 1 = more extreme than usual Less than 1 = Calm, reliably near-normal

  26. 20-year moving window Spring precipitation “irregularity” More than 1 = more extreme than usual Less than 1 = Calm, reliably near-normal Westwide average of 29 basins Calm Extreme

  27. Calm Typical Extreme 1961-80 1981-00 Where is spring precip more irregular? Now, especially in PNW and Southwest, whereas before it was very calm This matches decline in forecast skill

  28. In a nutshell: In CA/NV, extreme precip/snow is happening beforeApril 1st… Good for forecast skill. In Southwest, Colorado, Oregon extreme precip/snow is happening after April 1st… Bad for forecast skill. Skill trends dominated by climate

  29. Of course, the $100,000 question: Will trend continue or return to normal?

  30. Issues of climate non-stationarity Are rising temperatures a problem for water supply forecasts? Or just a problem for water managers?

  31. Basic Western US Hydrology Highly Simplified Watershed Pre-snowpack Snow normals precip Rainfall Snowpack snow Soil water Runoff runoff

  32. Basic Western US Hydrology Highly Simplified Watershed Peak of snowpack Snow precip Rainfall Snowpack snow Soil water Runoff runoff

  33. Basic Western US Hydrology Highly Simplified Watershed Snowmelt Snow precip Rainfall Snowpack snow Soil water Runoff runoff

  34. A “What if?” for a warmer climate precip snow Snow Rainfall Snowpack Soil water Runoff runoff

  35. A “What if?” for a warmer climate precip snow Snow Early Melt Rainfall Soil water Runoff More water into soils by April 1 Early Runoff runoff

  36. A “What if?” for a warmer climate To a statistical water supply forecast system that looks at Apr 1 snowpack as the predictor, these scenarios, An early melt to heavy snow The snow was never there are indistinguishable. However in one case the soil moisture is primed, the other not. Major implications for a forecast of Apr-Jul runoff! precip snow 1 2 1 2

  37. Summary Skill trends evident, trends dominated by climate Flows more variable, persistent Spring precip is more extreme Need to reconsider assumptions about climate stationarity, in context of “soil moisture” Need to know how a warmed climate affects regression forecasts

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