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Operational Drought Monitoring and Forecasting at the USDA-NRCS Tom Pagano 503 414 3010.

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Presentation on theme: "Operational Drought Monitoring and Forecasting at the USDA-NRCS Tom Pagano 503 414 3010."— Presentation transcript:

1 Operational Drought Monitoring and Forecasting at the USDA-NRCS Tom Pagano Tom.Pagano@por.usda.gov 503 414 3010

2 Monitoring networks Data products Seasonal forecasts Soil moisture Challenges Frontiers

3 Monitoring networks

4 Manual Snow Surveys Metal tube inserted into snow and weighed to measure water content. +300,000 snow course measurements as of June 2008 1906 2005

5 Snotel (SNOw TELemetry) network Automated, remote stations Primary variables: Snow water Precipitation Temperature Also: Snow depth Soil moisture SNOTEL and Snow course records often spliced together

6 Snowcourse (solid) and SNOTEL (hashed) active station installation dates Active year Number of sites

7 Soil climate analysis network (SCAN) Soil moisture/energy balance emphasis Short period of record (some from 1990s) Data available but few products

8 Manual snow-course SCAN SNOTEL

9 Data products

10 Time series charts

11

12 CSV flat files Google Earth

13 Forecast products

14 Location Forecasts are coordinated with the National Weather Service (NWS). Both agencies publish identical numbers.

15 Location Time Period Historical Average Forecasts are coordinated with the National Weather Service (NWS). Both agencies publish identical numbers.

16 Location Time Period “The” Forecast Water Volume Historical Average Error Bounds Forecasts are coordinated with the National Weather Service (NWS). Both agencies publish identical numbers.

17 Seasonal water supply volume forecasts (available in a variety of formats) NRCS formats:

18 Seasonal water supply volume forecasts (available in a variety of formats) NRCS formats:

19 Basic Forecasting Methods Statistical regression May 1 snowpack % avg Apr-Jul streamflow % avg S Fork Rio Grande, Colo

20 Statistical regression May 1 snowpack % avg Apr-Jul streamflow % avg S Fork Rio Grande, Colo Snow pack Soil water Snow Rainfall Runoff Heat Simulation modeling Basic Forecasting Methods

21 Principal Components Regression (Garen 1992) Prevents compensating variables. Filters “noise”.

22 Principal Components Regression (Garen 1992) Prevents compensating variables. Filters “noise”. Z-Score Regression (Pagano 2004) Prevents compensating variables. Aggregates like predictors, emphasizing best ones. Does not require serial completeness. Relative contribution of predictors

23 Daily forecast updates Existing seasonal forecasts issues once per month Why not develop 365 forecast equations/year and automate the guidance? We currently do Apr-Jul Streamflow = a * April 1 Snowpack + b Why not something like Apr-Jul Streamflow = a * April 8 Snowpack + b

24 1971-2000 avg Period of record median Period of record range (10,30,70,90 percentile)

25 1971-2000 avg Period of record median Period of record range (10,30,70,90 percentile) Official coordinated outlooks

26 1971-2000 avg Period of record median Period of record range (10,30,70,90 percentile) Official coordinated outlooks Daily Update Forecasts

27 Official forecasts

28 Daily forecast 50% exceedence Official forecasts Expected skill

29 SWSI Methodology varies by state Available 8 Western states Rescaled percentile of [reservoir + streamflow] Calibrated on observed, forced with streamflow forecasts (real-time variance too low) No consistent calibration period

30 Soil moisture and runoff efficiency

31 Expansion of soil moisture to SNOTEL network (data starts ~2003)

32 Blue Mesa Basin, Colorado Soil Moisture 2001-2008 (According to the Univ Washington Model- top 2 layers)

33 Blue Mesa Basin, Colorado Soil Moisture 2001-2008 (According to the Univ Washington Model- top 2 layers) (According to Park Cone Snotel- ~0-30” depth) Snotel does poorly in frozen soils, so that has been censored Model resembles snotel, but also remember we’re comparing basin average with point measurement

34 What influence humans? Does it matter? Blue Mesa For each site, all measurements Jan-Jun, Jul-Dec are averaged by year. Station half-year data then converted into standardized anomaly (o-avg(o))/std(o) vs period of record for the half year. Multiple stations are then averaged.

35 Spring precipitation, especially the sequencing with snowmelt is also important Runoff Snowmelt Rainfall Rainfall mixed with snowmelt “normal” April July

36 Spring precipitation, especially the sequencing with snowmelt is also important Runoff Snowmelt Rainfall Rainfall mixed with snowmelt “normal” Rainfall boosting snowmelt Larger volumes Snowmelt and rainfall separate Not enough “momentum” to produce big volumes All these interactions are tough to “cartoonize”; Simulation models can handle this… but still it’s tough to predict beyond 1-2 weeks. April July

37 Spring precipitation, especially the sequencing with snowmelt is also important April July Runoff Snowmelt Rainfall Rainfall mixed with snowmelt “normal” Rainfall boosting snowmelt Larger volumes Snowmelt and rainfall separate Not enough “momentum” to produce big volumes Even then, however, high heat and no rain can lead to “pouring sunshine” All these complex interactions are tough to “cartoonize”; Simulation models can handle this… but still it’s tough to predict beyond 1-2 weeks.

38 Challenges and frontiers

39 Seasonality/lag of drought in snowmelt regions Precipitation and impacts can be separated by months. Highly managed systems How to separate drought from poor planning or overbuilding? Also: Humans react to forecasts e.g. evacuating reservoirs Regional/local vulnerability Whose drought? Stickiness of drought When is the drought over? Never… (also risk of “Drought fatigue”)

40 Seasonality/lag of drought in snowmelt regions Precipitation and impacts can be separated by months. Highly managed systems How to separate drought from poor planning or overbuilding? Also: Humans react to forecasts e.g. evacuating reservoirs Regional/local vulnerability Whose drought? Stickiness of drought When is the drought over? Never… (also risk of “Drought fatigue”) Incomplete understanding of natural system (esp soil moist, sublim) Can we even close the water balance? Institutional and infrastructure barriers Limited agency resources, increasing restrictions Non-stationarity Could climate change be the new normal?

41 The future may have more and better: Products from and understanding of soil moisture data Automation and “smart” objectification of forecast process Quantification and use of anecdotal evidence Forecast transparency (i.e. access to raw guidance)

42 The future may have more and better: Products from and understanding of soil moisture data Automation and “smart” objectification of forecast process Quantification and use of anecdotal evidence Forecast transparency (i.e. access to raw guidance) Communication of uncertainty, especially graphically Understanding of local user vulnerabilities Consolidation of data from multiple networks: universal, uniform access and multi-agency products Understanding of the “long view”: how relevant is data from 10, 50, 100, 500 years ago?

43

44 Variable“Significance” Snow60-90 Fall precip 5-20 Winter precip30-60 Spring precip10-25 Baseflow 5-15 Soil Moisture 5-10 Temperature10-25 Wind 5-20 Radiation 5-15 Relative humidity 5-10 Source:1972 Engineering Handbook

45 Daily forecast Skill: (Correlation) 2 Variance Explained January 1

46 Daily forecast Skill: (Correlation) 2 Variance Explained April 1

47 NWS formats:

48

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