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The role of evaporative demand in drought monitoring

The role of evaporative demand in drought monitoring. Mike Hobbins, Andy Wood, and Kevin Werner Colorado Basin River Forecast Center (CBRFC) NWS-NOAA, Salt Lake City, UT. Outline. Background Current use of evaporation information in drought monitoring Complementarity of ET and E 0

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The role of evaporative demand in drought monitoring

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  1. The role of evaporative demand in drought monitoring Mike Hobbins, Andy Wood, and Kevin Werner Colorado Basin River Forecast Center (CBRFC) NWS-NOAA, Salt Lake City, UT

  2. Outline Background Current use of evaporation information in drought monitoring Complementarity of ET and E0 Potential roles for E0 in drought monitoring Summary

  3. 1. Background • ET is the supply of water from land to atmosphere • droughts: ET decreases due to moisture limitations at the surface • E0is the atmospheric demand for water from the surface, akin to potential ET • the maximal rate of ET: moisture not limiting • absent information on soil/plant moisture availability, E0 drives hydrologic bucket models and LSMs • observation-based: Epan • temperature-based: Thornthwaite, Hamon, Hargreaves, Blaney-Criddle • physically based: E0= f(T, RH, Wind, Rn, surface parameters) (Penman-related combination equations) • E0is driven by ET • land surface-atmosphere feedback mechanisms

  4. 2. Current use of evaporation information in drought monitoring ET deficits from NLDAS data: Xia and Ek, 2011 • NLDAS/LSM driven ET deficits (anomalies and percentiles) • taken up by US Drought Monitor

  5. 2. Current use of evaporation information in drought monitoring E0: Driving PDSI and other bucket models 10o S T PDSI’s native E0 is a function of T alone 50o S Hobbins et al. GRL 2008

  6. 2. Current use of evaporation information in drought monitoring E0: Driving PDSI and other bucket models – dangers T trends (°C/year) 1975-2004 29 (9) warming 6 (0) cooling Aus: +0.01 NZ: +0.02 Prcp trends (mm/yr2) 1975-2004 17 (1) increasing 18 (5) decreasing Aus: +0.10 NZ: -3.24 [Hobbins et al., Geophysical Research Letters, 2008] Hobbins et al., GRL, 2008

  7. 2. Current use of evaporation information in drought monitoring E0: Driving PDSI and other bucket models – dangers – drought trends Wetness Index: Annual Prcp/E0 Hobbins et al., GRL, 2008

  8. 2. Current use of evaporation information in drought monitoring E0: Driving PDSI and other bucket models – dangers – drought trends (1975-2004) E0PDSI-forced trends E0Pan-forced trends Soil moisture trends (mm/yr) Aus: -0.27 Aus: -0.05 NZ: -0.19 NZ: -0.05 E0 trends (mm/yr2) Aus: +1.1Aus: -2.0 NZ: +1.0 NZ: -1.2 No significant relation between SMPan and SMPDSI 7 sites show opposite SM-trends: 5 in SW WA, NSW and Vic. ET trends (mm/yr2) Aus: +0.5 Aus: +0.2 NZ: +0.5 NZ: -0.9 Hobbins et al., GRL, 2008

  9. 2. Current use of evaporation information in drought monitoring E0: Driving PDSI and other bucket models – dangers – drought trends Budyko curve 1.2 Energy limit 1.0 New Zealand Water limit 0.8 ET / Ep 0.6 0.4 Australia 0.2 0.0 0 1 2 3 4 5 Prcp / Ep General forcing of actual ET: sensitivity of ET to DE0 and DPrcp Prcp-forcing (constant E0) E0-forcing (constant Prcp) ETPan model Annual ET response (mm) ETPDSI model Annual Prcp anomaly (mm) Annual E0 anomaly (mm)

  10. 2. Current use of evaporation information in drought monitoring Contrary observations • CONUS - drought trends: • Variable Infiltration Capacity model • 1915-2003 • Prcp, T, U2, Vegetation and Topography • findings: • US generally wetting (exception is Southwest) • SM - 44% of US is wetting, 3% drying (95% sig.) • Runoff - 28% stations increasing, 3% decreasing • droughts shorter, less severe, less frequent, less widespread Annual soil moisture trends Andreadis and Lettenmaier, GRL, 2006 Ukraine - SM averaged across 150 stations (JJA): dPrcp/dt = -0.031 mm/yr2 dT/dt = +0.015 K/yr yet: dSM/dt > 0. Robock et al., GRL, 2005 China - comparing Ep trends: T increasing in all river basins, SW and U2 decreasing All basins, dEp[Thorn.]dt > 0 80% basins, dEp[Thorn.]/dt opposite to dEp[Pan]/dt Chen et al., Climate Research, 2005

  11. 3. The complementarity of ET and E0 • ETwWet Environment Evaporation - rate under conditions where the only limitation is the availability of energy; • ETActual Evapotranspiration - occurs under conditions of limited moisture availability; • E0Evaporative demand / potential evaporation - theoretical rate under conditions of limited moisture availability if the resulting excess in surface energy is used to evaporate further moisture. ET under traditional paradigm –q2 E0 +q1 q1 ETw E0 ET rates ET rates q2 q1=q2 ET ET Increasing moisture availability Increasing moisture availability Complementary relationship considers three evaporation measures:

  12. 3. The complementarity of ET and E0 Complementarity observed across CONUS E0 ET rates ET Moisture availability Hobbins et al. GRL 2004

  13. 4. Two potential roles of E0 in drought monitoring Climatology/forecast system driving bucket models and other tools ETrc = reference crop ET λ = latent heat of vaporization T = 2-m air temperature Δ = desat/dT at T γ = psychrometric constant Qn = net available energy for ET esat = saturated vapor pressure ea = actual vapor pressure U2 = 2-m wind speed Penman-Monteith ETrc (standard FAO-56 formulation) Mean annual Penman-Monteith ETrc • Weighted combination of radiative and advective drivers. • reference crop is specified: • well-watered grass, • actively growing, • 0.12 m in height, • completely shading the ground, • albedo of 0.23. ETrc then multiplied by factors describing soil moisture, stress, and phenology, to yield an actual ET estimate, e.g.: • January 1, 1979 to near-present • 1/8-degree resolution • Hourly input data • 6-hourly and daily output time-step • CONUS-wide

  14. 4. Two potential roles of E0 in drought monitoring Climatology/forecast system driving bucket models and other tools • Reanalysis: North American Land Data Assimilation System (NLDAS) • T, Air temperature at 2-m elevation • q, Specific humidity at 2 m • Rd, Down-welling SW radiation • Ld, Down-welling LW radiation • Pa, Station pressure • U10, Wind speed at 10 m • Hourly time-step • 0.125-deg (~12 km) resolution • Jan 1, 1980, to Dec 31, 2009 • Forecast: National Digital Forecast Database (NDFD) • T, Air temperature at 2 m • Tdew, Dewpoint at 2 m • U2, Wind speed at 2 m • ECA, Effective cloud amount • Hourly, 3-hourly, or 6-hourly time-steps • 2.5-km / 5-km resolution HRAP grid • Seven new NLDAS-driven reanalyses • and forecasts of E0: • Physically based models: • Penman-MonteithETrc • Kimberly Penman ETrc • Penman Ep • Penman-MonteithEp • PenPanEpan • Temperature-based models: • Hargreaves ETrc • HamonEp Climatologic annual ETrc (mm), 1980-2009 Forecast daily ETrc (mm), May 3, 2010

  15. 4. Two potential roles of E0 in drought monitoring Eo forecast across the CBRFC: 24 hr forecasts from 12Z 4/24/2010 PenPan Epan Penman-Monteith Ep Hamon Ep Kimberly Penman ETrc Penman Ep Hargreaves ETrc Penman-Monteith ETrc Temperature-based vs. Physically based data requirements vs. physics 0 mm/day 3 6 9 12

  16. 4. Two potential roles of E0 in drought monitoring Drivers of E0 variability, NLDAS-driven E0, 1980 - 2009 January Annual T q T SWdn covs July covs SWdn T U10 SWdn covs q q T = 2-m air temperature q = specific` humidity SWdn = downward shortwave U10 = 10-m wind speed covs = covaryingdrivers During summer growing season, T is not the dominant driver over much of CONUS: SWdn, covariances, and U10

  17. 4. Two potential roles of E0 in drought monitoring Issues with T-based models: Decomposing E0forcings – the PenPan model VPD = vapor pressure deficit dE0/dt= -2.0 mm/yr2 dE0,Advective/dt = -2.6 mm/yr2 dE0,Radiative/dt= +0.6 mm/yr2 dVPD/dt = -0.2 Pa/yr dE0,VPD/dt= 0.0 mm/yr2 dU2/dt = -0.01 m/sec/yrdE0,U2/dt= -2.7 mm/yr2 Roderick et al. GRL, 2007

  18. 4. Two potential roles of E0 in drought monitoring Evaporative Demand Drought Index, EDDI 2002 drought examined at Lakewood, CO > 0 positive anomaly in evaporative demand, DROUGHT X = 0 expected evaporative demand < 0 negative anomaly in evaporative demand

  19. 4. Two potential roles of E0 in drought monitoring Evaporative Demand Drought Index, EDDI 1979 2002 2011 • Penman-Monteith ETrc-driven EDDI • Climatology 1980-2009 • Jan 2, 1979 to Mar 8, 2011, for Lakewood, CO

  20. 4. Two potential roles of E0 in drought monitoring Evaporative Demand Drought Index, EDDI Penman-Monteith ETrc-driven EDDI April 1 – September 30, 2002 1980-2009 climatology SM percentiles from Surface Water Monitor September 1, 2002 1960-2003 climatology Wood, 2008

  21. 4. Two potential roles of E0 in drought monitoring Evaporative Demand Drought Index, EDDI EDDI: April 1 – September 30, 2002

  22. 5. Summary Applications • A verified, high-resolution E0 could make a significant contribution to US drought analysis and mitigation: • US Drought Monitor • River Forecast Center operations • daily forecasting flows - hydrologic drought • seasonal - water supply • utility districts’ demand-planning; trans-mountain diversions; reservoir operations; hydrologic science community. • long-term drought trends • improved PDSI-analyses in Hobbins et al. [2008] • significant differences in drought trend direction, depending on E0-type used

  23. 5. Summary • Take advantage of the complementarity of ET and E0 • drought: ET deficits, E0 surpluses • US Drought Monitor • current evaporation information limited to: • ET (using LSMs) • physics-poor implementations of E0 (PDSI) • no explicit E0 input • forecast drought development (daily to weekly time-scale) • E0 would have a ~five-day latency • Developed a 31+-year, daily, CONUS-wide climatology, of seven E0 measures, updated to near-real-time: • filling the latency gap (NLDAS ~5 days): • with quick-look (NLDAS) forcing data • with (NDFD) forecasts from start of data lacuna (i.e., ~5 days) • data will be available for 7 E0 models (5 physical, 2 T-based), Jan 2, 1979 – near-present • currently forecasting 1- and 7-day ETrc • (At least) two potential roles for E0 in drought monitoring: • driving current hydrologic operations (e.g., PDSI) • Evaporative Demand Drought Index (EDDI)

  24. Take Home Messages • E0reflects surface moisture anomalies, through ET • E0 can be a flexible driver for drought monitoring: • remotely sensed, land-based, or physically observed • forecastable • doesn’t rely on LSMs • E0can be estimated or observed simply, • but not too simply • T should not be used alone to drive E0 • often T-driven E0 is hidden

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