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Montana Drought Analysis
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Montana Drought Analysis

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A Montana Drought Analysis from the 2013 Clark Fork River Basin Task Force Meeting.

A Montana Drought Analysis from the 2013 Clark Fork River Basin Task Force Meeting.

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  • Global climate models are designed to simulate the large-scale patterns of climate, and they do so quite well (globe, the surface air temperature simulated by the German ECHAM5 model for January 1999). The ECHAM5 model in this simulation had a resolution of approximately 2°longitude by 2°latitude. For smaller-scale studies, however, the graininess of the models is evident (top right) and important features are missing like the east-west contrast across the Cascades. The process of translating these large-scale fields to the fine-scale topography (0.125° by 0.125° in the example shown) is called “downscaling”. The next slide shows the two primary approaches to downscaling.
  • More of generalized model that can be run anywhere globally Not yet operational, but can be with different driving climate data


  • 1. Most of these indices were designed to detect meteorological and/orhydrological drought without incorporating vegetation responses todrought.Common Drought Severity IndicesDrought Indices Input Data• Palmer Drought Severity Index (PDSI)• Palmer Hydrological Drought Index (PHDI)• Crop Moisture Index (CMI)• Surface Water Supply Index (SWSI)Precipitation, Temperature(T), Soil Moisture• Percent of Normal Precipitation (PNP)• Deciles• Standardized Precipitation Index (SPI)PrecipitationReclamation Drought Index (RDI) Precipitation and PETU.S. Drought Monitor 6 key indicators & manysupplementary indicatorsEvaporative Drought Index T, Rh, MODIS Rsw & NDVIDrought indices integrate large amounts of data such as precipitation, snowpack,streamflow and other water supply indicators to monitor drought severity in acomprehensive framework, and to measure how much the climate in a given timeperiod has deviated from historically established normal conditions.
  • 2. PENMAN-MONTEITH equation for EvapotranspirationWindspeed( ) ( )( ) iesatpnerreeCGRrE⋅+∆+⋅−⋅+−⋅⋅∆=γγρλ0Solar radiation HumidityAirTemperatureLand Water Balance = Precipitation – EvapotranspirationTHE PROBLEMTemperature + Precipitationdoes NOT show the landscape aridityVeg Leaf AreaFairbanks and Tucson have nearly identical annual precipitation,The difference is potential evaporation!
  • 3. Global annual DSI over 2000-2011(-) Drier than normal(+) Wetter than normal
  • 4. Monthly DSI over continental USA in 2012Strong drought impacts across US Corn-belt region in mid to late summer
  • 5. Downscaling global models for regional studies
  • 6. MODIS ET and Landscape-ScaleClimate Data for MontanaAvg. July ET: 2000-2012Jared W. OylerPhD Student, Software EngineerNumerical TerradynamicSimulation Group (NTSG),Montana Climate OfficeCollege of Forestry andConservation,University of Montana
  • 7. Remote Sensing of ET:MODIS ET• Penman-Monteith approach• ET = sum of:– Soil surface E– Canopy intercepted water E– VegetationT• 8-day, monthly, annual products• 1-km resolution• Main advantages– Generalized model that can be run globally– 8-day temporal resolution– Relatively straightforward to operationalize• Main disadvantages– Generalized model– Spatial resolutionAvg. July ET: 2000-2012
  • 8. Data InputsMODIS 1-km products•LAI (8-day)•% Veg Cover (8-day)•Albedo (16-day)•Land Cover (Static)Daily weather data•1/2° x 2/3°•~ 56 km x 51 km•Temperature•Humidity (RH,VPD)•RadiationModel Params by LC•Used in calculations ofconductances andresistances•9 totalMODISP-M ETModel8-day1-km ET Estimates
  • 9. Landscape ScaleWeather/Climate DatasetsInterpolated Datasets: spatiallyinterpolate point-source historicalweather observations onto aregular spatial grid– PRISM– DAYMET– MCO: wxTopo– U. Idaho: NLDAS + PRISMWeather StationsInterpolated Dataset
  • 10. Weather Station DataLong-term Station> 40 years of dataShort-term Station< 40 years> 5 years
  • 11. CoarseWeather DataCrown of the Continent Region
  • 12. CoarseWeather DataAvg.Values 2000-2009Solar Radiation Temperature Vapor Pressure Deficit
  • 13. WxTopo vs. CoarseWeather Data1948 -2012 Mean 1948 -2009 Mean
  • 14. Land SkinTemperatureTmax: MODIS 2003 – 2012 10 year average
  • 15. Interpolation Example1948 -2012 Daily Mean:Crown of the Continent RegionTmin Tmax
  • 16. Topographic DissectionGood at picking up cold air drainage potential (Holden et al. 2011)
  • 17. Stepwise MODIS ETImprovements for Montana1. Improved landscape-scale weather data2. Regionally and/or crop optimized land covermodel parameters3. Improved MODIS ET model4. Finer resolution (500m)