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

1. Most of these indices were designed to detect meteorological and/or
hydrological drought without incorporating vegetation responses to
drought.
Common Drought Severity Indices
Drought 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)
Precipitation
Reclamation Drought Index (RDI) Precipitation and PET
U.S. Drought Monitor 6 key indicators & many
supplementary indicators
Evaporative Drought Index T, Rh, MODIS Rsw & NDVI
Drought indices integrate large amounts of data such as precipitation, snowpack,
streamflow and other water supply indicators to monitor drought severity in a
comprehensive framework, and to measure how much the climate in a given time
period has deviated from historically established normal conditions.

2. PENMAN-MONTEITH equation for Evapotranspiration
Windspeed
( ) ( )
( ) ie
satpne
rr
eeCGRr
E
⋅+∆+⋅
−⋅+−⋅⋅∆
=
γγ
ρ
λ
0
Solar radiation HumidityAir
Temperature
Land Water Balance = Precipitation – Evapotranspiration
THE PROBLEM
Temperature + Precipitation
does NOT show the landscape aridity
Veg Leaf Area
Fairbanks and Tucson have nearly identical annual precipitation,
The difference is potential evaporation!

4. Global annual DSI over 2000-2011
(-) Drier than normal
(+) Wetter than normal

5. Monthly DSI over continental USA in 2012
Strong drought impacts across US Corn-
belt region in mid to late summer

6. Downscaling global models for regional studies

7. MODIS ET and Landscape-Scale
Climate Data for Montana
Avg. July ET: 2000-2012
Jared W. Oyler
PhD Student, Software Engineer
Numerical Terradynamic
Simulation Group (NTSG),
Montana Climate Office
College of Forestry and
Conservation,
University of Montana

8. 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 resolution
Avg. July ET: 2000-2012

9. Data Inputs
MODIS 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)
•Radiation
Model Params by LC
•Used in calculations of
conductances and
resistances
•9 total
MODIS
P-M ET
Model
8-day
1-km ET Estimates

10. Landscape Scale
Weather/Climate Datasets
Interpolated Datasets: spatially
interpolate point-source historical
weather observations onto a
regular spatial grid
– PRISM
– DAYMET
– MCO: wxTopo
– U. Idaho: NLDAS + PRISM
Weather Stations
Interpolated Dataset

11. Weather Station Data
Long-term Station
> 40 years of data
Short-term Station
< 40 years
> 5 years

12. CoarseWeather Data
Crown of the Continent Region

13. CoarseWeather Data
Avg.Values 2000-2009
Solar Radiation Temperature Vapor Pressure Deficit

14. WxTopo vs. CoarseWeather Data
1948 -2012 Mean 1948 -2009 Mean

15. Land SkinTemperature
Tmax: MODIS 2003 – 2012 10 year average

16. Interpolation Example
1948 -2012 Daily Mean:
Crown of the Continent Region
Tmin Tmax

17. Topographic Dissection
Good at picking up cold air drainage potential (Holden et al. 2011)

18. Stepwise MODIS ET
Improvements for Montana
1. Improved landscape-scale weather data
2. Regionally and/or crop optimized land cover
model parameters
3. Improved MODIS ET model
4. Finer resolution (500m)