Introduction


Welcome to the Upper Missouri River Basin (UMRB) drought indicators dashboard.


This website provides access to drought indices that are used by the Montana Governor’s Drought and Water Supply Advisory Committee (Monitoring Sub-Committee). All datasets are calculated daily and can be aggregated by watershed and county boundaries. In this document we focus on:


    1. Providing current drought information using the most current peer reviewed science
    1. A description of the theoretical basis for a metric
    1. Data required for calculation of each index
    1. Discuss the relative strengths and weaknesses of a metric
    1. Validating a metric using on the ground observations (soil moisture, streamflow and groundwater; ongoing)


This work is supported by a National Oceanic and Atmospheric Administration’s (NOAA) National Integrated Drought Information System (NIDIS) Drought Early Warning System (DEWS). More information on NIDIS and the DEWS program can be found at https://www.drought.gov/drought/what-nidis.


Select a drought indicator from the above tabs to analyze current conditions.

Current Data (All Variables)

Montana Mesonet


Overview:

The Montana Climate Office (MCO) is leading the development of a cooperative statewide soil moisture and meteorological information system. It is designed to support decision-making in agriculture, range and forested watershed contexts. This network will add new remote sites and integrate existing cooperator networks to develop the first statewide soil-climate network. No one entity can ensure sustained operation and success of a statewide climate and soil moisture information network. The MCO is embracing a cooperative context that will address a diverse set of information needs.


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Collaborators: NOAA National Mesonet Program, Department of Interior Bureau of Land Management, Montana Department of Agriculture, University of Montana - W.A. Franke College of Forestry and Conservation, Montana State University, MSU Extension, Montana Institute on Ecosystems, Montana Agricultural Experiment Stations, Crow Agency, Montana Department of Natural Resources and Conservation, Stillwater County, private landowners, ranchers, farmers and businesses.

Snow Water Equivalent

Snow Telemetry (SNOTEL)


Overview:

SNOTEL is an automated system of snowpack and related environmental sensors. SNOTEL is operated by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture in the Western United States. Plots in this module show total snow water equivalent (SWE) and total liquid precipitation (rain + SWE) and compares these to historical averages.


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Snow Water Equivalent

Accumulated Precipitation

Snow Telemetry (SNOTEL)


Overview:

SNOTEL is an automated system of snowpack and related environmental sensors. SNOTEL is operated by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture in the Western United States. Plots in this module show total snow water equivalent (SWE) and total liquid precipitation (rain + SWE) measured to date compared to historical averages.


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Accumulated Precipitation

Precipitation Percentiles

Precipitation Percentiles


Overview:

Precipitation is a strong driver of drought. In most cases, drought begins with a precipitation deficit which is the exacerbated by atmospheric conditions (such as temperature, humidity and wind speed). In this module we present precipitation percentiles calculated over timescales ranging from 15 to 365 days. As an example of how to interpret each map, 50 represents the normal (median; white shading) precipitation over a given time period, 100 would be the wettest period on record (dark blue) and 0 would represent the driest conditions observed (dark red). The analysis is conducted over a period from 1979 to present.


Data Requirements:

  • Precipitation


Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.


Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.

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Gridded Data

Counties

Watersheds

Tribal Lands

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Derivation:

For each timescale we calculate the accumulated precipitation for each year across the period of record (1979 - present) and compute the current percentile value.

Key Strengths:

  • Easy to calculate; uses only precipitation data.
  • Easy to interpret

Key Weaknesses:

  • Sensitive to biases in precipitation records over time
  • Doesn’t account for atmospheric demand on moisture (i.e. evapotranspiration), this limits identification of flash droughts due to high temperatures and large vapor pressure deficits
  • Does not account for the capacity of the landscape to store or release moisture (e.g. soil moisture, groundwater and streamflow)

Precipitation Anomaly (% of Normal)

Precipitation Anomaly


Overview:

Precipitation is a strong driver of drought. In most cases, drought begins with a precipitation deficit which is the exacerbated by atmospheric conditions (such as temperature, humidity and wind speed). In this module we present precipitation anomalies calculated over timescales that range from 15 to 365 days. For each timescale we calculate the average accumulated precipitation relative to the period of record (1979 - present) and compute the percent of average. As an example of how to interpret each map 100% would represent the average expected precipitation (white shading) during a given time period.


Data Requirements:

  • Precipitation


Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.


Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.

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Gridded Data

Counties

Watersheds

Tribal Lands

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Derivation:

For each timescale we calculate the average accumulated precipitation across the period of record (1979 - present) and compute the percent of average. 100% represents average expected precipitation.

Key Strengths:

  • Easy to calculate; uses only precipitation data.
  • Easy to interpret

Key Weaknesses:

  • Sensitive to biases in precipitation records over time
  • Doesn’t account for atmospheric demand of moisture, this limits identification of flash drought due to high temperature and large vapor pressure deficits
  • Does not account for the capacity of the landscape to store or release water (e.g. soil moisture, groundwater and streamflow)

Temperature Percentiles

Temperature Percentiles


Overview:

Temperature is a strong mediator of drought. In most cases, precipitation deficits are exacerbated by atmospheric dryness, which is strongly linked to temperature. In this module we present temperature percentiles calculated over timescales ranging from 15 to 365 days. As an example of how to interpret each map, 50 represents the normal (median; white shading) temperature over a given time period, 100 would be the warmest period on record (dark red) and 0 would represent the coldest conditions observed (dark blue). The analysis is conducted over a period from 1979 to present.


Data Requirements:

  • Temperature


Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.


Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.

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Gridded Data

Counties

Watersheds

Tribal Lands

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Derivation:

For each timescale we calculate the mean maximum temperature for each year across the period of record (1979 - present) and compute the current percentile value.

Key Strengths:

  • Easy to calculate; uses only temperature data.
  • Easy to interpret

Key Weaknesses:

  • Sensitive to biases in temperature records over time
  • Doesn’t account for precipitaion deficits (the primary driver of drought)
  • Does not account for the capacity of the landscape to store or release moisture (e.g. soil moisture, groundwater and streamflow)

Soil Moisture Percentiles (Modeled)

Soil Moisture Percentiles

National Weather Service, Climate Prediction Center


Overview:

Soil moisture is a strong indicator of drought. In this module we present modeled soil moisture percentiles calculated by the NOAAs Climate Prediction Center. This map is based on an ensemble of the MOSAIC, NOAH, VIC & SAC soil moisture models. The map is derived using a 30 day time scale and is updated every week. As an example of how to read this map, 50 represents the average (median) soil moisture relative to the period from 1980 to 2007. 100 and 0 represent the wettest and driest conditions observed respectively.


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Gridded Data

NDVI Anomaly

NDVI Anomaly


Overview:

The Normalized Difference Vegetation Index (NDVI) is a commonly used indicator of ecosystem photosynthesis and provides an indication of vegetation status. Here we present NDVI anomalies computed as a Z-Score. Positive values indicate greener than expected vegetation when compared to the same time period between 2002 – present. These data are provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Satellite.


Data Requirements:

  • NDVI


Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.


Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.

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Gridded Data

Counties

Watersheds

Tribal Lands

NDVI Trend

NDVI Anomaly


Overview:

The Normalized Difference Vegetation Index (NDVI) is a commonly used indicator of ecosystem photosynthesis and provides an indication of vegetation status. Here we present NDVI trends computed as a linear trend through time. Positive values indicate vegetation that is greening (NDVI increasing), while negative values indicate vegetation that is browning (NDVI decreasing) over the time period selected. These data are provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Satellite.


Data Requirements:

  • NDVI


Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.


Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.

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Gridded Data

Counties

Watersheds

Tribal Lands

Standardized Precipitation Index (SPI)

Standardized Precipitation Index (SPI)


Overview:

The SPI is a common metric which quantifies precipitation anomalies at various timescales. SPI is often used to estimate a range of hydrological processes that respond to precipitation from short to long periods of time. For example, SPI is related to soil moisture anomalies when calculated over short time scales (days to weeks) but is more related to groundwater and reservoir storage over longer timescales (months to years). The values of SPI can be interpreted as a number of standard deviations away from the average cumulative precipitation depth for a given time period. The SPI has unique statistical qualities in that it is directly related to precipitation probability and it can be used to represent both dry (negative values; represented here with warmer colors) and wet (positive values; represented here with cooler colors) conditions.


Data Requirements:

  • Precipitation


Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.


Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.

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Gridded Data

Counties

Watersheds

Tribal Lands

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Derivation:

The SPI quantifies precipitation as a standardized departure from a probability distribution function that models the raw precipitation data. The raw precipitation data are typically fitted to a gamma or a Pearson Type III distribution, and then transformed to a normal distribution (Keyantash and NCAR staff, 2018). Normalization of data is important because precipitation data is heavily right hand skewed. This is because smaller precipitation events are much more probable than large events. In our calculations we use a gamma distribution.

Key Strengths:

  • Easy to calculate; uses only precipitation data
  • Can be used to estimate effects of wet or dry time periods on hydrologic processes (e.g. soil moisture, groundwater, etc) using different time scales
  • Comparable across regions due to normalization of data
  • Can account for changes in climatology because the probability distributions are updated through time

Key Weaknesses:

  • Sensitive to biases in precipitation records over time
  • Doesn’t account for atmospheric demand of moisture, this limits identification of flash drought due to high temperature and large vapor pressure deficits
  • Does not account for the capacity of the landscape to store or release water (e.g. soil moisture, groundwater and streamflow)

UMRB Validation:

Validation in progress

UMRB Recommendation:

UMRB specific recommendations will be appended to this document as validation is completed

Much of the background information regarding this metric was contributed by NCAR/UCAR Climate Data Guide

Standardized Precipitation Evapotranspiration Index (SPEI)

Standardized Precipitation Evapotranspiration Index (SPEI)


Overview:

SPEI takes into account both precipitation and potential evapotranspiration to describe the wetness or dryness of a time period. SPEI can be calculated at various timescales to represent different drought timescales and its impacts on hydrological conditions ranging from short to long timescales. SPEI incorporates the important effect of atmospheric demand on drought.


Data Requirements:

  • Precipitation
  • Potential Evapotranspiration (PET)


Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.


Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.

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Gridded Data

Counties

Watersheds

Tribal Lands

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Derivation:

SPEI is an extension of the SPI in the sense that it uses a normalized probability distribution approximation of raw values to calculate deviation from normals. Similar to SPI, SPEI values are reported in units of standard deviation or z-score (Vicente-Serrano and NCAR staff, 2015). Although, the raw values for this metric are P-PET.

Key Strengths:

  • Combines information about water availability (precipitation) and atmospheric demand for moisture (potential evapotranspiration)
  • Relatively simple to calculate and only requires climatological data and statistical models
  • Does not incorporate assumptions about the behavior of the underlying system
  • Can be calculated where only T and P exist (if using Thornthwaite based PET)
  • Can be calculated with more sophisticated PET algorithms if data is available

Key Weaknesses:

  • Sensitive to differences in PET calculations
  • Requires climatology of data to have accurate statistical reference distributions
  • Sensitive to probability distribution used to normalize data distribution

Historical Validation:

The SPEI has been used in many studies to understand the effects of drought on hydrologic resource availability, including reservoir, stream discharge and groundwater. In general, SPEI calculated at longer timescales (>12 months) has shown greater correlation with water levels in lakes and reservoirs (McEvoy et al., 2012).

UMRB Validation:

Validation in progress

UMRB Recommendation:

UMRB specific recommendations will be appended to this document as validation is completed

Much of the background information regarding this metric was adapted from the NCAR/UCAR Climate Data Guide (here)

Evaporative Demand Drought Index (EDDI)

Evaporative Demand Drought Index (EDDI)


Overview:

EDDI calculates the rank of accumulated PET for a given region. EDDI does not standardize data based off of theoretical (parameterized) probability distributions (such as SPI and SPEI). Instead, EDDI uses a non-parametric approach to compute empirical probabilities using inverse normal approximation. This method calculates EDDI by ranking the data from smallest to largest and accounting for the number of observations. Therefore, maximum and minimum values of EDDI are constrained by the number of years on record (which determines the number of observations). Practically, this causes EDDI to show the relative ranking of year, with respect to the period of record.

Data Requirements:

  • Potential Evapotranspiration (PET)


Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.


Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.

Row

Gridded Data

Counties

Watersheds

Tribal Lands

Standardized Evapotranspiration Deficit Index (SEDI)

Standardized Evapotranspiration Deficit Index (SEDI)


Overview:

SEDI calculates the standardized difference between actual evapotranspiration (AET or ETa) and potential evapotranspiration (PET) for a given region. Similar to the SPI, SEDI can be calculated at various timescales to represent different drought timescales. As such, the SEDI can approximate different impacts of drought on hydrological conditions and processes depending on the timescale used. SEDI is a relatively new drought metric that has advantages over strictly meteorologically based drought metrics (such as SPI) because it uses a soil water balance model to estimate water available for evapotranspiration (AET or ETa). Therefore SEDI represents the standardized unmet atmospheric demand for moisture, which is a good representation of potential vegetation water stress.

Data Requirements:

  • Actual Evapotranspiration (AET or ETa)
  • Potential Evapotranspiration (PET)


Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.


Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.

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Gridded Data

Counties

Watersheds

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Tribal Lands

 

Produced by the Montana Climate Office

Montana Forest & Conservation Experiment Station

University of Montana

32 Campus Drive

Missoula, MT 59812

Phone: (406) 243-6793

state.climatologist@umontana.edu