The DRIP Model: Not Drought nor Deluge

How to find green growing plants in Arizona, a state famous for its long droughts and intermittent, but torrential, rains?  Previously I reviewed the available public models for drought, NDVI, and rainfall, and concluded that rainfall was most useful.  However, the most important factor for plant growth is regular consistent rain.  Not drought, but also not deluge.  I hypothesized that a consistent "drip" of at least 1/4 inch of rain each week would yield the best plant growth, and I created a GIS model to map this.  

Methods

Download data files from NOAA: https://water.noaa.gov/resources/downloads/precip/stageIV/2025/03/04/

Methods: https://water.noaa.gov/about/precipitation-data-access 

lots more info at the bottom link for PDF: NWPS Products and User Guide

GeoTIFF The new QPE GeoTIFFs generated from the NCEP Stage IV data are multi-band GeoTIFF. The bands they contain are: 

● Band 1 - Observation - Last 24 hours of QPE spanning 12Z to 12Z in inches 

● Band 2 - PRISM normals - PRISM normals in inches (see Appendix A- Normal Precipitation) 

● Band 3 - Departure from normal - The departure from normal in inches 

● Band 4 - Percent of normal - The percent of normal

I only use Band 1, for the previous week, not 24 hours.

I download the data using a Power Automate FTP query for: concat('https://water.noaa.gov/resources/downloads/precip/ ', variables('Date2'),  '/nws_precip_', 'last 7-days_', variables('CurrentDate'), '_conus.tif')

In GIS, I Clip rasters to extent and calculate threshold (0.25") for each week:

Then I use Cell Statistics to add all threshold files for a several month period.

Results

10/13-12/01, each week gets 1 point for rain over 0.25"

Northern CA, and areas NE of AZ received more regular precipitation. This beginning of the water year period is important for early germination of desert winter annuals that can lead to "superbloom" springs.  Because most desert areas in AZ did not get much precipitation, the indications were not good for 2025 spring.

12/8 to 3/5, each week gets 1 point for rain over 0.25"

The highest mountains in UT and CO got regular precipitation, as did northern CA. NM did not continue wetter than AZ.  This winter period is important for desert spring ephemeral flowers.  While some areas of the Mojave did get rain, there was basically no rain in the Sonoran desert during this period. 

How to Use Satellites to Find Growing Plants: A Practical and Theoretical Guide

USGS Maximum consecutive dry days, July 2024

Introduction

Knowing where and when plants are green and growing would help botanists and ecologists plan field work and help managers make real-time land management decisions.  There are many potential sources of geospatial data available but it is difficult to know which sources are most useful.

Over the past year I evaluated the accuracy and interpretability of dozens of indicators that could be used to assess current growing conditions.  These websites, maps, and data layers are mainly provided by US Government agencies to help managers respond to natural resource concerns such as rangeland management and drought impacts.

In my opinion, none of the available resources is a perfect fit to find growing plants.  However, understanding how plant growth is related to biophysical constraints can help identify the best available resources.

Plant Growth Theory

Plant growth (Net Primary Productivity, NPP), is proportional to total soil moisture (SM) and Growing Degree Days (GDD) of accumulated Temperature (T) since the start of that plant's growing season.  Soil moisture depends upon accumulated (total and frequency of ) precipitation (P) minus evapotranspiration (ET).  

 NPP = Sum (SM*T)

 SM = Sum (P-ET)

[In the above equations, "equals" is used to mean proportional, or depends upon. ]

The ideal resource for plant growth would be a direct measurement of NPP.  A second option would be measurement of soil moisture and temperature, with a way of combining them to estimate NPP.  A third option would be measurement of precipitation, which could be used to estimate SM. All of these variables can be measured via satellite, but delays make them difficult to use in real time.  Also, there are important details in how they are measured and modelled that make interpretation difficult.  

Details

Plant Growth: satellites can measure NPP via NDVI (Normalized Difference Vegetation Index), but available sources are delayed (DroughtView), low-resolution (GIMMS), weighted toward trees, and contain artefacts early in the growing season.  Existing models of NPP are not well-calibrated (VegDRI), or delayed and not mapped (RAP).

Soil Moisture: satellites can measure SM, but satellite data (SMAP) is not consistently available, may be inaccurate, and only provides a snapshot, not the sum.  Models of SM are not well-calibrated (NASA LIS).

Temperature: sensor networks can measure T and calculate Growing Degree Days (NPN) but aspect and micro variation are often more important than county-level temperatures.  This is helpful early in the growing season.  

Precipitation: radar and rain gauge networks can measure P and are timely and accurate.  Maps of accumulated P are available from NOAA and number of days with/without rain in the last month from USGS.  However, because P is not a direct measure of NPP, this can only be a general guide to plant growth.  

Conclusions

P - Best. Note that both total and frequency matter.

SM - not available.

T - GDD is helpful in Spring phenology, but don't matter later in the season.

NPP - difficult to use.  Good for viewing thinning and forest fires, not very helpful for spotting wildflower growth or distinguishing between grass and tree green-up.  

NOAA precipitation accumulation as percent of normal for July, 2024

Practical How-To

I recommend using a balance-of-evidence approach that combines the two best sources of precipitation information, NOAA for total accumulation, and USGS for frequency.  If both sources show good precipitation during the recent growing season, there is a good chance that plants are growing well in response to abundant and regular moisture. 

NOAA's National Water Prediction Service provides accumulated precipitation over any time period from 2005-present with less than 24 hour delay.  Their web viewer is currently the only way I know to view this data because I don't have the programming skills to use their API.  

Scroll down to Precipitation Estimate, where it is possible to set the time period of interest and map Precipitation in terms of Observed (totals), Normal (average), Departure from Normal (inches more/less than normal), and Percent of Normal.  The map is sometimes glitchy depending on connection speed.  More details about how precipitation is calculated are available in the help guide.  Note that this map is usually up to date for the previous "water day" ending in the early morning (so for AZ, selecting "Today" shows precipitation that occurred yesterday through 7 am today).

USGS's Drought Monitor provides access to several precipitation metrics, with a 48 hour delay. To assess precipitation frequency/regularity, I use Maximum Consecutive Dry Days (Past 30 days) and Days Since Precipitation.  They also offer total accumulated precipitation over the last 7 and 30 days (again, with a 2 day delay).  Their web-viewer is clunky and hard to use, but it is easily available.  Double click on the Dataset of interest in the left pane and resize the resulting window to view the area of interest.  The layers have no opacity, so to view the underlying map they have to be checked on/off in the Layers pane.  

They also make their data available as WMS that can be added to any ArcGIS desktop or online map.  The opacity can be adjusted on the Group layer that is created after the data is mapped.  However, the time-enabled settings can be difficult to use, and I haven't figured out how to show the data time period.  The web viewer is easier to change the time period and to see the data date range.

Note that USGS Drought Monitor also provides VegDRI and QuickDRI, two models that claim to incorporate all of the biophysical variables listed above (and then some!) to model NDVI difference from average.  These models are extremely complex, but they don't seem well-calibrated to Arizona because I have not found them to be very accurate or helpful.  

More Springtime NDVI Observations

 I recently took a driving tour from Prescott to Cottonwood and back.  Most areas have dried out already.  This post reviews the phenology, NDVI greenness maps, and rainfall patterns in the area.

Verde Valley  - wildflowers, hedgehog cactus flowers

2,437 32 AGDD 

501 50 AGDD

I-17 and General Crook  - mesquite just starting to flower, grasses dry

2,202 32 AGDD 

394  50 AGDD

Verde Valley native desert grassland with scattered juniper.  Some wildflowers are present, but most areas are dry.

Dugas - mesquite just starting to flower, grasses dry

2,414 

462 50 AGDD

169 and I-17 - Mesquite still leafing out, some grasses still green

2,224  32 AGDD 

400 50 AGDD

Hills near Dugas with dry invasive annual grasses.  Few to no flowers present.

89A at base of Mingus mountain grasses greening up, still early in the growing season

1,773  32 AGDD 

89A at base of Mingus mountain powerline ROW showing early spring green up of cool season grasses.

NDVI

DroughtView is still showing anomalously green areas between Cordes and Flower Pot along I-17, but these hills around Dugas are already quite dry (see image above).

Most recent (4/6-4/21) NDVI difference map

It matches the NDVI variance from that time period.  At this time there was still anomalous green up around Dugas, for example.  

MODIS NDVI (Near Real Time 8 Day) 4/6-4/21

DroughtView also shows straight NDVI, with a nice mask that only shows areas that are green, and has much more recent data.  For example, the current Near Real time NDVI shows greenness only in the mountains and areas of mesquite that have greened up, shows brown over much of the grasslands.  This is what I observed driving through the area: it has already browned out.  This tool allows visualize of current state of greenness on the landscape.

The difference a few weeks can make for springtime greenness.  Note the difference along I-17 from Cordes to Flower Pot.

MODIS NDVI (Near Real Time 8 Day) 4/22-5/7

QuickDRI (updated 5/6) is still showing "improvement" in drought stress around Congress and South of Cordes lakes, but NDVI (above) shows no greenness there.  I think it could be mesquite leaf out, but interesting that NDVI NRT doesn't show it.  Both maps continue to agree that the area around Yava still looks good.  QuickDRI shows "stress intensification" in the Bradshaw and Mingus  mountain areas that NDVI NRT shows as green.  Both could be true.  QuickDRI shows "improvement" in Chino Valley, an area that NDVI NRT doesn't show any greenness.  This area is still somewhat greening up, so QuickDRI may be more accurate there.

QuickDRI (updated 5/6)

Phenology and Accumulated Precipitation

Most areas are below normal 90 day precipitation, except a small area around Perkinsville.  This area doesn't show up as being anomalously green, or having any greenness in NDVI NRT, or having lower stress in QuickDRI.

Feb-May rainfall

Based on AGDD, it is possible that this area is still too early in Spring and needs more time to green up.  

The area around Congress has received some moisture in the last 60 days, but it may not be enough to compensate for the low rainfall in February, or the high temperatures and complete lack of precipitation over the last 30 days.  

March-May rainfall

April rainfall

The Knife Edge of Spring

"To what purpose, April, do you return again?

Beauty is not enough.

You can no longer quiet me with the redness

Of little leaves opening stickily."

-from Spring by Edna St. Vincent Millay

In AZ, the spring green up of grasses and trees makes use of a narrow window of time between declining winter moisture and increasing summer heat.  Our redbud trees blooms for three or four days (this year, from April 26-29), the lilac bushes hardly last much longer, and the spring green up of weedy lawns is over by the end of May.  June is the dry season in Arizona.  June is when spring dies.

The knife edge of spring in Arizona can be seen in the biomass production.  For example, the Rangeland Analysis Platform shows that peak spring growth in Dugas, AZ in 2020 lasted approximately 3 weeks, from 4/1 to Earth Day 4/22.  By May, the growth of the annual green up was already in freefall.  More examples can be seen in this previous blog post using RAP to investigate biomass production variability from year to year.  

Phenology Mapping

The National Phenology Network's Visualization Tool can be used to follow the spring green up via Accumulated Growing Degree Days (AGDD), which work by adding up all of the days and temperatures above some minimum threshold for growth, usually 32 F for cool season plants (winter annuals, cool season grasses, willows and cottonwoods and other early spring plants) or 50 F for warm season plants (mesquite, acacia, and other warm season trees like Chilopsis, walnut, and warm season grasses and forbs).

AGDD Details

A "growing degree day" (GDD) is calculated by subtracting the threshold temperature (T) from the average temperature for each day when the minimum temperature is above the threshold temperature of 32 or 50 degrees. 

Average T = (Maximum T - Minimum T)  / 2

GDD = Average T - Threshold T

 Accumulated growing degree days (AGDD) simply adds up all of the GDD since the beginning of the year.

AGDD = GDD (January 1) + GDD (January 2) + … + GDD (yesterday)

To use the NPN Visualization Tool for AGDD, choose Map>Base Layer>Category: Daily temperature accumulations>Layer:Current Day.

AGDD Example - 32 F

The figure below shows AGDD around Prescott, AZ from a threshold temperature of 32 as 5/2/24.  Most of the map shows AGDD of about 2,000; since there have been about 120 days since the beginning of the year, that works out to an average daily temperature of 48 degrees F (2,000/120 + 32 =  48).  Of course, some areas have been warmer than that and some have been cooler:  

Phenology AGDD from 32 F 

1,600 - cottonwoods leafed out (Prescott)

1,800 - still winter grasses not green (Kirkland junction)

2,200 -  annual grasses very green, early spring wildflowers blooming (Dugas)

2,245 - mesquite not leafed out yet, annual grasses still somewhat green (Yava)

2,500 - mesquite leafed out, annual grasses brown (Date)

2,800 mesquite flowering (Congress)

AGDD Example 50 F

Or maybe a 50 degree threshold better shows mesquite and grass green up? The average daily temperatures, of course, are the same, but the different threshold yields much lower AGDDs, mostly in the low 100's.  On this map, light green area are grasses already brown and mesquite leafed out, green areas actually are green fields of annual grasses, whereas white and blue are areas where willows, cottonwoods, and elms have leafed out, but herbaceous plants are just barely getting started.

Phenology AGDD from 50 F 

240 - oaks turning brown (Kirkland)

350 - mesquite not greened up yet, annual grasses and forbs green (Dugas)

400 - mesquite not leafed out yet, annual grasses still somewhat green (Yava)

550 - mesquite leafed out, annual grasses brown (Date)

800 - mesquite flowering (Congress)

AGDD Anomalies

The examples above of specific phenology AGDD values can be used in conjunction with NPN's Visualization Tool to predict plant growth stage.  Also, it is possible to use NPN's Visualization Tool to highlight geographic areas that may be ahead or behind the usual spring green up using the "Anomaly" visualization.  In the figure below, anomaly from 32 F AGDD, it can be seen that the Kirkland valley, Black Canyon City, and the Verde valley are behind (blue) normal phenology, whereas Prescott valley and especially the area north of Bagdad are ahead (red) normal phenology.  

"Life in itself 

Is nothing,

An empty cup, a flight of uncarpeted stairs.

It is not enough that yearly, down this hill,

April

Comes like an idiot, babbling and strewing flowers."

-from Spring by Edna St. Vincent Millay

Rangeland Analysis Platform

New data source for In-Season NDVI:  Rangeland Analysis Platform. (RAP) https://rangelands.app/rap/ 

RAP allows mapping of Cover and Biomass, and generates reports for an Area of Interest for Cover, Annual biomass, and 16-day biomass.  I'm hopeful they will upgrade the map to include 16-day biomass.  If they did, I could add it to the comparisons of the other NDVI sources.  Mapping would allow in-season management decisions based on forage production.

Case Example: Dugas, AZ

This series of years from 2018-2023 shows the variability in biomass production by season in a desert grassland at mid-elevation (4,000 ft) in AZ:

2018 shows a drought year, when there was little to no spring green-up due to a lack of winter precipitation, and a low green-up in response to summer monsoons.

2019 and 2020 were the "nonsoon" years, when the summer monsoons failed to materialize.  However, because the winter rains were good in 2019 and exceptional in 2020, total production was high.

2021 and 2022 show the potential for growth in years of good monsoon rains.  2023 shows a "normal" year with bimodal peaks in production corresponding to the spring green-up peaking in late March, and the summer monsoons peaking in mid-August.  However, for some reason this year had almost no annual biomass production associated with the monsoon.  Each year is different!

----

Case Example: Congress, AZ

This series from around Congress, AZ shows the extreme variability of plant growth in the Sonoran desert (2,500 ft).

In drought years like 2018 and 2022, there is almost no plant growth, whereas the extreme winter precipitation year of 2020 annuals produced almost 130 pounds/acre of spring growth.  None of the years hadmuch perennial herbaceous production, and monsoons inconsistently produce up to 40 pound/acre of growth in good years.  

----

Case Example:  Grand Canyon Junction 

SR-64 and SR 180 intersection, just south of Grand Canyon high-elevation grassland (6,000 ft).

Maximum production compared to the lower elevation sites is lower, only reaching 50 pounds/acre in good years.   However, total annual production is usually more consistent.  There is still the potential for bimodal production peaking in the late spring (early June) (2023 and 2017, not shown) and in the monsoons.  The monsoon peak seems to be most consistent, except in 2019 and 2020 when the monsoons failed - luckily those years had relatively good spring growth.   

In contrast to the low desert site, annual production (red) is usually less important than perennial production (green) at this site:

Spring Update: NDVI Differences

Last September, I wrote about Finding the Greenest Place in AZ.  This Spring, we have continued to compare and evaluate the different NDVI difference mapping applications and compare them to the actual growth of wildflowers and grasses we see when we go out hiking.  

Methods

I conduct pre-field research to identify predicted greenness/moisture from UA's Droughtview, USGS VegDRI, and NWS Accumulated Precipitation.  I take a screenshot of each product and assign the proposed site a scale from 1 (driest) to 10 (greenest).  We then visit the area and evaluate the plant production, recording example photos of overall landscape greenness, as well as assigning a score.  The data are organized in a OneNote table.  I then compare the numerical scores in an excel table, adding up the differences between each model and the observations.  

Results

So far, the UA model seems to slightly overestimate greenness, the USGS model greatly underestimates greenness, and the NWS precipitation record comes out closest to observation.

For the UA model, I think it might be helpful to have a difference from maximum, instead of the difference from period. The latter overestimates early spring greenness when the denominator NDVI is very small, so any amount of NDVI in the numerator saturates the index.  Using the maximum NDVI for that pixel could help with this phenology issue.  Plus, % of maximum NDVI may be more intuitive than “difference from average”.

The USGS VegDRI index consistently estimates pre-drought to severe drought in areas that have above average precipitation this water year and have an NDVI above average.  This leads me to think that VegDRI 7-Day eVIIRS is either not well calibrated to the desert southwest, or perhaps that it is better used as a predictive index – perhaps these areas are drying out even though they currently appear green?  However, SWCC does not show significant vegetation drying yet in the areas I assessed.  

Examples

Wingfield Mesa:  UA Droughtview shows this area at maximum NDVI (for this time of year)(=10/10), USGS showed it as pre-drought to moderate drought (4/10) , and NWS shows 125-200% of normal year to date precipitation (9/10).  It is quite green, but it is still early in the growing season and the mesquite have not leafed out yet.  We rated it 6 out of 10.  

Dugas Rd:  UA shows above average (8/10), while USGS showed Moderate drought (3.5/10) and NWS showed slightly below average precipitation (75-90%) (3.5/10).  It is quite green right now, but again not quite at maximum greenness production.  We rated it 8 out of 10.   

Finding the Greenest Place in Arizona

 I love plants, and I love places where plants are happy.  But how can I find the greenest places, especially when I live in Arizona?

In the Southwest deserts, plant phenology and growth are dependent on intermittent rains.  But the rains can be variable between nearby areas. 

Rainfall totals and percent of normal can be viewed using https://water.weather.gov/precip/ This uses observed (radar) precipitation. 

Displaying Last 90-Day Percent of Normal Precipitation from Sep. 20.

However, more important than total rainfall over the growing season is timing of rainfall.  A single deluge that brought 3 inches last week to an area that hasn't seen rain in 6 months, is not as effective in stimulating plant growth as regular weekly or biweekly 0.5 inch storms.

A better approach to locating areas of high plant growth is using NDVI (Normalized Difference Vegetation Index), or observed "greenness" from satellites.  Data updates every week or couple of weeks, depending on the satellite.

But some areas, like forests, will always look greener than other areas, like deserts.  What we really want to locate are areas of anomalous greenness.

This UA webpage calculates departures from last week, last year, and from the average year.

VIIRS NDVI difference from average from Sep 6.

Other resources include the USGS Vegetation Drought Model, VegDRI.  The Vegetation Drought Response Index (VegDRI) shows seasonal drought impacts on vegetation. The weekly index is produced using a model trained on the self-calibrated Palmer Drought Severity Index using MODIS satellite-derived greenness anomaly integrated with precipitation, land cover, soils, and other biophysical data sets. Updated every Monday by 10 AM. 

VIIRS NDVI as shown by the USGS VegDRI model.

Clicking on the map generates a time series, which can be useful to compare seasonal and yearly changes at a location.

Graph of VegDRI drought index for a location in Yavapai County over the last 4 years.

The United Nation's FAO also maintains an NDVI anomaly drought mapper that uses the European satellite AVHRR.  However, the data is only provided at the scale of entire nations.  

So where is the greenest place in Arizona?  As you may have noticed from the screenshots above, that depends on who you're asking!  I hope to "ground-truth" some of these models to learn which are most reliable for greenness-hunting.

Phenology, Accumulated Growing Degree Days, and Soil Moisture

US Crop Calendar

Source: https://ipad.fas.usda.gov/countrysummary/Default.aspx?id=US

Arizona had a good year for NDVI

Source: https://glam1.gsfc.nasa.gov/

NASA SMAP data.  Data is global.

This mapped layer is delayed by 2 weeks.  I haven't found a layer that shows real-time moisture.

NPN Visualization tool can view Historical, Current, and Anomaly Accumulated Growing Degree Days. Data is only for USA.

Source: https://data.usanpn.org/vis-tool/#/explore-phenological-findings