"Average" Rainfall in Arizona

 NOAA National Water Prediction Service reports daily, weekly, monthly, seasonal, and yearly accumulated precipitation and the totals can compared to "normal".  Their data access website states that normal precipitation is defined as the 30-year PRISM normal from 1981-2010.  The Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate mapping system developed at Oregon State University are considered the most detailed, highest-equality spatial climate datasets currently available.

But is the average still the same in the 16 years since 2010?

Methods

I downloaded total accumulated precipitation for water years ending September 30 for the last 10 years.  Unfortunately, the files for many of the years were corrupted and I was only able to look at the three years from October 2022-Oct 2025.  

I clipped the rasters to the Southwest US and took the mean for each grid cell.

Results

Over the last three years, some areas of the Southwest have had 50% of normal precipitation, and other areas have had 150% of normal. 

NOAA NWPS % of normal 2023-2025

Color key:

Zooming in to the area around Prescott, Arizona:

NOAA NWPS % of normal 2023-2025

Conclusions

Most of the area around Prescott is between 85-95% of normal (medium orange), confirming the presence of long-term drought.  Areas less than 75% of normal (dark orange/red) include much of the Bradshaw mountains around Prescott, the Dugas area along I-17, the Mogollon Rim - east of Payson and from Sedona to Ash Fork, and the south slopes of the San Francisco mountains around Flagstaff.

However, there are significant areas between 115 and 125% of normal (light green), including some areas above 125% of normal (dark green).  These above-average areas include the Juniper mountains and Upper Verde River, the Santa Maria river near I-93, a bit of Sedona and Schnebly Hill to Mormon Lake, and the north side of the San Francisco mountains. 

This shows how spatially heterogeneous average rainfall can be, and how a simple description of a state as being in drought or not can be misleading. For example, the excellent Drought Aware web app from Esri shows broad swaths of Arizona currently in drought, but my analysis shows that there is likely much more spatial variability.  

Desert People Without Water

I recently visited the ruins at Honanki and Palatki.  These are prehistoric settlements built into the red rock cliffs near Sedona, AZ.  Today, the people who built these dwellings are called "Sinagua", which comes from Spanish for "without water".  But everyone needs water, right?  I wondered where these people got drinking water.

I looked for springs around Honanki and Palatki and didn't find any.  That's weird!

Zoom in to see locations of Honanki (H) and Palatki (P) in relation to USGS-mapped springs (blue) and NAU-mapped springs (green).

Although springs have dried up in recent times, the USGS spring data was mapped in the late 1800s / early 1900s when many more springs were flowing.  It looks like the geology of the Sedona Red Rock cliffs just don't produce springs.  So even if the location of springs was different 800 years ago, it would be surprising if there were springs in the cliffs where these people lived.

The closest mapped spring (blue dot = unconfirmed water source) is 1.5 and 2.7 miles away, respectively, but there is no evidence of water in the aerial imagery.  The next closest (green dot = confirmed water source) is 4.7 and 3 miles away, respectively.   Neither Palatki nor Honanki is even built in one of the larger drainages that might flow more often/longer; the drainages that feed their valleys are quite short.  

I don't think these settlements had access to aboveground water throughout the year unless they dug wells or used cisterns to store water.

These and other prehistoric communities in the desert Southwest often built cliff dwellings high above canyon floors, far from surface water sources.  Archaeologists believe these people collected runoff during rainstorms using check dams and seeps, and stored water in cisterns or ceramic containers for later use.

Across the prehistoric Southwest, populations used ingenious methods to exploit scarce water:

• Rock overhangs and cisterns captured and stored rainwater.
• Seasonal mobility allowed families to occupy dry sites part of the year.
• Terraced fields, check dams, and soil-retention walls conserved moisture for crops.
• Small permanent settlements clustered near ephemeral water sources, such as seeps and seasonal pools.

In conclusion, while many large settlements in the prehistoric Southwest were built near springs or rivers, groups like the Anasazi, Sinagua, and others developed highly effective ways to survive in water-scarce environments through dry-land agriculture, runoff collection, and strategic mobility.

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 Estimate Emissions from Land Use Change

This blog post highlights the valuable role played by GIS layers in planning and complying with upcoming GHG reporting standards. New protocols will classify carbon released from land use changes as Scope 1 emissions, requiring stricter tracking.

There are several GIS layers (reviewed below) that can be used to estimate potential carbon emissions from biomass and soil carbon losses due to land development projects. While these layers may not be suitable for final reporting, they can be valuable for:

• Strategic planning: Identifying areas with high potential emissions and prioritizing mitigation efforts.
• Impact Assessment: Estimating the range of carbon emissions from projects.

These GIS layers, available in Esri's ArcGIS Online Living Atlas, have the potential to improve the ability of large businesses to plan for and comply with upcoming regulations related to land use change emissions.

UNEP Above and Below Ground Biomass Carbon 

Two datasets represent above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for the entire globe (2010).  The first layer layer estimates total biomass (i.e. plant parts such as roots, leaves, trunks) whereas the second layer includes soil organic carbon (SOC) and is therefore weighted to show the contribution of peat and permafrost-contained regions.  Both layers support direct analysis in GIS software.    

Left: First dataset shows plant biomass with large concentrations of C in the world's forests.  Right: Second dataset includes SOC and shows the large amounts of C in the world's arctic peat and permafrost.

USFS Predominant Major Forest Carbon Pools of the Continental United States

This layer layer depicts the predominant major forest carbon (short tons per pixel) pools of the Continental United States. The layer used USFS Forest Inventory & Analysis plot data and Landsat 8 Operational Land Imager scenes as inputs to an ecological climate model to estimate Live, Dead, and Organic Soil carbon pools.  However, the data is somewhat difficult to analyze because each pool is in separate raster image bands, and because the metric reported is short tons per pixel, where the pixel size varies across the map based on Web Mercator projection.  

USFS offers a faster and easier to use layer called CONUS Total Forest Carbon 2018, which provides short tons Carbon per pixel summed across all 8 individual carbon pools.  

Around Prescott, AZ the pixel size is 80 by 80 ft, so each pixel value must be multiplied by 6.8 to get tons per acre.  It looks like most of the pixels show a carbon pool of 30-36 tons/acre in this area.

To compare this layer with the UN layers mentioned above, it is necessary to convert US tons to metric tons and acres to hectares.  Overall this yields a correction factor of 2.24 to get from tons/acre to metric tons/hectare.  This yields a range of 67-80 metric tons/hectare carbon based on the USFS layer.  The UN Total Biomass layer estimates anywhere from 38-50 metric tons/hectare, while the UN layer that adds in SOC estimates 120-160 metric tons/hectare.  It seems that the USFS map estimates carbon pools in between these two ranges.

Northern AZ pine forests viewed in the USFS Forest Carbon layer.  It is not always clear how to interpret this data.

Biodiversity in the United States

Summary

NatureServe's Map of Biodiversity Importance (MoBI) is actually 4 main maps and 53 supporting maps.

The four maps showcase different aspects of biodiversity in the United States using Geographic Information System (GIS) technology. The maps are all based on data from NatureServe, which aims to assess the status and distribution of biodiversity across the United States. Each map uses a different metric to measure biodiversity and provides valuable insights into different facets of the complex and multifaceted concept of biodiversity. These maps can be used to inform conservation efforts and guide land use decisions to protect and preserve biodiversity in different regions of the United States. However, it is important to note that each map provides a limited view of biodiversity, and a comprehensive understanding of biodiversity requires consideration of multiple factors and metrics.

For example, it is difficult to determine which map shows the highest biodiversity in Arizona because each map uses a different definition of biodiversity. Map #1 shows the richness of imperiled species in the United States, but does not provide a total biodiversity count. The highest value in Arizona for this map is 11. Map #2 shows the summed range-size rarity of imperiled species in the United States, which measures the presence of imperiled species with small ranges, but does not necessarily capture total biodiversity. Maps #3 and #4 focus on areas of under-protected biodiversity importance of imperiled species and may not be as useful in determining total biodiversity.  It would be helpful to have a map that shows the total biodiversity of an ecoregion, which is not captured by any of these maps.

More resources

MoBI ESRI Overview

 Story map 

YouTube The Map of Biodiversity Importance | Dr. Healy Hamilton's Presentation at 2020 Esri UC

Detailed notes on each map

Map #1: Richness of Imperiled Species in the United States

1) Richness of Imperiled Species in the United States: link

a. High values identify areas where more imperiled species are most likely to occur.

b. Richness values are simply a tally of the number of species with habitat overlapping a cell.

c. Values range from 0 to 31, but the color ramp maxes out at 11.  

a. Highest value in AZ is 11.

b. Tenneessee has highest value upstream of Chattanooga and Knoxville along the Clinch river in the Cumberland river valley.

d. This is the prettiest map and easiest to interpret.

e. Most of the Sonoran desert has 0 imperiled species?  What about Pygmy owls?Sandhills east of Carlsbad only have 1 imperiled species (lesser prairie chicken).  What about lizards?  What about rare plants?

Map #2: Summed Range-size Rarity of Imperiled Species in the United States

2) Summed Range-size Rarity of Imperiled Species in the United States:  link

a. High values identify areas where species with very small ranges (and thus fewer places where they can be conserved) are likely to occur; the presence of multiple imperiled species contributes to higher scores.

b. Range-size rarity for each species is the inverse of the total area mapped as habitat. Summed range-size rarity is the sum of the range-size rarity values for all species with habitat that overlaps a cell.

c. The range for RSR values in cells containing species habitat is 0.0000002784 to 1.44584. A single species can have a value as high as 1.020335, which means just one 990-m cell contains all habitat for that species.  The RSR score for a species with habitat in two 990-m cells is 0.510167. 

d. This map is probably most important for conservation, and because its harder to interpret maybe is easier to use…less questions!

 Map #3: Protection-weighted Range-size Rarity of Imperiled Species in the United States

3) Protection-weighted Range-size Rarity of Imperiled Species in the United States: Link

a. High values identify areas where more unprotected, restricted-range species are likely to occur.  

b. Weighted based on how much of range is in protected areas.

c.  Each species was assigned a PWRSR score equal to the product of range-size rarity and the percent of habitat that is unprotected. The PWRSR raster sums these scores for all species with habitat that overlaps a cell.

d. Note:  Data based on USGS "GAP" analysis.  "Protected areas" include Wilderness and National Monument (GAP 1 and 2), but not Federal lands open to extraction like National Forests and BLM (GAP 3). 

Map #4: Areas of Unprotected Biodiversity Importance of Imperiled Species in the United States 

4)  Areas of Unprotected Biodiversity Importance of Imperiled Species in the United States: link

a. Values of “1” identify areas where under-protected and range-restricted species are most likely to occur, including areas where the presence of multiple imperiled species contributes to higher scores

b. This is the same as #3 above, but with a cutoff value to make the map black and white.

c. AUBIs (Areas of unprotected biodiversity importance)

Mapping Species Habitat with Appropriate-Sized Buffers

 Previously, I wrote that this Story Map shows small polygons of habitat as buffers around representative observations.  However, the actual locations are not accurate because the underlying observation data has been randomized to protect populations of rare species. 

The first map ("Preliminary Conservation Zones" and "Potential Dispersal Zones" for the American, Rusty-patched, Suckley's, and Western bumble bees) shows the correct kind of critical habitat (buffered observations) USFWS has designated for rusty patch and would likely designate for other proposed species, but the locations are incorrect.  For example, the mapped locations of Rusty patch on that map do not line up to the USFWS GIS for rusty patch critical habitat. 

 

Some of the other species may be are incorrect as well, depending on whether the data source (GBIF) considers the species endangered and so randomized the locations within a 0.2 degree lat/long box.  That seems to be the case for the Western Bumble bee, but not the American bumble bee. 

 

The map shows a mix of accurate and inaccurate, specific habitat points. This is confusing and potentially misleading, if the intent is to facilitate conservation planning.  For example, when I zoom to an area of interest, I might think there is no mapped habitat there. But if there is some nearby, I can't tell from if that habitat is or isn’t within my area of interest.

 

The easiest fix would be to increase the size of the buffers so that they include the entire randomized area (0.2 degree, lat/long) that each point comes from.  A note could say that critical habitat would likely be designated in a subset of those larger polygons based on the buffer size USFWS decides.

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