How Accurate Are Digital Thermometers?

I wanted to know my temperature, so I would know when I have a fever.  I was also curious how my temperature compares to "normal" people.  

But first, I had to figure out how accurate my digital thermometer is.

Accuracy is how close a measurement is to the true value; precision is how much variability there is in repeated measurements.  The thermometer says it is accurate to within +/- 0.2 F, but I have no way to test that because I have no other measurement of my temperature to compare it with.  I can test the precision by taking multiple readings in a row.  This usually yields the same number, indicating that the precision is very good (i.e. low variability).  However, sometimes, depending on how I hold the thermometer, the readings can vary by as much as 0.4.

What is my normal temperature?  

To test this, I took my temperature over 10 days in April, 2024.  I took 48 readings, a total of 3-4 for each hour of the day.  For each reading I lay down for a minute and then put the thermometer in my mouth, as far back under the tongue as possible.  Each reading took 90-120 seconds, so this effort included more than an hour of total time spent taking my temperature.

My average temperature is 97.4, almost a full degree less than the widely-quoted population average of 98.2.  Standard deviation is 0.6 degrees, so 68% of the time my temperature is between 96.8 and 98.  This variability include any precision variability in the thermometer.

My temperature usually reaches a minimum in the morning, rises to a maximum is in late afternoon / early evening, and then begins to fall before bedtime.  My highest temperatures were recorded after exercise, doing chores, eating a warm/fatty dinner, and lying in the sun.  Minimum temperatures were recorded lying in bed, after exercise, and after breakfast.  

Monarch Monitoring Results

 Here's a cool synthesis paper using data from the Integrated Monarch Monitoring Program (IMMP).  The headline conclusion is that ROWs had the most milkweed plants:

Site Type: ACL, Agricultural Conservation Land; DEV, Developed; PGS, Protected Grassland; ROW, Rights-of-way; UGS, Unclassified Grassland. 

However, this paper excludes Asclepias subverticillata (and A. verticillata), the most common milkweed species in our area.  Apparently this was so they could apply their total stems to the work of Thogmarten et al that calculated how many stems of milkweed are needed to support stable monarch butterfly populations.  But, since the main results of this paper compare milkweed plants between different site and habitat types, they should at least show what this analysis would look like with A. subverticillata.  

Actually, most of the paper's results are focused on Eastern U.S., and there is a companion Western paper forthcoming, but that is not obvious from the Abstract.  So excluding a common western milkweed plant may not have changed most of the results.  

What's also not obvious is that none of the figures show actual data, they only show the model results derived from the data.  Apparently that is how ecology works now-a-days:  create some kind of bespoke complicated statistical model and don't even bother to plot the underlying messy real-world data...

Citation

Front. Ecol. Evol., 23 May 2024

Sec. Conservation and Restoration Ecology

Volume 12 - 2024 | https://doi.org/10.3389/fevo.2024.1330583

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.  

Cutting Trees for Water: Are Thinned Forests Wetter or Drier?

Forest thinning can be controversial.  Concerned citizens want to know when logging counts as restoration;  can thinning a forest have beneficial ecological effects beyond reducing the risk of stand-replacing wildfires?  Will cutting trees increase soil moisture because there are less "straws sucking up water", or does it decrease soil moisture due to increased windspeed and more sunlight drying out the forest understory?

April 2017 - views of Rogers Lake, AZ overlooking untreated (left) and treated (right) areas.  Photos by Conor Flynn.  Click this link to play with the slider.  

Whether thinned forests are drier or wetter is complicated.  The excellent paper "Adapting western North American forests to climate change and wildfires: 10 common questions" by Prichard et al provides a good introduction to this question:

"Decreasing canopy bulk density can change site climatic conditions (Agee and Skinner 2005). Wildfire ignition potential is largely driven by fuel moisture, which can decrease on drier sites when canopy bulk density is reduced through commercial thinning (e.g., Reinhardt et al. 2006). Reduced canopy bulk density can lead to increased surface wind speed and fuel heating, which allows for increased rates of fire spread in thinned forests (Pimont et al. 2009, Parsons et al. 2018). Other studies show no effect of thinning on surface fuel moisture (Bigelow and North 2012, Estes et al. 2012), suggesting that thinning effects on surface winds and fuel moisture are complex, site specific, and likely vary across ecoregions and seasons."

Anecdotally, some people have noticed springs beginning to flow again after thinning and prescribed fire in AZ.  My research in NM pinyon noted increased soil moisture at thinned sites (unpublished data), however this could be due to the specifics of how thinning was accomplished at those sites.  Thinned slash was chipped and the chips were left on-site without follow-up prescribed fire.

In addition to water quantity, water quality should also be considered.  Prichard et al point out that "Treatments in watersheds that are distant from the WUI and protect municipal and agricultural water supplies are critical to minimizing high-severity fire impacts that can jeopardize clean water delivery (Bladon 2018, Hallema et al. 2018). For example, post-fire erosion and debris flows may cause more detrimental and longer-term impacts to watersheds than the wildfires themselves (Jones et al. 2018, Kolden and Henson 2019)."  However, even carefully managed thinning and prescribed fire can generate excess erosion from new roads, decreased large woody debris, and increased mobility of light charred wood.  Charcoal washing into local lakes can cause fish kills, even when not generated by catastrophic wildfire.  Creating erosion-control structures as part of forest thinning work could help to mitigate these risks.  

Further research is needed to ensure that large thinning projects adequately account for water cycle restoration in addition to natural stand density and fire interval restoration.  

Rain Walks

This is a story from Paul Krafel that I think should be more widely known and celebrated.  Thank you, Paul.

Rain Walks

A simple play I’ve made hundreds of times exemplifies “every play is two plays.” High in the drainage, as runoff increases, the rising runoff begins overflowing its shallow channels, spreading out into easily overlooked overflow routes. Sometimes a rock lies in the overflow channel, obstructing how much of the runoff can flow that way. I lift the rock out of the channel so that more of the runoff can flow in this new direction (Play One). I then place that rock in the main channel so that it shunts more of the runoff towards the overflow channel (Play Two). 

This simple moving of the rock is two plays. The broader, slower overflow route receives more runoff because of the removal of the rock, and the deeper main channel receives less because of the new placement of that rock. Though much of the water still flows down the main channel, more is now flowing along the broader, slower overflow route.

Rising water has a distinctive appearance. Surface tension holds the water’s surface against plant stems and rock edges so that this ring of contact lags behind the rising level of the surrounding water. This creates a dimpled surface around each stem and rock sticking up out of the water. These dimpled surfaces sparkle with focused light. I can watch this dance of light advance with the increased flow down the overflow route.

Read more here...

Another interesting article by Paul here.  

The case for using geography to understand phenology

 Alexandra Permar and Conor Flynn collaborated on this project.

Calochortus is an astonishingly diverse genus of flowering monocots, with common names that include Mariposa lily, Sego lily, and globe lily, as well as pussy ears, fairy-lantern, star-tulip, and others.  Their center of diversity is in CA, which has 48 out of the 78 total species in the genus.  In Arizona, there are 6 species, ranging in color from white, to pink, to yellow, and sometimes orange, with diagnostic darker markings on the inner base of the petals.  

    

    This genus in Arizona makes an interesting case study of biogeography and phenology because each species occurs in a relatively distinct (but overlapping) elevation and geographic location.  Also, because the flowers are very showy, there are more than 2,400 Research-grade observations documented on iNaturalist.   

    The plant grows quickly from underground tubers in the spring, flowers quickly, and then dies back; the growing stem and then the seed heads are relatively inconspicuous and are rarely photographed.  This means that almost all of the iNaturalist observations are of the flowers, which makes studying the phenology easier because there is less work needed to annotate images as flowering or not flowering.  Nevertheless, we still reviewed observations and annotated and removed any photographs that didn't show flowers from the phenology analysis below.  

Methods 

Research-grade Calochortus observations in AZ were exported from iNaturalist and processed in Excel and graphed in Excel and Tableau.  iNaturalist Research-grade observations are used for research, but observations with inaccurate locations can lead to misleading analyses and must be removed.  

A total of 2350 observations were exported.  Taxa were classified by species (even though subspecies IDs exist for several hundred observations).  Cleaned records with geopositional accuracy > 1000 m (excluding blanks; there are 594 blanks).  Removed 61 records with geoprivacy obscured. (In iNaturalist, obscured geoprivacy impacts the geopositional accuracy of an observation to +/- 500 square kilometers.)  8 observations were noted as not flowering and were removed from analysis.  After cleaning, the database contained 2066 records (observations).  This shows that about 88% had location precision that met our inclusion criteria.

 Calochortus kennedyi had the most observations (782),  C. ambiguus and C. flexuosus tied for 2nd most observations (534), while C. aureus (148) and C. nuttallii (61) had the least number of observations. 

Geocoding Elevation

iNaturalist does not record elevation of observations, so it is necessary to intersect the observation points with a Digital Elevation Model in GIS.  Observations were added to ArcGIS Pro and displayed using their XY coordinates.  Connect to elevation in Esri AGOL Living Atlas : Ground Surface Elevation - 30m (image service / raster DEM).  Use Geoprocessing tool Extract Multi Values to Points ( Need Spatial Analyst license).  Copy resulting data back to excel. 

        Tableau Public was used to graph data.  Calochortus Bio-Geo-Phenology | Tableau Public

Results: Biogeography and Elevation

    Ordered by average elevation, Calochortus kennedyi and C. flexuosus are the lowest elevation species, C. aureus, C. ambiguus, and C. nuttalli occur at middle elevations, and C. gunnisonnii occurs at the highest elevations.  

iNat allows visualization of biogeographic comparisons.  (Or new compare tool) 

C. kennedy (blue) is clearly visible at lower elevations along the foothills of the AZ mountains, and then extends to the West beyond the distribution of any other species.  C. flexuosus (green) is also visible to the NW of AZ, extending through central AZ in the lower elevation valleys of the Verde, Salt, and Gila rivers.  C. gunnisonnii (purple) is clearly visible in Colorado, barely extending down to the high country in AZ.  C. aureus (pink) stakes out a unique territory on the Navajo nation in NE AZ and SE Utah. C. ambiguus (red) is found throughout the mountains and high country of AZ.  

 Graphing both latitude and elevation help to illustrate this biogeographic comparison.  In the graph below, it can be seen that C. kennedyi is the most southerly of our Calochortus, and occurs at the lowest elevations.  C. flexuosus occurs farther north, but also at low elevations.  C. ambiguus marks out a consistent territory at higher elevations than other species, depending on elevation. Gunnisonii is a clear outlier, occuring only at high elevation in our study area.  There is a fair amount of overlap between C. aureus and C. nuttallii.

        This biogeographic comparison can help differentiate similar-looking species.  There are 4 white Calochortus in AZ.  C. gunnisonni is clearly only a high elevation species, although C. ambiguous can also occur at high elevations.  In the northern part of the state, C. flexulosus occurs at lower elevations, C. nuttallii at middle elevations, and C. ambiguous only occurs at the highest elevations. 

Results: Phenology

Overall, for Calochortus species in AZ, flowering begins at low elevations around week 13 (last week of March) and progresses to higher elevations, generally wrapping up around week 29 (third week of July).  Flowering species composition changes from C. flexuosus and C. kennedyi to C. ambiguus, with C. aureus and C. nuttalli thrown into the mix.  C. gunnisonni (barely visible at the far upper right) is, of course, the last to flower.  This chart also highlights how C. aureus is mainly restricted to 1600-1800 meters ( 5200-5900 feet).  

    Calochortus ambiguus shows the strongest relationship between elevation, latitude, and flowering date.  Based on this scatterplot, we were able to confirm that the location is not accurate for the observation at the far left side of the plot. 

Calochortus kennedyi, C. aureus, and C. flexulosus do not show as strong of a pattern, and C. gunnisonnii did not have enough observations in AZ to analyze.  

Example of C. kennedyi:

Graphing flowering week of C. kennedyi separately for Latitude and for elevation shows that there is some pattern, but it is clearly quite week.  The R squared values for these charts are 0.22 and 0.12.  Interesting, it looks like flowering actually starts at middle elevations (around week 11), then flowers appear at lower and higher elevations (week 16), until finally only the higher elevations are still flowering (week 20).  C. aureus and C. flexulosus show somewhat similar patterns (data not shown).  

Discussion

    There are many possible ecological and data collection reasons for weak r values:

1) Micro-site variation: south-facing aspect may have more impact than elevation.  Whether a site is forested, if there are shadows from cliffs, etc.

2) Genetic variation:  if a single species showed up with 2 distinct populations, that would be evidence for possible speciation or subspecies, but all populations showed continuous variation.  There may still be genetic variation between meta populations, or within single individuals.

3) Flowering period not known: our records only show flowering presence, not the start of flowering or the total period of flowering.  Correlations might be better if we had complete data, but our data is not complete: we only have point observations.  For example, if species A flowers from April 1-30 and Species B flowers from April 28-May 28, a visitor on April 28 would record both species as flowering at the same time, even though they do have distinct flowering periods.  

Joshua Trees Flower Episodically in Arizona

Intro

Joshua trees are large, visually-striking trees in the Mojave desert.  Most of them are in California and Nevada, but there is also a population in the western part of Arizona.  They are frequently photographed and there are more than 17,000 observations (1,000 in AZ) saved on iNaturalist, a website and app used to document biodiversity.  

Arizona Joshua Tree locations observed on iNaturalistiNaturalist.

Joshua trees produce showy clusters of white flowers at the tips of their branches in March, but this year I noticed they were not flowering.  I wondered how often they produce flowers and decided to answer this question using data from iNat.

53 observations in Arizona showed evidence of flowering, always in March and April.  

Methods

iNat observations of Joshua Trees (Yucca brevifolia) in Arizona were marked using the Plant Phenology option in the Annotation Field.  Plant phenology (flower budding, flowering, or fruiting) was determined based on visual inspection of the saved photos in iNat.

The iNat website has a nice phenology visualization tool.  This view is filtered to show only Arizona observations.  However, it cannot show differences in phenology from one year to the next.  

Observations were then filtered on the iNat Explore page by adding &term_id=12&term_value_id=13 to the URL and downloaded for analysis in Excel.

More info about using iNat search URLs.

Results

I graphed the results starting in 2017, because there are fewer iNat observations before 2017.  It appears Joshua Trees flower episodically.  According to this data, Joshua Trees flowered in four out of the last 7 years.  They appear to alternate years from 2017-2020, but then skipped 2021 and flowered in both 2022 and 2023.  

It is interesting to note that the Arizona Joshua Trees didn't flower in 2020, which was one of the wettest springs in the last 10 years. I wonder whether the spring moisture determines flowering, or if perhaps other climate variables, such as moisture in the fall, are more important.  Another possibility could be that the trees are only able to flower every other year, and that the trees were inhibited from flowering in 2020 because of the large number that flowered in 2019.  

According to this data they were able to flower in both 2022 and 2023, but at reduced numbers both years.  Perhaps the plants flowering in 2023 were different from the trees that had already flowered in 2022?  I can't answer that question with this iNat data.  While it appears that the areas of flowering in 2022 and 2023 were the same, not all of the Joshua trees in a given area necessarily flower, even in good years...

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.

The Biggest Problem in Conservation: Taxonomy

One of the interesting unspoken secrets of the conservation world is that taxonomists are in charge.  More specifically, what taxonomists consider interesting enough to name as a species or a subspecies determines what can be protected.  After all, it is the Endangered Species Act.  But what if the taxonomists can't agree on what a species is?

This excellent short article in the Atlantic provides examples form hawthorn trees, which, depending on who you talk to, are either in decline and in need of conservation, or so widely distributed and common that it would be like trying to preserve Kentucky Bluegrass.

"A few years ago, conservation groups were gearing up to assign the [balsam-mountain hawthorn] tree the rarest rank a species can receive, which would imply an urgent necessity to conserve it. But [a botanist] decided it was probably a hybrid of two other hawthorns. He still believed the tree should be protected, but instantly, the species went from critically rare to nonexistent, from a conservation point of view."

"A prominent evolutionary biologist, wrote in 1976,  that perhaps no true hawthorn species exist at all—that they make up a sort of genetic continuum that doesn’t allow for coherent species classification."

"[another] botanist... told me the biggest threat to the trees is not land-use changes but botanists themselves, who are unwilling to meet the taxonomic challenge. If no one takes on the task of categorizing hawthorns, then no conservation group can take any measures to save them."

"Now whatever solution [the botanists] come to will determine what we try to save."

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