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. 

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...

Land Development Releases Greenhouse Gases

Land use change releases stored carbon and should be counted under Greenhouse Gas (GHG) reporting.  

Example of a wildflower meadow (left) that was bulldozed to create a parking lot (right). This land use change results in direct emissions of stored soil carbon and plant biomass, as well as continuing opportunity costs: the meadow can no longer accumulate sequestered carbon. If this land is owned by the developing company, this would count as Scope 1 Emissions under GHG reporting requirements.

New GHG reporting standards for land use change are due to be finalized in 2024. According to these new standards,

"Companies shall:

-Account for land use change emissions from land carbon stock decreases across all carbon pools (biomass, soil organic carbon and dead organic matter).

-Account for and report direct land use change (dLUC) emissions or statistical land use change (sLUC) emissions in scope 1, scope 2, and scope 3."

This is important because, according to the IPCC AR6 (2023), land use change accounts for approximately 15% of anthropogenic emissions.  Interestingly, the parts of the land and ocean that have not been developed by humans still absorb 30% of our emissions.  As we degrade more and more land and water, the Earth loses this buffering capacity, in addition to the extra emissions created from land use change.

Climate Prediction Skill

The US Climate Prediction Center issues forecasts beyond the normal National Weather Service's 10-14 day window.  They provide weekly and monthly forecasts out to 3 months.  Given the timeframe and the fact that their forecasts cover the entire contintental US, its not surprising that the forecasts are often wrong.  But how wrong?  And is their skill improving over time?

I analyzed their 3 month temperature and precipitation forecast skill using data provided on their "Gridded Seasonal Verifications" webpage.  

Note that skill is measured on a scale from -50 to 100, where -50 would be a forecast that was exactly wrong in every area, 0 would be a prediction that did no better than chance, and 100 is a prediction that was exactly right in every area.  

They provide data starting in 1995.  Since that time in the mid 1990's, linear trendlines show that their forecast skill has slightly improved for both Temperature and Precipitation.  Precipitation skill started out lower, but has almost doubled (from 10 to 20) while Temperature skill started higher but has not increased as much (from 22 to 28).

However, the last 10 years have not been as successful:

Since 2012, neither Precipitation nor Temperature skill have increased.  In fact, mean temperature forecast skill has decreased markedly since 2018.  Before that, Temperature skill had been doing quite well in the period 2014-2018.  It is not clear what changed in 2018.  A similar transition may be happening with Precipitation, where the period 2019-2022 saw consistently good predictions, but since the beginning of 2023 the forecast skill has fallen off a cliff.

With increased use of machine learning, it seems likely that long-term weather forecast skill should increase.  However, complex chaotic weather patterns are most impactful to climate predictions in the 1-3 month time frame, so this area of weather/climate prediction may continue to have lower than hoped for success.  

2021 Arizona Monsoons

 It was a good monsoon in AZ this year, with some locations up 8 to 12 inches above normal (June-September 8).  But due to the uneven distribution of thunderstorms, even in this good year some areas barely received normal!

2020 Disasters

 

https://www.noaa.gov/stories/record-number-of-billion-dollar-disasters-struck-us-in-2020

Australian Wildfires

Extremely large pyrocumulus clouds tower over bushfires in New South Wales and spread over the Pacific Ocean. Sentinel-2A image, December 31, 2019, processed by @andrewmiskelly.  Source.

A pyrocumulus cloud is produced by the intense heating of the air over a fire. This induces convection, which causes the air mass to rise to a point of stability, where condensation occurs. If the fire is large enough, the cloud may continue to grow, becoming a cumulonimbus flammagenitus which may produce lightning and start another fire.  Source.

"Fuels management cannot prevent fires but can change their behavior" but fuels management is limited by budgets and time to burn, especially in droughts." Source.

The BBC has a good overview:

"We’re seeing recurrent fires in tall, wet eucalypt forests, which normally only burn very rarely. A swamp dried out near Port Macquarie, and organic sediments in the ground caught on fire. When you drop the water table, the soil is so rich in organic matter it will burn. We’ve seen swamps burning all around."

"Even Australia’s fire-adapted forest ecosystems are struggling because they are facing increasingly frequent events. In Tasmania, over the past few years we have seen environments burning that historically see fires very rarely, perhaps every 1000 years. The increasing tempo, spatial scale, and frequency of fires could see ecosystems extinguished." Source.


More Info.
Australian Fire Center
Case Study / Educational Info

Climate Change Belief Tree

An article in Nautilus magazine analogizes beliefs about climate change to branches on a tree.  I like that the tree diagram emphasizes the unity of thinking about climate change, even if we may be on different branches.  Also, I think it is OK for one person's beliefs to span different branches: belief about the future does not need to be certain but can be probabilistic and can change from one day to the next. 

Diagram and original article by Summer Praetorius.

The article concludes: "What if instead of feeling threatened by differences in opinion, we were to reconceptualize them in much the same way a tree will distribute a canopy to collect as much sunlight as possible—as a multi-pronged approach to getting the job done? In the same sense that both fast and slow processes contribute to Earth change, both steady progress and immediate local action will contribute to climate solutions. Let’s take stock of our pace and work together, thankful there is someone else to fill the space we can’t. After all, we are not lone trees, but a living, connected forest, and balance is essential for stability."

Logging to Save a Forest from Climate Change

A good article about logging in the forest where I did my graduate research in northern Michigan.  Researchers are using funding from a timber contract with Louisiana-Pacific to cut the aspen trees on part of the experimental forest.  They will study the soil and water impacts of cutting the trees, as well as looking at species composition changes.

In the article, a researcher is quoted as saying that it would be irresponsible not to cut the trees because of climate change.  "Aspen in the Great Lakes region are considered “climate change losers,” according to Nave, and are not expected to fare well as the region’s climate continues to warm in the coming decades."

"The high-emissions scenario projects an 11.2-degree Fahrenheit summer temperature increase in the assessment area by the end of the 21st century. At the same time, summer precipitation is projected to decline by 3.8 inches under that scenario. "

"It will take a decade or more to know which of the aspen-management treatments was most effective, Nave said. It is expected that future generations of Biological Station researchers and students will carry on with the work, he said."

Albuquerque 2016 Monsoon Season

From the ABQ NWS Homepage.

The monsoon began early in Albuquerque this year, with a week of good moisture at the end of June. But then June high pressure returned and most of July was hot and dry.  It wasn't until the beginning of June that the rains reliably returned.  Overall, the monsoon wasn't bad, but the hiccup in the beginning ended up dooming most annual plants.  Only perennials managed to reap the rewards of the late-breaking monsoon moisture.  Now, at the end of September, many monsoonal plants are still trying to finish flowering and set seed.  Many plant species are flowering late and show signs of stunted growth.

Bayesian Statistics

A recent post by Scientific American writer and blogger John Hogan got me thinking about Bayesian statistics again.

My favorite explanation of Bayesian statistics was by Nate Silver in The Signal and The Noise.  The basic approach involves incorporating prior estimates of probability into new measures of probability.  The opposing approach, which does not rely on prior knowledge, is termed "Frequentist" statistics and is exemplified Fisher's standard test used with p=0.05 (which implies that a given result would occur "by chance" only 5 in every 100 such tests).

Hogan uses the standard example of cancer tests to illustrate the importance and power of Bayesian thinking, but an astute commenter points out that the real power of Bayesian thinking comes when used in a process that tests, updates probabilities, and tests again, so that each test incorporates the learning from previous tests.

Silver offered a similar example in his book, but a review in the New Yorker points out that Silver got it wrong.   In Silver's case, he applies Bayesian statistics to the probability that global warming is occuring.  But the prior probability is estimated, and Bayesian approaches only improve on standard statistics when prior probabilities are well known.  So while Silver does present a rational means of updating beliefs, since the original belief is not based on statistical data, the resulting analysis cannot be called statistically valid.

Both the New Yorker review and Hogan's thoughts highlight the inherent power of confirmation bias to trump any statistical test, even Bayesian tests.

Top Conservation of Stories of 2015

Looking back on the year, I feel that victories and gained ground made good News:  US Congress acting(!) to ban microbeads,  Supreme court upheld Clean Power Plan to reduce emissions of  mercury by 1,000 million tonnes and thereby save more than 1,200 lives/year.  CO2 reduction plans from the 2015 Paris COP 16.  Administrative action to create a new office of ecosystem service financing (read: more support for restoration) and to standardize and promote mitigation banking.

However, there were some problems.  The gargantuan natural gas leak in S. Ca. highlighted the fact that natural gas leaks way too much to be a clean bridge fuel.  We either need to clean up natural gas or resolve to skip over it altogether.

A New Precipitation-Evapotranspiration Index to Map Global Drought

Standardized precipitation–evapotranspiration index (SPEI) is precipitation minus potential evapotranspiration (Vicente-Serrano et al 2010).  It is distinguished from other drought indices because it accounts for temperature through modelled evapotranspiration. This website maps global drought:

Drought in the US 2010-2011.

SPEI has recently been used to predict pine mortality in SW forests.  When the SPEI is below -1.68 for at least 11 months, pinyon and ponderosa pines cannot grow and mortality soon follows. (Kolb, T.E., 2015. A new drought tipping point for conifer mortality.Environmental Research Letters, 10(3), p.031002.)

Long-term drought graph for NM.

2015 New Mexico Weather Recap

The ABQ NWS office has an excellent recap of the state's weather over 2015.  For example, here is their summary of the summer monsoon:

The 2015 monsoon season got off to a quick start with heavy rainfall, floods, flash floods and severe weather in mid and late June, as well as the first two weeks of July.  A relatively quiet period ensued for most of the remainder of July. A resurgence of heavy rain returned from very late July through early August.  An outbreak of severe weather was the dominate weather story in mid August, and to a lesser extent on September 9th and 23rd.

By the numbers:  The Albuquerque NWS office issued 53 flash flood warnings between June 15 and September 30.

The biggest news of the year was probably the good precipitation that finally ended the drought that began in early 2011:

Drought conditions developed across New Mexico in early 2011, with few breaks in the drought through 2012, such that much of the state was gripped in the worst drought episode since the 1950s.  Near normal statewide precipitation in 2013 and 2014 did little to improve the drought.  Much of the precipitation in 2013 and 2014 fell during the monsoon season, rather than the much more needed winter mountain snowpack. Finally, New Mexico precipitation in 2015 was above normal for much of the year, and the period January through November was the fifth wettest on record since 1895.  As shown in the graph to the right, precipitation in New Mexico was well above average in January, May, July and October, with only two months below average - August and September.  These wetter than normal conditions supported a steady reduction in the intensity and coverage of the short term drought.  Finally, in early December 2015 New Mexico was drought free!  The last time the state was without any drought status was the week of November 23, 2010! By the numbers:  New Mexico went 263 weeks with a portion of the state in moderate or worse drought!
Source: U.S. Drought Monitor

A Very Strong El Nino

Dr. Jeff Masters discusses.

El Nino in the Spring

March, April, May and the first week of June have been quite wet for the East slopes of the Rockies and the Western Great Plains, with large regions receiving more than three or four times normal precipitation.  Meanwhile, the West has continued its drought, with CA looking especially dry.

NM has significant regions above 400% of normal precipitation.  While there is lush growth in some areas, other areas are not appreciably greener than they might otherwise be.  Sometimes this can be attributed phenology (e.g. to summer grasses not responding to early spring rains, or perhaps the exact timing is important for annual germination), but some must also be due to the severe productivity reduction of overgrazed and eroded soils.

El Nino has strengthened in recent months.  An active fall hurricane season supplyied NM with abundant moisture in the fall, In the winter a steady progression of Pacific storms brought an average amount of precipitation.  And since late May we have already experienced large moisture plumes from yet more unusually-strong Eastern Pacific hurricanes,  Andres and now, currently, Blanca.

Predicting Plant Phenology

Plants in the Southwestern deserts respond to water availability and temperature:

Map from AHPS Precipitation Analysis from January and February

NM is doing good on water so far this year!

Growing degree day map from PNWPest.org

On average NM is about 6 growind degree days (GDD) behind 2014, but 11 days ahead of 2013 and 6 days ahead of normal.

Scientific Statements Made by a Climate Change Skeptic

A recent op-ed in the Wall Street Journal concludes that "Climate Science Is Not Settled", contrary to activists' and scientists' claims that there is no longer even a debate.

The article, written by Steven Koonin, includes a number of interesting statements and is worth a read in its entirety. Unfortunately, responses to the article have not addressed many of his factual claims, so I wanted to list a few of them here.

Please feel free to comment or link to research that addresses or refutes these statements:

1) "For example, human additions to carbon dioxide in the atmosphere by the middle of the 21st century are expected to directly shift the atmosphere's natural greenhouse effect by only 1% to 2%. Since the climate system is highly variable on its own, that smallness sets a very high bar for confidently projecting the consequences of human influences."

2) "But feedbacks are uncertain. They depend on the details of processes such as evaporation and the flow of radiation through clouds. They cannot be determined confidently from the basic laws of physics and chemistry, so they must be verified by precise, detailed observations that are, in many cases, not yet available."

3) "Although the Earth's average surface temperature rose sharply by 0.9 degree Fahrenheit during the last quarter of the 20th century, it has increased much more slowly for the past 16 years, even as the human contribution to atmospheric carbon dioxide has risen by some 25%. This surprising fact demonstrates directly that natural influences and variability are powerful enough to counteract the present warming influence exerted by human activity."

4) "Even though the human influence on climate was much smaller in the past, the models do not account for the fact that the rate of global sea-level rise 70 years ago was as large as what we observe today—about one foot per century."

5) "[these model discrepancies] are not "minor" issues to be "cleaned up" by further research. Rather, they are deficiencies that erode confidence in the computer projections."

Hurricane Odile Rain in New Mexico

Just as September 2013 will be remembered for rain in New Mexico, so will September 2014.  The southern half of the state has been bombarded by a continuation of monsoonal tropical moisture, bolstered by the remnants from Hurricane Odile.

The area around Carlsbad Caverns in particular has had more than 20 inches in the last week, more than any other location in the U.S.  Most of the precipitation influx stalled south of I-40, bringing scant relief for the dry second half of the monsoon we had in August.

September 18-25 Observed Precipitation resulting from Hurricane Odile.  Source.  

Based on a weak, but developing, El Nino this autumn is forecast to continue above-average precipitation.

Review of Soil Moisture Measurement Techniques

Advances in efficient, broad measurement of soil moisture are needed to understand plant stress response to drought.  Crop growth and phenology can be predicted (link) with accurate modeling of soil-plant-atmosphere interactions.  These dynamics are also crucial for advances in meteorology, since most rain that falls in the U.S. is recycled rain that has already fallen and evaporated at least once before, but often several times.  Accurate prediction of rainfall will continue to elude meteorologists until soil moisture can be measured and predicted.

Soil moisture is critical for advancing plant and atmospheric sciences,  but the fact that different measurement techniques yield different values points to the fact that soil moisture is essentially an abstract idea.  While the water content of soil would seem to be straightforward, whether you calculate volumetric or gravimetric water content, and whether you consider chemically- and physically-immobilized water or only plant-available water (field capacity minus permanent wilting point) matters a great deal.

Diagram source.

Spatial and temporal scale also matters.  Do you want an instantaneous point measurement, or a daily weekly average for an entire county’s drought status?  Picking the right tool for the job means understanding the streghths and weaknesses of the entire gamut of technologies capable of reporting soil moisture.  This article will start with traditional in situ point measurement techniques and continue to review broad-scale soil moisture modeling and remote-sensing efforts.

from Shuttleworth 2013

Small-scale measurement can be accomplished using point-sampling with portable soil moisture probes, such as TDR and traditional (active) neutron probes.  Of course, any discussion of soil moisture measurement techniques would be incomplete without mentioning the gravimetric method, or simply weighing a soil sample wet and then dry.  But as with the other point techniques, this method can only measure hyperlocal conditions and must be replicated and averaged to inform landscape-scale management.

TDR, or time-domain reflectometry, uses the electrical properties of soil and water to calculate volumetric percent soil moisture.  For most soils, excluding those with very high organic matter (OM>10%), the TDR method without calibration provides water content in the range from zero to 50% with accuracy better than 1-2%.  While calibration and new TDR such as TRIME-TDR can improve accuracy by a factor of 10-100, the amount of microscale variability in soil means that these point measurements must be replicated dozens to hundreds of times to build up a picture of average site moisture. Microvariability can be important when precipitation preferentially flow along soil heterogeneities such as roots, textural changes, and bioturbation pathways.    Buried probes that use the TDR techniques, such as the Stevens Hydroprobes I used in my graduate research, are fixed in place and are therefore severely limited by their inability to average site variability.

  

Traditional neutron probes work by bombarding the soil with high-energy neutrons and recording the number of neutrons emitted by the soil.  Hydrogen absorbs neutrons so the amount of H2O can be calculated.  This technique solves many of the problems of TDR, but the sensors are expensive and the measurement still must be repeated several times to measure field soil moisture.  

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Meso-scale measurement can now be accomplished using the new COSMOS (Cosmic-ray Surface MOisture Sensor) program to measure whole ecosystem moisture.  Neutron moisture probes have been around for decades but COSMOS uses advances in particle-physics technology to increase sensitivity enough to rely solely on the background cosmic radiation as a uniform source of neutrons.  This advance makes possible, for the first time, instantaneous field-scale measurement of soil moisture.

These new sensors were originally deployed in 2010. They have the potential to revolutionize studies of soil moisture because they are the only technique to measure soil moisture at scales between the hyper-local point measurements and the kilometer-swaths of satellites.  They also are the only soil moisture probe that can account for water stores in living tissue.  According to Hydroinnova, one company that makes these $10,000 units, the measured soil footprint is 86% within 350 meters and the effective measuring depth changes with soil moisture, from a maximum of 70 cm in completely dry soil, to a minimum of 12 cm in saturated soil. 

Source.

While these sensors are few in number and relatively widely dispersed, they offer a whole new picture of soil moisture at the landscape level.  They are the only truly effective direct measure of soil moisture at the hectare level, and can be used to better calibrate the informational products discussed below.  However, as with all techniques, COSMOS must also be calibrated to take account of different soil types and changes in vegetation.

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Large-scale measurement of soil moisture can be accomplished using proxies, satellites, models, or some combination of techniques.

River flow data can reveal how much water is running off or through the soil from watersheds, so using a site like the USGSstreamflow network is a good proxy for large-scale short-term drought and deluge.

The current best methods for estimating large-scale soil moisture are the Drought.gov model products, which include the Palmer Drought Severity Index, soil moisture index, etc.  The Calculated Soil Moisture Anomaly is calculated based on observed precipitation and temperature.  Soil moisture, evaporation, and runoff for the entire US and globe are then modeled based on observations from a small area of eastern Oklahoma.  While this method is clearly biased, it is the best available.

The National Land Data Acquisition System is developing a more accurate model of soil moisture that incorporates soil textural properties and average percent vegetation (which impacts evapotranspiration).  The precipitation data used in the model is at approximately 25km resolution, interpolated to 13km grid cells:

Palmer Drought Indices are similar to soil moisture models in that precipitation, evapotranspiration, and runoff are used to calculate remaining water balance.  There are long-term (Palmer Drought Index (PDI) and Palmer Hydrological Drought Index (PHDI) indices that measure changes in groundwater and reservoir levels, and short-term indices (Palmer Z Index and Crop Moisture Index (CMI)) that affect agriculture during the growing season.

 Interestingly, the US Drought Monitor, which looks essentially like one of the Palmer indices, is subjectively drawn using “a blend of science and subjectivity”.