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. 

Problems Modeling Soil Moisture: Calculating Rainfall

All attempts to model soil moisture or drought condition use observed NEXRAD precipitation as inputs, but the raw data must first be corrected.

East of the Continental Divide, radar imagery is compared to ground rain gauges and a correction factor is calculated. In mountainous areas West of the Continental Divide, a different method is used to derive the "observed precipitation".  Ground rain gauge data is compared to average precipitation data (PRISM) and departures from that average are interpolated between gauge locations.  The end result is 4km resolution rainfall totals.  

Once observed precipitation is calculated, accumulated precipitation can be viewed for any time period using NOAA's website.

Clearly, 4km by 4km grid cells hide a great deal of local variability.  For example, the Rainlog network of rain gauges in Tucson records highly variable rainfall at locations less than a kilometer apart during the monsoons (W Miracle Mile is about 2 km):

Methodological problems in the way PRISM fills gaps using modeled historical data may bias against extreme or unusual rainfall patterns.  Also, numerous sources of bias in both the radar and the rain gauges have to be accounted for manually.  For example, radar can be biased by hail, angle, and artifacts created by birds and insects.  Rain gauges can also malfunction in an endless variety of ways, including sensor error, human error, and when ice and snow block the gauge.  These uncertainties in observed precipitation can jeopardize efforts to model soil moisture such as the PDI.  Also, they call into question research that has revealed an increase in extreme precipitation events.

Existing large-scale methods of modelling soil moisture are unconstrained by field measurements, so the advent of satellites offering weekly global measurements of soil moisture are an important step forward.  These satellites (such as SMOS) can image vast swaths of the Earth’s surface to infer average soil moisture at the surface, but this imagery has an accuracy of +/- 4% soil moisture over pixels that are 35-50 km on a side.  A new satellite launched this year (SMAP) has better resolution, approximately 9 km, but still nowhere near field-scale resolution.  Local hill slope, vegetation land cover, and soil texture differences mean that county-level averages aren't accurate enough to apply on individual acres.

There are some companies that claim to be able to remotely monitor acre-by-acre soil moisture for farmers, but that is not possible without field measurements.   

February Blocking Pattern

This computer model (GFS) of near-surface temperature (actually, just above the boundary layer) and atmospheric pressure shows an interesting pattern of high and low pressure areas.  A high pressure region over the Azores is predicted to hold steady for the next several weeks, in effect blocking the normal flow of the jet stream and Eastward-migrating low pressure regions.  These lows are forced to travel (clockwise) all the way around the high pressure region.  The consequence appears to be a trough in the jet stream over Eastern  North America, leading to large incursions of Arctic air, and very, very cold temperatures (see graph).

Desertification

Land-atmosphere feedbacks amplifying climate change in the Sahel. Click on image for a zoom.
From Dryland Systems in Ecosystems and Human Well-Being: Current State and Trends, part of the Millennium Assessment.


Service of Climate Regulation in Drylands. The central grey box --the components of biodiversity involved in service provision-- maintenance of soil moisture (bottom left) and modulation of rainfall (top). IN bold -- the major alternative/complementary function involved in the effect of live vegetation cover on rainfall: successive multiplication of signs along each trajectory generates an increase in rainfall (+) when service is ameliorated and a decrease in rainfall (-) when land is degraded. Land degradation (grey circle) degrade the service through affecting surface temperature; when surface temperatures increase along the albedo trajectory, it decreases along the evaporation trajectory; this trend is reversed when land is not degraded.

Much more information on desertification in Africa.

Information on Radioactive Particles from Japan

source: http://cliffmass.blogspot.com/2011/03/latest-on-japanese-radiation.html

20th Century Weather Data

It is possible to graphically plot weather data from any point on the Earth from any date over the last 100-or so years using GrADS software and data from NOAA's NOMADS data access platform. Or see a summary of other data sources.

The simplist explanation of Global Warming

The Earth is absorbing more heat than it is emitting.

The Day After Tomorrow

The movie "The Day After Tomorrow" is about the end of the world, and, unexpectedly, the cinematography is fairly good. I was surprised by how much I enjoyed seeing Los Angeles destroyed. New York is another matter: NY is destroyed every other weekend at the box office, and I've never lived there anyway, but I have lived near LA and I now believe that every good movie should destroy something important in your life.

Plus, the scientific side of the story was thought-provoking. Catastrophic (saltational) climate change can't be ruled out based on our present state of knowledge. The fossil record isn't precise enough to determine if past climate changes happened in 5, 50, or 500 hundred years. Also, the current level of Co2 is unprecedented for at least the last 400,000 years (the limit of our ice core data) and the rate of increase is likely unique in the entire history of the planet. We simply don't know enough from past experience to accurately anticipate how this is all going to play out.

The IPCC has issued a series of scenarios with likely average temperature increases, but a thorough understanding of the problems inherent in such models calls into question the precision of these averages. The most commonly cited average gives increases of of 2-6C over the next 100 years. But all of the models they currently use are inherently gradualist models (more on this below). One could be a skeptic whether these models are accurate at all, but their (retrograde) prediction of the cooling associated with the Mt. Pinatubo eruption convinced many of their validity. It is edifying to note the maturation of models that originally didn't even take into account atmospheric dust; this is certainly a new science and many problems remain to be solved, particularly the response of vegetation to increased CO2. (CF. McNaughton & Jarvis. Effects of spatial scale on stomatal control of transpiration. Agricultural and Forest Meteorology, 54, 279-301). There is a definite possibility that other, as yet undetermined, variables could play a significant role in future climate change.

But the problem with relying on IPCC averages as outer limits of possible climate changes goes deeper than possible 'out of left field' variables. A recent effort using contemporary day-to-day "matrix" models of weather (e.g. the kind that are used to predict the path of hurricanes) to predict long term climate change yielded slightly higher averages (up to 11C) than the IPCC,. But the most important result was buried in the calculation of these averages. It turns out that the researchers, like many other modelers, ran their simulation thousands of times and only averaged the results that seemed reasonable. A significant proportion of the models "crashed" to negative or positive infinity temperature. Obviously, these results could not be averaged with the simulations that "worked", but they instead point to the instability inherent in these equations.

The climate change models and the way they are interpreted are inherently biased toward gradualist results, but the real danger of climate change is unpredictability. A perfect storm probably won't jump-start a renewed ice age in a manner of days, as in The Day After Tomorrow, but more humble respect for the mysterious potentialities of nature may be wise. The hubris of those who calculate the economic cost of the IPCC predicted change are one example of what can go wrong in a cost-benefit analysis that doesn't factor in the possibility of catastrophes. Attaching specific temperatures to the future climate of the earth is an important undertaking, but it is not a perfect science. Nothing is.

Ecosphere vs. Microcosm

ecosphere vs. microcosm (distinction from Whole Earth Review):

Right off, to avoid confusion, the Ecosphere is not a "microcosm." It is not an aquarium or a farm in the woods or a greenhouse. Microcosms are small biological samples of the planet, physically removed from their natural surroundings, but not completely isolated from global matter exchange. Microcosms can be affected by the great material gifts that buffer the planet: water, vitamins, minerals, oxygen, carbon dioxide, chlorophyll, etc. The Ecosphere has no buffer, no huge water cycle or feedback loops outside its two-print interior. We may pretend to dwell in an isolated volume of the biosphere -- call it a watershed, a biogeographical region, a county, or a nation -- but compared to the Ecosphere, our lives are inseparable from wildly open-ended material influences.

The Tucson company Eco Sphere makes successful ecosystems. The Tucson experiment Biosphere II built a failed ecosystem. Anybody can make a successful (or unsuccessful) microcosm in an aquarium. Which is a good model system?

Architecture

I've been creating maximal strength sculptures using string dipped in glue. I tie the strings into a network and hang them from the underside of a table. It stretches into a perfect parabola that redistributes the weight equally onto all supports. This is a good tool to design buildings that use a minimum amount of materials while maximizing space. The glued strings are not very strong (you can easily bend them between thumb and forefinger) but because they are in perfect compression (everything in this model is in compression - my explorations of building with tensegrity haven't gone as well) along their length, they do not bend.

Note: because this structure was still wet when turned it over, this picture shows it slightly bent.