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.
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| 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.
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