Climate

Refreshing my memory about tropical light and temperature

image from flic.kr

On a visit to Manila some time ago, I visited a golf course and had a look around with the golf course superintendent. Our discussion turned to seasonal changes in weather and the relative impact on the grass. For example, in the winter, when the temperatures are a little cooler, is it lower temperatures that have a big impact on slowing the growth? Or might it be the light, because the days in winter are a little shorter and the sun is a little lower in the sky?

I made some calculations then, and reran the script now to refresh my memory about this.

I calculated what the photosynthetic light would be on a sunny day in each month.

Hourly

That's how the photosynthetic photon flux density (PPFD) will be throughout the year when there are no clouds. It varies a bit.

For the total light per day we can look at the daily light integral (DLI), and this is how the DLI changes through the year.

Dli

The PPFD is how much light reaches the turf every second. Adding together the light from each second, from sunrise until sunset, gives the total amount for the day -- the DLI.

What about temperature? Manila is 13 degrees north of the equator with a tropical climate. The coldest month is January, with an average temperature of 25.6°C. The hottest month is May, with an average temperature of 29.5°C. That's a difference of 3.9°C from the coldest to the hottest month.

The sunny day DLI ranges from about 44 to 59 mol m-2 d-1. So which changes more, the temperature, or the light?

To look at that, I plotted the standard score (z-score) for the DLI and for the temperature across 12 months. The z-score shows how many standard deviations a value is from the average (mean).

Temperature_light

If the z-score is less than 0, that means the value for that month is less than the average for the year. If the z-score is more than 0, that means the value for that month is more than the average for the year.

Because the z-scores are standardized, I can compare directly the DLI and the temperature, and how much they are changing in any one month, compared to their average value during the year.

At Manila on sunny days, the temperature varies relatively more than the light in January, February, April, and May. In March, and from June through December, the light varies relatively more than the temperature.


Applying the grammar of greenkeeping

Over the past two weeks, I've had multiple conversations about the way I think of turfgrass management. It all starts with a definition of greenkeeping as managing the growth rate of the grass. I wrote about this in A Short Grammar of Greenkeeping. You can get your copy here.

Application of the grammar allows for easy communication among turfgrass managers about the work they are doing. I'll use the creeping bentgrass greens at Hazeltine National GC as an example. Volunteers from near and far were at Hazeltine during the Ryder Cup.

Let's say that I was from Madrid, or San Francisco, or Sydney, and I wanted to get green conditions that were more like those at Hazeltine. One of the ways I would try to do that would be to apply a similar quantity of nitrogen. But how to compare locations?

I would use the temperature-based growth potential (GP). For Minneapolis, the GP looks like this.

Msp

If I set the maximum monthly N at 3 g/m2, and multiply by the GP, I get a maximum annual N of 13.3 g/m2 for that location (Minneapolis). Now I'll make up a number, because I don't know exactly what it is, but let's say the actual quantity of N applied at Hazeltine was 9 g/m2.

I'll use the log percentage (L%) difference for consistency. The L% is the natural logarithm of the ratio of two numbers, multiplied by 100:

If 9 g N were applied at Hazeltine, and the value calculated using GP as described above is 13.3 g, that is a 39 L% reduction.

If I want to apply proportionally the same amount of N at another location, I can calculate the GP amount, which I'll call a standard value, and then take a 39 L% reduction.

Msp_mad_sfo_syd

The standard using these calculations comes to 16.7 g at Madrid, 20.1 at San Francisco, and 28.9 at Sydney. Knowing that there was a 39 L% reduction at Hazeltine, my starting point for Madrid, after applying the same reduction, would be 11.3 g N/m2. At San Francisco, the N would go from the standard calculation of 20.1 down to 13.6 g, and at Sydney the 39 L% reduction takes N from 28.9 to 19.6.

This grammar facilitates the rapid sharing of relative inputs used to produce turf surfaces all over the world. Let's say we know there are amazing bentgrass greens in Sydney with N inputs of 10 g/m2/year. A corresponding quantity of N in Minneapolis would be 4.6 g.

This same approach can be applied for the quantity of water supplied in comparison to evapotranspiration (ET), to frequency of mowing, to evaluation of the growth rate, to assessment of the photosynthetic light, and so on. I find this approach quite useful in rapid implementation of maintenance practices that work well at location A, applied to location B. One then has a site specific starting point that can be further adjusted at location B, based on turfgrass response at that location.


Shiny app shows the temperature and sunshine combination for 11 cities in Japan

Selection_100

I made a Shiny app with climatological normals data from the Japan Meteorological Agency to show the combination of sunshine and temperature at 11 locations.

@naturalgolf_D asked "What kind of situation is Japan?" With these data, I think it is interesting to compare different locations of interest, and a Shiny app is an easy way to do that.


Six more Shiny apps from ATC are here.



Is it normal to be cloudy like this?

2016-07-17 10.23.40

On July 17, I was in the Tokyo area with Jim Brosnan. The daily light integral (DLI) in Tokyo on July 17 was 14.2 mol/m2. Jim asked me if it was exceptionally cloudy that day. Not really, I answered. I told him that the such cloudiness was normal.

Now that July 2016 is over, I looked at the DLI for every day in July at Tokyo and also at Batesville, Arkansas. Both are at about 35.7°N latitude, so the day lengths will be identical.

The lowest DLI at Batesville in July was 22.8 mol/m2 on July 29. In Tokyo, there were 10 days in July with a DLI less than 22.8 mol/m2, including 5 days with a DLI less than 10 mol/m2. In that context, the cloudiness on July 17 was not exceptional.

To see more, check out the average hourly PPFD and DLI values for Tokyo in this chart and for Batesville in this one.


Warm-season turfgrass growth rates and competition at 35°N

Mike Richardson pointed out that the growth rate of zoysia is less than bermuda, so by implication there must be something other than growth rate that allows zoysia to invade bermuda. That is, in the situations when bermuda and zoysia are growing together -- competing -- when zoysia appears to grow faster, Mike suggests it may be a factor such as turf density that allows such a result, because bermuda grows faster than zoysia.

I've outlined a hypothesis about grass growth rates and their required inputs, and have more to write about that later. In that hypothesis, I mention location, and in my recent discussion with Mike about the growth rate I said that there is a variety by climate interaction. By climate, I mean the same as location. I'll use these words interchangeably.

Let me try to explain what I mean by an interaction by climate. I'll use data from Tokyo, and from Batesville (2016 data) and Fort Smith (climatological normals data). These locations are all about 35°N.

Light, temperature, plant water status, and leaf nitrogen content all influence growth. In turfgrass management, light and temperature generally can't be controlled; plant water status and leaf nitrogen content can be modified by turfgrass managers. We can imagine that bermuda and zoysia are growing side by side, or together, and then think of what may happen with modifications to these growth-influencing factors.

On average, this is the part of the climate that can't be controlled, at Fort Smith and at Tokyo, shown in 2-dimensional space.

Fort_smith_tokyo_polygon

That's a similar temperature range but different amounts of sunshine. Thus, there is no overlap during the months when warm-season grasses are growing. I focus on light and temperature because the water and the nitrogen can be adjusted by the turf manager.

Temperatures for 2016 are pretty similar through July 30. I express temperature here as the cumulative sum of growing degree days.

2016_gdd_batesville_tokyo

Ok, so temperatures are similar. If it were only temperature that influences growth, one would expect the grasses to perform pretty much the same at these locations. If bermuda does have an inherently faster growth rate than zoysia, then in this side-by-side comparison, with the same temperature, then bermuda should grow faster at both locations.

I downloaded the global solar radiation data also and then converted it into photosynthetic radiation units. This is Batesville for the first 7 months of 2016.

2016 Batesville DLI and PPFD through July 31

This is Tokyo for the first 7 months of 2016.

2016 Tokyo DLI and PPFD through July 31

In 2016, there has been more photosynthetic light at Batesville than at Tokyo.

2016_dli_batesville_tokyo

The DLI was pretty much the same from January to March, but since the start of April Batesville has jumped ahead by about 1,000 moles/m2. In the past 4 months, Tokyo has accumulated about 4,000 mol/m2 and Batesville has accumulated about 5,000 mol/m2. That's a log percentage difference of 22%. The difference has been especially pronounced in June and July -- the hottest months of the year so far.

Imagine growing bermuda and zoysia in 10% shade at the same temperature. Bermuda may grow faster than zoysia. Now imagine 20% shade. Probably the same result. How about 30, 40, and 50% shade? 60% or 70% shade? At some point, the growth rate of zoysia will be greater than the growth rate of bermuda. The bermuda will die in shade under which the zoysia can still produce a turf.

Consider now that there are varying growth rates among bermudagrass varieties, and also among zoysia varieties. That's what I mean by the location (or climate) by variety interaction. Take an inherently faster-growing zoysia, mix it with bermuda, grow it in a climate with high temperatures combined with lower DLI, mow the grass and make sure plenty of water is applied during the dry season, and see which one grows faster. It's not bermuda.

Yes, with a high DLI, plenty of fertilizer, moderate water supply, and high temperatures, bermuda grows faster than zoysia. Here's a photo of the ATC research facility putting green during grow-in. It's easy to tell which plots are zoysia -- those closest to the camera.

grow-in 22 dec

But if one thinks of growth as something that happens over years, at a location, with the grasses maintained as turf, then one can find the growth rate of zoysia can be higher than that of bermuda.

I find it useful to look at growth rate in those terms, rather than trying to explain it as a response to density or as competition for some other factor.


Grass selection by normal temperature and sunshine hours

Plotting the normal temperatures and sunshine hours for a location places that location in a particular 2-dimensional space. I demonstrated that in these charts. @turfstuf suggested that a diagonal line might show a break point for classifying warm and cool-season grasses.

The idea is that the top right would be warm-season, the area around the line would be transition zone, and the area to the bottom left would be cool season. That chart looks like this.

Diagonal

I agree that different regions of the chart are indicative of over/under points for different grasses or growing conditions. I wouldn't separate by that diagonal line. Here's the break points I would use.

  • mean annual temperature less than 15°C, cool-season
  • mean annual temperature from 15 to 20°C, transition zone
  • mean annual temperature above 20°, warm-season

For those general breaks, one can estimate the annual mean from the monthly charts, or plot the locations by the mean annual temperature.

annual_cities_temperature_sunshine

Continuing with the breaks, specifically looking at which warm-season grasses will be suitable:

  • within warm-season, and more than 6 hours sunshine per day, bermudagrass
  • within warm-season, and less than 6 hours sunshine per day, zoysiagrass or other warm season grasses that are tolerant of low light conditions: bermudagrass will struggle
  • within transition zone, and less than 6 hours sunshine per day, if warm-season grasses are used, zoysiagrass or other warm season grasses that are tolerant of low light conditions: bermudagrass will struggle

A transition zone location like Atlanta looks like this when those points are marked on the plot.

Atlanta

Two warm-season locations, one where bermuda thrives (Honolulu) and another where bermuda is overgrown by more shade tolerant grasses (Hilo), are shown here.

Hnl_hilo

In the next plots I show some other locations: cool-season, warm-season, and transition zone. The break points I use seem to agree pretty well with grass distribution and performance around the world.

Bangkok_boston_dubai

Indianapolis_tokyo

Sydney

Cairns_hk_syd

Knoxville_tokyo


Visualizing climate differences

Of the factors that influence plant growth, turfgrass managers are able to modify in some way the plant water status and the nitrogen supply to the grass, but they can do little to adjust the temperature and the light. As a consequence, both the grass adaptation to a particular environment, and the management requirements for the grass, will be influenced or controlled by the combination of light and temperature.

I spoke about this at a conference in 2012 and shared this handout. From the start of the handout:

The weather, and specifically the temperature and the amount of sunshine, has a major influence on the growth of grass and therefore on the suitability of certain grasses for certain climates. By plotting the climatological normal weather data with temperature on the horizontal axis (x-axis) and sunshine hours on the vertical axis (y-axis), we can see which locations are similar in these parameters, and thus likely to be suitable for the same grasses, and to similar maintenance practices for grasses. Many locations in East, South, and Southeast Asia are distinguished by relatively low sunshine duration as compared with locations of similar temperature in North America, Oceania, Africa, and Europe. For additional information about the use of these charts, see www.climate.asianturfgrass.com.

The idea is that when temperature and sunshine are the same (or similar) at two or more locations, the growing conditions, and the energy available for grass growth, are the same (or similar). When the temperature and sunshine are different, with no overlap, then the growing conditions are clearly different.

I think this is interesting and informative because such an approach can help to identify places that we might think are similar, but are in fact different, and vice versa. The implications for maintenance requirements, grass selection, and location to location comparisons are also evident from such representations of climate data. I've made some more plots to illustrate this.

MiamiI start with Miami. The normal monthly mean temperature is shown on the x-axis and the mean daily sunshine hours for that month are shown on the y-axis. The polygon defined by each of the 12 months of the year expresses what the normal growing environment is like at Miami. Places that are similar to Miami should have overlap in light and temperature with Miami. Places that are different should have little or no overlap.

Moscow, for example, has no overlap with Miami. I don't think anyone would expect it to.

Miami_moscow

The hottest months of the year at Moscow are cooler (with more sunshine) than the coldest months at Miami. There is no overlap between these locations.

New York City has some overlap with both Miami and Moscow. If I plot New York on this chart, I can see which months at New York are similar to Miami or Moscow.
Miami_moscow_newYorkJune in New York is similar in temperature and sunshine to March in Miami, September in New York is almost the same as January in Miami, and July and August in New York are between March and October conditions in Miami. One can also see the seasonal overlap between New York and Moscow conditions.

How about another warm season location like Miami? This plot adds Singapore conditions.

3plusSingapore

There is no overlap between Singapore and Miami, even though both are warm-season locations. There is more overlap between New York and Miami (about 3 months) than there is between Singapore and Miami (0 months). This has implications for grass selection and management. That is, the grasses the work in Singapore may not do very well in Miami, and vice versa.

Some places are predictably similar. Portland, and Seattle, for example, have almost complete overlap.

Portland_seattle

Other locations that one might expect to be similar have no overlap at all. I often use Honolulu and Hilo as an example. And sure enough, one finds different grass species growing at these two locations.

Hilo_honolulu

This video discusses Hilo and Honolulu.

One can also look at transition zone locations, like Atlanta, where both warm and cool-season grasses are grown.

AtlantaHow does a cool-season location like London compare to Atlanta?

Atl_lonJune and July in London are similar to March and November in Atlanta. July and August in London have similar temperatures as October in Atlanta, but with less sunshine.

Melbourne is another transition zone location, with golf course fairways and sports fields usually planted to warm-season grasses, and golf course putting greens usually planted to cool-season grasses.

Atl_lon_melMelbourne has some overlap with both Atlanta and London.

 


December and January DLI in Everglades City, Florida

I've been reading about the rains and clouds in South Florida and how extraordinary the past couple months have been. I saw these charts from Travis Shaddox, and I wondered what the light would be in photosynthetic units.

I downloaded monthly summary data since March 2007 for Everglades City from the NOAA. I use these data because they include global solar radiation, and I converted from energy units of MJ/m2 to photosynthetic units of mol/m2 using the 2.04 conversion factor of Meek et al.

This shows the average daily light integral (DLI) each month. One can see the seasonal changes, and one can also see that December 2015 had the lowest DLI of any December and that January 2016 had the lowest DLI of any January. I plotted all the data I could get, which is since 2007; I don't know what the values would have been before that. In the past decade, though, these were the lowest.

image from farm2.staticflickr.comLooking just at December and January year by year, January 2016 really stands out for having a low DLI. Blue triangles are December DLIs and red circles are January DLIs; the vertical dashed lines (blue for December, red for January) show the averages prior to Dec 2015 and Jan 2016.

image from farm2.staticflickr.com

In a normal year at Everglades City, January would have more photosynthetic light than December. For seven out of the past eight years, the month of December had a lower DLI than January.  Only 2014 had a lower DLI in January than in December. But January 2016 is a big outlier; not only does January 2016 have the lowest DLI of any of the previous Januaries, but it also has a lower DLI than any of the previous Decembers.


GP and GDD: are they comparable?

Someone asked me at the Northern Green Expo if the temperature-based growth potential (GP) and temperature-based growing degree days (GDD) are comparable. They sort of are, with a couple of exceptions. Comparable, yeah, kind of. But they are not interchangeable.

I downloaded the weather data for every day in 2015 from the international airports at London (Heathrow), Minneapolis, Sydney, and Tokyo (Haneda). Then I calculated the GP, and the GDD, and I made the charts shown in this post. The script to download the data and produce the charts is here . I’ll try to explain this, but I think it is easiest to see how GP and GDD are similar, and how they differ, by making some comparisons yourself.

First, here are the mean daily temperatures in 2015. The points are daily mean temperatures for each day of the year, and the lines are a moving average. Sydney and Tokyo are both hot in the summer, Minneapolis is coldest in the winter and hotter than London in the summer, and London is coolest in the summer but has winter temperatures close to those of Tokyo.

4 cities, temperature in 2015

Those cities have fairly diverse temperature ranges and variation in temperature from winter to summer. One expects a different growing environment in each. The GP3 is a value with a minimum of 0 and a maximum of 1, showing the expected limitation (or potential) of temperature on growth.

4 cities, GP in 2015

What do we see there? It’s a bit different than the temperature. Looking at the moving average for each city, we see Tokyo has a big drop in mid-summer because it is too hot, and Sydney has a substantial drop too, and Minneapolis has a slight drop in GP during the hottest summer temperatures, and London has peak GP in mid-summer because the average temperature rarely exceeds the optimum growth temperatures.

In the winter, the GP3 drops to almost 0 at Minneapolis, London, and Tokyo, but at Sydney it drops just below 0.5 in mid-winter, indicating that C3 grasses should still be able to grow, albeit slowly.

That was GP through the year. Now we can look at GDD0 . That is, for each day with an average temperature above 0°C (32°F), I take that temperature and call that the GDD. In this case, I would be using a base temperature of 0°C. This is the basis for the growing degree day model of Kreuser for the reapplication of plant growth regulators.

4 cities, GDD0 in 2015

That’s not exactly like the GP plot above. It is like zooming in on the temperature chart, but only showing the portion of the chart with temperatures above 0°C. Compared to the GP chart, one notices that with GDD 0 there is no drop in mid-summer when it is too hot, and the GDD0 does not drop all the way to 0 in winter at Tokyo and London.

So far it seems the GP and GDD are sort of the same, and sort of different. Both are based on temperature. But GDD is a measure of heat accumulation. GP is generating a value with a minimum of 0 when temperatures are far from an optimum for photosynthesis, and then generating a value that gets closer to 1 as the temperatures get progressively closer to 1.

There are various ways to calculate the heat accumulation through growing degree days. The GDD10 only counts the degrees on those days when the average temperature is above 10°C (50°F). This is GDD with a base temperature of 10°C. That makes sense for some things, and this chart of GDD10 is similar to the GDD0 chart in that it is as if the temperature chart were cropped to omit all values less than 10°C.

4 cities, GDD10 in 2015

That’s what GDD10 is showing. Now we are looking only at the days in the year when the average temperature was above 10°C, and we can see how much heat accumulation there would be each day.

The big difference between GP and GDD is evident, because Sydney and Tokyo are peaking in GDD when temperatures are at their hottest, but GP would produce a value less than 1 when GDD was highest in those places, because the temperatures are considered too hot for optimum growth of C3 grass.

GDD is heat accumulation. GP is optimum growth temperature accumulation. Let’s look at the accumulation explicitly, by adding together the GP for every day of the year in order to get these lines showing the cumulative sum of GP in 2015.

4 cities, cumulative sum of GP in 2015

Sydney with the year-round growth, although with a dip in winter and also a dip in summer, has the highest sum of GP. Then Tokyo, and Minneapolis and London are similar.

We can do the same type of chart for GDD0 .

4 cities, cumulative sum of GDD0 in 2015

Sydney still has the highest sum, then Tokyo, but there is less of a distance between these two cities than with GP, because GDD is using all of Tokyo’s hot summer days, but GP in Tokyo drops when it is hot. London and Minneapolis are similar again, but notice that Minneapolis accumulates almost all its GDD0 from April to October, while the milder winter in London allows the GDD0 to accumulate slowly year-round.

The cumulative sum of GDD50 is just a little different.

4 cities,cumulative sum of GDD10 in 2015

Now Tokyo catches and exceeds Sydney in the northern hemisphere autumn, but Sydney catches up quickly as summer approaches. And when counting the heat accumulation now only above 10°C, Minneapolis now has a lot more of that than does London.

The GP and GDD with various base temperatures (0 and 10°C are two of the standard ones) can be used for different things. GDD is good for things that are heat dependent. Growth regulators, insects, diseases, weeds – certainly the growth of certain plants in the range of temperatures from the minimum temperature required for growth up to the optimum temperature for growth. The GP is formulated in a different way, where it decreases when it is too cold or too hot for optimum growth.

We can look at that a little more closely. Now let’s just look at 2 cities to reduce the overlap: London and Minneapolis. Here is the GDD0 for every day of 2015.

2 cities, temperature vs GDD0 2015

That’s a linear increase in GDD0 for every increase in temperature above 0°C. If we would plot GDD10 , there would also be a linear increase with temperature, but the line would start going up at a mean daily temperature of 10 rather than at 0.

The GP for that same range of temperatures at London and Minneapolis looks completely different.

4 cities, temperature vs GP in 2015

That’s because the GP has a minimum of 1 and a maximum of 0, and the value is dependent on how close the temperature is to the optimum temperatures for photosynthesis.

Now to get back to the original question, after all those examples, are GDD and GP comparable? For them to be comparable, there would have to be a linear relationship (or almost linear relationship) between the accumulated GP and the accumulated GDD through the year.

Here I’ve plotted just that; the cumulative sum of GP for 2015 is on the x-axis, and the cumulative sum of GDD0 is on the y-axis.

4 cities, gp vs GDD0 in 2015

Well, that is sort of linear, but has a few weird curves or shifts. London’s GDD goes up in winter when the GP is still low, and Tokyo’s GDD goes way up in mid-summer when the accumulated GP is increasing slowly. And the line for Sydney looks pretty straight by comparison, but we can take a closer look at that by checking GP vs GDD10 .

4 cities, gp vs GDD10 2015

Now we are looking at how GP accumulates through the year, and comparing that to how GDD10 accumulates. At some times of the year it is linear, but as temperatures get low or high, the slope of that line changes.

If you would make these calculations for data at your location, I think you would see the same thing and would see how GP and GDD are similar and how they are different.

For more information, see:


"How do you calculate how much water is needed for a given area?"

I received this inquiry last week:

"When looking at water quantity for a new golf course, you have to determine how much water is needed obviously so what I want to know is.
 
1) How do you calculate how much water is needed for a given area (the whole golf course?)
 
2)  I know you have to look at the driest year data and base it on that but I understand you have to measure how much a grass plant and soil will lose via Evapotranspiration so you know what you have to replace so what methods do you use to find this out?"

I suggest calculating a water budget for the location using the method described by Gelernter et al. with this supplement providing the details for the calculations.