FAQ

[Q] Weather generation in DSSAT?

Help!

I have been spending time the last few days trying to understand the kind of inputs that DSSAT requires to run it from the command line.  I hope you can coach me at the places I get stuck.  I have discovered that the DSSAT documentation lags years behind the program.  So in looking at the 3.5 manuals, they look at the inputs for FileX.  But I have not been able to see how FileX knows what climate variables to use in a simulation.  Could you please let me know where that occurs when using the command line?  Thanks!!!

Answer

Crop models in DSSAT operate on daily basis, thus daily weather data is needed. However, for future climate conditions, we only have monthly mean of climate variables (solar radiation, min/max temperature, rainfall, and rainy days) – so we are using a weather generator program to generate daily weather data. DSSAT conveniently comes with two weather generators, WGEN and SIMMETEO, and we’ve been using SIMMETEO, which uses monthly summaries to estimate parameters. Descriptions of the two weather generators can be found in the second volume of DSSAT v3.5 (yeah…) documentation. Soltani and Hoogenboom have published a number of articles on the comparison of two methodologies.

Opting to use SIMMETEO instead of recorded daily weather data is quite simple:

  1. Name your climate profile with four-character code (e.g., CLIM.CLI)
  2. In your X-file, in the FIELDS block, put the four-character code as the weather station ID [WSTA]
  3. In your X-file, in the SIMULATION CONTROLS block, set the option of WTHER to S (simulated)
  4. In your X-file, in the SIMULATION CONTROLS block, set the random seed value to your favorite 5-digit number

By the way, at this point I presume you constructed the climate profile for each grid cell. Just in case, the climate profile is a small text file containing monthly summary of climate variables (which are all included in the FutureClim database), and it should look like this:

*CLIMATE:Gainesville,Florida,USA
@ INSI      LAT     LONG  ELEV   TAV   AMP  SRAY  TMXY  TMNY  RAIY
  UFGA   29.630  -82.370    10  20.9   7.3  16.6  27.4  14.4  1310
@START  DURN  ANGA  ANGB REFHT WNDHT
  1958    49  0.25  0.50   2.0   3.0
@ GSST  GSDU
     1   365
*MONTHLY AVERAGES
@  MTH  SAMN  XAMN  NAMN  RTOT  RNUM
     1  10.9  19.6   6.0  86.4   8.1
     2  13.5  21.4   7.3 107.9   7.6
     3  17.3  24.5  10.1 101.7   7.8
     4  21.4  27.9  13.3  75.1   5.7
     5  22.3  31.0  17.0  95.3   7.6
     6  20.8  32.6  20.6 164.1  11.9
     7  20.5  33.1  21.8 166.9  15.8
     8  19.1  32.9  21.7 195.2  15.4
     9  16.7  31.6  20.5 134.5  11.1
    10  14.6  28.4  16.2  56.7   6.3
    11  11.9  24.4  11.1  57.4   6.0
    12   9.8  21.1   7.6  68.9   7.4

See DSSAT vol3 to learn more about this file format.

Couple of things to note:

  • Due to the nature of stochastic method, you won’t get any spatial correlation on daily weather. If you look at the generated daily data, you can easily spot that one grid cell has flood and its neighbor cell has drought in a same year.
  • Crop growth/water stress and yield are very sensitive to the rainfall distribution during the season, not necessarily to the total amount of rainfall. To (partly) overcome this, we run a number of realizations (about >100).
  • Especially, not having spatial correlation of rainfall is particularly problematic if you need to analyze yearly production from region to region and conduct trade/policy analysis, for example. One common method to address this is using a delta method. Which means, shift historic climatology (observed daily weather, if available) to the future condition (e.g., In January in this grid cell, temperature increases by 1 C and rainfall decreases by 5%). You can implement this by re-generating daily weather data (WTH file) or embedding the “shifters” in the ENVIRONMENTAL MODIFICATIONS block of X-file (see the second volume and find examples). For HarvestChoice studies, I pre-generated 100-year daily weather data for SSA countries (see here for more information) and use this method when I need to run under future climate conditions.

Hope this helps your understanding. I’ll be happy to discuss what’d be best for your particular study.

Cheers,
Jawoo

About Jawoo Koo

Research Fellow at International Food Policy Research Institute (IFPRI), Washington, DC. Working on the meso-scale crop systems modeling applicaitons for Sub-Saharan Africa region.

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