NCAR SUMMER COLLOQUIUM: July 24-Aug.6, 2011 Boulder, Colorado, USA. General Large Area Crop Model (GLAM) TUTORIAL

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1 NCAR SUMMER COLLOQUIUM: July 24-Aug.6, 2011 Boulder, Colorado, USA General Large Area Crop Model (GLAM) TUTORIAL Gizaw Mengistu, Dept. of Physics, Addis Ababa University, Ethiopia This document provides brief information on how to run GLAM crop model and make sensitivity studies to get better knowledge on how the process based crop model works. There are two documents (GLAM Documentation for Release 2 and GLAM model Practice) that are available and can be download from the web site: ' of Institute for Climate and Atmospheric Science, School of Earth and Environment, The University of Leeds, UK. The two documents are also included in the GLAM folder along with this document. The first section of this document gives an overview of the model. It gives brief description of the folder structure, input files and GLAM output files. The second section explains procedure to calibrate and run the model. The remaining sections will guide you how to explore the model and better understand how it works through different sensitivity exercises. Section 1: Overview of GLAM There are different folders that you will find in working directory 'GLAM'. These are 'Reference_setup', 'parameter_selection', 'GPCP_rainfall', 'High_temp_stress', 'rainfall_yield_relation', and 'planting_date' folders. Within these folders, you will find files and subfolders that contain the GLAM model, all the input data and files needed to run the model and some examples for model output in the 'ethio_exampe_output' folder for comparison with your results. The GLAM model All the files ending with a.f90 extension are different subroutines and deriver files that constitute the GLAM crop model. The Input data The input data is contained in the 'inputs' folder and includes: 1. Soil data (inputs/ascii/soil/ 2. Observed and simulated weather data. There are two sets of weather data: GPCP and RegCM4. The two data sets use simulated solar radiation, min and maximum temperatures as simulated by Regional Climate model (RegCM4) while the rainfall comes from the respective sources. The weather data is contained in 'inputs/ascii/wth/' folder. This folder contains RegCM4 simulation when you work from 'Reference_setup', 'parameter_selection', 'High_temp_stress', 'rainfall_yield_relation', and 'planting_date' folders. When you work from 'GPCP_rainfall', folders, you will find GPCP rainfall under column rainfall in the weather files. 3. Observed crop yields in the folder 'inputs/ascii/obs/'.

2 The input files These contain all of the information needed to run the GLAM model. These are: 1. The parameter files (glam-r2-param-swheat-cal-ethio.txt and glam-r2-param-swheat-setethio.txt ) contain all of the settings and parameter values of the model. The two parameter files are the same except the first (glam-r2-param-swheat-cal-ethio.txt) instructs the model to do calibration run based on observed yield whereas the second (glam-r2-param-swheat-setethio.txt) instructs the model to do full model run based on values selected for different parameters in the first run. There are different groups of parameters in parameter files: the parameters in the first group, for example, define which crop model to run, which method of optimization etc; the parameters in the second group are called model management parameters; parameters in the third, fourth, fifth, sixth and seventh group are called spatial management & LAI, soil spatial, drainage & uptake, evaporation and transpiration, biomass and phenology respectively. There are also other groups of parameters including crop specific in the remaining section of the parameter files. 2. The filenames files (filenames-swheat-cal-ethio.txt and filenames-swheat-set-ethio.txt) indicate where to find the input data and where to save the output data. The first is used in the calibration and the second in the full model run. Example model output This is in the 'ethio_exampe_output' sub-folder contained with the main folders: 'Reference_setup', 'parameter_selection', 'GPCP_rainfall', 'High_temp_stress', 'rainfall_yield_relation', and 'planting_date'. This sub-folder may contain: calibration output files such as swheat_ygp _loc.mer, swheat_extc _glo.mer, swheat_albedo _glo.mer, swheat_shf_cte _glo.mer etc. These are the files produced by the calibration run for YGP, EXTC, ALBEDO, SHF_CTE etc parameters. Local optimization (identified by 'loc') produces an optimal value of the chosen parameter for each location listed in the file. For each location, only the optimal value, and associated skill, is given. Global optimization (identified by 'glo') is not location-specific; instead of rows corresponding to different locations, they correspond to different values of the chosen parameter. Whilst local and global calibration output files differ, they do follow a common structure: Latitude identifier (set to -99 in global run) Longitude identifier (set to -99 in global run) Value of the parameter varied Root Mean Square Error (RMSE) Mean absolute error (sum of all Obs-Predicted ) Mean fractional error (sum of all Obs-Predicted /Obs) Correlation coefficient (omitted in global run) Observed minus predicted standard deviation (omitted in global run) Observed minus predicted mean yield (omitted in global run)

3 swheat.out: for every year of the simulation, this file contains information on the simulated crop. The information in this file are: 1. Year 2. Latitude 3. Longitude 4. Planting date 5. Final crop stage (ISTG) 6. Mean root length density by volume 7. LAI 8. Simulated yield 9. Biomass 10. Empty (irrigated fraction; SLA) 11. Harvest index 12. Cumulative rain 13. Solar radiation 14. Total soil water 15. Transpiration 16. ET 17. PET 18. Soil water stress factor 19. ET 20. Runoff 21. Cumulative runoff 22. Potential (root-limited) uptake 23. Cumulative potential uptake 24. Drainage 25. Cumulative drainage 26. Potential (energy-limited) transpiration 27. Cumulative potential transpiration 28. Cumulative evaporation 29. Cumulative potential evaporation 30. Cumulative transpiration 31. Root length density by area 32. Column 28/Column Rainfall 34. Change in soil moisture 35. Absorbed radiation 36. Duration 37. Mean vapour pressure deficit 38. Tot net radiation 39. Total percentage of pods setting (TOTPP) 40. Total percentage of pods setting considering temperature only (TOTPP_HIT) 41. Total percentage of pods setting considering water stress only (TOTPP_WAT) 42. Mean temperature during the crop season (planting to harvest) 43. Observed yield You will use this file from time to time for producing some graphs and for visual inspection when you compare different cases later on.

4 The planting date is in day-of-year where 1 st January refers to 1 and 31 st December refers to day 365. Some figures for comparison with what you will produce. Section 2: Calibrating and Running GLAM The model is currently set up for for calibration run and for full model run for 43 grid cells in Amhara regional state of Ethiopia using RegCM4 and GPCP rainfalls. You can see this information on line 9 of the two parameter files. a) Parameter Selection GLAM can be calibrated for a number of parameters by fixing, e.g. YGP (line 2), EXTC (line 21), SHF_CTE (line 20), ALBEDO (line 34), VPD_CTE (line 51) in the 'glam-r2-param-swheat-calethio.txt' file. To calibrate GLAM for some of this parameters, type 'cd parameter_selection' from GLAM working directory. Once you are in a 'parameter_selection' folder, open file 'glam-r2-param-swheat-cal-ethio.txt' and look for one of these parameters along row (line) number indicated above and enter a number under NVAL column to the number of times you want to run GLAM between the specified MIN and MAX values. Also make appropriate choice for IMERF (third row & fourth column). Enter 0 for local optimization and 1 for global optimization and save the file. Note that GLAM is calibrated for only one parameter at a time. Then type:./ethio_glamcal This will run the model as many times as the number you enter under NVAL column, each time with a different values of the chosen parameter. The values of the parameter which minimizes the RMSE between observed and simulated yields is the value chosen. The file containing this information is found in folder 'parameter_selection/output' as 'swheat_parametername _loc.mer' or 'swheat_parametername _glo.mer' depending on the optimization chosen in the previous step. This could be, for example, 'swheat_ygp _loc.mer' for YGP if IMERF=0. Open this file and compare the results with the corresponding results found in a file with the same name in 'ethio_exampe_output' folder. Repeat the above steps for other parameters this time with IMERF=1. You are now ready to do a full model run. If you have optimized other parameters rather than YGP, replace corresponding values under Value column with new ones in the file 'glam-r2-param-swheat-setethio.txt' and save the file. Then type:./ethio_glamrun This uses the chosen value(s) of YGP either from file produced by calibration run (e.g. 'swheat_ygp _loc.mer') if specified on line 7 of 'filenames-swheat-run-ethio.txt' or the parameter file itself if a single value is chosen for all grids.

5 After this run you should get 'swheat.out' ascii file in the 'parameter_selection/output' folder. Open this file and compare with the one in 'ethio_exampe_output' folder. Again, the two should be similar except for minor differences. To view the simulated and observed yield, open matlab and then open 'GLAM_ANALYSIS.m' from the matlab menu (click file and then click open). Uncomment one of the lines in this file which contains path to 'parameter_selection' folder and then click 'run' icon. You will find a number of pdf figures that can be compared with those files with the same name in 'ethio_exampe_output' folder. How well do the simulated and observed yields agree? Do the model capture the observed spatial and temporal variations? Do you see any particular part of the region which is not well simulated by GLAM? Why? Which years are better than others? Is there any possible explanation? b) GLAM run with preselected parameter values Now, change from 'parameter_selection' folder to 'Reference_setup' folder by typing 'cd../reference_setup/' at the Linux command prompt. Compare the parameter files ('glam-r2-paramswheat-cal-ethio.txt', 'glam-r2-param-swheat-set-ethio.txt') in this folder with the ones you have modified under section (a) above. Are they the same? Now, you carry out the model calibration and full runs by typing the following:./ethio_glamcal./ethio_glamrun and then comment one of the lines in 'GLAM_ANALYSIS.m' file which contains path to 'parameter_selection' folder and uncomment the one which contains path to 'Reference_setup' folder. Click 'run' icon. You will produce a number of pdf figures that can be compared with those files with the same name in your previous exercise. Are the graphs you generated and the ones in previous exercise the same? If there are discrepancies, what do they imply about tuning the model to capture reality? Section 3 High Temperature Stress The model run you have just completed used RegCM4 temperature data. Open the parameter files and check the T critical used in both parameter files (see line 90 ). Open 'swheat.out' in the 'Reference_setup/output' folder and choose year 1995 and find out the planting date for 1995 (column 4) and find out duration of the crop in the same year (column 36) for the first two grids (rows (lines) 1 & 2). You need this information to decide when to increase the average temperature (i.e. (maximum +minimum)/2 ) above the critical temperature. Now, change from 'Reference_setup' folder to 'High_temp_stress' folder by typing 'cd../high_temp_stress/' at the Linux command prompt. Open weather files for the two grids in the year 1995 ( i.e 'inputs/ascii/wth/amha wth' & 'inputs/ascii/wth/amha wth').

6 To do so you can use any text editor. For example you can use 'gedit' and type: gedit inputs/ascii/wth/amha wth at the Linux command prompt. You can now modify AMHA wth by raising the average temperature by a few degree centigrade above the critical temperature to 10 consecutive days after emergence (which is 8 days after planting). This can be done by adding the same number to both minimum and maximum temperatures. Do the same changes to the second grid. Did you note that emergence date for the two grids are different? Run the model without calibrating it (./ethio_glamrun). This is so that the same YGP found in the original run (i.e. in 'Reference_setup/output' folder ) is used again in this run. None of the simulated yields should change except for the year where you have raised the temperature. Comment one of the lines in 'GLAM_ANALYSIS.m' file which contains path to 'Reference_setup' folder and uncomment the one which contains path to 'High_temp_stress' folder. Then click 'run' icon. You have now pdf figures in 'High_temp_stress/output' folder that can be compared with corresponding figures in 'Reference_setup/output' folder produced a while ago. Is there any noticeable differences? Which year and which part of the simulation domain? What do the differences imply? Copy all pdf figures you have just produced to a folder called 'emergence_period' Now restore the original data of the two weather files you have modified by copying from 'Reference_setup' folder as follows. Type: cp../reference_setup/inputs/ascii/wth/amha wth inputs/ascii/wth/. cp../reference_setup/inputs/ascii/wth/amha wth inputs/ascii/wth/. Now edit the two restored input files as before by raising the mean temperature to a few degree above critical temperature for 10 consecutive days near the end of growing season (note that the day of the year that the crop is harvested is equal to: planting date+duration of the crop i.e column 4+column 36). Run the model without calibration so that you use the same YGP (./ethio_glamrun). Once the model run is finished, click 'run' icon on 'GLAM_ANALYSIS.m' file to get new set of pdf figures. You have now pdf figures in 'High_temp_stress/output' folder that can be compared with corresponding figures in 'Reference_setup/out' and 'emergence_period' folders. Is there any noticeable differences? Which year and parts of the simulation domain? What do the differences imply? What do you conclude about the high temperature impacts on crop yield at different times of crop developmental stage?

7 You may follow the above procedure and add temperature at different times of growing season to determine how crop growth responds to high temperature stress at the end of this tutorial class. Section 4 Rainfall and yield relationship a) GPCP Rainfall The model runs you have carried out are based on the RegCM4 rainfall data. Now, you will explore the GPCP dataset that has been put together using information from rain gauges and satellite observations. The solar radiation, minimum and maximum temperatures in the weather files are still from the previous RegCM4 data set. Now, change from 'High_temp_stress' folder to 'GPCP_rainfall' folder by typing 'cd../gpcp_rainfall/' at the Linux command prompt. Calibrate and then make full model run (./ethio_glamcal and./ethio_glamrun). Once the model run is finished, comment one of the lines in 'GLAM_ANALYSIS.m' file which contains path to 'High_temp_stress' folder and uncomment the one which contains path to 'GPCP_rainfall' folder. Then click 'run' icon on 'GLAM_ANALYSIS.m' file to get pdf figures. You have now pdf figures in 'GPCP_rainfall/output' folder that can be compared with corresponding figures in 'Reference_setup/output' folder. You can also make visual inspection of 'swheat.out' files in the two folders. Do you see any changes? What do the differences imply about the two data sets? Do the rainfall spatial temporal distribution affect planting date and crop growth duration? Which one seems to be appropriate? b) Adding extra rainfall to the input data files Now, change from 'GPCP_rainfall' folder to 'rainfall_yield_relation' folder by typing 'cd../rainfall_yield_relation/' at the Linux command prompt. Repeat exercises in Section 3 this time by modifying the rainfall column. Make the changes by adding 10 mm of rainfall to 10 consecutive days near the start of the growing season (i.e 10 days after planting date +8 days). Note that the same grids and year as in section 3 have to be considered. Make sure that 'swheat_ygp _loc.mer' file in 'Reference_setup/output' folder is identical to a file by the same name in 'rainfall_yield_relation/output' folder. Run the model without calibrating it (./ethio_glamrun). This is so that the same YGP found in the original run (in 'Reference_setup/output' folder) is used again in this run. Once the model run is finished, comment one of the lines in 'GLAM_ANALYSIS.m' file which contains path to 'GPCP_rainfall' folder and uncomment the one which contains path to 'rainfall_yield_relation' folder. Then click 'run' icon on 'GLAM_ANALYSIS.m' file to get pdf figures in 'rainfall_yield_relation/output' folder. You have now new pdf figures in 'rainfall_yield_relation/output' folder that can be compared with corresponding figures in 'Reference_setup/output' folder.

8 How does the simulated yield change? Move the pdf figures to 'emergence_period' folder and then restore the original weather data of the two files in the same manner as in section 3. Edit the two input files by adding 10 mm of rainfall to 10 consecutive days near the end of growing season. Run the model without calibration so that you use the same YGP (./ethio_glamrun). Then click 'run' icon on 'GLAM_ANALYSIS.m' file to get new set of pdf figures in 'rainfall_yield_relation/output' folder. How does the simulated yield change? What are the differences between adding rainfall at the beginning and end of growing season? You may follow the above procedure and add rainfall at different times of growing season to determine the optimal time of having more rainfall during growing period at the end of this tutorial class. Section 5 Role of planting date In GLAM, one can specify exact planting date or use 'intelligent planting routine'. In the intelligent planting routine, you can specify the date on which an intelligent sowing window begins. During this sowing window the crop is planted when a particular criterion is met and, if the criterion is never met and the crop is planted at the end of sowing window. There are two different criteria that can be used: I. The crop is planted if the soil water content exceeds half of the soil water holding capacity. II. The crop is planted if more than 1.5 cm of rain falls in one day. If this second criteria is chosen, the crop is also monitored for 20 days after planting and if it suffers from water stress in 10 or more of these days then it is considered failed and is replanted. GLAM is currently set up to start the intelligent sowing window on the ninth of June (this is day-ofyear 160, see line 22, IPDATE) and to plant the crop if the soil water content exceeds half of the soil water holding capacity (see line 24, SWF_THRESH). Now, change from 'rainfall_yield_relation' folder to 'planting_date' folder by typing 'cd../planting_date/' at the Linux command prompt. Change from current day of the year 160 to say 144 i.e. last week of May in both parameter files. Calibrate and make full run (./ethio_glamcal and./ethio_glamrun). Once the model run is finished, comment one of the lines in 'GLAM_ANALYSIS.m' file which contains path to 'rainfall_yield_relation' folder and uncomment the one which contains path to 'planting_date' folder. Then click 'run' icon on 'GLAM_ANALYSIS.m' file to get pdf figures in 'planting_date/output' folder. You have now pdf figures that can be compared with corresponding figures in 'Reference_setup/output' folder.

9 The switch for deciding which criteria is used to determine when the crop is planted is also in the parameter files. Scroll down to line 181 where you will see that ISPARE2 is currently set to -1. To use the second option, change this value to +1. Copy pdf figures from 'planting_date/output' folder to 'ISPARE2-1' folder. Calibrate and make full run (./ethio_glamcal and./ethio_glamrun). Click 'run' icon on 'GLAM_ANALYSIS.m' file to get new set of pdf figures in 'planting_date/output' folder which overwrite the previous figures. You can compare these figures with corresponding figures in 'Reference_setup/output' and 'ISPARE2-1' folders. Does changing the start of sowing window have impact on simulated yields? Does it affect some years more than others? Does it affect some places than others? Why do you think this is so? Which planting routine gives the best simulated yields? This is just a brief introduction to what you can do with GLAM model! It is at your disposal to explore further. GOOD LUCK!!!