Workflow
gaia is designed with a climate-driven empirical model
at its core, integrated into an efficient modular structure. This
architecture streamlines the entire workflow. This workflow includes raw
climate and crop data processing, empirical model fitting, yield shock
projections under future climate scenarios, and agricultural
productivity change calculation for GCAM. The modular design also
facilitates comprehensive diagnostic outputs, enhancing the tool’s
utility for researchers and policymakers.
The primary functionality of gaia is encapsulated in the
yield_impact wrapper function, which executes the entire
workflow from climate data processing to yield shock estimation. Users
can also execute individual functions to work through the main steps of
the process (Figure 1).
weighted_climate: Processes CMIP-ISIMIP climate NetCDF data and calculates cropland-weighted precipitation and temperature at the country level, differentiated by crop type and irrigation type. The function accepts both daily or monthly climate data that are consistent with the CMIP-ISIMIP NetCDF data format.crop_calendars: Generates crop planting months for each country and crop based on crop calendar data Sacks et al., (2020).data_aggregation: Calculates crop growing seasons using climate variables processed byweighted_climateand crop calendars for both historical and projected periods. This function prepares climate and yield data for subsequent model fitting.yield_regression: Performs regression analysis fitted with historical annual crop yields, monthly growing season temperature and precipitation, CO2 concentrations, GDP per capita, and year. The default econometric model applied ingaiais from Waldhoff et al., (2020). User can specify alternative formulas that are consistent with the data processed indata_aggregation.yield_shock_projection: Projects yield shocks for future climate scenarios using the fitted model and temperature, precipitation, and CO2 projections from the climate scenario.gcam_agprodchange: Remaps country-level yield shocks to GCAM-required spatial scales (e.g., region, basin, and intersections), based on harvested areas, and aggregates crops to GCAM commodities. This function applies the projected shocks to GCAM scenario agricultural productivity growth rates (the unit used to project future yields in GCAM) and creates ready-to-use XML outputs for GCAM.
Figure 1: The gaia workflow showing the functions and the corresponding outputs of modeling crop yield shocks to climate variations using empirical econometric model.
Example Climate Data
gaia requires global climate data from the
Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) or data formatted similarly
to ISIMIP data. Additionally, gaia supports climate data
aggregated to a monthly time step. Due to the large size of daily time
step climate data, we have provided an example of monthly aggregated
climate data covering the period from 2015 to 2100.
Example Data 1
Download the example data using the instructions below. This dataset includes both historical climate observations and future climate projections, already weighted by cropland areas and provided in .txt format. The cropland-area-weighted, country-level climate data contains monthly precipitation and temperature for 26 crop types, distinguishing between irrigated and rainfed areas.
# load gaia
library(gaia)
# Path to the output folder. Change it to your desired location
output_dir <- file.path(getwd(), 'gaia_output')
# Cropland-weighted historical and future climate data
data_dir <- gaia::get_example_data(
download_url = 'https://zenodo.org/records/13976521/files/weighted_climate.zip?download=1',
data_dir = output_dir
)
# Path to the folder that holds cropland-area-weighted precipitation and temperature TXT files
# historical climate observation
climate_hist_dir <- file.path(data_dir, 'climate_hist')
# future projected climate
climate_impact_dir <- file.path(data_dir, 'canesm5')NOTE! We recommend using the default historical
climate data provided in example data 1,
as it is based on WATCH climate observations (Weedon et al., (2011)).
If you do not plan to adjust any assumptions for empirical model
fitting, you can opt to use the default regression model, pre-fitted
with the WATCH historical climate data, by setting the argument
use_default_coeff = TRUE. After loading the
gaia package, type coef_default to view the
parameters of the default fitted model..
Example Data 2
To use a different climate model or scenario, you can provide your
own climate data in NetCDF format. Note that gaia adheres
to the ISIMIP climate data format as its standard, so your NetCDF files
should be formatted accordingly. You can download our example monthly
precipitation and temperature NetCDF files using the code below.
# load gaia
library(gaia)
# Path to the output folder. Change it to your desired location
output_dir <- file.path(getwd(), 'gaia_output')
# Future Climate Data
data_dir <- gaia::get_example_data(
download_url = 'https://zenodo.org/records/13976521/files/gaia_example_climate.zip?download=1',
data_dir = output_dir
)
# Path to the precipitation and temperature NetCDF files
# NOTE: Each variable can have more than one file
# projected climate data
pr_projection_file <- file.path(data_dir, 'pr_monthly_canesm5_w5e5_gcam-ref_2015_2100.nc')
tas_projection_file <- file.path(data_dir, 'tas_monthly_canesm5_w5e5_gcam-ref_2015_2100.nc')Run gaia!
Example 1
Warning!
This example demonstrates the complete gaia model workflow,
including processing the raw climate NetCDF data. Due to the large size
of the climate dataset, this process may take up to an hour. For a
quicker demonstration, please refer to Example
2.
This example uses both example data 1
and example data 2. You can run
gaia using the single function yield_impact,
which streamlines the entire workflow. For detailed explanations of each
argument in yield_impact, please refer to the reference
page.
# load gaia
library(gaia)
# Path to the output folder. Change it to your desired location
output_dir <- file.path(getwd(), 'gaia_output')
# Run gaia
# The full run with raw climate data can take up to an hour
gaia::yield_impact(
pr_hist_ncdf = NULL, # path to historical precipitation NetCDF file (must follow ISIMIP format); only if you wish to use your own historical precipitation observation
tas_hist_ncdf = NULL, # path to historical temperature NetCDF file (must follow ISIMIP format); only if you wish to use your own historical temperature observation
pr_proj_ncdf = pr_projection_file, # path to future projected precipitation NetCDF file (must follow ISIMIP format)
tas_proj_ncdf = tas_projection_file, # path to future projected temperature NetCDF file (must follow ISIMIP format)
timestep = 'monthly', # specify the time step of the NetCDF data (monthly or daily)
climate_hist_dir = climate_hist_dir, # path to the folder that holds cropland weighted historical climate observations
historical_periods = c(1960:2001), # vector of historical years selected for fitting
climate_model = 'canesm5', # label of climate model name
climate_scenario = 'gcam-ref', # label of climate scenario name
member = 'r1i1p1f1', # label of ensemble member name
bias_adj = 'w5e5', # label of climate data for bias adjustment for the global climate model (GCM)
cfe = 'no-cfe', # label of CO2 fertilization effect in the formula (default is no CFE)
gcam_version = 'gcam7', # output is different depending on the GCAM version (gcam6 or gcam7)
use_default_coeff = FALSE, # set to TRUE when there is no historical climate data available
base_year = 2015 # GCAM base year
start_year = 2015, # start year of the projected climate data
end_year = 2100, # end year of the projected climate data
smooth_window = 20, # number of years as smoothing window
co2_hist = NULL, # historical annual CO2 concentration. If NULL, will use default value
co2_proj = NULL, # projected annual CO2 concentration. If NULL, will use default value
diagnostics = TRUE, # set to TRUE to output diagnostic plots
output_dir = output_dir # path to the output folder
)NOTE! The arguments climate_model,
climate_scenario, member,
bias_adj, and cfe require specific strings
that provide climate model metadata in the output files. These arguments
do not impact the gaia model simulation itself; they are
only used to populate climate data metadata in the outputs.
Example 2
This example only uses the example of weighted climate
data as described in example data
2, which has been processed with cropland weights at the country
level. This weighted climate data was generated using
gaia::weighted_climate. This example serves as a guide to
help users format their own data to match the weighted climate data
structure if their raw climate data differs from the ISIMIP format.
Running gaia directly with weighted climate data requires
only a few minutes.
# load gaia
library(gaia)
# Path to the output folder. Change it to your desired location
output_dir <- file.path(getwd(), 'gaia_output')
# Run gaia
gaia::yield_impact(
climate_hist_dir = climate_hist_dir, # path to the folder that holds cropland weighted historical climate observations
climate_impact_dir = climate_impact_dir,# path to the folder that holds cropland weighted projected climate
timestep = 'monthly', # specify the time step of the NetCDF data (monthly or daily)
climate_model = 'canesm5', # label of climate model name
climate_scenario = 'gcam-ref', # label of climate scenario name
member = 'r1i1p1f1', # label of ensemble member name
bias_adj = 'w5e5', # label of climate data for bias adjustment
cfe = 'no-cfe', # label of CO2 fertilization effect in the formula (default is no CFE)
gcam_version = 'gcam7', # output is different depending on the GCAM version (gcam6 or gcam7)
use_default_coeff = FALSE, # set to TRUE when there is no historical climate data available
base_year = 2015, # GCAM base year
start_year = 2015, # start year of the projected climate data
end_year = 2100, # end year of the projected climate data
smooth_window = 20, # number of years as smoothing window
co2_hist = NULL, # historical annual CO2 concentration. If NULL, will use default value
co2_proj = NULL, # projected annual CO2 concentration. If NULL, will use default value
diagnostics = TRUE, # set to TRUE to output diagnostic plots
output_dir = output_dir # path to the output folder
)Explore the results in your output folder. For a detailed explanation of the output, checkout Example 3!
Example 3
As the gaia model runs, intermediate outputs will be
generated and saved to the user-specified output folder. For testing
purposes, if you follow Example 2 as outlined
above without modifying any input data or arguments, you can expect to
see the following key results. Below, we provide additional instructions
on using each function described in the Workflow
section and explain the associated example outputs.
weighted_climate
Once you have downloaded example data 1
to your desired location, you can use the weighted_climate
function to calculate the cropland-weighted monthly precipitation and
temperature for the projected climate. Please note that this step may
take up to an hour to complete.
library(gaia)
# Path to the output folder where you wish to save the outputs. Change it accordingly
output_dir <- file.path(getwd(), 'gaia_output')
# calculate weigted climate
weighted_climate(pr_ncdf = pr_projection_file ,
tas_ncdf = tas_projection_file ,
timestep = 'monthly',
climate_model = 'canesm5',
climate_scenario = 'gcam-ref',
time_periods = seq(2015, 2100, 1),
output_dir = output_dir,
name_append = NULL)This will create a folder based on the specified
climate_model argument (e.g.,
output_dir/weighted_climate/canesm5). Inside this folder,
you will find files containing precipitation and temperature data
weighted by the irrigated and rainfed cropland areas for 26 MIRCA crops.
The file structure is organized in columns as follows:
[year, month, 1, 2, 3, ..., 265], where the numbers
correspond to country IDs. To view the country names associated with
these IDs, simply type gaia::country_id in the R console
after loading the gaia package. Below is an example of the
structure for the weighted precipitation data for rainfed soybean
(output_dir/weighted_climate/canesm5/anesm5_gcam-ref_month_precip_country_rfc_crop08_2015_2100.txt).
## Error in get(paste0(generic, ".", class), envir = get_method_env()) :
## object 'type_sum.accel' not found| year | month | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2015 | 1 | -9999 | 6.49 | 53.48 | 49.14 | -9999 | 54.15 | 93.11 | -9999 | -9999 | -9999 | 76.99 | 20.35 | 40.77 | -9999 | 79.06 | 62.98 | 20.78 | -9999 | -9999 | -9999 | -9999 | 11.02 | -9999 | 63.19 | -9999 | 89.59 | 0.25 | -9999 | 22.57 | 117.14 | -9999 | 63.04 | 77.41 | -9999 | 166.14 | -9999 | -9999 | 17.54 | 0.03 | 3.03 | 57.71 | 0.37 | 2.15 | 150.80 | -9999 | -9999 | 6.52 | 0.13 | 3.12 | 7.73 | -9999 | -9999 | -9999 | 17.29 | -9999 | 75.87 | 115.34 | -9999 | -9999 | 94.45 | 77.29 | -9999 | -9999 | -9999 | 49.48 | 1.97 | 136.74 | 16.82 | -9999 | -9999 | 181.48 | 6.05 | 33.99 | 180.31 | 29.20 | 76.55 | 53.88 | -9999 | -9999 | -9999 | -9999 | -9999 | 50.44 | 81.47 | -9999 | -9999 | -9999 | 197.25 | -9999 | -9999 | 47.75 | 47.29 | 2.00 | -9999 | -9999 | 42.87 | -9999 | -9999 | -9999 | -9999 | 33.18 | -9999 | 0.47 | -9999 | 61.26 | -9999 | -9999 | 95.09 | 0.11 | -9999 | 50.32 | -9999 | 2.77 | 284.82 | 23.04 | 24.06 | -9999 | -9999 | 16.18 | 55.43 | -9999 | 129.33 | -9999 | -9999 | -9999 | 8.56 | -9999 | 20.43 | 70.29 | -9999 | -9999 | -9999 | 11.75 | 1.50 | 53.44 | 30.20 | 72.39 | 3.19 | -9999 | -9999 | 50.10 | -9999 | 1.33 | 20.00 | 325.07 | 181.61 | 71.04 | -9999 | 0.09 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 7.98 | -9999 | 11.79 | -9999 | 1.54 | -9999 | -9999 | 74.39 | 177.16 | 0.13 | -9999 | -9999 | 34.32 | -9999 | -9999 | -9999 | 118.04 | 0.08 | 2.69 | -9999 | -9999 | 21.08 | -9999 | -9999 | -9999 | 7.85 | -9999 | -9999 | -9999 | 16.46 | -9999 | 57.32 | 106.92 | 255.06 | -9999 | 36.17 | -9999 | -9999 | -9999 | -9999 | -9999 | 33.58 | 41.92 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 48.66 | 75.23 | -9999 | 3.81 | 107.75 | -9999 | 66.90 | -9999 | -9999 | 37.74 | 11.81 | 176.95 | -9999 | -9999 | -9999 | 129.63 | 17.21 | 2.59 | 37.34 | 128.07 | 0.26 | 219.38 | -9999 | -9999 | -9999 | 94.92 | -9999 | -9999 | 27.69 | -9999 | -9999 | -9999 | 55.33 | 20.96 | -9999 | -9999 | 102.45 | 97.20 | -9999 | -9999 | 32.49 | 4.08 | -9999 | -9999 | -9999 | -9999 | -9999 | 168.30 | 86.62 | -9999 |
| 2015 | 2 | -9999 | 30.41 | 8.91 | 3.78 | -9999 | 9.01 | 231.87 | -9999 | -9999 | -9999 | 92.16 | 53.70 | 5.37 | -9999 | 57.36 | 10.30 | 52.14 | -9999 | -9999 | -9999 | -9999 | 13.03 | -9999 | 33.06 | -9999 | 69.36 | 5.59 | -9999 | 0.98 | 197.79 | -9999 | 4.41 | 4.83 | -9999 | 220.05 | -9999 | -9999 | 14.17 | 0.31 | 11.54 | 82.93 | 4.42 | 10.48 | 87.01 | -9999 | -9999 | 5.75 | -9999.00 | 7.12 | 17.88 | -9999 | -9999 | -9999 | 56.89 | -9999 | 229.73 | 200.63 | -9999 | -9999 | 23.22 | 2.16 | -9999 | -9999 | -9999 | 18.48 | 7.22 | 69.84 | 12.10 | -9999 | -9999 | 292.88 | 25.45 | 34.55 | 346.70 | 19.66 | 44.34 | 29.91 | -9999 | -9999 | -9999 | -9999 | -9999 | 22.42 | 7.72 | -9999 | -9999 | -9999 | 550.00 | -9999 | -9999 | 79.74 | 23.53 | 10.17 | -9999 | -9999 | 61.48 | -9999 | -9999 | -9999 | -9999 | 28.05 | -9999 | 3.56 | -9999 | 78.72 | -9999 | -9999 | 94.81 | 19.56 | -9999 | 5.59 | -9999 | 0.08 | 275.20 | 38.65 | 62.77 | -9999 | -9999 | 56.22 | 0.51 | -9999 | 144.31 | -9999 | -9999 | -9999 | 21.65 | -9999 | 22.08 | 143.45 | -9999 | -9999 | -9999 | 35.97 | 9.91 | 36.29 | 65.92 | 50.82 | 12.01 | -9999 | -9999 | 28.44 | -9999 | 10.56 | 6.26 | 198.64 | 170.30 | 111.63 | -9999 | 0.03 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 11.89 | -9999 | 4.87 | -9999 | 1.38 | -9999 | -9999 | 3.86 | 47.89 | 2.37 | -9999 | -9999 | 5.58 | -9999 | -9999 | -9999 | 62.62 | 0.08 | 9.48 | -9999 | -9999 | 17.87 | -9999 | -9999 | -9999 | 20.32 | -9999 | -9999 | -9999 | 9.54 | -9999 | 293.98 | 87.49 | 344.87 | -9999 | 20.76 | -9999 | -9999 | -9999 | -9999 | -9999 | 33.45 | 79.03 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 17.67 | 4.22 | -9999 | 6.99 | 20.70 | -9999 | 75.88 | -9999 | -9999 | 111.26 | 32.49 | 177.82 | -9999 | -9999 | -9999 | 5.37 | 55.30 | 61.85 | 91.50 | 183.13 | 4.86 | 140.38 | -9999 | -9999 | -9999 | 62.97 | -9999 | -9999 | 124.53 | -9999 | -9999 | -9999 | 125.10 | 16.45 | -9999 | -9999 | 50.57 | 111.90 | -9999 | -9999 | 13.09 | 14.92 | -9999 | -9999 | -9999 | -9999 | -9999 | 101.86 | 17.83 | -9999 |
| 2015 | 3 | -9999 | 3.93 | 48.34 | 21.77 | -9999 | 25.94 | 91.85 | -9999 | -9999 | -9999 | 73.31 | 19.71 | 9.08 | -9999 | 94.52 | 39.98 | 16.60 | -9999 | -9999 | -9999 | -9999 | 135.66 | -9999 | 33.38 | -9999 | 66.68 | 14.48 | -9999 | 176.60 | 124.19 | -9999 | 55.18 | 24.74 | -9999 | 118.04 | -9999 | -9999 | 45.17 | 0.22 | 26.96 | 80.59 | 41.72 | 32.41 | 67.80 | -9999 | -9999 | 74.23 | -9999.00 | 2.10 | 27.10 | -9999 | -9999 | -9999 | 95.78 | -9999 | 103.92 | 107.08 | -9999 | -9999 | 5.61 | 49.68 | -9999 | -9999 | -9999 | 38.40 | 85.90 | 80.86 | 0.08 | -9999 | -9999 | 211.38 | 8.95 | 34.85 | 254.78 | 0.66 | 25.08 | 2.68 | -9999 | -9999 | -9999 | -9999 | -9999 | 18.87 | 60.03 | -9999 | -9999 | -9999 | 322.15 | -9999 | -9999 | 35.23 | 62.85 | 46.38 | -9999 | -9999 | 49.80 | -9999 | -9999 | -9999 | -9999 | 38.22 | -9999 | 74.27 | -9999 | 15.92 | -9999 | -9999 | 70.07 | 46.44 | -9999 | 36.65 | -9999 | 1.54 | 280.88 | 13.52 | 46.58 | -9999 | -9999 | 27.68 | 25.57 | -9999 | 102.58 | -9999 | -9999 | -9999 | 11.04 | -9999 | 18.16 | 118.73 | -9999 | -9999 | -9999 | 27.04 | 24.00 | 17.25 | 38.58 | 46.57 | 92.55 | -9999 | -9999 | 20.95 | -9999 | 15.69 | 23.05 | 241.56 | 108.53 | 237.30 | -9999 | 0.05 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 2.84 | -9999 | 17.44 | -9999 | 4.23 | -9999 | -9999 | 22.98 | 53.60 | 12.06 | -9999 | -9999 | 55.84 | -9999 | -9999 | -9999 | 27.79 | 0.08 | 28.07 | -9999 | -9999 | 36.94 | -9999 | -9999 | -9999 | 4.17 | -9999 | -9999 | -9999 | 21.25 | -9999 | 101.03 | 101.79 | 267.56 | -9999 | 21.80 | -9999 | -9999 | -9999 | -9999 | -9999 | 29.08 | 56.85 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 38.64 | 52.05 | -9999 | 0.04 | 46.38 | -9999 | 97.43 | -9999 | -9999 | 206.29 | 16.32 | 104.16 | -9999 | -9999 | -9999 | 112.89 | 40.00 | 50.59 | 64.15 | 167.43 | 11.41 | 176.53 | -9999 | -9999 | -9999 | 16.27 | -9999 | -9999 | 97.41 | -9999 | -9999 | -9999 | 93.03 | 26.51 | -9999 | -9999 | 82.77 | 118.53 | -9999 | -9999 | 14.18 | 54.60 | -9999 | -9999 | -9999 | -9999 | -9999 | 102.56 | 33.89 | -9999 |
| 2015 | 4 | -9999 | 1.50 | 44.38 | 14.86 | -9999 | 23.55 | 244.90 | -9999 | -9999 | -9999 | 45.70 | 24.52 | 7.51 | -9999 | 30.20 | 17.90 | 15.43 | -9999 | -9999 | -9999 | -9999 | 327.43 | -9999 | 44.39 | -9999 | 133.12 | 50.79 | -9999 | 363.37 | 113.79 | -9999 | 33.54 | 20.82 | -9999 | 82.18 | -9999 | -9999 | 49.03 | 15.40 | 108.84 | 114.57 | 55.26 | 88.19 | 54.10 | -9999 | -9999 | 104.93 | 1.07 | 0.89 | 56.10 | -9999 | -9999 | -9999 | 109.45 | -9999 | 128.66 | 211.58 | -9999 | -9999 | 28.85 | 13.82 | -9999 | -9999 | -9999 | 41.27 | 120.70 | 38.33 | 0.21 | -9999 | -9999 | 118.87 | 6.56 | 67.77 | 313.33 | 1.46 | 15.73 | 14.27 | -9999 | -9999 | -9999 | -9999 | -9999 | 10.64 | 35.37 | -9999 | -9999 | -9999 | 300.55 | -9999 | -9999 | 42.25 | 39.82 | 58.17 | -9999 | -9999 | 63.44 | -9999 | -9999 | -9999 | -9999 | 57.24 | -9999 | 149.08 | -9999 | 29.14 | -9999 | -9999 | 237.77 | 150.72 | -9999 | 29.88 | -9999 | 2.07 | 209.95 | 14.55 | 54.13 | -9999 | -9999 | 14.03 | 14.76 | -9999 | 144.03 | -9999 | -9999 | -9999 | 6.78 | -9999 | 23.51 | 185.21 | -9999 | -9999 | -9999 | 17.52 | 193.76 | 14.17 | 29.68 | 106.76 | 160.17 | -9999 | -9999 | 13.66 | -9999 | 81.39 | 16.03 | 105.71 | 71.19 | 216.92 | -9999 | 8.53 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 0.10 | -9999 | 17.74 | -9999 | 9.88 | -9999 | -9999 | 33.32 | 22.95 | 37.43 | -9999 | -9999 | 148.79 | -9999 | -9999 | -9999 | 124.01 | 0.75 | 78.13 | -9999 | -9999 | 48.24 | -9999 | -9999 | -9999 | 1.72 | -9999 | -9999 | -9999 | 25.20 | -9999 | 130.86 | 105.63 | 82.10 | -9999 | 23.37 | -9999 | -9999 | -9999 | -9999 | -9999 | 39.78 | 97.05 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 41.52 | 16.49 | -9999 | 0.12 | 85.58 | -9999 | 160.91 | -9999 | -9999 | 75.16 | 108.04 | 63.79 | -9999 | -9999 | -9999 | 53.02 | 41.26 | 120.54 | 33.50 | 115.39 | 165.10 | 178.29 | -9999 | -9999 | -9999 | 25.50 | -9999 | -9999 | 118.14 | -9999 | -9999 | -9999 | 180.97 | 38.53 | -9999 | -9999 | 65.40 | 80.64 | -9999 | -9999 | 25.39 | 143.12 | -9999 | -9999 | -9999 | -9999 | -9999 | 46.47 | 6.89 | -9999 |
| 2015 | 5 | -9999 | 1.00 | 42.54 | 9.60 | -9999 | 35.29 | 21.89 | -9999 | -9999 | -9999 | 25.63 | 46.34 | 35.32 | -9999 | 20.92 | 69.80 | 30.46 | -9999 | -9999 | -9999 | -9999 | 372.70 | -9999 | 46.52 | -9999 | 64.89 | 111.74 | -9999 | 363.37 | 16.58 | -9999 | 74.97 | 3.17 | -9999 | 43.44 | -9999 | -9999 | 29.32 | 29.68 | 159.61 | 115.83 | 130.54 | 122.46 | 128.53 | -9999 | -9999 | 219.04 | 11.84 | -9999.00 | 71.13 | -9999 | -9999 | -9999 | 263.29 | -9999 | 209.88 | 53.50 | -9999 | -9999 | 152.37 | 69.07 | -9999 | -9999 | -9999 | 49.95 | 163.57 | 97.06 | 18.27 | -9999 | -9999 | 88.78 | 0.41 | 121.05 | 240.85 | 7.72 | 48.76 | 50.09 | -9999 | -9999 | -9999 | -9999 | -9999 | 38.82 | 79.04 | -9999 | -9999 | -9999 | 199.49 | -9999 | -9999 | 40.81 | 68.88 | 132.68 | -9999 | -9999 | 27.64 | -9999 | -9999 | -9999 | -9999 | 109.31 | -9999 | 208.63 | -9999 | 179.98 | -9999 | -9999 | 93.67 | 136.28 | -9999 | 107.96 | -9999 | 7.73 | 198.90 | 9.33 | 21.02 | -9999 | -9999 | 2.47 | 84.36 | -9999 | 134.05 | -9999 | -9999 | -9999 | 1.27 | -9999 | 10.49 | 190.78 | -9999 | -9999 | -9999 | 25.36 | 186.99 | 40.14 | 2.86 | 4.94 | 321.55 | -9999 | -9999 | 26.52 | -9999 | 60.64 | 26.58 | 112.07 | 4.18 | 135.64 | -9999 | 24.52 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 1.21 | -9999 | 14.85 | -9999 | 12.90 | -9999 | -9999 | 9.02 | 5.27 | 0.50 | -9999 | -9999 | 214.40 | -9999 | -9999 | -9999 | 116.91 | 14.29 | 125.58 | -9999 | -9999 | 134.35 | -9999 | -9999 | -9999 | 3.57 | -9999 | -9999 | -9999 | 110.80 | -9999 | 100.00 | 36.91 | 225.72 | -9999 | 32.41 | -9999 | -9999 | -9999 | -9999 | -9999 | 38.78 | 141.63 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 64.10 | 110.17 | -9999 | 41.83 | 9.48 | -9999 | 196.79 | -9999 | -9999 | 136.68 | 133.25 | 448.10 | -9999 | -9999 | -9999 | 168.21 | 11.61 | 64.06 | 28.30 | 53.48 | 190.77 | 257.67 | -9999 | -9999 | -9999 | 107.09 | -9999 | -9999 | 33.21 | -9999 | -9999 | -9999 | 208.63 | 18.99 | -9999 | -9999 | 120.63 | 107.58 | -9999 | -9999 | 173.95 | 160.52 | -9999 | -9999 | -9999 | -9999 | -9999 | 0.45 | 1.15 | -9999 |
| 2015 | 6 | -9999 | 4.13 | 69.80 | 2.75 | -9999 | 7.10 | 17.49 | -9999 | -9999 | -9999 | 14.12 | 22.80 | 10.47 | -9999 | 136.45 | 93.56 | 8.97 | -9999 | -9999 | -9999 | -9999 | 546.80 | -9999 | 73.68 | -9999 | 219.43 | 271.11 | -9999 | 545.22 | 53.24 | -9999 | 94.84 | 0.65 | -9999 | 55.70 | -9999 | -9999 | 95.81 | 184.62 | 162.74 | 2.72 | 96.42 | 169.98 | 52.69 | -9999 | -9999 | 157.08 | 79.99 | -9999.00 | 97.69 | -9999 | -9999 | -9999 | 238.34 | -9999 | 16.06 | 21.23 | -9999 | -9999 | 368.82 | 95.78 | -9999 | -9999 | -9999 | 51.36 | 196.97 | 100.18 | 15.66 | -9999 | -9999 | 24.78 | 0.07 | 244.32 | 237.93 | 34.98 | 52.83 | 91.85 | -9999 | -9999 | -9999 | -9999 | -9999 | 27.30 | 35.88 | -9999 | -9999 | -9999 | 69.81 | -9999 | -9999 | 44.35 | 62.13 | 227.70 | -9999 | -9999 | 35.91 | -9999 | -9999 | -9999 | -9999 | 152.41 | -9999 | 235.35 | -9999 | 117.48 | -9999 | -9999 | 302.65 | 198.67 | -9999 | 173.93 | -9999 | 97.16 | 133.62 | 2.77 | 5.29 | -9999 | -9999 | -9999.00 | 60.35 | -9999 | 204.54 | -9999 | -9999 | -9999 | -9999.00 | -9999 | 28.89 | 63.46 | -9999 | -9999 | -9999 | 15.41 | 284.80 | 44.04 | 0.13 | 3.34 | 372.58 | -9999 | -9999 | 25.87 | -9999 | 94.42 | 62.27 | 44.15 | 0.57 | 77.01 | -9999 | 140.55 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 11.63 | -9999 | 91.76 | -9999 | 43.40 | -9999 | -9999 | 0.49 | 12.28 | 0.77 | -9999 | -9999 | 231.75 | -9999 | -9999 | -9999 | 359.83 | 161.03 | 187.05 | -9999 | -9999 | 103.79 | -9999 | -9999 | -9999 | 32.72 | -9999 | -9999 | -9999 | 218.47 | -9999 | 125.50 | 29.08 | 202.34 | -9999 | 68.18 | -9999 | -9999 | -9999 | -9999 | -9999 | 50.75 | 11.89 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 144.82 | 144.77 | -9999 | 13.52 | 10.35 | -9999 | 155.21 | -9999 | -9999 | 122.24 | 48.05 | 288.06 | -9999 | -9999 | -9999 | 126.78 | 1.15 | 84.59 | 13.78 | 2.19 | 129.76 | 82.15 | -9999 | -9999 | -9999 | 143.71 | -9999 | -9999 | 3.95 | -9999 | -9999 | -9999 | 43.08 | 68.74 | -9999 | -9999 | 141.61 | 53.95 | -9999 | -9999 | 168.99 | 197.31 | -9999 | -9999 | -9999 | -9999 | -9999 | 0.09 | 2.64 | -9999 |
| 2015 | 7 | -9999 | 14.89 | 7.15 | 0.73 | -9999 | 7.97 | 12.78 | -9999 | -9999 | -9999 | 26.30 | 24.96 | 11.53 | -9999 | 17.42 | 49.66 | 15.09 | -9999 | -9999 | -9999 | -9999 | 1631.77 | -9999 | 73.38 | -9999 | 305.47 | 246.39 | -9999 | 1854.65 | 17.44 | -9999 | 20.24 | 3.56 | -9999 | 33.81 | -9999 | -9999 | 29.32 | 144.70 | 149.18 | 17.59 | 107.21 | 243.23 | 80.11 | -9999 | -9999 | 88.98 | 180.63 | -9999.00 | 117.73 | -9999 | -9999 | -9999 | 233.21 | -9999 | 17.07 | 33.23 | -9999 | -9999 | 432.28 | 22.23 | -9999 | -9999 | -9999 | 42.06 | 142.39 | 121.87 | 38.47 | -9999 | -9999 | 4.06 | 0.04 | 307.71 | 10.83 | 244.26 | 98.82 | 246.67 | -9999 | -9999 | -9999 | -9999 | -9999 | 64.38 | 17.64 | -9999 | -9999 | -9999 | 14.67 | -9999 | -9999 | 21.17 | 55.24 | 115.33 | -9999 | -9999 | 1.44 | -9999 | -9999 | -9999 | -9999 | 236.84 | -9999 | 257.32 | -9999 | 125.48 | -9999 | -9999 | 322.91 | 286.98 | -9999 | 36.35 | -9999 | 490.54 | 190.94 | 4.20 | 1.07 | -9999 | -9999 | 0.57 | 33.29 | -9999 | 192.56 | -9999 | -9999 | -9999 | 0.69 | -9999 | 45.31 | 254.87 | -9999 | -9999 | -9999 | 8.38 | 122.14 | 79.95 | 0.83 | 7.51 | 306.20 | -9999 | -9999 | 71.10 | -9999 | 97.10 | 4.80 | 31.95 | 0.13 | 124.70 | -9999 | 115.84 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 20.65 | -9999 | 28.48 | -9999 | 52.17 | -9999 | -9999 | 1.01 | 10.05 | 2.65 | -9999 | -9999 | 1243.94 | -9999 | -9999 | -9999 | 440.23 | 158.48 | 254.94 | -9999 | -9999 | 334.90 | -9999 | -9999 | -9999 | 104.76 | -9999 | -9999 | -9999 | 169.22 | -9999 | 97.54 | 5.53 | 164.51 | -9999 | 44.15 | -9999 | -9999 | -9999 | -9999 | -9999 | 65.03 | 37.12 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 30.35 | 70.56 | -9999 | 52.33 | 13.67 | -9999 | 403.34 | -9999 | -9999 | 190.22 | 160.92 | 244.95 | -9999 | -9999 | -9999 | 75.49 | 1.00 | 204.67 | 11.02 | 5.64 | 48.30 | 61.28 | -9999 | -9999 | -9999 | 117.63 | -9999 | -9999 | 4.16 | -9999 | -9999 | -9999 | 298.24 | 48.65 | -9999 | -9999 | 21.67 | 53.06 | -9999 | -9999 | 183.24 | 140.30 | -9999 | -9999 | -9999 | -9999 | -9999 | 0.02 | 6.11 | -9999 |
| 2015 | 8 | -9999 | 1.52 | 20.66 | 3.24 | -9999 | 7.05 | 5.65 | -9999 | -9999 | -9999 | 46.95 | 5.90 | 12.00 | -9999 | 28.24 | 49.56 | 3.88 | -9999 | -9999 | -9999 | -9999 | 574.15 | -9999 | 69.10 | -9999 | 162.74 | 157.76 | -9999 | 651.03 | 28.57 | -9999 | 32.26 | 0.17 | -9999 | 53.68 | -9999 | -9999 | 73.86 | 203.95 | 134.30 | 9.02 | 158.68 | 193.61 | 100.55 | -9999 | -9999 | 129.69 | 286.23 | 0.74 | 152.13 | -9999 | -9999 | -9999 | 156.38 | -9999 | 20.40 | 22.59 | -9999 | -9999 | 481.77 | 37.02 | -9999 | -9999 | -9999 | 31.07 | 122.49 | 63.50 | 96.00 | -9999 | -9999 | 21.30 | 0.30 | 172.84 | 42.62 | 297.98 | 116.14 | 349.86 | -9999 | -9999 | -9999 | -9999 | -9999 | 87.33 | 15.05 | -9999 | -9999 | -9999 | 30.36 | -9999 | -9999 | 12.91 | 43.72 | 126.20 | -9999 | -9999 | 10.88 | -9999 | -9999 | -9999 | -9999 | 122.96 | -9999 | 185.58 | -9999 | 101.57 | -9999 | -9999 | 299.00 | 115.59 | -9999 | 40.19 | -9999 | 196.76 | 94.33 | 0.83 | 0.01 | -9999 | -9999 | 0.14 | 26.23 | -9999 | 118.32 | -9999 | -9999 | -9999 | -9999.00 | -9999 | 23.53 | 42.51 | -9999 | -9999 | -9999 | 1.17 | 131.75 | 83.76 | 0.10 | 18.59 | 243.42 | -9999 | -9999 | 40.49 | -9999 | 39.58 | 21.80 | 11.99 | 2.11 | 186.93 | -9999 | 171.94 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 29.66 | -9999 | 87.03 | -9999 | 72.18 | -9999 | -9999 | 0.65 | 2.72 | 0.59 | -9999 | -9999 | 291.40 | -9999 | -9999 | -9999 | 494.82 | 204.14 | 167.37 | -9999 | -9999 | 161.26 | -9999 | -9999 | -9999 | 13.17 | -9999 | -9999 | -9999 | 301.03 | -9999 | 85.56 | 21.95 | 210.58 | -9999 | 30.94 | -9999 | -9999 | -9999 | -9999 | -9999 | 77.89 | 23.05 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 38.89 | 108.64 | -9999 | 70.13 | 11.57 | -9999 | 210.42 | -9999 | -9999 | 84.58 | 190.87 | 93.67 | -9999 | -9999 | -9999 | 65.52 | 0.23 | 96.63 | 0.71 | 5.48 | 102.88 | 25.92 | -9999 | -9999 | -9999 | 114.18 | -9999 | -9999 | 1.40 | -9999 | -9999 | -9999 | 100.27 | 61.34 | -9999 | -9999 | 60.14 | 190.72 | -9999 | -9999 | 126.60 | 125.23 | -9999 | -9999 | -9999 | -9999 | -9999 | 0.46 | 0.20 | -9999 |
| 2015 | 9 | -9999 | 1.77 | 59.01 | 10.26 | -9999 | 15.90 | 84.73 | -9999 | -9999 | -9999 | 28.41 | 51.11 | 4.86 | -9999 | 17.02 | 81.38 | 42.12 | -9999 | -9999 | -9999 | -9999 | 225.45 | -9999 | 47.80 | -9999 | 184.97 | 106.49 | -9999 | 252.38 | 27.40 | -9999 | 132.07 | 12.47 | -9999 | 41.08 | -9999 | -9999 | 41.93 | 70.92 | 180.29 | 2.74 | 273.61 | 181.46 | 146.95 | -9999 | -9999 | 320.11 | 60.54 | 0.04 | 57.08 | -9999 | -9999 | -9999 | 199.34 | -9999 | 74.73 | 68.04 | -9999 | -9999 | 375.46 | 200.83 | -9999 | -9999 | -9999 | 34.48 | 198.01 | 39.75 | 13.12 | -9999 | -9999 | 23.44 | 0.03 | 255.94 | 325.39 | 68.07 | 25.13 | 73.52 | -9999 | -9999 | -9999 | -9999 | -9999 | 23.56 | 25.64 | -9999 | -9999 | -9999 | 148.08 | -9999 | -9999 | 59.75 | 28.40 | 121.04 | -9999 | -9999 | 21.88 | -9999 | -9999 | -9999 | -9999 | 188.75 | -9999 | 330.30 | -9999 | 150.61 | -9999 | -9999 | 384.65 | 148.40 | -9999 | 150.70 | -9999 | 17.87 | 80.15 | 9.94 | 0.70 | -9999 | -9999 | 0.20 | 85.16 | -9999 | 120.89 | -9999 | -9999 | -9999 | 0.20 | -9999 | 19.47 | 61.11 | -9999 | -9999 | -9999 | 2.07 | 199.18 | 14.05 | 3.77 | 65.84 | 481.03 | -9999 | -9999 | 21.35 | -9999 | 76.85 | 26.20 | 33.93 | 2.49 | 300.41 | -9999 | 69.29 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 34.18 | -9999 | 47.17 | -9999 | 19.45 | -9999 | -9999 | 9.20 | 9.43 | 0.96 | -9999 | -9999 | 82.93 | -9999 | -9999 | -9999 | 464.37 | 25.35 | 117.22 | -9999 | -9999 | 141.38 | -9999 | -9999 | -9999 | 5.18 | -9999 | -9999 | -9999 | 208.82 | -9999 | 46.18 | 29.86 | 141.54 | -9999 | 27.74 | -9999 | -9999 | -9999 | -9999 | -9999 | 37.71 | 6.79 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 40.67 | 286.62 | -9999 | 34.68 | 50.85 | -9999 | 150.92 | -9999 | -9999 | 100.73 | 27.70 | 26.88 | -9999 | -9999 | -9999 | 37.11 | 2.55 | 63.42 | 2.08 | 6.95 | 260.32 | 1.45 | -9999 | -9999 | -9999 | 90.24 | -9999 | -9999 | 54.93 | -9999 | -9999 | -9999 | 56.55 | 33.86 | -9999 | -9999 | 97.57 | 81.11 | -9999 | -9999 | 115.32 | 116.44 | -9999 | -9999 | -9999 | -9999 | -9999 | 1.79 | 0.82 | -9999 |
| 2015 | 10 | -9999 | 8.42 | 114.76 | 10.99 | -9999 | 19.21 | 110.07 | -9999 | -9999 | -9999 | 33.55 | 68.82 | 13.08 | -9999 | 66.63 | 52.64 | 64.54 | -9999 | -9999 | -9999 | -9999 | 67.11 | -9999 | 17.70 | -9999 | 202.50 | 249.85 | -9999 | 12.82 | 16.43 | -9999 | 119.37 | 11.78 | -9999 | 48.07 | -9999 | -9999 | 19.16 | 98.19 | 160.32 | 109.91 | 111.15 | 189.21 | 131.06 | -9999 | -9999 | 285.62 | 92.25 | 0.97 | 64.17 | -9999 | -9999 | -9999 | 100.95 | -9999 | 170.35 | 123.20 | -9999 | -9999 | 260.69 | 123.50 | -9999 | -9999 | -9999 | 31.98 | 173.37 | 97.53 | 0.53 | -9999 | -9999 | 33.18 | 3.21 | 194.26 | 540.25 | 1.18 | 45.68 | 20.51 | -9999 | -9999 | -9999 | -9999 | -9999 | 43.85 | 33.21 | -9999 | -9999 | -9999 | 360.42 | -9999 | -9999 | 86.84 | 28.61 | 135.37 | -9999 | -9999 | 161.52 | -9999 | -9999 | -9999 | -9999 | 142.34 | -9999 | 343.80 | -9999 | 45.83 | -9999 | -9999 | 322.71 | 10.27 | -9999 | 79.54 | -9999 | 1.06 | 184.08 | 12.16 | 34.29 | -9999 | -9999 | 15.35 | 52.01 | -9999 | 204.25 | -9999 | -9999 | -9999 | 8.47 | -9999 | 26.07 | 236.41 | -9999 | -9999 | -9999 | 40.00 | 46.57 | 30.27 | 31.70 | 60.42 | 419.62 | -9999 | -9999 | 15.35 | -9999 | 13.83 | 40.91 | 31.85 | 0.50 | 442.25 | -9999 | 89.79 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 21.69 | -9999 | 5.85 | -9999 | 21.78 | -9999 | -9999 | 4.40 | 11.17 | 0.20 | -9999 | -9999 | 7.15 | -9999 | -9999 | -9999 | 237.62 | 70.70 | 203.90 | -9999 | -9999 | 88.16 | -9999 | -9999 | -9999 | 5.83 | -9999 | -9999 | -9999 | 165.11 | -9999 | 61.57 | 36.14 | 206.20 | -9999 | 15.12 | -9999 | -9999 | -9999 | -9999 | -9999 | 32.70 | 150.52 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 32.20 | 140.12 | -9999 | 11.39 | 44.51 | -9999 | 152.61 | -9999 | -9999 | 334.43 | 99.15 | 20.14 | -9999 | -9999 | -9999 | 53.48 | 28.41 | 19.57 | 64.58 | 48.50 | 98.19 | 16.93 | -9999 | -9999 | -9999 | 61.84 | -9999 | -9999 | 136.15 | -9999 | -9999 | -9999 | 218.75 | 12.70 | -9999 | -9999 | 89.20 | 20.79 | -9999 | -9999 | 98.09 | 65.22 | -9999 | -9999 | -9999 | -9999 | -9999 | 1.27 | 4.05 | -9999 |
| 2015 | 11 | -9999 | 0.23 | 113.53 | 1.98 | -9999 | 22.55 | 124.04 | -9999 | -9999 | -9999 | 93.71 | 26.15 | 29.03 | -9999 | 51.93 | 57.70 | 20.77 | -9999 | -9999 | -9999 | -9999 | 152.09 | -9999 | 31.51 | -9999 | 39.51 | 10.00 | -9999 | 66.28 | 71.24 | -9999 | 129.24 | 32.18 | -9999 | 82.31 | -9999 | -9999 | 78.60 | 0.62 | 76.21 | 35.59 | 74.14 | 12.55 | 73.98 | -9999 | -9999 | 80.09 | -9999.00 | 3.49 | 47.40 | -9999 | -9999 | -9999 | 170.87 | -9999 | 161.26 | 137.06 | -9999 | -9999 | 77.05 | 116.61 | -9999 | -9999 | -9999 | 34.59 | 83.54 | 90.89 | 0.31 | -9999 | -9999 | 32.59 | 3.32 | 21.54 | 893.66 | -9999.00 | 37.27 | 4.79 | -9999 | -9999 | -9999 | -9999 | -9999 | 28.26 | 36.66 | -9999 | -9999 | -9999 | 816.61 | -9999 | -9999 | 44.62 | 28.34 | 36.78 | -9999 | -9999 | 170.43 | -9999 | -9999 | -9999 | -9999 | 20.20 | -9999 | 87.78 | -9999 | 65.06 | -9999 | -9999 | 105.98 | 11.17 | -9999 | 86.86 | -9999 | 13.22 | 216.99 | 9.19 | 33.06 | -9999 | -9999 | 28.36 | 39.88 | -9999 | 150.24 | -9999 | -9999 | -9999 | 4.73 | -9999 | 12.98 | 74.98 | -9999 | -9999 | -9999 | 5.13 | 64.99 | 24.13 | 53.36 | 83.45 | 83.50 | -9999 | -9999 | 18.07 | -9999 | 9.73 | 48.68 | 172.62 | 69.02 | 402.99 | -9999 | 0.34 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 11.98 | -9999 | 30.71 | -9999 | 2.82 | -9999 | -9999 | 1.60 | 58.32 | 3.99 | -9999 | -9999 | 15.24 | -9999 | -9999 | -9999 | 89.10 | 0.05 | 7.25 | -9999 | -9999 | 38.75 | -9999 | -9999 | -9999 | 0.24 | -9999 | -9999 | -9999 | 151.21 | -9999 | 102.27 | 78.17 | 148.40 | -9999 | 26.12 | -9999 | -9999 | -9999 | -9999 | -9999 | 22.33 | 43.15 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 73.14 | 103.44 | -9999 | 1.09 | 68.66 | -9999 | 107.85 | -9999 | -9999 | 172.92 | 12.43 | 85.51 | -9999 | -9999 | -9999 | 44.01 | 33.73 | 15.72 | 14.55 | 37.31 | 32.34 | 174.07 | -9999 | -9999 | -9999 | 79.49 | -9999 | -9999 | 119.46 | -9999 | -9999 | -9999 | 58.22 | 24.91 | -9999 | -9999 | 49.73 | 77.83 | -9999 | -9999 | 78.66 | 133.38 | -9999 | -9999 | -9999 | -9999 | -9999 | 56.87 | 37.94 | -9999 |
| 2015 | 12 | -9999 | 3.79 | 7.83 | 18.22 | -9999 | 25.63 | 299.60 | -9999 | -9999 | -9999 | 89.58 | 31.74 | 31.75 | -9999 | 34.77 | 8.66 | 40.55 | -9999 | -9999 | -9999 | -9999 | 3.06 | -9999 | 15.47 | -9999 | 70.73 | 0.87 | -9999 | 2.93 | 191.35 | -9999 | 13.47 | 59.56 | -9999 | 188.32 | -9999 | -9999 | 5.81 | 0.06 | 16.18 | 57.40 | 37.31 | 5.07 | 75.78 | -9999 | -9999 | 0.73 | 0.10 | 1.13 | 17.71 | -9999 | -9999 | -9999 | 36.32 | -9999 | 140.27 | 226.73 | -9999 | -9999 | 73.69 | 12.62 | -9999 | -9999 | -9999 | 7.94 | 7.03 | 93.70 | 0.24 | -9999 | -9999 | 78.73 | 28.09 | 24.89 | 317.79 | 0.54 | 37.33 | 4.78 | -9999 | -9999 | -9999 | -9999 | -9999 | 42.05 | 29.48 | -9999 | -9999 | -9999 | 553.96 | -9999 | -9999 | 34.33 | 14.63 | 3.12 | -9999 | -9999 | 15.47 | -9999 | -9999 | -9999 | -9999 | 18.12 | -9999 | 1.43 | -9999 | 103.55 | -9999 | -9999 | 103.18 | 38.14 | -9999 | 9.64 | -9999 | 0.32 | 259.02 | 23.44 | 9.51 | -9999 | -9999 | 28.70 | 6.61 | -9999 | 106.50 | -9999 | -9999 | -9999 | 4.39 | -9999 | 42.80 | 37.88 | -9999 | -9999 | -9999 | 32.09 | 29.41 | 24.07 | 31.17 | 53.12 | 11.58 | -9999 | -9999 | 16.83 | -9999 | 17.12 | 3.10 | 140.56 | 310.22 | 292.03 | -9999 | 0.01 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 17.09 | -9999 | 0.94 | -9999 | 5.23 | -9999 | -9999 | 35.43 | 207.67 | 0.22 | -9999 | -9999 | 0.28 | -9999 | -9999 | -9999 | 95.41 | 0.10 | 2.13 | -9999 | -9999 | 32.51 | -9999 | -9999 | -9999 | 1.01 | -9999 | -9999 | -9999 | 44.34 | -9999 | 81.50 | 83.27 | 161.89 | -9999 | 7.86 | -9999 | -9999 | -9999 | -9999 | -9999 | 34.70 | 58.09 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | -9999 | 8.69 | 15.56 | -9999 | 2.45 | 70.07 | -9999 | 64.12 | -9999 | -9999 | 373.31 | 1.25 | 273.71 | -9999 | -9999 | -9999 | 24.05 | 32.43 | 56.14 | 57.83 | 161.55 | 13.60 | 211.62 | -9999 | -9999 | -9999 | 103.21 | -9999 | -9999 | 132.74 | -9999 | -9999 | -9999 | 13.63 | 3.59 | -9999 | -9999 | 83.89 | 116.45 | -9999 | -9999 | 40.10 | 75.98 | -9999 | -9999 | -9999 | -9999 | -9999 | 271.86 | 113.18 | -9999 |
| Note: | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| This only shows the first 12 lines of the example data. Value -9999 indicates there is no cropland area for such crop in the country. |
crop_calendars
The crop calendar outlines the planting and harvesting months for
each predefined crop across different countries. Users can modify the
crop calendar file to add additional crops, following the established
format. Below is an example of the crop calendar structure. You can also
access the crop calendar output at
output_dir/data_processed/crop_calendar.csv.
# Path to the output folder where you wish to save the outputs. Change it accordingly
output_dir <- file.path(getwd(), 'gaia_output')
# calculate crop calendars
crop_cal <- crop_calendars(output_dir = output_dir)
# print result
crop_cal| iso | wheat | rice | maize | cassava | soybean | sugarcane | sugarbeet | cotton | sorghum | root_tuber | sunflower | plant | harvest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| alb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 6 |
| alb | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 9 |
| alb | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 8 |
| alb | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 3 |
| alb | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 9 |
| alb | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 10 |
| alb | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 7 |
| alb | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 8 |
| alb | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 6 |
| alb | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 6 |
| Note: | |||||||||||||
| This only shows the first 10 lines of the example data. |
data_aggregation
The data_aggregation function calculates crop growing
seasons using climate variables processed by
weighted_climate along with crop calendars for both
historical and projected periods. This function prepares climate and
yield data for subsequent model fitting.
The data_aggregation function creates two types of files
in the output_dir/data_processed folder:
historic_vars_[crop-name].csv: This file contains historical data for each crop. For example, historical information for soybean is illustrated in Table 3.weather_[climate-model]_[climate-scenario]_[crop-name].csv: This file includes aggregated climate data for the specified crop. An example of climate information for soybean is shown in Table 4.
# Path to the output folder where you wish to save the outputs. Change it accordingly
output_dir <- file.path(getwd(), 'gaia_output')
# aggregate crop and climate information at the country level
data_agg <- data_aggregation(climate_hist_dir = climate_hist_dir,
climate_impact_dir = climate_impact_dir,
climate_model = 'canesm5',
climate_scenario = 'gcam-ref',
output_dir = output_dir)| iso | year | gdp_pcap_ppp | crop | area_harvest | irr_rf | irr_equip | co2_conc | yield | grow_month | grow_season | temp | precip |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 1 | 1 | 17.49 | 71.90 |
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 2 | 1 | 15.76 | 171.47 |
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 3 | 1 | 15.33 | 64.36 |
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 4 | 1 | 12.31 | 65.97 |
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 5 | 1 | 8.92 | 28.31 |
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 6 | 0 | 0.00 | 0.00 |
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 7 | 0 | 0.00 | 0.00 |
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 8 | 0 | 0.00 | 0.00 |
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 9 | 0 | 0.00 | 0.00 |
| arg | 1961 | 7.116181 | soybean | 980 | rf | 5.233 | 317.64 | 9765 | 10 | 0 | 0.00 | 0.00 |
| Note: | ||||||||||||
| This only shows the first 10 lines of the example data. |
| year | iso | crop | temp_mean | temp_max | temp_min | precip_mean | precip_max | precip_min | GCAM_region_name | co2_conc |
|---|---|---|---|---|---|---|---|---|---|---|
| 2015 | arg | soybean | 13.870 | 16.80 | 9.42 | 62.758 | 92.16 | 25.63 | Argentina | 403.1340 |
| 2016 | arg | soybean | 14.870 | 18.54 | 10.30 | 76.034 | 139.59 | 35.53 | Argentina | 406.0092 |
| 2017 | arg | soybean | 14.342 | 17.58 | 10.87 | 77.414 | 93.54 | 53.58 | Argentina | 408.8844 |
| 2018 | arg | soybean | 14.518 | 17.20 | 10.33 | 57.868 | 77.41 | 32.60 | Argentina | 411.7596 |
| 2019 | arg | soybean | 14.284 | 18.37 | 9.33 | 112.450 | 248.10 | 28.94 | Argentina | 414.6348 |
| 2020 | arg | soybean | 14.164 | 16.46 | 9.52 | 94.260 | 177.09 | 48.33 | Argentina | 417.5100 |
| 2021 | arg | soybean | 14.372 | 18.51 | 10.69 | 81.306 | 130.18 | 41.56 | Argentina | 420.9428 |
| 2022 | arg | soybean | 14.638 | 18.34 | 10.35 | 66.506 | 106.83 | 25.89 | Argentina | 424.3756 |
| 2023 | arg | soybean | 14.088 | 16.64 | 10.58 | 84.342 | 154.99 | 30.03 | Argentina | 427.8084 |
| 2024 | arg | soybean | 14.548 | 18.03 | 10.60 | 82.828 | 126.91 | 15.84 | Argentina | 431.2412 |
| Note: | ||||||||||
| This only shows the first 10 lines of the example data. |
yield_regression
The gaia::yield_regression function performs empirical
model fitting and generates diagnostic plots comparing modeled yields to
FAO recorded yields for each crop type at the GCAM region scale. An
example of this comparison for soybean is illustrated in Figure 2.
# Path to the output folder where you wish to save the outputs. Change it accordingly
output_dir <- file.path(getwd(), 'gaia_output')
# perform empirical regression
yield_regression(diagnostics = TRUE,
output_dir = output_dir)
Figure 2: Model fitted yields versus FAO yields for soybean.
The parameters of the fitted model can be found in the file located
at
output_dir/data_processed/reg_out_[crop-name]_fit_lnyield_mmm_quad_noco2_nogdp.csv.
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -17.3684531 | 0.9409990 | -18.457461 | 0.0000000 |
| year | 0.0140001 | 0.0004217 | 33.203002 | 0.0000000 |
| temp_mean | -0.2550105 | 0.1173558 | -2.172969 | 0.0300334 |
| temp_mean_2 | 0.0084446 | 0.0038428 | 2.197496 | 0.0282299 |
| temp_max | 0.1501057 | 0.0757098 | 1.982646 | 0.0476996 |
| temp_max_2 | -0.0044598 | 0.0019971 | -2.233172 | 0.0257738 |
| temp_min | 0.0436482 | 0.0189879 | 2.298740 | 0.0217397 |
| temp_min_2 | -0.0022589 | 0.0009430 | -2.395559 | 0.0167910 |
| precip_mean | -0.0027821 | 0.0011221 | -2.479350 | 0.0133377 |
| precip_mean_2 | 0.0000092 | 0.0000049 | 1.871032 | 0.0616531 |
| Note: | ||||
| This only shows the first 10 lines of the example data. |
yield_shock_projection
Once the gaia model has completed fitting, the
gaia::yield_shock_projection function calculates the
projected annual yield shocks based on the input climate data. For
coarse-scale models like GCAM, gaia also computes smoothed
yield shocks using a user-specified smoothing window (the default window
is 20 years). In the smoothed outputs, the yield shocks at the base year
will be set to 1. The results are provided in both CSV
outputs and diagnostic plots. An example of annual soybean yield shocks
is illustrated in Figure 3.
# Path to the output folder where you wish to save the outputs. Change it accordingly
output_dir <- file.path(getwd(), 'gaia_output')
# calculate projected yield shocks
out_yield_shock <- yield_shock_projection(use_default_coeff = FALSE,
climate_model = 'canesm5',
climate_scenario = 'gcam-ref',
base_year = 2015,
start_year = 2015,
end_year = 2100,
smooth_window = 20,
diagnostics = TRUE,
output_dir = output_dir)
Figure 3: Projected yield shocks for soybean.
The output data for annual and smoothed yield shocks projection are
located under folders output_dir/yield_impacts_annual
(Table 6) and output_dir/yield_impacts_smooth (Table
7).
| GCAM_region_name | iso | crop | year | yield_impact |
|---|---|---|---|---|
| Africa_Southern | tza | soybean | 2015 | 1.0057519 |
| Africa_Western | nga | soybean | 2015 | 1.0377940 |
| Argentina | arg | soybean | 2015 | 1.0151492 |
| Canada | can | soybean | 2015 | 1.0049194 |
| China | chn | soybean | 2015 | 0.9769140 |
| Colombia | col | soybean | 2015 | NA |
| EU-15 | ita | soybean | 2015 | 0.9985774 |
| Europe_Non_EU | tur | soybean | 2015 | 1.0566004 |
| India | ind | soybean | 2015 | 1.0545602 |
| Mexico | mex | soybean | 2015 | 0.9773951 |
| Note: | ||||
| This only shows the first 10 lines of the example data. |
| crop | model | scenario | iso | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 | 2031 | 2032 | 2033 | 2034 | 2035 | 2036 | 2037 | 2038 | 2039 | 2040 | 2041 | 2042 | 2043 | 2044 | 2045 | 2046 | 2047 | 2048 | 2049 | 2050 | 2051 | 2052 | 2053 | 2054 | 2055 | 2056 | 2057 | 2058 | 2059 | 2060 | 2061 | 2062 | 2063 | 2064 | 2065 | 2066 | 2067 | 2068 | 2069 | 2070 | 2071 | 2072 | 2073 | 2074 | 2075 | 2076 | 2077 | 2078 | 2079 | 2080 | 2081 | 2082 | 2083 | 2084 | 2085 | 2086 | 2087 | 2088 | 2089 | 2090 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| soybean | canesm5 | gcam-ref | arg | 1 | 0.9993807 | 0.9987614 | 0.9981420 | 0.9975227 | 0.9969034 | 0.9968355 | 0.9967676 | 0.9966996 | 0.9966317 | 0.9965638 | 0.9964959 | 0.9964280 | 0.9963600 | 0.9962921 | 0.9962242 | 0.9958960 | 0.9955678 | 0.9952396 | 0.9949114 | 0.9945832 | 0.9942550 | 0.9939268 | 0.9935986 | 0.9932704 | 0.9929422 | 0.9919359 | 0.9909297 | 0.9899234 | 0.9889172 | 0.9879110 | 0.9869047 | 0.9858985 | 0.9848922 | 0.9838860 | 0.9828797 | 0.9821853 | 0.9814908 | 0.9807964 | 0.9801019 | 0.9794075 | 0.9787130 | 0.9780186 | 0.9773241 | 0.9766297 | 0.9759352 | 0.9760368 | 0.9761383 | 0.9762399 | 0.9763414 | 0.9764430 | 0.9765446 | 0.9766461 | 0.9767477 | 0.9768492 | 0.9769508 | 0.9767368 | 0.9765229 | 0.9763089 | 0.9760950 | 0.9758811 | 0.9756671 | 0.9754532 | 0.9752393 | 0.9750253 | 0.9748114 | 0.9749882 | 0.9751650 | 0.9753418 | 0.9755187 | 0.9756955 | 0.9758723 | 0.9760491 | 0.9762259 | 0.9764028 | 0.9765796 |
| soybean | canesm5 | gcam-ref | bol | 1 | 1.0004914 | 1.0009828 | 1.0014743 | 1.0019657 | 1.0024571 | 1.0022937 | 1.0021304 | 1.0019670 | 1.0018036 | 1.0016403 | 1.0014769 | 1.0013135 | 1.0011502 | 1.0009868 | 1.0008234 | 0.9995553 | 0.9982872 | 0.9970191 | 0.9957510 | 0.9944829 | 0.9932148 | 0.9919467 | 0.9906786 | 0.9894105 | 0.9881424 | 0.9874969 | 0.9868514 | 0.9862059 | 0.9855603 | 0.9849148 | 0.9842693 | 0.9836238 | 0.9829782 | 0.9823327 | 0.9816872 | 0.9814435 | 0.9811997 | 0.9809560 | 0.9807123 | 0.9804686 | 0.9802248 | 0.9799811 | 0.9797374 | 0.9794937 | 0.9792500 | 0.9789164 | 0.9785829 | 0.9782493 | 0.9779157 | 0.9775822 | 0.9772486 | 0.9769151 | 0.9765815 | 0.9762480 | 0.9759144 | 0.9772488 | 0.9785831 | 0.9799174 | 0.9812517 | 0.9825860 | 0.9839204 | 0.9852547 | 0.9865890 | 0.9879233 | 0.9892576 | 0.9890063 | 0.9887549 | 0.9885035 | 0.9882521 | 0.9880008 | 0.9877494 | 0.9874980 | 0.9872466 | 0.9869952 | 0.9867439 |
| soybean | canesm5 | gcam-ref | can | 1 | 0.9973227 | 0.9946455 | 0.9919682 | 0.9892909 | 0.9866137 | 0.9872040 | 0.9877942 | 0.9883845 | 0.9889748 | 0.9895651 | 0.9901554 | 0.9907457 | 0.9913359 | 0.9919262 | 0.9925165 | 0.9917872 | 0.9910579 | 0.9903287 | 0.9895994 | 0.9888701 | 0.9881408 | 0.9874115 | 0.9866823 | 0.9859530 | 0.9852237 | 0.9842641 | 0.9833044 | 0.9823448 | 0.9813851 | 0.9804255 | 0.9794658 | 0.9785062 | 0.9775466 | 0.9765869 | 0.9756273 | 0.9761157 | 0.9766042 | 0.9770926 | 0.9775811 | 0.9780695 | 0.9785580 | 0.9790465 | 0.9795349 | 0.9800234 | 0.9805118 | 0.9802660 | 0.9800202 | 0.9797744 | 0.9795287 | 0.9792829 | 0.9790371 | 0.9787913 | 0.9785455 | 0.9782997 | 0.9780539 | 0.9784909 | 0.9789279 | 0.9793649 | 0.9798019 | 0.9802388 | 0.9806758 | 0.9811128 | 0.9815498 | 0.9819868 | 0.9824238 | 0.9821757 | 0.9819277 | 0.9816797 | 0.9814316 | 0.9811836 | 0.9809356 | 0.9806875 | 0.9804395 | 0.9801915 | 0.9799434 |
| soybean | canesm5 | gcam-ref | chn | 1 | 0.9967303 | 0.9934605 | 0.9901908 | 0.9869211 | 0.9836513 | 0.9831567 | 0.9826620 | 0.9821673 | 0.9816727 | 0.9811780 | 0.9806833 | 0.9801887 | 0.9796940 | 0.9791993 | 0.9787047 | 0.9789300 | 0.9791552 | 0.9793805 | 0.9796058 | 0.9798311 | 0.9800564 | 0.9802816 | 0.9805069 | 0.9807322 | 0.9809575 | 0.9808478 | 0.9807382 | 0.9806285 | 0.9805188 | 0.9804092 | 0.9802995 | 0.9801899 | 0.9800802 | 0.9799705 | 0.9798609 | 0.9800423 | 0.9802237 | 0.9804052 | 0.9805866 | 0.9807680 | 0.9809495 | 0.9811309 | 0.9813123 | 0.9814938 | 0.9816752 | 0.9813962 | 0.9811172 | 0.9808383 | 0.9805593 | 0.9802803 | 0.9800014 | 0.9797224 | 0.9794434 | 0.9791644 | 0.9788855 | 0.9788117 | 0.9787379 | 0.9786641 | 0.9785903 | 0.9785165 | 0.9784427 | 0.9783689 | 0.9782951 | 0.9782213 | 0.9781475 | 0.9782861 | 0.9784247 | 0.9785633 | 0.9787019 | 0.9788405 | 0.9789791 | 0.9791177 | 0.9792564 | 0.9793950 | 0.9795336 |
| soybean | canesm5 | gcam-ref | col | 1 | 1.0020314 | 1.0040628 | 1.0060942 | 1.0081256 | 1.0101570 | 1.0111680 | 1.0121789 | 1.0131899 | 1.0142008 | 1.0152118 | 1.0162227 | 1.0172336 | 1.0182446 | 1.0192555 | 1.0202665 | 1.0203511 | 1.0204358 | 1.0205204 | 1.0206051 | 1.0206897 | 1.0207744 | 1.0208590 | 1.0209437 | 1.0210283 | 1.0211130 | 1.0202092 | 1.0193055 | 1.0184017 | 1.0174980 | 1.0165942 | 1.0156905 | 1.0147868 | 1.0138830 | 1.0129793 | 1.0120755 | 1.0109141 | 1.0097526 | 1.0085912 | 1.0074297 | 1.0062683 | 1.0051068 | 1.0039454 | 1.0027839 | 1.0016225 | 1.0004610 | 1.0005619 | 1.0006628 | 1.0007636 | 1.0008645 | 1.0009654 | 1.0010663 | 1.0011672 | 1.0012680 | 1.0013689 | 1.0014698 | 1.0021532 | 1.0028366 | 1.0035199 | 1.0042033 | 1.0048867 | 1.0055701 | 1.0062535 | 1.0069369 | 1.0076202 | 1.0083036 | 1.0087079 | 1.0091123 | 1.0095166 | 1.0099209 | 1.0103253 | 1.0107296 | 1.0111339 | 1.0115383 | 1.0119426 | 1.0123469 |
| soybean | canesm5 | gcam-ref | guy | 1 | 1.0016505 | 1.0033010 | 1.0049515 | 1.0066020 | 1.0082525 | 1.0076475 | 1.0070426 | 1.0064376 | 1.0058326 | 1.0052276 | 1.0046227 | 1.0040177 | 1.0034127 | 1.0028078 | 1.0022028 | 1.0005761 | 0.9989495 | 0.9973228 | 0.9956961 | 0.9940695 | 0.9924428 | 0.9908161 | 0.9891895 | 0.9875628 | 0.9859362 | 0.9851151 | 0.9842941 | 0.9834731 | 0.9826521 | 0.9818311 | 0.9810101 | 0.9801891 | 0.9793680 | 0.9785470 | 0.9777260 | 0.9781414 | 0.9785568 | 0.9789722 | 0.9793877 | 0.9798031 | 0.9802185 | 0.9806339 | 0.9810493 | 0.9814647 | 0.9818801 | 0.9811923 | 0.9805044 | 0.9798165 | 0.9791287 | 0.9784408 | 0.9777529 | 0.9770651 | 0.9763772 | 0.9756894 | 0.9750015 | 0.9756735 | 0.9763456 | 0.9770176 | 0.9776897 | 0.9783617 | 0.9790338 | 0.9797058 | 0.9803778 | 0.9810499 | 0.9817219 | 0.9824824 | 0.9832430 | 0.9840035 | 0.9847640 | 0.9855245 | 0.9862850 | 0.9870455 | 0.9878061 | 0.9885666 | 0.9893271 |
| soybean | canesm5 | gcam-ref | ind | 1 | 0.9947227 | 0.9894454 | 0.9841681 | 0.9788907 | 0.9736134 | 0.9764248 | 0.9792362 | 0.9820476 | 0.9848590 | 0.9876704 | 0.9904818 | 0.9932931 | 0.9961045 | 0.9989159 | 1.0017273 | 1.0023780 | 1.0030288 | 1.0036795 | 1.0043303 | 1.0049810 | 1.0056318 | 1.0062825 | 1.0069333 | 1.0075840 | 1.0082348 | 1.0052712 | 1.0023076 | 0.9993440 | 0.9963804 | 0.9934168 | 0.9904532 | 0.9874896 | 0.9845260 | 0.9815624 | 0.9785988 | 0.9800357 | 0.9814725 | 0.9829094 | 0.9843463 | 0.9857831 | 0.9872200 | 0.9886568 | 0.9900937 | 0.9915306 | 0.9929674 | 0.9936697 | 0.9943720 | 0.9950743 | 0.9957765 | 0.9964788 | 0.9971811 | 0.9978833 | 0.9985856 | 0.9992879 | 0.9999902 | 1.0049641 | 1.0099381 | 1.0149121 | 1.0198860 | 1.0248600 | 1.0298340 | 1.0348080 | 1.0397819 | 1.0447559 | 1.0497299 | 1.0480319 | 1.0463339 | 1.0446359 | 1.0429379 | 1.0412399 | 1.0395419 | 1.0378439 | 1.0361459 | 1.0344479 | 1.0327499 |
| soybean | canesm5 | gcam-ref | ita | 1 | 1.0027258 | 1.0054515 | 1.0081773 | 1.0109031 | 1.0136289 | 1.0120174 | 1.0104059 | 1.0087944 | 1.0071829 | 1.0055714 | 1.0039599 | 1.0023485 | 1.0007370 | 0.9991255 | 0.9975140 | 0.9961448 | 0.9947757 | 0.9934065 | 0.9920374 | 0.9906682 | 0.9892991 | 0.9879299 | 0.9865608 | 0.9851916 | 0.9838225 | 0.9836711 | 0.9835197 | 0.9833683 | 0.9832170 | 0.9830656 | 0.9829142 | 0.9827628 | 0.9826114 | 0.9824601 | 0.9823087 | 0.9814239 | 0.9805390 | 0.9796542 | 0.9787694 | 0.9778845 | 0.9769997 | 0.9761148 | 0.9752300 | 0.9743452 | 0.9734603 | 0.9733874 | 0.9733144 | 0.9732415 | 0.9731685 | 0.9730955 | 0.9730226 | 0.9729496 | 0.9728766 | 0.9728037 | 0.9727307 | 0.9725583 | 0.9723859 | 0.9722135 | 0.9720411 | 0.9718687 | 0.9716963 | 0.9715239 | 0.9713515 | 0.9711791 | 0.9710067 | 0.9695777 | 0.9681486 | 0.9667195 | 0.9652905 | 0.9638614 | 0.9624323 | 0.9610033 | 0.9595742 | 0.9581451 | 0.9567161 |
| soybean | canesm5 | gcam-ref | mex | 1 | 1.0030933 | 1.0061865 | 1.0092798 | 1.0123731 | 1.0154663 | 1.0161667 | 1.0168670 | 1.0175673 | 1.0182677 | 1.0189680 | 1.0196684 | 1.0203687 | 1.0210690 | 1.0217694 | 1.0224697 | 1.0214565 | 1.0204433 | 1.0194302 | 1.0184170 | 1.0174038 | 1.0163906 | 1.0153774 | 1.0143642 | 1.0133510 | 1.0123378 | 1.0116733 | 1.0110087 | 1.0103442 | 1.0096796 | 1.0090151 | 1.0083505 | 1.0076860 | 1.0070214 | 1.0063569 | 1.0056923 | 1.0063728 | 1.0070533 | 1.0077338 | 1.0084142 | 1.0090947 | 1.0097752 | 1.0104557 | 1.0111362 | 1.0118167 | 1.0124971 | 1.0118407 | 1.0111843 | 1.0105278 | 1.0098714 | 1.0092150 | 1.0085585 | 1.0079021 | 1.0072457 | 1.0065892 | 1.0059328 | 1.0049872 | 1.0040416 | 1.0030960 | 1.0021504 | 1.0012048 | 1.0002592 | 0.9993136 | 0.9983679 | 0.9974223 | 0.9964767 | 0.9966744 | 0.9968720 | 0.9970696 | 0.9972672 | 0.9974648 | 0.9976625 | 0.9978601 | 0.9980577 | 0.9982553 | 0.9984530 |
| soybean | canesm5 | gcam-ref | nga | 1 | 1.0004543 | 1.0009086 | 1.0013630 | 1.0018173 | 1.0022716 | 1.0032933 | 1.0043150 | 1.0053368 | 1.0063585 | 1.0073802 | 1.0084019 | 1.0094236 | 1.0104453 | 1.0114670 | 1.0124887 | 1.0131240 | 1.0137593 | 1.0143945 | 1.0150298 | 1.0156650 | 1.0163003 | 1.0169356 | 1.0175708 | 1.0182061 | 1.0188414 | 1.0192797 | 1.0197181 | 1.0201565 | 1.0205948 | 1.0210332 | 1.0214716 | 1.0219099 | 1.0223483 | 1.0227867 | 1.0232250 | 1.0234941 | 1.0237632 | 1.0240322 | 1.0243013 | 1.0245704 | 1.0248394 | 1.0251085 | 1.0253776 | 1.0256466 | 1.0259157 | 1.0254327 | 1.0249496 | 1.0244666 | 1.0239836 | 1.0235005 | 1.0230175 | 1.0225345 | 1.0220515 | 1.0215684 | 1.0210854 | 1.0213908 | 1.0216963 | 1.0220017 | 1.0223072 | 1.0226126 | 1.0229180 | 1.0232235 | 1.0235289 | 1.0238344 | 1.0241398 | 1.0248050 | 1.0254701 | 1.0261353 | 1.0268005 | 1.0274656 | 1.0281308 | 1.0287960 | 1.0294611 | 1.0301263 | 1.0307915 |
| Note: | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| This only shows the first 10 lines of the example data. |
gcam_agprodchange
The gcam_agprodchange function is specifically designed
for the Global Change Analysis Model (GCAM). GCAM requires the
agricultural productivity growth rate (APG) as an input, which is
calculated based on yield shocks and the baseline APG, varying according
to different SSP scenarios. Additionally, the APG for GCAM must be at
the region-basin scale. The gaia::gcam_agprodchange
function directly remaps country-level yield shocks to region-basin APG.
In the example below, since climate_scenario = 'gcam-ref',
it will use the APG without SSP impact (Reference scenario APC) to
calculate the climate-impacted APG based on the yield shocks computed in
the previous step.
# Path to the output folder where you wish to save the outputs. Change it accordingly
output_dir <- file.path(getwd(), 'gaia_output')
# calculate region-basin agricultural productivity growth rate for GCAM
gcam_apg <- gcam_agprodchange(data = out_yield_shock,
climate_model = 'canesm5',
climate_scenario = 'gcam-ref',
member = 'r1i1p1f1',
bias_adj = 'w5e5',
cfe = 'no-cfe',
gcam_version = 'gcam7',
diagnostics = TRUE,
output_dir = output_dir)Once the run is complete, a folder named
[gcam-version]_agprodchange_[cfe] (e.g.,
gcam7_agprodchange_no-cfe) will be created in your
specified output location. This folder contains the XML
file of the agricultural productivity growth rate (APG) for GCAM.
Additionally, it includes diagnostic figures illustrating the APG by
region-basin. An example of APG for soybean is shown in Figure 4 below.
Figure 4: Agricultural productivity growth rate for soybean. Each line under a region indicates the APG for the region-basin intersection.