library(HELPS)
-library(ncdf4)
library(raster)
#> Loading required package: sp
library(dplyr)
@@ -97,8 +96,6 @@ HELPS-vignette
#> intersect, setdiff, setequal, union
library(ggplot2)
library(knitr)
-library(sf)
-#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(ggpattern)HELPS has been tested with both ISIMIP data and outputs from BASD.
-Besides the climate projections, HELPS also takes crop
-calendar (https://zenodo.org/records/5062513) and harvested area
-(https://doi.org/10.7910/DVN/SWPENT) data as input for
+
Besides the climate projections, HELPS also takes GGCMI
+Phase 3 crop calendar (Jägermeyr et al. 2021) (https://zenodo.org/records/5062513) and SPAM harvested
+area (IFPRI, 2024) (https://doi.org/10.7910/DVN/SWPENT) data as input for
temporal and spatial aggregation in the package.
HELPS provides measurement of crop specific labor heat
stress exposure and heat-induced physical work capacity loss for 93
sectors, consisting of 46 SPAM crops by 2 irrigation practices (rain-fed
and irrigated) and 1 noncrop sector. Below is the fUll list of sectors
-supported in HELPS:
HELPS, and refer to Table 1 for crop
+mapping:
SECTOR_ALL <- c("WHEA_I", "RICE_I", "MAIZ_I", "SOYB_I", "BARL_I", "MILL_I", "PMIL_I", "SORG_I", "OCER_I",
"POTA_I", "SWPO_I", "YAMS_I", "CASS_I", "BEAN_I", "CHIC_I", "COWP_I", "PIGE_I", "LENT_I",
"GROU_I", "SUNF_I", "RAPE_I", "SESA_I", "SUGC_I", "SUGB_I", "COTT_I", "OFIB_I", "BANA_I",
@@ -165,6 +162,296 @@ Prepare your own input data"REST_R",
"NONCROP")
| SPAM | +SPAM Crop Name | +ggcmi | +ggcmi Crop Name | +
|---|---|---|---|
| WHEA | +Wheat | +wh | +wheat | +
| RICE | +Rice | +ri | +rice | +
| MAIZ | +Maize | +mai | +maize | +
| SOYB | +Soybean | +soy | +soybean | +
| BARL | +Barley | +bar | +barley | +
| MILL | +Small Millet | +mil | +millet | +
| PMIL | +Pearl Millet | +mil | +millet | +
| SORG | +Sorghum | +sor | +sorghum | +
| OCER | +Other Cereals | +rye | +rye | +
| POTA | +Potato | +pot | +potato | +
| SWPO | +Sweet Potato | +cas | +cassava | +
| YAMS | +Yams | +cas | +cassava | +
| CASS | +Cassava | +cas | +cassava | +
| BEAN | +Bean | +bea | +beans | +
| CHIC | +Chickpea | +pea | +field peas | +
| COWP | +Cowpea | +pea | +beans | +
| PIGE | +Pigeon Pea | +pea | +beans | +
| LENT | +Lentil | +pea | +field peas | +
| GROU | +Groundnut | +nut | +groundnut | +
| SUNF | +Sunflower | +sun | +sunflower | +
| RAPE | +Rapeseed | +rap | +rapeseed | +
| SESA | +Sesame Seed | +sun | +sunflower | +
| SUGC | +Sugarcane | +sgc | +sugar cane | +
| SUGB | +Sugarbeet | +sgb | +sugar beet | +
| COTT | +Cotton | +cot | +cotton | +
| OFIB | +Other Fiber Crops | ++ | + |
| BANA | +Banana | ++ | + |
| PLNT | +Plantain | ++ | + |
| CITR | +Citrus | ++ | + |
| TROF | +Other Tropical Fruit | ++ | + |
| TEMF | +Temperate Fruit | ++ | + |
| TOMA | +Tomato | ++ | + |
| ONIO | +Onion | ++ | + |
| VEGE | +Other Vegetables | ++ | + |
| ORTS | +Other Roots | ++ | + |
| OPUL | +Other Pulses | ++ | + |
| CNUT | +Coconut | ++ | + |
| OILP | +Oilpalm | ++ | + |
| OOIL | +Other Oil Crops | ++ | + |
| COFF | +Arabic Coffee | ++ | + |
| RCOF | +Robust Coffee | ++ | + |
| COCO | +Cocoa | ++ | + |
| RUBB | +Rubber | ++ | + |
| TEAS | +Tea | ++ | + |
| TOBA | +Tobacco | ++ | + |
| REST | +Rest Of Crops | ++ | + |
Sheng, D. et al. Omitting labor responses to heat stress underestimates future climate impact on agriculture. (Under review) doi:https://doi.org/10.21203/rs.3.rs-5000229/v1.
+Sheng, D. et al. Omitting labor responses to heat stress underestimates future climate impact on agriculture. (Under review). doi: https://doi.org/10.21203/rs.3.rs-5000229/v1.
Open R studio:
install.packages('devtools')
-devtools::install_github('dsheng1026/HELPS')
+devtools::install_github('JGCRI/HELPS')
renv::restore()
devtools::load_all()renv::restore() helps to install package dependencies for HELPS, but users might need to download a few R packages individually through RStudio’s guidance. You should now be set to run the driver without running into any package version issues. Note that if you have completed steps related to renv once, your R session should automatically connect to a private library when you open HELPS.Rproj, and you can run the code below to use the package.
To run the vignette, users need first download a set of example data into folder HELPS_Example_Data by running
To run vignette/HELPS-vignette.Rmd, users need first download a set of example data into folder HELPS_Example_Data by running
devtools::load_all()
get_example_data()The HELPS package operates on 0.5 degree resolution, bias-corrected outputs from Earth System Models and General Circulation Models participating in the CMIP process. Several options to access such data exist: - The ISIMIP2b repository (https://data.isimip.org/search/tree/ISIMIP2b/InputData/climate/atmosphere/) contains outputs from CMIP5-era models (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) for specific scenarios - The ISIMIP3b repository (https://data.isimip.org/search/tree/ISIMIP3b/InputData/climate/atmosphere/) contains outputs from CMIP6-era models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKSEM1-0-LL) for specific scenarios - If a user wishes to explore other CMIP models or bias-correct model data against different observational data than that used by ISIMIP, the BASD python package (https://github.com/JGCRI/basd) is available for ease of use. BASD implements an extension of the bias adjustment and statistical downscaling method used in ISIMIP3b (ISIMIP3BASD, Lange 2021 https://zenodo.org/records/4686991 and https://gmd.copernicus.org/articles/12/3055/2019/). The ISIMIP3BASD code base can also be used directly. For additional information, please see https://www.isimip.org/documents/413/ISIMIP3b_bias_adjustment_fact_sheet_Gnsz7CO.pdf - If a user wishes to explore novel scenarios not covered by CMIP models or the ISIMIP collection, we suggest a combination of STITCHES emulation (https://github.com/JGCRI/stitches) and BASD bias correction and downscaling.
The HELPS package operates on 0.5 degree resolution, bias-corrected outputs from Earth System Models and General Circulation Models participating in the CMIP process. Several options to access such data exist: - The ISIMIP2b repository contains outputs from CMIP5-era models (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) for specific scenarios - The ISIMIP3b repository contains outputs from CMIP6-era models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKSEM1-0-LL) for specific scenarios - If a user wishes to explore other CMIP models or bias-correct model data against different observational data than that used by ISIMIP, the BASD python package is available for ease of use. BASD implements an extension of the bias adjustment and statistical downscaling method used in ISIMIP3b (ISIMIP3BASD, Lange 2021). The ISIMIP3BASD code base can also be used directly. For additional information, please see here - If a user wishes to explore novel scenarios not covered by CMIP models or the ISIMIP collection, we suggest a combination of STITCHES emulation and BASD bias correction and downscaling.
HELPS has been tested with both ISIMIP data and outputs from BASD.