An update to global gridded monthly crop datasets. This new dataset uses the crop categories established by the Global Agro-Ecological Zones (GAEZ) Version 3 model, which is based on the Food and Agricultural Organization of the United Nations (FAO) crop production data. We used publicly available data from the GAEZ+_2015 Annual global gridded dataset (Frolking et al., 2000) along with data from the MIRCA2000 dataset (Portmann et al. 2008) on crop rotations, cropping intensity, and planting and harvest dates to generate c.2015 monthly crop physical area by crop production system (irrigated and rainfed) for 26 crops and crop categories globally at 5-minute resolution. Each crop category can have up to 5 subcrop categories to represent crop rotations, resulting in a total of 93 individual crop/subcrop/production system combinations (93 files total). These data are in standard georeferenced gridded format (netCDF files), and can be used by any global hydrology, land surface, or other earth system model that requires gridded annual or monthly crop data inputs.
|Citation||Grogan, DS, A Prusevich, S Frolking, D Wisser and S Glidden (2021): GAEZ+_2015 Monthly Cropland Data: Global gridded monthly crop physical area for 26 irrigated and rainfed crops, MyGeoHUB, DOI:10.13019/J2BH-VB41.|
|Associated Paper||Grogan, DS, S Frolking, D Wisser, A Prusevich and S Glidden (2022): Global Gridded Crop Harvested Area, Production, Yield, and Monthly Physical Area Data circa 2015. Scientific Data, 9 (1): 1–16. https://doi.org/10.1038/s41597-021-01115-2.|
To accurately represent the routing and storage of water through terrestrial river networks, macro-scale hydrologic models require accurate representations of impounded water-bodies throughout the model domain. The Hydrologically-Consistent Dams (HydroConDams) dataset provides a critical compilation and correction of available datasets for the Conterminous United States (CONUS). HydroConDams provides locations for the single major point of outflow of reservoirs, maximum capacity information, inundated surface area, upstream catchment area, dam construction year, and primary dam purpose. These data are considered appropriate for representing the routing and storage effects of impounded waterbodies along river networks for meso- to macro-scale models of terrestrial hydrology and the land-surface (pixel-sizes from 100’s m to 100’s km).
Here, we combine a high-resolution weather product (PRISM) with fine-scale outputs of a hydrologic model (WBM) to construct functional indicators of compound hydroclimatic extremes for agriculture. Data for US counties for 1981-2015. The main variables are: degree days above 29C; degree days from 10C to 29C; seasonal mean volumetric soil moisture; seasonal mean soil moisture fraction; seasonal mean evapotranspiration; cumulative precipitation; corn irrigated area share; and metrics for the daily interaction of heat and soil moisture.
|Citation||Haqiqi, I, DS Grogan, TW Hertel and W Schlenker (2020): Data Sets for: Quantifying the Impacts of Compound Extremes on Agriculture and Irrigation Water Demand, Purdue University Research Repository, DOI:10.4231/0M14-EY38.|
|Associated Paper|| Haqiqi, I, DS Grogan, TW Hertel and W Schlenker (2021): Quantifying the impacts of compound extremes on agriculture, Hydrology and Earth System Sciences, 25(2), 551-564, DOI: 10.5194/hess-25-551-2021.|
This utility summarizes the USGS Water Use data available in the public domain in a convenient Jupyter notebook utilizing python.
This Stata code estimates a model to investigate the impacts of compound extremes, water stress, and heat stress on crop yields. It requires "soilMoistureData.dta", which can be found in the data product cited above, Haqiqi et al 2020 (DOI:10.4231/0M14-EY38). The code will estimate 1) the marginal impacts of heat stress on crop yields; 2) the marginal impact of daily soil moisture extremes on crop yields, and 3) the conditional marginal impact of heat and soil moisture on crop yields. This code can be used for studying the impacts of compound extremes on agriculture. The code generates figures and tables too.
|Citation||Haqiqi, I, DS Grogan, TW Hertel and W Schlenker (2020): Model Code for: Quantifying the Impacts of Compound Extremes on Agriculture and Irrigation Water Demand, Purdue University Research Repository, DOI:10.4231/Q07D-J369|
|Associated Paper||Haqiqi, I, DS Grogan, TW Hertel and W Schlenker (2021): Quantifying the impacts of compound extremes on agriculture, Hydrology and Earth System Sciences, 25(2), 551-564, DOI: 10.5194/hess-25-551-2021.|
Code for interpolating inundation return periods for parcels of land between the 10 year and 500 year FEMA flood water surface elevations. The code's goal is to improve flood risk communication and understanding by interpolating flood probabilities from existing FEMA flood maps and data. This analysis focuses on two locations: a model testing case in the Sims Bayou, Houston, TX. and an application case at the town of Muncy, PA.