Interactive Jupyter dashboard visualizing the relative importance of different uncertainty sources in local climate projections. Learn more about creating and using dashboards for interactive visualization of scientific data and results with Panel in
this blog post by David Lafferty.
This is release v1.0.0 of the WBM open source code.
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.
SIMPLE-on-a-Grid (SIMPLE-G) is a multi-region, partial equilibrium model of gridded cropland use, crop production, consumption and trade. It is an extension of the SIMPLE model that has been applied to study long run sustainability issues in the global food-water-environment nexus. Rather than looking at regions or country aggregates, SIMPLE-G divides the world into georeferenced grid-cell units. This allows SIMPLE-G to explicitly incorporate local environmental constraints in its projections, account for sub-national heterogeneity of global drivers such as climate change and water scarcity, and assess local land and water use given future trends the global farm and food system.
In SIMPLE, the world is split into sixteen economic regions. Regional consumption is disaggregated into four commodities (crops, livestock, processed foods and biofuels). Regional demand is driven by population, per capita income, and biofuel mandates (all exogenous in the model) as well as prices (endogenous to the model). SIMPLE-G extends the existing SIMPLE model by disaggregating rainfed and irrigated production and modeling these processes at the individual grid-cell level. Regional crop output is obtained by aggregating across the grid cells (30 arc-min resolution) within each region. Crop production follows a nested constant elasticity of substitution (CES) function. Water is an explicit input used by the irrigated sector only. Water consumption is computed as the product of gridded irrigated cropland area and a grid cell-specific consumptive water use parameter in m3/ha. By aggregating water use across grid cells within a sub-basin (defined below), we obtain total irrigation consumption. Water availability at each grid cell is exogenous in SIMPLE-on-a-Grid, and is obtained from the hydrological model.
| MyGeoHub |
SIMPLE-on-a-Grid (SIMPLE-G) model |
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Global Trade Analysis Project |
Information hub for the SIMPLE-G model at Purdue Univrsity |
| Associated Paper |
Baldos, U. L. C., Haqiqi, I., Hertel, T., Horridge, M., and Liu, J. (2020): SIMPLE-G: A Multiscale Framework for Integration of Economic and Biophysical Determinants of Sustainability. Environmental Modelling & Software, 133: 104805. https://www.sciencedirect.com/science/article/pii/S1364815220304205. |
