<p dir="ltr">This repository contains three Jupyter notebooks designed to simulate crop growth and soil processes under different water management scenarios:</p><ul><li>Drip irrigation</li><li>Sprinkler irrigation</li><li>Rainfed (serving as a control scenario without irrigation)</li></ul><p dir="ltr">Each notebook performs a growing season simulation of:</p><ul><li>Vertically integrated soil moisture dynamics</li><li>Crop biomass growth</li><li>Soil greenhouse gas (GHG) emissions</li></ul><p dir="ltr">The notebooks take as input a NumPy array representing rainfall distribution. As an example, the file rain_Lubbock_lbd0p18_aphp0096.npy, provided here, contains 200 seasons of a typical 140-day growing season of rainfall representative of the Texas High Plains.</p><p dir="ltr">For the example simulations, we use soil, weather, and crop parameters representative of the corn growing season in the Texas High Plains, a major agricultural region in the United States that relies heavily on irrigation for sustainable production.</p>
Funding
Climate-Smart Irrigation: An Opportunity for Water Resource Conservation, Climate Change Mitigation, and Carbon Market Integration
This repository contains three Jupyter notebooks developed to simulate crop growth and soil processes under different water management scenarios, including drip irrigation, sprinkler irrigation, and rainfed conditions. Each notebook performs a growing-season simulation of vertically integrated soil moisture dynamics, crop biomass growth, and soil microbial respiration.
Use limitations
The model is designed for daily field-scale dynamics with limited consideration of topography. Moreover, daily rainfall in the simulation setup is assumed to be statistically stationary at the growing-season scale.