USDA-NIFA Water for Agriculture
Research Grant:
2016-67026-25067
NSF EPSCoRNSF EPSCoR
Cooperative Agreement:
#EPS-1101342
Montana NASA EPSCoR
Research Grant:
80NSSC18M0025M
Data Assimilation Water Use and Agricultural Productivity
Overview
The data assimilation Water Use and Agricultural Productivity (daWUAP) project is a hydro-economic model that couples an economic model of agricultural production calibrated using positive mathematical programming (PMP) and a semidistributed rainfall-runoff-routing model that simulates water available to producers.
Read our recent peer-reviewed paper in Environmental Modelling & Software!
daWUAP is an open-source, cross-platform Python library for conducting hydro-economic assessments of agricultural water use. User-provided hydrology data (stream network, basins, and climate data) are combined with economic and productivity data on water users (i.e., farmers) to estimate changes in water availability and/ or crop land allocations under varying hydrologic regimes or farmer decision-making scenarios.
Check out the daWUAP Python library on Bitbucket.
Features
- Hydrologic rainfall routing model to simulate water availability;
- Spatially and temporally explicit snowmelt and AET estimates along with stream-flow time series;
- Economic modeling of agricultural production under Constant Elasticity of Substituion for water, land constraints using Positive Mathematical Programming (PMP);
- Stochastic data assimilation (via Kalman filter) of farm observational data for estimating variance in economic model parameters;
- Simulation of farmer behavior and agricultural production;
- Coupled hydrologic-economic modeling of water availability, farmer behavior and agricultural production in order to quantify how farming decisions affect agricultural water supplies;
Documentation and Interactive Demos
Want to learn how to use daWUAP? Interactive demos are available as Jupyter Notebooks on Bitbucket. You can see rendered (HTML) versions of the demos listed on our "Python Demos" page.