A satellite-driven hydro-economic model to support agricultural water resources management. (December 2020)

• Record Type: Journal Article
• Authors: Maneta, M.P.; Cobourn, K.; Kimball, J.S.; He, M.; Silverman, N.L.; Chaffin, B.C.; Ewing, S.; Ji, X.; Maxwell, B.
• Is Part Of: Environmental modelling & software. Volume 134(2020)
• Publication Date: 2020-12
• Subjects: Hydro-economic models -- Positive mathematical programming -- Data assimilation -- Decision support systems; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104836
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The management of water resources among competing uses presents a complex technical and policy challenge. Integrated hydro-economic models capable of simulating the hydrologic system in irrigated and non-irrigated regions including the response of farmers to hydrologic constraints and economic and policy incentives, provide a framework to understand biophysical and socioeconomic implications of changing water availability. We present a transformative hydro-economic model of agricultural production driven by multi-sensor satellite observations, outputs from regional climate models, and socioeconomic data. Our approach overcomes the limitations of current decision support systems for agricultural water management and provides policymakers and natural resource managers with satellite data-driven, state-wide, operational models capable of anticipating how farmers allocate water, land, and other resources when confronted with new climate patterns, policy rules, or market signals. The model can also quantify how farming decisions affect agricultural water supplies. We demonstrate the model through an application in the state of Montana. Highlights: Open-source hydro-economic model of agricultural production written in Python. Model calibrated with stochastic version of positive mathematical programming. Hydro-economic model can be calibrated using remote sensing observations. A recursive Bayesian filter permits dynamic updating of model parameters. Model can trace the spatial hydrologic impact of producer choices.