Stochastic and recursive calibration for operational, large-scale, agricultural land and water use management models

Abstract:

Managing the impact of climatic cycles on agricultural production, on land allocation, and on the state of active and projected water sources is challenging. This is because in addition to the uncertainties associated with climate projections, it is difficult to anticipate how farmers will respond to climatic change or to economic and policy incentives. Some sophisticated decision support systems available to water managers consider farmers’ adaptive behavior but they are data intensive and difficult to apply operationally over large regions. Satellite-based observational technologies, in conjunction with models and assimilation methods, create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents at seasonal scales.

We present an integrated modeling framework that can be driven by satellite remote sensing to enable robust regional assessment and prediction of climatic and policy impacts on agricultural production, water resources, and management decisions. The core of this framework is a widely used model of agricultural production and resource allocation adapted to be used in conjunction with remote sensing inputs to quantify the amount of land and water farmers allocate for each crop they choose to grow on a seasonal basis in response to reduced or enhanced access to water due to climatic or policy restrictions. A recursive Bayesian update method is used to adjust the model parameters by assimilating information on crop acreage, production, and crop evapotranspiration as a proxy for water use that can be estimated from high spatial resolution satellite remote sensing. The data assimilation framework blends new and old information to avoid over-calibration to the specific conditions of a single year and permits the updating of parameters to track gradual changes in the agricultural system.

This integrated framework provides an operational means of monitoring and forecasting what crops will be grown and how farmers will allocate land and water under expected adverse conditions, and the resulting consequences for other water users. The Bayesian update framework constitutes an efficient method for the identification of the production function parameters and provides valuable information on the associated uncertainty of the forecasts.