Abstract

Commonly perceived as ‘the cause’ of water scarcity, agriculture is by far the largest water user globally. Modeling of policies and investments, which aim at improving productivity and efficiency in the agricultural water sector, involve complex tradeoffs where spatial modeling scale usually plays an important role. In contrast to local scale modeling for which data is available and the physical processes and institutional arrangement are generally well understood, macro-scale water management modeling faces the challenges of inadequate information and aggregation bias. The fundamental cause of aggregation bias lies in the heterogeneity of water resource and agricultural systems and the nonlinearities in relationships within and across system components. Using three hydro-economic modeling studies geographically ranging from local scale to global scale, we explore the challenges of spatial scales in policy modeling for agricultural water management and discuss potential solutions.