Challenges in Representing Hydroclimatic Effects of Agriculture Management in Earth-System Models

Advancing the understanding of the nexus among food, energy, and water systems has recently emerged as a new science frontier, and the research community started modeling agricultural management in earth-system models to develop an integrated modeling tool for investigating relevant land-atmosphere interactions and agriculture sustainability issues. However, the development of crop-growth and agriculture management models is still in its infancy and substantial discrepancies exist in simulating land-atmosphere interactions over cultivated fields. As an example, this paper uses recent efforts in developing an integrated agriculture-system model in the regional-climate Weather Research and Forecasting (WRF) model to highlight the great need and challenges for earth-system models to capture interactions between agricultural management, hydrology, and climate systems. The newly added WRF crop-modeling capability, based on the community Noah-MP (multiple-physics) land model, involves: 1) coupling the Noah-MP photosynthesis and soil hydrology components with crop-growth models (Noah-MP-Crop), and 2) developing crop-specific parameters required by crop-growth models. At field scales, comparison with in-situ data reveals a good performance of Noah-MP-Crop in simulating the seasonal and annual variability of crop phenology (e.g., leaf area index and yield) and evapotranspiration. This model is able to differentiate corn and soybean with regards to their peak values of LAI as well as the length of growing seasons. However, the application of the WRF-Crop modeling system to continental scales has great difficulty in correctly capturing regional differences in crop yield due to the lack of representation of agriculture management. Similar challenges exist when transitioning irrigation modeling from field scales to continental scales, but have not been thoroughly investigated. We discuss those uncertainties related to realistically simulating regional differences in agriculture management and irrigation effects that vary with crop species and growth stages, and the need for including better agriculture data to constrain crop- and irrigation-models across various spatial and temporal scales.