Monday, 20 June 2016
Many of the world's breadbasket regions, such as the US Corn Belt, are located in humid continental climates where growing seasons experience convective precipitation. While the Corn Belt has typically been dominated by rainfed agriculture, recent years have seen a substantial increase in irrigation. However, irrigation scheduling is more challenging than it is in more arid regions because of the episodic nature of summer precipitation and the day-to-day variability of evapotranspiration (ET). At present, the most sophisticated techniques employ measurements of antecedent conditions and a checkbook approach for decision making. A significant impediment to this method is that it fails to account for future conditions, which could drastically alter decision making. While producers may have access to precipitation forecasts, there are few cases where predicted ET is available. We have developed ETool, a system based on the Weather Research and Forecasting (WRF) model, where predicted ET and precipitation (P) are provided at 3 km resolution over a 7 day period for the state of Minnesota. The goal is to provide a tool that improves irrigation scheduling in an agricultural region where properly managing for episodic precipitation and transient drought through supplemental irrigation can manifest significant yield benefits. We use WRF coupled to the Noah land surface scheme and release forecasts once per week. Initially we used the standard parameterized leaf area index (LAI) functions within the model, however, we found that this was too static and unable to account for inter-annual variability in crop growth. We therefore implemented observationally constrained LAI based on MODIS data, which significantly improved forecasting skill for ET. Here, we describe the forecasting system, and model evaluation against precipitation observations and tall tower eddy covariance measurements of ET.
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