A23E-0384
How does irrigation modulate regional climate under wet and dry conditions?

Tuesday, 15 December 2015
Poster Hall (Moscone South)
Lisi Pei1, Nathan J Moore2, Shiyuan Sharon Zhong3, Anthony D Kendall3 and David W Hyndman3, (1)Michigan State University, East Lansing, United States, (2)Center for Global Change and Earth Observation, Michigan State University, East Lansing, MI, United States, (3)Michigan State University, East Lansing, MI, United States
Abstract:
Nationwide, the impacts of irrigation on summer (June-July-August) weather over the United States are examined for a pair of unusually wet (2010) and dry (2012) years using an integrated regional climate-irrigation modeling system based on the WRF-Noah-Mosaic framework. A novel irrigation scheme was developed for WRF to mimic center pivot irrigation practices for farmlands across the U.S., over areas defined using MODIS-based irrigation extent data. Results show that this new irrigation approach can dynamically generate irrigation water amounts that are in good agreement with the observed irrigation water use estimates across the High Plains, a region where the prescribed parameters in the irrigation scheme best match actual irrigation practice. Synoptic conditions are significantly altered by the simulated irrigation under both wet and dry conditions, but in different patterns. For the dry summer, irrigation helps strengthen the dominant continental high pressure system in the central U.S. and favors the downwind transport of moisture generated over irrigated lands, contributing to increased downwind rainfall. For the wet summer, the cyclonic vortex system is deepened by irrigation in the central U.S. along the Great Plains low-level jet corridor, enhancing rainfall locally over the heavily irrigated lands. In both cases, introducing irrigation into the model reduced overall mean biases and root-mean-square errors in the simulated daily precipitation over the entire United States. This highlights the importance of integrating anthropogenic influences on the land surface in regional climate modeling.