Joint modeling of crop and irrigation in the Central United States using
the Noah-MP land surface model
Abstract
Representing climate-crop interactions is critical to earth system
modeling. Despite recent progress in modeling dynamic crop growth and
irrigation in land surface models (LSMs), transitioning these models
from field to regional scales is still challenging. This study applies
the Noah-MP LSM with dynamic crop-growth and irrigation schemes to
jointly simulate the crop yield and irrigation amount for corn and
soybean in the central U.S. The model performance of crop yield and
irrigation amount are evaluated at county-level against the USDA reports
and USGS water withdrawal data, respectively. The bulk simulation (with
uniform planting/harvesting management and no irrigation) produces
significant biases in crop yield estimates for all planting regions,
with root-mean-square-errors (RMSEs) being 28.1% and 28.4% for corn
and soybean, respectively. Without an irrigation scheme, the crop yields
in the irrigated regions are reduced due to water stress with RMSEs of
48.7% and 20.5%. Applying a dynamic irrigation scheme effectively
improves crop yields in irrigated regions and reduces RMSEs to 22.3%
and 16.8%. In rainfed regions, the model overestimates crop yields.
Applying spatially-varied planting and harvesting dates at state-level
reduces crop yields and irrigation amount for both crops, especially in
northern states. A “nitrogen-stressed” simulation is conducted and
found that the improvement of irrigation on crop yields are limited when
the crops are under nitrogen stress. Several uncertainties in modeling
crop growth are identified, including yield-gap, planting date, rubisco
capacity, and discrepancies between available datasets, pointing to
future efforts to incorporating spatially-varying crop parameters to
better constrain crop growing seasons.