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 United States. 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 is 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 data sets, pointing to future efforts to incorporating spatially varying crop parameters to better constrain crop growing seasons.

中文翻译：

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使用Noah-MP地表模型对美国中部的作物和灌溉进行联合建模

代表气候－作物相互作用对地球系统建模至关重要。尽管最近在土地表面模型（LSMs）的动态作物生长和灌溉模型中取得了进展，但将这些模型从田间规模转换为区域规模仍然具有挑战性。这项研究将Noah-MP LSM与动态作物生长和灌溉方案结合起来，共同模拟美国中部玉米和大豆的作物产量和灌溉量。分别根据美国农业部的报告和美国地质调查局的取水数据，对县级作物产量和灌溉量的模型表现进行了评估。大量模拟（采用统一的播种/收获管理且不灌溉）在所有播种地区的农作物产量估算中均产生明显偏差，玉米和大豆的均方根误差（RMSE）分别为28.1％和28.4％，分别。如果没有灌溉计划，由于水分胁迫，灌溉区域的农作物产量会下降，RMSE分别为48.7％和20.5％。采用动态灌溉方案可有效提高灌溉地区的农作物产量，并将RMSE降低至22.3％和16.8％。在雨育地区，该模型高估了农作物的单产。在州一级应用空间变化的种植和收获日期会降低两种作物的单产和灌溉量，尤其是在北部各州。进行了“氮胁迫”模拟，发现当作物处于氮胁迫下时，灌溉对作物产量的改善作用有限。确定了作物生长模型的一些不确定性，包括产量差距，播种日期，rubisco容量以及可用数据集之间的差异，