Cross-scale evaluation of dynamic crop growth in WRF and Noah-MP-Crop
Cross-scale evaluation of dynamic crop growth in WRF and Noah-MP-Crop
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WRF 和 Noah-MP-Crop 中作物动态生长的跨尺度评估
DOI：
10.1016/j.agrformet.2020.108217
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发表时间：
2021
影响因子：
6.2
通讯作者：
Hyndman, David W.
中科院分区：
文献类型：
--
作者：
Partridge, Trevor F.;Winter, Jonathan M.;Kendall, Anthony D.;Hyndman, David W.
在气候模式中准确地表示农田对于模拟陆地和大气之间的水和能量通量以及评估气候变化对农业的影响非常重要。最近，Noah陆面模式与多参数化（Noah- mp - crop）整合了动态作物生长，这有可能大大推进地球系统建模，并被纳入最新发布的天气研究与预报（WRF）区域气候模式。将动态作物生长纳入WRF提供了一个独特的机会，可以同时评估与区域气候模型耦合的作物模型相关的偏差，并解决有关农业生态系统在调节区域气候中的作用的悬而未决的问题。在此，我们分析了WRF在三个模拟空间尺度（25km、5km和1km）上的作物生长动态，分别为降水高于（2010年）、低于（2012年）和近似等于（2015年）的季节。在WRF中纳入动态作物生长显著降低了美国中部农田模拟叶面积指数相对于观测值的偏差。然而，与不含作物的动态植被模块（WRF- dv）相比，含动态作物的WRF模拟（WRF- crop）与含动态作物的WRF模拟（WRF- dv）在计算的日蒸散量、平均生长季温度或生长季总降水量方面没有显著差异。模拟玉米（大豆）平均绝对误差（MAE）占观测到的年平均产量的百分比，根据年份和空间分辨率的不同在24.7% -101%（28.1% - 109%）之间，在高度灌溉的县偏差最显著。在观测到的气候条件下，强迫Noah-MP-Crop将玉米（大豆）产量MAE的范围大幅降低至9.5% - 55.1%（15.0% - 37.5）。模型分辨率的提高一直导致世界粮食计划署作物产量估计值的降低。
Accurately representing croplands in climate models is important for simulating water and energy fluxes between the land and atmosphere, as well as evaluating the impacts of climate change on agriculture. The recent integration of dynamic crop growth in the Noah land surface model with multiparameterization (Noah-MP-Crop) has the potential to substantially advance Earth system modeling and is included in the latest release of the Weather Research and Forecasting (WRF) regional climate model. The addition of dynamic crop growth to WRF provides a unique opportunity to simultaneously evaluate biases associated with a crop model coupled to a regional climate model and address outstanding questions regarding the role of agroecosystems in modulating regional climate. Here, we analyze dynamic crop growth in WRF across three simulated spatial scales (25km, 5km, and 1km) for growing seasons with precipitation above (2010), below (2012), and approximately equal (2015) to the seasonal average. Including dynamic crop growth in WRF significantly reduces biases in simulated leaf area index over croplands in the central U.S. relative to observations. However, there is no substantial difference in calculated daily evapotranspiration, average growing season temperature, or total growing season precipitation between WRF simulations with dynamic crops (WRF-Crop) compared to the dynamic vegetation module without crops (WRF-DV). Simulated corn (soy) mean absolute error (MAE), as a percentage of observed annual average yield, ranges from 24.7% -101% (28.1% - 109%) depending on year and spatial resolution, with the most significant biases in highly irrigated counties. Forcing Noah-MP-Crop with observed climate substantially reduces the range of corn (soy) yield MAE to 9.5% - 55.1% (15.0% - 37.5). Increased model resolution consistently leads to lower corn and soy yield estimates within WRF-Crop.
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