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Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Journal Article · · Computers and Electronics in Agriculture
 [1];  [2];  [3];  [4];  [1];  [5];  [6];  [1];  [1]
  1. Chinese Academy of Sciences (CAS), Changchun (China)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Changchun Normal University (China)
  4. Chinese Academy of Sciences (CAS), Beijing (China); Nanjing Univ. of Information Science and Technology (China)
  5. China Meteorological Administration, Shenyang (China)
  6. Chinese Academy of Sciences (CAS), Shenyang (China)
+ Show Author Affiliations

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE; Chinese Academy of Sciences; National Natural Science Foundation of China (NSFC); Department of Science and Technology of JiLin Province
Grant/Contract Number:
AC05-76RL01830; XDA28080503; 42071025; 42075168; 20220101154JC; 2022CXTD02
OSTI ID:
1888505
Report Number(s):
PNNL-SA-177052
Journal Information:
Computers and Electronics in Agriculture, Vol. 201; ISSN 0168-1699
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
cropland and atmosphere interactions
dynamic crop model
WRF
crop yield
Northeast China