Data and Code for: Observation-constrained agroecosystem model inversion reveals continental-scale variation of winter wheat traits

This repository contains the simulation outputs and processing scripts associated with the study of winter wheat traits across the United States, utilizing the Ecosys agroecosystem model. The dataset includes model results for both rainfed and irrigated winter wheat systems, supporting the findings presented in the manuscript titled "Observation-constrained agroecosystem model inversion reveals continental-scale variation of winter wheat traits." Data includes the original Ecosys simulation outputs (archived in .db format within the compressed .zip files) and extracted analysis data (stored in .pkl files for efficient processing). Python code for data processing and figure generation is provided in a Jupyter notebook. External Observational Datasets should refer to the following official repositories for the input and validation data used in this study. The eddy covariance data from the AmeriFlux network (https://ameriflux.lbl.gov/). Climate-forcing data of NLDAS-2 from NASA LDAS (https://ldas.gsfc.nasa.gov/nldas/nldas-2-forcing-data). Soil data from the Gridded Soil Survey Geographic Database (gSSURGO), available at (https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database). Crop yields, planting and harvest dates from the USDA public databases (https://quickstats.nass.usda.gov/; https://webapp.rma.usda.gov/apps/actuarialinformationbrowser/CropCriteria.aspx). Satellite-derived SLOPE GPP data from ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1786). Land use and crop progress information from the USDA Crop Data Layer and Crop Progress and Condition Gridded Layers (https://www.nass.usda.gov/Research_and_Science/). The Ecosys model code is available online at https://github.com/jinyun1tang/ECOSYS.本仓库包含与美国冬小麦性状研究相关的模拟输出与处理脚本，本研究采用了Ecosys农业生态系统模型（Ecosys agroecosystem model）。数据集涵盖雨养与灌溉两类冬小麦系统的模型结果，用以支撑题为《观测约束下的农业生态系统模型反演揭示冬小麦性状的大陆尺度变异》的研究论文的核心发现。 数据包含原始Ecosys模拟输出（压缩.zip归档文件内为.db格式存档）与提取后的分析数据（存储为.pkl文件以提升处理效率）。数据处理与图表生成所用的Python代码已封装在Jupyter Notebook中。 本研究使用的输入与验证数据所对应的外部观测数据集，请参考以下官方仓库获取： 1. 来自AmeriFlux网络的涡度协方差数据（https://ameriflux.lbl.gov/）； 2. 来自NASA陆面数据同化系统（LDAS）的NLDAS-2气候强迫数据（https://ldas.gsfc.nasa.gov/nldas/nldas-2-forcing-data）； 3. 来自栅格化土壤调查地理数据库（gSSURGO, Gridded Soil Survey Geographic Database）的土壤数据，可从https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database获取； 4. 来自美国农业部（USDA）公共数据库的作物产量、播种与收获日期数据（https://quickstats.nass.usda.gov/；https://webapp.rma.usda.gov/apps/actuarialinformationbrowser/CropCriteria.aspx）； 5. 来自橡树岭国家实验室分布式主动存档中心（ORNL DAAC）的卫星反演SLOPE GPP数据（https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1786）； 6. 来自美国农业部作物数据层与作物进度状况栅格图层的土地利用与作物进度信息（https://www.nass.usda.gov/Research_and_Science/）。 Ecosys模型代码可从https://github.com/jinyun1tang/ECOSYS在线获取。