Datasets for simulating heat and CO2 fluxes in Beijing using SUEWS V2020b

Name: Datasets for simulating heat and CO2 fluxes in Beijing using SUEWS V2020b
Creator: Zenodo
Published: 2023-06-09T08:58:46+08:00

Zenodo2023-06-09 更新2026-05-26 收录

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Data, model runs and codes used in the manuscript "Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance". The source code for Surface Urban Energy and Water balance Scheme (SUEWS) V2020b is also included. [Latest Version] June 9, 2023: Version 2 was created. (1) Modifications in LAI: the input data of LAI (LAI/source_data/Beijing_LAI.csv) was previously derived from a MODIS product MOD15A2H; it was replaced with the LAI time series at a higher spatial resolution derived from data provided by Landsat 7 ETM+; the code for data retrieval (LAI/LAI_from_GEE.txt) was added. (2) Modifications in ModelRuns: some of the external parameters given to SUEWS were adjusted, including the optimized LAI parameters (for case LAI and case gs_LAI), and Frnonheat (from 0.2 to 0.5); a read_me.txt was added. (3) Modifications in Fig&Statistics: the shaded area for the line charts were changed from standard deviation to the interquartile range. [Data&Code Description] The folders are: 1. Observations The units for CO2 flux (Fc), latent heat flux (QE), and sensible heat flux (QH) are μmol m-2 s-1, W m-2, and W m-2, respectively, unless otherwise stated. 1.1. Observation datasets 1.1.1. radiation_flux_2010-2011.csv is radiation flux observations from May 2010 to July 2011 at 140 m on the IAP tower, Beijing. 1.1.2. turbulent_flux_before_QC_2016.csv is turbulent flux observations before quality control (QC) obtained with the eddy covariance (EC) technique for the year 2016 at 47 m on the IAP tower. 1.1.3. turbulent_flux_after_QC_2016.csv is turbulent flux observations after quality control (QC). 1.1.4. Fc_mean_diurnal_by_season.csv is the mean diurnal seasonal cycle of Fc observations. 1.1.5. Fc_gapfilled_with_MDC.csv is the Fc time series for the year 2016 gap-filled with Mean Diurnal Cycle (MDC) method. 1.1.6. meteorological_observations is a folder including meteorological observations at different heights measured at the IAP tower for the years 2010-2012. 1.2 Codes for observations processing 1.2.1. turbulent_flux_QC.py is for turbulent flux QC covering wind direction filtering, stationarity test, friction velocity filtering, and nighttime filtering. 1.2.2. mean_diurnal_cycle_of_flux_observations.py is for obtaining the mean diurnal cycle of flux observations. 1.2.3. Fc_gap_filling.py is for filling the gaps in the Fc time series with the MDC method. 2. LAI This folder includes a project aiming to obtain the parameters for the leaf area index (LAI) model adopted by SUEWS. In this LAI model, the LAI is related to air temperature only. Incorporating hourly air temperature observations and the 'real' LAI (e.g. observed LAI, remotely sensed LAI), the optimization method Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES) will derive the optimized parameters for the LAI model. For more details, please read the description text file (read_me.txt) under this folder. 2.1. read_me.txt. 2.2. source_data is a folder including the input for CMA-ES optimization. The input files of Beijing are included, allowing a quick run to reproduce the results demonstrated in the manuscript. 2.3. result_data is a folder including the output for CMA-ES optimization. 2.4. The code for data retrieval from the Google Earth Engine platform (LAI/LAI_from_GEE.txt) was added. The others are Python scripts regarding CMA-ES, including the LAI model, data de-spiking, interpolation, CMA-ES training, and result visualization. 3. ModelRuns This folder includes SUEWS source code, four model runs conducted to test the sensitivity to vegetation-related parameters (i.e., maximum conductance, LAI model parameters), and four model runs to test sensitivity to the radius of the model domain. 3.1. SUEWS_SourceCode is a folder including SUEWS V2020b source Fortran codes. For detailed descriptions, readers are referred to SUEWS webpage (https://suews.readthedocs.io/en/latest/). 3.2. Input is a folder including the necessary input files shared by each of the model runs. Please copy all the files to a model run folder (e.g. VegetationSensitivity\case-base\Input\) before starting a SUEWS run. 3.3. VegetationSensitivity is a folder including four SUEWS runs to test the model sensitivity to vegetation-related parameters: case-base is the control run, case-gs is a model run where maximum conductance of vegetation has been modified to more site-specific values, case-LAI is a model run where LAI parameters have been optimized with CMA-ES, and case-gs_LAI is a model run where both maximum conductance and LAI parameters have been modified. Note that all the model runs have selected 1000 m as the radius of the model domain. To conduct a quick model run to reproduce the output, enter the command "./SUEWS_V2020b" in the model run folder (e.g. VegetationSensitivity\case-base\) under a Linux environment (e.g., Ubuntu). 3.4. RadiusSensitivity is a folder including four SUEWS runs to test the model sensitivity to the size of the model domain, covering circles with a 500 m, 750 m, 1000 m, 1500 m radius, respectively. Note that the vegetation-related parameters are the same as the model run VegetationSensitivity\case-gs_LAI. 3.5. read_me.txt is a note on how to reproduce the SUEWS cases shown in the manuscript. 4. Fig&Statistics This folder includes Python scripts and data to reproduce a portion of the figures and statistics in the manuscript quickly. They are sorted into the following sub-folders: 4.1 validation_WFDE5, 4.2 evaluation_radiation, 4.3 evaluation_turbulent, 4.4 accumulated_Fc, 4.5 Fc_component_MDC.

本数据集包含论文《使用SUEWS V2020b模拟北京地区热量与二氧化碳通量：植被物候与最大导度敏感性》中使用的数据集、模型运行结果与代码，同时附带了城市地表能量与水平衡方案（Surface Urban Energy and Water balance Scheme, SUEWS）V2020b的源代码。【最新版本】2023年6月9日：发布版本2。 (1) 叶面积指数（Leaf Area Index, LAI）相关修改：原LAI输入数据（`LAI/source_data/Beijing_LAI.csv`）源自MODIS产品MOD15A2H，现替换为基于Landsat 7 ETM+数据生成的更高空间分辨率LAI时间序列；新增数据获取代码（`LAI/LAI_from_GEE.txt`）。 (2) 模型运行（ModelRuns）相关修改：调整了部分输入至SUEWS的外部参数，包括优化后的LAI参数（对应案例LAI与案例gs_LAI），以及非热通量占比Fr_nonheat（从0.2调整至0.5）；新增`read_me.txt`说明文档。 (3) 图表与统计（Fig&Statistics）相关修改：折线图的阴影区域从标准差改为四分位距。【数据与代码说明】数据集的文件夹结构如下： 1. **观测数据（Observations）** 若无特别说明，二氧化碳通量（Fc）、潜热通量（QE）与感热通量（QH）的单位分别为μmol·m⁻²·s⁻¹、W·m⁻²与W·m⁻²。 1.1 **观测数据集** 1.1.1 `radiation_flux_2010-2011.csv`为2010年5月至2011年7月，北京中国科学院大气物理研究所（IAP）铁塔140米高度处的辐射通量观测数据。 1.1.2 `turbulent_flux_before_QC_2016.csv`为2016年IAP铁塔47米高度处，通过涡动协方差（eddy covariance, EC）技术获取的质控前湍流通量观测数据。 1.1.3 `turbulent_flux_after_QC_2016.csv`为质控后的湍流通量观测数据。 1.1.4 `Fc_mean_diurnal_by_season.csv`为按季节划分的Fc平均日循环观测数据。 1.1.5 `Fc_gapfilled_with_MDC.csv`为采用平均日循环（Mean Diurnal Cycle, MDC）方法插补后的2016年Fc时间序列数据。 1.1.6 `meteorological_observations`文件夹包含2010-2012年IAP铁塔不同高度处的气象观测数据。 1.2 **观测数据处理代码** 1.2.1 `turbulent_flux_QC.py`用于湍流通量质控，涵盖风向筛选、平稳性检验、摩擦速度筛选与夜间数据筛选流程。 1.2.2 `mean_diurnal_cycle_of_flux_observations.py`用于获取通量观测的平均日循环特征。 1.2.3 `Fc_gap_filling.py`用于采用MDC方法对Fc时间序列进行缺失值插补。 2. **叶面积指数（LAI）** 本文件夹包含用于获取SUEWS所用叶面积指数（LAI）模型参数的相关项目。该LAI模型中，LAI仅与气温相关。结合逐小时气温观测数据与“真实”LAI（如实测LAI、遥感反演LAI），协方差矩阵自适应进化策略（Covariance Matrix Adaptation Evolutionary Strategies, CMA-ES）优化方法可推导得到LAI模型的最优参数。更多细节请参阅本文件夹下的说明文档`read_me.txt`。 2.1 `read_me.txt`：本文件夹说明文档。 2.2 `source_data`：包含CMA-ES优化所需输入数据的文件夹，其中附带了北京地区的输入文件，可快速复现论文中展示的结果。 2.3 `result_data`：包含CMA-ES优化输出结果的文件夹。 2.4 新增了从谷歌地球引擎（Google Earth Engine, GEE）平台获取数据的代码（`LAI/LAI_from_GEE.txt`）；其余为与CMA-ES相关的Python脚本，涵盖LAI模型、数据去尖峰、插值、CMA-ES训练与结果可视化。 3. **模型运行（ModelRuns）** 本文件夹包含SUEWS源代码，以及4组用于测试植被相关参数（即植被最大导度）敏感性的模型运行结果，另有4组用于测试模型域半径敏感性的运行结果。 3.1 `SUEWS_SourceCode`：包含SUEWS V2020b的Fortran源代码文件夹。详细说明请参阅SUEWS官方网页（https://suews.readthedocs.io/en/latest/）。 3.2 `Input`：包含所有模型运行共享的必要输入文件的文件夹。启动SUEWS运行前，请将所有文件复制至对应模型运行文件夹（如`VegetationSensitivitycase-baseInput`）。 3.3 `VegetationSensitivity`：包含4组SUEWS运行结果，用于测试模型对植被相关参数的敏感性：`case-base`为对照运行；`case-gs`为将植被最大导度修改为更贴合站点实际值的模型运行；`case-LAI`为采用CMA-ES优化LAI参数的模型运行；`case-gs_LAI`为同时修改植被最大导度与LAI参数的模型运行。请注意，所有模型运行均选取1000米作为模型域半径。若需快速复现结果，可在Linux环境（如Ubuntu）下的模型运行文件夹（如`VegetationSensitivitycase-base`）中执行命令`"./SUEWS_V2020b"`。 3.4 `RadiusSensitivity`：包含4组SUEWS运行结果，用于测试模型对模型域尺寸的敏感性，涵盖半径分别为500米、750米、1000米与1500米的圆形域。请注意，植被相关参数与`VegetationSensitivitycase-gs_LAI`模型运行一致。 3.5 `read_me.txt`：关于如何复现论文中展示的SUEWS案例的说明文档。 4. **图表与统计（Fig&Statistics）** 本文件夹包含可快速复现论文中部分图表与统计结果的Python脚本与数据，按以下子文件夹分类： 4.1 `validation_WFDE5` 4.2 `evaluation_radiation` 4.3 `evaluation_turbulent` 4.4 `accumulated_Fc` 4.5 `Fc_component_MDC`

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创建时间：

2023-06-09

Datasets for simulating heat and CO2 fluxes in Beijing using SUEWS V2020b

资源简介：

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