{ "cells": [ { "cell_type": "markdown", "id": "bb98be55", "metadata": {}, "source": [ "# Soil Heat‑Flux Estimation & Validation \n", "This notebook demonstrates how to derive **surface ground‑heat flux (G₀)** from a SoilVUE profile using the helper functions included in the project repository, and then compares the derived values with the *in‑situ* heat‑flux‑plate measurements (“G”) available in the eddy‑covariance flux file.\n", "\n", "Data files used:\n", "\n", "| File | Contents | Native resolution |\n", "|------|----------|-------------------|\n", "| `21026_Statistics_AmeriFlux0.dat` | SoilVUE profile statistics (T, VWC, κ etc.) | Site‑logger native (≈ 5–10 min) |\n", "| `21025_Flux_AmeriFluxFormat_30.dat` | 30‑min AmeriFlux‑format flux file (includes **G**) | 30 min |\n", "\n", "The analysis proceeds as follows:\n", "\n", "1. **Load & tidy** the two datasets. \n", "2. **Estimate** ground‑heat flux from the SoilVUE temperatures and soil‑moisture–dependent conductivity using `soil_heat.compute_heat_flux_conduction`. \n", "3. **Merge** the estimated series with the measured 30‑min “G” record. \n", "4. **Evaluate** performance using RMSE & NME (functions in `gao_et_al.py`) and quick visualisations.\n", "\n", "> **Note** – The SoilVUE statistics file shipped with this example has a single timestamp header (`2024‑07‑11`). Rows are therefore back‑filled with that value and treated as measurements for **11 July 2024**. If your logger exports full timestamps, simply remove the forward‑fill block below.\n" ] }, { "cell_type": "markdown", "id": "23d73e0c", "metadata": {}, "source": [ "## Import relevant libraries" ] }, { "cell_type": "code", "execution_count": null, "id": "d65311a5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "id": "94e3ab7d", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pathlib import Path\n", "import sys\n", "import micromet\n", "# Project helper modules (all live in /mnt/data after upload)\n", "sys.path.append('../../src')\n", "import soil_heat # gradient & calorimetric methods\n", "#import gao_et_al as gao # RMSE & NME helpers\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "f622aee6", "metadata": {}, "outputs": [], "source": [ "station_path = Path(\"G:/Shared drives/UGS_Flux/Data_Downloads/Dugout_Ranch/\")" ] }, { "cell_type": "markdown", "id": "a839f1aa", "metadata": {}, "source": [ "## Load SoilVUE Data" ] }, { "cell_type": "code", "execution_count": 4, "id": "0eef8837", "metadata": {}, "outputs": [], "source": [ "dfs = {}\n", "\n", "for file in station_path.rglob(\"*21031_Statistics_AmeriFlux*.dat\"):\n", " fileno = file.stem.split(\"_\")[-1].replace(\"AmeriFlux\", \"\")\n", " dfs[fileno] = pd.read_csv(file, na_values=['NAN'], engine='python',)\n", "df_sv = pd.concat(dfs,ignore_index=True).iloc[50:,:57]\n", "\n", "# Back‑fill the one timestamp so that every row carries a datetime label\n", "df_sv['TIMESTAMP_START'] = df_sv['TIMESTAMP_START'].ffill()\n", "\n", "# Parse to pandas datetime (format YYYYMMDDhhmm)\n", "df_sv['datetime'] = pd.to_datetime(df_sv['TIMESTAMP_START'], format='%Y%m%d%H%M')\n", "\n", "# For this demo we keep only the channels needed for the gradient method\n", "df_sv = df_sv.astype({'T_1_1_1': float,\n", " 'T_1_2_1': float,\n", " 'T_1_3_1': float,\n", " 'T_1_4_1': float,\n", " 'T_1_5_1': float,\n", " 'T_1_6_1': float,\n", " 'T_1_7_1': float,\n", " 'VWC_1_1_1': float,\n", " 'VWC_1_2_1': float,\n", " 'VWC_1_3_1': float,\n", " 'VWC_1_4_1': float,\n", " 'VWC_1_5_1': float,\n", " 'VWC_1_6_1': float,\n", " 'VWC_1_7_1': float,\n", " 'T_CANOPY': float, \n", " 'NETRAD': float,\n", " 'SW_IN': float,\n", " 'SW_OUT': float,\n", " 'LW_IN': float,\n", " 'LW_OUT': float,\n", " })\n", "df_sv = df_sv.set_index('datetime').sort_index().drop_duplicates()\n", "df_sv = df_sv.loc[pd.to_datetime('2023-07-01'):]\n", "\n", "#df_sv['datetime'] = pd.date_range(start='2024-07-11 05:30', periods=589, freq='30min')\n", "# Package expects the DataFrame columns to be named explicitly\n", "df_grad =df_sv.rename(columns={\n", " 'T_CANOPY':'T00cm',\n", " 'T_1_1_1':'T05cm',\n", " 'T_1_2_1':'T10cm',\n", " 'T_1_3_1':'T20cm',\n", " 'T_1_4_1':'T30cm',\n", " 'T_1_5_1':'T40cm',\n", " 'T_1_6_1':'T50cm',\n", " 'T_1_7_1':'T60cm', \n", " 'VWC_1_1_1':'VWC05cm',\n", " 'VWC_1_2_1':'VWC10cm',\n", " 'VWC_1_3_1':'VWC20cm',\n", " 'VWC_1_4_1':'VWC30cm',\n", " 'VWC_1_5_1':'VWC40cm',\n", " 'VWC_1_6_1':'VWC50cm',\n", " 'VWC_1_7_1':'VWC60cm',\n", "})\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "b75ed660", "metadata": {}, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "datetime", "rawType": "datetime64[ns]", "type": "datetime" }, { "name": "TIMESTAMP_START", "rawType": "float64", "type": "float" }, { "name": "TIMESTAMP_END", "rawType": "float64", "type": "float" }, { "name": "ALB", "rawType": "float64", "type": "float" }, { "name": "NETRAD", "rawType": "float64", "type": "float" }, { "name": "SW_IN", "rawType": "float64", "type": "float" }, { "name": "SW_OUT", "rawType": "float64", "type": "float" }, { "name": "LW_IN", "rawType": "float64", "type": "float" }, { "name": "LW_OUT", "rawType": "float64", "type": "float" }, { "name": "T00cm", "rawType": "float64", "type": "float" }, { "name": "T_SI111_body", "rawType": "float64", "type": "float" }, { "name": "PPFD_IN", "rawType": "float64", "type": "float" }, { "name": "VWC05cm", "rawType": "float64", "type": "float" }, { "name": "Ka_1_1_1", "rawType": "float64", "type": "float" }, { "name": "T05cm", "rawType": "float64", "type": "float" }, { "name": "BulkEC_1_1_1", "rawType": "float64", "type": "float" }, { "name": "VWC10cm", "rawType": "float64", "type": "float" }, { "name": "Ka_1_2_1", "rawType": "float64", "type": "float" }, { "name": "T10cm", "rawType": "float64", "type": "float" }, { "name": "BulkEC_1_2_1", "rawType": "float64", "type": "float" }, { "name": "VWC20cm", "rawType": "float64", "type": "float" }, { "name": "Ka_1_3_1", "rawType": "float64", "type": "float" }, { "name": "T20cm", "rawType": "float64", "type": "float" }, { "name": "BulkEC_1_3_1", "rawType": "float64", "type": "float" }, { "name": "VWC30cm", "rawType": "float64", "type": "float" }, { "name": "Ka_1_4_1", "rawType": "float64", "type": "float" }, { "name": "T30cm", "rawType": "float64", "type": "float" }, { "name": "BulkEC_1_4_1", "rawType": "float64", "type": "float" }, { "name": "VWC40cm", "rawType": "float64", "type": "float" }, { "name": "Ka_1_5_1", "rawType": "float64", "type": "float" }, { "name": "T40cm", "rawType": "float64", "type": "float" }, { "name": "BulkEC_1_5_1", "rawType": "float64", "type": "float" }, { "name": "VWC50cm", "rawType": "float64", "type": "float" }, { "name": "Ka_1_6_1", "rawType": "float64", "type": "float" }, { "name": "T50cm", "rawType": "float64", "type": "float" }, { "name": "BulkEC_1_6_1", "rawType": "float64", "type": "float" }, { "name": "VWC60cm", "rawType": "float64", "type": "float" }, { "name": "Ka_1_7_1", "rawType": "float64", "type": "float" }, { "name": "T60cm", "rawType": "float64", "type": "float" }, { "name": "BulkEC_1_7_1", "rawType": "float64", "type": "float" }, { "name": "VWC_1_8_1", "rawType": "float64", "type": "float" }, { "name": "Ka_1_8_1", "rawType": "float64", "type": "float" }, { "name": "T__1_8_1", "rawType": "float64", "type": "float" }, { "name": "BulkEC_1_8_1", "rawType": "float64", "type": "float" }, { "name": "VWC_1_9_1", "rawType": "float64", "type": "float" }, { "name": "Ka_1_9_1", "rawType": "float64", "type": "float" }, { "name": "T_1_9_1", "rawType": "float64", "type": "float" }, { "name": "BulkEC_1_9_1", "rawType": "float64", "type": "float" }, { "name": "WS", "rawType": "float64", "type": "float" }, { "name": "WD", "rawType": "float64", "type": "float" }, { "name": "LWmV_1_1_1", "rawType": "float64", "type": "float" }, { "name": "LWMDry_1_1_1", "rawType": "float64", "type": "float" }, { "name": "LWMCon_1_1_1", "rawType": "float64", "type": "float" }, { "name": "LWMWet_1_1_1", "rawType": "float64", "type": "float" }, { "name": "LWmV_1_1_2", "rawType": "float64", "type": "float" }, { "name": "LWMDry_1_1_2", "rawType": "float64", "type": "float" }, { "name": "LWMCon_1_1_2", "rawType": "float64", "type": "float" }, { "name": "LWMWet_1_1_2", "rawType": "float64", "type": "float" } ], "ref": "9ef0070d-207e-45a4-97b5-b5337473e35b", "rows": [ [ "2023-07-20 09:41:00", "202307200941.0", "202307201000.0", "28.26583", "318.8813", "637.4134", "180.1522", "357.5272", "495.9071", "32.17941", "28.71401", "1411.436", "0.0136386", "2.771102", "23.57", "0.0074917", "0.0582224", "4.228928", "22.58157", "0.0054145", "0.1226994", "6.880218", "22.81857", "0.0547476", "0.1007037", "5.903605", "23.02734", "0.0362762", "0.0889185", "5.411079", "22.87811", "0.0299398", "0.0706616", "4.690443", "22.58157", "0.0165667", "0.0796858", "5.040216", "21.73529", "0.0131326", "0.1071956", "6.184079", "20.76797", "0.0254701", "0.1340948", "7.415563", "19.74359", "0.0550575", "2.701577", "306.6066", "257.1425", "62.5", "0.0", "0.0", "255.5927", "62.5", "0.0", "0.0" ], [ "2023-07-20 10:00:00", "202307201000.0", "202307201030.0", "28.1589", "356.0205", "707.7589", "199.2483", "358.8571", "511.3472", "34.38037", "29.93085", "1567.31", "0.0134196", "2.764697", 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| datetime | \n", "\n", " | \n", " | \n", " | \n", " | \n", " |
| 2023-07-20 | \n", "24.01 | \n", "14.31 | \n", "6.49 | \n", "2.49 | \n", "1.28 | \n", "
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| 2023-07-22 | \n", "27.36 | \n", "19.15 | \n", "9.20 | \n", "3.77 | \n", "1.97 | \n", "
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| 2023-07-24 | \n", "26.79 | \n", "16.73 | \n", "8.07 | \n", "2.93 | \n", "1.42 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 2025-05-09 | \n", "16.61 | \n", "11.11 | \n", "7.85 | \n", "3.66 | \n", "2.21 | \n", "
| 2025-05-10 | \n", "18.50 | \n", "9.19 | \n", "6.55 | \n", "3.04 | \n", "1.83 | \n", "
| 2025-05-11 | \n", "14.63 | \n", "7.97 | \n", "5.55 | \n", "2.48 | \n", "1.39 | \n", "
| 2025-05-12 | \n", "14.91 | \n", "9.42 | \n", "6.57 | \n", "2.96 | \n", "1.73 | \n", "
| 2025-05-13 | \n", "11.74 | \n", "3.12 | \n", "1.94 | \n", "1.52 | \n", "1.00 | \n", "
664 rows × 5 columns
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| 3 | \n", "2025-03-28 01:00:00 | \n", "11.065370 | \n", "
| 4 | \n", "2025-03-28 01:30:00 | \n", "-7.604448 | \n", "
| ... | \n", "... | \n", "... | \n", "
| 2227 | \n", "2025-05-13 09:00:00 | \n", "-36.622820 | \n", "
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| 2231 | \n", "2025-05-13 11:00:00 | \n", "7.689254 | \n", "
2232 rows × 2 columns
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| 2024-07-11 06:30:00 | \n", "-19.676980 | \n", "-60.82810 | \n", "
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| 2024-07-11 07:30:00 | \n", "-6.595578 | \n", "49.94262 | \n", "