{ "cells": [ { "cell_type": "markdown", "id": "92fe4842", "metadata": {}, "source": [ "# From k value to Gardner via HYPAGS + Curve Fitting Routine\n", "\n", "*Martin Vonk (2025)*\n", "\n", "## Overview\n", "\n", "This notebook demonstrates an integrated workflow that combines parameter estimation techniques:\n", "\n", "1. **HYPAGS estimation** - Get initial van Genuchten parameters from a single saturated conductivity measurement\n", "2. **Data generation** - Create water retention and conductivity data from the estimated model\n", "3. **Curve fitting** - Fit a different soil model (Gardner) to this data using least-squares optimization\n", "\n", "This workflow is useful for:\n", "- **Model conversion** - Converting between different soil model formulations (van Genuchten → Gardner, etc.)\n", "- **Hypothesis testing** - Comparing how well different models fit the same data\n", "- **Parameter refinement** - Using HYPAGS as an initial guess, then refining with additional data\n", "- **Workflow demonstration** - Showing how `pedon` components integrate together" ] }, { "cell_type": "markdown", "id": "9c52e27f", "metadata": {}, "source": [ "## Workflow Steps\n", "\n", "This notebook follows these steps:\n", "1. Import libraries and define helper plotting functions\n", "2. Estimate van Genuchten parameters using HYPAGS from a k value\n", "3. Generate synthetic water retention and conductivity data from the van Genuchten model\n", "4. Set up and visualize initial Gardner model parameters\n", "5. Fit the Gardner model to the synthetic data\n", "6. Compare the fitted Gardner model with the original van Genuchten curve\n", "\n", "## Setup" ] }, { "cell_type": "markdown", "id": "e8c2b6e8", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "id": "6c5e347e", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "import pedon as pe\n", "\n", "# pe.show_versions()" ] }, { "cell_type": "markdown", "id": "cff77c90", "metadata": {}, "source": [ "### Helper plot" ] }, { "cell_type": "code", "execution_count": 2, "id": "14ca8132", "metadata": {}, "outputs": [], "source": [ "def plot_compare(\n", " soilsample: pe.SoilSample, soilmodel: pe.SoilModel\n", ") -> np.typing.NDArray[plt.Axes]:\n", " f, ax = plt.subplots(1, 2, sharey=True, figsize=(7.0, 6.0))\n", " ax[0].scatter(soilsample.theta, soilsample.h, c=\"k\", s=10, label=\"Soil Sample\")\n", " _ = pe.soilmodel.plot_swrc(\n", " soilmodel, ax=ax[0], label=f\"Soil Model {soilmodel.__class__.__name__}\"\n", " )\n", " ax[0].set_yscale(\"log\")\n", " ax[0].set_xlim(0, 0.5)\n", " ax[0].set_yticks(soilsample.h)\n", " ax[0].set_xticks(np.linspace(0, 0.5, 6))\n", " ax[0].set_ylim(min(soilsample.h), max(soilsample.h))\n", " ax[1].scatter(soilsample.k, soilsample.h, c=\"k\", s=10)\n", " _ = pe.soilmodel.plot_hcf(soilmodel, ax=ax[1])\n", "\n", " ax[1].set_yscale(\"log\")\n", " ax[1].set_xscale(\"log\")\n", "\n", " k_left = 10 ** (np.floor(np.log10(min(soilsample.k))) - 1)\n", " k_right = 10 ** (np.ceil(np.log10(max(soilsample.k))) + 1)\n", " ax[1].set_xlim(k_left, k_right)\n", " ax[0].set_ylabel(r\"|$\\psi$| [cm]\")\n", " ax[0].set_xlabel(r\"$\\theta$ [-]\")\n", " ax[1].set_xlabel(r\"$K_s$ [cm/d]\")\n", " ncol = 3\n", " ax[0].legend(\n", " loc=(-0.02, 1),\n", " fontsize=6,\n", " frameon=False,\n", " ncol=ncol,\n", " columnspacing=0.8,\n", " handlelength=2.5,\n", " )\n", "\n", " f.align_xlabels()\n", " return ax" ] }, { "cell_type": "markdown", "id": "364af67c", "metadata": {}, "source": [ "## Step 1: Estimate van Genuchten Parameters via HYPAGS\n", "\n", "We start with a single measurement: saturated hydraulic conductivity of 100 cm/d (typical for sandy soil). HYPAGS will estimate the complete van Genuchten parameter set from this single value.\n", "\n", "Note: HYPAGS expects k in m/s, so we convert from cm/d." ] }, { "cell_type": "code", "execution_count": 3, "id": "24f8d4a4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Genuchten(k_s=100.0, theta_r=0.108, theta_s=np.float64(0.260064701261806), alpha=np.float64(3.2905002387762474), n=np.float64(1.7228201552429758), l=0.5)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "k_cmd = 100.0 # cm/d\n", "k_ms = k_cmd / 86400 / 100 # m/s since HYPAGS expects that\n", "genuchten = pe.SoilSample(k=np.array([k_ms])).hypags()\n", "genuchten.k_s = k_cmd # convert back to cm/d\n", "genuchten" ] }, { "cell_type": "markdown", "id": "f8d6a59a", "metadata": {}, "source": [ "## Step 2: Convert to Gardner Model via Curve Fitting\n", "\n", "Now we'll fit a Gardner model to the van Genuchten data. This demonstrates model conversion: taking one model's predictions and fitting a different model to match them.\n", "\n", "### Why This Workflow?\n", "\n", "Sometimes you need to convert between models because:\n", "- Your simulation software only accepts a specific model\n", "- You want to compare model performance on the same data\n", "- You're testing whether a simpler model (like Gardner) works as well as a more complex one (like van Genuchten)\n", "\n", "### Sample Pressure Heads\n", "\n", "We create synthetic data by evaluating the van Genuchten model at various pressure head values. Note the careful range selection to avoid numerical issues where conductivity becomes extremely small." ] }, { "cell_type": "markdown", "id": "a794e0c7", "metadata": {}, "source": [ "### Sample Genuchten curve for data points" ] }, { "cell_type": "code", "execution_count": 4, "id": "79faf394", "metadata": {}, "outputs": [], "source": [ "h = np.logspace(\n", " -3, 2, 100\n", ") # carefull with sampling h since k goes to zero very fast for large h which gives trouble with logs\n", "k = genuchten.k(h)\n", "theta = genuchten.theta(h)\n", "soilsample = pe.SoilSample(h=h, k=k, theta=theta)" ] }, { "cell_type": "markdown", "id": "e4ebeda5", "metadata": {}, "source": [ "### Setting Parameter Bounds\n", "\n", "Before fitting, we define reasonable bounds for each Gardner parameter. The bounds have three values:\n", "- **p_ini**: Initial guess for the optimizer\n", "- **p_min**: Minimum allowed value\n", "- **p_max**: Maximum allowed value\n", "\n", "Good bounds are crucial for successful fitting - they guide the optimization and prevent unrealistic values." ] }, { "cell_type": "code", "execution_count": 5, "id": "7006da85", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:No default parameter bounds for SoilModel type Gardner\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " p_ini p_min p_max\n", "k_s 100.000000 0.0000 110.0\n", "theta_s 0.260065 0.2000 0.3\n", "m 0.300000 0.0001 0.5\n", "c 0.150000 0.0001 0.5" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pbounds = pe.get_params(\"Gardner\")\n", "pbounds.loc[\"k_s\"] = [k_cmd, k_cmd - 100.0, k_cmd + 10.0]\n", "pbounds.loc[\"theta_s\"] = [genuchten.theta_s, 0.2, 0.3]\n", "pbounds.loc[\"c\"] = [0.15, 0.0001, 0.5]\n", "pbounds.loc[\"m\"] = [0.3, 0.0001, 0.5]\n", "pbounds" ] }, { "cell_type": "markdown", "id": "d7985f8d", "metadata": {}, "source": [ "#### Visualize Initial Fit\n", "\n", "Let's see how the initial Gardner model parameters compare with the van Genuchten data. These plots show the data points (black dots) and the initial Gardner model curve. The fit will likely be poor at this stage." ] }, { "cell_type": "code", "execution_count": 6, "id": "a2b63118", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(np.float64(0.001), np.float64(100.0))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# compare initial guesses with genuchten\n", "gardner_ini = pe.Gardner(**pbounds[\"p_ini\"].to_dict())\n", "axes = plot_compare(soilsample, gardner_ini)\n", "axes[0].set_ylim(h[0], h[-1])" ] }, { "cell_type": "markdown", "id": "d08db099", "metadata": {}, "source": [ "### Perform the Curve Fitting\n", "\n", "Now we use the least-squares algorithm to optimize Gardner parameters. The `W1` parameter controls how we weight the hydraulic conductivity data relative to water content data. A value of 0.1 means we weight conductivity less heavily than water content, which is common practice because conductivity data tends to be noisier and covers a wider range." ] }, { "cell_type": "code", "execution_count": 7, "id": "7bbd1087", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Gardner(k_s=np.float64(1.7140071030290098), theta_s=np.float64(0.22527115097409922), m=np.float64(0.025716662197584478), c=np.float64(0.30552460435612094))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gardner = soilsample.fit(\n", " pe.Gardner,\n", " pbounds=pbounds,\n", " # k_s=k_cmd, # fix option to fix k_s and only fit k_r\n", " W1=0.1, # how to weight k vs theta data\n", ")\n", "gardner" ] }, { "cell_type": "markdown", "id": "36fb1389", "metadata": {}, "source": [ "## Results and Interpretation\n", "\n", "The plots above show the fitted Gardner model (curves) and the original van Genuchten data (points). \n" ] }, { "cell_type": "code", "execution_count": 8, "id": "bf957645", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(np.float64(0.001), np.float64(100.0))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# compare initial guesses with genuchten\n", "axes = plot_compare(soilsample, gardner)\n", "axes[0].set_ylim(h[0], h[-1])" ] } ], "metadata": { "kernelspec": { "display_name": "pedon (3.13.5)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }