diff --git a/autotest/conftest.py b/autotest/conftest.py index fe174893..b8335775 100644 --- a/autotest/conftest.py +++ b/autotest/conftest.py @@ -2,7 +2,6 @@ import yaml - def pytest_addoption(parser): parser.addoption( "--domain_yaml", diff --git a/autotest/run_swb_test.cmd b/autotest/run_swb_test.cmd new file mode 100644 index 00000000..34dd99bd --- /dev/null +++ b/autotest/run_swb_test.cmd @@ -0,0 +1 @@ +pytest --pdb --domain_yaml=../test_data/hru_1/hru_1.yaml test_swb_rootzone.py diff --git a/autotest/test_swb_rootzone.py b/autotest/test_swb_rootzone.py new file mode 100644 index 00000000..799f2653 --- /dev/null +++ b/autotest/test_swb_rootzone.py @@ -0,0 +1,85 @@ +import pathlib as pl + +import pytest + +from pywatershed.base.control import Control +from pywatershed.hydrology.SWBRootZone import SWBRootZone + +from pywatershed import Parameters +from pywatershed.utils.netcdf_utils import NetCdfCompare + + +def test_init(domain, tmp_path): + tmp_path = pl.Path(tmp_path) + params = Parameters.from_yaml(domain["swb_param_file"]) + + # TODO fix this. make it a data of DatasetDict.from_yaml() + # nc_file = tmp_path / "swb_params_hru_1.nc" + # params.to_netcdf(nc_file, use_xr=True) + + # Set information from the control file + control = Control.load(domain["control_file"]) + + # load csv files into dataframes + swb_output_dir = domain["swb_output_dir"] + prms_output_dir = domain["prms_output_dir"] + input_variables = {} + for key in SWBRootZone.get_inputs(): + nc_path = pl.Path(prms_output_dir) / f"{key}.nc" + input_variables[key] = nc_path + + swb_rz = SWBRootZone( + control, + None, + params, + **input_variables, + budget_type="warn", + ) + + nc_parent = tmp_path / domain["domain_name"] + swb_rz.initialize_netcdf(nc_parent) + + output_compare = {} + vars_compare = ("swb_soil_storage",) + + for key in SWBRootZone.get_variables(): + if key not in vars_compare: + continue + + base_nc_path = ( + pl.Path(swb_output_dir) + / f"hru_1_5000__{key.removeprefix('swb_')}__1979-01-01_to_2019-12-31__1_by_1.nc" + ) + compare_nc_path = tmp_path / domain["domain_name"] / f"{key}.nc" + output_compare[key] = (base_nc_path, compare_nc_path) + + print(f"base_nc_path: {base_nc_path}") + print(f"compare_nc_path: {compare_nc_path}") + + for istep in range(control.n_times): + control.advance() + swb_rz.advance() + swb_rz.calculate(float(istep)) + swb_rz.output() + + swb_rz.finalize() + + # breakpoint() + + assert_error = False + for key, (base, compare) in output_compare.items(): + success, diff = NetCdfCompare(base, compare).compare() + breakpoint() + if not success: + print( + f"comparison for {key} failed: " + + f"maximum error {diff[key][0]} " + + f"(maximum allowed error {diff[key][1]}) " + + f"in column {diff[key][2]}" + ) + assert_error = True + assert not assert_error, "comparison failed" + + # breakpoint() + + return diff --git a/examples/03_swb_model.ipynb b/examples/03_swb_model.ipynb new file mode 100644 index 00000000..4683242c --- /dev/null +++ b/examples/03_swb_model.ipynb @@ -0,0 +1,421 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "08f842ce-586b-490c-9e9e-97321918e5b8", + "metadata": {}, + "source": [ + "# 03 - SWB Model in pynhm" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "816e410d-c806-4467-8d56-e6add8c7f516", + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib as pl\n", + "import pynhm\n", + "import tempfile\n", + "\n", + "pynhm_root = pynhm.constants.__pynhm_root__\n", + "domain = \"hru_1\"\n", + "domain_dir = pynhm_root.parent / f\"test_data/{domain}\"\n", + "input_dir = domain_dir / \"output\"\n", + "run_dir = pl.Path(tempfile.mkdtemp())\n", + "\n", + "params = pynhm.PrmsParameters.load(domain_dir / \"myparam.param\")\n", + "control = pynhm.Control.load(domain_dir / \"control.test\", params=params)\n", + "\n", + "swb_model = pynhm.Model(\n", + " pynhm.SWBRootZone,\n", + " control=control,\n", + " input_dir=input_dir,\n", + " budget_type=\"error\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d0baa109-233b-498a-8097-2e25258a0cfa", + "metadata": {}, + "source": [ + "First we import the pynhm package and ask it where it was located (pynhm_root). Relative to this we know where our test data are and we use that to assign the `domain_dir` variable, which is where our control and parameter files are found for running our model domain. We chose to use the \"hru_1\" domain for this model. For our `input_dir` we selected the `output` subdirectory of the domain directory. We are choosing to have the framework look for our inputs amongst the PRMS outputs; for this first cut, we'll drive our SWB root zone model with fluxes calculated by PRMS." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "95574d33-f8ff-42f4-ae9b-316dcc152a7b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Displaying SVG written to temp file: C:\\Users\\smwesten\\AppData\\Local\\Temp\\1\\tmpu9twxc_v\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "\n", + "Files\n", + "\n", + "\n", + "\n", + "Files\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pynhm.analysis.ModelGraph(swb_model, hide_variables=False).SVG(verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e99798e6-8d98-4e24-ae07-817717f5e72a", + "metadata": {}, + "source": [ + "The plot shows inputs and (public) variables of the PRMSChannel process (the only process in our streamflow_model). The graph shows its inputs coming from file. It's inputs are mass flux volumes from groundwater (gwres_flow_vol), surface runoff (sroff_vol), and the subsurface reservoir (ssres_flow_vol). The files use for these inputs are those fluxes calculated by PRMS (in this case they've been converted to volumes instead of inches output natively by PRMS). We are using the PRMS calculated values out of convenience, but also because if our `pynhm.PRMSChannel` process representation matches that of PRMS, our outputs will be identical. We will verify that momentarily. Also of note in the plot are the mass balance/budget terms outlined in blue. Stream channel has a \"global\" budget which is calculate over the entire spatial domain whereas HRU-based processes track a budget on each HRU. \n", + "\n", + "Returning to the previous code block, a temporary directory is created for the output of this model. Next, the parameter and control files are read in from the domain directory and the control object is given the parameters to manage. Finally, the `streamflow_model` is instantiated with the single process, providing the control object, a directory to search for input data, and the type of budget which is to error on imbalance. \n", + "\n", + "To run this model with netcdf output, we do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67554718-c7a1-4d72-9f2e-523c3436b534", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "streamflow_model.run(netcdf_dir=run_dir, finalize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "870aa0da-14dd-405a-b836-fd4146172603", + "metadata": {}, + "outputs": [], + "source": [ + "import process_plot\n", + "gis_dir = pynhm_root.parent / f\"examples/pynhm_gis/{domain}\"\n", + "proc_plot = process_plot.ProcessPlot(gis_dir)\n", + "\n", + "proc_name = 'PRMSChannel'\n", + "var_name = 'seg_outflow'\n", + "proc = streamflow_model.processes[proc_name]\n", + "display(proc_plot.plot_seg_var(var_name, proc))" + ] + }, + { + "cell_type": "markdown", + "id": "a51cb35e-4921-4e7a-b518-bd997f178beb", + "metadata": {}, + "source": [ + "Now we can easily check the results of our pynhm model against PRMS. The model only maintains the current state, not the full timeseries, " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee8a9aa5-b98a-4bec-ae1c-308060059819", + "metadata": {}, + "outputs": [], + "source": [ + "streamflow_model.processes['PRMSChannel'].seg_outflow.shape #tolower on the names as they are instances?" + ] + }, + { + "cell_type": "markdown", + "id": "5ba72503-01b7-4815-805c-e9e02ffefa3d", + "metadata": {}, + "source": [ + "So we'll evaluate the full timeseries of output retrieved from disk." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "068e17bc-61ba-407d-9266-32e93d5f08fc", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "def compare_results(var, verbose=False):\n", + " ans_file = input_dir / f\"{var}.nc\"\n", + " if not ans_file.exists():\n", + " print(f\"PRMS does not output {var}\")\n", + " return\n", + " ans_prms = xr.open_dataset(ans_file) \n", + " result_pynhm = xr.open_dataset(run_dir / f\"{var}.nc\")\n", + " outflow_errs = result_pynhm - ans_prms\n", + " outflow_rel_errs = outflow_errs / ans_prms\n", + " tol = np.finfo(np.float32).resolution\n", + " assert (\n", + " (abs(outflow_errs).max() < tol)\n", + " | (abs(outflow_rel_errs).max() < tol)\n", + " )\n", + " \n", + " if verbose:\n", + " display(ans_prms)\n", + " display(result_pynhm)\n", + " display(abs(outflow_errs).max().values())\n", + " display(abs(outflow_rel_errs).max().values())\n", + " \n", + " return\n", + " \n", + " \n", + "for vv in pynhm.PRMSChannel.get_variables():\n", + " print(f\"comparing variable: {vv}\")\n", + " verbose = False\n", + " if vv == 'seg_outflow': \n", + " verbose = True\n", + " compare_results(vv, verbose=verbose)\n", + " print(\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "c2d263e0-a612-421a-95e9-19376ae44e9c", + "metadata": {}, + "source": [ + "The above code is similar to the test of pynhmPRMSChannel against PRMS5.2.1 output. We see that the absolute differences have a maximum of 0.02661 while the absolute differences normalized by the PRMS magnitudes are less than about 7.1e-7. That is, errors are in the range of single precision floating point accuracy. We call this result good enough as PRMS has a mixture of single and double precision types. \n", + "\n", + "Note that the (static)method `get_variables()` is defined on the `PRMSChannel` class but could also be obtained on the instance in the model (`streamflow_model.processes['PRMSChannel']` which should probably not be capitalized todo). This is one way in which process models are self-describing. Also apparent in the netcdf output is metadata describing the variable (particularly if you click on the little piece of paper looking icon to the right of `seg_outflow`. If you want a full description of the public inputs, variables, and parameters for a given process model you can do the following: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bda755fc-e31e-4e06-8f13-80a0cf2f6365", + "metadata": {}, + "outputs": [], + "source": [ + "from pprint import pprint, pformat\n", + "#pprint(pynhm.PRMSChannel.description(), sort_dicts=False) ## to save space here, we'll just print the first 50 lines... but feel free to run this line yourself.\n", + "whole_repr = pformat(pynhm.SWBRootZone.description(), sort_dicts=False)\n", + "for line in whole_repr.splitlines()[:50]:\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "id": "696a2772-49cd-4442-bc4d-6c91879677a7", + "metadata": {}, + "source": [ + "We will discuss how process models are self-describing and the metadata in later notebooks.\n", + "\n", + "## Multi-process model\n", + "\n", + "Usually, a single process is just not enough! Let's make a model with more processes. For example we are interested in processes in the soil and groundwater and how these models contribute to changes in streamflow. We simply add the process classes we want to include as the first arguments to `pynhm.Model()`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd3db6a2-b6e1-4b31-9476-b90ec30f28d7", + "metadata": {}, + "outputs": [], + "source": [ + "params = pynhm.PrmsParameters.load(domain_dir / \"myparam.param\") # this line is necessary to avoid error in channel graph construction. apparently the parameters are edited. should probably copy them\n", + "control = pynhm.Control.load(domain_dir / \"control.test\", params=params)\n", + "\n", + "multi_proc_model = pynhm.Model(\n", + " pynhm.PRMSSoilzone,\n", + " pynhm.PRMSGroundwater,\n", + " pynhm.PRMSChannel,\n", + " control=control,\n", + " input_dir=input_dir,\n", + " budget_type=\"error\")" + ] + }, + { + "cell_type": "markdown", + "id": "91854ce8-21b6-4ba1-bee0-6cc37958cf8d", + "metadata": {}, + "source": [ + "As before, we can plot the model graph. This time we adopt the standard color palette for the NHM processes that's already defined in the package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93e2562b-d3fd-4d52-8a60-903123a1bb2e", + "metadata": {}, + "outputs": [], + "source": [ + "palette = pynhm.analysis.utils.colorbrewer.nhm_process_colors(multi_proc_model)\n", + "pynhm.analysis.utils.colorbrewer.jupyter_palette(palette)\n", + "pynhm.analysis.ModelGraph(multi_proc_model, process_colors=palette, hide_variables=False).SVG(verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "54e081b0-c96c-4c75-9ef7-3a19b9528a93", + "metadata": {}, + "source": [ + "We run the model just as before" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02071b0f-87ff-418b-a8ed-6aa517253255", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "run_dir_multi_proc = pl.Path(tempfile.mkdtemp())\n", + "multi_proc_model.run(netcdf_dir=run_dir_multi_proc, finalize=True)" + ] + }, + { + "cell_type": "markdown", + "id": "fa9ce5aa-53ca-4f18-b529-7c28c8119369", + "metadata": {}, + "source": [ + "Let's plot some variables, say soil recharge storage, at the final time of the simulation. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1675ca28-ce7f-4554-b34b-e32dff2e1255", + "metadata": {}, + "outputs": [], + "source": [ + "import process_plot\n", + "gis_dir = pynhm_root.parent / f\"examples/pynhm_gis/{domain}\"\n", + "proc_plot = process_plot.ProcessPlot(gis_dir)\n", + "\n", + "proc_name = 'PRMSSoilzone'\n", + "var_name = 'soil_rechr'\n", + "proc = multi_proc_model.processes[proc_name]\n", + "display(proc_plot.plot_hru_var(var_name, proc))" + ] + }, + { + "cell_type": "markdown", + "id": "1ac44b16-64e3-4401-be5d-040fe012efda", + "metadata": {}, + "source": [ + "We can also inspect the mass budget at the final time. Processes on HRUs check budgets on individual HRUs (or spatial units) whereas the `PRMSChannel` tracks a global budget, checking if the total water entering and leaving the channel model is consistent with its stoage changes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85a5c1c3-701e-4c9b-9e10-3aba9abcf3d3", + "metadata": {}, + "outputs": [], + "source": [ + "pprint(multi_proc_model.processes['PRMSSoilzone'].get_mass_budget_terms())\n", + "multi_proc_model.processes['PRMSSoilzone'].budget" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e09b19f5-edc0-4660-880a-6cfd8b8bbbf3", + "metadata": {}, + "outputs": [], + "source": [ + "pprint(multi_proc_model.processes['PRMSChannel'].get_mass_budget_terms())\n", + "multi_proc_model.processes['PRMSChannel'].budget" + ] + }, + { + "cell_type": "markdown", + "id": "acbed031-66ba-4255-9a26-f09d9a69e741", + "metadata": {}, + "source": [ + "## NHM Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c855f32-c165-4a62-ba84-cfa25f0aa2f9", + "metadata": {}, + "outputs": [], + "source": [ + "params = pynhm.PrmsParameters.load(domain_dir / \"myparam.param\") \n", + "control = pynhm.Control.load(domain_dir / \"control.test\", params=params)\n", + "\n", + "nhm = pynhm.Model(\n", + " pynhm.PRMSSolarGeometry,\n", + " pynhm.PRMSAtmosphere,\n", + " pynhm.PRMSCanopy,\n", + " pynhm.PRMSSnow,\n", + " pynhm.PRMSRunoff,\n", + " pynhm.PRMSGroundwater,\n", + " pynhm.PRMSSoilzone,\n", + " pynhm.PRMSChannel,\n", + " control=control,\n", + " input_dir=domain_dir, # the PRMS/NHM inputs are all in this level\n", + " budget_type=\"error\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85978af5-278f-464d-8ef2-64b2bd49f4a6", + "metadata": {}, + "outputs": [], + "source": [ + "palette = pynhm.analysis.utils.colorbrewer.nhm_process_colors(nhm)\n", + "pynhm.analysis.utils.colorbrewer.jupyter_palette(palette)\n", + "pynhm.analysis.ModelGraph(nhm, process_colors=palette, hide_variables=False).SVG(verbose=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyclass", + "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": "3.10.8" + }, + "vscode": { + "interpreter": { + "hash": "f56f627c4c60f28539445070c8502106decdac8a9871af2a51c29ec1f4468ecc" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pywatershed/__init__.py b/pywatershed/__init__.py index 538b6170..068ab16f 100644 --- a/pywatershed/__init__.py +++ b/pywatershed/__init__.py @@ -18,6 +18,7 @@ from .hydrology.prms_snow import PRMSSnow from .hydrology.prms_soilzone import PRMSSoilzone from .hydrology.starfit import Starfit +from .hydrology.SWBRootZone import SWBRootZone from .utils import ( ControlVariables, NetCdfCompare, @@ -33,5 +34,6 @@ "atmosphere", "base", "hydrology", + "functions", "utils", ] diff --git a/pywatershed/base/data_model.py b/pywatershed/base/data_model.py index 73d0ecdd..b0b42fe9 100644 --- a/pywatershed/base/data_model.py +++ b/pywatershed/base/data_model.py @@ -1,7 +1,8 @@ import warnings from copy import deepcopy +import pathlib as pl from pprint import pprint -from typing import Iterable, Literal +from typing import Iterable, Literal, Union import cftime import netCDF4 as nc4 @@ -194,7 +195,7 @@ def __init__( if metadata is None: metadata = {} if encoding is None: - encoding = {} + encoding = {kk: {} for kk in metadata.keys()} self._data_vars = data_vars self._dims = dims @@ -292,6 +293,22 @@ def _keys(self) -> list: # def __str__(self): # return + @classmethod + def from_yaml(cls, the_file: Union[str, pl.Path]) -> dict: + """Read from a YAML file""" + import yaml + + with pl.Path(the_file).open("r") as file_stream: + dd = yaml.load(file_stream, Loader=yaml.Loader) + + for cv in ["coords", "data_vars"]: + for vv in dd[cv].keys(): + # TODO, introduce dims for shape if necessary + if not isinstance(dd[cv][vv], np.ndarray): + dd[cv][vv] = np.array(dd[cv][vv]) + + return cls(**dd) + @classmethod def from_dict(cls, dict_in, copy=False): """Return this class from a passed dictionary. @@ -804,6 +821,7 @@ def dd_to_xr_dd(dd: dict) -> dict: cv = "coords" key_enc = {} + if key in encoding.keys(): key_enc = encoding[key] dd[cv][key] = { diff --git a/pywatershed/functions/actual_et_thornthwaite_mather.py b/pywatershed/functions/actual_et_thornthwaite_mather.py new file mode 100644 index 00000000..61d62f35 --- /dev/null +++ b/pywatershed/functions/actual_et_thornthwaite_mather.py @@ -0,0 +1,79 @@ +import numpy as np + +TM_SLOPE_TERM_IN = 0.478769194198665 +TM_SLOPE_TERM_MM = 0.539815721014123 +TM_EXPONENT_TERM = -1.03678439421169 + +def thornthwaite_mather_soil_moisture_inches(max_soil_moisture, apwl): + """ + Return the current soil moisture (in inches) given the current soil moisture maximum (in inches), + as well as the current accumulated potential water loss (APWL). APWL is the running sum of the difference + between the potential ET and the actual ET. Equation and constants come from an R equation fitting exercise + applied to the original soil-mosture retention tables included in Thornthwaite and Mather (1957). + + max_soil_moisture Maximum moisture content of a soil at field capacity, in inches. + apwl Running sum of daily difference between PET and AET, in inches. + """ + + soil_moisture = np.where( max_soil_moisture > 0., + max_soil_moisture * 10**(-TM_SLOPE_TERM_IN*apwl*max_soil_moisture**TM_EXPONENT_TERM), + 0.0 + ) + return(soil_moisture) + + +def thornthwaite_mather_soil_moisture_millimeters(max_soil_moisture, apwl): + """ + Return the current soil moisture (in millimeters) given the current soil moisture maximum (in millimeters), + as well as the current accumulated potential water loss (APWL). APWL is the running sum of the difference + between the potential ET and the actual ET. Equation and constants come from an R equation fitting exercise + applied to the original soil-mosture retention tables included in Thornthwaite and Mather (1957). + + max_soil_moisture Maximum moisture content of a soil at field capacity, in millimeters. + apwl Running sum of daily difference between PET and AET, in millimeters. + """ + + soil_moisture = np.where( max_soil_moisture > 0., + max_soil_moisture * 10**(-TM_SLOPE_TERM_MM*apwl*max_soil_moisture**TM_EXPONENT_TERM), + 0.0 + ) + return(soil_moisture) + + +def thornthwaite_mather_accumulated_potential_water_loss_inches(max_soil_moisture, soil_moisture): + """ + Return the accumulated potential water loss (in inches) given the current soil moisture (in inches), + as well as maximum soil moisture (in inches). APWL is the running sum of the difference + between the potential ET and the actual ET. Equation and constants come from an R equation fitting exercise + applied to the original soil-mosture retention tables included in Thornthwaite and Mather (1957). + """ + apwl = np.where(max_soil_moisture > 0., + - (np.log(soil_moisture / max_soil_moisture))/(np.log(10)*TM_SLOPE_TERM_IN*max_soil_moisture**TM_EXPONENT_TERM), + 0.0 + ) + return(apwl) + + + +def thornthwaite_mather_accumulated_potential_water_loss_millimeters(max_soil_moisture, soil_moisture): + """ + Return the accumulated potential water loss (in millimeters) given the current soil moisture (in millimeters), + as well as maximum soil moisture (in millimeters). APWL is the running sum of the difference + between the potential ET and the actual ET. Equation and constants come from an R equation fitting exercise + applied to the original soil-mosture retention tables included in Thornthwaite and Mather (1957). + """ + apwl = np.where(max_soil_moisture > 0., + - (np.log(soil_moisture) - np.log(max_soil_moisture))/(np.log(10)*TM_SLOPE_TERM_MM*max_soil_moisture**TM_EXPONENT_TERM), + 0.0 + ) + return(apwl) + + +def actual_et_references(): + """ + Thornthwaite, C.W., and Mather, J.R., 1957, Instructions and tables for computing potential evapotranspiration + and the water balance: Publications in Climatology, v. 10, no. 3, p. 1-104. + + + """ + pass \ No newline at end of file diff --git a/pywatershed/functions/pet_hargreaves_samani.py b/pywatershed/functions/pet_hargreaves_samani.py new file mode 100644 index 00000000..7a094d6c --- /dev/null +++ b/pywatershed/functions/pet_hargreaves_samani.py @@ -0,0 +1,64 @@ +from numpy import nanmax, float_power +from ..constants import DEGREES_TO_RADIANS, RADIANS_TO_DEGREES +from .solar_and_meteorological_functions import relative_earth_sun_distance__D_r, \ + solar_declination__delta, \ + sunrise_sunset_angle__omega_s, \ + extraterrestrial_radiation__Ra, \ + equivalent_evaporation + +def et0_hargreaves_samani(extraterrestrial_radiation__Ra, air_temp_min, air_temp_max, air_temp_mean, + et_slope=0.0023, et_constant=17.8, et_exponent=0.5): + """ + Return the daily reference evapotranspriation in millimeters given extraterrestrial + radiation (expressed as the number of millimeters of water that could be evaporated by + the applied radiation) and the minimum and maximum daily air temperatures in Celsius degrees. + + Implemented as equation 4 in Hagreaves and Samani (1985) and as equation 50 in Allen and others (1998). + + """ + air_temp_delta = air_temp_max - air_temp_min + + et0 = nanmax((0.0, + et_slope * extraterrestrial_radiation__Ra * (air_temp_mean + et_constant) \ + * float_power(air_temp_delta,et_exponent)) + ) + + return et0 + + +def calculate_et0_hargreaves_samani(day_of_year, number_of_days_in_year, latitude, + air_temp_min, air_temp_max, air_temp_mean): + """ + Return the calculated reference evapotranspiration in millimeters per day, + given the day number, latitude, and min, max, and mean air temperatures in Celsius. + + day_of_year Day number of the current solar year. + number_of_days_in_year Number of days in the solar year. + latitude Latitude (in degrees) at which calculation is to be made. + air_temp_min Minimum daily air temperature, in degrees Celsius + air_temp_max Maximum daily air temperature, in degrees Celsius + air_temp_mean Mean daily air temperature, in degrees Celsius + """ + latitude_radians = latitude * DEGREES_TO_RADIANS + + d_r = relative_earth_sun_distance__D_r(day_of_year, number_of_days_in_year) + delta = solar_declination__delta(day_of_year, number_of_days_in_year) + omega_s = sunrise_sunset_angle__omega_s(latitude_radians, delta) + + Ra = equivalent_evaporation(extraterrestrial_radiation__Ra(latitude_radians, delta, omega_s, d_r)) + ref_ET = et0_hargreaves_samani(Ra, air_temp_min, air_temp_max, air_temp_mean) + print(f'ref_ET: {ref_ET}') + return ref_ET + + + +def references(): + """ + Allen, R.G., Pereira, L.S., Raes, D., and Smith, M., 1998, Crop evapotranspiration-Guidelines + for computing crop water requirements-FAO Irrigation and drainage paper 56: Food and Agriculture + Organization of the United Nations, Rome, 333 p. + + Hargreaves, G.H., and Samani, Z.A., 1985, Reference Crop Evapotranspiration from Ambient Air + Temperature: American Society of Agricultural Engineers 85–2517, 12 p., + accessed December 21, 2015, at http://www.zohrabsamani.com/papers/Hargreaves_Samani_85.pdf. + """ \ No newline at end of file diff --git a/pywatershed/functions/runoff_curve_number.py b/pywatershed/functions/runoff_curve_number.py new file mode 100644 index 00000000..04302d2b --- /dev/null +++ b/pywatershed/functions/runoff_curve_number.py @@ -0,0 +1,127 @@ +import numpy as np + + +def calculate_cn_S_inches(curve_number): + """ + Return the curve number storage (S) term, in inches. Equation 2-4, Cronshey and others (1986). + + """ + + S_inches = ( 1000.0 / curve_number ) - 10.0 + return S_inches + + + +def calculate_cn_S_millimeters(curve_number): + """ + Return the curve number storage (S) term, in millimeters. Equation 2-4, Cronshey and others (1986), + with constants multiplied by 25.4 (mm to inches). + + """ + + S_mm = ( 25400.0 / curve_number ) - 254.0 + return S_mm + + + +def calculate_cn_runoff(inflow, storage_S, initial_abstraction_Ia = 0.05): + """ + Return the runoff value given the inflow (precip), storage, and initial abstraction. + Equation 2-3, Cronshey and others (1986). + """ + Ia = initial_abstraction_Ia + runoff = np.where(inflow > Ia, + np.float_power(inflow - Ia * storage_S, 2.0) / (inflow + (1.0 - Ia) * storage_S), + 0.0 + ) + + return runoff + + + +def calculate_cn_alternative_S_0_05(storage_S): + """ + Return the curve number storage term, assuming that the initial abstraction is 0.05, rather than 0.2. + Equation 8, Woodward and others (2003). + """ + return 1.33 * (storage_S^1.15) + + + +def calculate_cn_arc2_to_arc1(curve_number_arc2): + """ + Return a curve number corresponding to antecedant runoff condition 1, given a + curve number corresponding to antecedant runoff condition 2. + Implemented as equation 3.145 of "SCS Curve Number Methodology", Mishra and Singh (2003), + and as equation 15 in Ponce and Hawkins (1996). + + Resulting curve numbers are clipped to the range 30-100. + """ + return np.clip((curve_number_arc2 / (2.281 - 0.01281 * curve_number_arc2 )), + 30.0, + 100.0 + ) + + +def calculate_cn_arc2_to_arc3(curve_number_arc2): + """ + Return a curve number corresponding to antecedant runoff condition 3, given a + curve number corresponding to antecedant runoff condition 2. + Implemented as equation 3.146 of "SCS Curve Number Methodology", Mishra and Singh (2003), + and as equation 16 in Ponce and Hawkins (1996). + + Resulting curve numbers are clipped to the range 30-100. + """ + return np.clip((curve_number_arc2 / (0.427 - 0.00573 * curve_number_arc2 )), + 30.0, + 100.0 + ) + +def adjust_curve_number(curve_number, inflow_5_day_sum, is_growing_season=False, cfgi=0, cfgi_ll=55, cfgi_ul=85): + + ARC_DRY_GROWING = 1.40 + ARC_DRY_DORMANT = 0.50 + ARC_WET_GROWING = 2.10 + ARC_WET_DORMANT = 1.10 + + if( cfgi > cfgi_ll ): + + p_er = calculate_probability_of_enhanced_runoff(cfgi, cfgi_ll, cfgi_ul) + + curve_number_adj = curve_number * (1. - p_er) + curve_number_arc3 * p_er + + elif( is_growing_season ): + + curve_number_adj = np.where(inflow_5_day_sum > ARC_WET_GROWING, curve_number_arc3, + np.where(inflow_5_day_sum < ARC_DRY_GROWING, curve_number_arc1, curve_number)) + + else: + + curve_number_adj = np.where(inflow_5_day_sum > ARC_WET_DORMANT, curve_number_arc3, + np.where(inflow_5_day_sum < ARC_DRY_DORMANT, curve_number_arc1, curve_number)) + + return curve_number_adj + + + +def cn_references(): + """ + Cronshey, R., McCuen, R., Miller, N., Rawls, W., Robbins, S., and Woodward, D., 1986, Urban Hydrology + for Small Watersheds - Technical release 55: US Dept. of Agriculture, Soil Conservation Service, + Engineering Division, accessed at http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/16/stelprdb1044171.pdf. + + Mishra, S.K., and Singh, V.P., 2003, Soil Conservation Service Curve Number (SCS-CN) Methodology: Water Science + and Technology Library, Springer Netherlands, Dordrecht, 534 p. + + Ponce, V.M., and Hawkins, R.H., 1996, Runoff Curve Number: Has It Reached Maturity? Journal of Hydrologic + Engineering, v. 1, no. 1, p. 11-19. + + Woodward, D.E., Hawkins, R.H., Jiang, R., Hjelmfelt, J., Van Mullem, J.A., and Quan, Q.D., 2003, + Runoff Curve Number Method: Examination of the Initial Abstraction Ratio, + in World Water and Environmental Resources Congress 2003, + American Society of Civil Engineers, Philadelphia, Pennsylvania, p. 1-10. + + """ + pass + +# \ No newline at end of file diff --git a/pywatershed/functions/solar_and_meteorological_functions.py b/pywatershed/functions/solar_and_meteorological_functions.py new file mode 100644 index 00000000..c5d340f9 --- /dev/null +++ b/pywatershed/functions/solar_and_meteorological_functions.py @@ -0,0 +1,128 @@ +from numpy import arccos, sin, cos, tan, pi + + +def daylight_hours( omega_s ): + """ + Calculate the number of daylight hours at a location. + + omega_s Sunset hour angle in Radians. + + Implementation follows equation 34, Allen and others (1998). + """ + return 24.0 / pi * omega_s + + + +def sunrise_sunset_angle__omega_s(latitude, delta): + """ + Return the sunset angle in radians, given the latitude and solar declination, in radians. + + latitude Latitude, in Radians + delta Solar declination, in Radians + + Implementation follows equation 25, Allen and others (1998). + """ + omega_s = arccos( - tan(latitude) * tan(delta) ) + return omega_s + + + +def day_angle__Gamma(day_of_year, number_of_days_in_year): + """ + Return the day angle in Radians. + + day _of_year Integer day of the year (January 1 = 1) + number_of_days_in_year Number of days in the current year + + Implementation follows equation 1.2.2 in Iqbal (1983) + """ + day_angle = 2.0 * pi * ( day_of_year - 1.0 ) / number_of_days_in_year + return day_angle + + + +def solar_declination__delta(day_of_year, number_of_days_in_year): + """ + Return the solar declination for a given day of the year in Radians. + + day _of_year Integer day of the year (January 1 = 1) + number_of_days_in_year Number of days in the current year + + Implementation follows equation 1.3.1 in Iqbal (1983). + Iqbal (1983) reports maximum error of 0.0006 radians; if the last two terms are omitted, + the reported accuracy drops to 0.0035 radians. + """ + Gamma = day_angle__Gamma( day_of_year, number_of_days_in_year ) + + delta = 0.006918 \ + - 0.399912 * cos( Gamma ) \ + + 0.070257 * sin( Gamma ) \ + - 0.006758 * cos( 2.0 * Gamma ) \ + + 0.000907 * sin( 2.0 * Gamma ) \ + - 0.002697 * cos( 3.0 * Gamma ) \ + + 0.00148 * sin( 3.0 * Gamma ) + + return delta + + +def relative_earth_sun_distance__D_r(day_of_year, number_of_days_in_year): + """ + Return the inverse relative Earth-Sun distance (unitless) for a given day of the year. + + day_of_year Integer day of the year (January 1 = 1) + number_of_days_in_year Number of days in the current year + + Implementation follows equation 23, Allen and others (1998). + See also Equation 1.2.3 in Iqbal, Muhammad (1983-09-28). + """ + d_r = 1.0 + 0.033 \ + * cos( 2.0 * pi * day_of_year ) \ + / number_of_days_in_year + + return d_r + + +def extraterrestrial_radiation__Ra(latitude, delta, omega_s, d_r): + """ + Return the extraterrestrial solar radiation for a point above earth, in MJ / m^2 / day + + latitude latitude in Radians + delta solar declination in Radians + omega_s sunset hour angle in Radians + d_r Inverse relative sun-earth distance (unitless) + + Implemented as equation 21, Allen and others (1998). + """ + Gsc = 0.0820 # MJ / m^2 / min + + part_a = omega_s * sin( latitude ) * sin( delta ) + part_b = cos( latitude ) * cos( delta ) * sin( omega_s ) + + Ra = 24.0 * 60.0 * Gsc * d_r * ( part_a + part_b ) / pi + return Ra + + + +def equivalent_evaporation(radiation_energy): + """ + Returns the equivalent depth of water (in millimeters) that would be evaporated + per day for a given amount of solar radiation (expressed in MJ/m^2-day). + + radiation_energy Solar radiation energy expressed in MJ per sq. meter per day + + Implementation follows equation 20, Allen and others (1998). + """ + + return radiation_energy * 0.408 + + +def references(): + """ + Allen, R.G., Pereira, L.S., Raes, D., and Smith, M., 1998, Crop evapotranspiration-Guidelines for + computing crop water requirements-FAO Irrigation and drainage paper 56: Food and Agriculture Organization + of the United Nations, Rome, 333 p. + + Iqbal, M., 1983, An Introduction To Solar Radiation: Academic Press Canada, Ontario, Canada, 617 p. + + """ + pass \ No newline at end of file diff --git a/pywatershed/hydrology/SWBRootZone.py b/pywatershed/hydrology/SWBRootZone.py new file mode 100644 index 00000000..69316aa7 --- /dev/null +++ b/pywatershed/hydrology/SWBRootZone.py @@ -0,0 +1,223 @@ +from ..base.conservative_process import ConservativeProcess +from ..parameters import Parameters + +from pywatershed.base.adapter import adaptable +from pywatershed.base.control import Control +from pywatershed.constants import nan, zero +import pywatershed.functions.runoff_curve_number as cn +import pywatershed.functions.actual_et_thornthwaite_mather as aet +import numpy as np + + +class SWBRootZone(ConservativeProcess): + """SWB Root Zone Storage object. + + Args: + + """ + + def __init__( + self, + control: Control, + discretization: Parameters, + parameters: Parameters, + net_rain: adaptable, + snowmelt: adaptable, + potet: adaptable, + budget_type: str = None, + calc_method: str = None, + verbose: bool = False, + load_n_time_batches: int = 1, + ): + super().__init__( + control=control, + discretization=discretization, + parameters=parameters, + ) + self.name = "SWBRootZone" + + self._set_inputs(locals()) + self._set_options(locals()) + + self._set_budget(budget_type) + # self._init_calc_method() + self._calc_method = str(calc_method) + + @staticmethod + def get_dimensions() -> tuple: + """Get dimensions""" + return ("nhru",) + + @staticmethod + def get_parameters() -> tuple: + """Get root zone reservoir parameters + Returns: + parameters: input parameters + """ + return ( + "base_curve_number", + "max_net_infiltration_rate", + "available_water_capacity", + "rooting_depth", + "swb_soil_storage_init", + ) + + @staticmethod + def get_inputs() -> tuple: + """Get root zone reservoir input variables + Returns: + variables: input variables + """ + return ( + "net_rain", + "snowmelt", + "potet", + ) + + @staticmethod + def get_init_values() -> dict: + """Get SWB rootzone initial values + Returns: + dict: initial values for named variables + """ + return { + "swb_runoff": zero, + "swb_actual_et": zero, + "swb_soil_storage": zero, + "swb_soil_storage_old": zero, + "swb_soil_storage_change": zero, + "swb_net_infiltration": zero, + "swb_soil_storage_max": zero, + "swb_accumulated_potential_water_loss": zero, + "swb_accumulated_potential_water_loss_old": zero, + } + + @staticmethod + def get_mass_budget_terms(): + return { + "inputs": [ + "net_rain", + "snowmelt", + ], + "outputs": [ + "swb_runoff", + "swb_net_infiltration", + ], + "storage_changes": [ + "swb_soil_storage_change", + ], + } + + def _set_initial_conditions(self): + self.swb_soil_storage[:] = self.swb_soil_storage_init.copy() + self.swb_soil_storage_max[:] = ( + self.available_water_capacity * self.rooting_depth + ) + return + + def _advance_variables(self) -> None: + """Advance the SWBRunoffCurveNumber + Returns: + None + """ + self.swb_soil_storage_old[:] = self.swb_soil_storage + return + + def _calculate(self, simulation_time): + """Calculate SWBRunoffCurveNumber for a time step + + Args: + simulation_time: current simulation time + + Returns: + None + + """ + + self._simulation_time = simulation_time + + # self.accumulated_potential_water_loss_old[:] = self.swb_accumulated_potential_water_loss[:].copy() + + ( + self.swb_runoff[:], + self.swb_soil_storage[:], + self.swb_accumulated_potential_water_loss[:], + self.swb_actual_et[:], + self.swb_net_infiltration[:], + ) = self._calculate_numpy( + base_curve_number=self.base_curve_number, + net_rain=self.net_rain, + snowmelt=self.snowmelt, + reference_et=self.potet, + apwl=self.swb_accumulated_potential_water_loss, + storage_max=self.swb_soil_storage_max, + storage_old=self.swb_soil_storage_old, + ) + return + + @staticmethod + def _calculate_numpy( + base_curve_number, + net_rain, + snowmelt, + reference_et, + apwl, + storage_max, + storage_old, + ): + apwl_new = np.full_like(apwl, fill_value=0.0) + actual_et = np.full_like(apwl, fill_value=0.0) + storage = np.full_like(storage_old, fill_value=0.0) + net_infiltration = np.full_like(apwl, fill_value=0.0) + + inflow = net_rain + snowmelt + curve_number_storage_S = cn.calculate_cn_S_inches(base_curve_number) + runoff = cn.calculate_cn_runoff( + inflow=inflow, storage_S=curve_number_storage_S + ) + + P_minus_PE = inflow - runoff - reference_et + + P_minus_PE_ge_0 = np.where(P_minus_PE >= 0.0) + P_minus_PE_lt_0 = np.where(P_minus_PE < 0.0) + + # handle cases where P minus PET is >= 0 + if len(P_minus_PE_ge_0[0]) > 0: + actual_et[P_minus_PE_ge_0] = reference_et[P_minus_PE_ge_0] + temp_storage = ( + storage_old[P_minus_PE_ge_0] + P_minus_PE[P_minus_PE_ge_0] + ) + zeros = np.full_like(temp_storage, fill_value=0.0) + storage[P_minus_PE_ge_0] = np.min( + (storage_max[P_minus_PE_ge_0], temp_storage) + ) + net_infiltration[P_minus_PE_ge_0] = np.max( + (zeros, temp_storage - storage_max[P_minus_PE_ge_0]) + ) + + apwl_new[ + P_minus_PE_ge_0 + ] = aet.thornthwaite_mather_accumulated_potential_water_loss_inches( + max_soil_moisture=storage_max[P_minus_PE_ge_0], + soil_moisture=storage, + ) + actual_et[P_minus_PE_ge_0] = reference_et[P_minus_PE_ge_0] + + # now handle cases where P minus PET < 0 + if len(P_minus_PE_lt_0[0]) > 0: + apwl_new[P_minus_PE_lt_0] = apwl_new[P_minus_PE_lt_0] + np.abs( + P_minus_PE[P_minus_PE_lt_0] + ) + storage[ + P_minus_PE_lt_0 + ] = aet.thornthwaite_mather_soil_moisture_inches( + max_soil_moisture=storage_max[P_minus_PE_lt_0], + apwl=apwl_new[P_minus_PE_lt_0], + ) + actual_et[P_minus_PE_lt_0] = ( + storage_old[P_minus_PE_lt_0] - storage[P_minus_PE_lt_0] + ) + + # breakpoint() + + return (runoff, storage, apwl_new, actual_et, net_infiltration) diff --git a/pywatershed/static/metadata/parameters.yaml b/pywatershed/static/metadata/parameters.yaml index 82efe03f..ef9b1472 100644 --- a/pywatershed/static/metadata/parameters.yaml +++ b/pywatershed/static/metadata/parameters.yaml @@ -132,6 +132,28 @@ azrh: - stream_temp type: F units: radians +available_water_capacity: + default: 2.0 + desc: Available water capacity for a soil, in inches per foot + dimensions: + 0: nhru + help: Available water capacity for a soil, in inches per foot + maximum: 10.0 + minimum: 0.0 + modules: + type: F + units: inches per foot +base_curve_number: + default: 70.0 + desc: National Soil Conservation Service curve number for type 2 antecedant runoff condition + dimensions: + 0: nhru + help: National Soil Conservation Service curve number for type 2 antecedant runoff condition + maximum: 100.0 + minimum: 20.0 + modules: + type: F + units: radians basin_solsta: default: 0 desc: Index of main solar radiation station @@ -1592,6 +1614,17 @@ max_missing: - temp_1sta_laps type: I units: none +max_net_infiltration_rate: + default: 0.5 + desc: Maximum daily net_infiltration rate, in inches + dimensions: + 0: nhru + help: Maximum daily net_infiltration rate, in inches + maximum: 20.0 + minimum: 0.0 + modules: + type: F + units: inches max_psta: default: 0 desc: Maximum number of precipitation stations to distribute to an HRU @@ -2434,6 +2467,17 @@ ratetbl_lake: - muskingum_lake type: I units: none +rooting_depth: + default: 1.0 + desc: Rooting depth for an SWB soil storage reservoir + dimensions: + 0: nhru + help: Rooting depth, in feet + maximum: 12.0 + minimum: 0.0 + modules: + type: F + units: feet runoff_units: default: 0 desc: Measured streamflow units diff --git a/pywatershed/static/metadata/variables.yaml b/pywatershed/static/metadata/variables.yaml index 9e706e0b..12179f94 100644 --- a/pywatershed/static/metadata/variables.yaml +++ b/pywatershed/static/metadata/variables.yaml @@ -3064,6 +3064,60 @@ swrad: 0: nhru type: float64 units: Langleys +swb_actual_et: + desc: Actual evapotranspiration + dims: + 0: nhru + type: float64 + units: inches +swb_accumulated_potential_water_loss: + desc: Accumulated potential water loss as per Thornthwaite-Mather (1957) + dims: + 0: nhru + type: float64 + units: inches +swb_accumulated_potential_water_loss_old: + desc: Accumulated potential water loss as per Thornthwaite-Mather (1957) + dims: + 0: nhru + type: float64 + units: inches +swb_net_infiltration: + desc: Infiltrated water that leaves the root zone + dims: + 0: nhru + type: float64 + units: inches +swb_runoff: + desc: Runoff calculated by a SWB method + dims: + 0: nhru + type: float64 + units: inches +swb_soil_storage: + desc: Storage in the SWB root zone + dims: + 0: nhru + type: float64 + units: inches +swb_soil_storage_old: + desc: Storage old in the SWB root zone + dims: + 0: nhru + type: float64 + units: inches +swb_soil_storage_change: + desc: Storage change in the SWB root zone + dims: + 0: nhru + type: float64 + units: inches +swb_soil_storage_max: + desc: Storage maximum in the SWB root zone + dims: + 0: nhru + type: float64 + units: inches t_ground: desc: ground temperature dims: diff --git a/pywatershed/utils/netcdf_utils.py b/pywatershed/utils/netcdf_utils.py index 6ca6744e..2c454c3c 100644 --- a/pywatershed/utils/netcdf_utils.py +++ b/pywatershed/utils/netcdf_utils.py @@ -50,7 +50,7 @@ def __init__( nc_read_vars: list = None, load_n_times: int = None, load_n_time_batches: int = 1, - ) -> "NetCdfRead": + ): self.name = "NetCdfRead" self._nc_file = name self._nc_read_vars = nc_read_vars diff --git a/test_data/hru_1/hru_1.yaml b/test_data/hru_1/hru_1.yaml index 9574d288..3e61b33f 100644 --- a/test_data/hru_1/hru_1.yaml +++ b/test_data/hru_1/hru_1.yaml @@ -7,9 +7,11 @@ cbh_inputs: control_file: control.test param_file: myparam.param +swb_param_file: ../test_data/hru_1/swb_parameters.yaml prms_run_dir: ./ prms_output_dir: ./output +swb_output_dir: E:\projects\enterprise_capacity\git\pywatershed\test_data\hru_1\swb_output cbh_nc: cbh.nc diff --git a/test_data/hru_1/hru_1_weather.txt b/test_data/hru_1/hru_1_weather.txt new file mode 100644 index 00000000..dae095ea --- /dev/null +++ b/test_data/hru_1/hru_1_weather.txt @@ -0,0 +1,14976 @@ +date prcp tmin tmax +1979-01-01 0.5 42.65 56.49 +1979-01-02 0.99 46.73 58.02 +1979-01-03 0.05 14.59 49.4 +1979-01-04 0 12.48 27.55 +1979-01-05 0.08 19.37 28.79 +1979-01-06 0.52 21.1 35.88 +1979-01-07 1.42 22.62 43.44 +1979-01-08 0.76 25.87 43.06 +1979-01-09 0 15.78 30.89 +1979-01-10 0 12.88 26.75 +1979-01-11 0 8.61 23.75 +1979-01-12 0.28 7.15 28.48 +1979-01-13 0.57 24.02 38.61 +1979-01-14 0.21 27.37 41.86 +1979-01-15 0 19.5 31.02 +1979-01-16 0.1 19.25 34.53 +1979-01-17 0.28 20.26 37.07 +1979-01-18 0.01 6.54 35.04 +1979-01-19 0.03 1.07 19.84 +1979-01-20 1.74 13 35.45 +1979-01-21 2.78 30.48 42.92 +1979-01-22 0 27.98 38.89 +1979-01-23 0.07 24.61 36.83 +1979-01-24 1.41 28.81 43.48 +1979-01-25 0.95 30.99 44.65 +1979-01-26 0.01 30.5 40.11 +1979-01-27 0 28.39 45.49 +1979-01-28 0 28.89 43.8 +1979-01-29 0 27.67 39.89 +1979-01-30 0 25.4 39.55 +1979-01-31 0 26.05 36.49 +1979-02-01 0 16.65 31.45 +1979-02-02 0 13.56 29.52 +1979-02-03 0 15.57 34.15 +1979-02-04 0 17.58 37.1 +1979-02-05 0 14.13 31.45 +1979-02-06 0 8.42 24.84 +1979-02-07 0.36 12.04 27.77 +1979-02-08 0 17.26 31.99 +1979-02-09 0 4.69 21.91 +1979-02-10 0 -0.65 16.2 +1979-02-11 0 -3.12 12.6 +1979-02-12 0.03 -3.33 20.11 +1979-02-13 0 2.18 16.67 +1979-02-14 0 -4.56 15.91 +1979-02-15 0 8.37 22.45 +1979-02-16 0.01 8.37 21.24 +1979-02-17 0 -3.64 12.6 +1979-02-18 0.02 -6.47 19.44 +1979-02-19 0.35 14.42 35.71 +1979-02-20 0.16 10.16 35.44 +1979-02-21 0.11 17.32 40.34 +1979-02-22 0 30.47 43.88 +1979-02-23 0.31 29.73 41.53 +1979-02-24 1.15 34.43 45.18 +1979-02-25 0.44 33.12 45.86 +1979-02-26 0.97 32.97 44.26 +1979-02-27 0.02 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29.54 55.24 +2019-02-05 0 30.73 59.26 +2019-02-06 0.46 32.59 41.96 +2019-02-07 0.19 36.39 42.22 +2019-02-08 0.06 21.82 53.04 +2019-02-09 0 18.17 32.3 +2019-02-10 0 20.75 34.2 +2019-02-11 0 24.21 35.43 +2019-02-12 0.82 21.13 36 +2019-02-13 0 30.04 40.35 +2019-02-14 0 30.69 43.23 +2019-02-15 0 31.69 49.96 +2019-02-16 0 21.89 42.26 +2019-02-17 0.12 24.21 35.01 +2019-02-18 0.03 18.86 39.49 +2019-02-19 0 20.5 32.76 +2019-02-20 0.64 22.56 34.29 +2019-02-21 0 18.97 32.53 +2019-02-22 0 22.9 46.01 +2019-02-23 0 27.55 52.57 +2019-02-24 0 34.28 56.16 +2019-02-25 0.17 41.74 49.9 +2019-02-26 0.88 42.17 47.32 +2019-02-27 0 26.96 42.61 +2019-02-28 0 25.93 39.24 +2019-03-01 0 21.03 35.13 +2019-03-02 0.03 29.86 39.49 +2019-03-03 1.07 31.72 41.61 +2019-03-04 0 16.03 38.87 +2019-03-05 0 16.12 31.87 +2019-03-06 0 12.43 26.91 +2019-03-07 0 11.76 31.21 +2019-03-08 0 19.37 37.04 +2019-03-09 0.19 28.46 45.91 +2019-03-10 0.34 34.18 44.84 +2019-03-11 0 32.97 52.57 +2019-03-12 0 25.47 43.6 +2019-03-13 0 31.42 45.13 +2019-03-14 0 41.05 54.91 +2019-03-15 0.34 42.76 63.74 +2019-03-16 0 30.51 51.01 +2019-03-17 0 23.2 44.11 +2019-03-18 0 24.75 45.55 +2019-03-19 0 26.19 48.48 +2019-03-20 0 33.08 51.13 +2019-03-21 0.84 39.79 47.36 +2019-03-22 0.06 32.01 48.36 +2019-03-23 0 29.59 48.03 +2019-03-24 0 36.45 57.06 +2019-03-25 0 29.82 51.16 +2019-03-26 0 27.67 47.97 +2019-03-27 0 28.38 44.53 +2019-03-28 0 36.3 50.67 +2019-03-29 0 43.66 56.68 +2019-03-30 0 48.26 64.26 +2019-03-31 0.26 31.39 58.23 +2019-04-01 0 26.38 47.34 +2019-04-02 0.04 36.01 51.39 +2019-04-03 0 39.82 68.83 +2019-04-04 0 33.96 58.67 +2019-04-05 0.31 35.45 43.83 +2019-04-06 0 37.97 66.79 +2019-04-07 0.22 47.35 66.03 +2019-04-08 0 42.94 72.16 +2019-04-09 0.06 38.93 50.71 +2019-04-10 0 35.33 59.68 +2019-04-11 0 39.47 51.29 +2019-04-12 0.95 43.09 64.52 +2019-04-13 0.03 51.39 74.96 +2019-04-14 0.49 54.6 70.78 +2019-04-15 0.19 40.83 65.62 +2019-04-16 0 44.69 65.21 +2019-04-17 0 45.72 63.41 +2019-04-18 0.08 47.76 60.69 +2019-04-19 0.41 57.73 74.75 +2019-04-20 0.93 51.56 66.87 +2019-04-21 0 47.73 63.75 +2019-04-22 0.42 52.05 65.77 +2019-04-23 0.01 52.81 75.8 +2019-04-24 0 43.13 67.73 +2019-04-25 0.17 48.66 66.31 +2019-04-26 1.54 46.42 60.03 +2019-04-27 0 42.06 56.67 +2019-04-28 0.03 34.1 52.25 +2019-04-29 0.18 44.2 59.59 +2019-04-30 0.01 44.95 58.59 +2019-05-01 0.02 46.76 53.45 +2019-05-02 0.05 47.16 71.06 +2019-05-03 0.28 48.19 54.4 +2019-05-04 0.3 53.44 68.17 +2019-05-05 0.83 46.89 56.48 +2019-05-06 0 48.91 67.48 +2019-05-07 0.01 52.45 68.33 +2019-05-08 0 47.5 70.46 +2019-05-09 0 50.69 57.72 +2019-05-10 0.09 52.29 70.71 +2019-05-11 0.37 45.52 68.24 +2019-05-12 0.96 40.75 46.8 +2019-05-13 0.56 41.95 48.91 +2019-05-14 0.08 43.08 50.91 +2019-05-15 0.03 47.59 68.01 +2019-05-16 0 50.7 70.95 +2019-05-17 0.05 49.88 73.96 +2019-05-18 0 53.74 73.31 +2019-05-19 0.42 61.6 75.54 +2019-05-20 0.1 55.46 83.89 +2019-05-21 0 46.97 71.64 +2019-05-22 0.09 54.66 73.24 +2019-05-23 0.08 56.95 70.28 +2019-05-24 0 50.79 73.31 +2019-05-25 0.08 56.99 69.83 +2019-05-26 0 59.49 84.67 +2019-05-27 0 58.64 79.99 +2019-05-28 0.17 52.89 65.12 +2019-05-29 0.83 53.31 64.32 +2019-05-30 0.89 54.97 66.28 +2019-05-31 0 56.77 79.73 +2019-06-01 0 59.06 77.31 +2019-06-02 0.04 53.17 79.29 +2019-06-03 0 43.52 71.29 +2019-06-04 0 57.47 71.33 +2019-06-05 0 64.13 79.65 +2019-06-06 0 60.14 82.71 +2019-06-07 0 57.29 78.71 +2019-06-08 0 51.95 80.35 +2019-06-09 0 51.07 75 +2019-06-10 0.6 60.37 70.41 +2019-06-11 0.03 51.24 77.12 +2019-06-12 0 56.71 74.93 +2019-06-13 0.41 55.51 63.67 +2019-06-14 0 52.37 74.32 +2019-06-15 0.07 63.04 80.52 +2019-06-16 0.41 64.87 76.24 +2019-06-17 0.08 65.99 76.89 +2019-06-18 0.96 64.34 70.74 +2019-06-19 0.07 63.47 71.33 +2019-06-20 0.07 65.48 75.73 +2019-06-21 0.04 58.05 77.66 +2019-06-22 0 57.07 80.44 +2019-06-23 0 58.1 84.45 +2019-06-24 0.01 65.57 84.22 +2019-06-25 0.35 65.6 80.34 +2019-06-26 0 65.06 87.25 +2019-06-27 0 62.22 88.6 +2019-06-28 0 68.2 89.87 +2019-06-29 0.21 68.14 90.38 +2019-06-30 0.41 61.53 82.94 +2019-07-01 0 63.15 84.99 +2019-07-02 0 67.88 83.96 +2019-07-03 0 65.93 86.3 +2019-07-04 0 65.08 89.33 +2019-07-05 0 70.63 82.87 +2019-07-06 0.43 72.42 87.34 +2019-07-07 0 65.6 82.77 +2019-07-08 0.04 63.19 78.38 +2019-07-09 0 62.67 86.35 +2019-07-10 0 69.12 87.72 +2019-07-11 0.96 70.83 84.86 +2019-07-12 0 66.06 87.38 +2019-07-13 0 69.17 84.94 +2019-07-14 0 64.34 90.02 +2019-07-15 0 62.43 85.93 +2019-07-16 0 73.04 89.28 +2019-07-17 2.57 72.44 91.97 +2019-07-18 1.03 69.04 77.91 +2019-07-19 0 71.95 88.2 +2019-07-20 0 76.54 95.17 +2019-07-21 0 73.23 97.49 +2019-07-22 2.2 65.4 87.6 +2019-07-23 0.25 64.35 74.4 +2019-07-24 0 60.77 83.04 +2019-07-25 0 62.24 84.01 +2019-07-26 0 65.15 85.88 +2019-07-27 0.03 68.34 85 +2019-07-28 0 70.17 89.56 +2019-07-29 0 71.29 91.21 +2019-07-30 0 70.71 92.18 +2019-07-31 0.37 66.98 87.31 +2019-08-01 0 65.39 86 +2019-08-02 0 66.89 82.81 +2019-08-03 0.23 66.52 85.04 +2019-08-04 0 64.5 86.65 +2019-08-05 0 64.65 80.87 +2019-08-06 0 68.22 79.07 +2019-08-07 0.96 66.97 83.88 +2019-08-08 0 64.13 85.47 +2019-08-09 0 59.32 83.83 +2019-08-10 0 55.5 80.94 +2019-08-11 0 57.78 80.34 +2019-08-12 0.02 68.44 85.12 +2019-08-13 0.22 67.89 76.37 +2019-08-14 0.01 65.29 79.79 +2019-08-15 0 63.68 82.09 +2019-08-16 0 68.36 79.97 +2019-08-17 0.13 68.47 83.53 +2019-08-18 0.09 68.2 88.19 +2019-08-19 0.84 68.32 89.71 +2019-08-20 0 69.79 87.45 +2019-08-21 0.12 70.49 86.27 +2019-08-22 0.67 64.24 89.59 +2019-08-23 0.07 57.56 75.45 +2019-08-24 0 58.78 75.71 +2019-08-25 0 55.03 75.01 +2019-08-26 0 53.38 73.52 +2019-08-27 0 59.13 73.13 +2019-08-28 0.8 63.13 73.26 +2019-08-29 0 56.3 81.87 +2019-08-30 0 61.65 84.62 +2019-08-31 0 56.3 78.39 +2019-09-01 0 61.38 76 +2019-09-02 0.14 59.93 76.27 +2019-09-03 0 61.21 80.28 +2019-09-04 0.38 57.62 83.65 +2019-09-05 0 55.82 72.63 +2019-09-06 0.31 53.47 66.68 +2019-09-07 0 55.1 74.57 +2019-09-08 0 57.91 77.77 +2019-09-09 0 59.79 75.22 +2019-09-10 0 63.39 75.65 +2019-09-11 0.15 68.15 85.4 +2019-09-12 0.01 50.93 76.5 +2019-09-13 0 53.92 69.95 +2019-09-14 0.07 61.61 73.64 +2019-09-15 0.04 60.47 80.11 +2019-09-16 0 50.26 74.78 +2019-09-17 0 50.73 75.34 +2019-09-18 0 41.43 67.85 +2019-09-19 0 43.82 68.83 +2019-09-20 0 50.46 77.96 +2019-09-21 0 52.92 81.51 +2019-09-22 0 62.57 84.19 +2019-09-23 0.06 61.22 86.6 +2019-09-24 0 49.46 76.89 +2019-09-25 0 52.38 76.57 +2019-09-26 0.32 48.42 79.25 +2019-09-27 0 53.96 73.79 +2019-09-28 0 62.3 80.01 +2019-09-29 0 53.95 78.31 +2019-09-30 0 56.18 68.86 +2019-10-01 0 64.31 79.3 +2019-10-02 0.09 51.85 90.65 +2019-10-03 0.48 50.23 58.48 +2019-10-04 0 38.76 64.11 +2019-10-05 0 43.28 61.93 +2019-10-06 0.02 59.5 69.9 +2019-10-07 0.44 56.07 76.53 +2019-10-08 0 53.27 63.62 +2019-10-09 0.52 50.85 57.19 +2019-10-10 0 52.27 69.25 +2019-10-11 0 53.75 63.92 +2019-10-12 0 49.17 66.43 +2019-10-13 0 49.57 66.08 +2019-10-14 0 42.64 71.55 +2019-10-15 0 41.93 63.85 +2019-10-16 2.51 48.39 65.29 +2019-10-17 0.02 48.48 58.59 +2019-10-18 0 35.92 60.3 +2019-10-19 0 38.58 60.19 +2019-10-20 0.3 43.11 58.8 +2019-10-21 0 47.82 66.9 +2019-10-22 0.72 51.88 62.76 +2019-10-23 0 38.86 67.12 +2019-10-24 0 40.99 67.52 +2019-10-25 0 45.67 64.61 +2019-10-26 0.05 50.69 63.24 +2019-10-27 2.65 48.99 63.67 +2019-10-28 0.03 50.41 63.34 +2019-10-29 0.18 51.88 59.2 +2019-10-30 0.38 56.8 65.01 +2019-10-31 0.37 45.64 70.56 +2019-11-01 0 31.09 57.02 +2019-11-02 0 33.06 52.2 +2019-11-03 0 30.84 54.81 +2019-11-04 0 35.48 56.33 +2019-11-05 0 36.36 60.79 +2019-11-06 0 34.32 55.14 +2019-11-07 0.17 28.54 56.59 +2019-11-08 0 20 39.67 +2019-11-09 0 24.31 41.34 +2019-11-10 0 34.09 51.88 +2019-11-11 0 39.89 61.95 +2019-11-12 0.06 20.49 54.54 +2019-11-13 0 19.75 33.82 +2019-11-14 0 24 45.51 +2019-11-15 0 29.04 52.15 +2019-11-16 0 25.17 40.63 +2019-11-17 0 29.16 41.53 +2019-11-18 0.86 36.2 44.58 +2019-11-19 0 38 47.07 +2019-11-20 0 33.01 45.85 +2019-11-21 0 35.97 53.3 +2019-11-22 0.12 28.96 53.1 +2019-11-23 0.85 31.63 46.95 +2019-11-24 0 31.75 32.99 +2019-11-25 0 31.81 52.4 +2019-11-26 0 34.47 60.42 +2019-11-27 0.03 40.26 57.61 +2019-11-28 0 29.09 51.67 +2019-11-29 0 27.4 41.85 +2019-11-30 0 23.86 42.49 +2019-12-01 0.65 24.65 37.43 +2019-12-02 0.12 27.32 36.28 +2019-12-03 0 26.04 36.17 +2019-12-04 0 30.54 38.06 +2019-12-05 0 30.68 41.86 +2019-12-06 0 26.46 46.79 +2019-12-07 0 15.79 37.38 +2019-12-08 0.3 18.17 42.89 +2019-12-09 1.26 40.12 55.13 +2019-12-10 0.44 29.19 54.92 +2019-12-11 0.04 20.2 34.67 +2019-12-12 0 20.27 34.04 +2019-12-13 2.18 31.04 50.73 +2019-12-14 0.11 41.43 55.45 +2019-12-15 0 29.27 46.89 +2019-12-16 0.31 28.99 34.68 +2019-12-17 0.36 27.02 33.19 +2019-12-18 0 14.11 35.33 +2019-12-19 0 14.33 24.27 +2019-12-20 0 17.15 30.92 +2019-12-21 0 17.77 32.41 +2019-12-22 0 23.22 44.4 +2019-12-23 0 29.54 54.79 +2019-12-24 0 23.45 47.21 +2019-12-25 0 24.26 42.65 +2019-12-26 0 30.6 42.59 +2019-12-27 0 34.17 50.4 +2019-12-28 0 28.63 47.86 +2019-12-29 0.76 28.84 41.31 +2019-12-30 0.64 34.44 39.2 +2019-12-31 0.08 34.86 43.59 diff --git a/test_data/hru_1/landuse_nlcd.txt b/test_data/hru_1/landuse_nlcd.txt new file mode 100644 index 00000000..578db157 --- /dev/null +++ b/test_data/hru_1/landuse_nlcd.txt @@ -0,0 +1,16 @@ +lu_code description lu_group First_day_of_growing_season Last_day_of_growing_season note__curve_numbers cn_1 cn_2 cn_3 cn_4 max_net_infil_1 max_net_infil_2 max_net_infil_3 max_net_infil_4 interception__note growing_season_interception_a growing_season_interception_b growing_season_interception_n nongrowing_season_interception_a nongrowing_season_interception_b nongrowing_season_interception_n note__root_zone reference__root_zone rz_1 rz_2 rz_3 rz_4 +11 Open Water openwat 05-01 10-15 99 99 99 99 0 0 0 0 0 0 1 0 0 1 no rooting depth for open water 0 0 0 0 +21 "Developed, Open Space" devopen 05-01 10-15 "open space, fair" 49 68.3 79.0 83.3 4 2 0.24 0.12 meadow grass 0.005 0.08 1 0.0001 0.027 1 "weak evidence suggesting shallowest rooting depths for loamy soils, with greater depths for sand, clay" "Sheffer, K.M., Dunn, J.H., and Minner, D.D., 1987, Summer Drought Response and Rooting Depth of Three Cool-season Turfgrasses: HortScience, v. 22, no. 2, p. 296?297." 1.5 1.35 1.395 1.44 +22 "Developed, Low Intensity" devlow 05-01 10-15 "developed, residential, 1/8 acre or less" 77 85.7 90.5 92.5 4 2 0.24 0.12 "maples, hedges and open" 0.03 0.23 1 0.0001 0.033 1 1.5 1.35 1.395 1.44 +23 "Developed, Medium Intensity" devmed 05-01 10-15 "developed, industrial" 81 86.0 91.0 93.8 4 2 0.24 0.12 2/3 of interception for lu 22 0.02 0.15 1 0.0001 0.027 1 1.5 1.35 1.395 1.44 +24 "Developed, High Intensity" devhi 05-01 10-15 "developed, commercial" 89 86.0 91.0 94.0 4 2 0.24 0.12 2/3 of interception for lu 23 0.01 0.10 1 0.0001 0.027 1 1.5 1.35 1.395 1.44 +31 Barren Land barren 05-01 10-15 "newly graded, no vegetation" 77 85.7 90.5 92.5 4 2 0.24 0.12 0 0 1 0 0 1 1 0.9 0.93 0.96 +41 Deciduous Forest decfrst 05-01 10-15 curve number for 'Evergreen Forest' times 1.1 49.5 68.6 79.2 83.4 4 2 0.24 0.12 oak woods 0.05 0.18 0.5 0.0001 0.06 0.5 weak evidence suggests needle-leaf evergreens have somewhat smaller mean rooting depths than deciduous trees "Fan, Y., Miguez-Macho, G., Jobb gy, E.G., Jackson, R.B., and Otero-Casal, C., 2017, Hydrologic regulation of plant rooting depth: Proceedings of the National Academy of Sciences, v. 114, no. 40, p. 10572?10577." 4 3.6 3.72 3.84 +42 Evergreen Forest evfrst 05-01 10-15 "woods, poor; CNs for forested LU tend to be too low" 45 65.8 77.4 82.0 4 2 0.24 0.12 "hemlock and pines, woods" 0.05 0.2 0.5 0.0065 0.2 0.5 3 2.7 2.79 2.88 +43 Mixed Forest mixfrst 05-01 10-15 curve number for 'Evergreen Forest' times 1.05 47.25 67.2 78.3 82.7 4 2 0.24 0.12 "hemlock and pines, open" 0.03 0.2 0.5 0.0001 0.095 0.5 3.5 3.15 3.255 3.36 +52 Shrub/Scrub shrub 05-01 10-15 "brush, poor" 48 67.7 78.6 82.9 4 2 0.24 0.12 "maple, hedges and open" 0.03 0.23 1 0.0001 0.0265 1 2.5 2.25 2.325 2.4 +71 Herbaceous grsherb 05-01 10-15 "grassland, fair" 49 68.3 79.0 83.3 4 2 0.24 0.12 meadow grass 0.01 0.16 1 0.0001 0.05 1 2.7 2.43 2.511 2.592 +81 Hay/Pasture hay 05-01 10-15 "pasture, good" 39 62.1 74.9 80.0 4 2 0.24 0.12 "Forage, alfalfa" 0.02 0.2 1 0.0001 0.05 1 2.5 2.25 2.325 2.4 +82 Cultivated Crops gencrop 05-01 10-15 "row crops, contoured, good" 65 78.2 85.6 88.5 4 2 0.24 0.12 "beans, potatoes" 0.02 0.15 1 0.0001 0.033 1 2 1.8 1.86 1.92 +90 Woody Wetlands wdwetl 05-01 10-15 "woods, fair" 43 64.5 76.6 81.3 4 2 0.24 0.12 "hemlock and pines, open" 0.03 0.2 0.5 0.0001 0.06 1 assume a rather large storage reservoir for wetlands 3.25 2.925 3.0225 3.12 +95 Emergent Herbaceous Wetlands hbwetl 05-01 10-15 "meadow, continuous grass, protected from grazing" 30 56.5 71.2 77.0 4 2 0.24 0.12 small grains 0.015 0.15 1 0.0001 0.05 1 3.25 2.925 3.0225 3.12 diff --git a/test_data/hru_1/scripts/cleanup_swb.bat b/test_data/hru_1/scripts/cleanup_swb.bat new file mode 100644 index 00000000..181e285a --- /dev/null +++ b/test_data/hru_1/scripts/cleanup_swb.bat @@ -0,0 +1,2 @@ +del /S /Q ..\swb_output\*.* +del /S /Q ..\swb_logfiles\*.* \ No newline at end of file diff --git a/test_data/hru_1/scripts/daily_swb_plot.py b/test_data/hru_1/scripts/daily_swb_plot.py new file mode 100644 index 00000000..edfa1d53 --- /dev/null +++ b/test_data/hru_1/scripts/daily_swb_plot.py @@ -0,0 +1,172 @@ +# -*- coding: UTF-8 -*- +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.backends.backend_pdf import PdfPages +from datetime import timedelta +import sys + +csv_filename = '..\swb_output\SWB2_variable_values__col_1__row_1__x_1866006__y_2234241.csv' + +bits = csv_filename.replace('___','_').replace('__','_').replace('.','_').split('_') +output_file_prefix = csv_filename.split('.')[0] +locator_text = 'x: ' + bits[8] + ', y: ' + bits[10] +df = pd.read_csv(csv_filename) + +# eliminate unwanted spaces before and after items in the column name list +df.columns = map(str.strip, df.columns) +df['date'] = pd.to_datetime(df['date']) +df.set_index('date', inplace=True) + +years = np.unique(df.index.year) + +output_filename = 'daily_mass_balance_plot__' + output_file_prefix + '.pdf' + +with PdfPages(output_filename) as pdf: + + for n in range(0,len(years)): + fig, ax = plt.subplots(10,1,figsize=(15, 25),sharex=False) + df_ss = df.loc[df.index.year==years[n]] + y0 = np.zeros(len(df_ss)) + + p_temp = 0 + p_inp = 1 + p_snow_stor = 2 + p_int_stor = 3 + p_soil_stor = 4 + p_fao56 = 5 + p_fao56_evap = 6 + p_et = 7 + p_out = 8 + p_mb = 9 + + + lu = str(int(df_ss['landuse_code'][0])) + soil_group = str(int(df_ss['soil_group'][0])) + + ax.flat[p_temp].plot(df_ss.index, y0+32, marker=' ', linestyle='-', color='black', linewidth=0.15) + ax.flat[p_inp].plot(df_ss.index, y0, marker=' ', linestyle='-', color='black', linewidth=0.15) + ax.flat[p_snow_stor].plot(df_ss.index, y0, marker=' ', linestyle='-', color='black', linewidth=0.15) + ax.flat[p_int_stor].plot(df_ss.index, y0, marker=' ', linestyle='-', color='black', linewidth=0.15) + ax.flat[p_soil_stor].plot(df_ss.index, y0, marker=' ', linestyle='-', color='black', linewidth=0.15) + ax.flat[p_et].plot(df_ss.index, y0, marker=' ', linestyle='-', color='black', linewidth=0.15) + ax.flat[p_out].plot(df_ss.index, y0, marker=' ', linestyle='-', color='black', linewidth=0.15) + ax.flat[p_mb].plot(df_ss.index, y0, marker=' ', linestyle='-', color='black', linewidth=0.15) + + # Temperature plot + ax.flat[p_temp].fill_between(df_ss.index, df_ss['tmin'],df_ss['tmax'], color='grey', alpha=0.4) + ax.flat[p_temp].fill_between(df_ss.index, df_ss['tmin'],df_ss['tmax'], color='cyan', alpha=0.5, + where=df_ss['tmax'] <= 32) + ax.flat[p_temp].set(ylabel='Air temp (°F)') + + # Inputs plot + ax.flat[p_inp].bar(df_ss.index,df_ss['rainfall'],color='blue',alpha=0.7, label='rainfall') + ax.flat[p_inp].bar(df_ss.index, df_ss['snowmelt'], bottom=df_ss['rainfall'],color='green',alpha=0.6, + label='snowmelt') + ax.flat[p_inp].bar(df_ss.index, df_ss['irrigation'], bottom=df_ss['snowmelt']+df_ss['rainfall'],color='rebeccapurple', alpha=0.6,label='irrigation') + + ax2 = ax.flat[p_inp].twinx() + + gross_precip = df_ss['rainfall'] + df_ss['snowfall'] + + ax2.plot(df_ss.index, np.cumsum(gross_precip), color="darkblue", label='cumulative gross precip') + ax2.plot(df_ss.index, np.cumsum(df_ss['irrigation']), color="darkviolet", label='cumulative irrigation') + + ax.flat[p_inp].legend() + ax2.legend() + ax2.set(ylabel='Cumulative input (inches)') + ax.flat[p_inp].set(ylabel='Input (inches)') + + # Snow storage plot + ax.flat[p_snow_stor].fill_between(df_ss.index, y0,df_ss['snow_storage'],color='blue',alpha=0.4, + where=df_ss['snow_storage'] > 0, interpolate=True) + ax.flat[p_snow_stor].set(ylabel='Snow storage (inches)') + + # Interception storage plot + ax.flat[p_int_stor].fill_between(df_ss.index, y0,df_ss['interception_storage'],color='green', + alpha=0.8, where=df_ss['interception_storage'] > 0, interpolate=True) + ax.flat[p_int_stor].set(ylabel='Interception storage (inches)') + + # Soil storage plot + ax.flat[p_soil_stor].fill_between(df_ss.index, y0,df_ss['soil_storage'],color='tan',alpha=0.4) + ax.flat[p_soil_stor].plot(df_ss.index, df_ss['soil_storage_max'],color='brown') + ax.flat[p_soil_stor].set(ylabel='Soil storage\n (inches)') + + # FAO-56 params plot + ax.flat[p_fao56].plot(df_ss.index, df_ss['plant_stress_coef_ks'],color='green', + marker=' ', linestyle='--', alpha=0.4, label='plant stress coefficient (Ks)') + ax.flat[p_fao56].plot(df_ss.index, df_ss['crop_coefficient_kcb'],color='orange', + marker='.', linestyle="-", alpha=0.5, label='crop coefficient (Kcb)') + + ax2 = ax.flat[p_fao56].twinx() + ax2.plot(df_ss.index, df_ss['current_rooting_depth'],color='brown', + marker=' ', alpha=0.7, label='current_rooting_depth') + + ax.flat[p_fao56].legend() + ax.flat[p_fao56].set(ylabel='Coefficient value\n(dimensionless)') + ax2.legend() + ax2.set(ylabel='Rooting depth (feet)') + + # FAO-56 evap params plot + ax.flat[p_fao56_evap].plot(df_ss.index, df_ss['evaporable_water_storage'],color='green', + marker=' ', linestyle='--', alpha=0.4, label='evaporable water storage') + ax.flat[p_fao56_evap].plot(df_ss.index, df_ss['evaporable_water_deficit'],color='red', + marker=' ', alpha=0.7, label='evaporable_water_deficit') + + ax2 = ax.flat[p_fao56_evap].twinx() + ax2.plot(df_ss.index, df_ss['evap_reduction_coef_kr'],color='purple', + marker=' ', linestyle="-.", alpha=0.5, label='evap reduction coef (Kr)') + ax2.plot(df_ss.index, df_ss['surf_evap_coef_ke'],color='blue', + marker=' ', linestyle="-", alpha=0.5, label='surface evap coef (Ke)') + ax2.plot(df_ss.index, df_ss['crop_coefficient_kcb'],color='orange', + marker='.', linestyle="-", alpha=0.5, label='crop coefficient (Kcb)') + ax.flat[p_fao56_evap].legend() + ax2.legend() + ax.flat[p_fao56_evap].set(ylabel='Evaporation storage') + ax2.set(ylabel='Coefficient value\n(dimensionless)') + + # Actual ET plot + ax.flat[p_et].bar(df_ss.index, -df_ss['bare_soil_evap'], + color='purple', label='bare_soil_evap') + ax.flat[p_et].bar(df_ss.index, -df_ss['crop_etc'], color='gold', + label='crop_etc', bottom= -df_ss['bare_soil_evap']) + ax.flat[p_et].bar(df_ss.index, -df_ss['actual_et_interception'], + color='green', bottom = -df_ss['actual_et_soil'], label='actual_et_interception') + ax.flat[p_et].plot(df_ss.index, -df_ss['actual_ET'],marker=' ',linestyle='dashdot',color='black', + label='total actual ET') + ax.flat[p_et].plot(df_ss.index, -df_ss['reference_ET0'],marker=' ',linestyle='--',color='orange', + label='reference ET0') + ax.flat[p_et].set(ylabel='Evapotranspiration\n from soil (inches)') + + ax2 = ax.flat[p_et].twinx() + ax2.plot(df_ss.index, np.cumsum(-df_ss['actual_ET']), color="darkgreen", label='cumulative actual ET') + ax2.legend() + ax.flat[p_et].legend() + + # Ouflows + ax.flat[p_out].bar(df_ss.index,-df_ss['runoff'],color='red',alpha=0.5, label='runoff') + ax.flat[p_out].bar(df_ss.index, -df_ss['net_infiltration'],color='blue',alpha=0.7, + label='net infiltration', bottom = -df_ss['runoff']) + ax.flat[p_out].bar(df_ss.index, -df_ss['rejected_net_infiltration'],color='orange',alpha=0.7, + label='rejected net infiltration', bottom = -df_ss['net_infiltration']) + ax2 = ax.flat[p_out].twinx() + ax2.plot(df_ss.index, np.cumsum(-df_ss['runoff']), color="darkred", label='cumulative runoff') + ax2.plot(df_ss.index, np.cumsum(-df_ss['net_infiltration']), color="darkblue", + label='cumulative net infiltration') + ax2.legend() + ax.flat[p_out].legend() + + mb = df_ss['rainfall'] + df_ss['snowmelt'] + df_ss['runon'] + df_ss['irrigation'] \ + - df_ss['actual_ET'] \ + - df_ss['runoff'] - df_ss['delta_soil_storage'] - df_ss['net_infiltration'] \ + - df_ss['rejected_net_infiltration'] + + ax.flat[p_mb].plot(df_ss.index,mb) + + ax.flat[p_mb].set(ylabel="Mass balance (inches)") + ax.flat[0].set(title='water balance for ' + str(years[n]) + '; land-use: ' + lu + ' soil group: ' + soil_group + ' ' + locator_text) + + fig.tight_layout() + #fig.show() + pdf.savefig() # saves the current figure into a pdf page + plt.close() \ No newline at end of file diff --git a/test_data/hru_1/scripts/run_swb.bat b/test_data/hru_1/scripts/run_swb.bat new file mode 100644 index 00000000..2dd814d9 --- /dev/null +++ b/test_data/hru_1/scripts/run_swb.bat @@ -0,0 +1,8 @@ +call set_run_variables.bat +%SWB2% --output_prefix=%OUTPUT_FILE_PREFIX% ^ + --output_dir=%OUTPUT_DIR% ^ + --logfile_dir=%LOGFILE_DIR% ^ + --data_dir=%DATA_DIR% ^ + --lookup_dir=%LOOKUP_DIR% ^ + %SWB_CONTROL_FILE% + diff --git a/test_data/hru_1/scripts/set_run_variables.bat b/test_data/hru_1/scripts/set_run_variables.bat new file mode 100644 index 00000000..4332d666 --- /dev/null +++ b/test_data/hru_1/scripts/set_run_variables.bat @@ -0,0 +1,14 @@ +:: Hail Mary attempt to keep netCDF from erroring out based on this SO entry: +::https://stackoverflow.com/questions/49317927/errno-101-netcdf-hdf-error-when-opening-netcdf-file +:: +set RES=5000 +set HDF5_USE_FILE_LOCKING=FALSE +set LOGFILE_DIR=../swb_logfiles +set OUTPUT_DIR=../swb_output +set DATA_DIR=../ +set LOOKUP_DIR=../ +set SWB_CONTROL_FILE=../swb_control_file.ctl +set OUTPUT_FILE_PREFIX=hru_1_%RES%__ + +set SWB2=swb2.exe +set SWBSTATS2=swbstats2.exe diff --git a/test_data/hru_1/swb_control_file.ctl b/test_data/hru_1/swb_control_file.ctl new file mode 100644 index 00000000..9066d4d1 --- /dev/null +++ b/test_data/hru_1/swb_control_file.ctl @@ -0,0 +1,73 @@ +GRID 1 1 1864520 2231406 5000 +BASE_PROJECTION_DEFINITION +proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23.0 +lon_0=-96.0 +x_0=0.0 +y_0=0.0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs + +%% Define methods +----------------- + +INTERCEPTION_METHOD BUCKET +EVAPOTRANSPIRATION_METHOD HARGREAVES +RUNOFF_METHOD CURVE_NUMBER +SOIL_MOISTURE_METHOD THORNTHWAITE +PRECIPITATION_METHOD TABULAR +GROWING_DEGREE_DAY_METHOD BASKERVILLE_EMIN +FOG_METHOD NONE +FLOW_ROUTING_METHOD NONE +IRRIGATION_METHOD NONE #NONE turns off all irrigation #FAO-56 uses that method +ROOTING_DEPTH_METHOD NONE +CROP_COEFFICIENT_METHOD NONE #Used to calculate actual ET with soil moisture +DIRECT_RECHARGE_METHOD NONE +SOIL_STORAGE_MAX_METHOD CALCULATED +AVAILABLE_WATER_CONTENT_METHOD GRIDDED + + +%% define location, projection, and conversions for weather data +---------------------------------------------------------------- + + +INITIAL_CONTINUOUS_FROZEN_GROUND_INDEX CONSTANT 100.0 +UPPER_LIMIT_CFGI 83. +LOWER_LIMIT_CFGI 55. + +%% specify location and projection for input GIS grids +------------------------------------------------------ + +HYDROLOGIC_SOILS_GROUP CONSTANT 2 + +LAND_USE CONSTANT 22 + +AVAILABLE_WATER_CONTENT CONSTANT 2.5 + + +%% specify location and names for all lookup tables +--------------------------------------------------- + +LAND_USE_LOOKUP_TABLE landuse_nlcd.txt +WEATHER_DATA_LOOKUP_TABLE hru_1_weather.txt + +%% initial conditions for soil moisture and snow storage amounts +%% may be specified as grids, but using a constant amount and +%% allowing the model to "spin up" for a year is also acceptable. + +INITIAL_PERCENT_SOIL_MOISTURE CONSTANT 100.0 +INITIAL_SNOW_COVER_STORAGE CONSTANT 2.0 + + +%% OUTPUT CONTROL SECTION: +OUTPUT DISABLE snow_storage +OUTPUT ENABLE soil_storage delta_soil_storage +OUTPUT DISABLE crop_et reference_ET0 +OUTPUT DISABLE runon interception tmin tmax + +OUTPUT DISABLE gross_precipitation +OUTPUT ENABLE runoff_outside rejected_net_infiltration +OUTPUT ENABLE runoff actual_et +OUTPUT_DISABLE rainfall snowmelt + +DUMP_VARIABLES COORDINATES 1866006 2234241 + +%% start and end date may be any valid dates in SWB version 2.0 +%% remember to allow for adequate model spin up; running the +%% model for just a month or two will give questionable results + +START_DATE 01/01/1979 +END_DATE 12/31/2019 diff --git a/test_data/hru_1/swb_parameters.yaml b/test_data/hru_1/swb_parameters.yaml new file mode 100644 index 00000000..205962eb --- /dev/null +++ b/test_data/hru_1/swb_parameters.yaml @@ -0,0 +1,78 @@ +dims: + nhru: 1 + +coords: + nhm_id: + - 1 + +data_vars: + max_net_infiltration_rate: 0.24 + available_water_capacity: 3.0 + rooting_depth: 1.2 + base_curve_number: 87 + swb_soil_storage_init: .5 + +metadata: + global: + model: "SWB Coolness" + + nhm_id: + desc: NHM HRU identifier + dims: + - nhru + type: int32 + units: none + + available_water_capacity: + default: 2.0 + desc: Available water capacity for a soil, in inches per foot + dims: + - nhru + help: Available water capacity for a soil, in inches per foot + maximum: 10.0 + minimum: 0.0 + type: float32 + units: inches per foot + + base_curve_number: + default: 70.0 + desc: National Soil Conservation Service curve number for type 2 antecedant runoff condition + dims: + - nhru + help: National Soil Conservation Service curve number for type 2 antecedant runoff condition + maximum: 100.0 + minimum: 20.0 + type: float32 + units: none + + max_net_infiltration_rate: + default: 0.5 + desc: Maximum daily net_infiltration rate, in inches + dims: + - nhru + help: Maximum daily net_infiltration rate, in inches + maximum: 20.0 + minimum: 0.0 + type: float32 + units: inches + + rooting_depth: + default: 1.0 + desc: Rooting depth for an SWB soil storage reservoir + dims: + - nhru + help: Rooting depth, in feet + maximum: 12.0 + minimum: 0.0 + type: float32 + units: feet + + swb_soil_storage_init: + default: .5 + desc: Initial (cold start) swb_soil_storage + dims: + - nhru + maximum: 4.0 + minimum: 0.0 + type: float32 + units: inches