A Python implementation of a modified TR-55 stormwater runoff model with STEP-L like water quality routines.
You can install the latest version from PyPI
pip install tr55The simulate_day is the function most likely to be of direct interest for users of this module.
The simulate_water_quality and simulate_modifications functions are two other functions which can be used to create simulations with additional behaviors beyond those supplied by simulate_day.
This function takes four arguments: a census of the area of interest (see the description given below in the discussion of simulate_modifications), an amount of precipitation in inches, an optional cell resolution (the size of a cell in square meters), and an optional Boolean to control whether or not a Pre-Columbian simulation is done.
For each cell type present in the area of interest, it calculates runoff, infiltration, evapotranspiration, and pollutant loads caused by that cell type. The algorithm used to calculate the water volumes is close to TR-55, the algorithm found in the USDA's Technical Release 55, revised 1986, but with a few differences. The main difference is the use of Pitt Small Storm Hydrology Model for low levels of precipitation when the land use is a built-type. STEP-L like routines are used for the water quality calculations.
The simulate_water_quality function does a water quality calculation over an entire area of interest. The arguments are:
tree, the tree-like dictionary which contains the distribution of cell types in the area of interest. For example:
{
"cell_count": 8,
"distribution": {
"c:developed_high": {"cell_count": 5},
"a:deciduous_forest": {
"cell_count": 3
"distribution": {
"a:deciduous_forest": {"cell_count": 1},
"a:no_till": {"cell_count": 1},
"d:barren_land": {"cell_count", 1}
}
}
}
}represents an area of interest that is eight cells in size, with five of those cells of type "c:developed_high" (Developed High Intensity land use on top of type C soil), and one cell each of deciduous forest, no-till farming, and barren land.
The single cells of deciduous forest, no-till, and the barren land are all underneath a node of three cells of type deciduous forest. That indicates a land use modification has taken place: in this case, two of three original cells of deciduous forest have undergone modifications.
-
The
cell_resparameter gives the resolution (size) of each cell. It is used for converting runoff, evapotranspiration, and infiltration amounts from inches to volumes. -
fnis the function that is used to perform the runoff, evapotranspiration, and infiltration calculation. It takescellandcell_countas arguments. -
The optional parameter
current_cellcontains the string description of the cell currently being worked on (e.g. "a:deciduous_forest"). -
The optional Boolean
precolumbiandetermines whether to simulate the cell type as-shown or under Pre-Columbian circumstances. When a Pre-Columbian simulation is done, all developed land (NLCD 21-24) uses are treated as mixed forest.
In all probability, the fourth parameter will not need to be supplied if you are calling this function from external code.
This function is used to simulate the effects of land use modifications. The arguments are:
censuscontains the distribution of cell types in the area of interest, along with an array of modifications. For example, the following:
{
"cell_count": 8,
"distribution": {
"c:developed_high": {"cell_count": 5},
"a:deciduous_forest": {"cell_count": 3}
},
"modifications": [
{
"change": "::no_till",
"cell_count": 1,
"distribution": {
"a:deciduous_forest": {"cell_count": 1},
}
},
{
"change": "d:barren_land:",
"cell_count": 1,
"distribution": {
"a:deciduous_forest": {"cell_count": 1}
}
}
]
}is the census that corresponds to the tree given in the discussion of simulate_water_quality above. There is an area of interest eight cells in size, with five of type "c:developed_high" and three of type "a:deciduous_forest".
Modifications are given as an array of dictionaries. Each dictionary contains a change key whose value encodes the modification that has taken place. In the example above, "::no_till" indicates that the no-till farming BMP has been applied, while "a:barren_land:" means that that particular area has been reclassified as being most barren_land sitting on top of A-type soil.
-
The
fnargument is as described previously in the discussion ofsimulate_water_quality. It is responsible for performing the simulation at each cell. -
The
cell_resargument is as described previously. -
The optional Boolean parameter
precolumbiandetermines whether to simulate the cell type as-shown or under Pre-Columbian circumstances. When a Pre-Columbian simulation is done, all land uses other than water and wetland are treated as mixed forest.
The output is dictionary with two keys, modified and unmodified. These respectively contain modified and unmodified trees (the trees are as described in the discussion of simulate_water_quality) with runoff, evapotranspiration, infiltration, and pollutant loads included.
The following land use values are implemented and correspond to the keys listed:
- 'open_water' - NLCD Type 11, Open Water
- 'developed_open' - NLCD Type 21, Developed, Open Space
- 'developed_low'- NLCD Type 22, Developed, Low Intensity
- 'developed_med' - NLCD Type 23, Developed, Medium Intensity
- 'developed_high' - NLCD Type 24, Developed, High Intensity
- 'barren_land' - NLCD Type 31, Barren Land (Rock/Sand/Clay)
- 'deciduous_forest' - NLCD Type 41, Deciduous Forest
- 'evergreen_forest' - NLCD Type 42, Evergreen Forest
- 'mixed_forest' - NLCD Type 43, Mixed Forest
- 'shrub' - NLCD Type 52, Shrub/Scrub
- 'grassland' - NLCD Type 71, Grassland/Herbaceous
- 'pasture' - NLCD Type 81, Pasture/Hay
- 'cultivated_crops' - NLCD Type 82, Cultivated Crops
- 'woody_wetlands' - NLCD Type 90, Woody Wetlands
- 'herbaceous_wetlands' - NLCD Type 95, Emergent Herbaceous Wetlands
The output of the following program:
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
import pprint
from tr55.model import simulate_day
census = {
"cell_count": 147,
"distribution": {
"d:developed_med": {"cell_count": 33},
"c:developed_high": {"cell_count": 42},
"a:deciduous_forest": {"cell_count": 72}
},
"modifications": [
{
"change": "::no_till",
"cell_count": 30,
"distribution": {
"d:developed_med": {"cell_count": 10},
"c:developed_high": {"cell_count": 20}
}
},
{
"change": "d:barren_land:",
"cell_count": 5,
"distribution": {
"a:deciduous_forest": {"cell_count": 5}
}
}
]
}
pprint.pprint(simulate_day(census, 0.984))is partially reproduced here:
u'unmodified': {u'bod': 43.11309178874012,
u'cell_count': 147,
u'distribution': {u'a:deciduous_forest': {u'bod': 0.0,
u'cell_count': 72,
u'et': 0.14489999999999997,
u'inf': 0.8391,
u'runoff': 0.0,
u'tn': 0.0,
u'tp': 0.0,
u'tss': 0.0},
u'c:developed_high': {u'bod': 28.342317499361275,
u'cell_count': 42,
u'et': 0.012322664565942195,
u'inf': 0.0,
u'runoff': 0.9716773354340579,
u'tn': 0.2079960397130545,
u'tp': 0.03291365903151632,
u'tss': 5.780461367410053},
u'd:developed_med': {u'bod': 14.770774289378844,
u'cell_count': 33,
u'et': 0.037259999999999995,
u'inf': 0.26946506358880085,
u'runoff': 0.6772749364111992,
u'tn': 0.08574559651037718,
u'tp': 0.014395246129479379,
u'tss': 1.764982351527472}},
u'et': 0.08285667967190184,
u'inf': 0.47147991223422064,
u'runoff': 0.4296634080938776,
u'tn': 0.2937416362234317,
u'tp': 0.0473089051609957,
u'tss': 7.545443718937525}The output shown is a tree-like dictionary, akin to the one in the discussion of the first parameter of the simulate_water_quality function, except with additional keys and values attached to each node in the tree. The additional keys, runoff, tss, and so on, have associated values which are the water volumes and pollutant loads that have been calculated. The volumes and loads at the leaves of the tree are those returned by the fn function (the second parameter of the simulate_modifications function), while those of internal nodes are the sums of the amounts found in their child nodes.
Run python setup.py test from within the project directory.
Deployments to PyPi are handled through Travis-CI. The following git flow commands approximate a release using Travis:
$ git flow release start 0.1.0
$ vim CHANGELOG.md
$ vim setup.py
$ git commit -m "0.1.0"
$ git flow release publish 0.1.0
$ git flow release finish 0.1.0After you've completed the git flow steps, you'll need to push the changes from your local master and develop branches back to the main repository.
$ git checkout develop
$ git push origin develop
$ git checkout master
$ git push origin master
# Trigger PyPi deployment
$ git push --tagsThis project is licensed under the terms of the Apache 2.0 license.