libcbm run of tutorial 2 from CBM-CFS3¶

[1]:

import os, json
import pandas as pd

%matplotlib inline

Import the required packages from libcbm

- sit_cbm_factory: a module for initializing the CBM model from the CBM Standard import tool format
- cbm_simulator: simulates the sit dataset using the CBM model
- libcbm.resources: gets files for tutorial 2 that are bundled in libcbm

[9]:

from libcbm.input.sit import sit_cbm_factory
from libcbm.model.cbm import cbm_simulator
from libcbm.model.cbm.cbm_output import CBMOutput
from libcbm import resources
from libcbm.storage.backends import BackendType

Setup¶

Load the standard import tool configuration at the specified path. This configuration encompasses the data source for the various sit inputs (sit_inventory, sit_classifiers etc.) and also the relationships of classifiers, and disturbance types to the default CBM parameters.

[10]:

config_path = os.path.join(
    resources.get_test_resources_dir(), "cbm3_tutorial2", "sit_config.json"
)
sit = sit_cbm_factory.load_sit(config_path)

Initialize and validate the inventory in the sit dataset

[11]:

classifiers, inventory = sit_cbm_factory.initialize_inventory(sit)

Create storage for CBM simulation results. This is a built in method, and it is not mandatory since it is possible to interact with the simulation dataframes directly as well.

[12]:

cbm_output = CBMOutput(
    classifier_map=sit.classifier_value_names,
    disturbance_type_map=sit.disturbance_name_map,
)

Simulation¶

The following line of code spins up the CBM inventory and runs it through 100 timesteps.

[13]:

with sit_cbm_factory.initialize_cbm(sit) as cbm:
    # Create a function to apply rule based disturbance events and transition rules based on the SIT input
    rule_based_processor = sit_cbm_factory.create_sit_rule_based_processor(
        sit, cbm
    )
    # The following line of code spins up the CBM inventory and runs it through 200 timesteps.
    cbm_simulator.simulate(
        cbm,
        n_steps=200,
        classifiers=classifiers,
        inventory=inventory,
        pre_dynamics_func=rule_based_processor.pre_dynamics_func,
        reporting_func=cbm_output.append_simulation_result,
        backend_type=BackendType.numpy,
    )

Pool Results¶

[17]:

pi = cbm_output.classifiers.to_pandas().merge(
    cbm_output.pools.to_pandas(),
    left_on=["identifier", "timestep"],
    right_on=["identifier", "timestep"],
)

[18]:

pi.head()

[18]:

	identifier	Working_Species_Or_Leading_Species	Site_Quality	Density_Class	Working_Status	Input	SoftwoodMerch	SoftwoodFoliage	...	BelowGroundSlowSoil	SoftwoodStemSnag	SoftwoodBranchSnag	CO2	CH4	CO
0	1	B	G	D	W	200.0	0.000000	0.000000	...	10529.242115	5568.605479	1544.809522	690032.809697	631.259013	5681.261733
1	2	B	G	D	W	100.0	0.003124	0.376897	...	5273.531854	2666.157167	666.417417	345255.294728	315.629506	2840.630866
2	3	B	G	D	W	100.0	0.030782	1.867345	...	5278.708266	2553.024910	574.973378	345471.445778	315.629506	2840.630866
3	4	B	G	D	W	100.0	0.117354	4.744316	...	5281.125622	2444.693447	496.077048	345669.654559	315.629506	2840.630866
4	5	B	G	D	W	100.0	0.303283	9.168135	...	5281.488695	2340.959458	428.006664	345853.464520	315.629506	2840.630866

5 rows × 33 columns

[19]:

biomass_pools = [
    "SoftwoodMerch",
    "SoftwoodFoliage",
    "SoftwoodOther",
    "SoftwoodCoarseRoots",
    "SoftwoodFineRoots",
    "HardwoodMerch",
    "HardwoodFoliage",
    "HardwoodOther",
    "HardwoodCoarseRoots",
    "HardwoodFineRoots",
]

dom_pools = [
    "AboveGroundVeryFastSoil",
    "BelowGroundVeryFastSoil",
    "AboveGroundFastSoil",
    "BelowGroundFastSoil",
    "MediumSoil",
    "AboveGroundSlowSoil",
    "BelowGroundSlowSoil",
    "SoftwoodStemSnag",
    "SoftwoodBranchSnag",
    "HardwoodStemSnag",
    "HardwoodBranchSnag",
]

biomass_result = pi[["timestep"] + biomass_pools]
dom_result = pi[["timestep"] + dom_pools]
total_eco_result = pi[["timestep"] + biomass_pools + dom_pools]

annual_carbon_stocks = pd.DataFrame(
    {
        "Year": pi["timestep"],
        "Biomass": pi[biomass_pools].sum(axis=1),
        "DOM": pi[dom_pools].sum(axis=1),
        "Total Ecosystem": pi[biomass_pools + dom_pools].sum(axis=1),
    }
)

annual_carbon_stocks.groupby("Year").sum().plot(
    figsize=(10, 10), xlim=(0, 160), ylim=(0, 5.4e6)
)

[19]:

<AxesSubplot:xlabel='Year'>

State Variable Results¶

[23]:

si = cbm_output.state.to_pandas()

[24]:

si.head()

[24]:

	identifier	last_disturbance_type	time_since_last_disturbance	time_since_land_class_change	growth_enabled	enabled	age	growth_multiplier
0	1	N	0	-1	1	1	0	1.0
1	2	N	1	-1	1	1	1	1.0
2	3	N	2	-1	1	1	2	1.0
3	4	N	3	-1	1	1	3	1.0
4	5	N	4	-1	1	1	4	1.0

[25]:

state_variables = [
    "timestep",
    "time_since_last_disturbance",
    "time_since_land_class_change",
    "growth_enabled",
    "enabled",
    "land_class",
    "age",
    "growth_multiplier",
    "regeneration_delay",
]
si[state_variables].groupby("timestep").mean().plot(figsize=(10, 10))

[25]:

<AxesSubplot:xlabel='timestep'>

Flux Indicators¶

[30]:

fi = cbm_output.flux.to_pandas()

[31]:

fi.head()

[31]:

	identifier	...
0	1	...
1	2	...
2	3	...
3	4	...
4	5	...

5 rows × 54 columns

[32]:

annual_process_fluxes = [
    "DecayDOMCO2Emission",
    "DeltaBiomass_AG",
    "DeltaBiomass_BG",
    "TurnoverMerchLitterInput",
    "TurnoverFolLitterInput",
    "TurnoverOthLitterInput",
    "TurnoverCoarseLitterInput",
    "TurnoverFineLitterInput",
    "DecayVFastAGToAir",
    "DecayVFastBGToAir",
    "DecayFastAGToAir",
    "DecayFastBGToAir",
    "DecayMediumToAir",
    "DecaySlowAGToAir",
    "DecaySlowBGToAir",
    "DecaySWStemSnagToAir",
    "DecaySWBranchSnagToAir",
    "DecayHWStemSnagToAir",
    "DecayHWBranchSnagToAir",
]

[33]:

fi[["timestep"] + annual_process_fluxes].groupby("timestep").sum().plot(
    figsize=(15, 10)
)

[33]:

<AxesSubplot:xlabel='timestep'>

Appendix¶

SIT source data¶

[34]:

sit.sit_data.age_classes

[34]:

	name	class_size	start_year	end_year
0	AGEID0	0	0	0
1	AGEID1	10	1	10
2	AGEID2	10	11	20
3	AGEID3	10	21	30
4	AGEID4	10	31	40
5	AGEID5	10	41	50
6	AGEID6	10	51	60
7	AGEID7	10	61	70
8	AGEID8	10	71	80
9	AGEID9	10	81	90
10	AGEID10	10	91	100
11	AGEID11	10	101	110
12	AGEID12	10	111	120
13	AGEID13	10	121	130
14	AGEID14	10	131	140
15	AGEID15	10	141	150
16	AGEID16	10	151	160
17	AGEID17	10	161	170
18	AGEID18	10	171	180
19	AGEID19	10	181	190
20	AGEID20	10	191	200

[35]:

sit.sit_data.inventory

[35]:

	Working_Species_Or_Leading_Species	Site_Quality	Density_Class	Working_Status	age	area	delay	land_class	historical_disturbance_type	last_pass_disturbance_type
0	BF	GOOD	D1	W	0	200	0	0	DISTID1	DISTID1
1	BF	GOOD	D1	W	1	100	0	0	DISTID1	DISTID1
2	BF	GOOD	D1	W	2	100	0	0	DISTID1	DISTID1
3	BF	GOOD	D1	W	3	100	0	0	DISTID1	DISTID1
4	BF	GOOD	D1	W	4	100	0	0	DISTID1	DISTID1
...	...	...	...	...	...	...	...	...	...	...
196	BF	GOOD	D1	W	196	100	0	0	DISTID1	DISTID1
197	BF	GOOD	D1	W	197	100	0	0	DISTID1	DISTID1
198	BF	GOOD	D1	W	198	100	0	0	DISTID1	DISTID1
199	BF	GOOD	D1	W	199	100	0	0	DISTID1	DISTID1
200	BF	GOOD	D1	W	200	100	0	0	DISTID1	DISTID1

201 rows × 10 columns

[36]:

sit.sit_data.classifiers

[36]:

	id	name
0	1	Working_Species_Or_Leading_Species
1	2	Site_Quality
2	3	Density_Class
3	4	Working_Status

[37]:

sit.sit_data.classifier_values

[37]:

	classifier_id	name	description
1	1	BF	Balsam Fir
2	1	BS	Black Spruce
3	1	JL	Western Larch
4	1	JP	Jack Pine
5	1	OS	Other Spruce
6	1	RP	Red Pine
7	1	SH	Unspecified Softwood
8	1	LT	Larch
9	1	WS	White Spruce
11	2	GOOD	Good
12	2	MEDIUM	Medium
13	2	POOR	Poor
15	3	D0	No Crown Closure
16	3	D1	75% - > Crown Closure
17	3	D2	51% - 74% Crown Closure
18	3	D3	26% - 50% Crown Closure
19	3	NN	Null Value For The Non-forested Polyg
21	4	W	Working forest
22	4	R	Reserve Forest

[38]:

sit.sit_data.disturbance_types

[38]:

	sit_disturbance_type_id	id	name
0	1	DISTID1	Natural forest fire
1	2	DISTID2	Senescence
2	3	DISTID3	Mountain pine beetle infestation
3	4	DISTID4	ClearCut Harvesting

[41]:

sit.sit_data.disturbance_events

[41]:

	Working_Species_Or_Leading_Species	Site_Quality	Density_Class	Working_Status	min_age	max_age	MinYearsSinceDist	MaxYearsSinceDist	LastDistTypeID	MinTotBiomassC	...	MinSWMerchStemSnagC	MaxSWMerchStemSnagC	MinHWMerchStemSnagC	MaxHWMerchStemSnagC	efficiency	sort_type	target_type	target	disturbance_type	time_step
0	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	1
1	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	2
2	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	3
3	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	4
4	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
155	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	156
156	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	157
157	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	158
158	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	159
159	BF	GOOD	D1	W	80	200	-1	-1	-1	-1	...	-1	-1	-1	-1	1	SORT_BY_SW_AGE	Area	200	DISTID4	160

160 rows × 33 columns

[39]:

sit.sit_data.yield_table

[39]:

	Working_Species_Or_Leading_Species	Site_Quality	Density_Class	Working_Status	leading_species	v0	v1	v2	v3	v4	...	v11	v12	v13	v14	v15	v16	v17	v18	v19	v20
0	BF	GOOD	D1	W	29	0	0	0	8	20	...	112	113	113	113	113	113	113	113	113	113

1 rows × 26 columns

[40]:

print(json.dumps(sit.config, indent=4, sort_keys=True))

{
    "import_config": {
        "age_classes": {
            "params": {
                "path": "age_classes.csv"
            },
            "type": "csv"
        },
        "classifiers": {
            "params": {
                "path": "classifiers.csv"
            },
            "type": "csv"
        },
        "disturbance_types": {
            "params": {
                "path": "disturbance_types.csv"
            },
            "type": "csv"
        },
        "events": {
            "params": {
                "path": "disturbance_events.csv"
            },
            "type": "csv"
        },
        "inventory": {
            "params": {
                "path": "inventory.csv"
            },
            "type": "csv"
        },
        "transitions": {
            "params": {
                "path": "transition_rules.csv"
            },
            "type": "csv"
        },
        "yield": {
            "params": {
                "path": "growth_and_yield.csv"
            },
            "type": "csv"
        }
    },
    "mapping_config": {
        "disturbance_types": [
            {
                "default_dist_type": "Clearcut harvesting without salvage",
                "user_dist_type": "ClearCut Harvesting"
            },
            {
                "default_dist_type": "Wildfire",
                "user_dist_type": "Natural forest fire"
            },
            {
                "default_dist_type": "Stand\u2013replacing natural succession",
                "user_dist_type": "Senescence"
            },
            {
                "default_dist_type": "Insect disturbance",
                "user_dist_type": "Mountain pine beetle infestation"
            }
        ],
        "nonforest": null,
        "spatial_units": {
            "admin_boundary": "Ontario",
            "eco_boundary": "Boreal Shield East",
            "mapping_mode": "SingleDefaultSpatialUnit"
        },
        "species": {
            "species_classifier": "Working Species Or Leading Species",
            "species_mapping": [
                {
                    "default_species": "Balsam fir",
                    "user_species": "Balsam Fir"
                },
                {
                    "default_species": "Black spruce",
                    "user_species": "Black Spruce"
                },
                {
                    "default_species": "Western larch",
                    "user_species": "Western Larch"
                },
                {
                    "default_species": "Jack pine",
                    "user_species": "Jack Pine"
                },
                {
                    "default_species": "Other spruce",
                    "user_species": "Other Spruce"
                },
                {
                    "default_species": "Red pine",
                    "user_species": "Red Pine"
                },
                {
                    "default_species": "Unspecified softwood species",
                    "user_species": "Unspecified Softwood"
                },
                {
                    "default_species": "Tamarack/larch",
                    "user_species": "Larch"
                },
                {
                    "default_species": "White spruce",
                    "user_species": "White Spruce"
                }
            ]
        }
    }
}

[ ]:

[ ]:

[ ]:

libcbm run of tutorial 2 from CBM-CFS3¶

Setup¶

Simulation¶

Pool Results¶

State Variable Results¶

Flux Indicators¶

Appendix¶

SIT source data¶

Table of Contents

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	identifier	last_disturbance_type	time_since_last_disturbance	time_since_land_class_change	growth_enabled	enabled	age	growth_multiplier
0	1	N	0	-1	1	1	0	1.0
1	2	N	1	-1	1	1	1	1.0
2	3	N	2	-1	1	1	2	1.0
3	4	N	3	-1	1	1	3	1.0
4	5	N	4	-1	1	1	4	1.0

	identifier	...
0	1	...
1	2	...
2	3	...
3	4	...
4	5	...

	identifier	last_disturbance_type	time_since_last_disturbance	time_since_land_class_change	growth_enabled	enabled	age	growth_multiplier
0	1	N	0	-1	1	1	0	1.0
1	2	N	1	-1	1	1	1	1.0
2	3	N	2	-1	1	1	2	1.0
3	4	N	3	-1	1	1	3	1.0
4	5	N	4	-1	1	1	4	1.0

	identifier	...
0	1	...
1	2	...
2	3	...
3	4	...
4	5	...

	identifier	last_disturbance_type	time_since_last_disturbance	time_since_land_class_change	growth_enabled	enabled	age	growth_multiplier
0	1	N	0	-1	1	1	0	1.0
1	2	N	1	-1	1	1	1	1.0
2	3	N	2	-1	1	1	2	1.0
3	4	N	3	-1	1	1	3	1.0
4	5	N	4	-1	1	1	4	1.0

	identifier	...
0	1	...
1	2	...
2	3	...
3	4	...
4	5	...