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Improved forest management / wood harvest strategies for FATES #1459
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…per size bin as history variable
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Description:
Enabled to simulate different improved forest management (IFM) strategies, including the harvest priority by size/age, updates on carbon-based wood harvest, and some more history outputs.
In this PR, scaling factors of harvest rates at different model structure level are introduced to enable FATES to simulate the consequence of different harvest plans (Fig. 1, manuscript under progress) by giving specific size and age priority to a harvest event. For age priority, we derive from the pseudo-age concept and collapse the pseudo age (landuse x PFT x age) into pure age dimension through min-heap merge algorithm and calculate patch-level scaling factor based on patch age information. For size priority, 4 different size priority options are available and are described in Table 1. Scaling factor for considering size priority may cause deviation from the target harvest request, another site-level scaling factor is calculated to enforce the simulation to meet the harvest request.
Figure 1. The schematic diagram shows the design of different available options (medium column) that change the scale factor of harvest rate under each model hierarchical level (left column) in the FATES IFM module. The combination of different options can enable a range of different forestry and IFM (right column, Kaarakka et al., 2021) practices, including clear-cut, selective logging, reduced impact logging through building infrastructure (Huang et al., 2020) and the change of harvest rotation length. Harvest rate in the unit of either mass/volume or area at site level is determined by the rotation length. Under both patch and cohort level, a harvest rate scaling factor is defined to tailor the actual harvest rate for each patch/cohort, calculated based on detailed harvest plan options. Two age preference options (old patch first and no preference) and four size preference options (small, medium, large trees and no preference, see details on how to calculate the scaling factor in Table 1) are defined and applied to calculate scaling factors.
Table 1. List of different size preferences for harvest represented in FATES IFM module. Functions applied here describes the size dependent scaling factor on cohort level harvest rate that equivalent to the harvest probability of trees (fsize) with a certain diameter at breast height (dbh). In following abbreviations, dbhmin and dbhmax are lower and upper bound of harvestable trees, k=0.1 is the logistic growth rate we picked by assuming a relative low change of elasticity of harvest probability to DBH that close to linear, and$\sigma$ =5 cm is the standard deviation of harvested woods dbh which can cover 99% of harvested wood within 10 - 50 cm, the DBH range of harvestable trees in this study.
Note: A previous version using bubble sort is a more rigorous way to sort patch with age but will increase computational cost. I would like to see if there's any suggestion on how we can optimize the current method first. As a backup plan, we can either add bubble sort back as an alternative option or roll back to the version using bubble sort as default method.
Collaborators:
@jenniferholm @ckoven @JessicaNeedham
Expectation of Answer Changes:
No under default settings. We added more harvest priority parameters to FATES so different answers are expected.
Checklist
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If satellite phenology regressions are not b4b, please hold merge and notify the FATES development team.
Documentation
Test Results:
CTSM (or) E3SM (specify which) test hash-tag:
e9515ed7a81c679233e6e38a79e4f0a43fc073f7
CTSM (or) E3SM (specify which) baseline hash-tag:
9eba6d6be2b1965da45f433652a4129684416e32
FATES baseline hash-tag:
3bd2378
Test Output: