pyPhenology.models.MSB

class pyPhenology.models.MSB(parameters={})

Macroscale Species-specific Budburst model.

Extension of the Alternating model which adds a correction (\(d\)) using the mean spring temperature.

\[\sum_{t=t1}^{DOY}R_{f}(T_{i})\geq a + be^{cNCD_{i}} +dT_{mean}\]

where:

\[R_{f}(T_{i}) = max(T_{i}-threshold, 0)\]

Spring is DOY 1-60 as described in Jeong et al. 2013.

Parameters:

a : int | float

\(a\) - Intercept of chill day curve

default : (-1000,1000)

b : int | float, > 0

\(b\) - Slope of chill day curve

default : (0,5000)

c : int | float, < 0

\(c\) - scale parameter of chill day curve

default : (-5,0)

d : int | float

\(d\) - Correction factor using spring temperature

threshold : int | float

\(threshold\) - Degree threshold above which forcing accumulates, and below which chilling accumulates.

default : 5

t1 : int

:math:`` - DOY which forcing and chilling accumulationg starts.

default : 1 (Jan 1)

Notes:

Jeong, S.-J., Medvigy, D., Shevliakova, E., & Malyshev, S. (2013). Predicting changes in temperate forest budburst using continental-scale observations and models. Geophysical Research Letters, 40(2), 359–364. https://doi.org/10.1029/2012Gl054431

__init__(parameters={})

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([parameters])	Initialize self.
fit(observations, predictors[, …])	Estimate the parameters of a model
get_params()	Get the fitted parameters
predict([to_predict, predictors])	Make predictions
save_params(filename[, overwrite])	Save the parameters for a model
score([metric, doy_observed, to_predict, …])	Evaluate a prediction given observed doy values