Crate reservoirs[−][src]
Expand description
Reservoirs - A library for modeling Bolin & Rodhe reservoirs.
Bolin & Rodhe (1973) describe methods for characterizing the turnover rate of mass accumulating in a reservoir. The distribution of ages of particles in the reservoir constrain the possible input and output rates that could produce the observed record. The functions in this crate allow the user to compare synthetic accumulation records to observed records using the K-S and Kuiper statistics, to determine the best-fitting input/output pair for an observed record.
In my research at Oregon State University, I estimate the transit times of stream sediments moving through headwater valleys of the Coast Range by fitting reservoir models to a record of charcoal ages sampled from stream bank deposits. Inherited age refers to the age of charcoal when it enters a stream deposit. If we do not account for inherited age in the model, then transit times become artificially inflated. I have added an inherited age capacity to reservoirs, and while this is not a traditional feature of Bolin & Rodhe reservoirs, it is useful for dealing with charcoal ages.
This library includes the full code base used to estimate transit times for my ongoing dissertation, published here in the interest of academic transparency.
- Please direct questions, comments or insults to the github repository.
- View the crate documentation on docs.rs.
Quick Start
To use reservoirs, add it to your Cargo.toml
[dependencies]
reservoirs = "^0.1.7"- Load the crate prelude in the preamble of your
main.rs. - Load charcoal data from headwaters of the OCR:
use reservoirs::prelude::*;
fn main() -> Result<(), ResError> {
use reservoirs::prelude::*;
// mean expected deposit age and inherited age by facies
let dep = Sample::read("https://github.com/crumplecup/reservoirs/blob/master/examples/dep.csv")?;
let iat = Sample::read("https://github.com/crumplecup/reservoirs/blob/master/examples/iat.csv")?;
// subset mean ages of debris flows
let df: Vec<f64> = dep.iter()
.filter(|x| x.facies == "DF")
.map(|x| x.age)
.collect();
// subset inherited ages
let ia: Vec<f64> = iat.iter()
.map(|x| x.age)
.collect();
let mut debris_flows = Reservoir::new()
.input(&0.687)?
.output(&0.687)?
.inherit(&ia);
// model parameters
let period = 30000.0; // run simulations for 30000 years
let runs = 1000; // run 1000 simulated accumulations per candidate pair for goodness-of-fit
let bins = 500; // split observation into bins for deriving CDF
// create reservoir model using builder pattern
let mut model = Model::new(debris_flows)
.runs(runs);
// sample a stereotypical record from 1000 runs of 30000 years
let eg = model.stereotype(bins);
// compare the CDF of the synthetic example to the observed debris-flow deposit record
plot::comp_cdf(&eg, &df, "examples/df_cdf.png");
Ok(())
}
Create reservoirs using a builder pattern. First make a ‘blank’ reservoir using new, then assign it features using the input, output and inherit methods.
use reservoirs::prelude::*;
fn main() -> Result<(), ResError> {
// build step by step
let mut res = Reservoir::new();
res = res.input(&0.78)?;
res = res.output(&0.78)?;
res = res.inherit(&vec![10.0, 20.0, 27.0, 100.3, 7000.0, 10000.0]);
// or inline, same result
let res_b = Reservoir::new()
.input(&0.78)?
.output(&0.78)?
.inherit(&vec![10.0, 20.0, 27.0, 100.3, 7000.0, 10000.0]);
Ok(())
}