Crate reservoirs

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 = "^0.1.1"

• 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>{
// mean expected deposit age and inherited age by facies
    let dep = Sample::read("https://crumplecup.github.io/reservoirs/examples/dep.csv")?;
    let iat = Sample::read("https://crumplecup.github.io/reservoirs/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 res = Reservoir::new().input(&0.78)?.output(&0.78)?.inherit(&ia);
    let eg = res.stereotype(&30000.0, 1000, 200);
    plot::comp_cdf(res.mass, df, "https://crumplecup.github.io/reservoirs/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::*;

// 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]);

assert_eq!(res, res_b);

Modules

plot
prelude
reservoir	Structs and methods for Bolin & Rodhes reservoir models.
utils