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https://github.com/kmarkert/hymod.jl
HYMOD model implementation in Julia
https://github.com/kmarkert/hymod.jl
hydrology hymod-model julia
Last synced: 4 days ago
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HYMOD model implementation in Julia
- Host: GitHub
- URL: https://github.com/kmarkert/hymod.jl
- Owner: KMarkert
- License: mit
- Created: 2020-09-04T11:31:27.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-06-21T05:33:27.000Z (over 3 years ago)
- Last Synced: 2024-12-11T14:47:28.267Z (about 1 month ago)
- Topics: hydrology, hymod-model, julia
- Language: Julia
- Homepage: https://kmarkert.github.io/Hymod.jl/dev/
- Size: 467 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Hymod.jl
Hymod model implementation in Julia
Simple package with functionality to calibrate/simulate river discharge with the Hymod model.
## Installation
The Hymod package is available through the Julia package system and can be installed using the following commands:.
```
julia> using Pkg
julia> Pkg.add("Hymod")
```
## Example use
```julia
using HTTP, CSV, DataFrames, Hymod
# define url to test data
testDataUrl = "https://raw.githubusercontent.com/JuliaHydro/Hymod.jl/master/test/data/test_forcings.csv"
# get response
response = HTTP.get(testDataUrl)
# read response as a DataFrame
df = CSV.File(response.body) |> DataFrame
# create a column in the data frame for potential evapotranspiration
df[!,:pet] = hargreaves(df,tmincol=:tmin,tmaxcol=:tmax,dtcol=:date)
# get a random set of parameters
pars = randomparams()
# run a simulation
q = simulate(df,precipcol=:precip, petcol=:pet; pars...)
```
### Calibrating a model
```julia
using Dates
# define dates to calibrate
calstart = Date(1986,1,1)
calend = Date(2001,12,31)
# filter dataframe between start and end calibration times
caldf = filter(row -> row[:date] >= calstart && row[:date] <= calend, df)
# get a dictionary of parameter ranges
paramspace = Dict(
:cmax => Dict(:lower => 1.0, :upper => 100),
:bexp => Dict(:lower => 0.0, :upper => 2.0),
:alpha => Dict(:lower => 0.2, :upper => 0.99),
:ks => Dict(:lower => 0.01, :upper => 0.5),
:kq => Dict(:lower => 0.5, :upper => 1.2)
)
# set number of iterations to run calibration
niterations = 5000
# run calibration
calq, calpars, calloss = calibrate(caldf,paramspace,niterations)
# get the remainder of dataframe to test calibrated parameters
testdf = filter(row -> row[:date] > calend, df)
# run simulation with calibrated parameters
testdf[!,:q] = simulate(testdf,precipcol=:precip, petcol=:pet; calpars...)
```
When you plot the simulated results compared to observed values, you should get a plot similar to one below.
## Interactive Examples
A Docker image is provided for users that want to run a contained installation of the Hymod model interactively in the browser using [Pluto notebooks](https://github.com/fonsp/Pluto.jl). To start the Pluto server with Hymod, run the following command in your terminal:
```
$ docker run --rm -v julia -e 'import Pluto; Pluto.run()' -p 1234:1234 kmarkert/julia-hymod:latest
```
Next, paste the following URL into the "Open from file" prompt: https://github.com/KMarkert/Hymod.jl/blob/master/examples/hymod_interactive_parameters.jl