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https://github.com/kmarkert/geoglows.jl
Julia package to request data from the GEOGloWS streamflow service API
https://github.com/kmarkert/geoglows.jl
api geoglows julia-language streamflow
Last synced: about 1 month ago
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Julia package to request data from the GEOGloWS streamflow service API
- Host: GitHub
- URL: https://github.com/kmarkert/geoglows.jl
- Owner: KMarkert
- License: mit
- Created: 2021-10-16T13:57:56.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2021-10-27T18:58:46.000Z (about 3 years ago)
- Last Synced: 2024-10-12T23:20:39.990Z (about 1 month ago)
- Topics: api, geoglows, julia-language, streamflow
- Language: Julia
- Homepage: https://kmarkert.github.io/GEOGloWS.jl/dev/
- Size: 106 KB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# GEOGloWS.jl
Julia package to request data from the [GEOGloWS ECMWF Streamflow service](https://geoglows.ecmwf.int/)
Information regarding the GEOGloWS ECMWF Streamflow service can be found at the [About page](https://geoglows.ecmwf.int/about)
## Installation
```
$ julia
julia> ]
pkg> add GEOGloWS
julia> using GEOGloWS
```
## Quick start
The GEOGloWS services provides critical historical and forecast information on streamflow for every reach across the globe. This quick start example illustrates how to request data for the Brahmaputra River near Guwahati, India.
### Requesting historical data
```julia term=true
using GEOGloWS
df = GEOGloWS.historic_simulation(26.1807, 91.7108)
df
15553×2 DataFrame
Row │ datetime streamflow_m^3/s
│ DateTime… Float64
───────┼───────────────────────────────────────
1 │ 1979-01-01T00:00:00 0.0
2 │ 1979-01-02T00:00:00 3.41469
3 │ 1979-01-03T00:00:00 26.3249
4 │ 1979-01-04T00:00:00 68.7416
⋮ │ ⋮ ⋮
15550 │ 2021-07-28T00:00:00 24899.9
15551 │ 2021-07-29T00:00:00 23278.6
15552 │ 2021-07-30T00:00:00 22384.6
15553 │ 2021-07-31T00:00:00 22817.1
15545 rows omitted
```
### Requesting forecast data
```julia
using GEOGloWS
df = GEOGloWS.forecast_ensembles(26.1807, 91.7108)
df
145×53 DataFrame
Row │ datetime ensemble_01_m^3/s ensemble_10_m^3/s ensemble_11_m^3/s ensemble_12_m^3/s ensemble_13_m^3/s ensemble_14_m^3/s ensemble_15_m^3/s ensemble_16_m^3/s ensembl ⋯
│ DateTime… Float64? Float64? Float64? Float64? Float64? Float64? Float64? Float64? Float64 ⋯
─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 2021-10-16T00:00:00 10738.7 10738.7 10738.7 10738.7 10738.7 10738.7 10738.7 10738.7 ⋯
2 │ 2021-10-16T01:00:00 missing missing missing missing missing missing missing missing
3 │ 2021-10-16T02:00:00 missing missing missing missing missing missing missing missing
4 │ 2021-10-16T03:00:00 10687.7 10687.6 10687.7 10687.6 10687.7 10687.6 10687.6 10687.6
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
142 │ 2021-10-30T06:00:00 18810.5 20950.2 18868.2 19794.4 16530.8 20430.2 17165.0 14943.7 ⋯
143 │ 2021-10-30T12:00:00 19617.2 21530.3 19545.2 20586.4 17056.5 21485.9 17189.7 15129.7
144 │ 2021-10-30T18:00:00 20127.3 21759.9 19995.9 21154.5 17334.7 22283.9 17025.3 15103.7
145 │ 2021-10-31T00:00:00 20292.8 21616.3 20157.9 21426.9 17329.0 22724.2 16667.9 14857.3
44 columns and 137 rows omitted
```
For more information on the methods the signatures, please see the [API documentation]()