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Description
The caravan datset has already been added PR #407 as discussed in #398.
This was relatively simple as we had the NetCDF files availible from the source, we only had to combined them.
The downside is it used era5-Land data, the evaporation can be quite far from realistic.
See this article on the issue.
As part of my thesis I used the original Camels-USA dataset, which has better forcing. But the forcing is in text files, split per type: forcing/streamflow/characteristics.
I ran models for all 671 catchments, in the process already making the conversion to netcdf. I only used a 5 year period, there is data for the period 1980-2010 (some cases 14).
I used custom forcing in the HBV mode to achieve this.
It would also be nice to include the catchment characteristics. These are currently spread across different files and comparing your results to them requires a bit of pandas effort as shown in this messy notebook, or if you want to view it online use this link. Definetely doable, but effort.
Loading observations is shown here
Tl;dr: original camels forocing is better than the caravan. Code exists but still some effort to polish.
One main discussion points. Do we:
- update the existing caravan data set? This might be confusing as the forcing is different.
- create a new
_forcing.camels
? Then we have a bit of repeated code but the dataset structure is likley different so kinda needed.
Todo:
- Use exisiting code to make NetCDF files for forcing of the whole data set. Using all three sources: Daymet, NLDAS and Maurer
Summary from my thesis
- Daymet has the finest resolution at 1x1km, whilst the other two sources have resolutions of 1/8th a degree.
- Daymet aims to reproduce the weather conditions in the whole of the USA.
- NLDAS is more focussed on the soil moisture stores and energy.
- Both Daymet and NLDAS are products by NASA.
- The dataset by Maurer et al is a baseline for climate predictions
- Also load in characteristics per catchmetns
- Combine the forcing and characteristics
- merge all 671 catchments
- Optionally load streamflow
- could use the USGS link already availible in ewatercycle
- then again the data is availible and might as well if we go through the effort hand a complete product
- Check errors and flags are handled correctly. See: Flag not processed wel by camels forcing Daafip/ewatercycle-hbv#59
- Add to OpenDap