[GRASS-user] r.watershed identify inland watershed

ming han dustming at gmail.com
Fri Apr 2 07:37:04 PDT 2021


Maybe I am the only one who has this demand. Following is just a
recommendation to GRASS r.watershed function.
Maybe it is worth having an option to avoid r.watershed overcome
depressions.
The reasons are 1) there are many hydrologically pre condition DEM data
available globally, such as:HydroSHEDS, MERIT
                            2) the depression in these DEM are real
depressions, overcome these depressions will make the entire drainage
system incorrectly.

I understand GRASS has other functions to solve this problem, but just a
user recommendation. I use GRASS a lot.

Thanks
Ming

ming han <dustming at gmail.com> 于2021年3月30日周二 上午8:06写道:

> Got it, thanks everyone~
> Ming
>
> Micha Silver <tsvibar at gmail.com> 于2021年3月29日周一 下午2:40写道:
>
>> Hello:
>>
>> You might try `r.param.scale`, or even better `r.geomorphons` modules to
>> identify geomorphology features, then filter out all pixels identified
>> as pits.
>>
>>
>> r.watershed is purposely designed to overcome depressions, and find flow
>> routing thru these spots. So I don't think you can use that module to
>> identify depressions.
>>
>>
>> On 3/27/21 8:49 PM, ming han wrote:
>> > Hi  Everyone
>> >
>> >      When I do watershed delineation using r.watershed for great salt
>> > lake watershed. I found r.watershed always tried to assign an outlet
>> > for a great salt lake, which does actually not exist because it is an
>> > inland lake and the great salt lake has no watershed outlet at all.
>> >
>> >       I noticed that there is a depression option. But is there any
>> > way that  r.watershed can automatically identify depressions while
>> > defining flow accumulation and stream network?
>> >
>> > Thanks
>> > Ming
>> >
>> > _______________________________________________
>> > grass-user mailing list
>> > grass-user at lists.osgeo.org
>> > https://lists.osgeo.org/mailman/listinfo/grass-user
>>
>> --
>> Micha Silver
>> Ben Gurion Univ.
>> Sde Boker, Remote Sensing Lab
>> cell: +972-523-665918
>>
>>
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