[GRASS-user] temporal raster accumulation

GRASS GRASS user
1

Dear GRASS users

I'm a little bit confused about which can be the best module/tool to perform the following task
I have a strd having a daily granularity. It contains total daily cumulated rainfall.

I would like to create a new strd where, for each day, I have the cumulated rainfall in the antecedent 5 days

can you kindly suggest which is the best tool (or combination of tools) to reach this aim?

thank you very much

Ivan

2

Hi Ivan,

A simple, quick-and-dirty approach could be:
t.rast.algebra basename=rain5 expression="rain5=rain + rain[-1] + rain[-2] + rain[-3] + rain[-4] + rain[-5]"

Temporal selection (start_date(rain)...) is probably more elegant...

See:
https://grass.osgeo.org/grass78/manuals/t.rast.algebra.html

Cheers
Stefan

-----Original Message-----
From: grass-user <grass-user-bounces@lists.osgeo.org> On Behalf Of Ivan Marchesini
Sent: tirsdag 9. august 2022 09:45
To: GRASS user list <grass-user@lists.osgeo.org>
Subject: [GRASS-user] temporal raster accumulation

Dear GRASS users

I'm a little bit confused about which can be the best module/tool to perform the following task I have a strd having a daily granularity. It contains total daily cumulated rainfall.

I would like to create a new strd where, for each day, I have the cumulated rainfall in the antecedent 5 days

can you kindly suggest which is the best tool (or combination of tools) to reach this aim?

thank you very much

Ivan

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3

Hi Ivan,

In addition, see also

https://grasswiki.osgeo.org/wiki/Temporal_data_processing#Aggregation_of_seasonal_data_using_time_ranges

best,
Markus

On Tue, Aug 9, 2022 at 11:40 AM Stefan Blumentrath
<Stefan.Blumentrath@nina.no> wrote:

Hi Ivan,

A simple, quick-and-dirty approach could be:
t.rast.algebra basename=rain5 expression="rain5=rain + rain[-1] + rain[-2] + rain[-3] + rain[-4] + rain[-5]"

Temporal selection (start_date(rain)...) is probably more elegant...

See:
https://grass.osgeo.org/grass78/manuals/t.rast.algebra.html

Cheers
Stefan

-----Original Message-----
From: grass-user <grass-user-bounces@lists.osgeo.org> On Behalf Of Ivan Marchesini
Sent: tirsdag 9. august 2022 09:45
To: GRASS user list <grass-user@lists.osgeo.org>
Subject: [GRASS-user] temporal raster accumulation

Dear GRASS users

I'm a little bit confused about which can be the best module/tool to perform the following task I have a strd having a daily granularity. It contains total daily cumulated rainfall.

I would like to create a new strd where, for each day, I have the cumulated rainfall in the antecedent 5 days

can you kindly suggest which is the best tool (or combination of tools) to reach this aim?

thank you very much

Ivan

4

Thank you Stefan and Markus

I'll check out which one of the different solutions works better for me

thank you again!!!

On 09/08/22 13:43, Markus Neteler wrote:

Hi Ivan,

In addition, see also

https://grasswiki.osgeo.org/wiki/Temporal_data_processing#Aggregation_of_seasonal_data_using_time_ranges

best,
Markus

On Tue, Aug 9, 2022 at 11:40 AM Stefan Blumentrath
<Stefan.Blumentrath@nina.no> wrote:

Hi Ivan,

A simple, quick-and-dirty approach could be:
t.rast.algebra basename=rain5 expression="rain5=rain + rain[-1] + rain[-2] + rain[-3] + rain[-4] + rain[-5]"

Temporal selection (start_date(rain)...) is probably more elegant...

See:
https://grass.osgeo.org/grass78/manuals/t.rast.algebra.html

Cheers
Stefan

-----Original Message-----
From: grass-user <grass-user-bounces@lists.osgeo.org> On Behalf Of Ivan Marchesini
Sent: tirsdag 9. august 2022 09:45
To: GRASS user list <grass-user@lists.osgeo.org>
Subject: [GRASS-user] temporal raster accumulation

Dear GRASS users

I'm a little bit confused about which can be the best module/tool to perform the following task I have a strd having a daily granularity. It contains total daily cumulated rainfall.

I would like to create a new strd where, for each day, I have the cumulated rainfall in the antecedent 5 days

can you kindly suggest which is the best tool (or combination of tools) to reach this aim?

thank you very much

Ivan