Provided by: grass-doc_7.6.1-3_all bug

NAME

       t.rast.accumulate  - Computes cyclic accumulations of a space time raster dataset.

KEYWORDS

       temporal, accumulation, raster, time

SYNOPSIS

       t.rast.accumulate
       t.rast.accumulate --help
       t.rast.accumulate  [-nr] input=name output=name  [lower=name]   [upper=name]  start=string
       [stop=string]   cycle=string   [offset=string]     [granularity=string]    basename=string
       [suffix=string]    limits=lower,upper    [scale=float]    [shift=float]    [method=string]
       [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

   Flags:
       -n
           Register empty maps in the output space time raster dataset, otherwise  they  will  be
           deleted

       -r
           Reverse time direction in cyclic accumulation

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       input=name [required]
           Name of the input space time raster dataset

       output=name [required]
           Name of the output space time raster dataset

       lower=name
           Input  space  time  raster dataset that defines the lower threshold, values lower than
           this threshold are excluded from accumulation

       upper=name
           Input space time raster dataset that defines the upper threshold, values  higher  than
           this threshold are excluded from accumulation

       start=string [required]
           The temporal starting point to begin the accumulation, eg ’2001-01-01’

       stop=string
           The temporal date to stop the accumulation, eg ’2009-01-01’

       cycle=string [required]
           The temporal cycle to restart the accumulation, eg ’12 months’

       offset=string
           The temporal offset to the beginning of the next cycle, eg ’6 months’

       granularity=string
           The granularity for accumulation ’1 day’
           Default: 1 day

       basename=string [required]
           Basename of the new generated output maps
           A  numerical  suffix  separated  by  an underscore will be attached to create a unique
           identifier

       suffix=string
           Suffix to add to the basename. Set ’gran’ for granularity, ’time’ for  the  full  time
           format, ’num’ for numerical suffix with a specific number of digits (default %05)
           Default: gran

       limits=lower,upper [required]
           Use  these  limits in case lower and/or upper input space time raster datasets are not
           defined or contain NULL values

       scale=float
           Scale factor for input space time raster dataset

       shift=float
           Shift factor for input space time raster dataset

       method=string
           This method will be applied to compute the accumulative values from the input maps  in
           a single granule
           Growing  Degree Days or Winkler indices; Mean: sum(input maps)/(number of input maps);
           Biologically Effective Degree Days; Huglin Heliothermal index
           Options: mean, gdd, bedd, huglin
           Default: mean

DESCRIPTION

       t.rast.accumulate is designed to perform  temporal  accumulations  of  space  time  raster
       datasets.   This  module expects a space time raster dataset as input that will be sampled
       by a given granularity. All maps that have the start time during the actual  granule  will
       be  accumulated  with  the predecessor granule accumulation result using the raster module
       r.series.accumulate. The default granularity is 1 day, but any temporal granularity can be
       set.

       The  start  time  and  the  end  time  of  the  accumulation  process  must  be  set,  eg.
       start="2000-03-01" end="2011-01-01". In addition, a cycle, eg. cycle="8  months",  can  be
       specified,  that  defines  after which interval of time the accumulation process restarts.
       The offset option specifies the time that  should  be  skipped  between  two  cycles,  eg.
       offset="4 months".

       The  lower  and upper limits of the accumulation process can be set, either by using space
       time raster datasets or by using fixed values for all raster cells  and  time  steps.  The
       raster maps that specify the lower and upper limits of the actual granule will be detected
       using the following temporal relations: equals, during, overlaps, overlapped and contains.
       First,  all maps with time stamps equal to the current granule will be detected, the first
       lower map and the first upper map found will be used as limit definitions.   If  no  equal
       maps  are  found,  then  maps with a temporal during relation are detected, then maps that
       temporally overlap the actual granules, until maps that have a temporal  contain  relation
       are  detected.  If no maps are found or lower/upper STRDS are not defined, then the limits
       option is used, eg. limits=10,30.

       The upper limit is only used in the Biologically Effective Degree Days calculation.

       The options shift, scale and method are passed to r.series.accumulate.   Please  refer  to
       the manual page of r.series.accumulate for detailed option description.

       The  output  is a new space time raster dataset with the provided start time, end time and
       granularity containing the accumulated raster maps. The base name of  the  generated  maps
       must  always  be  set.   The  output  space time raster dataset can then be analyzed using
       t.rast.accdetect to detect specific accumulation patterns.

EXAMPLE

       This is an example how to accumulate the daily mean temperature of  Europe  from  1990  to
       2000  using  the growing-degree-day method to detect grass hopper reproduction cycles that
       are critical to agriculture.
       # Get the temperature data
       wget http://www-pool.math.tu-berlin.de/~soeren/grass/temperature_mean_1990_2000_daily_celsius.tar.gz
       # Create a temporary location directory
       mkdir -p /tmp/grassdata/LL
       # Start GRASS and create a new location with PERMANENT mapset
       grass76 -c EPSG:4326 /tmp/grassdata/LL/PERMANENT
       # Import the temperature data
       t.rast.import input=temperature_mean_1990_2000_daily_celsius.tar.gz \
             output=temperature_mean_1990_2000_daily_celsius directory=/tmp
       # We need to set the region correctly
       g.region -p raster=`t.rast.list input=temperature_mean_1990_2000_daily_celsius column=name | tail -1`
       # We can zoom to the raster map
       g.region -p zoom=`t.rast.list input=temperature_mean_1990_2000_daily_celsius column=name | tail -1`
       #############################################################################
       #### ACCUMULATION USING GDD METHOD ##########################################
       #############################################################################
       # The computation of grashopper pest control cycles is based on:
       #
       #   Using Growing Degree Days For Insect Management
       #   Nancy E. Adams
       #   Extension Educator, Agricultural Resources
       #
       # available here: http://extension.unh.edu/agric/gddays/docs/growch.pdf
       # Now we compute the Biologically Effective Degree Days
       # from 1990 - 2000 for each year (12 month cycle) with
       # a granularity of one day. Base temperature is 10°C, upper limit is 30°C.
       # Hence the accumulation starts at 10°C and does not accumulate values above 30°C.
       t.rast.accumulate input="temperature_mean_1990_2000_daily_celsius" \
             output="temperature_mean_1990_2000_daily_celsius_accumulated_10_30" \
             limits="10,30" start="1990-01-01" stop="2000-01-01" cycle="12 months" \
             basename="temp_acc_daily_10_30" method="bedd"
       #############################################################################
       #### ACCUMULATION PATTERN DETECTION #########################################
       #############################################################################
       # Now we detect the three grasshopper pest control cycles
       # First cycle at 325°C - 427°C GDD
       t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_30@PERMANENT \
             occ=leafhopper_occurrence_c1_1990_2000 start="1990-01-01" stop="2000-01-01" \
             cycle="12 months" range=325,427 basename=lh_c1 indicator=leafhopper_indicator_c1_1990_2000
       # Second cycle at 685°C - 813°C GDD
       t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_30@PERMANENT \
             occ=leafhopper_occurrence_c2_1990_2000 start="1990-01-01" stop="2000-01-01" \
             cycle="12 months" range=685,813 basename=lh_c2 indicator=leafhopper_indicator_c2_1990_2000
       # Third cycle at 1047°C - 1179°C GDD
       t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_30@PERMANENT \
             occ=leafhopper_occurrence_c3_1990_2000 start="1990-01-01" stop="2000-01-01" \
             cycle="12 months" range=1047,1179 basename=lh_c3 indicator=leafhopper_indicator_c3_1990_2000
       #############################################################################
       #### YEARLY SPATIAL OCCURRENCE COMPUTATION OF ALL CYCLES ####################
       #############################################################################
       # Extract the areas that have full cycles
       t.rast.aggregate input=leafhopper_indicator_c1_1990_2000 gran="1 year" \
             output=leafhopper_cycle_1_1990_2000_yearly method=maximum basename=li_c1
       t.rast.mapcalc input=leafhopper_cycle_1_1990_2000_yearly basename=lh_clean_c1 \
                      output=leafhopper_cycle_1_1990_2000_yearly_clean \
                      expression="if(leafhopper_cycle_1_1990_2000_yearly == 3, 1, null())"
       t.rast.aggregate input=leafhopper_indicator_c2_1990_2000 gran="1 year" \
             output=leafhopper_cycle_2_1990_2000_yearly method=maximum basename=li_c2
       t.rast.mapcalc input=leafhopper_cycle_2_1990_2000_yearly basename=lh_clean_c2 \
                      output=leafhopper_cycle_2_1990_2000_yearly_clean \
                      expression="if(leafhopper_cycle_2_1990_2000_yearly == 3, 2, null())"
       t.rast.aggregate input=leafhopper_indicator_c3_1990_2000 gran="1 year" \
             output=leafhopper_cycle_3_1990_2000_yearly method=maximum basename=li_c3
       t.rast.mapcalc input=leafhopper_cycle_3_1990_2000_yearly basename=lh_clean_c3 \
                      output=leafhopper_cycle_3_1990_2000_yearly_clean \
                      expression="if(leafhopper_cycle_3_1990_2000_yearly == 3, 3, null())"
       t.rast.mapcalc input=leafhopper_cycle_1_1990_2000_yearly_clean,leafhopper_cycle_2_1990_2000_yearly_clean,leafhopper_cycle_3_1990_2000_yearly_clean \
                      basename=lh_cleann_all_cycles \
                      output=leafhopper_all_cycles_1990_2000_yearly_clean \
                      expression="if(isnull(leafhopper_cycle_3_1990_2000_yearly_clean), \
                     if(isnull(leafhopper_cycle_2_1990_2000_yearly_clean), \
                  if(isnull(leafhopper_cycle_1_1990_2000_yearly_clean), \
                  null() ,1),2),3)"
       cat > color.table << EOF
       3 yellow
       2 blue
       1 red
       EOF
       t.rast.colors input=leafhopper_cycle_1_1990_2000_yearly_clean rules=color.table
       t.rast.colors input=leafhopper_cycle_2_1990_2000_yearly_clean rules=color.table
       t.rast.colors input=leafhopper_cycle_3_1990_2000_yearly_clean rules=color.table
       t.rast.colors input=leafhopper_all_cycles_1990_2000_yearly_clean rules=color.table
       #############################################################################
       ################ DURATION COMPUTATION #######################################
       #############################################################################
       # Extract the duration in days of the first cycle
       t.rast.aggregate input=leafhopper_occurrence_c1_1990_2000 gran="1 year" \
             output=leafhopper_min_day_c1_1990_2000 method=minimum basename=occ_min_day_c1
       t.rast.aggregate input=leafhopper_occurrence_c1_1990_2000 gran="1 year" \
             output=leafhopper_max_day_c1_1990_2000 method=maximum basename=occ_max_day_c1
       t.rast.mapcalc input=leafhopper_min_day_c1_1990_2000,leafhopper_max_day_c1_1990_2000 \
                      basename=occ_duration_c1 \
                      output=leafhopper_duration_c1_1990_2000 \
                      expression="leafhopper_max_day_c1_1990_2000 - leafhopper_min_day_c1_1990_2000"
       # Extract the duration in days of the second cycle
       t.rast.aggregate input=leafhopper_occurrence_c2_1990_2000 gran="1 year" \
             output=leafhopper_min_day_c2_1990_2000 method=minimum basename=occ_min_day_c2
       t.rast.aggregate input=leafhopper_occurrence_c2_1990_2000 gran="1 year" \
             output=leafhopper_max_day_c2_1990_2000 method=maximum basename=occ_max_day_c2
       t.rast.mapcalc input=leafhopper_min_day_c2_1990_2000,leafhopper_max_day_c2_1990_2000 \
                      basename=occ_duration_c2 \
                      output=leafhopper_duration_c2_1990_2000 \
                      expression="leafhopper_max_day_c2_1990_2000 - leafhopper_min_day_c2_1990_2000"
       # Extract the duration in days of the third cycle
       t.rast.aggregate input=leafhopper_occurrence_c3_1990_2000 gran="1 year" \
             output=leafhopper_min_day_c3_1990_2000 method=minimum basename=occ_min_day_c3
       t.rast.aggregate input=leafhopper_occurrence_c3_1990_2000 gran="1 year" \
             output=leafhopper_max_day_c3_1990_2000 method=maximum basename=occ_max_day_c3
       t.rast.mapcalc input=leafhopper_min_day_c3_1990_2000,leafhopper_max_day_c3_1990_2000 \
                      basename=occ_duration_c3 \
                      output=leafhopper_duration_c3_1990_2000 \
                      expression="leafhopper_max_day_c3_1990_2000 - leafhopper_min_day_c3_1990_2000"
       t.rast.colors input=leafhopper_duration_c1_1990_2000 color=rainbow
       t.rast.colors input=leafhopper_duration_c2_1990_2000 color=rainbow
       t.rast.colors input=leafhopper_duration_c3_1990_2000 color=rainbow
       #############################################################################
       ################ MONTHLY CYCLES OCCURRENCE ##################################
       #############################################################################
       # Extract the monthly indicator that shows the start and end of a cycle
       # First cycle
       t.rast.aggregate input=leafhopper_indicator_c1_1990_2000 gran="1 month" \
             output=leafhopper_indi_min_month_c1_1990_2000 method=minimum basename=occ_indi_min_month_c1
       t.rast.aggregate input=leafhopper_indicator_c1_1990_2000 gran="1 month" \
             output=leafhopper_indi_max_month_c1_1990_2000 method=maximum basename=occ_indi_max_month_c1
       t.rast.mapcalc input=leafhopper_indi_min_month_c1_1990_2000,leafhopper_indi_max_month_c1_1990_2000 \
                      basename=indicator_monthly_c1 \
                      output=leafhopper_monthly_indicator_c1_1990_2000 \
                      expression="if(leafhopper_indi_min_month_c1_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c1_1990_2000 == 3, 3, 2))"
       # Second cycle
       t.rast.aggregate input=leafhopper_indicator_c2_1990_2000 gran="1 month" \
             output=leafhopper_indi_min_month_c2_1990_2000 method=minimum basename=occ_indi_min_month_c2
       t.rast.aggregate input=leafhopper_indicator_c2_1990_2000 gran="1 month" \
             output=leafhopper_indi_max_month_c2_1990_2000 method=maximum basename=occ_indi_max_month_c2
       t.rast.mapcalc input=leafhopper_indi_min_month_c2_1990_2000,leafhopper_indi_max_month_c2_1990_2000 \
                      basename=indicator_monthly_c2 \
                      output=leafhopper_monthly_indicator_c2_1990_2000 \
                      expression="if(leafhopper_indi_min_month_c2_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c2_1990_2000 == 3, 3, 2))"
       # Third cycle
       t.rast.aggregate input=leafhopper_indicator_c3_1990_2000 gran="1 month" \
             output=leafhopper_indi_min_month_c3_1990_2000 method=minimum basename=occ_indi_min_month_c3
       t.rast.aggregate input=leafhopper_indicator_c3_1990_2000 gran="1 month" \
             output=leafhopper_indi_max_month_c3_1990_2000 method=maximum basename=occ_indi_max_month_c3
       t.rast.mapcalc input=leafhopper_indi_min_month_c3_1990_2000,leafhopper_indi_max_month_c3_1990_2000 \
                      basename=indicator_monthly_c3 \
                      output=leafhopper_monthly_indicator_c3_1990_2000 \
                      expression="if(leafhopper_indi_min_month_c3_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c3_1990_2000 == 3, 3, 2))"
       cat > color.table << EOF
       3 red
       2 yellow
       1 green
       EOF
       t.rast.colors input=leafhopper_monthly_indicator_c1_1990_2000 rules=color.table
       t.rast.colors input=leafhopper_monthly_indicator_c2_1990_2000 rules=color.table
       t.rast.colors input=leafhopper_monthly_indicator_c3_1990_2000 rules=color.table
       #############################################################################
       ################ VISUALIZATION ##############################################
       #############################################################################
       # Now we use g.gui.animation to visualize the yearly occurrence, the duration and the monthly occurrence
       # Yearly occurrence of all reproduction cycles
       g.gui.animation strds=leafhopper_all_cycles_1990_2000_yearly_clean
       # Yearly duration of reproduction cycle 1
       g.gui.animation strds=leafhopper_duration_c1_1990_2000
       # Yearly duration of reproduction cycle 2
       g.gui.animation strds=leafhopper_duration_c2_1990_2000
       # Yearly duration of reproduction cycle 3
       g.gui.animation strds=leafhopper_duration_c3_1990_2000
       # Monthly occurrence of reproduction cycle 1
       g.gui.animation strds=leafhopper_monthly_indicator_c1_1990_2000
       # Monthly occurrence of reproduction cycle 2
       g.gui.animation strds=leafhopper_monthly_indicator_c2_1990_2000
       # Monthly occurrence of reproduction cycle 3
       g.gui.animation strds=leafhopper_monthly_indicator_c3_1990_2000

SEE ALSO

        t.rast.accdetect, t.rast.aggregate, t.rast.mapcalc, t.info, g.region, r.series.accumulate

REFERENCES

           ·   Jones, G.V., Duff, A.A., Hall, A., Myers, J.W., 2010.  Spatial Analysis of Climate
               in Winegrape Growing Regions in the Western United States. Am. J. Enol. Vitic. 61,
               313-326.

AUTHOR

       Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

       Last changed: $Date: 2018-08-31 17:01:15 +0200 (Fri, 31 Aug 2018) $

SOURCE CODE

       Available at: t.rast.accumulate source code (history)

       Main index | Temporal index | Topics index | Keywords index | Graphical index | Full index

       © 2003-2019 GRASS Development Team, GRASS GIS 7.6.1 Reference Manual