1t.rast.accumulate(1) GRASS GIS User's Manual t.rast.accumulate(1)
2
6 t.rast.accumulate - Computes cyclic accumulations of a space time
7 raster dataset.
8
10 temporal, accumulation, raster, time
11
13 t.rast.accumulate
14 t.rast.accumulate --help
15 t.rast.accumulate [-nr] input=name output=name [lower=name] [up‐
16 per=name] start=string [stop=string] cycle=string [offset=string]
17 [granularity=string] basename=string [suffix=string] lim‐
18 its=lower,upper [scale=float] [shift=float] [method=string]
19 [--overwrite] [--help] [--verbose] [--quiet] [--ui]
20 Flags:
22 -n
23 Register empty maps in the output space time raster dataset, other‐
24 wise they will be deleted
25 -r
27 Reverse time direction in cyclic accumulation
28 --overwrite
30 Allow output files to overwrite existing files
31 --help
33 Print usage summary
34 --verbose
36 Verbose module output
37 --quiet
39 Quiet module output
40 --ui
42 Force launching GUI dialog
43 Parameters:
45 input=name [required]
46 Name of the input space time raster dataset
47 output=name [required]
49 Name of the output space time raster dataset
50 lower=name
52 Input space time raster dataset that defines the lower threshold,
53 values lower than this threshold are excluded from accumulation
54 upper=name
56 Input space time raster dataset that defines the upper threshold,
57 values higher than this threshold are excluded from accumulation
58 start=string [required]
60 The temporal starting point to begin the accumulation, eg
61 ’2001-01-01’
62 stop=string
64 The temporal date to stop the accumulation, eg ’2009-01-01’
65 cycle=string [required]
67 The temporal cycle to restart the accumulation, eg ’12 months’
68 offset=string
70 The temporal offset to the beginning of the next cycle, eg ’6
71 months’
72 granularity=string
74 The granularity for accumulation ’1 day’
75 Default: 1 day
76 basename=string [required]
78 Basename of the new generated output maps
79 A numerical suffix separated by an underscore will be attached to
80 create a unique identifier
81 suffix=string
83 Suffix to add to the basename. Set ’gran’ for granularity, ’time’
84 for the full time format, ’num’ for numerical suffix with a spe‐
85 cific number of digits (default %05)
86 Default: gran
87 limits=lower,upper [required]
89 Use these limits in case lower and/or upper input space time raster
90 datasets are not defined or contain NULL values
91 scale=float
93 Scale factor for input space time raster dataset
94 shift=float
96 Shift factor for input space time raster dataset
97 method=string
99 This method will be applied to compute the accumulative values from
100 the input maps in a single granule
101 Growing Degree Days or Winkler indices; Mean: sum(input maps)/(num‐
102 ber of input maps); Biologically Effective Degree Days; Huglin He‐
103 liothermal index
104 Options: mean, gdd, bedd, huglin
105 Default: mean
106
108 t.rast.accumulate is designed to perform temporal accumulations of
109 space time raster datasets. This module expects a space time raster
110 dataset as input that will be sampled by a given granularity. All maps
111 that have the start time during the actual granule will be accumulated
112 with the predecessor granule accumulation result using the raster mod‐
113 ule r.series.accumulate. The default granularity is 1 day, but any tem‐
114 poral granularity can be set.
115 The start time and the end time of the accumulation process must be
117 set, eg. start="2000-03-01" end="2011-01-01". In addition, a cycle, eg.
118 cycle="8 months", can be specified, that defines after which interval
119 of time the accumulation process restarts. The offset option specifies
120 the time that should be skipped between two cycles, eg. offset="4
121 months".
122 The lower and upper limits of the accumulation process can be set, ei‐
124 ther by using space time raster datasets or by using fixed values for
125 all raster cells and time steps. The raster maps that specify the lower
126 and upper limits of the actual granule will be detected using the fol‐
127 lowing temporal relations: equals, during, overlaps, overlapped and
128 contains. First, all maps with time stamps equal to the current granule
129 will be detected, the first lower map and the first upper map found
130 will be used as limit definitions. If no equal maps are found, then
131 maps with a temporal during relation are detected, then maps that tem‐
132 porally overlap the actual granules, until maps that have a temporal
133 contain relation are detected. If no maps are found or lower/upper
134 STRDS are not defined, then the limits option is used, eg. lim‐
135 its=10,30.
136 The upper limit is only used in the Biologically Effective Degree Days
138 calculation.
139 The options shift, scale and method are passed to r.series.accumulate.
141 Please refer to the manual page of r.series.accumulate for detailed op‐
142 tion description.
143 The output is a new space time raster dataset with the provided start
145 time, end time and granularity containing the accumulated raster maps.
146 The base name of the generated maps must always be set. The output
147 space time raster dataset can then be analyzed using t.rast.accdetect
148 to detect specific accumulation patterns.
149
151 This is an example how to accumulate the daily mean temperature of Eu‐
152 rope from 1990 to 2000 using the growing-degree-day method to detect
153 grass hopper reproduction cycles that are critical to agriculture.
154 # Get the temperature data
155 wget http://www-pool.math.tu-berlin.de/~soeren/grass/temperature_mean_1990_2000_daily_celsius.tar.gz
156 # Create a temporary location directory
157 mkdir -p /tmp/grassdata/LL
158 # Start GRASS and create a new location with PERMANENT mapset
159 grass -c EPSG:4326 /tmp/grassdata/LL/PERMANENT
160 # Import the temperature data
161 t.rast.import input=temperature_mean_1990_2000_daily_celsius.tar.gz \
162 output=temperature_mean_1990_2000_daily_celsius directory=/tmp
163 # We need to set the region correctly
164 g.region -p raster=`t.rast.list input=temperature_mean_1990_2000_daily_celsius column=name | tail -1`
165 # We can zoom to the raster map
166 g.region -p zoom=`t.rast.list input=temperature_mean_1990_2000_daily_celsius column=name | tail -1`
167 #############################################################################
168 #### ACCUMULATION USING GDD METHOD ##########################################
169 #############################################################################
170 # The computation of grashopper pest control cycles is based on:
171 #
172 # Using Growing Degree Days For Insect Management
173 # Nancy E. Adams
174 # Extension Educator, Agricultural Resources
175 #
176 # available here: http://extension.unh.edu/agric/gddays/docs/growch.pdf
177 # Now we compute the Biologically Effective Degree Days
178 # from 1990 - 2000 for each year (12 month cycle) with
179 # a granularity of one day. Base temperature is 10°C, upper limit is 30°C.
180 # Hence the accumulation starts at 10°C and does not accumulate values above 30°C.
181 t.rast.accumulate input="temperature_mean_1990_2000_daily_celsius" \
182 output="temperature_mean_1990_2000_daily_celsius_accumulated_10_30" \
183 limits="10,30" start="1990-01-01" stop="2000-01-01" cycle="12 months" \
184 basename="temp_acc_daily_10_30" method="bedd"
185 #############################################################################
186 #### ACCUMULATION PATTERN DETECTION #########################################
187 #############################################################################
188 # Now we detect the three grasshopper pest control cycles
189 # First cycle at 325°C - 427°C GDD
190 t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_30@PERMANENT \
191 occ=leafhopper_occurrence_c1_1990_2000 start="1990-01-01" stop="2000-01-01" \
192 cycle="12 months" range=325,427 basename=lh_c1 indicator=leafhopper_indicator_c1_1990_2000
193 # Second cycle at 685°C - 813°C GDD
194 t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_30@PERMANENT \
195 occ=leafhopper_occurrence_c2_1990_2000 start="1990-01-01" stop="2000-01-01" \
196 cycle="12 months" range=685,813 basename=lh_c2 indicator=leafhopper_indicator_c2_1990_2000
197 # Third cycle at 1047°C - 1179°C GDD
198 t.rast.accdetect input=temperature_mean_1990_2000_daily_celsius_accumulated_10_30@PERMANENT \
199 occ=leafhopper_occurrence_c3_1990_2000 start="1990-01-01" stop="2000-01-01" \
200 cycle="12 months" range=1047,1179 basename=lh_c3 indicator=leafhopper_indicator_c3_1990_2000
201 #############################################################################
202 #### YEARLY SPATIAL OCCURRENCE COMPUTATION OF ALL CYCLES ####################
203 #############################################################################
204 # Extract the areas that have full cycles
205 t.rast.aggregate input=leafhopper_indicator_c1_1990_2000 gran="1 year" \
206 output=leafhopper_cycle_1_1990_2000_yearly method=maximum basename=li_c1
207 t.rast.mapcalc input=leafhopper_cycle_1_1990_2000_yearly basename=lh_clean_c1 \
208 output=leafhopper_cycle_1_1990_2000_yearly_clean \
209 expression="if(leafhopper_cycle_1_1990_2000_yearly == 3, 1, null())"
210 t.rast.aggregate input=leafhopper_indicator_c2_1990_2000 gran="1 year" \
211 output=leafhopper_cycle_2_1990_2000_yearly method=maximum basename=li_c2
212 t.rast.mapcalc input=leafhopper_cycle_2_1990_2000_yearly basename=lh_clean_c2 \
213 output=leafhopper_cycle_2_1990_2000_yearly_clean \
214 expression="if(leafhopper_cycle_2_1990_2000_yearly == 3, 2, null())"
215 t.rast.aggregate input=leafhopper_indicator_c3_1990_2000 gran="1 year" \
216 output=leafhopper_cycle_3_1990_2000_yearly method=maximum basename=li_c3
217 t.rast.mapcalc input=leafhopper_cycle_3_1990_2000_yearly basename=lh_clean_c3 \
218 output=leafhopper_cycle_3_1990_2000_yearly_clean \
219 expression="if(leafhopper_cycle_3_1990_2000_yearly == 3, 3, null())"
220 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 \
221 basename=lh_cleann_all_cycles \
222 output=leafhopper_all_cycles_1990_2000_yearly_clean \
223 expression="if(isnull(leafhopper_cycle_3_1990_2000_yearly_clean), \
224 if(isnull(leafhopper_cycle_2_1990_2000_yearly_clean), \
225 if(isnull(leafhopper_cycle_1_1990_2000_yearly_clean), \
226 null() ,1),2),3)"
227 cat > color.table << EOF
228 3 yellow
229 2 blue
230 1 red
231 EOF
232 t.rast.colors input=leafhopper_cycle_1_1990_2000_yearly_clean rules=color.table
233 t.rast.colors input=leafhopper_cycle_2_1990_2000_yearly_clean rules=color.table
234 t.rast.colors input=leafhopper_cycle_3_1990_2000_yearly_clean rules=color.table
235 t.rast.colors input=leafhopper_all_cycles_1990_2000_yearly_clean rules=color.table
236 #############################################################################
237 ################ DURATION COMPUTATION #######################################
238 #############################################################################
239 # Extract the duration in days of the first cycle
240 t.rast.aggregate input=leafhopper_occurrence_c1_1990_2000 gran="1 year" \
241 output=leafhopper_min_day_c1_1990_2000 method=minimum basename=occ_min_day_c1
242 t.rast.aggregate input=leafhopper_occurrence_c1_1990_2000 gran="1 year" \
243 output=leafhopper_max_day_c1_1990_2000 method=maximum basename=occ_max_day_c1
244 t.rast.mapcalc input=leafhopper_min_day_c1_1990_2000,leafhopper_max_day_c1_1990_2000 \
245 basename=occ_duration_c1 \
246 output=leafhopper_duration_c1_1990_2000 \
247 expression="leafhopper_max_day_c1_1990_2000 - leafhopper_min_day_c1_1990_2000"
248 # Extract the duration in days of the second cycle
249 t.rast.aggregate input=leafhopper_occurrence_c2_1990_2000 gran="1 year" \
250 output=leafhopper_min_day_c2_1990_2000 method=minimum basename=occ_min_day_c2
251 t.rast.aggregate input=leafhopper_occurrence_c2_1990_2000 gran="1 year" \
252 output=leafhopper_max_day_c2_1990_2000 method=maximum basename=occ_max_day_c2
253 t.rast.mapcalc input=leafhopper_min_day_c2_1990_2000,leafhopper_max_day_c2_1990_2000 \
254 basename=occ_duration_c2 \
255 output=leafhopper_duration_c2_1990_2000 \
256 expression="leafhopper_max_day_c2_1990_2000 - leafhopper_min_day_c2_1990_2000"
257 # Extract the duration in days of the third cycle
258 t.rast.aggregate input=leafhopper_occurrence_c3_1990_2000 gran="1 year" \
259 output=leafhopper_min_day_c3_1990_2000 method=minimum basename=occ_min_day_c3
260 t.rast.aggregate input=leafhopper_occurrence_c3_1990_2000 gran="1 year" \
261 output=leafhopper_max_day_c3_1990_2000 method=maximum basename=occ_max_day_c3
262 t.rast.mapcalc input=leafhopper_min_day_c3_1990_2000,leafhopper_max_day_c3_1990_2000 \
263 basename=occ_duration_c3 \
264 output=leafhopper_duration_c3_1990_2000 \
265 expression="leafhopper_max_day_c3_1990_2000 - leafhopper_min_day_c3_1990_2000"
266 t.rast.colors input=leafhopper_duration_c1_1990_2000 color=rainbow
267 t.rast.colors input=leafhopper_duration_c2_1990_2000 color=rainbow
268 t.rast.colors input=leafhopper_duration_c3_1990_2000 color=rainbow
269 #############################################################################
270 ################ MONTHLY CYCLES OCCURRENCE ##################################
271 #############################################################################
272 # Extract the monthly indicator that shows the start and end of a cycle
273 # First cycle
274 t.rast.aggregate input=leafhopper_indicator_c1_1990_2000 gran="1 month" \
275 output=leafhopper_indi_min_month_c1_1990_2000 method=minimum basename=occ_indi_min_month_c1
276 t.rast.aggregate input=leafhopper_indicator_c1_1990_2000 gran="1 month" \
277 output=leafhopper_indi_max_month_c1_1990_2000 method=maximum basename=occ_indi_max_month_c1
278 t.rast.mapcalc input=leafhopper_indi_min_month_c1_1990_2000,leafhopper_indi_max_month_c1_1990_2000 \
279 basename=indicator_monthly_c1 \
280 output=leafhopper_monthly_indicator_c1_1990_2000 \
281 expression="if(leafhopper_indi_min_month_c1_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c1_1990_2000 == 3, 3, 2))"
282 # Second cycle
283 t.rast.aggregate input=leafhopper_indicator_c2_1990_2000 gran="1 month" \
284 output=leafhopper_indi_min_month_c2_1990_2000 method=minimum basename=occ_indi_min_month_c2
285 t.rast.aggregate input=leafhopper_indicator_c2_1990_2000 gran="1 month" \
286 output=leafhopper_indi_max_month_c2_1990_2000 method=maximum basename=occ_indi_max_month_c2
287 t.rast.mapcalc input=leafhopper_indi_min_month_c2_1990_2000,leafhopper_indi_max_month_c2_1990_2000 \
288 basename=indicator_monthly_c2 \
289 output=leafhopper_monthly_indicator_c2_1990_2000 \
290 expression="if(leafhopper_indi_min_month_c2_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c2_1990_2000 == 3, 3, 2))"
291 # Third cycle
292 t.rast.aggregate input=leafhopper_indicator_c3_1990_2000 gran="1 month" \
293 output=leafhopper_indi_min_month_c3_1990_2000 method=minimum basename=occ_indi_min_month_c3
294 t.rast.aggregate input=leafhopper_indicator_c3_1990_2000 gran="1 month" \
295 output=leafhopper_indi_max_month_c3_1990_2000 method=maximum basename=occ_indi_max_month_c3
296 t.rast.mapcalc input=leafhopper_indi_min_month_c3_1990_2000,leafhopper_indi_max_month_c3_1990_2000 \
297 basename=indicator_monthly_c3 \
298 output=leafhopper_monthly_indicator_c3_1990_2000 \
299 expression="if(leafhopper_indi_min_month_c3_1990_2000 == 1, 1, if(leafhopper_indi_max_month_c3_1990_2000 == 3, 3, 2))"
300 cat > color.table << EOF
301 3 red
302 2 yellow
303 1 green
304 EOF
305 t.rast.colors input=leafhopper_monthly_indicator_c1_1990_2000 rules=color.table
306 t.rast.colors input=leafhopper_monthly_indicator_c2_1990_2000 rules=color.table
307 t.rast.colors input=leafhopper_monthly_indicator_c3_1990_2000 rules=color.table
308 #############################################################################
309 ################ VISUALIZATION ##############################################
310 #############################################################################
311 # Now we use g.gui.animation to visualize the yearly occurrence, the duration and the monthly occurrence
312 # Yearly occurrence of all reproduction cycles
313 g.gui.animation strds=leafhopper_all_cycles_1990_2000_yearly_clean
314 # Yearly duration of reproduction cycle 1
315 g.gui.animation strds=leafhopper_duration_c1_1990_2000
316 # Yearly duration of reproduction cycle 2
317 g.gui.animation strds=leafhopper_duration_c2_1990_2000
318 # Yearly duration of reproduction cycle 3
319 g.gui.animation strds=leafhopper_duration_c3_1990_2000
320 # Monthly occurrence of reproduction cycle 1
321 g.gui.animation strds=leafhopper_monthly_indicator_c1_1990_2000
322 # Monthly occurrence of reproduction cycle 2
323 g.gui.animation strds=leafhopper_monthly_indicator_c2_1990_2000
324 # Monthly occurrence of reproduction cycle 3
325 g.gui.animation strds=leafhopper_monthly_indicator_c3_1990_2000
326
328 • Jones, G.V., Duff, A.A., Hall, A., Myers, J.W., 2010. Spatial
329 Analysis of Climate in Winegrape Growing Regions in the Western
330 United States. Am. J. Enol. Vitic. 61, 313-326.
331
333 t.rast.accdetect, t.rast.aggregate, t.rast.mapcalc, t.info, g.region,
334 r.series.accumulate
335
337 Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
338
340 Available at: t.rast.accumulate source code (history)
341 Accessed: Mon Jun 20 16:47:47 2022
343 Main index | Temporal index | Topics index | Keywords index | Graphical
345 index | Full index
346 © 2003-2022 GRASS Development Team, GRASS GIS 8.2.0 Reference Manual
348GRASS 8.2.0 t.rast.accumulate(1)