1r.series.accumulate(1)      GRASS GIS User's Manual     r.series.accumulate(1)
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NAME

6       r.series.accumulate  - Makes each output cell value a accumulationfunc‐
7       tion of the values assigned to the corresponding  cells  in  the  input
8       raster map layers.
9

KEYWORDS

11       raster, series, accumulation
12

SYNOPSIS

14       r.series.accumulate
15       r.series.accumulate --help
16       r.series.accumulate  [-nzf]   [basemap=name]    [input=name[,name,...]]
17       [file=name]  output=name  [scale=float]   [shift=float]    [lower=name]
18       [upper=name]   [range=min,max]   [limits=lower,upper]   [method=string]
19       [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]
20
21   Flags:
22       -n
23           Propagate NULLs
24
25       -z
26           Do not keep files open
27
28       -f
29           Create a FCELL map (floating point single precision) as output
30
31       --overwrite
32           Allow output files to overwrite existing files
33
34       --help
35           Print usage summary
36
37       --verbose
38           Verbose module output
39
40       --quiet
41           Quiet module output
42
43       --ui
44           Force launching GUI dialog
45
46   Parameters:
47       basemap=name
48           Existing map to be added to output
49
50       input=name[,name,...]
51           Name of input raster map(s)
52
53       file=name
54           Input file with raster map names, one per line
55
56       output=name [required]
57           Name for output raster map
58
59       scale=float
60           Scale factor for input
61           Default: 1.0
62
63       shift=float
64           Shift factor for input
65           Default: 0.0
66
67       lower=name
68           The raster map specifying the lower accumulation limit, also called
69           baseline
70
71       upper=name
72           The raster map specifying the upper accumulation limit, also called
73           cutoff. Only applied to BEDD computation.
74
75       range=min,max
76           Ignore values outside this range
77
78       limits=lower,upper
79           Use these limits in case lower and/or upper input maps are not  de‐
80           fined
81           Default: 10,30
82
83       method=string
84           This method will be applied to compute the accumulative values from
85           the input maps
86           Options: gdd, bedd, huglin, mean
87           Default: gdd
88           gdd: Growing Degree Days or Winkler indices
89           bedd: Biologically Effective Degree Days
90           huglin: Huglin Heliothermal index
91           mean: Mean: sum(input maps)/(number of input maps)
92

DESCRIPTION

94       r.series.accumulate calculates (accumulated) raster value using growing
95       degree  days  (GDDs)/Winkler  indices’s,  Biologically Effective Degree
96       Days (BEDD), Huglin heliothermal indices or an  average  approach  from
97       several  input  maps for a given day. Accumulation of e.g.  degree-days
98       to growing degree days (GDDs) can be done by providing a  basemap  with
99       GDDs of the previous day.
100
101       The  flag  -a  determines  the  average computation of the input raster
102       maps.  In case the flag is not set, the average calculation is:
103           average = (min + max) / 2
104       In case the flag was set, the calculation changes to arithmetic mean
105           average = sum(input maps) / (number of input maps)
106
107       GDD Growing Degree Days are calculated as
108           gdd = average - lower
109
110       In case the -a is set, the Winkler indices are  calculated  instead  of
111       GDD,  usually  accumulated  for  the  period  April 1st to October 31st
112       (northern hemisphere) or the period October 1st to April 30th (southern
113       hemisphere).
114
115       BEDDs Biologically Effective Degree Days are calculated as
116           bedd = average - lower
117       with  an  optional  upper  cutoff applied to the average instead of the
118       temperature values.
119
120       The Huglin heliothermal index is calculated as
121           huglin = (average + max) / 2 - lower
122       usually accumulated for the period April 1st to September 30th  (north‐
123       ern  hemisphere)  or  the  period September 1st to April 30th (southern
124       hemisphere).
125
126       Mean raster values are calculated as
127           mean = average
128
129       For all the formulas min is the minimum value, max  the  maximum  value
130       and average the average value.  The min, max and average values are au‐
131       tomatically calculated from the input maps.
132
133       The shift and scale values are applied directly to  the  input  values.
134       The  lower  and upper maps, as well as the range options are applied to
135       constrain the accumulation. In case the lower and upper  maps  are  not
136       provided the limits option with default values will be applied.
137
138       If  an  existing map is provided with the basemap option, the values of
139       this map are added to the output.
140

NOTES

142       The scale and shift parameters are used to transform input values with
143           new = old * scale + shift
144
145       With the -n flag, any cell for which any  of  the  corresponding  input
146       cells  are NULL is automatically set to NULL (NULL propagation) and the
147       accumulated value is not calculated.
148
149       Negative results are set to 0 (zero).
150
151       Without the -n flag, all non-NULL cells are used for calculation.
152
153       If the range= option is given, any values which fall outside that range
154       will be treated as if they were NULL. Note that the range is applied to
155       the scaled and shifted input data. The range parameter can  be  set  to
156       low,high  thresholds:  values outside of this range are treated as NULL
157       (i.e., they will be ignored by most aggregates, or will cause  the  re‐
158       sult  to  be NULL if -n is given). The low,high thresholds are floating
159       point, so use -inf or inf for a single threshold (e.g., range=0,inf  to
160       ignore  negative  values,  or  range=-inf,-200.4 to ignore values above
161       -200.4).
162
163       The maximum number of raster maps that can be processed is given by the
164       user-specific limit of the operating system. For example, the soft lim‐
165       its for users are typically 1024 files. The soft limit can  be  changed
166       with  e.g.  ulimit -n 4096 (UNIX-based operating systems) but it cannot
167       be higher than the hard limit. If the latter is too low, you can as su‐
168       peruser add an entry in:
169       /etc/security/limits.conf
170       # <domain>      <type>  <item>         <value>
171       your_username  hard    nofile          4096
172       This  will  raise the hard limit to 4096 files. Also have a look at the
173       overall limit of the operating system
174       cat /proc/sys/fs/file-max
175       which on modern Linux systems is several 100,000 files.
176
177       Use the -z flag to analyze large amounts of raster maps without hitting
178       open files limit and the file option to avoid hitting the size limit of
179       command line arguments.  Note that the computation using the  file  op‐
180       tion is slower than with the input option.  For every single row in the
181       output map(s) all input maps are opened and closed. The amount  of  RAM
182       will  rise  linearly with the number of specified input maps. The input
183       and file options are mutually exclusive: the former is  a  comma  sepa‐
184       rated list of raster map names and the latter is a text file with a new
185       line separated list of raster map names.
186

EXAMPLES

188       Example with MODIS Land Surface Temperature, transforming  values  from
189       Kelvin * 50 to degrees Celsius:
190       r.series.accumulate in=MOD11A1.Day,MOD11A1.Night,MYD11A1.Day,MYD11A1.Night out=MCD11A1.GDD \
191             scale=0.02 shift=-273.15 limits=10,30
192

SEE ALSO

194        g.list, g.region, r.series, r.series.interp
195
196       Hints for large raster data processing
197

REFERENCES

199           •   Jones,  G.V., Duff, A.A., Hall, A., Myers, J.W., 2010.  Spatial
200               analysis of climate in winegrape growing regions in the Western
201               United States. Am. J. Enol. Vitic. 61, 313-326.
202

AUTHORS

204       Markus Metz and Soeren Gebbert (based on r.series)
205

SOURCE CODE

207       Available at: r.series.accumulate source code (history)
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209       Accessed: Mon Jun 20 16:46:25 2022
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211       Main  index  | Raster index | Topics index | Keywords index | Graphical
212       index | Full index
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214       © 2003-2022 GRASS Development Team, GRASS GIS 8.2.0 Reference Manual
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218GRASS 8.2.0                                             r.series.accumulate(1)
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