Overview simulated data

Simulated Climate Change: CC85

Names of the restructured GPKGs

gpkg <- unique(rslt$gpkg)
print(gpkg)

##  [1] "MV_Hartola"   "MV_Kitee"     "MV_Korsnas"   "MV_Parikkala" "MV_Pori"     
##  [6] "MV_Pyhtaa"    "MV_Raasepori" "MV_Simo"      "MV_Vaala"     "MV_Voyri"

Simulated branching_groups

All simulated branching_groups must be listed in the file /params/regimes.csv !! Based on this the regime names are merged to the results.

##  [1] NA                                    "Selection cut_1"                    
##  [3] "Selection cut_2"                     "Selection cut_3"                    
##  [5] "Selection cut_4"                     "Tapio thinning"                     
##  [7] "Tapio thinning nature"               "Short rotation thinning 5"          
##  [9] "Long rotation thinning 5"            "Long rotation thinning 10"          
## [11] "Long rotation thinning 15"           "Long rotation thinning 30"          
## [13] "Tapio harvest without thinnings -20" "Short rotation harvest 5"           
## [15] "Tapio harvest"                       "Tapio harvest nature scene"         
## [17] "Tapio harvest without thinnings"     "Long rotation harvest 5"            
## [19] "Long rotation harvest 10"            "Tapio harvest without thinnings 10" 
## [21] "Long rotation harvest 15"            "Long rotation harvest 30"

##  [1] "SA_DWextract" "CCF_1"        "CCF_2"        "CCF_3"        "CCF_4"       
##  [6] "BAUwT"        "BAUwT_GTR"    "BAUwT_m5"     "BAUwT_5"      "BAUwT_10"    
## [11] "BAUwT_15"     "BAUwT_30"     "BAUwoT_m20"   "BAU_m5"       "BAU"         
## [16] "BAUwGTR"      "BAUwoT"       "BAU_5"        "BAU_10"       "BAUwoT_10"   
## [21] "BAU_15"       "BAU_30"       "SA"

Number of simulated stands per GPKG and their size

stands <- rslt %>% 
  group_by(gpkg) %>% 
  summarise(simulated_stands = n_distinct(id),
            min_stand_size = min(AREA),
            max_stand_size = max(AREA),
            mean_size = mean(AREA))

kable(stands) %>% kable_styling()

gpkg	simulated_stands	min_stand_size	max_stand_size	mean_size
MV_Hartola	154	0.126	8.209	1.112990
MV_Kitee	29	0.316	2.404	1.152039
MV_Korsnas	297	0.109	11.691	1.038666
MV_Parikkala	284	0.091	4.483	1.045226
MV_Pori	207	0.112	7.130	1.062409
MV_Pyhtaa	120	0.154	8.052	1.287290
MV_Raasepori	200	0.025	5.350	1.086739
MV_Simo	290	0.070	10.892	1.421866
MV_Vaala	24	0.183	4.682	1.682645
MV_Voyri	190	0.078	4.524	0.903005

Development of average stand volume V (m3/ha)

for certain regimes

Volume development of “SA” and “SA_DWextract” should be identical

meanV <- rslt[rslt$regime %in% c("BAU", "SA", "CCF_2", "BAUwGTR", "SA_DWextract"), ] %>% 
   group_by(year, regime, gpkg) %>% 
   summarise(meanV = mean(V)) %>% 
   ggplot(aes(year, meanV)) + 
   geom_line( aes(color = regime)) +
   theme(axis.text.x = element_text(angle = 90)) +
   scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
   scale_y_continuous(limit = c(0,700)) +
   facet_wrap(. ~gpkg)
 plot(meanV)

only for CCF regimes

meanV_CCF <- rslt[rslt$regime %in% c("CCF_1", "CCF_2", "CCF_3", "CCF_4"), ] %>% 
   group_by(year, regime, gpkg) %>% 
   summarise(meanV = mean(V)) %>% 
   ggplot(aes(year, meanV)) + 
   geom_line( aes(color = regime)) +
   theme(axis.text.x = element_text(angle = 90)) +
   scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
   # scale_y_continuous(limit = c(0,700)) +
   facet_wrap(. ~gpkg)
 plot(meanV_CCF)

Development of average h_dom for certain regimes

meanH_dom <- rslt[rslt$regime %in% c("BAU", "SA", "CCF_2", "BAUwGTR", "SA_DWextract") , ] %>% 
   group_by(year, regime, gpkg) %>% 
   summarise(meanH_dom = mean(H_dom, na.rm = TRUE )) %>% 
   ggplot(aes(year, meanH_dom)) + 
   geom_line( aes(color = regime)) +
   theme(axis.text.x = element_text(angle = 90)) +
   scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
   facet_wrap(. ~gpkg)
 plot(meanH_dom)

What is the maximum H_dom per watershed, and which regime is causing it

require(data.table)
dt <- data.table(rslt)
max <- dt[ , max(H_dom, na.rm = TRUE ), by = gpkg]
max <- max %>% rename(H_dom = V1 )

maxH_dom <- rslt %>% 
   semi_join(max, by = c("gpkg","H_dom")) %>% 
   select(gpkg, id, year, regime, H_dom)

kable(maxH_dom) %>% kable_styling()

gpkg	id	year	regime	H_dom
MV_Hartola	9914063	2106	CCF_2	32.95013
MV_Kitee	24153294	2111	CCF_4	33.77057
MV_Korsnas	13141993	2111	CCF_3	30.70894
MV_Parikkala	24718720	2106	CCF_2	36.28994
MV_Pori	27925448	2106	CCF_4	36.09263
MV_Pyhtaa	32627001	2111	BAUwT_30	31.21540
MV_Raasepori	29606206	2101	CCF_3	40.58453
MV_Simo	29010751	2111	CCF_4	29.94783
MV_Vaala	32442373	2111	CCF_3	26.32676
MV_Voyri	10882587	2111	CCF_3	31.70397

Development of average V_total_deadwood (m3/ha) for certain regimes

meanDW <- rslt[rslt$regime %in% c("BAU", "SA", "CCF_2", "BAUwGTR", "SA_DWextract") , ] %>% 
   group_by(year, regime, gpkg) %>% 
   summarise(mean_DW = mean(V_total_deadwood)) %>% 
   ggplot(aes(year, mean_DW)) + 
   geom_line( aes(color = regime)) +
   theme(axis.text.x = element_text(angle = 90)) +
   scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
   facet_wrap(. ~gpkg)
 plot(meanDW)

Development of average CARBON_STORAGE (kg/ha) for certain regimes

meanCS <- rslt[rslt$regime %in% c("BAU", "SA", "CCF_2", "BAUwGTR")  , ] %>% 
   group_by(year, regime, gpkg) %>% 
   summarise(mean_CS = mean(CARBON_STORAGE, na.rm = TRUE )/1000) %>% 
   ggplot(aes(year, mean_CS)) + 
   geom_line( aes(color = regime)) +
   theme(axis.text.x = element_text(angle = 90)) +
   scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
   facet_wrap(. ~gpkg)
 plot(meanCS)

Average harvested timber volume (m3/ha) for certain regimes

Cash flow = The sum of all revenues and costs for a specific forest stand

meanHarvested_V <- rslt[rslt$regime %in%c("BAU", "SA", "CCF_2", "BAUwGTR") , ] %>% 
   group_by(year, regime, gpkg) %>% 
   mutate(Harvested_V = ifelse(is.na(Harvested_V), 0, Harvested_V)) %>% 
   summarise(meanHarvested_V = mean(Harvested_V), na.rm = TRUE) %>% 
   ggplot(aes(year, meanHarvested_V)) + 
   geom_line( aes(color = regime)) +
   theme(axis.text.x = element_text(angle = 90)) +
   scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
   facet_wrap(. ~gpkg)
 plot(meanHarvested_V)

Average cash flow (Euro/ha) for certain regimes