---
title: "Overview simulated data"
author: "CB"
date: "`r format(Sys.time(), '%d %m, %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, include=FALSE}
path <- paste0(getwd(),"/")
# source(paste0(path,"Main.R"))
### DEFINE simulation variant ###
# only the following three, special set aside simulations are bind
#
# sim_variant: "Without"
# "CC45"
# "CC85"
sim_variant = "CC85"
library(tidyr)
library(dplyr)
library(ggplot2)
library(knitr)
library(kableExtra)
#### Load the data
# Simulation results of all management regimes, SA includes DW extraction
rslt <- read.csv(paste0(path, "output/rslt_", sim_variant, "_all.csv" ), sep = ";", header = TRUE, stringsAsFactors = FALSE)
# Simulation results only for set aside without DW extraction
rslt_SA <- read.csv(paste0(path, "output/rslt_", sim_variant,"_SA_all.csv" ), sep = ";", header = TRUE, stringsAsFactors = FALSE)
rslt <- rslt %>% rbind(rslt_SA)
```
### Simulated Climate Change: `r sim_variant `
### Names of the restructured GPKGs
```{r, echo=TRUE}
gpkg <- unique(rslt$gpkg)
print(gpkg)
```
### 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.
```{r, echo = FALSE}
# simulated_regimes <- unique(rslt$regime)
# print(simulated_regimes)
sim_branching_group <- unique(rslt$branching_group)
print(sim_branching_group)
print(unique(rslt$regime))
```
### Number of simulated stands per GPKG and their size
```{r, echo = TRUE}
stands <- rslt %>%
group_by(gpkg) %>%
summarise(simulated_stands = n_distinct(id),
min_stand_size = min(AREA),
max_stand_size = max(AREA),
mean_size = mean(AREA))
kable(stands) %>% kable_styling()
```
### Development of average stand volume V (m3/ha)
#### for certain regimes
Volume development of "SA" and "SA_DWextract" should be identical
```{r}
meanV <- rslt[rslt$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
```{r}
meanV_CCF <- rslt[rslt$regime %in% c("CCF_1", "CCF_2", "CCF_3", "CCF_4"), ] %>%
group_by(year, regime, gpkg) %>%
summarise(meanV = mean(V)) %>%
ggplot(aes(year, meanV)) +
geom_line( aes(color = regime)) +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
# scale_y_continuous(limit = c(0,700)) +
facet_wrap(. ~gpkg)
plot(meanV_CCF)
```
### Development of average h_dom for certain regimes
```{r}
meanH_dom <- rslt[rslt$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
```{r, warning=FALSE, message=FALSE}
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()
```
### Development of average V_total_deadwood (m3/ha) for certain regimes
```{r}
meanDW <- rslt[rslt$regime %in% c("BAU", "SA", "CCF_2", "BAUwGTR", "SA_DWextract") , ] %>%
group_by(year, regime, gpkg) %>%
summarise(mean_DW = mean(V_total_deadwood)) %>%
ggplot(aes(year, mean_DW)) +
geom_line( aes(color = regime)) +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
facet_wrap(. ~gpkg)
plot(meanDW)
```
### Development of average CARBON_STORAGE (kg/ha) for certain regimes
```{r}
meanCS <- rslt[rslt$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
```{r}
meanHarvested_V <- rslt[rslt$regime %in%c("BAU", "SA", "CCF_2", "BAUwGTR") , ] %>%
group_by(year, regime, gpkg) %>%
mutate(Harvested_V = ifelse(is.na(Harvested_V), 0, Harvested_V)) %>%
summarise(meanHarvested_V = mean(Harvested_V), na.rm = TRUE) %>%
ggplot(aes(year, meanHarvested_V)) +
geom_line( aes(color = regime)) +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks=c(2016, 2026, 2036, 2046, 2056, 2066, 2076, 2086, 2096, 2106)) +
facet_wrap(. ~gpkg)
plot(meanHarvested_V)
```
### Average cash flow (Euro/ha) for certain regimes
Cash flow = The sum of all revenues and costs for a specific forest stand
```{r}
meanCash <- rslt[rslt$regime %in%c("BAU", "SA", "CCF_2", "BAUwGTR") , ] %>%
group_by(year, regime, gpkg) %>%
mutate(cash_flow = ifelse(is.na(cash_flow), 0, cash_flow)) %>%
summarise(meanCash = mean(cash_flow), na.rm = TRUE) %>%
ggplot(aes(year, meanCash)) +
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(meanCash)
```