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CliffEBM.R
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CliffEBM.R
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################################################################################
# ICE CLIFF ENERGY BALANCE MODEL
#
#
# AUTHORS: Pascal Buri(1), Marin Kneib(1,2)
# (1) Swiss Federal Institute for Forest, Snow and Landscape Research, WSL
# (2) Université Grenoble Alpes, France
#
#
#
# MODEL DETAILS: Buri et al., 2016a (https://doi.org/10.3189/2016AoG71A059)
# Kneib et al., 2022 (https://doi.org/10.5194/tc-16-4701-2022)
#
#
#
# CONTACT: Dr. Pascal Buri
# Swiss Federal Institute for Forest, Snow and Landscape Research, WSL
# Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
# pascal.buri@wsl.ch
################################################################################
#===============================================================================
# INITIALIZE WORKSPACE
#===============================================================================
## clear entire workspace (excl. packages)
rm(list = ls())
gc()
## define &-sign for pasting string-elements
'&' <- function(...) UseMethod('&')
'&.default' <- .Primitive('&')
'&.character' <- function(...) paste(...,sep='')
################################################################################
################################################################################
# PRIMARY DEFINITIONS (TO CHANGE REGULARLY)
################################################################################
################################################################################
## define cliff ID
CL<-2
## define simulation folder name (will be created)
sim_folder<-'Lirung-C-'&CL
## define root path of all data
root<-'D:/CliffEBM'
## define the reduction of cores to be used (max. number - reduction)
reduction<-3
# reduction<-0
## define parameter file name
## Parameter file defines for single or multiple (e.g. for sensitivity analysis, Monte Carlo analysis etc.) runs:
## albedos, emissivities, structural parameters, meteorological offsets
parfn<-'param_20220103.txt'
## define cliff-shapefile name
clifn<-'Cliff2_May2013.shp'
## define glacier shapefile name
glafn<-'glacier_mask_2014.shp'
## define primary dem1 file name (high resolution, covering cliff and close surrounding)
demfn1<-'UAV-DEM_1m_May2013_Cliff2.tif'
## define secondary dem1 file name (coarse resolution, covering glacier and relevant far surrounding)
demfn2<-'ALOS100m.tif'
## define meteodata file name
metfn<-'meteodata_AWS2013.txt'
## define target resolution for primary DEM [m]
resol_calc<-1
## define if additional outputs should be generated from model (TRUE) or not (FALSE)
## (e.g. mean & sd of fluxes per season, model parameters, horizons per cell, rasters)
additionalOutput<-TRUE
# additionalOutput<-FALSE
## Time settings
localTZ<-'Europe/Zurich' ##local timezone on machine
targetTZ<-'Asia/Kathmandu' ##studysite-specific timezone
deltaGMT<-5.75 ##difference to GMT [decimal h]
# define melt model loop (has to be inside of meteodata-timestamps)
# [mm-dd HH:MM:SS]
simStart<-"2013-05-19 00:00:00"
simStop<-"2013-10-22 23:00:00"
## define interval of intermediate outputs
## e.g. daily ('%j'), weekly ('%W'), monthly ('%m') or all timesteps ('ALL')
## (call '?strptime' to see other formats)
# interv<-'%W' #weekly
interv<-'%m' #monthly
# interv<-'ALL' #run over all timesteps
################################################################################
################################################################################
# SECONDARY DEFINITIONS (TO CHANGE RARELY)
################################################################################
################################################################################
## define path to data folder (existing)
path_data<-root
# path_data<-root&'/Data'
## define path to horizon files folder (existing)
path_hor<-path_data&'/Horizons'
## define path to model output folder (existing)
path_out<-path_data&'/ModelOutputs'
## define path to code folder (existing)
path_code<-root
## define path to functions file (existing)
path_func<-path_code&'/Functions.r'
## define how many neighboring cells to use to compute slope/aspect for any cell:
## ?raster::terrain
## 4: 'Fleming and Hoffer (1979)', 'Ritter (1987)', better for smoother surfaces
## 8: 'Horn (1981)', best for rough surfaces
neighbCells<-8
# desired squared gridsize in which surrounding terrain has to be considered
# for debrisview-calculations [m], e.g.
# 200: uses 200m x 200m grid
newgridsize<-100
## define UTM projection of all geographical data (otherwise specified within code)
projec<-'+proj=utm +zone=45 +datum=WGS84 +units=m +no_defs'
################################################################################
################################################################################
# LOAD PACKAGES
################################################################################
################################################################################
## install missing packages, e.g.:
## install.packages("zoo") )
## or via a github repository, e.g.:
## library(devtools)
## install_github("laubblatt/cleaRskyQuantileRegression")
library(cleaRskyQuantileRegression)
library(doParallel)
library(foreach)
library(grDevices)
library(iterators)
library(methods)
library(parallel)
library(raster)
library(rgdal)
library(rgeos)
library(sf)
library(sp)
library(stats)
library(utils)
library(zoo) #used in Functions.R-file only
## define no. of digits printed in console
options('scipen'=100, 'digits'=4)
## change local time from german to english (check in sessionInfo()) for plotting
Sys.setlocale('LC_TIME', 'C')
Sys.setenv(TZ=targetTZ)
## no. of cores to be used
n.cores<-parallel::detectCores(all.tests = FALSE, logical = TRUE) - reduction
## register cores for parallel functionality
registerDoParallel(cores=n.cores) #put "stopImplicitCluster()" at the end
#===============================================================================
# LOAD GLACIER-MASK POLYGON
#===============================================================================
p<-st_read(path_data&'/Polygons/'&glafn,quiet=T)
p<-as(p,'Spatial')
projection(p)<-projec
buffer_gla_msk<-buffer(p,50) # add buffer around glacier [m]
rm(p)
#===============================================================================
# READ PARAMETER-FILE
#===============================================================================
paramfile<-read.csv(path_data&'/Parameters/'&parfn,header=TRUE,dec='.',sep='\t')
paramfile$Run<-as.character(paramfile$Run)
#===============================================================================
# LOAD CLIFF POLYGONS
#===============================================================================
cliff_p<-st_read(path_data&'/Polygons/'&clifn,quiet=T)
cliff_p<-as(cliff_p,'Spatial')
projection(cliff_p)<-projec
#===============================================================================
# READ PRIMARY DEM
#===============================================================================
dem1<-raster(paste(path_data,'/DEMs/',demfn1,sep=''))
f<-resol_calc/round(res(dem1)[1],digits=3) # conversion factor
##resample if needed
if(f > 1){dem1<-aggregate(dem1,fact=f,method='bilinear')}
if(f == 1){dem1<-dem1}
projection(dem1)<-projec
resol1<-res(dem1)[1]
#===============================================================================
# READ SECONDARY DEM
#===============================================================================
dem2<-raster(paste(path_data,'/DEMs/',demfn2,sep=''))
projection(dem2)<-projec
dem2<-mask(dem2,buffer_gla_msk,inverse=TRUE) #mask glacier from dem2
resol2<-res(dem2)[1]
################################################################################
################################################################################
# LOOP OVER SENSITIVITY RUNS
# (N = NUMBER OF ROWS IN PARAMETERS-FILE)
################################################################################
################################################################################
for(SRN in 1:nrow(paramfile)){
runID<-paramfile[SRN,'Run']
id<-'C-'&CL&'___SR-'&runID
print('Modelling '&id)
#===============================================================================
# EXTRACT PARAMETERS
#===============================================================================
# << ad >>
# ice albedo [-]
alpha_i<-paramfile[SRN,'ai']
# << ai >>
# debris albedo [-]
alpha_d<-paramfile[SRN,'ad']
# << ed >>
# debris emissivity [-]
epsilon_d<-paramfile[SRN,'ed']
# << ei >>
# ice emissivity [-]
epsilon_i<-paramfile[SRN,'ei']
# << ft >>
# value for filtering (smoothing) of dem1 in terms of aspect:
# "filter_val x filter_val" mean filter around pixel (e.g. '3')
filter_val<-paramfile[SRN,'ft']
# << lw >>
# longwave radiation deviation [W/m2]
LWdev<-paramfile[SRN,'lw']
# << rh >>
# relative humidity deviation [%]
RHdev<-paramfile[SRN,'rh']
# << sw >>
# shortwave radiation deviation [W/m2]
SWdev<-paramfile[SRN,'sw']
# << ta >>
# T air deviation [K]
TAdev<-paramfile[SRN,'ta']
# << ts >>
# T surface deviation [K]
TSdev<-paramfile[SRN,'ts']
# << ws >>
# wind speed multiplier [-]
WSmult<-1
# << z0 >>
# surface roughness [m]
z0<-paramfile[SRN,'z0']
###cliff polygon
# simplify with Ramer-Douglas-Peucker algorithm
# https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm
cliff_p<-rgeos::gSimplify(cliff_p,tol=0.1,topologyPreserve=TRUE)
#plot(dem1)
#plot(cliff_p,add=TRUE)
## Access polygon information (ID, area,...)
area<-as.numeric(sapply(slot(cliff_p,'polygons'),function(x)slot(x,'area')))
centr<-sapply(slot(cliff_p,'polygons'),function(x)slot(x,'labpt'))
centrX<-centr[1,]
centrY<-centr[2,]
cliffs_df<-as.data.frame(cbind(area,centrX,centrY))
cliffs_df$ID<-CL
utmcoor<-SpatialPoints(cbind(centrX,centrY),proj4string=CRS(projec))
longlatcoor<-spTransform(utmcoor,CRS("+proj=longlat"))
rm(area,centr,centrX,centrY)
#===============================================================================
# CREATE DIRECTORIES & TIMESTAMPS
#===============================================================================
# Model output directory
path_newdir<-path_out&'/'&sim_folder&'/Run-'&id
dir.create(path_newdir,recursive=TRUE,showWarnings=FALSE)
# specific output folders
path_outrast<-path_newdir&'/Rasters'
dir.create(path_outrast,showWarnings=FALSE)
path_outpol<-path_newdir&'/Polygons'
dir.create(path_outpol,showWarnings=FALSE)
path_outtab<-path_newdir&'/Tables'
dir.create(path_outtab,showWarnings=FALSE)
path_outlist<-path_newdir&'/Lists'
dir.create(path_outlist,showWarnings=FALSE)
path_outplot<-path_newdir&'/Plots'
dir.create(path_outplot,showWarnings=FALSE)
#===============================================================================
# CALCULATE GEOMETRY
#===============================================================================
# calculate terrain values of clipped initial dem1
slope_r<-raster::terrain(dem1,opt='slope',unit='degrees',
neighbors=neighbCells)
aspect_r<-raster::terrain(dem1,opt='aspect',unit='degrees',
neighbors=neighbCells)
# stack geometry raster layers
gl_geom_st<-raster::stack(dem1,slope_r,aspect_r)
names(gl_geom_st)<-c('Elevation','Slope','Aspect')
rm(slope_r,aspect_r)
# plot(gl_geom_st$Elevation)
# plot(cliff_p,add=TRUE)
# rasterize cliff polygons (clipped dem1-raster needed only for extent)
cliff_r<-rasterize(cliff_p,gl_geom_st)
# plot(cliff_r)
# mask clipped rasters with cliff raster
cl_geom_r<-mask(gl_geom_st,cliff_r)
#=================================================================
# FILTER OUT ABNORMAL ASPECT VALUES
#=================================================================
# Aspect, component-wise:
# convert degrees to radians & calculate sines
# (x-axis component, W-E, zonal direction)
WEcomp_r<-sin(cl_geom_r$Aspect*pi/180)
# plot(WEcomp_r)
# convert degrees to radians & calculate cosines
# (y-axis component, S-N, meridional direction)
SNcomp_r<-cos(cl_geom_r$Aspect*pi/180)
# plot(SNcomp_r)
# take the arctan of the averages,
# rotate to positive values,
# and then convert back to degrees
aspect_r<-atan2(WEcomp_r,SNcomp_r)
rm(WEcomp_r,SNcomp_r)
# plot(aspect_r)
# Median filter window (e.g. 9x9)
aspect_r<-focal(aspect_r,w=matrix(1,nrow=filter_val,ncol=filter_val),fun=median,
na.rm=TRUE)
# plot(aspect_r)
aspect_v<-as.vector(aspect_r)
idx<-which(aspect_v<0,arr.ind=FALSE)
aspect_v[idx]<-aspect_v[idx]+(2*pi)
aspect_v<-aspect_v*180/pi
aspect_r<-setValues(aspect_r,aspect_v)
# plot(crop(cl_geom_r$Aspect,extent(cliff_p)+10)) # old
# plot(cliff_p,add=TRUE)
# plot(crop(aspect_r,extent(cliff_p)+10))# filtered
# plot(cliff_p,add=TRUE)
cl_geom_r$Aspect<-aspect_r
rm(aspect_v,aspect_r)
# mask clipped rasters with cliff raster
cl_geom_r<-mask(cl_geom_r,cliff_r)
#=======================================================================
# GET GEOMETRY VALUES + CLIFF-ID FOR EVERY CLIFF PIXEL
#=======================================================================
# extract elevation/slope/aspect values from raster (incl. NAs)
cliffs_geom_df<-data.frame(raster::extract(cl_geom_r,extent(cl_geom_r)))
# select only cliff-pixels
cliffs_geom_df<-cliffs_geom_df[complete.cases(cliffs_geom_df),]
# use pixel numbers (relative to "new_geometry_r") as index
idx<-as.numeric(rownames(cliffs_geom_df))
# extract coordinates of cliff pixels
cliff_coord_m<-xyFromCell(cl_geom_r,idx)
rm(idx)
# dataframe with elevation/slope/aspect/x/y
cliffs_geom_df<-data.frame(cbind(cliffs_geom_df,cliff_coord_m))
colnames(cliffs_geom_df)<-c('Elevation','Slope','Aspect','x','y')
# add cliff ID
# query cliff polygon which contains specific pixel coordinate
xy_pts<-SpatialPoints(cbind(cliffs_geom_df[,'x'],cliffs_geom_df[,'y']),
proj4string=CRS(projec))
projection(cliff_p)<-projec
cliffs_geom_df$ID<-over(xy_pts,cliff_p)
horcalc<-cliffs_geom_df[,c(1,4:6)] # only elevation, coordinates & ID
horcalc<-cbind(1:nrow(cliffs_geom_df),horcalc)# add specific number per point
horcalc[,] <- as.numeric(as.character(unlist(horcalc[,])))
colnames(horcalc)<-c('cell','elevation','X','Y','ID')
#plot(gl_geom_st$Elevation)
#plot(cliff_p,add=TRUE)
gc()
#===============================================================================
# GEOMETRY CALCULATIONS
#===============================================================================
ID_cliffs<-as.character(cliffs_df$ID)
calculateInitHorizon<-TRUE
cliff_p$ID<-ID_cliffs
if (calculateInitHorizon == TRUE) {
#################################
# horizon angle from primary DEM ->comput. expensive!
#################################
N<-nrow(horcalc)
source(path_func)
print('*** CLIFF_CLOSEHORIZON @t0 ||| '&id&' ||| '&N&'px ***')
horfile_dem1<-foreach(i = icount(N),.combine=cbind) %dopar%
CLIFF_CLOSEHORIZON(horcalc[i,],gl_geom_st$Elevation,cl_geom_r,cliff_p,
resol1,newgridsize)
print('************************************************************************* DONE ***')
horfile_dem1<-data.frame(horfile_dem1)
horfile_dem1<-cbind(1:360,horfile_dem1)
horfile_dem1[horfile_dem1== -9999]<-NA
###################################
# horizon angle from secondary DEM ->comput. expensive!
###################################
print('**** CLIFF_FARHORIZON @t0 ||| '&id&' ||| '&N&'px ****')
horfile_dem2<-foreach(i = icount(N),.combine=cbind) %dopar%
CLIFF_FARHORIZON(horcalc[i,],dem2,resol2,resol1)
print('************************************************************************* DONE ***')
rm(N,horcalc)
horfile_dem2<-data.frame(horfile_dem2)
horfile_dem2<-cbind(as.numeric(1:360),horfile_dem2)
elem<-ncol(horfile_dem2)-1
colnames(horfile_dem2)<-c('Azimuth',rep('Px_'&1:elem,1))
###########################################
# define 360° skyview angle for every point
###########################################
# select only 'el_all' (highest elevation/horizon angle overall) from UAV
horHRDEM_all<-horfile_dem1[,seq(2,ncol(horfile_dem1),by=2)]
# select only 'el_ice' (highest elevation/horizon angle only on ice) from UAV
horHRDEM_ice<-horfile_dem1[,seq(1,ncol(horfile_dem1),by=2)]
rm(horfile_dem1)
horHRDEM_ice<-horHRDEM_ice[,-1] # to get rid of azimuth column
resulthor<-vector('list')
for (cell in 1:nrow(cliffs_geom_df)){ # cells 1-n on cliff
# horizon angle for shortwave radiation:
# [hor_skyI]
# combine horizon angles derived from ASTER & UAV
# & choose highest angle for every degree of azimuth
hor1<-data.frame(cbind(horfile_dem2[,cell+1],horHRDEM_all[,cell]))
colnames(hor1)<-c('horang_ASTER','horang_UAV')
hor_skyI<-apply(hor1,1,max,na.rm=TRUE)
hor_skyL<-hor1$horang_UAV
# debris portion angle (for longwave radiation from debris)
# [prt_deb]
# combine horizon angles derived UAV
# & choose effective debris portion angle for every degree of azimuth
hor2<-data.frame(cbind(horHRDEM_all[,cell],horHRDEM_ice[,cell]))
prt_deb<-cbind(hor2,rep(NA,nrow(hor2)))
colnames(prt_deb)<-c('horang_HRDEMall','horang_HRDEMice','debris_portion')
# [case 1] azimuth direction where horizon angle is defined by ice surface:
idx1<-which(prt_deb$horang_HRDEMall == prt_deb$horang_HRDEMice)
prt_deb[idx1,3]<-0
# [case 2] azimuth direction where horizon angle is higher than ice surface:
idx2<-which(prt_deb$horang_HRDEMall > prt_deb$horang_HRDEMice)
prt_deb[idx2,3]<-prt_deb[idx2,1]-prt_deb[idx2,2]
# [case 3] azimuth direction where horizon angle only for ice is not defined
# (debris can be assumed in this direction):
idx3<-which(is.na(prt_deb$horang_HRDEMice))
prt_deb[idx3,3]<-prt_deb[idx3,1]+90
hor3<-data.frame(cbind(hor_skyI,hor_skyL,prt_deb[,3]))
colnames(hor3)<-c('horizon_angle_I','horizon_angle_L','deb_prt_angle')
resulthor[[cell]]<-hor3
names(resulthor[[cell]])<-c('hor_angleI_'&cell,'hor_angleL_'&cell,'deb_prt_angle'&cell)
}
hor_all<-do.call('cbind',resulthor)
rm(resulthor)
hor_all<-data.frame(hor_all)
# output-file of horizon/debris angles per cliff cell:
# convert output to 3 digits after decimal point & avoid spacing between columns
hor_all_output<-format(hor_all,digits=4,trim=TRUE)
hor_all_output<-cbind(1:360,hor_all_output)
colnames(hor_all_output)[1]<-'Azimuth'
filename_horDEF<-path_hor&'/horDEF___C-'&ID_cliffs&'___'&
round(resol1,digits=1)&'m.txt'
write.table(hor_all_output,filename_horDEF,dec='.',sep=',',quote=FALSE,
row.names=FALSE)
rm(hor_all_output,filename_horDEF)
hor_all<-cbind(1:360,hor_all)
colnames(hor_all)[1]<-'Azimuth'
horDEF<-hor_all
rm(hor_all)
##################################################################
# define skyviewfactor (I, L) & debrisviewfactor from every point
##################################################################
horDEF<-horDEF[,-1] # get rid of azimuth column
# horizon angle for sky SHORTWAVE (0?=horizontal, 90?=vertical upwards)
obstr_s_I<-horDEF[,seq(1, ncol(horDEF), by = 3)]
# horizon angle for sky LONGWAVE (0?=horizontal, 90?=vertical upwards)
obstr_s_L<-horDEF[,seq(2, ncol(horDEF), by = 3)]
# obstruction angle for debris (angle between lower and upper limit of debrisview)
obstr_d<-horDEF[,seq(3, ncol(horDEF), by = 3)]
skyview_factor_I<-vector('list')
skyview_factor_L<-vector('list')
debrisview_factor<-vector('list')
for (cell in 1:ncol(obstr_s_I)){ # cells 1-n on cliff
# skyview factor SHORTWAVE
# ->convert horizon angles I appropriately as used in literature
# (=angle between vertical up (solar zenith) and horizon)
sky_I<-abs(obstr_s_I[,cell]-90)
# calculate portion of visible sky per azimuth direction
sky_I<-sky_I/90
# get mean of every portion of skyview around 360?
skyview_factor_I[[cell]]<-mean(sky_I,na.rm=TRUE)
# skyview factor LONGWAVE
# ->convert horizon angles L appropriately as used in literature
# (=angle between vertical up (solar zenith) and horizon)
sky_L<-abs(obstr_s_L[,cell]-90)
# calculate portion of visible sky per azimuth direction
sky_L<-sky_L/90 #
# get mean of every portion of skyview around 360?
skyview_factor_L[[cell]]<-mean(sky_L,na.rm=TRUE)
# debrisview factor
# ->calculate portion of visible debris per azimuth direction
# ->cliffs: lowest horizon is -90?, maximum 90?
debris<-obstr_d[,cell]/180
# get mean of every portion of skyview around 360?:
debrisview_factor[[cell]]<-mean(debris,na.rm=TRUE)
}
# skyview factors I
VsI<-do.call('rbind',skyview_factor_I)
VsI<-round(VsI,3)
# skyview factors L
VsL<-do.call('rbind',skyview_factor_L)
VsL<-round(VsL,3)
# debrisview factors
Vd<-do.call('rbind',debrisview_factor)
Vd<-round(Vd,3)
# combine with cliff geometry
cliffs_geom_df<-cbind(cliffs_geom_df,VsI,VsL,Vd)
rm(skyview_factor_I,skyview_factor_L,debrisview_factor,VsI,VsL,Vd)
# add cliff ID
cliffs_geom_df$ID<-as.character(cliff_p$ID)
rm(xy_pts)
# define format of output
cliffs_geom_df<-within(cliffs_geom_df,
{Elevation<-formatC(Elevation,format='f',digits=2)
Slope<-formatC(Slope,format='f',digits=1)
Aspect<-formatC(Aspect,format='f',digits=1)
ID<-formatC(ID,format='f',digits=0)
x<-formatC(x,format='f',digits=3)
y<-formatC(y,format='f',digits=3)
VsI<-formatC(VsI,format='f',digits=3)
VsL<-formatC(VsL,format='f',digits=3)
Vd<-formatC(Vd,format='f',digits=3)
})
filename_geom<-path_hor&'/geom___C-'&ID_cliffs&'___'&
round(resol1,digits=1)&'m.txt'
#
write.table(cliffs_geom_df,filename_geom,dec='.',sep=',',
quote=FALSE,row.names=FALSE)
rm(filename_geom)
}
gc()
#===============================================================================
# READ GEOMETRY CALCULATIONS IF NOT CALCULATED BEFORE
#===============================================================================
t_load_horizons<-system.time(
if (calculateInitHorizon == FALSE) {
# read horizon files if not calculated above
horDEF<-read.csv(file=path_data&'horizons/horDEF___C-'&ID_cliffs&'___'&
round(resol1,digits=1)&'m.txt',header=TRUE,dec='.',sep=',')
horDEF<-horDEF[,-1] # get rid of azimuth column
}
)
t_load_horizons
# read geometry file anyway (numeric)
cliffs_geom_df<-read.csv(file=path_hor&'/geom___C-'&ID_cliffs&'___'&
round(resol1,digits=1)&'m.txt',header=TRUE,dec='.',sep=',')
# write file also into model output folder
filename_geom<-path_newdir&'/geom_ts0___'&id&'_'&
round(resol1,digits=1)&'m.txt'
write.table(cliffs_geom_df,filename_geom,dec='.',sep=',',
quote=FALSE,row.names=FALSE)
rm(filename_geom)
#===============================================================================
# READ METEOROLOGICAL DATA
#===============================================================================
# read hourly data from AWS
AWSdata<-read.csv(file=paste(path_data,'/Meteodata/'&metfn,sep=''),
header=TRUE,dec='.',sep='\t')
# merge columns to date (POSIXct-format)
AWSdata<-within(AWSdata,Date<-as.POSIXct(paste(year,doy,sprintf("%04d",time)),
format='%Y %j %k'))
# subset data according to defined start and end dates
AWSdata<-subset(AWSdata,Date >= simStart)
AWSdata<-subset(AWSdata,Date <= simStop)
# plot(AWSdata$RH,type='l') # test
# create vectors of AWSdata-columns
Date<-AWSdata$Date
# # check if no duplicated dates are in the dataset:
# which(duplicated(Date))
doy<-as.numeric(strftime(Date,format='%j'))
hour<-as.numeric(strftime(Date,format='%H'))
year<-as.numeric(strftime(Date,format='%Y'))
ws<-AWSdata$WS
rH<-AWSdata$RH
SWin<-AWSdata$SWIN
LWin<-AWSdata$LWIN
LWout<-AWSdata$LWOUT
Ta<-AWSdata$TA
sigma <- 5.6704e-8; # Stefan-Boltzmann constant (W m^-2 K^-4)
# Tsurf<-((LWout - ((1-epsilon_d)*LWin))/(epsilon_d*sigma))^(1/4)-273.15
Tsurf<-AWSdata$TS
#===============================================================================
# APPLY DEVIATIONS TO METEO-VARIABLES FROM UNCERTAINTY ANALYSIS
#===============================================================================
LWin<-LWin+LWdev
rH<-rH+RHdev
rH[rH<0]<-0
rH[rH>100]<-100
SWin<-SWin+SWdev
Ta<-Ta+TAdev
Ts<-Tsurf+TSdev
ws<-ws*WSmult
# avoid zero wind speed (numerical problems for turbulent fluxes)
ws[ws < 0.01]<-0.01
#===============================================================================
# DEFINE REMAINING CONSTANTS
#===============================================================================
# define constants
Env_LR<-0.0065 # Environmental temperature lapse rate [K m^-1]
# (used for pressure in turbulent flux)
Lat<-extent(longlatcoor)[3] # Latitude (pos. for N-hemisphere) [°]
Lon<-extent(longlatcoor)[1] # Longitude (pos. for W-hemisphere) [°]
phi<-Lat*pi/180 # Latitude in radians
sigma<-5.67e-8 # Stefan-Boltzmann constant [W m^-2 K^-4]
Ti<-0 # Ice cliff surface temperature [C]
g<-9.81 # Gravitational acceleration [m s^-2]
k<-0.41 # Von Karman's constant
Rgas<-8.31447 # Gas constant [J mol^-1 K^-1]
Mair<-0.0289644 # Molar mass of dry air [kg mol^-1]
P_0<-101.3 # Standard sea level air pressure [kPa]
T_0<-288.15 # Standard sea level air temperature [K]
rho_0<-1.29 # Standard sea level air density [kg m^-3]
L_e<-2514000 # Latent heat of evaporation of water [J kg^-1]
L_f<-334000 # Latent heat of fusion of ice [J kg^-1]
c_p<-1004 # Specific heat capacity of air at constant pressure
rho_i<-900 # Ice density [kg m^-3]
z<-2 # Height of meteorological measurements [m]
e_s<-0.611 # Saturated vapour pressure at 0C [kPa]
nu<-1.35e-5 # Viscosity of air
#===============================================================================
# PREPARE MODEL OUTPUT TIMESTEPS
#===============================================================================
# predefine timesteps in a vector, where intermediate results should be
# performed resp. the cliff geometry should be updated
PERIOD<-Date[1:length(Date)]
# intermediate output interval
if(interv == 'ALL'){iOutput_ts<-length(PERIOD)} ##run over all timesteps
if(interv != 'ALL'){
iOutput_ts<-as.numeric(format(PERIOD,interv))
iOutput_ts<-which(diff(iOutput_ts) != 0)
iOutput_ts<-c(iOutput_ts,length(PERIOD))
}
#===========================================================================
# PREPARE RASTERS ETC. FOR MODEL
#===========================================================================
##
# Raster brick with horizon angles
# get cell numbers from cliff-cells for embedding back in rectangular raster
cliffcell_nr<-cellFromXY(gl_geom_st$Elevation,cliff_coord_m)
rm(cliff_coord_m)
# create index selecting column with shortwave sky horizon for every point
columnsSkyangle_I<-seq(1,ncol(horDEF),by=3)
hors_I<-horDEF[,columnsSkyangle_I]
rm(horDEF)
hors_I<-data.frame(t(hors_I))
#structure 'hors' with only 3 cliff-cells::
# V1 V2 (...) V360
# 9 34.75 35.02 (...) 35.51
# 13 34.66 34.94 (...) 35.37
# 17 34.52 34.79 (...) 35.05
# create data frame with NAs in every cell
grid_df<-data.frame(replicate(360,sample(NA,ncell(gl_geom_st$Elevation),
rep=TRUE)))
#strucute 'grid_df':
# X1 X2 (...) X360
# 1 NA NA (...) NA
# 2 NA NA (...) NA
# (...) (...) (...) (...) (...)
# ncells NA NA (...) NA
# merge all cell numbers
grid_df[cliffcell_nr,]<-hors_I
rm(hors_I)
# create rasterbrick with 360 layers, each with the horizon angle per
# cliff cell in the correspondent azimuth direction
horI_m<-data.matrix(grid_df)
rm(grid_df)
clip_br<-brick(gl_geom_st$Elevation) #used later in model!
# horI_br<-setValues(clip_br,horI_m) #expensive!
# horI_df<-as.data.frame(horI_br)
horI_m<-horI_m[complete.cases(horI_m),] # only cliff-pixels
##
# Raster with skyview/debrisview factor
viewfactors<-data.frame(cbind(as.numeric(cliffs_geom_df$VsI),
as.numeric(cliffs_geom_df$VsL),
as.numeric(cliffs_geom_df$Vd)))
colnames(viewfactors)<-c('VsI','VsL','Vd')
#> head(viewfactors)
# VsI VsL Vd
# 0.680 0.812 0.510
# 0.660 0.777 0.514
# 0.640 0.746 0.531
# create data frame with NAs in every cell
grid_df2<-data.frame(replicate(3,sample(NA,ncell(gl_geom_st$Elevation),
rep=TRUE)))
colnames(grid_df2)<-c('VsI','VsL','Vd')
#> head(grid_df2)
# VsI VsL Vd
# NA NA NA
# NA NA NA
# NA NA NA
# merge all cell numbers
grid_df2[cliffcell_nr,]<-viewfactors
rm(cliffcell_nr,viewfactors)
# create rasterbrick with 3 layers, one with the skyview factor I,
# one with skyviewfactor L and one with the debrisview factor
# per cliff cell
view_m<-data.matrix(grid_df2)
view_br<-setValues(clip_br,view_m)
rm(clip_br,grid_df2,view_m)
# stack rasters & convert slope & aspect from deg to rad
# (names: elevation, slope, aspect, VsI, VsL, Vd)
cl_geom_r<-stack(cl_geom_r,view_br)
cl_geom_r$Slope<-cl_geom_r$Slope*pi/180
cl_geom_r$Aspect<-cl_geom_r$Aspect*pi/180
#plot(cl_geom_r)
##
# prepare rasterstacks for calculation of
# diurnal cycle in melt model loop
# make a vector of the unique hour values to see which are there
hrs<-as.POSIXlt(Date)$hour
hrs<-unique(hrs)
# take an existing raster to create (24) layers with the correct extent
empty_r<-cl_geom_r$Aspect
empty_r<-setValues(empty_r,0)
stack_dc<-stack(mget(rep('empty_r',length(hrs))))
DC_names<-paste('Hour',hrs,sep='')
names(stack_dc)<-DC_names
# write initial cliff polygon to shapefile
fn<-path_outpol&'/Cliffs_ts0_p.shp'
st_write(st_as_sf(cliff_p),fn,driver="ESRI Shapefile",
delete_dsn=TRUE,quiet=TRUE)
rm(fn)
# plot cliffs
plot(cliff_p,col='orange',border='orange')
# Histograms
# Aspect:
png(file=path_outplot&'/CliffAspect_ts0_hist.png',units='in',
width=6,height=6,res=300)
ti='Aspect '&as.character(Date[1])&' (after filtering)'
hist(cl_geom_r$Aspect*180/pi,main=ti,xlab='Aspect',
breaks=seq(0,360,by=45),labels=c('NNE','ENE','ESE','SSE','SSW','WSW',
'WNW','NNW'))
dev.off()
# Slope
png(file=path_outplot&'/CliffSlope_ts0_hist.png',units='in',
width=6,height=6,res=300)
ti='Slope '&as.character(Date[1])
hist(cl_geom_r$Slope*180/pi,main=ti,xlab='Slope [°]',breaks=seq(0,90,by=5))
dev.off()
##
# Write model settings to file
# switch to local time zone for current machine timestamp
Sys.setenv(TZ=localTZ)
generated<-as.character(Sys.time())
print(generated)
#switch back to target timezone
Sys.setenv(TZ=targetTZ)
print(Sys.time())
simulation<-
sink(path_newdir&'/MODELSETTINGS_'&sim_folder&'_'&id&'.txt',append=FALSE)
sim_folder
generated
# modelversion
print(ls.str(),max.level=0)
sink()
gc()
################################################################################
################################################################################
#===============================================================================
# DISTRIBUTED MELT MODEL
#===============================================================================
################################################################################
################################################################################
stop<-FALSE
# LOOP FOR INTERMEDIATE GEOMETRY UPDATES
for(loop in 1:length(iOutput_ts)){
## for testing only:
# loop=1
# loop=2
ifelse(loop == 1,
TS<-1:iOutput_ts[loop],
TS<-(iOutput_ts[loop-1]+1):iOutput_ts[loop]
)
# YEAR
idx_yr<-1900+unique(as.POSIXlt(Date[TS])$year)
#===============================================================================
# CLIFF ALTITUDE
#===============================================================================
### MEDIAN CLIFF ELEVATION ###
# calculate median elevation per cliff
MedianCliffEle<-aggregate(cliffs_geom_df[,'Elevation'],
list(cliffs_geom_df$ID),median)
colnames(MedianCliffEle)<-c('ID','ele')
# write median cliff elevations to file for documentation
MedianCliffEle_tab<-within(MedianCliffEle,
{ele<-formatC(ele,format='f',digits=2)})
filename_medelev<-path_outtab&'/MedianCliffEle_ts'&TS[1]-1&'_tab.txt'
write.table(MedianCliffEle,filename_medelev,dec='.',sep=',',quote=FALSE,
row.names=FALSE)
rm(filename_medelev,MedianCliffEle_tab)
#===============================================================================
# SHORTWAVE RADIATION
#===============================================================================
############################################################################
## I_E: potential clear sky shortwave radiation
# Calculation following Renner et al., 2019 (https://doi.org/10.1029/2019EA000686)
# https://github.com/laubblatt/cleaRskyQuantileRegression/
I_E<-calc_PotRadiation_CosineResponsePower(doy = doy[TS],
hour = hour[TS],
latDeg = Lat,
longDeg = Lon,
timeZone = 0,
isCorrectSolartime = FALSE,
cosineResponsePower = 1.2 )
############################################################################
# SWin can not be larger than I_E (in cases where this happens,
# numerical probelms occur, leading to model inaccuracies)
SW_IN<-SWin[TS]
I_in_obs<-pmin(SW_IN,I_E)
# SWin can not be smaller than 0
I_in_obs<-pmax(0,I_in_obs)
# ##check
# plot(I_E,type='l')
# lines(I_in_obs,col='red')
############################################################################
## Calculate sun variables (improved algorithm)
delta<-vector()
omega<-vector()
solaraz<-vector()
h<-vector()
for(i in 1:length(TS)){
DOY<-doy[TS][i]
HH<-hour[TS][i]
## Solar declination
delta_S<-23.45*pi/180*cos(2*pi/365*(172 - DOY))
## Time difference between standard and local meridian
ifelse(Lon < 0,
Delta_TSL<- -1/15*(15*abs(deltaGMT) - abs(Lon)),
Delta_TSL<- 1/15*(15*abs(deltaGMT) - abs(Lon))
)
## Integration intervals (needs to be adapted?)
t_bef<- 0.5
t_aft<- 0.5
t<-seq(HH-t_bef,HH+t_aft,0.0166666)
omega_S<-vector()
for(j in 1:length(t)){
## Solar hour angle
ifelse(t[j] < (12 + Delta_TSL),
omega_S[j]<- 15*pi/180*(t[j] + 12 - Delta_TSL),
omega_S[j]<- 15*pi/180*(t[j] - 12 - Delta_TSL)
)
}
## Solar elevation
sinh_S<-sin(phi)*sin(delta_S) + cos(phi)*cos(delta_S)*cos(omega_S);
h_S<-asin(sinh_S)
h_S<-mean(h_S)
## Solar azimuth
zeta_S<-atan(-sin(omega_S)/(tan(delta_S)*cos(phi) - sin(phi)*cos(omega_S)))
for(j in 1:length(t)){
if(omega_S[j] >0 && omega_S[j] <= pi){
ifelse(zeta_S[j] > 0,
zeta_S[j]<- zeta_S[j] + pi,
zeta_S[j]<-zeta_S[j] + (2*pi)
)
}
if(omega_S[j] >=pi && omega_S[j] <= 2*pi){
if(zeta_S[j] < 0){zeta_S[j]<-zeta_S[j] + pi}
}