Chapter 5: Simulating the Alerce glacier surface mass balance

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
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Introduction

In the previous chapters we deal with ideal or synthetic cases. This purely academic examples, were though as a gentle (and pedagogical) introduction to the HBV.IANIGLA package. In this chapter we are going to face our first real world case: the simulation of a glacier surface mass balance.

Glacier surface mass balaces

The simulation of glacier mass balances is relevant in the Andes Cordillera, where these ice bodies have an important contribution to catchment discharge [@pellicciotti:2008; @ragettli:2012; @frenierre:2014; @ayala:2016; @masiokas:2016; @ayala:2020].

In mountain areas with scarce meteorological information, temperature index models are widely used to simulate snow and ice melting [@hock:2003; @konz:2010; @finger:2015; @ayala:2017; @bravo:2017]. Since air temperature is the most readily available meteorological data in remote areas, the temperature index approach has been widely used in glaciological and hydrological modeling [@ohmura:2001]. This package has been built with the SnowGlacier_HBV function, a module that uses this empirical approach to simulate snow, clean ice and debris covered melting.

Methods

In this section, we simulate the glacier mass balance for the Alerce glacier. Located on Monte Tronador (41.15º S ; 71.88º W), nearby the border between Argentina and Chile in the Andes of Northern Patagonia, Alerce is a medium size mountain glacier with an area of about 2.33 km^2^ that ranges between 1629 and 2358 m elevation showing a SE aspect [@ruiz:2017; @ing_manso:2018].

Since 2013 it is part of the monitoring network of the Inventario Nacional de Glaciares [@ing_fundamentos:2010]. Measurements are carried out following the glaciological method for seasonal mass balance computation [@kaser_manual:2003]. Precipitation series from Puerto Montt (Dirección General de Aguas, Chile) and air temperature series from Bariloche (Servicio Meteorológico Nacional - Argentina) were used as meteorological forcing to simulate the annual glacier mass balance (data(alerce_data)).

When calibrating the model parameters, we considered as acceptable simulations those showing an annual mass balance in the range of MB ± 400 mm, where MB is the glacier annual mass balance.

library(HBV.IANIGLA)

## loading the data-set
data(alerce_data)

  # now extract 
    meteo_data   <- alerce_data$meteo_data   # meteorological forcing series
    mass_balance <- alerce_data$mass_balance # annual glacier mass balances
    mb_dates     <- alerce_data$mb_dates     # fix seasonal dates
    gl_topo      <- alerce_data$topography   # elevation bands

    z_tair   <- alerce_data$station_height[1] # topographic elevation air temp.
    z_precip <- alerce_data$station_height[2] # topographic elevation of precipitation gauge 

Despite the fact that we have a global surface mass balance estimation (derived from measurements), in order to take into consideration the topographic effect on it, we discretised our ice body in elevation bands. This mean that we have to build a semi-distributed glacier surface mass balance model.

# in this example we incorporate the precipitation and 
# air temperature extrapolation functions into our model

## agruments description
  # topograpphy: data frame with the elevation bands (gl_topo in this script).
  # meteo: data frame with dates, air temperature and precipitation series.
  # z_topo: numeric vector with air temperature and precipitation gauge elevation.
  # param_tair: numeric vector with air temperature module parameters.
  # param_precip: numeric vector with precipitation module parameters.
  # param_ice: numeric vector with the glacier module parameters.
  # init_ice: numeric value with initial snow water equivalent. Default value being 00 mm.
## output
  # data frame with two columns: the date and the daily mass balance.

glacier_hbv <- function(topography,
                        meteo,
                        z_topo,
                        param_tair,
                        param_precip,
                        param_ice,
                        init_ice = 0){

  n_it    <- nrow(topography) # to get the number of elevation bands
  n_dates <- nrow(meteo) # to get number of rows

  precip_model  <- tair_model <- matrix(NA_real_, nrow = n_dates, ncol = n_it)
  glacier_model <- list()

  for(i in 1:n_it){
    # we first distribute the meteo forcing among the elevation bands
    precip_model[ , i] <- Precip_model(model = 1,
                                       inputData = meteo[ , "P(mm/d)"],
                                       zmeteo = z_topo[2],
                                       ztopo = topography[i , "mean"],
                                       param = param_precip)

    tair_model[ , i] <- Temp_model(model = 1,
                                   inputData = meteo[ , "Tair(ºC)"],
                                   zmeteo = z_topo[1],
                                   ztopo = topography[i , "mean"],
                                   param = param_tair)

    # now we use the extrapolated values in the glacier module
    glacier_model[[ i ]] <-
      SnowGlacier_HBV(model = 1,
                      inputData = cbind( tair_model[ , i], precip_model[ , i]),
                      initCond = c(init_ice, 1, topography[i, "area_rel"]),
                      param = param_ice )
  }

  # we aggregate the mass balance series for the whole glacier
  cum_mb <- lapply(X = 1:n_it, FUN = function(x){
    out <- glacier_model[[x]][ , 7] * topography[x, "area_rel"]
  })
  cum_mb <- Reduce(f = `+`, x = cum_mb)

  # return the column Cum = Psnow - Mtot (aggregated at glacier scale)
  cum_out <- data.frame(date = meteo[ , "Date"],
                        "cum_mb(mm)" = cum_mb,
                        check.names = FALSE)

  return(cum_out)
}

When the measures and simulations are at different time-scale (annual vs daily) is always useful to construct an aggregation function (apart from the model).

# this function will use the glacier_hbv output data frame
# in order to get the annual mass balance

## arguments
  # x: data frame resulting from glacier_hbv model
  # start_date: string vector with starting dates.
  # end_date: string vector with ending dates.
## output
  # data frame with the annual mass balances

agg_mb <- function(x, start_date, end_date){
  n_it <- length(start_date)

  annual_mb <- c()
  for(i in 1:n_it){
    flag_start <- which(x[ , 1] == start_date[i])
    flag_end   <- which(x[ , 1] == end_date[i])

    annual_mb[i] <- sum( x[flag_start:flag_end, 2] )

  }

  # build output data frame
  out <- data.frame(start = start_date,
                    end = end_date,
                    "mb(mm we)" = annual_mb,
                    check.names = FALSE)

  return(out)

}

# in this chunk of code I will also build the GOF function (you can also take a look 
# at the hydroGOF package)

## arguments
  # obs_upp: numeric vector with the acceptable upper limit
  # obs_lwr: numeric vector with the acceptable lower limit
  # sim: numeric vector with simulated mass balance
## output
  # numeric value with the number of times that the simulation fits in between 
  # the lower and upper limits.


my_gof <- function(obs_upp, obs_lwr, sim){
  n_it <- length(sim)
  out  <- 0

  for(i in 1:n_it){

    if(sim[i] >= obs_lwr[i] & sim[i] <= obs_upp[i]){
      out <- 1 + out

    } else {
      out <- 0 + out

    }

  }

  return(out)

}

Calibrating the parameters

As in the previous chapters (3 and 4), we are going to sample the parameter space in order to get the acceptable parameter sets.

 # air temperature model
tair_range <- rbind(
  t_grad  = c(-9.8, -2)
)

# precip model
precip_range <- rbind(
  p_grad  = c(5, 25)
)

# glacier module
glacier_range <- rbind(
  sfcf = c(1, 2),
  tr   = c(0, 3),
  tt   = c(0, 3),
  fm   = c(1, 4),
  fi   = c(4, 8)
)

## we aggregate them in a matrix
param_range <-
  rbind(
    tair_range,
    precip_range,
    glacier_range
  )

In the next step we generate the random parameter sets,

# set the number of model runs that you want to try
n_run <- 10000

# build the matrix
n_it <- nrow(param_range)

param_sets <- matrix(NA_real_, nrow = n_run, ncol = n_it)

colnames(param_sets) <- rownames(param_range)

set.seed(123) # just for reproducibility 
for(i in 1:n_it){

  param_sets[ , i] <- runif(n = n_run,
                            min = param_range[i, 1],
                            max = param_range[i, 2]
  )

}

head(param_sets)

Now we combine our functions and extract our best simulations,

# goodness of fit vector
gof <- c()

# make a loop
for(i in 1:n_run){

  # run the model
  glacier_sim <- glacier_hbv(topography = gl_topo,
                             meteo = meteo_data,
                             z_topo = c(z_tair, z_precip),
                             param_tair = param_sets[i, rownames(tair_range)],
                             param_precip = param_sets[i, rownames(precip_range) ],
                             param_ice = param_sets[i, rownames(glacier_range)] )

  # aggregate the simulation
  annual_mb <- agg_mb(x = glacier_sim,
                      start_date = as.Date( mb_dates$winter[-4] ),
                      end_date = as.Date( mb_dates$winter[-1] ) - 1 )

  # compare the simulations with measurements
  gof[i] <- my_gof(obs_upp = mass_balance$upp,
                   obs_lwr = mass_balance$lwr,
                   sim = annual_mb[ , 3])

  rm(glacier_sim, annual_mb)
}

param_sets <- cbind(param_sets, gof)

# we apply a filter
param_subset <- subset(x = param_sets, subset = gof == 3)

Once we have the subsetted our parameter matrix, we run the simulations to obtain a mean value (one per year).

# now we run the model again to get our simulations
n_it <- nrow(param_subset)

mb_sim <- matrix(NA_real_, nrow = 3, ncol = n_it)

for(i in 1:n_it){
  glacier_sim <- glacier_hbv(topography = gl_topo,
                             meteo = meteo_data,
                             z_topo = c(z_tair, z_precip),
                             param_tair = param_subset[i, rownames(tair_range)],
                             param_precip = param_subset[i, rownames(precip_range) ],
                             param_ice = param_subset[i, rownames(glacier_range)] )

  annual_mb <- agg_mb(x = glacier_sim,
                      start_date = as.Date( mb_dates$winter[-4] ),
                      end_date = as.Date( mb_dates$winter[-1] ) - 1 )

  mb_sim[ , i] <- annual_mb[ , 3]

  rm(i, glacier_sim, annual_mb)

}

# now we are going to make a data frame with the mean surface mass balance simulation
mean_sim <- cbind( mass_balance,
                   "mb_sim" =  rowMeans(mb_sim)    )

# make the plot
library(ggplot2)
g1 <-
  ggplot(data =  mean_sim, aes(x = year)) +
  geom_pointrange(aes(y = `mb(mm we)`, ymin = `lwr`, color = 'obs',
                      ymax = `upp` ), size = 1,  fill = "white", shape = 21) +
  geom_point(aes(y = `mb_sim`, fill = 'sim'), shape = 23,
             size = 3) +
  geom_hline(yintercept = 0) +
  scale_y_continuous(limits = c(-1500, 500), breaks = seq(-1500, 500, 250) ) +
  scale_color_manual(name = '', values = c('obs' = 'blue') ) +
  scale_fill_manual(name = '', values = c('sim' = 'red') ) +
  ggtitle('') +
  xlab('') + ylab('mb (mm we)') +
  theme_minimal() +
  theme(
    title = element_text(color = "black", size = 12, face = "bold"),
    axis.title.x = element_text(color = "black", size = 12, face = "bold"),
    axis.title.y = element_text(color = "black", size = 12, face = "bold"),
    legend.text = element_text(size = 11),
    axis.text = element_text(size = 11))

g1

Is your turn

References



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HBV.IANIGLA documentation built on Jan. 23, 2021, 1:07 a.m.