# Chapter 5: Simulating the Alerce glacier surface mass balance

#### Ezequiel Toum

#### 2021-01-22

## 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 et al. 2008; Ragettli and Pellicciotti 2012; La Frenierre and Mark 2014; Ayala et al. 2016, 2020; Masiokas et al. 2016).

In mountain areas with scarce meteorological information, temperature index models are widely used to simulate snow and ice melting (Hock 2003; Konz and Seibert 2010; Finger et al. 2015; Ayala et al. 2017; Bravo et al. 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 km2 that ranges between 1629 and 2358 m elevation showing a SE aspect (Ruiz et al. 2017; IANIGLA-ING 2018).

Since 2013 it is part of the monitoring network of the Inventario Nacional de Glaciares (IANIGLA-ING 2010). Measurements are carried out following the glaciological method for seasonal mass balance computation (Kaser, Fountain, and Jansson 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)
    #>         t_grad    p_grad     sfcf        tr        tt       fm       fi
    #> [1,] -7.556895 11.211834 1.991123 0.2636769 0.6044820 1.639739 5.692968
    #> [2,] -3.651220 11.490401 1.302231 2.3388344 2.9345710 2.331002 7.141930
    #> [3,] -6.609980 22.405084 1.433759 0.8435020 1.2381374 1.384493 6.407414
    #> [4,] -2.912464 11.573476 1.160521 1.6490971 1.4259374 2.434075 4.327736
    #> [5,] -2.464355  7.514025 1.823027 0.5838206 0.0531312 3.157230 5.180723
    #> [6,] -9.444659 12.124429 1.208091 1.0183195 1.1042341 3.717423 5.354845

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

- Can you do the job without looking at the vignette?
- If you have your own data, maybe is a good time to apply HBV.IANIGLA on them.

## References

Ayala, A., F. Pellicciotti, S. MacDonell, J. McPhee, S. Vivero, C. Campos, and P. Egli. 2016. “Modelling the Hydrological Response of Debris-Free and Debris-Covered Glaciers to Present Climatic Conditions in the Semiarid Andes of Central Chile.” Hydrol. Process. 30 (22): 4036–58. https://doi.org/10.1002/hyp.10971.

Ayala, A., F. Pellicciotti, N. Peleg, and P. Burlando. 2017. “Melt and Surface Sublimation Across a Glacier in a Dry Environment: Distributed Energy-Balance Modelling of Juncal Norte Glacier, Chile.” Journal of Glaciology 63 (241): 803–22. https://doi.org/10.1017/jog.2017.46.

Ayala, Álvaro, David Farías-Barahona, Matthias Huss, Francesca Pellicciotti, James McPhee, and Daniel Farinotti. 2020. “Glacier Runoff Variations Since 1955 in the Maipo River Basin, in the Semiarid Andes of Central Chile.” The Cryosphere 14 (6): 2005–27. https://doi.org/https://doi.org/10.5194/tc-14-2005-2020.

Bravo, Claudio, Thomas Loriaux, Andrés Rivera, and Ben W. Brock. 2017. “Assessing Glacier Melt Contribution to Streamflow at Universidad Glacier, Central Andes of Chile.” Hydrology and Earth System Sciences 21 (7): 3249–66. https://doi.org/https://doi.org/10.5194/hess-21-3249-2017.

Finger, David, Marc Vis, Matthias Huss, and Jan Seibert. 2015. “The Value of Multiple Data Set Calibration Versus Model Complexity for Improving the Performance of Hydrological Models in Mountain Catchments.” Water Resour. Res. 51 (4): 1939–58. https://doi.org/10.1002/2014WR015712.

Hock, Regine. 2003. “Temperature Index Melt Modelling in Mountain Areas.” Journal of Hydrology 282 (1): 104–15. https://doi.org/10.1016/S0022-1694(03)00257-9.

IANIGLA-ING. 2010. “Inventario Nacional de Glaciares Y Ambiente Periglacial:Fundamentos Y Cronograma de Ejecución.” Mendoza: CONICET. http://www.glaciaresargentinos.gob.ar/wp-content/uploads/legales/fundamentos_cronograma_ejecucion.pdf.

———. 2018. “IANIGLA-Inventario Nacional de Glaciares. 2018. Informe de Las Subcuencas de Los Ríos Manso, Villegas Y Foyel. Cuenca de Los Ríos Manso Y Puelo. IANIGLA-CONICET, Ministerio de Ambiente Y Desarrollo Sustentable de La Nación.” IANIGLA. http://www.glaciaresargentinos.gob.ar.

Kaser, George, Andrew Fountain, and Peter Jansson. 2003. “A Manual for Monitoring the Mass Balance of Mountain Glaciers.” Manual 59. Paris: UNESCO.

Konz, Markus, and Jan Seibert. 2010. “On the Value of Glacier Mass Balances for Hydrological Model Calibration.” Journal of Hydrology 385 (1): 238–46. https://doi.org/10.1016/j.jhydrol.2010.02.025.

La Frenierre, Jeff, and Bryan G. Mark. 2014. “A Review of Methods for Estimating the Contribution of Glacial Meltwater to Total Watershed Discharge.” Progress in Physical Geography: Earth and Environment 38 (2): 173–200. https://doi.org/10.1177/0309133313516161.

Masiokas, M. H., D. A. Christie, C. Le Quesne, P. Pitte, L. Ruiz, R. Villalba, B. H. Luckman, et al. 2016. “Reconstructing the Annual Mass Balance of the Echaurren Norte Glacier (Central Andes, \(33.5^{\circ}\) S) Using Local and Regional Hydroclimatic Data.” The Cryosphere 10 (April): 927–40. https://doi.org/10.5194/tc-10-927-2016.

Ohmura, Atsumu. 2001. “Physical Basis for the Temperature-Based Melt-Index Method.” Journal of Applied Meteorology 40 (4): 753–61. https://doi.org/10.1175/1520-0450(2001)040<0753:PBFTTB>2.0.CO;2.

Pellicciotti, Francesca, Jakob Helbing, Andrés Rivera, Vincent Favier, Javier Corripio, Jose Araos, Jean-Emmanuel Sicart, and Marco Carenzo. 2008. “A Study of the Energy Balance and Melt Regime on Juncal Norte Glacier, Semi-Arid Andes of Central Chile, Using Melt Models of Different Complexity.” Hydrol. Process. 22 (19): 3980–97. https://doi.org/10.1002/hyp.7085.

Ragettli, S., and F. Pellicciotti. 2012. “Calibration of a Physically Based, Spatially Distributed Hydrological Model in a Glacierized Basin: On the Use of Knowledge from Glaciometeorological Processes to Constrain Model Parameters.” Water Resour. Res. 48 (3): W03509. https://doi.org/10.1029/2011WR010559.

Ruiz, L., E. Berthier, M. Viale, P. Pitte, and M. H. Masiokas. 2017. “Recent Geodetic Mass Balance of Monte Tronador Glaciers, Northern Patagonian Andes.” The Cryosphere 11 (1): 619–34. https://doi.org/10.5194/tc-11-619-2017.