ecorisk (version 0.2.1)

risk: Calculate Risk Scores Using Expert-Based or Model-Derived Vulnerability and Status Scores

Description

The risk function calculates risk scores using the output from of the status and the vulnerability functions. For each state indicator-pressure combination the function adds the status score to the vulnerability score to derive the risk score.

Usage

risk(vulnerability_results, status_results)

Value

a data frame containing the exposure, sensitivity, adaptive capacity, vulnerability, and risk scores as well as their associated uncertainty for each pressure - state indicator - type combination.

Arguments

vulnerability_results

A data frame with the output from the vulnerability function.

status_results

A data frame with status scores for each state indicator. The first column MUST contain the indicator names. The second and third column have to be named status and score.

Details

Final risk scores are in a range from -10 (severe risk for the state indicator due to the assessed pressure) to +10 (good opportunities for the state indicator due to the assessed pressure). The risk scores are specific for each combination of state indicator and pressure and do NOT take into account cumulative effects. The risk scores can be aggregated in an additive manner with the aggregate_risk function.

See Also

vulnerability, status, aggregate_risk

Examples

# Using demo output data from the vulnerability() and status() functions:
risk(
  vulnerability_results = ex_output_vulnerability_model,
  status_results = ex_output_status
)

# \donttest{
  ### Demo Expert-Based Pathway
  # - using the example scoring datasets 'ex_expert_exposure',
  #   'ex_expert_sensitivity' and 'ex_expert_status'

  # Calculate (mean) exposure score:
  exp_expert <- calc_exposure(
    pressures = ex_expert_exposure$pressure,
    components = ex_expert_exposure[ ,2:5],
    uncertainty = ex_expert_exposure[ ,6:9],
    method = "mean" # default
  )
  # Calculate (mean) sensitivity (and adaptive capacity) score:
  sens_ac_expert <- calc_sensitivity(
    indicators = ex_expert_sensitivity$indicator,
    pressures = ex_expert_sensitivity$pressure,
    type = ex_expert_sensitivity$type,
    sensitivity_traits = ex_expert_sensitivity[ ,4:8],
    adaptive_capacities = ex_expert_sensitivity[ ,9:13],
    uncertainty_sens = ex_expert_sensitivity[ ,14:18],
    uncertainty_ac = ex_expert_sensitivity[ ,19:23],
    method = "mean" # default
  )
  # Calculate (mean) vulnerability score:
  vuln_expert <- vulnerability(
    exposure_results = exp_expert,
    sensitivity_results = sens_ac_expert,
    method_vulnerability = "mean", # default
    method_uncertainty = "mean" # default
  )
  # Calculate risk score:
  risk(
    vulnerability_results = vuln_expert,
    status_results = ex_expert_status
  )


  ### Demo Model-Based Pathway
  # - using the demo time series 'pressure_ts_baltic' and 'indicator_ts_baltic'

  # Model exposure score:
  exp_model <- model_exposure(
    pressure_time_series = pressure_ts_baltic,
    base_years = c(start = 1984, end = 1994),
    current_years = c(start = 2010, end = 2016)
  )

  # Model sensitivity score:
  sens_ac_model <- model_sensitivity(
    indicator_time_series = indicator_ts_baltic,
    pressure_time_series = pressure_ts_baltic,
    current_years = c(start = 2010, end = 2016)
  )
  # Add manually adaptive capacity scores (otherwise zero):
  sens_ac_model$adaptive_capacity <- c(rep(1, 8), rep(-1, 8))

  # Calculate (mean) vulnerability score:
  vuln_model <- vulnerability(
    exposure_results = exp_model,
    sensitivity_results = sens_ac_model
  )
  # Calculate status score:
  status_model <- status(
    indicator_time_series = indicator_ts_baltic,
    base_years = c(start = 1984, end = 2010),
    current_years = c(start = 2011, end = 2016)
  )
  # Calculate risk score:
  risk(
    vulnerability_results = vuln_model,
    status_results = status_model
  )

# }