func_P

A tool to create hydroclimate scenarios, stress test systems and visualize system performance in scenario-neutral climate change impact assessments. Scenario-neutral approaches 'stress-test' the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) <doi:10.1029/2012EO410001> and Prudhomme et al. (2010) <doi:10.1016/j.jhydrol.2010.06.043>). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) <doi:10.1002/2015WR018253>) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) <doi:10.1016/j.jhydrol.2016.03.025>). It incorporates 'Richardson-type' weather generator model configurations documented in Richardson (1981) <doi:10.1029/WR017i001p00182>, Richardson and Wright (1984), as well as latent variable type model configurations documented in Bennett et al. (2018) <doi:10.1016/j.jhydrol.2016.12.043>, Rasmussen (2013) <doi:10.1002/wrcr.20164>, Bennett et al. (2019) <doi:10.5194/hess-23-4783-2019> to generate hydroclimate variables on a daily basis (e.g. precipitation, temperature, potential evapotranspiration) and allows a variety of different hydroclimate variable properties, herein called attributes, to be perturbed. Options are included for the easy integration of existing system models both internally in R and externally for seamless 'stress-testing'. A suite of visualization options for the results of a scenario-neutral analysis (e.g. plotting performance spaces and overlaying climate projection information) are also included. Version 1.0 of this package is described in Bennett et al. (2021) <doi:10.1016/j.envsoft.2021.104999>. As further developments in scenario-neutral approaches occur the tool will be updated to incorporate these advances.

David McInerney

foreSIGHT

Systems Insights from Generation of Hydroclimatic Timeseries

Bree Bennett

Sam Culley

Anjana Devanand

Seth Westra

Danlu Guo

Holger Maier

func_P function

<dl><dt>data</dt>
<dd>is a vector, representing a time series</dd>
<dt>attArgs</dt>
<dd>is a list, with attArgs$quant denoting the probability of the quantile</dd></dl>

Arguments

Calculates a quantile value — func_P

<dl>

<dt>data</dt>
<dd>is a vector, representing a time series</dd>


<dt>attArgs</dt>
<dd>is a list, with attArgs$quant denoting the probability of the quantile</dd>

</dl>

Calculates a quantile value

func_P: Calculates a quantile value

Description

Usage

Arguments