TY - JOUR
T1 - An inverse approach to perturb historical rainfall data for scenario-neutral climate impact studies
AU - Guo, Danlu
AU - Westra, Seth
AU - Maier, Holger R.
PY - 2018/1
Y1 - 2018/1
N2 - Scenario-neutral approaches are being used increasingly for climate impact assessments, as they allow water resource system performance to be evaluated independently of climate change projections. An important element of these approaches is the generation of perturbed series of hydrometeorological variables that form the inputs to hydrologic and water resource assessment models, with most scenario-neutral studies to-date considering only shifts in the average and a limited number of other statistics of each climate variable. In this study, a stochastic generation approach is used to perturb not only the average of the relevant hydrometeorological variables, but also attributes such as the intermittency and extremes. An optimization-based inverse approach is developed to obtain hydrometeorological time series with uniform coverage across the possible ranges of rainfall attributes (referred to as the 'exposure space'). The approach is demonstrated on a widely used rainfall generator, WGEN, for a case study at Adelaide, Australia, and is shown to be capable of producing evenly-distributed samples over the exposure space. The inverse approach expands the applicability of the scenario-neutral approach in evaluating a water resource system's sensitivity to a wider range of plausible climate change scenarios. (C) 2016 Elsevier B.V. All rights reserved.
AB - Scenario-neutral approaches are being used increasingly for climate impact assessments, as they allow water resource system performance to be evaluated independently of climate change projections. An important element of these approaches is the generation of perturbed series of hydrometeorological variables that form the inputs to hydrologic and water resource assessment models, with most scenario-neutral studies to-date considering only shifts in the average and a limited number of other statistics of each climate variable. In this study, a stochastic generation approach is used to perturb not only the average of the relevant hydrometeorological variables, but also attributes such as the intermittency and extremes. An optimization-based inverse approach is developed to obtain hydrometeorological time series with uniform coverage across the possible ranges of rainfall attributes (referred to as the 'exposure space'). The approach is demonstrated on a widely used rainfall generator, WGEN, for a case study at Adelaide, Australia, and is shown to be capable of producing evenly-distributed samples over the exposure space. The inverse approach expands the applicability of the scenario-neutral approach in evaluating a water resource system's sensitivity to a wider range of plausible climate change scenarios. (C) 2016 Elsevier B.V. All rights reserved.
KW - Exposure space
KW - Inverse approach
KW - Optimization
KW - Scenario-neutral climate impact study
KW - Stochastic generator
KW - Wgen
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=anu_research_portal_plus2&SrcAuth=WosAPI&KeyUT=WOS:000423641300068&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1016/j.jhydrol.2016.03.025
DO - 10.1016/j.jhydrol.2016.03.025
M3 - Article
SN - 0022-1694
VL - 556
SP - 877
EP - 890
JO - Journal of Hydrology
JF - Journal of Hydrology
ER -