Abstract
Statistical post-processing of climate model outputs is a common hydroclimatic modelling practice aiming to produce climate scenarios that better fit in-situ observations and to produce reliable stream flows forcing calibrated hydrologic models. Such practice is however criticized for disrupting the physical consistency between simulated climate variables and affecting the trends in climate change signals imbedded within raw climate simulations. It also requires abundant good-quality meteorological observations, which are not available for many regions in the world. A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations, nor for statistical post-processing of climate model outputs, nor for calibrating hydrologic models. By combining asynchronous hydroclimatic modelling, an alternative framework designed to construct hydrologic scenarios without resorting to meteorological observations, and quantile perturbation applied to streamflow observations, the proposed workflow produces sound and plausible hydrologic scenarios considering: (1) they preserve trends and physical consistency between simulated climate variables, (2) are implemented from a modelling cascades despite observation scarcity, and (3) support the participation of end-users in producing and interpreting climate change impacts on water resources. The proposed modelling workflow is implemented over four subcatchments of the Chaudière River, Canada, using 9 North American CORDEX simulations and a pool of lumped conceptual hydrologic models. Forced with raw climate model outputs, hydrologic models are calibrated over the reference period according to a calibration metric designed to function with temporally uncorrelated observed and simulated streamflow values. Perturbation factors are defined by relating each simulated streamflow quantiles over both reference and future periods. Hydrologic scenarios are finally produced by applying perturbation factors to available streamflow observations.
Highlights
Assessments of climate change impacts are commonly oriented in a top-down perspective favoring the implementation of a modelling cascade from climate simulations to impact models (e.g. Poulin et al, 2011; Seiller and Anctil, 2014; Seo et al, 30 2016)
The application of statistical post-processed climate model outputs is criticized for three main reasons (e.g. Alfieri et al, 2015b, Chen et al, 2018; Lee et al, 2018): (1) it disrupts the physical consistency between simulated climate variables; (2) it affects the trends in climate change signals imbedded within raw climate simulations; (3) it requires abundant good-quality meteorological observations, which are not available for many regions of the world, including some less common 40 meteorological fields such as wind speed, relative humidity, and radiations (Ricard et al, 2020)
The current study aims to display the implementation of the modelling workflow and to demonstrate its applicability by producing time ordered hydrologic scenarios from raw CORDEX simulations over a mid-scale catchment located in Southern Québec, Canada, using a pool of lump conceptual hydrologic models
Summary
Assessments of climate change impacts are commonly oriented in a top-down perspective favoring the implementation of a modelling cascade from climate simulations to impact models (e.g. Poulin et al, 2011; Seiller and Anctil, 2014; Seo et al, 30 2016). More marginal critics raise the fact that statistical post-processing hides raw climate model outputs biases from end-users (Ehret et al, 2012), potentially blurring confidence attributed to resulting impact scenarios, and potentially misleading adaptation to climate change. Even if these limitations are generally acknowledged, statistical post-processing is close to be considered as mandatory for climate change impact assessments on water resources. Trend-preserving and multivariate approaches (e.g. Cannon et al, 45 2018; Nguyen et al, 2020) have been developed in order to limit the above-mentioned post-processing drawbacks These latter employ, a fairly high level of complexification, and requires specific expertise in postprocessing technologies
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