Abstract
The study of climate change impact on water resources has accelerated worldwide over the past two decades. An important component of such studies is the bias correction step, which accounts for spatiotemporal biases present in climate model outputs over a reference period, and which allows realistic streamflow simulations using future climate scenarios. Most of the literature on bias correction focuses on daily scale climate model temporal resolution. However, a large amount of regional and global climate simulations are becoming increasingly available at the sub-daily time step, and even extend to the hourly scale, with convection-permitting models exploring sub-hourly time resolution. Recent studies have shown that the diurnal cycle of variables simulated by climate models is also biased, which raises issues respecting the necessity (or not) of correcting such biases prior to generating streamflows at the sub-daily time scale. This paper investigates the impact of bias-correcting the diurnal cycle of climate model outputs on the computation of streamflow over 133 small to large North American catchments. A standard hydrological modeling chain was set up using the temperature and precipitation outputs from a high spatial (12-km) and temporal (1-hour) regional climate model large ensemble (ClimEx-LE). Two bias-corrected time series were generated using a multivariate quantile mapping method, with and without correction of the diurnal cycles of temperature and precipitation. The impact of this correction was evaluated on three small (< 500 km2), medium and large (> 1000 km2) surface area catchment size classes. Results show small but systematic improvements of streamflow simulations when bias-correcting the diurnal cycle of precipitation and temperature. The greatest improvements were seen on the small catchments, and least noticeable on the largest. The diurnal cycle correction allowed for hydrological simulations to accurately represent the diurnal cycle of summer streamflow on small catchments. Bias-correcting the diurnal cycle of precipitation and temperature is therefore recommended when conducting impact studies at the sub-daily time scale on small catchments.
Highlights
The potential impacts of climate change have become a crucial concern for public safety, the environment and the economy 35 of the twenty-first century (Raza et al, 2019; Walsh et al, 2019; Vogel et al, 2019)
Should we bias-correct the diurnal cycles of 85 climate model outputs? If so how? Do we have reliable reference datasets at the sub-daily time scale? Will this even influence the results of impact studies? To provide an answer to these questions, this paper examines the impact of bias-correcting the diurnal cycle on the hydrology of several North American catchments
The left-hand side shows that ClimEx simulates a good temperature diurnal cycle, which is fairly close to the observed ones and for all seasons
Summary
The potential impacts of climate change have become a crucial concern for public safety, the environment and the economy 35 of the twenty-first century (Raza et al, 2019; Walsh et al, 2019; Vogel et al, 2019). General circulation models (GCMs) and Earth System Models (ESMs) are invaluable tools for simulating the present and future climates (Panday et al, 2015; Alfieri et al, 2015) These models do require substantial computational power 50 and disk space, which significantly limits both the spatial and temporal resolution at which they can be run, and the frequency at which their outputs can be archived. This explains why output data from these models have typically been limited to a relatively coarse spatial resolution of 100 km (or more), and been archived at the daily time scale These spatial and temporal resolutions are too coarse to allow studying the potential hydrological impacts of climate change on small catchments (Trzaska and Schnarr, 2014; Bajracharya et al, 2018; 55 Fatichi et al, 2014) To overcome this issue, regional climate models (RCMs) have been used to dynamically downscale GCM outputs at a higher spatial and temporal resolution over limited area domains. This increase in spatial resolution requires a corresponding increase in temporal resolution (for numerical stability), and such models are limited to even smaller computational domains
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