Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins

Chen, Jie; Brissette, François; Chaumont, Diane; Braun, Marco

doi:10.1016/j.jhydrol.2012.11.062

Cited by 260 publications

(197 citation statements)

References 43 publications

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“…A 'distribution mapping' procedure was employed to bias-correct the RCM-simulated precipitation and temperature series on a monthly basis. Distribution mapping, which is also known as 'probability mapping' (Block et al 2009), 'quantile mapping' (Chen et al 2013), 'intensity-based statistical downscaling' (Piani et al 2010), 'histogram equalization' , the Integrated Catchment model for Carbon (INCA-C) (Futter et al 2007), and the Riparian flowconcentration Integrated model (RIM) ). Hydrologically effective rainfall (HER) represents the available water from rainfall and snowmelt that eventually contributes to runoff.…”

Section: Monitoring Datamentioning

confidence: 99%

Cross-scale ensemble projections of dissolved organic carbon dynamics in boreal forest streams

et al. 2014

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Climate is an important driver of dissolved organic carbon (DOC) dynamics in boreal catchments characterized by networks of streams within forest-wetland landscape mosaics. In this paper, we assess how climate change may affect stream DOC concentrations ([DOC]) and export from boreal forest streams with a multi-model ensemble approach. First, we apply an ensemble of regional climate models (RCMs) to project soil temperatures and stream-flows. These data are then used to drive two biogeochemical models of surface water DOC: (1) The Integrated Catchment model for Carbon (INCA-C), a detailed process-based model of DOC operating at the catchment scale, and (2) The Riparian Integration Model (RIM), a simple dynamic hillslope scale model of stream [DOC]. All RCMs project a consistent increase in temperature and precipitation as well as a shift in spring runoff peaks from May to April. However, they present a considerable range of possible future runoff conditions with an ensemble median increase of 31 % between current and future (2061-2090) conditions. Both biogeochemical models perform well in describing the dynamics of presentday stream [DOC] and fluxes, but disagree in their future projections. Here, we assess possible futures in three boreal catchments representative of forest, mire and mixed landscape elements. INCA-C projects a wider range of stream [DOC] due to its temperature sensitivity, whereas RIM gives consistently larger inter-annual variation and a wider range of exports due to its sensitivity to hydrological variations. The uncertainties associated with modeling complex processes that control future DOC dynamics in boreal and temperate catchments are still the main limitation to our understanding of DOC mechanisms under changing climate conditions. Novel, currently overlooked or unknown drivers may appear that will present new challenges to modelling DOC in the future.

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Section: Monitoring Datamentioning

confidence: 99%

Cross-scale ensemble projections of dissolved organic carbon dynamics in boreal forest streams

et al. 2014

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“…In addition, impact modelers are also facing a risk of improper RCM simulations (Christensen et al, 2008;Teutschbein and Seibert, 2010;Varis et al, 2004) due to systematic (i.e., biases) and random model errors. Mismatching scales in combination with such errors have led to many recently developed correction approaches (Chen et al, 2013;Johnson and Sharma, 2011;Maraun et al, 2010;Teutschbein and Seibert, 2012;Themeßl et al, 2011) that help impact modelers to cope with the various problems linked to biased RCM output.…”

Section: Introductionmentioning

confidence: 99%

“…The correction procedures usually identify possible differences between observed and simulated climate variables, which provide the basis for correcting both control and scenario RCM runs with a transformation algorithm. Although the correction of RCM climate variables can considerably improve hydrological simulations under current climate conditions (Chen et al, 2013;Teutschbein and Seibert, 2012), there is a major drawback: most methods follow the assumption of stationarity of model errors, which means that the correction algorithm and its parameterization for current climate conditions are assumed to also be valid for a time series of changed future climate conditions. Whether or not this condition is actually fulfilled for our future climate cannot be evaluated directly.…”

Section: Introductionmentioning

confidence: 99%

Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

Teutschbein

Seibert

2013

Hydrol. Earth Syst. Sci.

344

251

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Abstract. In hydrological climate-change impact studies, regional climate models (RCMs) are commonly used to transfer large-scale global climate model (GCM) data to smaller scales and to provide more detailed regional information. Due to systematic and random model errors, however, RCM simulations often show considerable deviations from observations. This has led to the development of a number of correction approaches that rely on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well correction methods perform for conditions different from those used for calibration with the relatively simple differential split-sample test.For five Swedish catchments, precipitation and temperature simulations from 15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF)) were corrected with different commonly used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with nonstationary biases. More advanced correction methods performed better, whereas large deviations remained for climate model simulations corrected with simpler approaches. Therefore, we question the use of simple correction methods such as the widely used delta-change approach and linear transformation for RCM-based climate-change impact studies. Instead, we recommend using higher-skill correction methods such as distribution mapping.

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“…Daily data is available from all sources; however, only Regional Climate Models, such as models in NARCCAP and NA-CORDEX projects, are able to produce rainfall output on a sub-daily level of 3-hours or less. When the engineering application is not limited to a sub-daily time step, additional resources are available at the daily level through sources that utilize "empirical downscaling" techniques, which rely on existing statistical relationships between large-scale climate systems and local weather patterns (Abatzoglou and Brown 2012;Khan et al 2006;Murphy 1999;Chen et al 2013). These techniques are less computationally intense than Regional Climate Models (RCMs), and can provide higher resolution output (4 -12 km) for long simulation periods (1950 -2100), and multiple emissions scenarios and global climate models (Cooney 2012).…”

Section: Model Output Attributesmentioning

confidence: 99%

Framework for Incorporating Downscaled Climate Output into Existing Engineering Methods: Application to Precipitation Frequency Curves

Cook

Anderson

Samaras

2017

J. Infrastruct. Syst.

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Abstract:To improve the resiliency of designs, particularly for long-lived infrastructure, current engineering practice must be updated to incorporate a range of future climate conditions that are likely to be different from the past. However, a considerable mismatch exists between climate model outputs and the data inputs needed for engineering designs. The present work provides a framework for incorporating climate trends into design standards and applications, including: selecting the appropriate climate model source based on the intended application, understanding model performance and uncertainties, addressing differences in temporal and spatial scales, and interpreting results for engineering design. The framework is illustrated through an application to depth-duration-frequency curves, which are commonly used in stormwater design. A change factor method is used to update the curves in a case study of Pittsburgh, PA. Extreme precipitation depth is expected to increase in the future for Pittsburgh for all return periods and durations examined, requiring revised standards and designs. Doubling the return period and using historical, stationary values may enable adequate design for short duration storms; however, this method is shown to be insufficient to enable protective designs for larger duration storms.

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Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins

Cited by 260 publications

References 43 publications

Cross-scale ensemble projections of dissolved organic carbon dynamics in boreal forest streams

Cross-scale ensemble projections of dissolved organic carbon dynamics in boreal forest streams

Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

Framework for Incorporating Downscaled Climate Output into Existing Engineering Methods: Application to Precipitation Frequency Curves

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