Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

Switanek, B. Matthew; Troch, A. Peter; Castro, Lorena; Leuprecht, Armin; Chang, Hsin I.; Mukherjee, Rajarshi; Demaria, E. M.

doi:10.5194/hess-21-2649-2017

Cited by 177 publications

(132 citation statements)

References 51 publications

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“…However, Switanek et al (2017) showed this assumption of a stationary error correction function to be invalid, and as a result, the altering of the raw model projected changes to precipitation and temperature was found to be unjustified. SDM, in contrast, does not rely on a stationary error correction function, but rather attempts to best preserve the raw model projected changes across the entire distribution.…”

Section: Error Correctionmentioning

confidence: 99%

“…The novel method Scaled distribution mapping (SDM) is used to bias correct the model precipitation and temperature data time series (Switanek et al, 2017). The commonly used bias correction method of quantile mapping (Wood et al, 2004;Piani et al, 2010;Themeßl et al, 2011;Teutschbein and Seibert, 2013) assumes that error correction functions can be treated as stationary from one time period to another.…”

Section: Error Correctionmentioning

confidence: 99%

“…An improved bias correction technique (Switanek et al, 2017) is used to minimize error propagation throughout the modelling chain. The study area is located in South-Eastern Austria (Styria), where local flash floods are the predominant flood type 15 (e.g., Merz and Blöschl, 2003).…”

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confidence: 99%

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Convection-permitting regional climate simulations for representing floods in small and medium sized catchments in the Eastern Alps

Reszler¹,

Switanek²,

Truhetz³

2018

Preprint

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Abstract. Small scale floods are a consequence of high precipitation rates in small areas that can occur along frontal activity and convective storms. This situation is expected to become more severe due to a warming climate, when single precipitation events resulting from deep convection become more intense ("Super Clausius-Clapeyron effect"). Regional climate model (RCM) evaluations and inter-comparisons have shown that there is evidence that an increase in regional climate model 10 resolution and in particular, at the convection permitting scale, will lead to a better representation of the spatial and temporal characteristics of heavy precipitation at small and medium scales. In this paper, the benefit of grid size reduction and bias correction in climate models are evaluated in their ability to properly represent flood generation in small and medium sized catchments. The climate models are coupled with a distributed hydrological model. The study area is the Eastern Alps, where small scale storms often occur along with heterogeneous rainfall distributions leading to a very local flash flood 15 generation. The work is carried out in a small multi-model (ensemble) framework using two different RCMs (CCLM and WRF) in different grid sizes. Bias correction is performed by the use of the novel Scaled Distribution Mapping (SDM) method. The results show, that for small catchments (< 200 km²) a resolution of 3 km is essential to accurately simulate the magnitude of flood events. Flood frequency and seasonality are both represented well in all catchments. In the larger catchments resolutions of 12.5 km and 50 km already yield statistically satisfying results, but poor results regarding 20 seasonality. Also, due to the short response times in the small sub-catchments a time step of 1 hour is required. In all setups a bias still exists in precipitation and temperature, which sometimes leads to unrealistic hydrological conditions demonstrating the necessity of bias correction. The results show the added value of reducing grid size and bias correction in climate models that can be used to model flood mechanisms in small and medium sized catchments.

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Section: Error Correctionmentioning

confidence: 99%

Section: Error Correctionmentioning

confidence: 99%

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Convection-permitting regional climate simulations for representing floods in small and medium sized catchments in the Eastern Alps

Reszler¹,

Switanek²,

Truhetz³

2018

Preprint

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“…Here, the equidistant quantile mapping (EQM) approach (Li et al, 2010;10 Srivastav et al, 2014;Sachindra et al, 2014) was employed to bias-correct the ICRA precipitation time-series. EQM is an adaptation of the original quantile mapping method that accounts for non-stationarity in the moments of the biased time-series and helps to preserve changes in the cumulative distribution function of the precipitation data that may have occurred over time (Switanek et al, 2017;Cannon et al, 2015). To evaluate the effectiveness of the bias-correction procedure, the R 2 correlation score was calculated between the bias-corrected ICRA data and the measured AWS1 data.…”

Section: Precipitationmentioning

confidence: 99%

Glacio-hydrological melt and runoff modelling: a limits of acceptability framework for model selection

Mackay

Barrand

Hannah

et al. 2018

Preprint

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Abstract. Glacio-hydrological models (GHMs) underpin our understanding how future climate change will affect river flow regimes in glaciated watersheds. A variety of simplified GHM structures and parameterisations exist, yet the performance of these are rarely quantified at the process-level or with metrics beyond global summary statistics. A fuller understanding of the deficiencies in competing model structures and parameterisations and the ability of models to simulate physical processes require performance metrics utilising the full range of uncertainty information within input observations. Here, the glacio-5 hydrological characteristics of the Virkisá river basin in southern Iceland are characterised using 33 'signatures' derived from observations of ice melt, snow coverage and river discharge. The uncertainty of each set of observations are harnessed to define 'limits of acceptability' (LOA), a set of criteria used to objectively evaluate the acceptability of different GHM structures and parameterisations. This framework is used to compare and diagnose deficiencies in three melt and three runoff-routing model structures. Increased model complexity is shown to improve acceptability when evaluated against specific signatures, but does 10 not always result in better consistency across all signatures, emphasising the difficulty in appropriate model selection and the need for multi-model prediction approaches to account for model selection uncertainty. Melt and runoff-routing structures demonstrate a hierarchy of influence on river discharge signatures with melt model structure having the most influence on discharge hydrograph seasonality and runoff-routing structure on shorter-timescale discharge events. None of the tested GHM structural configurations returned acceptable simulations across the full population of signatures. The framework outlined here 15 provides a comprehensive and rigorous assessment tool for evaluating the acceptability of different GHM process hypotheses.Future melt and runoff model forecasts should seek to diagnose structural model deficiencies and evaluate diagnostic signatures of system behaviour using the LOA framework.

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“…High-quality observational datasets of surface downwelling radiation are of interest in many fields of climate science, including energy budget estimation (Kiehl and Trenberth, 1997;Trenberth et al, 2009;Wild et al, 2013) and climate model evaluation (Garratt, 1994;Ma et al, 2014;Wild et al, 2015). As part of so-called climate or meteorological forcing datasets such as those generated within the Global Soil Wetness Project (GSWP; Zhao and Dirmeyer, 2003), at Princeton University (Sheffield et al, 2006), and within the Integrated Project Water and Global Change (WATCH; Weedon et al, 2011), the longwave and shortwave components of surface downwelling radiation (abbreviated as rlds and rsds or just longwave and shortwave radiation in the following) are used to correct model biases in climate model output (Hempel et al, 2013;Iizumi et al, 2017;Cannon, 2017) and drive simulations of climate impacts, for example (Müller Schmied et al, 2016;Veldkamp et al, 2017;Chang et al, 2017;Krysanova and Hattermann, 2017;Ito et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

Lange

2018

Earth Syst. Dynam.

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Abstract. Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.

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Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

Cited by 177 publications

References 51 publications

Convection-permitting regional climate simulations for representing floods in small and medium sized catchments in the Eastern Alps

Convection-permitting regional climate simulations for representing floods in small and medium sized catchments in the Eastern Alps

Glacio-hydrological melt and runoff modelling: a limits of acceptability framework for model selection

Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

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