Bias correction for hydrological impact studies – beyond the daily perspective

Addor, Nans; Seibert, Jan

doi:10.1002/hyp.10238

Cited by 60 publications

(53 citation statements)

References 57 publications

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“…Despite this quite weak performance of all the four satellite rainfall products, they provide spatio-temporal estimate of rainfall not available from gauges. Besides, that estimate can be improved by bias correction (Addor and Seibert 2014;AghaKouchak et al 2009;Müller and Thompson 2013) or by merged/blended improvements (Chappell et al 2013;Li and Shao 2010;Woldemeskel et al 2013) with gauge data as reference. Geophysical and climatological constraints (Jia et al 2011), such as elevation applied in this study, eventually also distance to the sea (Abtew et al 2011;Johansson and Chen 2003) and wind direction (Castro et al 2014), can also improve the performance of satellite rainfall products in contrast to climatology zonation that was not particularly beneficial.…”

Section: Discussionmentioning

confidence: 99%

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

Rahmawati

Lubczynski

2017

Theor Appl Climatol

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Satellite rainfall products have different performances in different geographic regions under different physical and climatological conditions. In this study, the objective was to select the most reliable and accurate satellite rainfall products for specific, environmental conditions of Bali Island. The performances of four spatio-temporal satellite rainfall products, i.e., CMORPH 25 , CMORPH 8 , TRMM, and PERSIANN, were evaluated at the island, zonation (applying elevation and climatology as constraints), and pixel scales, using (i) descriptive statistics and (ii) categorical statistics, including bias decomposition. The results showed that all the satellite products had low accuracy because of spatial scale effect, daily resolution and the island complexity. That accuracy was relatively lower in (i) dry seasons and dry climatic zones than in wet seasons and wet climatic zones; (ii) pixels jointly covered by sea and mountainous land than in pixels covered by land or by sea only; and (iii) topographically diverse than uniform terrains. CMORPH 25 , CMORPH 8 , and TRMM underestimated and PERSIANN overestimated rainfall when comparing them to gauged rain. The CMORPH 25 had relatively the best performance and the PERSIANN had the worst performance in the Bali Island. The CMORPH 25 had the lowest statistical errors, the lowest miss, and the highest hit rainfall events; it also had the lowest miss rainfall bias and was relatively the most accurate in detecting, frequent in Bali, ≤ 20 mm day −1 rain events. Lastly, the CMORPH 25 coarse grid better represented rainfall events from coastal to inlands areas than other satellite products, including finer grid CMORPH 8 .

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Section: Discussionmentioning

confidence: 99%

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

Rahmawati

Lubczynski

2017

Theor Appl Climatol

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“…Downscaling is sometimes combined with bias correction (e.g. , however several recent papers have cast doubt on the assumption that bias correction improves the reliability of results from subsequent hydrological modelling (Addor and Seibert 2014, Huang et al 2014, Ehret et al 2012. , as well as for snow modelling.…”

Section: Use Of Rcm Datamentioning

confidence: 99%

An assessment of the possible impacts of climate change on snow and peak river flows across Britain

et al. 2016

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The NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. 1An assessment of the possible impacts of climate change on snow and peak river flows across Britain AbstractA temperature-based snow module has been coupled with a grid-based distributed hydrological model, to improve simulations of river flows in upland areas of Britain subject to snowfall and snowmelt. The coupled model has been driven with data from an 11-member perturbedparameter climate model ensemble, for two time-slices (1960-1990 and 2069-2099), to investigate the potential impacts of climate change. The analysis indicates large reductions in the ensemble mean of the number of lying snow days across the country. This in turn affects the seasonality of peak river flows in some parts of the country; for northerly regions, annual maxima tend to occur earlier in the water year in future. For more southerly regions the changes are less straightforward, and likely driven by changes in rainfall patterns rather than snow. The modelled percentage changes in peak flows illustrate high spatial variability in hydrological response to projected climate change, and large differences between ensemble members. When changes in projected future peak flows are compared to an estimate of current natural variability, more changes fall outside the range of natural variability in southern Britain than in the north.

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“…The question of whether or not to apply a bias correction to overcome these biases in the climate models is widely discussed in the scientific community [25][26][27]. Bias correction is mostly limited to temperature and precipitation and leads (by definition) to a better agreement with the historical observations.…”

Section: Ensembles Climate Modelsmentioning

confidence: 99%

“…On the other hand, bias correction methods affect the advantages of dynamic climate models, like the tempo-spatial coherence of the climate variables (covariance structure) and the compliance of physical conservation principles [27,28]. Additionally, bias corrections are based on the assumption that statistical errors remain stationary in the future, but the bias will not necessarily stay the same in the next decades [25][26][27][28][29]. Hence, it is not certain if a bias correction leads to more realistic results in hydrological impact studies [30].…”

Section: Ensembles Climate Modelsmentioning

confidence: 99%

Impacts of Climate Change on the Hydrological Regime of the Danube River and Its Tributaries Using an Ensemble of Climate Scenarios

Stagl

Hattermann

2015

Water

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Information about the potential impacts of climate change on river runoff is needed to prepare efficient adaptation strategies. This study presents scenario projections for the future hydrological runoff regime in the Danube River Basin. The eco-hydrological watershed model Soil and Water Integrated Model (SWIM) was applied for the entire Danube River catchment, considering 1224 subbasins. After calibration and validation of the model, a set of high-resolution climate projections (bias-corrected and non-bias-corrected) served as meteorological drivers with which future daily river discharge under different climate warming scenario conditions was simulated. Despite existing uncertainties, robust trends could be identified. In the next 30 years, the seasonal stream-flow regime of the Danube and its tributaries is projected to change considerably. Our results show a general trend towards a decrease in summer runoff for the whole Danube basin and, additionally, in autumn runoff for the Middle and Lower Danube basin, aggravating the existing low flow periods. For the winter and early spring seasons, mainly January-March, an increase in river runoff is projected. Greater uncertainties show up in particular for winter runoff in the Dinaric Alps and the Lower Danube basin. The existing trends become very distinct until the end of the 21st century, especially for snow-influenced river regimes.

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Bias correction for hydrological impact studies – beyond the daily perspective

Cited by 60 publications

References 57 publications

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

An assessment of the possible impacts of climate change on snow and peak river flows across Britain

Impacts of Climate Change on the Hydrological Regime of the Danube River and Its Tributaries Using an Ensemble of Climate Scenarios

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