Articles | Volume 17, issue 6
https://doi.org/10.5194/hess-17-2147-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-17-2147-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction
E. P. Maurer
Civil Engineering Dept., Santa Clara University, Santa Clara, CA, USA
T. Das
CH2MHill, 402 W. Broadway, San Diego, CA, USA
D. R. Cayan
Division of Climate, Atmospheric Sciences, and Physical Oceanography, Scripps Institution of Oceanography and Water Resources Division, US Geological Survey, La Jolla, CA, USA
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35 citations as recorded by crossref.
- Variable sensitivity analysis in groundwater level projections under climate change adopting a hybrid machine learning algorithm A. Sharghi et al. 10.1016/j.envsoft.2024.106264
- Assessing the sensitivity of hydro-climatological change detection methods to model uncertainty and bias Z. Jiang et al. 10.1016/j.advwatres.2019.103430
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- Evaluating the Time-Invariance Hypothesis of Climate Model Bias Correction: Implications for Hydrological Impact Studies J. Velázquez et al. 10.1175/JHM-D-14-0159.1
- Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean E. Maurer & D. Pierce 10.5194/hess-18-915-2014
- Evaluation of Reanalysis Precipitation Data and Potential Bias Correction Methods for Use in Data-Scarce Areas V. Garibay et al. 10.1007/s11269-021-02804-8
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- Patterns in Climate-Related Parameters as Proxy for Rainfall Deficiency and Aridity: Application to Burkina Faso A. Garcia-Aristizabal et al. 10.1061/AJRUA6.0000860
- Assessing the limits of bias‐correcting climate model outputs for climate change impact studies J. Chen et al. 10.1002/2014JD022635
- Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting T. Schmith et al. 10.5194/hess-25-273-2021
- What are the impacts of bias correction on future drought projections? F. Johnson & A. Sharma 10.1016/j.jhydrol.2015.04.002
- Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach J. Nahar et al. 10.1002/2017WR021293
- Observed and Projected Scaling of Daily Extreme Precipitation with Dew Point Temperature at Annual and Seasonal Scales across the Northeast United Sates S. Steinschneider & N. Najibi 10.1175/JHM-D-21-0183.1
- A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations J. Nahar et al. 10.1016/j.jhydrol.2017.05.045
- Analysis of Expected Climate Extreme Variability with Regional Climate Simulations over Napoli Capodichino Airport: A Contribution to a Climate Risk Assessment Framework E. Bucchignani et al. 10.3390/earth2040058
- Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping B. Thrasher et al. 10.5194/hess-16-3309-2012
- Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes P. Paredes et al. 10.1016/j.agwat.2018.08.003
- Assessing the extent of non-stationary biases in GCMs J. Nahar et al. 10.1016/j.jhydrol.2017.03.045
- Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies E. Maurer et al. 10.5194/hess-20-685-2016
- Bias adjustment to preserve changes in variability: the unbiased mapping of GCM changes C. Chadwick et al. 10.1080/02626667.2023.2201450
- Effects of climate change on shallow landslides in a small coastal catchment in southern Italy F. Ciervo et al. 10.1007/s10346-016-0743-1
- Design considerations for riverine floods in a changing climate – A review B. François et al. 10.1016/j.jhydrol.2019.04.068
- Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium J. Van de Velde et al. 10.5194/hess-26-2319-2022
- Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods A. Werner & A. Cannon 10.5194/hess-20-1483-2016
- How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? D. Rastogi et al. 10.1029/2022EF002734
- Projected changes in future climate over the Midwest and Great Lakes region using downscaled CMIP5 ensembles K. Byun & A. Hamlet 10.1002/joc.5388
- Estimating Future Surface Water Availability Through an Integrated Climate‐Hydrology‐Management Modeling Framework at a Basin Scale Under CMIP6 Scenarios M. Shao et al. 10.1029/2022WR034099
- Enumerating the Effects of Climate Change on Water Resources Using GCM Scenarios at the Xin’anjiang Watershed, China M. Zaman et al. 10.3390/w10101296
- Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling J. Chen et al. 10.1016/j.jhydrol.2018.03.040
- Evaluating the effect of climate change on areal reduction factors using regional climate model projections J. Li et al. 10.1016/j.jhydrol.2015.06.067
- Assessing bias corrections of oceanic surface conditions for atmospheric models J. Beaumet et al. 10.5194/gmd-12-321-2019
- An integrated assessment of extreme hydrometeorological events in Bangladesh S. Moghim & A. Takallou 10.1007/s00477-023-02404-5
2 citations as recorded by crossref.
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- The stationarity of two statistical downscaling methods for precipitation under different choices of cross‐validation periods Y. Wang et al. 10.1002/joc.5375
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