Hydrological Modeling to Evaluate Climate Model Simulations and Their Bias Correction

Improved bias correction techniques for hydrological 1 simulations of climate change

Discharge simulations performed with a hydrological model using bias corrected regional climate model input

Studies have demonstrated that precipitation on Northern Hemisphere mid-latitudes has increased in the last decades and that it is likely that this trend will continue. This will have an influence on discharge of the river Meuse. The use of bias correction methods is important when the effect of precipitation change on river discharge is studied. The objective of this paper is to investigate th...

A novel bias correction methodology for climate impact simulations

Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies ...

Joint bias correction of temperature and precipitation in climate model simulations

Bias correction of meteorological variables from climate model simulations is a routine strategy for circumventing known limitations of state-of-the-art general circulation models. Although the assessment of climate change impacts often depends on the joint variability of multiple variables, commonly used bias correction methodologies treat each variable independently and do not consider the re...

Climate model bias correction and the role of timescales

It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However, the improvements to the statistical properties of the data are ...