scholarly journals Impact of correcting sub-daily climate model biases for hydrological studies

2021 ◽  
Author(s):  
Mina Faghih ◽  
François Brissette ◽  
Parham Sabeti
Keyword(s):  
Diurnal Cycle ◽  
Bias Correction ◽  
Time Scale ◽  
Climate Model ◽  
Temperature And Precipitation ◽  
Diurnal Cycle Of Precipitation ◽  
The Impact ◽  
Small Catchments ◽  
Precipitation And Temperature ◽  
Daily Time

Abstract. The study of climate change impact on water resources has accelerated worldwide over the past two decades. An important component of such studies is the bias correction step, which accounts for spatiotemporal biases present in climate model outputs over a reference period, and which allows realistic streamflow simulations using future climate scenarios. Most of the literature on bias correction focuses on daily scale climate model temporal resolution. However, a large amount of regional and global climate simulations are becoming increasingly available at the sub-daily time step, and even extend to the hourly scale, with convection-permitting models exploring sub-hourly time resolution. Recent studies have shown that the diurnal cycle of variables simulated by climate models is also biased, which raises issues respecting the necessity (or not) of correcting such biases prior to generating streamflows at the sub-daily time scale. This paper investigates the impact of bias-correcting the diurnal cycle of climate model outputs on the computation of streamflow over 133 small to large North American catchments. A standard hydrological modeling chain was set up using the temperature and precipitation outputs from a high spatial (12-km) and temporal (1-hour) regional climate model large ensemble (ClimEx-LE). Two bias-corrected time series were generated using a multivariate quantile mapping method, with and without correction of the diurnal cycles of temperature and precipitation. The impact of this correction was evaluated on three small (< 500 km2), medium and large (> 1000 km2) surface area catchment size classes. Results show small but systematic improvements of streamflow simulations when bias-correcting the diurnal cycle of precipitation and temperature. The greatest improvements were seen on the small catchments, and least noticeable on the largest. The diurnal cycle correction allowed for hydrological simulations to accurately represent the diurnal cycle of summer streamflow on small catchments. Bias-correcting the diurnal cycle of precipitation and temperature is therefore recommended when conducting impact studies at the sub-daily time scale on small catchments.

scholarly journals Effects of climate model radiation, humidity and wind estimates on hydrological simulations

2012 ◽  
Vol 16 (2) ◽  
pp. 305-318 ◽  
Author(s):  
I. Haddeland ◽  
J. Heinke ◽  
F. Voß ◽  
S. Eisner ◽  
C. Chen ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Correction Method ◽  
Hydrological Models ◽  
Temperature And Precipitation ◽  
Effects Of Radiation ◽  
Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

Using a high-resolution regional climate model large ensemble to simulate the impact of extreme precipitation on flooding over small to medium-size catchments

2021 ◽  
Author(s):  
Mina Faghih ◽  
François Brissette ◽  
Parham Sabeti ◽  
Mostafa Tarek
Keyword(s):  
Return Period ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Large Ensemble ◽  
Catchment Scale ◽  
Time Step ◽  
Daily Time Step ◽  
The Impact ◽  
Small Catchments ◽  
Daily Time

&lt;p&gt;Recent studies show that the frequency and intensity of extreme precipitation will increase under a warmer climate. It is expected that extreme convective precipitation will scale at a larger than Clausius&amp;#8211;Clapeyron rate and especially so for short-duration rainfall. This has implication on flooding risk, and especially so on small catchments (&lt;500 km&lt;sup&gt;2&lt;/sup&gt;) which have a quick response time and are therefore particularly vulnerable to short duration rainfall.&amp;#160;The impact of the amplification of extreme precipitation as a function of catchment scale has not been widely studied because most of the climate change impact studies have been conducted at the daily time step or higher. This is because until recently the vast majority of climate model outputs have only been available at the daily time step.&lt;/p&gt;&lt;p&gt;This study has looked at the amplification of sub-daily, daily, and multiday extreme precipitation and flooding and its dependency on catchment scale. This work uses outputs from the Climex large-ensemble to study the amplification of extreme streamflow with return period from 2 to 300 years and durations from 1 to 24 hours over 133 North-American catchments. Using a large ensemble allows for the accurate empirical computation of extreme events with very large return periods.&amp;#160; Results indicate that future extreme streamflow relative increases are largest for smaller catchments, longer return period, and shorter rainfall durations. Small catchments are therefore more vulnerable to future extreme rainfall than their larger counterparts.&lt;/p&gt;

scholarly journals Effects of climate model radiation, humidity and wind estimates on hydrological simulations

2011 ◽  
Vol 8 (4) ◽  
pp. 7919-7945 ◽  
Author(s):  
I. Haddeland ◽  
J. Heinke ◽  
F. Voß ◽  
S. Eisner ◽  
C. Chen ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Correction Method ◽  
Hydrological Models ◽  
Temperature And Precipitation ◽  
Effects Of Radiation ◽  
Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

scholarly journals Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

2018 ◽  
Vol 22 (6) ◽  
pp. 3175-3196 ◽  
Author(s):  
Mathieu Vrac
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Grid Cell ◽  
Dependence Structure ◽  
Historical Period ◽  
Temporal Properties ◽  
Temperature And Precipitation ◽  
Climate Simulations ◽  
Site Dependence

Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R2D2) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R2D2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid cell  ×  number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure – making it possible to deal with a high number of statistical dimensions – that lets the climate model drive the temporal properties and their changes in time. R2D2 is applied on temperature and precipitation reanalysis time series with respect to high-resolution reference data over the southeast of France (1506 grid cell). Bivariate, 1506-dimensional and 3012-dimensional versions of R2D2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071–2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R2D2 is only satisfying in the inter-variable context, 1506d-R2D2 strongly improves inter-site properties and 3012d-R2D2 is able to account for both. Applications of the proposed R2D2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.

scholarly journals Seasonal Surface Air Temperature and Precipitation in the FSU Climate Model Coupled to the CLM2

2005 ◽  
Vol 18 (16) ◽  
pp. 3217-3228 ◽  
Author(s):  
D. W. Shin ◽  
S. Cocke ◽  
T. E. LaRow ◽  
James J. O’Brien
Keyword(s):  
Heat Flux ◽  
Air Temperature ◽  
Climate Model ◽  
Initial Conditions ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Community Land Model ◽  
Temperature And Precipitation ◽  
The Impact ◽  
Convective Scheme

Abstract The current Florida State University (FSU) climate model is upgraded by coupling the National Center for Atmospheric Research (NCAR) Community Land Model Version 2 (CLM2) as its land component in order to make a better simulation of surface air temperature and precipitation on the seasonal time scale, which is important for crop model application. Climatological and seasonal simulations with the FSU climate model coupled to the CLM2 (hereafter FSUCLM) are compared to those of the control (the FSU model with the original simple land surface treatment). The current version of the FSU model is known to have a cold bias in the temperature field and a wet bias in precipitation. The implementation of FSUCLM has reduced or eliminated this bias due to reduced latent heat flux and increased sensible heat flux. The role of the land model in seasonal simulations is shown to be more important during summertime than wintertime. An additional experiment that assimilates atmospheric forcings produces improved land-model initial conditions, which in turn reduces the biases further. The impact of various deep convective parameterizations is examined as well to further assess model performance. The land scheme plays a more important role than the convective scheme in simulations of surface air temperature. However, each convective scheme shows its own advantage over different geophysical locations in precipitation simulations.

Impact of climate change on water requirements and growth of potato in different climatic zones of Montenegro

2018 ◽  
Vol 9 (4) ◽  
pp. 657-671 ◽  
Author(s):  
Mirko Knežević ◽  
Ljubomir Zivotić ◽  
Nataša Čereković ◽  
Ana Topalović ◽  
Nikola Koković ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Regional Climate ◽  
Baseline Period ◽  
Yield Reduction ◽  
Adaptation Measures ◽  
Impact Of Climate Change ◽  
Temperature And Precipitation ◽  
Viable Solution ◽  
The Impact

Abstract The impact of climate change on potato cultivation in Montenegro was assessed. Three scenarios (A1B, A1Bs and A2) for 2001–2030, 2071–2100 and 2071–2100, respectively, were generated by a regional climate model and compared with the baseline period 1961–1990. The results indicated an increase of temperature during the summer season from 1.3 to 4.8 °C in the mountain region and from 1 to 3.4 °C in the coastal zone. The precipitation decreased between 5 and 50% depending on the scenario, region and season. The changes in temperature and precipitation influenced phenology, yield and water needs. The impact was more pronounced in the coastal areas than in the mountain regions. The growing season was shortened 13.6, 22.9 and 29.7 days for A1B, A1Bs and A2, respectively. The increase of irrigation requirement was 4.0, 19.5 and 7.3 mm for A1B, A1Bs and A2, respectively. For the baseline conditions, yield reduction under rainfed cultivation was lower than 30%. For A1B, A1Bs and A2 scenarios, yield reductions were 31.0 ± 8.2, 36.3 ± 11.6 and 34.1 ± 10.9%, respectively. Possible adaptation measures include shifting of production to the mountain (colder) areas and irrigation application. Rainfed cultivation remains a viable solution when the anticipation of sowing is adopted.

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

scholarly journals AMO Forcing of Multidecadal Pacific ITCZ Variability

2018 ◽  
Vol 31 (14) ◽  
pp. 5749-5764 ◽  
Author(s):  
Aaron F. Z. Levine ◽  
Dargan M. W. Frierson ◽  
Michael J. McPhaden
Keyword(s):  
Northern Hemisphere ◽  
Climate Model ◽  
Atlantic Multidecadal Oscillation ◽  
Intertropical Convergence Zone ◽  
Hadley Cell ◽  
Convergence Zone ◽  
Proxy Record ◽  
Temperature And Precipitation ◽  
The Pacific ◽  
The Impact

The Atlantic multidecadal oscillation (AMO) has been shown to play a major role in the multidecadal variability of the Northern Hemisphere, impacting temperature and precipitation, including intertropical convergence zone (ITCZ)-driven precipitation across Africa and South America. Studies into the location of the intertropical convergence zone have suggested that it resides in the warmer hemisphere, with the poleward branch of the Hadley cell acting to transport energy from the warmer hemisphere to the cooler one. Given the impact of the Atlantic multidecadal oscillation on Northern Hemisphere temperatures, we expect the Atlantic multidecadal oscillation to have an impact on the location of the intertropical convergence zone. We find that the positive phase of the Atlantic multidecadal oscillation warms the Northern Hemisphere, resulting in a northward shift of the intertropical convergence zone, which is evident in the Pacific climate proxy record. Using a coupled climate model, we further find that the shift in the intertropical convergence zone is consistent with the surface energy imbalance generated by the Atlantic multidecadal oscillation. In this model, the Pacific changes are driven in large part by the warming of the tropical Atlantic and not the extratropical Atlantic.

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

2017 ◽  
Vol 21 (6) ◽  
pp. 2649-2666 ◽  
Author(s):  
Matthew B. Switanek ◽  
Peter A. Troch ◽  
Christopher L. Castro ◽  
Armin Leuprecht ◽  
Hsin-I Chang ◽  
...  
Keyword(s):  
Error Correction ◽  
Bias Correction ◽  
Climate Model ◽  
Correction Method ◽  
Climate Change Signal ◽  
Quantile Mapping ◽  
Temperature And Precipitation ◽  
Distribution Mapping ◽  
Projected Changes ◽  
Time Invariant

Abstract. Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.

scholarly journals Joint bias correction of temperature and precipitation in climate model simulations

2014 ◽  
Vol 119 (23) ◽  
pp. 13,153-13,162 ◽  
Author(s):  
Chao Li ◽  
Eva Sinha ◽  
Daniel E. Horton ◽  
Noah S. Diffenbaugh ◽  
Anna M. Michalak
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Climate Model Simulations
Sign in / Sign up

Export Citation Format

Share Document