scholarly journals Multiple bias-correction of dynamically downscaled CMIP5 climate models temperature projection: a case study of the transboundary Komadugu-Yobe river basin, Lake Chad region, West Africa

2020 ◽  
Vol 2 (7) ◽  
Author(s):  
O. E. Adeyeri ◽  
P. Laux ◽  
A. E. Lawin ◽  
K. S. A. Oyekan
Keyword(s):  
West Africa ◽  
River Basin ◽  
Bias Correction ◽  
Climate Models ◽  
Lake Chad ◽  
Cmip5 Climate Models ◽  
Temperature Projection

scholarly journals Bias correction capabilities of quantile mapping methods for rainfall and temperature variables

2020 ◽  
Author(s):  
Maedeh Enayati ◽  
Omid Bozorg-Haddad ◽  
Javad Bazrafshan ◽  
Somayeh Hejabi ◽  
Xuefeng Chu
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Correction Method ◽  
Quantile Mapping ◽  
Topographic Features ◽  
Temperature Variable ◽  
Transformation Functions ◽  
Karkheh River Basin ◽  
The Karkheh River

Abstract This study aims to conduct a thorough investigation to compare the abilities of QM techniques as a bias correction method for the raw outputs from GCM/RCM combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the CORDEX dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.

Assessment of land-cover changes in a sub-catchment of the Oti basin (West Africa): A case study of the Kara River basin

2014 ◽  
Vol 2014 (1) ◽  
pp. 151-170 ◽  
Author(s):  
Hèou Maléki Badjana ◽  
Peter Selsam ◽  
Kpérkouma Wala ◽  
Wolfgang-Albert Flügel ◽  
Manfred Fink ◽  
...  
Keyword(s):  
Land Cover ◽  
West Africa ◽  
River Basin ◽  
Land Cover Changes

scholarly journals Bias Correction of Climate Model’s Precipitation Using the Copula Method and Its Application in River Basin Simulation

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 600 ◽  
Author(s):  
Georgia Lazoglou ◽  
Christina Anagnostopoulou ◽  
Charalampos Skoulikaris ◽  
Konstantia Tolika
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Monthly Precipitation ◽  
Distributed Hydrological Model ◽  
Copula Method ◽  
Corrected Data ◽  
Precipitation Analysis ◽  
Obs Data

During the last few decades, the utilization of the data from climate models in hydrological studies has increased as they can provide data in the regions that lack raw meteorological information. The data from climate models data often present biases compared to the observed data and consequently, several methods have been developed for correcting statistical biases. The present study uses the copula for modeling the dependence between the daily mean and total monthly precipitation using E-OBS data in the Mesta/Nestos river basin in order to use this relationship for the bias correction of the MPI climate model monthly precipitation. Additionally, both the non-corrected and bias corrected data are tested as they are used as the inputs to a spatial distributed hydrological model for simulating the basin runoff. The results showed that the MPI model significantly overestimates the E-OBS data while the differences are reduced sufficiently after the bias correction. The outputs from the hydrological models were proven to coincide with the precipitation analysis results and hence, the simulated discharges in the case of copula corrected data present an increased correlation with the observed flows.

scholarly journals Addressing Climate Change Impacts on Streamflow in the Jinsha River Basin Based on CMIP5 Climate Models

Water ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 910 ◽  
Author(s):  
Jun Yin ◽  
Zhe Yuan ◽  
Denghua Yan ◽  
Zhiyong Yang ◽  
Yongqiang Wang
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Jinsha River ◽  
Cmip5 Climate Models

scholarly journals Using a Hydrologic Model to Assess the Performance of Regional Climate Models in a Semi-Arid Watershed in Brazil

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 170 ◽  
Author(s):  
Carlos Santos ◽  
Felizardo. Rocha ◽  
Tiago Ramos ◽  
Lincoln Alves ◽  
Marcos Mateus ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Northeastern Brazil ◽  
Impact Of Climate Change ◽  
Monthly Streamflow ◽  
The Impact

This study assessed the impact of climate change on the hydrological regime of the Paraguaçu river basin, northeastern Brazil. Hydrological impact simulations were conducted using the Soil and Water Assessment Tool (SWAT) for 2020–2040. Precipitation and surface air temperature projections from two Regional Climate Models (Eta-HadGEM2-ES and Eta-MIROC5) based on IPCC5—RCP 4.5 and 8.5 scenarios were used as inputs after first applying two bias correction methods (linear scaling—LS and distribution mapping—DM). The analysis of the impact of climate change on streamflow was done by comparing the maximum, average and reference (Q90) flows of the simulated and observed streamflow records. This study found that both methods were able to correct the climate projection bias, but the DM method showed larger distortion when applied to future scenarios. Climate projections from the Eta-HadGEM2-ES (LS) model showed significant reductions of mean monthly streamflow for all time periods under both RCP 4.5 and 8.5. The Eta-MIROC5 (LS) model showed a lower reduction of the simulated mean monthly streamflow under RCP 4.5 and a decrease of streamflow under RCP 8.5, similar to the Eta-HadGEM2-ES model results. The results of this study provide information for guiding future water resource management in the Paraguaçu River Basin and show that the bias correction algorithm also plays a significant role when assessing climate model estimates and their applicability to hydrological modelling.

scholarly journals Evaluation and selection of CORDEX-SA datasets and bias correction methods for a hydrological impact study in a humid tropical river basin, Kerala

2021 ◽  
Author(s):  
M. S. Saranya ◽  
V. Nair Vinish
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Circulation Model ◽  
Climate Variables ◽  
Tropical River ◽  
Hydrological Impact ◽  
Selection Of

Abstract It is well recognised that the performance of climate model simulations and bias correction methods is region specific, and, therefore, careful validation should always be performed. This study evaluates the performance of five general circulation model–regional climate model (GCM–RCM) combinations selected from CORDEX–SA datasets over a humid tropical river basin in Kerala, India, for climate variables such as precipitation, maximum and minimum temperatures. This involves ranking of the selected climate models based on an EDAS (Evaluation Based on Distance from Average Solution) method and the selection of an appropriate bias correction method for the selected three climate variables. A range of indices are used to evaluate the performance of the bias-corrected climate models to simulate observed climate data. Finally, the hydrological impact of the bias-corrected ranked models is assessed by simulating streamflow over the river basin using individual models and different combinations of models based on rank. According to the findings, hydrological simulation using an average of all GCM–RCM pairs provides the best model output in simulating streamflow, with an NSE value of 0.72. The results confirm the importance of a multimodel ensemble for improving the reliability and minimising the uncertainty of climate predictions for impact studies.

Sign in / Sign up

Export Citation Format

Share Document