Assessment of various bias correction methods and future projection of minimum and maximum temperatures using regional climate model over Thanjavur district

Sundaram, Gunavathi; Radhakrishnan, Selvakumar

doi:10.1007/s12517-022-10403-z

Cited by 4 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to the reference period, the annual average growth rates of Tmin rise by 1.55, 2.33, and 2.86 • C under RCP4.5 and by 1.74, 3.12 and 4.83 • C under RCP8.5, respectively, for the (2025-2049), (2050-2074) and (2075-2098) periods. Our results are slightly higher than those obtained by Sundaram and Radhakrishnan [44] in the Thanjavur district. The authors found that annual Tmin is projected to increase by about 1.06-2.32 • C under the RCP4.5 scenario, and by 1.59-3.87 • C under the RCP8.5 scenario for short and long-term scenarios.…”

Section: Projected Changes In Bias-corrected Tmax and Tmin Under Both...contrasting

confidence: 87%

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

Latrech,

Hermassi,

Yacoubi

et al. 2024

Agriculture

View full text Add to dashboard Cite

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET0). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET0 model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET0 values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET0 increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET0 are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET0 patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region.

show abstract

Section: Projected Changes In Bias-corrected Tmax and Tmin Under Both...contrasting

confidence: 87%

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

Latrech,

Hermassi,

Yacoubi

et al. 2024

Agriculture

View full text Add to dashboard Cite

show abstract

“…It is based on the assumption that changes in climate data are location-specific and occur only over large distances [9,10]. However, due to its simple transfer function, this method does not capture changes in extreme events [11]. The linear scaling method corrects the mean of future data by adjusting the long-term monthly mean of model data to that of the observation [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Multi Criteria Decision Analysis Approach for Regional Climate Model Selection and Future Climate Assessment in the Mono River Basin, Benin and Togo

2022

View full text Add to dashboard Cite

Regional climate models (RCMs) are key in the current context of global warming, and they are increasingly used to support decision-making and to identify adaptation measures in response to climate change. However, considering the wide range of available RCMs, it is important to identify the most suitable ones prior to climate impact studies, especially at small scales like catchments. In this study, a multicriteria decision analysis approach, namely the technique for order preferences by similarity to an ideal solution (TOPSIS) was applied to select the best performing RCMs in the Mono River Basin of Benin and Togo (West Africa). The TOPSIS method was used to systematically rank 15 RCMs accessed from the coordinated regional downscaling experiment (CORDEX) database. Six RCMs were finally selected and averaged into an ensemble to assess the future climate in the Mono River Basin until 2070 compared to the period 1966–2015. Two climate change scenarios, RCP 4.5 and RCP 8.5, were considered. The results show that under both climate change scenarios, the annual temperature has an increasing trend during the period 1966–2070, whereas annual rainfall for the next 50 years presents high variability and no statistically significant trend. Furthermore, seasonal cycles of rainfall are expected to change in the different parts of the catchment with delayed onset of rainfall, longer dry seasons, and rainfall intensification. In response to the projected changes, impact studies and risk assessments need to be carried out to evaluate potential implications for human security in the Mono River Basin and to provide adequate adaptation measures.

show abstract

Reservoirs Response to Climate Change Under Medium Emission Scenario in Upper Krishna Basin, India Using Geospatial Inputs

Arivoli,

Saravanan,

Chandrasekar

et al. 2024

J Indian Soc Remote Sens

View full text Add to dashboard Cite

Assessment of various bias correction methods and future projection of minimum and maximum temperatures using regional climate model over Thanjavur district

Cited by 4 publications

References 51 publications

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

A Multi Criteria Decision Analysis Approach for Regional Climate Model Selection and Future Climate Assessment in the Mono River Basin, Benin and Togo

Reservoirs Response to Climate Change Under Medium Emission Scenario in Upper Krishna Basin, India Using Geospatial Inputs

Contact Info

Product

Resources

About