Abstract:This study analyzed the performance of four (REgional MOdel (REMO2009), High-Resolution Hamburg Climate Model 5 (HIRAM5), Climate Limited-Area Modeling Community (CCLM4-8) and Rossby Centre Regional Atmospheric Model (RCA4)) Regional Climate Models (RCMs) simulations from Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa program. The simulation period of 1985-2005 was evaluated considering how each RCM simulated the observed rainfall and air temperature over southwest Ethiopia. It was found t… Show more
“…In the 2050s and 2080s, the average annual temperature is expected to rise in all seasons in all RCP scenarios. Demissie & Sime (2021) had undertaken the research in the rainfall and temperature simulation capabilities of CORDEX regional climate models like ICHEC-HIRAM5, MPI-CCLM4, and CNRM-RCA4. Figure 8 Average (minimum and minimum) seasonal and annual temperature at each station of the basin.…”
Section: Resultsmentioning
confidence: 99%
“…The magnitude and distribution of the AET were determined using MPI-CCLM4, ICHECHIRAM, and CNRMRCA4 outputs. The RECMO2009 model's output specifically does not fit the Gilgel Gibe district's bimodal rainfall distribution pattern ( Demissie and Sime, 2021 ).…”
Section: Methodsmentioning
confidence: 99%
“…In spite of the rainfall patterns, the average temperature will practically increase throughout the basin and in all seasons (Figure 13a-e). The simulation of temperature and rainfall employing CORDEX regional climate models ICHEC-HIRAM5, MPI-CCLM4, and CNRM-RCA4 demonstrated effectiveness for the accuracy assessment (Elshamy et al, 2009;Worku et al, 2020;Demissie and Sime, 2021;Rahimpour et al, 2021). The WetSpass-M model's global and local model parameters were examined for their impact on AET variation throughout the referral period.…”
Section: Preview Of the Projected Temperaturementioning
“…In the 2050s and 2080s, the average annual temperature is expected to rise in all seasons in all RCP scenarios. Demissie & Sime (2021) had undertaken the research in the rainfall and temperature simulation capabilities of CORDEX regional climate models like ICHEC-HIRAM5, MPI-CCLM4, and CNRM-RCA4. Figure 8 Average (minimum and minimum) seasonal and annual temperature at each station of the basin.…”
Section: Resultsmentioning
confidence: 99%
“…The magnitude and distribution of the AET were determined using MPI-CCLM4, ICHECHIRAM, and CNRMRCA4 outputs. The RECMO2009 model's output specifically does not fit the Gilgel Gibe district's bimodal rainfall distribution pattern ( Demissie and Sime, 2021 ).…”
Section: Methodsmentioning
confidence: 99%
“…In spite of the rainfall patterns, the average temperature will practically increase throughout the basin and in all seasons (Figure 13a-e). The simulation of temperature and rainfall employing CORDEX regional climate models ICHEC-HIRAM5, MPI-CCLM4, and CNRM-RCA4 demonstrated effectiveness for the accuracy assessment (Elshamy et al, 2009;Worku et al, 2020;Demissie and Sime, 2021;Rahimpour et al, 2021). The WetSpass-M model's global and local model parameters were examined for their impact on AET variation throughout the referral period.…”
Section: Preview Of the Projected Temperaturementioning
“…In this study, the recorded data had missed values randomly and the percentage of the missing data was less than 4%. Hence linear regression was used to ll the missing rainfall data while the simple average method was used to ll the temperature data using XLSTAT 2019 tool (Demissie and Sime, 2021;Sime and Demissie, 2022).…”
Hydrological measurements are vital for Water Resources Planning and Management. Modeling rainfall-runoff may be used to understand the impact of future hydrological changes such as flooding, soil loss, and sedimentation effect on lakes and reservoirs for irrigation and hydropower. But the difficult one is the selection of the best models. All hydrological models may not be equally performed due to being influenced by input data and over models system that will be used for the analysis. Therefore, two hydrological models; Soil and Water Assessment Tool (SWAT), and Hydrologic Engineering Centers Hydrological Modeling System (HEC-HMS) models to simulate rainfall runoff. These models were calibrated and validated using daily observed streamflow data. The R2 and NSE were used to check the performance of the models and the result indicates that during HEC-HMS model calibration and validation, R2 of 0.6 and 0.61, NSE of 0.55 and 0.53, generated respectively. During the SWAT model accuracy assessment, the values of model performance indicators were R2 of 0.69 and 0.68, NSE of 0.58 and 0.52 for calibration and validation respectively. This indicates both SWAT and HEC-HMS models have been satisfied in streamflow simulations. The SWAT model overestimated the daily streamflow more than the observed and HEC-HMS simulated streamflow. These models overestimated peak streamflow during both the calibration and validation period. The curve number grid generated by HEC-GeoHMS was slightly lower than the curve number simulated by the SWAT model.
“…In this study, data homogeneity was checked using the standard normal homogeneity test (SNHT) in XLSTAT. This method was used to identify a variation in a time series of precipitation data by comparing the mean of the rst k years of the record with the last n-k years (Agha et al, 2017;Demissie & Sime, 2021a;Elzeiny et al, 2019). Errors such as minimum temperature above maximum temperature and negative precipitation values were corrected using nearby stations.…”
Using regional climate models (RCMs) and ensembles of multiple model simulation outputs without assessing their modeling performance did not always ensure the best agreement between observed and modeled climate variables. To this end, assessing the modeling performance of regional climate models (RCMs) is indispensable in selecting the most effective model to use for climate change impact studies. In this study, the performance of ten Coordinated Regional Climate Downscaling Experiments (CORDEX) in Africa was examined against observational datasets from 1986 to 2005 across the entire Omo Gibe River Basin (OGRB). The output of RCMs was evaluated based on their ability to reproduce the magnitude and pattern of monthly, seasonal, and annual precipitation and air temperature, precipitation characteristics, and statistical metrics. The results confirm the difference between RCMs in capturing climate conditions at both spatial and temporal scales. The spatial pattern of mean annual precipitation was better reproduced by the ensemble mean and RACMO22T (EC-EARTH). CCLM4-8-17 (MPI) and the ensemble mean reproduced the annual patterns of observed precipitation, even though the amounts were different. Except for peak precipitation, all RCMs simulated seasonal precipitation, and the pattern was reasonably captured by RACMO22T (EC-EARTH), CCLM4-8-17 (CNRM), RCA4 (CNRM), CCLM4-8-17 (MPI), and REMO2009 (MPI). The interannual and seasonal variability of precipitation was higher than the variability of air temperature. It was found that observed and RCM precipitation simulations using CCLM4-8-17 (MPI), REMO2009 (MPI), and RCA4 (CNRM) showed better agreement at several individual stations in the Omo Gibe River Basin (OGRB. Likewise, RCA4 (MPI) and CCLM4-8-17 (MPI) were superior in capturing minimum and maximum air temperatures. The cumulative distribution of extreme precipitation was better captured by RCA4 (MIROC5), and all RCMs, including their ensemble mean, overestimated the return period. Overall, the study emphasizes that the selection of robust RCMs that better reproduce observed climate conditions and the use of multi-model ensembles of models with the best performance after systematic bias correction are fundamentally necessary for any study of climate change impacts and adaptation in the OGRB.
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