Evaluation of Regional Climate Models (RCMs) Performance in Simulating Seasonal Precipitation over Mountainous Central Pindus (Greece)

Abstract

During the last few years, there is a growing concern about climate change and its negative effects on water availability. This study aims to evaluate the performance of regional climate models (RCMs) in simulating seasonal precipitation over the mountainous range of Central Pindus (Greece). To this end, observed precipitation data from ground-based rain gauge stations were compared with RCMs grid point’s simulations for the baseline period 1974–2000. Statistical indexes such as root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficient, and standard deviation (SD) were used in order to evaluate the model’s performance. The results demonstrated that RCMs fail to represent the temporal variability of precipitation time series with exception of REMO. Although, concerning the model’s prediction accuracy, it was found that better performance was achieved by the RegCM3 model in the study area. In addition, regarding a future projection (2074–2100), it was highlighted that precipitation will significantly decrease by the end of the 21st century, especially in spring (−30%). Therefore, adaption of mountainous catchment management to climate change is crucial to avoid water scarcity.

Trend Analysis of Rainfall Using Gridded Data over a Region of Southern Italy

“…For example, Rudolf et al [35] studied the influence of the station density on a 2.5 • × 2.5 • gridded dataset evaluated over different land regions with high station coverage, evidencing an error between ±7 and 40% when 5 rain gauges per grid cell are considered. Stefanidis et al [36] compared data of rain gauge stations of the mountainous range of Central Pindus (Greece) with the RCMs simulations in 1974-2000, demonstrating that RCMs gridded data often fail to characterize the temporal variability of rainfall series.…”

Estimation of Threshold Rainfall in Ungauged Areas Using Machine Learning

“…This study uses MAE, RMSE, and RMSLE to compare the performance of different models. Most studies use the above three indicators a lot for data comparison [38][39][40]. They are widely used to objectively assess the accuracy of a regression equation by analyzing differences between observations and estimates.…”

Future Flood Hazard Assessment for the City of Pamplona (Spain) Using an Ensemble of Climate Change Projections

“…Consequently, several studies have considered the impact of climate change in smaller river catchments, avoiding the extrapolation of the results from large-scale studies. They evaluate the climate change impact on the water cycle with different objectives, as climate change can have implications in sustainable management of ecosystem services [23], daily, monthly, and yearly streamflow patterns [24], seasonal precipitation [25], or engineering hydraulic design [10]. Indeed, the use of RCMs in the Mediterranean area at the river basin scale also demonstrated that different parts of southern Europe could have problems related to water scarcity in the future, as precipitation will decrease by the end of the 21st century in the central part of Greece [25], and a reduction of water yield mainly forced by decreasing precipitations has been detected in the Taro river basin (Italy) [23].…”