A CMIP6-ensemble-based evaluation of precipitation and temperature projections
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Wien
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Understanding climate change's effects on dam basins is very important for water resource management because of their important role in providing essential functions such as water storage, irrigation, and energy production. This study aims to investigate the impact of climate change on temperature and precipitation variables in the Alt & imath;nkaya Dam Basin, which holds significant potential for hydroelectric power generation in T & uuml;rkiye. These potential impacts were investigated by using ERA5 reanalysis data, six GCMs from the current CMIP6 archive, and two Shared Socioeconomic Pathways (SSP2 - 4.5 and SSP5 - 8.5) scenario data. Four Multi-Model Ensemble (MME) models were developed by using an Artificial Neural Network (ANN) approach (ENS1), simple averaging (ENS2), weighted correlation coefficients (ENS3), and the MARS algorithm (ENS4), and the results were compared to each other. Moreover, quantile delta mapping (QDM) bias correction was used. The 35-year period (1980-2014) was chosen as the reference period, and further evaluations were conducted by dividing it into three future periods (near (2025-2054), mid-far (2055-2084), and far (2085-2100)). Considering the results achieved from the MMEs, variations are expected in the monthly, seasonal, and annual assessments. Projections until the year 2100 indicate that under optimistic and pessimistic scenarios, temperature increases could reach up to 3.11 degrees C and 5.64 degrees C, respectively, while precipitation could decrease by as much as 19% and 43%, respectively. These results suggest that the potential changes in temperature and precipitation within the dam basin could significantly impact critical elements such as future water flow and energy production.
Açıklama
Anahtar Kelimeler
Artificial Neural-Network, Adaptive Regression Splines, Bias Correction Methods, Climate-Change Impacts, Multimodel Ensemble, Water-Resources, River-Basin, Models, Prediction, Cmip6
Kaynak
Theoretical and Applied Climatology
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
155
Sayı
8
