The prediction of precipitation changes in the Aji-Chay watershed using CMIP6 models and the wavelet neural network

Abstract

Greenhouse gases affect climate system disturbances. This research employs sixth generation CMIP6 models in the SSP5.85 scenario and extends the use of the neural wavelet network to predict precipitation variations for the future (2025–2065). Kendall's trend test is used to assess changes in precipitation trends for observed and projected periods. An analysis of variance (ANOVA) validates models under SSP5.85 by comparing observed precipitation with model predictions. A multi-layer perceptron neural network assesses climate change's impact on future precipitation. Findings indicate future precipitation is projected to fluctuate from −0.146 to over −2.127 mm compared to the baseline period. The observed period showed a significant 3.37% monthly precipitation decrease within the watershed. The CanESM5 model predicts a 3.916 reduction in precipitation with 95% confidence, while INM-CM4-8 and MRI-ESM2-0 models are less certain. The minor difference between CanESM5's predicted (−5.91) and observed (−5.05) precipitation suggests a slight variance. On the other hand, the wavelet neural network (WNN) model predicts that precipitation in this region will increase in the future. In general, this study predicts a decrease in precipitation for the Aji-Chay watershed in Iran over the next decade, could lead to serious issues like lower crop yields, rising food prices, and even droughts. HIGHLIGHT This research stands as a beacon of progress in climate science. Through its use of cutting-edge modeling, innovative methodology, statistical rigor, and policy-relevant conclusions, it thoroughly investigates the critical issue of climate change's impact on rainfall and its broader implications for water resources.;

Keywords

• anova
• canesm5
• neural wavelet network
• the aji-chay watershed