Editorial: Assessment of Climate Change Impact on Water Resources Using Machine Learning Algorithms

Machine learning (ML) algorithms bring about a game changer tool in developing estimation models in various fields of research, including water resources and climate change.These techniques can be used for solving various problems when assessing climate change impacts on water resources.For instance, they can be utilized to downscale outputs of Global Climate Models (GCMs) to investigate climate change effects on hydroclimatic variables.Furthermore, ML can be employed to study variations of water quantity and quality under a changing climate.Moreover, they can be exploited to explore climate change impacts on rivers, groundwater, and water supply systems.Because of the importance of the topic, this special issue intends to provide an opportunity to collect recent investigations focusing on evaluating climate change impacts on water resources.The scientific peer-reviewed papers contributed to this special issue are summarized in the following:• Statistical computation for hydrological assessment of climate change Understanding how hydroclimatic variables change over time considering climate change impacts is crucial.Nguyen et al.(2023) evaluated two ML models, i.e., convolutional neural network (CNN) and long short-term memories (LSTM), for estimating hydroclimatic variables at the 3S River Basin.For assessing climate change impacts, three climate models, i.e., CMCC-CMS, HadGEM-AO2, and MIROC5, and two climate scenarios, i.e., Representative Concentration Pathways (RCPs) 4.5 and 8.5, were considered for three future periods.An increase in the mean annual temperature and fluctuations in the annual precipitation were detected.Furthermore, ML-based future projections yield a rise in the streamflow in the Srepok and Sesan Rivers, a reducing trend of streamflow in the Sekong, and increasing flood risk in the Sekong and Sesan basins.Patel & Mehta (2023) conducted a statistical analysis of climate change over the Hanumangarh district.They exploited (i) graphical (Innovative Trend Analysis method) and (ii) statistical (Mann-Kendall's test and Sen's Slope estimator) trend analysis methods to explore monthly, seasonal, and annual variations of precipitation for 122 years.Their results indicated an increasing trend in southwest monsoon season and annual precipitation based on the graphical trend analysis method, which was identified as the most robust model in their study.