Climate indices and hydrological extremes: Deciphering the best fit model

Area of Science:

• Climatology
• Hydrology
• Water Resource Management

Background:

• India faces significant hydrological extremes impacting agriculture and water security.
• Accurate prediction of these extremes is crucial for effective water resource management.
• Traditional models often struggle with the complexity of Indian hydroclimatic patterns.

Purpose of the Study:

• To review climate indices and non-linear models for analyzing Indian hydrological extremes.
• To compare different modeling techniques for predicting precipitation extremes.
• To identify the best-fit approach for Indian hydroclimatic forecasting.

Main Methods:

• Comprehensive review of large-scale climate indices and non-linear models.
• Analysis of long-term precipitation data and global climate indices.
• Comparison of statistical operations and modeling techniques for hydroclimatic tele-connections.

Main Results:

• Global atmospheric phenomena outperform traditional geospatial models in predicting Indian precipitation extremes.
• Wavelet-based non-linear models demonstrate superior performance.
• The use of large-scale climate indices for local hydrological prediction is increasing.

Conclusions:

• Wavelet-based non-linear models are best suited for Indian hydrological extreme prediction.
• Large-scale climate indices are essential for understanding the Indian monsoon.
• Findings support improved water resource management for Indian agriculture and water security.