Incorporation of non-stationarity in precipitation intensity-duration-frequency curves for Kerala, India

Abstract

Intensity-Duration-Frequency (IDF) curve is an essential hydrologic tool used for the design of hydraulic infrastructure. In a changing climate scenario, the assumption of a static return period of precipitation extremes is not valid; the under-estimation of rainfall intensity values may lead to the failure of critical infrastructure. This paper is focused on identifying the extent of non-stationary behaviour at different locations in Kerala. The annual maxima rainfall data prepared from daily time series was idealized into non-stationary Generalized Extreme Value (GEV) distributions, with time-varying parameters after rainfall disaggregation operation. Non-stationary rainfall intensities were estimated using different non-stationary models, and the best model was identified using Akaike Information Criteria (AIC). From the analysis using station-wise data, it was found that districts including Palakkad, Malappuram, Idukki and Pathanamthitta were experiencing significant non-stationarity in extreme precipitation events. Non-stationary rainfall extremes were concentrated at Eastern regions of Kerala compared to coastal and midlands of Kerala.

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

“…The authors observed that the stationary assumption underestimated extreme precipitation by as much as 60% in a 2 h duration, 2-year return period rainfall, which could potentially increase the frequency of flood risks and infrastructure failure several folds. Another study conducted by Soumya et al [73] in Kerala, India, found that non-stationary IDF curves had higher rainfall intensities than stationary IDF curves, with an increase in rainfall intensity of greater than 50% for the 100-year return period under a non-stationary assumption compared to that of a stationary assumption. Similarly, a study conducted in the metropolitan region of Ontario, Canada, revealed similar findings.…”