Modeling Rainfall Intensity-Duration-Frequency (IDF) and Establishing Climate Change Existence in Uyo-Nigeria Using Non-Stationary Approach

Abstract

This study aims at establishing if climate change exists in the Niger Delta environment using non-stationary rainfall Intensity-Duration-Frequency (IDF) modelling incorporating time-variant parameters. To compute the intensity levels, the open-access R-studio software was used based on the General Extreme Value (GEV) distribution function. Among the four linear parameter models adopted for integrating time as a covariate, the fourth linear model incorporating scale and location with the shape function constant produced the least corrected Akaike Information Criteria (AICc), varying between 306.191 to 101.497 for 15 and 1440 minutes, respectively, selected for calibration of the GEV distribution equation. The non-stationary intensities yielded higher values above those of stationary models, proving that the assumption of stationary IDF models underestimated extreme events. The difference of 13.71 mm/hr (22.71%) to 14.26 mm/hr (17.0%) intensities implies an underestimation of the peak flood from a stationary IDF curve. The statistical difference at a 95% confidence level between stationary and non-stationary models was significant, confirming evidence of climatic change influenced by time-variant parameters. Consequently, emphasis should be on applying shorter-duration storms for design purposes occurring with higher intensities to help reduce the flood risk and resultant infrastructural failures.

Comparative Performance of Non-Stationary Intensity-Duration-Frequency (NS-IDF) Models for Selected Gauge Stations in the Niger Delta

“…The basic steps required to follow for the development of a 24-hour GEV distribution function Curve fitting for both stationary and Non-stationary rainfall intensity are well cataloged in our earlier publications [13,14]. The procedure required are as follows:i.…”

“…The resulting solutions and the performances of the different statistical parameters expressed as a function of time with their values for the various stations are presented in Table 1. The best behavioral parameter extreme model was selected based on the corrected Akaike Information Criteria (AICc) [13,14]. The model that had the lowest AICc was selected as the model that best represents the time series data.…”

“…The measured rainfall annual maximum time series data used in this analysis was the 24-hourly time series data with the Modified Chowdhury Indian Meteorological Department (MCIMD) downscaling model applied to generate the time series data for the analysis [13,14]. In developing the Non-stationary IDF models, preference was placed on the MCIMD model downscaled time series data over others for the reason that the calibrated MCIMD model produced higher predicted rainfall intensities than the Indian Meteorological Department (IMD) method.…”

Flood Estimation and Control in a Micro-Watershed Using GIS-Based Integrated Approach

“…The maximum daily (24 h) precipitation data for 1968-2017 in Samaru was reduced to a shorter time scale of 10, 20, 30, 60, 120, 360, 720, and 1440 min using the formula recommended by the Indian Meteorological Department [42,43]. The formula has been widely used and has gained acceptance worldwide [44][45][46][47][48].…”