A novel statistical model for flood prediction in the Eel River watershed, New Brunswick, Canada

Abstract

ABSTRACTA strong correlation between the effect of climate change and the increase in flooding frequency and magnitude has been reported in Canada. Consequently, there is a crucial need to examine the effects of future climate change scenarios on flooding conditions. The main objective of this research is to better understand the destructive effects of flood events under historical and future climate change conditions for a small watershed (Eel River watershed) in New Brunswick (NB), Eastern Canada. A practical model had been developed using the modified Artificial Neural Network (ANN) in MATLAB by the authors of this study. The architecture and data structure of ANN is characterized by a back propagation with the Levenberg–Marquardt method. The observed daily total precipitation, daily maximum and minimum air temperatures, daily discharge for the period 1967 to 1983, the simulated monthly maximum and minimum air temperatures, and monthly total precipitation for the period of 1996–2099 from the CanESM2, the second-generation Canadian Earth System Model (CGCM), were used as input of the model. The Representative Concentration Pathways (RCP 4.5 and 8.5), as suitable climate change scenarios, were selected based on the Intergovernmental Panel on Climate Change (IPCC) recommendations for flood studies. Daily values of temperatures, precipitations, and discharges were converted to monthly mean values for better prediction of the output results. In addition, two series of observed discharges were prepared using mean monthly (Qavg) and daily maximum discharges (Qd) as the Target of the model. For more accurate analysis, the time frames of 1996–2012 (for the historical) and 2022–2038, 2039–2055, 2056–2072, 2073–2089, and 2083–2099 (for the future) were considered with a duration of 16 years for each time frame. The output results of ANN were predicted daily maximum (Qd) and mean (Qavg) discharges under the impact of climate change scenarios. As a part of the developed model, Flood Frequency Analysis (FFA) was undertaken using the generalized extreme value (GEV) and the three-parameter lognormal (LN3) distributions based on the predicted and observed discharges. The performance of FFA and ANN were demonstrated using the Anderson–Darling (AD), the Chi-square (CS) tests and coefficient of correlation (R) and mean squared error (MSE), respectively. In conclusion, the three most critical time frames with the highest values of predicted discharges were 2022–2038, 2056–2072, and 2073–2089 for RCP4.5 and 2039–2055, 2073–2089, and 2083–2099 for RCP8.5. Also, based on the FFA, the magnitudes of flood recurrence for the future time period of 100 years will dramatically increase according to the most critical time frames of 2056–2072 and 2039–2055 for RCP 4.5 and 8.5, respectively. Findings indicated that the Eel River watershed will encounter severe floods, and about a 50% increase in mean discharge, especially for the critical time frames. Finally, flood occurrences show increasing trends due to climate change effects in the most critical time frames.

Keywords

• ANN
• flood prediction
• climate change
• CGCM
• New Brunswick