Evaluation of additional physiographical variables characterising drainage network systems in regional frequency analysis, a Quebec watersheds case-study

• Published in: Stochastic environmental research and risk assessment 2022-02, Vol.36 (2), p.331-351
• Main Authors: Msilini, A., Ouarda, T. B. M. J., Masselot, P.
• Format: Article
• Language: English
• Subjects: Aquatic Pollution; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Correlation analysis; Datasets; Drainage; Drainage density; Drainage patterns; Drainage systems; Earth and Environmental Science; Earth Sciences; Environment; Flood predictions; Frequency analysis; Hydrology; Math. Appl. in Environmental Science; Original Paper; Physics; Probability Theory and Stochastic Processes; Quantiles; Regional analysis; Regression analysis; Regression models; Ruggedness; Statistics for Engineering; Variables; Waste Water Technology; Water Management; Water Pollution Control; Watersheds
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-021-02109-7

Regional Frequency Analysis (RFA) relies on a wide range of physiographical and meteorological variables to estimate hydrological quantiles at ungauged sites. However, additional catchment characteristics related to its drainage network are not yet fully understood and integrated in RFA procedures. The aim of the present paper is to propose the integration of several physiographical variables characterizing the drainage network systems in RFA, and to evaluate their added value in predicting quantiles at ungauged sites. The proposed extended dataset (EXTD) includes several variables characterising drainage network characteristics. To evaluate the new variables, a number of commonly used RFA approaches are applied to the extended data representing 151 stations in Quebec (Canada) and compared to a standard dataset (STA) that excludes the new variables. The considered RFA approaches include the combination of two neighborhood methods namely the canonical correlation analysis (CCA) and the region of influence (ROI) with two regional estimation (RE) models which are the log-linear regression model (LLRM) and the generalized additive model (GAM). The RE models are also applied without the hydrological neighborhood. Results show that regional models using the extended dataset lead to significantly better flood quantile predictions, especially for large basins. Indeed, the variable selection performed with EXTD consistently includes some of the new variables, in particular the drainage density, the stream length ratio, and the ruggedness number. Two other new variables are also identified and included in the DHR step: the circularity ratio and the texture ratio. This leads to better predictions with relative errors about 29% for EXTD, versus around 42% for STA in the case of the best combination of RFA approaches. Thus, the proposed new variables allow for a better representation of the physical dynamics within the watersheds.