A method for low-flow estimation at ungauged sites: a case study in Wallonia (Belgium)

M Grandry,S Gailliez,A Verstraete,C Sohier,A Degré

doi:10.5194/hess-17-1319-2013

Abstract

Abstract. Well-integrated water management can notably require estimating low flows at any point of a river. Depending on the management practice, it can be needed for various return periods. This is seldom addressed in the literature. This paper shows the development of a full analysis chain including quality analysis of gauging stations, low-flow frequency analysis, and building of a global model to assess low-flow indices on the basis of catchment physical parameters. The most common distributions that fit low-flow data in Wallonia were two-parameter lognormal and gamma. The recession coefficient and percolation were the most explanatory variables, regardless of the return period. The determination coefficients of the models ranged from 0.51 to 0.67 for calibration and from 0.61 to 0.80 for validation. The regression coefficients were found to be linked to the return period. This was used to design a complete equation that gives the low-flow index based on physical parameters and the desired return period (in a 5 to 50 yr range). The interest of regionalisation and the development of regional models are also discussed. Four homogeneous regions are identified, but to date the global model remains more robust due to the limited number of 20-yr-long gauging stations. This should be reconsidered in the future when enough data will be available.

Highlights

It is recognised that river low flows can lead to severe consequences in water quality and river ecological status (Whitehead et al, 2009)
We developed a full analysis chain allowing us to estimate low flows anywhere in gauged and ungauged catchments in Wallonia, and this for any desired return period between 5 and 50 yr
This method puts together the selection of gauging stations for low-flow calculation, frequency analysis to fit a frequency distribution to low-flow data, an optional cluster analysis to delineate homogeneous regions if enough data are available, regression analysis to develop models predicting low flows from catchment characteristics, and a new approach that evaluates the relationships between regression coefficients and the return period

Summary

Introduction

It is recognised that river low flows can lead to severe consequences in water quality and river ecological status (Whitehead et al, 2009). As pressures on rivers become more important during low flows, some conflicts between the different water uses can arise, especially between instream water use and water abstraction demand (Hebert et al, 2003). Water managers need to be able to quantify low flows at any point of a river, in magnitude as well as in frequency. Low flows can have different meanings depending on the definitions of authors. Low flows are considered as the lowest discharge values observed in a river, which usually occur between May and November in Wallonia (Belgium). The index chosen to characterise low flows is MAM7 which stands for mean annual minimum flow on a 7-day average basis

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