Delineating modelling uncertainty in river flow indicators with representative parameter sets

Anna E Sikorska-Senoner

doi:10.1016/j.advwatres.2021.104024

Abstract

For mitigating negative effects of floods and droughts, estimates of flow indicators and their uncertainties are essential. The recently introduced concept of the representative parameter sets (RPSs) enables modelling uncertainty to be represented in the flow frequency space at low computational cost, using a small subset of pre-selected model parameter sets. This concept is here adapted to assess hazards of three flow indicators: annual maximal flow, annual 7-day-average low flow, and annual mean flow. An additional in-depth analysis assesses the RPS transferability to other flow indicators and to hydrological signatures. RPS-based simulations are benchmarked with a random selection of parameter sets. The results show that i) RPSs can be successfully transferred between flow indicators with only a small drop in model performance; and ii) RPSs can be used to represent modelling uncertainty in hydrological signatures. The RPS concept has thus great potential for delineating modelling uncertainty of any environmental model.

Highlights

Both drought and flood hazards were responsible for almost 50% of all natural disasters that occurred globally over the years 1998–2017, with floods affecting the largest number of people (>2 billion), followed by droughts (1.5 billion) (UNISDR and CRED, 2018)
To evaluate which parameter sets are chosen for different flow conditions, box-plots with selected representative parameter sets (RPSs) are presented in Fig. 3, together with standard evaluation metrics used in hydrology: Kling-Gupta efficiency (KGE) (Gupta et al, 2009), Peak efficiency (Seibert, 2003) and MARE measure (Dawson et al, 2007)
With respect to different efficiency metrics considered here, it is evident that RPSs selected for LQ and HQ lie in the same range for KGE and MARE, whereas for MQ a much wider spread in the efficiency is observed

Summary

Introduction

Both drought and flood hazards were responsible for almost 50% of all natural disasters that occurred globally over the years 1998–2017, with floods affecting the largest number of people (>2 billion), followed by droughts (1.5 billion) (UNISDR and CRED, 2018). To mitigate such negative effects of floods and droughts, accurate estimates of flow indicators (e.g., extreme low and high flows) and their hazards are essential. A commonly applied flow indicator is the magnitude of discharge, which can refer to minimum (low flows) or maximum (high flows) magnitude These flow indicators can be derived from observed or simulated river flow data. For reliable hazard estimates based on direct statistical analysis, at least 20 years of observations are required, whereas further increasing the record length greatly deceases the uncertainty attached to the hazard estimates (Hu et al, 2020)

Objectives

Methods

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Discussion

Conclusion