A Rating Curve Model Accounting for Cyclic Stage‐Discharge Shifts due to Seasonal Aquatic Vegetation

Abstract

Introduction 1.1. Rating Curve Management Establishing the streamflow time series and its uncertainty is a priority for most hydrological studies and water management applications. The streamflow record is commonly computed by transforming a continuous water level record into water discharge using a stage-discharge relation, also known as a rating curve (World Meteorological Organization, 2010). Rating curves are site-specific and are usually built using occasional measurements of water discharge Q and stage h (i.e., gaugings), combined with information about hydraulic controls (section or channel controls) at the hydrometric station (Rantz, 1982a, 1982b). The process of building a rating curve is subject to a large uncertainty that needs to be quantified in order to make important water-related decisions (McMillan et al., 2017). Many methods exist to estimate rating curve uncertainty and have been applied with increasing frequency (Kiang et al., 2018). One of the main problems for rating curve management is to deal with rating changes or "shifts" (Mansanarez et al., 2019). Indeed, stage-discharge relations are not always stable and can evolve in time, which leads to non-unique h-Q relations. Rating instabilities can be caused by sudden changes (e.g., morphological evolution induced by a flood, human constructions that affect the hydraulic control) and/or by transient changes (e.g., vegetation growth, accumulation of debris or ice). The challenge is to detect these changes and update the curve accordingly, for example, modifying the rating curve model and/or re-estimating the model parameters. Some procedures exist to deal with sudden changes and are reported in Mansanarez et al. (2019). However, these methods are not suitable for transient changes due to aquatic vegetation. In that case, the stage-discharge relation varies continuously in time according to the vegetation evolution. Water discharge therefore needs to be estimated from the stage and other variables depending on time. To the best of our knowledge, no rating curve models accounting for aquatic vegetation exist in the literature.

Enhancing urban runoff modelling using water stable isotopes and ages in complex catchments

“…Missed peak events in modelling the upper catchment could be due to multiple factors including the complex distribution of SWO thresholds that were spatially variable within the catchment or high heterogeneity of urban precipitation inputs (Liu & Niyogi, 2019; Lorenz et al, 2019). Given the limited number of these events, and their occurrence during summer, peak flows also may be uncertain due to limited measurements to characterize peak flow rating curves (Sikorska et al, 2013; Westerberg & McMillan, 2015) and/or the impact of vegetation in the channel overbank increasing rating curve uncertainty during peak flow events when over bank flow occurs (Perret et al, 2020). Furthermore, while heterogeneity in precipitation sources was incorporated within the modelling framework, known uncertainties particularly of convective precipitation heterogeneities within urban landscapes likely affect event‐based discharge simulations during summer (Meierdiercks et al, 2010; Singh et al, 2020).…”

Sharing perceptual models of uncertainty: On the use of soft information about discharge data

“…The potential for temporal variability that results from seasonal changes in roughness due to weed and vegetation growth (Perret et al, 2021) can be assessed from photos, aerial images and site visits. Vegetation such as leaves and debris can often block small v-notch weirs, resulting in artificially raised water levels and temporally varying systematic overestimation.…”

Detection of Stage‐Discharge Rating Shifts Using Gaugings: A Recursive Segmentation Procedure Accounting for Observational and Model Uncertainties

“…Reitan and Petersen-Øverleir (2011) developed a dynamic model based on time-varying RC parameters within a hierarchical Bayesian framework. Finally, in the specific context of sites affected by aquatic vegetation, Puechberty et al (2017) proposed time-varying stage corrections and Perret et al (2021) introduced the Bayesian estimation of a time-dependent rating curve model accounting for vegetation growth and decay, with year-specific parameters.…”

“…(2017) proposed time‐varying stage corrections and Perret et al. (2021) introduced the Bayesian estimation of a time‐dependent rating curve model accounting for vegetation growth and decay, with year‐specific parameters.…”