The role of rating curve uncertainty in real‐time flood forecasting

Human alterations to hydrology have globally impacted wetland ecosystems. Preventing or reversing these impacts is a principal focus of restoration efforts. However, restoration effectiveness is often hampered by limited information on historical landscape properties and hydrologic regime. To help address this gap, we developed a novel statistical approach for inferring flows and inundation frequency (i.e., hydroperiod, HP) in wetlands where changes in spatial vegetation and geomorphic patterns have occurred due to hydrologic alteration. We developed an analytical expression for HP as a transformation of the landscape‐scale stage‐discharge relationship. We applied this model to the Everglades “ridge‐slough” (RS) landscape, a patterned, lotic peatland in southern Florida that has been drastically degraded by compartmentalization, drainage, and flow diversions. The new method reliably estimated flow and HP for a range of RS landscape patterns. Crucially, ridge‐patch anisotropy and elevation above sloughs were strong drivers of flow‐HP relationships. Increasing ridge heights markedly increased flow required to achieve sufficient HP to support peat accretion. Indeed, ridge heights inferred from historical accounts would require boundary flows 3–4 times greater than today, which agrees with restoration flow estimates from more complex, spatially distributed models. While observed loss of patch anisotropy allows HP targets to be met with lower flows, such landscapes likely fail to support other ecological functions. This work helps inform restoration flows required to restore stable ridge‐slough patterning and positive peat accretion in this degraded ecosystem, and, more broadly, provides tools for exploring interactions between landscape and hydrology in lotic wetlands and floodplains.

Section: The Rating Curve Transformation (Rct) Method: Theorymentioning

confidence: 99%

Doing ecohydrology backward: Inferring wetland flow and hydroperiod from landscape patterns

Acharya

Kaplan

Jawitz

et al. 2017

“…To date, there has been little coordination between the diverse research groups developing discharge uncertainty estimation methods. Limited previous studies have compared some pairs of methods (Mason et al ; Ocio et al, ; Storz, ), but we know of no broader comparisons.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Methods for Streamflow Uncertainty Estimation

Kiang

Gazoorian

McMillan

et al. 2018

139

106

Streamflow time series are commonly derived from stage‐discharge rating curves, but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage‐discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage‐discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), full width 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast, uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time‐varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.

“…The process of building a rating curve is affected by many sources of uncertainty, including the imperfection of the specified rating curve equation, the measurement uncertainty of calibration gaugings, and the uncertainty in estimated parameters. Rating curve uncertainty impacts any analysis that makes use of discharge time series: flood frequency analysis (e.g., Steinbakk et al, ), hydrological model calibration (e.g., Sikorska & Renard, ), real‐time flood forecasting (e.g., Ocio et al, ), hydrological change detection (e.g., Juston et al, ; Lang et al, ), hydrological signatures (e.g., Westerberg & McMillan, ), among many others. Consequently, quantifying rating curve uncertainty and using it in decision making may lead to better decisions, as illustrated by McMillan et al ().…”

Section: Introductionmentioning

confidence: 99%

Shift Happens! Adjusting Stage‐Discharge Rating Curves to Morphological Changes at Known Times

Mansanarez

Renard²,

Coz³

et al. 2019

Establishing streamflow time series in unstable rivers is challenged by changes in the stage‐discharge relation after floods. Then, the field hydrologist must develop a new stage‐discharge rating curve using new calibration data but also using some information from previous calibration data and previous rating curves. The process includes a large amount of informal expert knowledge and hydraulic assumptions seldom made explicit. This paper develops a stage‐period‐discharge (SPD) model based on the physical interpretation of changes in the stage‐discharge relation across a series of stability periods defined by known dates and times. Using simple hydraulic equations, the user provides prior knowledge on the controls, their static and varying parameters, and their possible changes. As a single model is used for all the periods, the estimation of all rating curves can be performed in one go: All gaugings hence provide information to estimate the static parameters and the varying parameters for the relevant periods. Bayesian inference is used, providing a natural way to include prior knowledge and to quantify uncertainty. The generality and some key properties of the method are demonstrated through application to two hydrometric stations, differing in hydraulic configuration and in number and type of changes. Specific experiments demonstrate the ability of the SPD model to transfer information across periods. Consequently, rating curves are more precisely estimated than by separately estimating them for each period. The SPD model provides a hydraulically based, transparent, and user‐friendly approach to replace manual shift corrections traditionally applied in operational practice, with a quantification of uncertainties.