Reply to Comment on &amp;quot;A dynamic  rating curve approach to indirect discharge measurement  by Dottori et al. (2009)&amp;quot; by Koussis (2009)

Dottori, Francesco; Todini, E.

doi:10.5194/hess-14-1099-2010

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Empirical methods such as multipliers for the rating curve have been used as well (Aronica et al ., ). The rating curve approach has also been extended to account for uncertainties because of unsteady flow, for example, by including longitudinal variation in water surface slope using simultaneous stage measurements at two adjacent cross sections (Leonard et al ., ; Schmidt and Yen, ; Dottori et al ., ; Dottori and Todini, ; Koussis, ). In large river systems especially, extrapolations may be needed in both directions (high and low flows) from gauged relationships for effective resource management (Sefe, ).…”

Section: River Discharge Uncertaintymentioning

confidence: 99%

Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality

McMillan

Krueger

Freer

2012

Hydrological Processes

405

366

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This review and commentary sets out the need for authoritative and concise information on the expected error distributions and magnitudes in observational data. We discuss the necessary components of a benchmark of dominant data uncertainties and the recent developments in hydrology which increase the need for such guidance. We initiate the creation of a catalogue of accessible information on characteristics of data uncertainty for the key hydrological variables of rainfall, river discharge and water quality (suspended solids, phosphorus and nitrogen). This includes demonstration of how uncertainties can be quantified, summarizing current knowledge and the standard quantitative results available. In particular, synthesis of results from multiple studies allows conclusions to be drawn on factors which control the magnitude of data uncertainty and hence improves provision of prior guidance on those uncertainties. Rainfall uncertainties were found to be driven by spatial scale, whereas river discharge uncertainty was dominated by flow condition and gauging method. Water quality variables presented a more complex picture with many component errors. For all variables, it was easy to find examples where relative error magnitudes exceeded 40%. We consider how data uncertainties impact on the interpretation of catchment dynamics, model regionalization and model evaluation. In closing the review, we make recommendations for future research priorities in quantifying data uncertainty and highlight the need for an improved ‘culture of engagement’ with observational uncertainties. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: River Discharge Uncertaintymentioning

confidence: 99%

Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality

McMillan

Krueger

Freer

2012

Hydrological Processes

405

366

View full text Add to dashboard Cite

show abstract

“…Being physically based in nature, the first type approach often requires the estimation of hydraulic parameters like velocity or celerity corresponding to a discharge and stage. The latter is generally evaluated by the Jones approach (Jones, 1915; Koussis, 2009) and Koussis approach (Koussis, 1975, 2009, 2010a, 2010b). Further, the literature reveals that mostly discharge is used as the primary operating variable in such reverse routing approaches with the exception of Spada et al.…”

Section: Introductionmentioning

confidence: 99%

A Physically‐Based Reverse‐Stage Routing Model Considering Lateral Flow for Establishing Normal Rating Curves at Ungauged Upstream River Sections

Pati

Sahoo

Perumal

2023

Water Resources Research

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The development of water resource projects of a river often requires information on stage and discharge to design hydraulic control structures at desired locations across the river. However, the rivers of many countries are often ungauged or poorly gauged due to inadequate streamflow monitoring networks. Even with the available limited monitoring stations, only a few of them monitor both stage and discharge data. To arrive at this information at the desired locations along a river, one may estimate this information by transferring the related information from nearby monitored gauging stations.Generally, a river reach is termed ungauged if the discharge measurements are unavailable, even if the associated stage information is available (Hrachowitz et al., 2013). As a limited number of stations are gauged along the rivers, conventionally, the downstream stage and discharge hydrographs are estimated by the forward routing models with the known upstream stage or discharge hydrographs. There are many river basins worldwide

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Development of discharge-stage curves affected by hysteresis using time varying models, model trees and neural networks

Wolfs

Willems

2014

Environmental Modelling & Software

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Reply to Comment on "A dynamic rating curve approach to indirect discharge measurement by Dottori et al. (2009)" by Koussis (2009)

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Cited by 5 publications

References 12 publications

Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality

Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality

A Physically‐Based Reverse‐Stage Routing Model Considering Lateral Flow for Establishing Normal Rating Curves at Ungauged Upstream River Sections

Development of discharge-stage curves affected by hysteresis using time varying models, model trees and neural networks

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