Response to the slug injection of a tracer—a large-scale experiment in a natural river / Réponse à l'injection impulsionnelle d'un traceur—expérience à grande échelle en rivière naturelle

Rowiński, Paweł M.; Guymer, I.; Kwiatkowski, Kamil

doi:10.1623/hysj.53.6.1300

Cited by 28 publications

(10 citation statements)

References 19 publications

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“…Longitudinal dispersion coefficients were determined with a passive tracer (NaCl). The tracer dispersion in the far‐field downstream of an instant‐tracer injection was measured at the up‐ and downstream longitudinal positions along the reaches following common procedures for tracer experiments in rivers and streams [ Domenico and Schwartz , ; Leibundgut et al ., ; Rowinski et al ., ]. The electrical conductivity was monitored at both sites using conductivity sensors (TetraCon 925, Wissenschaftlich Technische Werkstätten) following a NaCl slug injections of mass m .…”

Section: Methodsmentioning

confidence: 99%

“…Longitudinal dispersion coefficients were determined with a passive tracer (NaCl). The tracer dispersion in the far-field downstream of an instant-tracer injection was measured at the up-and downstream longitudinal positions along the reaches following common procedures for tracer experiments in rivers and streams [Domenico and Schwartz, 1998;Leibundgut et al, 2011;Rowinski et al, 2008]. The electrical conductivity was monitored at both sites using conductivity sensors (TetraCon 925, Wissenschaftlich Technische Werkst€ atten) following a NaCl slug injections of mass m. To verify that vertical and lateral gradients of the tracer distribution were negligible and that transport and mixing can be described by the longitudinal advection-dispersion equation, electrical conductivity at the upstream cross section was simultaneously measured close to the left-and right-bank as well as close to the centerline [Rutherford, 1994].…”

Section: Flow Resistance and Dispersionmentioning

confidence: 99%

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Roughness, resistance, and dispersion: Relationships in small streams

Noß

Lorke

2016

Water Resources Research

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Although relationships between roughness, flow, and transport processes in rivers and streams have been investigated for several decades, the prediction of flow resistance and longitudinal dispersion in small streams is still challenging. Major uncertainties in existing approaches for quantifying flow resistance and longitudinal dispersion at the reach scale arise from limitations in the characterization of riverbed roughness. In this study, we characterized the riverbed roughness in small moderate‐gradient streams (0.1–0.5% bed slope) and investigated its effects on flow resistance and dispersion. We analyzed high‐resolution transect‐based measurements of stream depth and width, which resolved the complete roughness spectrum with scales ranging from the micro to the reach scale. Independently measured flow resistance and dispersion coefficients were mainly affected by roughness at spatial scales between the median grain size and the stream width, i.e., by roughness between the micro‐ and the mesoscale. We also compared our flow resistance measurements with calculations using various flow resistance equations. Flow resistance in our study streams was well approximated by the equations that were developed for high gradient streams (>1%) and it was overestimated by approaches developed for sand‐bed streams with a smooth riverbed or ripple bed.

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Section: Methodsmentioning

confidence: 99%

Section: Flow Resistance and Dispersionmentioning

confidence: 99%

Roughness, resistance, and dispersion: Relationships in small streams

Noß

Lorke

2016

Water Resources Research

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“…Figure 3 shows that the tests were conducted in the reach of Point 16 (injection point) to Point 20 (monitoring point). A fluorescent dye, Rhodamine WT, was used as a tracer material, which is a widelyused conservative tracer [43,[71][72][73]. In Test 1 and Test 2, 15 and 7.5 L, respectively, of 20% Rhodamine WT solution were injected.…”

Section: Study Site and Field Tracer Testmentioning

confidence: 99%

Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis

Kwon

Noh

Seo

et al. 2021

IJERPH

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To minimize the damage from contaminant accidents in rivers, early identification of the contaminant source is crucial. Thus, in this study, a framework combining Machine Learning (ML) and the Transient Storage zone Model (TSM) was developed to predict the spill location and mass of a contaminant source. The TSM model was employed to simulate non-Fickian Breakthrough Curves (BTCs), which entails relevant information of the contaminant source. Then, the ML models were used to identify the BTC features, characterized by 21 variables, to predict the spill location and mass. The proposed framework was applied to the Gam Creek, South Korea, in which two tracer tests were conducted. In this study, six ML methods were applied for the prediction of spill location and mass, while the most relevant BTC features were selected by Recursive Feature Elimination Cross-Validation (RFECV). Model applications to field data showed that the ensemble Decision tree models, Random Forest (RF) and Xgboost (XGB), were the most efficient and feasible in predicting the contaminant source.

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“…The best way of estimating the dispersion coefficients for the actual river is a tracer experiment, but usually in practical applications -since such an experiment is expensive and time consuming -impossible to be carried out. Moreover, the practice shows that tracer tests are feasible in one-dimensional situations only and they allow for the determination of just the longitudinal dispersion coefficients (Deng et al, 2001(Deng et al, , 2002Guymer, 1998;Kumar and Dalal, 2010;Rowiński et al, 2008;Sukhodolov et al, 1998). In cases when such tracer tests are not available, reliable estimation of dispersion coefficients becomes extremely difficult and can be a source of large uncertainty.…”

Section: Dispersion Coefficientsmentioning

confidence: 99%

Uncertainty in computations of the spread of warm water in a river – lessons from Environmental Impact Assessment case study

Kalinowska

Rowiński

2012

Hydrol. Earth Syst. Sci.

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Abstract. The present study aims at the evaluation of sources of uncertainty in modelling of heat transport in a river caused by the discharge coming from a cooling system of a designed gas-stem power plant. This study was a part of an Environmental Impact Assessment and was based on twodimensional modelling of temperature distribution in an actual river. The problems with the proper description of the computational domain, velocity field and hydraulic characteristics were considered in the work. An in-depth discussion on the methods of evaluation of the dispersion coefficients in the model comprising of all four components of the dispersion tensor was carried out. It was shown that in natural rivers all components of a dispersion tensor should be taken into account to qualitatively reflect the proper shape of temperature distributions. The results considerably depend on the 2-D velocity field as well as hydraulic and morphometric characteristics of the flow. Numerical methods and their influence on the final results of computations were also discussed. All computations were based upon a real case study performed in Vistula River in Poland.

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Response to the slug injection of a tracer—a large-scale experiment in a natural river / Réponse à l'injection impulsionnelle d'un traceur—expérience à grande échelle en rivière naturelle

Cited by 28 publications

References 19 publications

Roughness, resistance, and dispersion: Relationships in small streams

Roughness, resistance, and dispersion: Relationships in small streams

Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis

Uncertainty in computations of the spread of warm water in a river – lessons from Environmental Impact Assessment case study

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