Probabilistic Forecasting of Nitrogen Dynamics in Hyporheic Zone

Boso, Francesca; Marzadri, Alessandra; Tartakovsky, Daniel M.

doi:10.1029/2018wr022525

Cited by 11 publications

(14 citation statements)

References 46 publications

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“…and the moment equations satisfied by hðxÞ and σ 2 h ðxÞ, as in Boso and Tartakovsky (2016), Boso et al (2018), and Yang et al (2019). These expressions assume the random variable h(x) and the corresponding CDF F h (U; x) to be defined on the interval ½H min ; H max .…”

Section: Appendix A: Cdf Equation For Flow In Composite Porous Mediamentioning

confidence: 99%

Method of Distributions for Quantification of Geologic Uncertainty in Flow Simulations

Yang

Boso

Tartakovsky

2020

Water Resources Research

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Probabilistic models of subsurface flow and transport are required for risk assessment and reliable decision making under uncertainty. These applications require accurate estimates of confidence intervals, which generally cannot be ascertained with statistical moments such as mean (unbiased estimate) and variance (a measure of uncertainty) of a quantity of interest (QoI). The method of distributions provides this information by computing either the probability density function or the cumulative distribution function (CDF‐) of the QoI. The standard method can be orders of magnitude faster than Monte Carlo simulations (MCS) but is applicable to stationary, mildly to moderately heterogeneous porous media in which the coefficient of variation of input parameters (e.g., log‐conductivity) is below four. Our CDF‐random domain decomposition (RDD) framework alleviates these limitations by combining the method of distributions and RDD; it also accounts for uncertainty in the geologic makeup of a subsurface environment. For a given realization of the geological map, we derive a deterministic equation for the conditional CDF of hydraulic head of steady single‐phase flow. The solutions of this equation are then averaged over realizations of the geological map‐ to compute the hydraulic head CDF. Our numerical experiments reveal that the CDF‐RDD method remains accurate for two‐dimensional flow in a porous medium composed of two heterogeneous hydrofacies, a setting in which the original CDF method fails. For the same accuracy, the CDF‐RDD method is an order of magnitude faster than MCS.

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Section: Appendix A: Cdf Equation For Flow In Composite Porous Mediamentioning

confidence: 99%

Method of Distributions for Quantification of Geologic Uncertainty in Flow Simulations

Yang

Boso

Tartakovsky

2020

Water Resources Research

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“…Empirical or phenomenological selection of the closure variables (Haworth, 2010;Pope, 2001;Raman et al, 2005) does not automatically guarantee an accurate reproduction of the first and second statistical moment of the distribution, that is, meanh(x) and variance 2 h (x). Following Boso and Tartakovsky (2016) and Boso et al (2018), we construct the closure variables and in a way that ensures that the CDF equation 7gives rise to the moment equations satisfied byh and 2 h . We start by recalling that if a random variable h is defined on an interval [H min , H max ], then the mean and variance of the CDF F h (H) arē…”

Section: Cdf Equation For Hydraulic Headmentioning

confidence: 99%

“…The self-consistent closure of Boso & Tartakovsky (2016) ameliorates this deficiency by preserving both the mean and variance. It has been used to quantify uncertainty in advection-dispersion (Boso & Tartakovsky, 2016) and advection-dispersion-reaction (Boso et al, 2018) problems.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Forecast of Single‐Phase Flow in Porous Media With Uncertain Properties

Yang

Boso

Tartakovsky

2019

Water Resources Research

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Uncertainty about geologic makeup and properties of the subsurface renders inadequate a unique quantitative prediction of flow and transport. Instead, multiple alternative scenarios have to be explored within the probabilistic framework, typically by means of Monte Carlo simulations (MCS). These can be computationally expensive, and often prohibitively so, especially when the goal is to compute the tails of a distribution, that is, probabilities of rare events, which are necessary for risk assessment and decision making under uncertainty. We deploy the method of distributions to derive a deterministic equation for the cumulative distribution function (CDF) of hydraulic head in an aquifer with uncertain (random) hydraulic conductivity. The CDF equation relies on a self‐consistent closure approximation, which ensures that the resulting CDF of hydraulic head has the same mean and variance as those computed with statistical moment equations. We conduct a series of numerical experiments dealing with steady‐state two‐dimensional flow driven by either a natural hydraulic head gradient or a pumping well. These experiments reveal that the CDF method remains accurate and robust for highly heterogeneous formations with the variance of log conductivity as large as five. For the same accuracy, it is also up to four orders of magnitude faster than MCS in computing hydraulic head with a required degree of confidence (probability).

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“…The authors showed that hyporheic exchange flux, solute transport, and consumption increase during individual flow events. Boso et al (2018) adopted a probabilistic approach to quantify the impact of uncertainty in both the temporally-variable river water and the rate constants on predictions of N 2 O emissions at the bedform scale. On the other hand, Zheng and Cardenas (2018) focused on the effects of temperature on nitrogen cycling and nitrate removal-production efficiency in bedform-induced hyporheic zones under steady flow conditions.…”

Section: Introductionmentioning

confidence: 99%

Impacts of peak-flow events on hyporheic denitrification potential

Singh,

Gupta,

Chiogna

et al. 2021

Preprint

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Subsurface flows, particularly hyporheic exchange fluxes, driven by streambed topography, permeability, channel gradient and dynamic flow conditions provide prominent ecological services such as nitrate removal from streams and aquifers. Stream flow dynamics cause strongly nonlinear and often episodic contributions of nutrient concentrations in river-aquifer systems. Using a fully coupled transient flow and reactive transport model, we investigated the denitrification potential of hyporheic zones during peak-flow events. The effects of streambed permeability, channel gradient and bedform amplitude on the spatio-temporal distribution of nitrate and dissolved organic carbon in streambeds and the associated denitrification potential were explored. Distinct peak-flow events with different intensity, duration and hydrograph shape were selected to represent a wide range of peak-flow scenarios. Our results indicated that the specific hydrodynamic characteristics of individual flow events largely determine the average positive or negative nitrate removal capacity of hyporheic zones, however the magnitude of this capacity is controlled by geomorphological settings (i.e. channel slope, streambed permeability and bedform amplitude). Specifically, events with longer duration and higher intensity were shown to promote higher nitrate removal efficiency with higher magnitude of removal efficiency in the scenarios with higher slope and permeability values. These results are essential for better assessment

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Probabilistic Forecasting of Nitrogen Dynamics in Hyporheic Zone

Cited by 11 publications

References 46 publications

Method of Distributions for Quantification of Geologic Uncertainty in Flow Simulations

Method of Distributions for Quantification of Geologic Uncertainty in Flow Simulations

Probabilistic Forecast of Single‐Phase Flow in Porous Media With Uncertain Properties

Impacts of peak-flow events on hyporheic denitrification potential

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