Identifying Stagnation Zones and Reverse Flow Caused by River-Aquifer Interaction: An Approach Based on Polynomial Chaos Expansions

Fluctuating stream stages and peak-flow events can significantly influence the interactions between streams and aquifers and modify the hydraulic gradient, the flux exchange and the subsurface flow paths. As a result, stagnation zones and reverse flow may appear in different parts of an aquifer and at different times. These features of the flow field play a relevant role in the transport, transformation, and residence time of solutes, pollutants, and nutrients in the subsurface. However, their identification using numerical models is complex not only because of highly nonlinear dynamics, but also due to significant uncertainties in the model input data which propagate into the quantities of interest. In this work, we use an approach based on polynomial chaos expansions to map the probability of occurrence of stagnation zones and reverse flow during a flood event. We quantify the propagation of uncertainty into the groundwater flow field due to the applied river boundary conditions. Then, we evaluate the responses of the posterior probabilities in an element-wise fashion using a set of flow classification criteria and kernel density estimations. The proposed methodology is flexible because it employs a nonintrusive pseudo-spectral technique and, consequently, it can be applied straightforwardly in preexisting models. The regions near the confluence of two streams in the studied area are prone to present transient stagnation and reverse flow.publishedVersio

A novel framework for uncertainty propagation in river systems based on performance graphs using two-dimensional hydrodynamic modeling

This thesis presents a novel approach for propagation of uncertainty in river systems. Errors in data observations and predictions (e.g., stream inflows), in model parameters, and resulting from the discretization of continuous systems, all point to the need to accurately quantify the amount of uncertainty carried through the modeling process. In the proposed framework, stochastic processes are incorporated directly into the physical description of the system (e.g., river flow dynamics) with the goal of better modeling uncertainties (both aleatoric and epistemic) and hence, reducing the ranges of the confidence intervals on quantities of interest. We represent uncertainty in stream inflows via an error term modeled as a stochastic process. Stochastic collocation is then used to discretize random space. This non-intrusive approach is both more efficient than Monte-Carlo methods and is as flexible in its application. The flow dynamics are simulated efficiently using the performance graphs approach implemented in the OSU Rivers model. For one-dimensional unsteady flow routing, the performance graph (PG) approach has been shown to be accurate, numerically efficient, and robust. The Hydraulic Performance Graph (HPG) of a channel reach graphically summarizes the dynamic relation between the flow through and the stages at the ends of the reach under gradually varied flow (GVF) conditions, while the Volumetric Performance Graph (VPG) summarizes the corresponding storage. The hydraulic routing for the entire system consists of dividing the river system into reaches and pre-computing the hydraulics for each of these reaches independently using a steady flow model. Then, a non-linear system of equations is solved that is assembled based on information summarized in the systems' performance graphs, the reach-wise equation of conservation of mass, continuity and water stage compatibility conditions at the union of reaches (nodes), and the system boundary conditions. For complex flows in river systems such as when there is flow over floodplains, the dynamic relation between water stages and flow in a river reach is best represented by depth averaged two-dimensional hydrodynamic models. The applicability of two-dimensional flow modeling for the construction of PGs for unsteady flow routing in complex river networks is explored. To illustrate application of the uncertainty propagation framework and PGs derived from two-dimensional flow models, a test case is presented that examines uncertainty quantification and flood routing through a complex section of the Fraser River in British Columbia

Advanced Bayesian framework for uncertainty estimation of sediment transport models

2018 Summer.Includes bibliographical references.Numerical sediment transport models are widely used to forecast the potential changes in rivers that might result from natural and/or human influences. Unfortunately, predictions from those models always possess uncertainty, so that engineers interpret the model results very conservatively, which can lead to expensive over-design of projects. The Bayesian inference paradigm provides a formal way to evaluate the uncertainty in model forecasts originating from uncertain model elements. However, existing Bayesian methods have rarely been used for sediment transport models because they often have large computational times. In addition, past research has not sufficiently addressed ways to treat the uncertainty associated with diverse sediment transport variables. To resolve those limitations, this study establishes a formal and efficient Bayesian framework to assess uncertainty in the predictions from sediment transport models. Throughout this dissertation, new methodologies are developed to represent each of three main uncertainty sources including poorly specified model parameter values, measurement errors contained in the model input data, and imperfect sediment transport equations used in the model structure. The new methods characterize how those uncertain elements affect the model predictions. First, a new algorithm is developed to estimate the parameter uncertainty and its contribution to prediction uncertainty using fewer model simulations. Second, the uncertainties of various input data are described using simple error equations and evaluated within the parameter estimation framework. Lastly, an existing method that can assess the uncertainty related to the selection and application of a transport equation is modified to enable consideration of multiple model output variables. The new methodologies are tested with a one-dimensional sediment transport model that simulates flume experiments and a natural river. Overall, the results show that the new approaches can reduce the computational time about 16% to 55% and produce more accurate estimates (e.g., prediction ranges can cover about 6% to 46% more of the available observations) compared to existing Bayesian methods. Thus, this research enhances the applicability of Bayesian inference for sediment transport modeling. In addition, this study provides several avenues to improve the reliability of the uncertainty estimates, which can help guide interpretation of model results and strategies to reduce prediction uncertainty

Flood Prediction and Mitigation in Data-Sparse Environments

In the last three decades many sophisticated tools have been developed that can accurately predict the dynamics of flooding. However, due to the paucity of adequate infrastructure, this technological advancement did not benefit ungauged flood-prone regions in the developing countries in a major way. The overall research theme of this dissertation is to explore the improvement in methodology that is essential for utilising recently developed flood prediction and management tools in the developing world, where ideal model inputs and validation datasets do not exist. This research addresses important issues related to undertaking inundation modelling at different scales, particularly in data-sparse environments. The results indicate that in order to predict dynamics of high magnitude stream flow in data-sparse regions, special attention is required on the choice of the model in relation to the available data and hydraulic characteristics of the event. Adaptations are necessary to create inputs for the models that have been primarily designed for areas with better availability of data. Freely available geospatial information of moderate resolution can often meet the minimum data requirements of hydrological and hydrodynamic models if they are supplemented carefully with limited surveyed/measured information. This thesis also explores the issue of flood mitigation through rainfall-runoff modelling. The purpose of this investigation is to assess the impact of land-use changes at the sub-catchment scale on the overall downstream flood risk. A key component of this study is also quantifying predictive uncertainty in hydrodynamic models based on the Generalised Likelihood Uncertainty Estimation (GLUE) framework. Detailed uncertainty assessment of the model outputs indicates that, in spite of using sparse inputs, the model outputs perform at reasonably low levels of uncertainty both spatially and temporally. These findings have the potential to encourage the flood managers and hydrologists in the developing world to use similar data sets for flood management

Development and field-installation of a mathematical simulation model in support of irrigation canal management

Mathematical models / Simulation models / Flow / Hydraulics / Irrigation canals / Decision making / Research / Sri Lanka / Kirindi Oya

Ranking sources of uncertainty in flood damage modelling: a case study on the cost-benefit analysis of a flood mitigation project in the Orb Delta, France

International audienceCost-benefit analyses (CBA) of flood management plans usually require estimating expected annual flood damages on a study area, and rely on a complex modelling chain including hydrological, hydraulic and economic modelling as well as GIS-based spatial analysis. As most model-based assessments, these CBA are fraught with uncertainty. In this paper, we consider as a case-study the CBA of a set of flood control structural measures on the Orb Delta, France. We demonstrate the use of variance-based global sensitivity analysis (VB-GSA) to i) propagate uncertainty sources through the modelling chain and assess their overall impact on the outcomes of the CBA, and ii) rank uncertainty sources according to their contribution to the variance of the CBA outcomes. All uncertainty sources prove to explain a significant share of the overall output variance. Results show that the ranking of uncertainty sources depends not only on the economic sector considered (private housing, agricultural land, other economic activities), but also on a number of averaging-out effects controlled by the number and surface area of the assets considered, the number of land use types or the number of damage functions