Abstract: Publication date: Available online 16 January 2020Source: Advances in Water ResourcesAuthor(s): Vitali Diaz, Gerald A. Corzo Perez, Henny A.J. Van Lanen, Dimitri Solomatine, Emmanouil A. VarouchakisThe spatiotemporal monitoring of droughts is a complex task. In the past decades, drought monitoring has been increasingly developed, while the consideration of its spatio-temporal dynamics is still a challenge. This study proposes a method to build the spatial tracks and paths of drought, which can enhance its monitoring. The steps for the drought tracks calculation are (1) identification of spatial units (areas), (2) centroids localisation, and (3) centroids linkage. The spatio-temporal analysis performed here to extract the areas and centroids builds upon the Contiguous Drought Area (CDA) analysis. The potential of the proposed methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. The method to calculate the drought tracks allows for identification of drought paths delineated by an onset and an end in space and time. Tracks, severity and duration of the drought are identified, as well as localisation (onset and end position), and rotation. The response of the drought tracking method to different combinations of parameters is also analysed. Further research is in progress to set up a model to predict the drought tracks for particular regions across the world, including India.Graphical abstract

Abstract: Publication date: Available online 15 January 2020Source: Advances in Water ResourcesAuthor(s): Daniel Horna Munoz, George ConstantinescuAbstractThis paper presents a 3-D, non-hydrostatic, Reynolds-Averaged Navier-Stokes (RANS) model using the volume of fluid (VOF) approach to simulate dam-break flows. Good agreement is observed between the 3-D model predictions and results of dam-break experiments performed in the laboratory. The 3-D model is then applied to predict flood-wave propagation induced by the sudden failure of two flood-protection dams in Iowa, USA. Results are also compared with predictions of 2-D, hydrostatic, depth-averaged models. The 2-D model simulations using the precalibrated values of the Manning's coefficients underpredict the speed of propagation of the flood wave and the area inundated by the flood compared to the 3-D model predictions. A methodology is presented to recalibrate the 2-D model which improves the agreement with the 3-D model predictions. Simulation results also show that strong 3-D effects are generated in regions of strong curvature of the river channel, near sudden constrictions and obstacles, and during the times the mean flow direction varies significantly over the flow depth. Such 3-D effects cannot be captured by the 2-D model even after recalibration, pointing toward the need to use 3-D models for detailed flood mapping.

Abstract: Publication date: Available online 15 January 2020Source: Advances in Water ResourcesAuthor(s): Pablo Ouro, Carmelo Juez, Mário FrancaAbstractLarge-Eddy Simulations (LES) are used to investigate the governing processes involved in mass and momentum transfer between the flow in the main channel and symmetrically-distributed lateral bank cavities. In-cavity free-surface velocities, based on laboratory measurements made in an open channel, are used to validate the numerical results. A main vortical structure dominates the in-cavity flow which, despite the shallow nature of the flow, features a remarked three dimensional dynamics. LES results outline the largest velocities through the mouth of the cavity are attained in two thin regions near the bottom-bed and free-surface. In the shear layers established between the main channel and cavities is where the main transfer of turbulent momentum is made between these two flow regions, and the numerical simulations capture well the instantaneous coherent flow structures, e.g. Kelvin-Helmholtz vortices. LES captures a low-frequency standing wave phenomenon even with a rigid-lid approximation adopted at the free-surface boundary. Momentum exchange between cavities and main channel is analysed using the Reynolds Averaged momentum equation in the transverse direction, revealing that the pressure gradient term is the unique contributor to flushing momentum out of the cavities whilst convection and Reynolds normal stress terms are responsible for its entraining into the cavity. Furthermore, sediment deposition areas documented in the laboratory experiments are linked with the simulated hydrodynamics, which correlate with regions of low turbulent kinetic energy and vertical velocities near the bottom of the channel. Overall, the results shed new light into the complex mechanisms involved in mass and momentum transfer; this will aid to design embayments more efficiently regarding sediment transport processes.

Abstract: Publication date: Available online 15 January 2020Source: Advances in Water ResourcesAuthor(s): David A. Benson, Stephen Pankavich, Michael J. Schmidt, Guillem Sole-MariAbstractTraditional random-walk particle-tracking (PT) models of advection and dispersion do not track entropy, because particle masses remain constant. However, newer mass-transfer particle tracking (MTPT) models have the ability to do so because masses of all compounds may change along trajectories. Additionally, the probability mass functions (PMF) of these MTPT models may be compared to continuous solutions with probability density functions, when a consistent definition of entropy (or similarly, the dilution index) is constructed. This definition reveals that every discretized numerical model incurs a computational entropy. Similar to Akaike’s (1974, 1992) entropic penalty for larger numbers of adjustable parameters, the computational complexity of a model (e.g., number of nodes or particles) adds to the entropy and, as such, must be penalized. Application of a new computational information criterion reveals that increased accuracy is not always justified relative to increased computational complexity. The MTPT method can use a particle-collision based kernel or an adaptive kernel derived from smoothed-particle hydrodynamics (SPH). The latter is more representative of a locally well-mixed system (i.e., one in which the dispersion tensor equally represents mixing and solute spreading), while the former better represents the separate processes of mixing versus spreading. We use computational means to demonstrate the fitness of each of these methods for simulating 1-D advective-dispersive transport with uniform coefficients.

Abstract: Publication date: Available online 10 January 2020Source: Advances in Water ResourcesAuthor(s): Andrew D. Gronewold, Joeseph P. Smith, Laura Read, James L. CrooksAbstractWater balance models are commonly employed to improve understanding of drivers behind changes in the hydrologic cycle across multiple space and time scales. Generally, these models are physically-based, a feature that can lead to unreconciled biases and uncertainties when a model is not encoded to be faithful to changes in water storage over time. Statistical methods represent one approach to addressing this problem. We find, however, that there are very few historical hydrological modeling studies in which bias correction and uncertainty quantification methods are routinely applied to ensure fidelity to the water balance. Importantly, we know of none (aside from preliminary applications of the model we advance in this study) applied specifically to large lake systems. We fill this gap by developing and applying a Bayesian statistical analysis framework for inferring water balance components specifically in large lake systems. The model behind this framework, which we refer to as the L2SWBM (large lake statistical water balance model), includes a conventional water balance model encoded to iteratively close the water balance over multiple consecutive time periods. Throughout these iterations, the L2SWBM can assimilate multiple preliminary estimates of each water balance component (from either historical model simulations or interpolated in situ monitoring data, for example), and it can accommodate those estimates even if they span different time periods. The L2SWBM can also be executed if data for a particular water balance component are unavailable, a feature that underscores its potential utility in data scarce regions. Here, we demonstrate the utility of our new framework through a customized application to the Laurentian Great Lakes, the largest system of lakes on Earth. Through this application, we find that the L2SWBM is able to infer new water balance component estimates that, to our are knowledge, are the first ever to close the water balance over a multi-decadal historical period for this massive lake system. More specifically, we find that posterior predictive intervals for changes in lake storage are consistent with observed changes in lake storage across this period over simulation time intervals of both 6 and 12 months. In additional to introducing a framework for developing definitive long-term hydrologic records for large lake systems, our study provides important insights into the origins of biases in both legacy and state-of-the-art hydrological models, as well as regional and global hydrological data sets.

Abstract: Publication date: Available online 9 January 2020Source: Advances in Water ResourcesAuthor(s): Colin L. Clark, C. Larrabee Winter, Tim CorleyAbstractWe develop a phenomenological model for the effective conductivity of porous media that consist of two distinct materials characterized by different values of hydraulic conductivity. Our focus is two-fold: first, to relate the terms and parameters of macroscale conductivity to the spatial variability of local flow and conductivity observed in computational experiments, and second to develop a reduced order model for effective conductivity based on those observations. Darcys law is assumed to hold at local (mesoscopic) and large (macroscopic) scales. At the mesoscale a composite medium is a configuration of irregular subdomains consisting of two different materials. The resulting heterogeneities force fluid to flow along irregular paths, and this produces spatial variability in the magnitude and the orientation of the Darcy flux. This variability, along with the macroscopic effective conductivity, depends on the proportion of the total volume allocated to each material, the ratio of the two conductivity values, and the spatial connectivity of material subvolumes. Computational experiments indicate that the effects of heterogeneity are most pronounced when the two conductivity values are very different, and when the volume fraction of the more conductive material is near a percolation threshold. The percolation threshold is the critical volume fraction of the more conductive material at which the medium is no longer traversed by connected paths through that material. The percolation threshold determines the existence of three regimes in effective conductivity, two where the effective conductivity obeys power laws and is dominated by one of the materials, and a third intermediate regime that interpolates between the power laws.

Abstract: Publication date: Available online 7 January 2020Source: Advances in Water ResourcesAuthor(s): Luqman K. Abidoye, Diganta B. DasAbstractContrary to report that dynamic capillary pressure effect was insignificant in the supercritical CO2-water (scCO2-water) flow system, this work found the effect to be considerable in the displacement of water or brine by injected scCO2in the geological carbon sequestration, especially prior to the attainment of equilibrium in the system. Series of controlled laboratory scale experimental measurements and numerical simulations of the dynamic capillary pressure effect and its magnitude (dynamic coefficient, τ) for supercritical CO2-water(scCO2-water) system are reported in unconsolidated silica sand. Novel measurement technique has been developed to achieve this purpose, by applying the concept of two-phase flow system in the context of geological carbon sequestration. This work considers the injection of scCO2 into storage aquifer as a two-phase flow system, where the CO2 displaces the resident fluid (brine or water). Using a high-pressure and high-temperature experimental rig, capillary pressure–saturation relationships (Pc-S) for this flow system and the saturation rate dependencies of the Pc-S relationships (quantified by dynamic coefficient, τ), known as dynamic capillary pressure effect were determined. This τ was previously unreported for scCO2-water system. In scCO2-water flow system, τ ranges from 2 × 105 to 6 × 105 Pa s at high water saturation and 1.3 × 106 to 8 × 106 Pa saround the irreducible saturation. τ increases with rising temperature but decreases with increase in porous medium permeability. Numerically determined τ-S relationships compare well with the corresponding experimental results for wide range of water saturation.The implication was that water saturation of the porous media will be considerably underestimated, if the dynamic capillary pressure effect was ignored in the characterization of the scCO2-water flow system, i.e., if only equilibrium Pcrelationship was used.

Abstract: Publication date: Available online 7 January 2020Source: Advances in Water ResourcesAuthor(s): Douglas E. Meisenheimer, James E. McClure, Mark L. Rivers, Dorthe WildenschildAbstractEmpirical relationships that describe two-phase flow in porous media have been largely hysteretic in nature, thereby requiring different relationships depending on whether the system is undergoing drainage or imbibition. Recent studies have suggested using interfacial area to close the well-known capillary pressure-saturation relationship, while others expand upon this by including the Euler characteristic for a geometric description of the system. With the advancement of fast x-ray microtomography at synchrotron facilities, three-dimensional experiments of two-phase quasi- and non-equilibrium flow experiments were conducted to quantify the uniqueness of constitutive relationships under different flow conditions. We find that the state functions that include the Euler characteristic provide the most unique prediction of the state of the system for both quasi- and non-equilibrium flow. Of these functions, those that infer volume fraction from the other state variables are independent of flow condition (quasi- or non-equilibrium). This enhances the applicability of new constitutive relationships allowing for more robust models of two-phase flow.

Abstract: Publication date: February 2020Source: Advances in Water Resources, Volume 136Author(s): Arianna Miniussi, Marco Marani, Gabriele VillariniAbstractThis study analyzes daily mean streamflow records from 5,311 U.S. Geological Survey stream gages in the continental United States and develops a Metastatistical Extreme Value Distribution (MEVD) tailored for flood frequency analysis. We compare the new tool with the Generalized Extreme Value (GEV) and Log-Pearson Type III (LP3) distributions and investigate the role of El Niño Southern Oscillation (ENSO) in the generation of floods. Hence, we formulate the MEVD in terms of mixture of distributions to describe the occurrence of flood peaks generated under different ENSO phases. We find that the MEVD outperforms GEV and LP3 distributions respectively in about 76% and 86% of the stations, with a significant improvement in the accuracy of quantiles corresponding to return periods much larger than the calibration sample size. The ENSO signature detected in the distributions of the daily peak flows does not necessarily improve the estimation of high return period flow values.

Abstract: Publication date: February 2020Source: Advances in Water Resources, Volume 136Author(s): Jory S. Hecht, Richard M. VogelOrdinary least squares (OLS) regression offers a decision-oriented approach for modeling trends in annual peak flows. We introduce a two-stage OLS approach for nonstationary flood frequency analysis that (i) models changes in their central tendency (median) in response to environmental perturbations with one regression and then (ii) examines changes in the coefficient of variation (Cv) by running a second regression on Anscombe-transformed residuals from the first regression. Monte Carlo simulations show that this approach yields 100-year flood estimates with mean squared errors comparable to estimates made with an advanced generalized linear model-based method. Also, this second-stage regression often produces approximately normal residuals, which permits statistical inferences on Cv trends. Case studies illustrate the dramatic impact that decreasing and increasing Cv trends can have on 100-year floods. Findings motivate the incorporation of trends in variability in infrastructure design along with further research examining asymmetric changes in urban flood variability.Graphical abstract

Abstract: Publication date: February 2020Source: Advances in Water Resources, Volume 136Author(s): Maria Klepikova, Bernard Brixel, Mohammadreza JalaliAbstractIn sparse fracture systems flow and transport patterns are often dominated by main flowpaths and characterized by a strong complexity induced by the fractures’ surface roughness, hierarchical arrangement in networks, and from the interaction of the fractures with the surrounding rocks. The accurate characterization of the hydraulic properties and connectivity of major fracture zones is essential to model flow, solute and heat transport and fluid pressure propagation in fractured media. In this study, we present a novel deterministic inverse modeling method for imaging the connectivity and spatial variability of transmissivity and storativity of a network of fractures. The method is based on a numerical model that simulates fluid flow in a simple three-dimensional (3-D) discrete fracture network (DFN) with a fixed fracture network structure. The forward model is coupled to an inverse algorithm to match observed pressure transients obtained from sequential hydraulic cross-hole tests. This method has been successfully tested for constant rate injection tests carried out at the Grimsel Test Site (GTS) in Switzerland. Cross-hole injection tests were conducted in a tomographic configuration, with hydraulic responses monitored at four observation intervals at various depths in two different boreholes. A discrete fracture network approach with a simple parametrization is developed to estimate log-transformed transmissivity (T) and storativity (S) values of hydraulically active fractures between boreholes by inverting the pressure transients, under the hypothesis that T and S are independent variables. We identified several permeable fractures and their connectivity without attempting to represent explicitly the true fracture network geometrical properties (length, orientation, dip), focusing instead on the calibration of a simple model capturing the observed pressure main behavior. The identified inter-borehole connectivity structure agrees well with independent information, including additional data from a cross-borehole step rate injection and hydrogeophysical data. Hence, the proposed tomography approach appears to be a promising approach for characterizing the connectivity structure and hydraulic properties of the main flowpaths in sparsely fractured rock.

Abstract: Publication date: February 2020Source: Advances in Water Resources, Volume 136Author(s): Tohid Erfani, Kevis Pachos, Julien J. HarouAbstractStaged water infrastructure capacity expansion optimization models help create flexible plans under uncertainty. In these models exogenous uncertainty can be incorporated into the optimization using an a priori hydrological and demand scenario ensemble. However some water supply intervention uncertainties cannot be considered in this way, such as demand management or technological options. In these cases the uncertainty is endogenous or ‘decision-dependent’, i.e., the optimized timing and selection of interventions determines when and which uncertainties must be considered. We formulate a multistage real-options water supply capacity expansion optimization model incorporating such uncertainty and describe its effect on cost and option selection.

Abstract: Publication date: February 2020Source: Advances in Water Resources, Volume 136Author(s): Yonghui Wu, Linsong Cheng, Sidong Fang, Shijun Huang, Pin JiaAbstractAdvances in hydraulic fracturing technology are promoting global interest in the simulation of the transient flow in fractured porous media. Boundary element method (BEM) is widely used in pressure transient analysis because of its high precision and simple discretization of the boundaries and the fractures. However, BEM is computationally expensive when real heterogeneities and large numbers of fractures are modeled. A Green element method (GEM)-based discrete fracture model (DFM) is proposed in this paper to address this problem, and this is the first study to enrich the GEM in modeling the transient behavior of heterogeneous porous media with discrete fracture networks.GEM is applied to handle formation heterogeneities and discrete fractures. First, a new mathematic model and integral formulation are proposed with the consideration of formation heterogeneities and discrete fractures. Next, structured Cartesian grids are used to discretize the domain and characterize the heterogeneities, and discrete fracture networks are embedded into the domain grids. Then, GEM is used to handle fluid flow between connected domain blocks and between a domain block and connected discrete fracture segments. The finite difference method (FDM) is used to model fluid flow between connected discrete fracture segments.The solution of the model is obtained by coupling the two systems. Two validation cases and several synthetic cases are used to verify the precision, efficiency, and application of the proposed GEM-based DFM. The results show that the proposed model is precise and efficient by comparing it to the BEM and a commercial simulator. The number of integrals and the computation are largely decreased by applying the boundary integral equation to each local block. Finally, a field example with several synthetic cases is studied to show the model's application. The proposed GEM-based DFM serves as a good tool for simulation of the transient flow in heterogeneous fractured porous media.

Abstract: Publication date: February 2020Source: Advances in Water Resources, Volume 136Author(s): Minh Tran, Birendra JhaAbstractWe investigate the influence of poroelasticity on solute transport processes during viscously unstable flows in a deformable aquifer. We hypothesize that viscous fingering-induced fluctuations in the flow field can increase the strength of coupling between poroelastic stresses and mixing and spreading characteristics of the solute slug. To test the hypothesis, we develop a coupled flow, transport and geomechanics framework to simulate spreading, mixing and deformation mechanisms during viscous fingering in a stress-sensitive aquifer. We show quantitatively that concentration-dependent fluid mobility affects the pressure and effective stress fields, and deformation-driven changes in porosity affects advective and diffusive fluxes. We perform a sensitivity analysis to evaluate the dependence of global transport characteristics—the solute breakthrough time, breakthrough curve and the degree of mixing—on the strength of flow-deformation coupling defined in terms of poroelastic moduli. Results are obtained for different magnitudes of the Peclet number, solute viscosity contrast, Biot coefficient, rock bulk modulus, fluid compressibility, and permeability heterogeneity. We find that, in presence of less consolidated formations, softer rocks, or more compressible fluids, the injected solute breaks through earlier and mixes with the resident fluid better. Our results provide important insights for modeling hydrodynamically unstable contaminant remediation and fluid injection processes in stress-sensitive reservoirs.

Abstract: Publication date: February 2020Source: Advances in Water Resources, Volume 136Author(s): Priyanka Agrawal, Amir Raoof, Oleg Iliev, Mariëtte WolthersInjection of CO2 into carbonate rocks causes dissolution and alters rock transport properties. The extent of the permeability increases, due to the increased pore volume and connectivity, strongly depends on the regimes of transport and dissolution reactions. Identification of these regimes and their parametrization at the microscopic scale is required for an understanding of the injection processes, and, afterward, for calculating the effective macroscopic parameters for field-scale simulations. Currently, a commonly used approach for calculating the rock effective parameters is the Pore Network Method, PNM, but a better understanding of the validity of its basic assumptions and their areas of applicability is essential. Here, we performed a combined microscopic experimental and numerical study to explore pore-shape evolution over a wide range of transport and dissolution reaction regimes. Experiments were conducted by flowing an acidic solution through a microscopic capillary channel in a calcite crystal at two different flow rates. The experimental results were used to validate our pore-scale reactive transport model that could reproduce the measured effluent composition as well as pore shape changes. Two key stages in pore shape evolution were observed, a transient phase and a quasi-steady-state phase. During the first stage, the shape of the single pore evolved very fast, depending on the flow regime. Under advective-dominant flow, the pore shape remained nearly cylindrical, while under diffusive-dominant transport, the pore shape developed into a half-hyperboloid shape. During the quasi-steady-state stage, the pore volume continued to increase, however, without or with diminutive change of the pore shape. In this stage, only a long period of injection may result in a significant deviation of the pore shape from its original cylinder shape, which is a common assumption in PNMs. Furthermore, we quantitatively evaluated the impact of evolved pore shape spectrum on the conductance calculations and compared it to the formulations currently used for pore network modeling of reactive transport. Under low flow rates, neglecting the developed non-uniform pore shape during the non-steady stage may lead to an overestimation of pore conductance up to 80%.Graphical abstract

Abstract: Publication date: February 2020Source: Advances in Water Resources, Volume 136Author(s): David Cross, Christian Onof, Hugo WinterAbstractWe present a new approach for estimating the frequency of sub-hourly rainfall extremes in a warming climate with simulation by conditioning Bartlett–Lewis rectangular pulse (BLRP) rainfall model parameters on the mean monthly near surface air temperature. We use a censored modelling approach with multivariate regression to capture the sensitivity of the full set of BLRP parameter estimators to temperature enabling the parameter estimators to be updated. The downscaling framework incorporates uncertainty in climate model projections for moderate and severe carbon forcing scenarios by using an ensemble of climate model outputs. Linear regression on the logarithm of BLRP parameter estimators offers a robust model for parameter estimation with uncertainty. The approach is tested with 5 min rainfall data from Bochum in Germany, and Atherstone in the United Kingdom. We find that the approach is highly effective at estimating rainfall extremes in the present climate, and the estimation of future rainfall extremes appears highly plausible.

Abstract: Publication date: Available online 2 January 2020Source: Advances in Water ResourcesAuthor(s): A. Al-Shukaili, H. Al- Busaidi, A.R. KacimovAbstractChannels are widely used for conveying natural flash floods or managed aquifer recharge (MAR) released treated wastewater. Adequate models are needed for describing the interaction between hydraulics of surface flow downslope the channel and vertical seepage into a porous bed for evaluation of the depth of water and the length to which surface water “jet” propagates over a porous bed. Experiments with surface “jets” (consumed by bed infiltration) are compared with numerical and analytical modeling of these coupled flows. In the field (Al-Hail site, Oman), at a pedestal and slope of a dune, two rectangular trenches had a sand bed and a mild and steep slope S (3° and 25°, correspondingly). After applying constant discharges at the inlet of the trenches, the length, L, of the water “jets” propagating until complete extinction has been measured. The wetting front into the subjacent sand has been detected by measuring the volumetric soil moisture content at different depths in an excavation, immediately after termination of discharging the surface water. Analytically (by the hodograph method), 2-D steady phreatic seepage flow from a rectangular channel was examined as a special case of Vedernikov's solution for a soil without capillarity. Since the depth of water was small, the Riesenkampf analytical solution, involving capillarity in the tension-saturated flanks of the channel, has been also utilized. Numerically, Richard's equation has been solved by a finite element method, HYDRUS2D for arbitrary soils. The boundary condition was a hydrostatic pressure head along the wetted perimeters of the channels with water depth gradually dropping downslope. Coupling of surface and subsurface flows has been done by the “kinematic waves” approximation with Manning's friction slope equal to S. In the ODE for conservation of mass, the sink term (infiltration) was computed by either HYDRUS or analytical solution. The corresponding boundary value problem was solved by computer algebra routines.

Abstract: Publication date: Available online 24 December 2019Source: Advances in Water ResourcesAuthor(s): Paolo Filippucci, Angelica Tarpanelli, Christian Massari, Andrea Serafini, Virginia Strati, Matteo Alberi, Kassandra Giulia Cristina Raptis, Fabio Mantovani, Luca BroccaAbstractThe global warming effects put in danger global water availability and make necessary to decrease water wastage, e.g., by monitoring global irrigation. Despite this, global irrigation information is scarce due to the absence of a solid estimation technique. In this study, we applied an innovative approach to retrieve irrigation water from high spatial and temporal resolution Soil Moisture (SM) data obtained from an advanced sensor based on Proximal Gamma-Ray (PGR) spectroscopy, in a field located in Emilia Romagna (Italy).The results show that SM is a key variable to obtain information about the amount of water applied to plants, with Pearson correlation between observed and estimated daily irrigation data ranges from 0.88 to 0.91 by using different calibration methodology. With the aim of reproducing the working conditions of satellites measuring soil moisture, we sub-sampled SM hourly time series at larger time steps. The results demonstrated that the methodology is still capable to perform the daily (weekly) irrigation estimation with Pearson Correlation around 0.6 (0.7) if the time step is not greater than 36 (48) hours.

Abstract: Publication date: Available online 24 December 2019Source: Advances in Water ResourcesAuthor(s): Zhuangji Wang, Dennis Timlin, Mikhail Kouznetsov, David Fleisher, Sanai Li, Katherine Tully, Vangimalla ReddyAbstractSurface runoff has been recognized as an important component in agricultural water management. Extensive studies have been developed to measure surface runoff, and numerical methods have been applied to simulate surface water dynamics. For simulations of surface runoff in agricultural systems, three processes should be considered: (1) the movement of water along the soil surface, i.e., surface runoff; (2) the accumulation of water in depressions; and (3) the water fluxes across the soil/atmosphere interface (i.e. infiltration, evaporation and exfiltration through seepage faces). The objective of this study is to develop a physical based surface runoff model that includes all the three processes. Numerical implementation of the new runoff model was developed and incorporated within 2DSOIL, a simulation package for soil water, energy and solute movement. The new model describes the subsurface flow near the soil surface following a unified boundary condition, expressed with the Heaviside step function. This expression enables continuous and automatic changes of boundary conditions between infiltration and runoff. The surface water flow is simulated using Saint-Venant equations. The ponded water height on the soil surface and the infiltration rate are adjusted based on the runoff flux and topography. Numerical tests based on an experimental dataset are used to evaluate the accuracy of this model, and numerical examples of surface water flow along a variety of topography are used to demonstrate model performance. The simulations match the experimental results, and the surface water mass balance errors of the numerical examples are less than 1%. A practical example of using the surface runoff model to estimate the runoff efficiency in a ridge-furrow water harvesting is carried out. In conclusion, the newly developed surface runoff model can successfully simulate surface water dynamics. This model can further support the design and evaluation of agricultural water management strategies and field water budgets.

Abstract: Publication date: Available online 23 December 2019Source: Advances in Water ResourcesAuthor(s): Dylan Wood, Ethan J. Kubatko, Mehrzad Rahimi, Abdollah Shafieezadeh, Colton J. ConroyAbstractIn coastal regions, accurate prediction of the inundation risks posed by tropical cyclones and other severe weather is key to mitigating disaster impacts. Predictions of these risks are often provided by numerical models; however, these models also frequently do not consider inundation due to wind-sea wave run-up. Neglecting such effects may be misleading for evaluating the risks posed to areas protected by flood defense systems such as levees and dykes. To effectively evaluate the flooding risks posed by storm waves, accurate wind-sea modeling should be undertaken in parallel with storm surge modeling, and accurate wave-overtopping flow rates at flood barriers must be prescribed based on the modeled sea conditions and structure geometries. To this end, we present development of a loosely coupled finite element based shallow-water equations and parametric spectral wave model. Wave-overtopping flow rates are computed by formulas provided in the guidance of the EurOtop manual. We demonstrate that the parametric spectral wave modeling approach is sufficiently accurate for modeling of wave-overtopping events, despite its simplifying assumptions, such as those based on deep water (e.g., discounting foreshore effects such as wave shoaling and breaking), and disregarding internal forces due to wave breaking and turbulence, as opposed to more commonly used discrete (third generation) spectral wave models, which provide more detailed descriptions of wave fields but are much more computationally expensive. Comparisons of numerically modeled wave-overtopping flow rates with measurements from the CLASH database show good agreement by linear regression (slope: 0.9911, R2: 0.9584) for more than 90% of the test cases considered, where for the relatively few outlier cases the model performs poorly due to either uncertainties in the EurOtop guidance or the lack of foreshore effects in the wave model.

Abstract: Publication date: Available online 30 November 2019Source: Advances in Water ResourcesAuthor(s): P. Frey, H. Lafaye de Micheaux, C. Bel, R. Maurin, K. Rorsman, T. Martin, C. DucottetAbstractSediment transport in mountain and gravel-bed-rivers is characterized by bedload transport of a wide range of grain sizes. When the bed is moving, dynamic void openings permit downward infiltration of the smaller particles. This process, termed here ‘kinetic sieving’, has been studied in industrial contexts, but more rarely in fluvial sediment transport. We present an experimental study of two-size mixtures of coarse spherical glass beads entrained by turbulent and supercritical steady water flows down a steep channel with a mobile bed. The particle diameters were 4mm and 6mm, and the channel inclination 10%. The spatial and temporal evolution of the segregating smaller 4mm diameter particles was studied through the introduction of the smaller particles at a low constant rate into the large particle bedload flow at transport equilibrium. Particle flows were filmed from the side by a high-speed camera. Using original particle tracking algorithms, the position and velocity of both small and large particles were determined. Results include the time evolution of the layer of segregating smaller beads, assessment of segregation velocity and particle depth profiles. Segregation resulted in the progressive establishment of a quasi-continuous region of small particles reaching a steady-state penetration depth. The segregation dynamics showed a logarithmic time decreasing trend. This evolution was demonstrated to be dependent on the particle streamwise shear rate which decays downwards exponentially. This result is comparable to theories initially developed for dry granular flows.

Abstract: Publication date: Available online 27 November 2019Source: Advances in Water ResourcesAuthor(s): Georgia Papacharalampous, Hristos Tyralis, Demetris Koutsoyiannis, Alberto MontanariAbstractPredictive hydrological uncertainty can be quantified by using ensemble methods. If properly formulated, these methods can offer improved predictive performance by combining multiple predictions. In this work, we use 50-year-long monthly time series observed in 270 catchments in the United States to explore the performances provided by an ensemble learning post-processing methodology for issuing probabilistic hydrological predictions. This methodology allows the utilization of flexible quantile regression models for exploiting information about the hydrological model's error. Its key differences with respect to basic two-stage hydrological post-processing methodologies using the same type of regression models are that (a) instead of a single point hydrological prediction it generates a large number of “sister predictions” (yet using a single hydrological model), and that (b) it relies on the concept of combining probabilistic predictions via simple quantile averaging. A major hydrological modelling challenge is obtaining probabilistic predictions that are simultaneously reliable and associated to prediction bands that are as narrow as possible; therefore, we assess both these desired properties of the predictions by computing their coverage probabilities, average widths and average interval scores. The results confirm the usefulness of the proposed methodology and its larger robustness with respect to basic two-stage post-processing methodologies. Finally, this methodology is empirically proven to harness the “wisdom of the crowd” in terms of average interval score, i.e., the average of the individual predictions combined by this methodology scores no worse –usually better− than the average of the scores of the individual predictions.

Abstract: Publication date: Available online 23 November 2019Source: Advances in Water ResourcesAuthor(s): Georgia Papacharalampous, Demetris Koutsoyiannis, Alberto MontanariAbstractWe introduce an ensemble learning post-processing methodology for probabilistic hydrological modelling. This methodology generates numerous point predictions by applying a single hydrological model, yet with different parameter values drawn from the respective simulated posterior distribution. We call these predictions “sister predictions”. Each sister prediction extending in the period of interest is converted into a probabilistic prediction using information about the hydrological model's errors. This information is obtained from a preceding period for which observations are available, and is exploited using a flexible quantile regression model. All probabilistic predictions are finally combined via simple quantile averaging to produce the output probabilistic prediction. The idea is inspired by the ensemble learning methods originating from the machine learning literature. The proposed methodology offers larger robustness in performance than basic post-processing methodologies using a single hydrological point prediction. It is also empirically proven to “harness the wisdom of the crowd” in terms of average interval score, i.e., the obtained quantile predictions score no worse –usually better− than the average score of the combined individual predictions. This proof is provided within toy examples, which can be used for gaining insight on how the methodology works and under which conditions it can optimally convert point hydrological predictions to probabilistic ones. A large-scale hydrological application is made in a companion paper.

Abstract: Publication date: Available online 9 November 2019Source: Advances in Water ResourcesAuthor(s): Mohamed E. Ammar, Amr Gharib, Zahidul Islam, Evan G.R. Davies, Michael Seneka, Monireh FaramarziAbstractNonstationary flood frequency analysis (NFFA) has increasingly been applied to predict future floods under climate change. The inference of nonstationarity from trend tests (INTT) on historical floods is a widespread practice used to justify the application of NFFA; however, its reliability has seldom been investigated. This research examines future floods from interpretations of INTT compared to those obtained from cause-and-effect processes by a hydrological model (INCE). The study uses INCE to quantify the changes in the regime and magnitude of floods due to potential climate change in the mid-21st century using multi-model ensemble simulations under two greenhouse gas emissions scenarios (i.e., RCP 2.6 and RCP 8.5). Independent peaks over threshold from simulated streamflow were used to estimate the changes in future (2040-2064) flood regimes and magnitudes compared to their historical (1983-2007) counterparts for 29 unregulated catchments across Alberta, Canada. Separation of extreme events and their fittings to generalized Pareto distributions (GPD) were based on a hybrid approach that combined two developed automated threshold selection methods and four estimators for the parameters of the GPD. Based on comparing the results of INTT and INCE, we show that future changes in floods contradict. We also show that the future frequency curves shifted differently at different return periods compared to historical curves, while in some instances, future climate tended to decrease small floods and increase larger floods or vice versa. Finally, flood magnitudes in 2/3 of the studied catchments in Alberta are predicted to intensify, accompanied by increases in the rate of occurrence and earlier shifts in the timing of floods for both climate scenarios, whereas no considerable change in the duration was recognized.

Abstract: Publication date: Available online 4 November 2019Source: Advances in Water ResourcesAuthor(s): N. Hosseini, Z. Bajalan, A.R. KhoeiAbstractIn this paper, a numerical model is developed based on the X-FEM technique to simulate the transport of dense solute in a single fluid phase through the fractured porous media. The governing equation is based on the mass conservation law which is applied to the fluid phase and the solute in both matrix and fracture domain. The integral governing equations of the mass exchange between the fracture and the surrounding matrix is derived. The extended finite element method (X-FEM) is applied by employing appropriate enrichment functions to model the fractured porous domain. The superiority of the X-FEM is that the FE mesh is not necessary to be conformed to the fracture geometry, so the regular mesh is utilized independent of the position of the fracture. Finally, several numerical examples of dense brine transport in a water aquifer are studied to validate the proposed computational algorithm. Moreover, the effects of various parameters of the fracture, such as the aperture and interconnectivity, as well as the matrix medium, such as the permeability and diffusion are investigated. It is shown that the proposed computational model provides an accurate prediction of subsurface hydrology for a field-scale closed desert basin.

Abstract: Publication date: Available online 4 November 2019Source: Advances in Water ResourcesAuthor(s): Giada Varra, Veronica Pepe, Luigi Cimorelli, Renata Della Morte, Luca CozzolinoAbstractA novel differential porosity model for urban flooding, namely the Binary Single Porosity model (BSP), is proposed in the present paper. The BSP model, which is derived from the Single Porosity (SP) model by constraining the porosity to attain only the values zero inside the buildings and one in the voids among the buildings, is local and independent on the existence of a Representative Elementary Volume (REV). The BSP model satisfies the Galilean invariance, while the corresponding wave speeds are identical to those of the Shallow water Equations model, and its integral formulation coincides with the original integral model by Sanders et al. (2008).The structure of the BSP model implies that the solution of the SP Riemann problem is the numerical building block for the construction of the corresponding Finite Volume schemes. This observation prompts a further study of the SP model and its solutions, demonstrating that the exact SP Riemann problem solution has the potential to take into account the transient energy losses due to wave reflections through the urban fabric in BSP models. Nonetheless, a further comparison with the two-dimensional SWE results demonstrates that additional stationary energy dissipations must be accurately taken into account through porosity reductions in the case of supercritical flow. The numerical experiments show that available approximate SP Riemann solvers may cause a systematic underestimation of the energy dissipation through the urban fabric and an overestimation of the flood celerity. The improvement of SP Riemann solvers could limit the resort to additional drag and momentum dissipation terms that are frequently added in numerical models.Finally, the investigation of the differential porosity models where different definitions are used for storage and conveyance porosity shows that these models suffer from a fundamental lack of physical congruence, implying that they cannot be used for the analysis of flood wave propagation through urban fabric.

Abstract: Publication date: Available online 31 October 2019Source: Advances in Water ResourcesAuthor(s): Paula A. Gago, Ali Q. Raeini, Peter KingAbstractFluid flow through dense granular packs or soft sands can be described as a Darcy’ s flow for low injection rates, as the friction between grain-grain and grain-walls dominate the solid system behaviour. For high injection rates, fluid forces can generate grain displacement forming flow channels or “fractures”, which in turn modify local properties within the system, such as permeability and stress distribution. Due to this kind of “self organized” behaviour, a spatially resolved model for these interactions is required to capture the dynamics of these systems. In this work, we present a resolved model based on the approach taken by the CFDEM open source project which uses LIGGGHTS – a discrete elements method (DEM)– to model the granular behaviour and OpenFoam finite volume library for computational fluid dynamics (CFD), to simulate the fluid behaviour. The capabilities provided by the DEM engine allows the properties of the solid phase, such as inter-grain cohesion and solid confinement stress to be controlled. In this work the original solver provided by the CFDEM project was modified so as to deal with dense granular packs more effectively. Advantages of the approach presented are that it does not require external “scaling parameters” to reproduce well known properties of porous materials and that it inherits the performance provided by the CFDEM project. The model is validated by reproducing the well-known properties of static porous materials, such as its permeability as a function of the system porosity, and by calculating the drag coefficient for a sphere resting inside a uniform flow. Finally, we present fracture patterns obtained when modelling water injection into a Hele-Shaw cell, filled with a dense granular pack.