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Most Cited Advances in Water Resources Articles
The most cited articles published since 2011, extracted from Scopus.
Volume 51, Issue , January 2013, Pages 197-216
Martin J. Blunt | Branko Bijeljic | Hu Dong | Oussama Gharbi | Stefan Iglauer | Peyman Mostaghimi | Adriana Paluszny | Christopher Pentland
Pore-scale imaging and modelling - digital core analysis - is becoming a routine service in the oil and gas industry, and has potential applications in contaminant transport and carbon dioxide storage. This paper briefly describes the underlying technology, namely imaging of the pore space of rocks from the nanometre scale upwards, coupled with a suite of different numerical techniques for simulating single and multiphase flow and transport through these images. Three example applications are then described, illustrating the range of scientific problems that can be tackled: dispersion in different rock samples that predicts the anomalous transport behaviour characteristic of highly heterogeneous carbonates; imaging of super-critical carbon dioxide in sandstone to demonstrate the possibility of capillary trapping in geological carbon storage; and the computation of relative permeability for mixed-wet carbonates and implications for oilfield waterflood recovery. The paper concludes by discussing limitations and challenges, including finding representative samples, imaging and simulating flow and transport in pore spaces over many orders of magnitude in size, the determination of wettability, and upscaling to the field scale. We conclude that pore-scale modelling is likely to become more widely applied in the oil industry including assessment of unconventional oil and gas resources. It has the potential to transform our understanding of multiphase flow processes, facilitating more efficient oil and gas recovery, effective contaminant removal and safe carbon dioxide storage. © 2012 Elsevier Ltd.
Volume 51, Issue , January 2013, Pages 3-26
Adrian D. Werner | Mark Bakker | Vincent E A Post | Alexander Vandenbohede | Chunhui Lu | Behzad Ataie-Ashtiani | Craig T. Simmons | D. A. Barry
Seawater intrusion (SI) is a global issue, exacerbated by increasing demands for freshwater in coastal zones and predisposed to the influences of rising sea levels and changing climates. This review presents the state of knowledge in SI research, compares classes of methods for assessing and managing SI, and suggests areas for future research. We subdivide SI research into categories relating to processes, measurement, prediction and management. Considerable research effort spanning more than 50. years has provided an extensive array of field, laboratory and computer-based techniques for SI investigation. Despite this, knowledge gaps exist in SI process understanding, in particular associated with transient SI processes and timeframes, and the characterization and prediction of freshwater-saltwater interfaces over regional scales and in highly heterogeneous and dynamic settings. Multidisciplinary research is warranted to evaluate interactions between SI and submarine groundwater discharge, ecosystem health and unsaturated zone processes. Recent advances in numerical simulation, calibration and optimization techniques require rigorous field-scale application to contemporary issues of climate change, sea-level rise, and socioeconomic and ecological factors that are inseparable elements of SI management. The number of well-characterized examples of SI is small, and this has impeded understanding of field-scale processes, such as those controlling mixing zones, saltwater upconing, heterogeneity effects and other factors. Current SI process understanding is based mainly on numerical simulation and laboratory sand-tank experimentation to unravel the combined effects of tides, surface water-groundwater interaction, heterogeneity, pumping and density contrasts. The research effort would benefit from intensive measurement campaigns to delineate accurately interfaces and their movement in response to real-world coastal aquifer stresses, encompassing a range of geological and hydrological settings. © 2012 Elsevier Ltd.
Volume 51, Issue , January 2013, Pages 104-122
Denis O'Carroll | Brent Sleep | Magdalena Krol | Hardiljeet Boparai | Christopher Kocur
Since the late 1990s, the use of nano zero valent iron (nZVI) for groundwater remediation has been investigated for its potential to reduce subsurface contaminants such as PCBs, chlorinated solvents, and heavy metals. nZVI shows tremendous promise in the environmental sector due to its high reactivity and as such, numerous laboratory and field studies have been performed to assess its effectiveness. This paper reviews the current knowledge of nZVI/bimetallic technology as it pertains to subsurface remediation of chlorinated solvents and heavy metals. The manuscript provides background on the technology, summarizing nZVI reactions with chlorinated solvents and metals, and examines the factors affecting nZVI reactivity. Studies on subsurface transport of bare and coated nZVI particles are also reviewed and challenges with field implementation are discussed. This manuscript offers a comprehensive review of nZVI technology and highlights the work still needed to optimize it for subsurface remediation. © 2012 Elsevier Ltd.
X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems
Volume 51, Issue , January 2013, Pages 217-246
Dorthe Wildenschild | Adrian P. Sheppard
We report here on recent developments and advances in pore-scale X-ray tomographic imaging of subsurface porous media. Our particular focus is on immiscible multi-phase fluid flow, i.e., the displacement of one immiscible fluid by another inside a porous material, which is of central importance to many natural and engineered processes. Multiphase flow and displacement can pose a rather difficult problem, both because the underlying physics is complex, and also because standard laboratory investigation reveals little about the mechanisms that control micro-scale processes. X-ray microtomographic imaging is a non-destructive technique for quantifying these processes in three dimensions within individual pores, and as we report here, with rapidly increasing spatial and temporal resolution. © 2012 Elsevier Ltd.
An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering
Volume 34, Issue 7, July 2011, Pages 844-864
Haiyan Zhou | J. Jaime Gómez-Hernández | Harrie Jan Hendricks Franssen | Liangping Li
The ensemble Kalman filter (EnKF) is a commonly used real-time data assimilation algorithm in various disciplines. Here, the EnKF is applied, in a hydrogeological context, to condition log-conductivity realizations on log-conductivity and transient piezometric head data. In this case, the state vector is made up of log-conductivities and piezometric heads over a discretized aquifer domain, the forecast model is a groundwater flow numerical model, and the transient piezometric head data are sequentially assimilated to update the state vector. It is well known that all Kalman filters perform optimally for linear forecast models and a multiGaussian-distributed state vector. Of the different Kalman filters, the EnKF provides a robust solution to address non-linearities; however, it does not handle well non-Gaussian state-vector distributions. In the standard EnKF, as time passes and more state observations are assimilated, the distributions become closer to Gaussian, even if the initial ones are clearly non-Gaussian. A new method is proposed that transforms the original state vector into a new vector that is univariate Gaussian at all times. Back transforming the vector after the filtering ensures that the initial non-Gaussian univariate distributions of the state-vector components are preserved throughout. The proposed method is based in normal-score transforming each variable for all locations and all time steps. This new method, termed the normal-score ensemble Kalman filter (NS-EnKF), is demonstrated in a synthetic bimodal aquifer resembling a fluvial deposit, and it is compared to the standard EnKF. The proposed method performs better than the standard EnKF in all aspects analyzed (log-conductivity characterization and flow and transport predictions). © 2011 Elsevier Ltd.
Volume 51, Issue , January 2013, Pages 438-456
P. M. Reed | D. Hadka | J. D. Herman | J. R. Kasprzyk | J. B. Kollat
This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with four or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are given for the new algorithms that should serve as the benchmarks for innovations in the water resources literature. The future of MOEAs in water resources needs to emphasize self-adaptive search, new technologies for visualizing tradeoffs, and the next generation of computing technologies. © 2012 Elsevier Ltd.
Volume 36, Issue , February 2012, Pages 146-164
Erica R. Siirila | Alexis K. Navarre-Sitchler | Reed M. Maxwell | John E. McCray
Leakage of CO 2 and associated gases into overlying aquifers as a result of geologic carbon capture and sequestration may have adverse impacts on aquifer drinking-water quality. Gas or aqueous-phase leakage may occur due to transport via faults and fractures, through faulty well bores, or through leaky confining materials. Contaminants of concern include aqueous salts and dissolved solids, gaseous or aqueous-phase organic contaminants, and acidic gas or aqueous-phase fluids that can liberate metals from aquifer minerals. Here we present a quantitative risk assessment framework to predict potential human health risk from CO 2 leakage into drinking water aquifers. This framework incorporates the potential release of CO 2 into the drinking water aquifer; mobilization of metals due to a decrease in pH; transport of these metals down gradient to municipal receptors; distributions of contaminated groundwater to multiple households; and exposure and health risk to individuals using this water for household purposes. Additionally, this framework is stochastic, incorporates detailed variations in geological and geostatistical parameters and discriminates between uncertain and variable parameters using a two-stage, or nested, Monte Carlo approach. This approach is demonstrated using example simulations with hypothetical, yet realistic, aquifer characteristics and leakage scenarios. These example simulations show a greater risk for arsenic than for lead for both cancer and non-cancer endpoints, an unexpected finding. Higher background groundwater gradients also yield higher risk. The overall risk and the associated uncertainty are sensitive to the extent of aquifer stratification and the degree of local-scale dispersion. These results all highlight the importance of hydrologic modeling in risk assessment. A linear relationship between carcinogenic and noncarcinogenic risk was found for arsenic and suggests action levels for carcinogenic risk will be exceeded in exposure situations before noncarcinogenic action levels, a reflection of the ratio of cancer and non-cancer toxicity values. Finally, implications for ranking aquifer vulnerability due to geologic configuration, aquifer mineralogy, and leakage scenarios are discussed. © 2010 Elsevier Ltd.
Capillary pressure and heterogeneity for the CO 2/water system in sandstone rocks at reservoir conditions
Volume 38, Issue , March 2012, Pages 48-59
Ronny Pini | Samuel C M Krevor | Sally M. Benson
A novel method is presented to measure drainage capillary pressure curves both at the core and sub-core scale using CO 2 and water at reservoir conditions. The experimental configuration is very similar to the one used in traditional steady-state relative permeability experiments. Capillary pressure measurements are made at the inlet face of the sample by successively increasing the flow rate of the non-wetting phase while measuring the saturation with a medical X-ray Computed Tomography (CT) scanner. The method requires that the wetting phase pressure is uniform across the core and can be measured in the outlet end-cap. A capillary pressure curve is obtained in less than two days, as compared to weeks for existing methods that use porous plates. Drainage capillary pressure curves of CO 2 and water are measured for two sandstones rock cores with different lithology and pore size distribution. Experiments are carried out at 25 and 50°C and at 9MPa pore pressure, while keeping the confining pressure on the core at 12MPa. There is excellent agreement between the new method and data from mercury intrusion porosimetry; beside providing confidence in the new technique, such comparison allows for an estimate of the wetting and interfacial properties of the CO 2/water system. X-ray CT scanning allows for precise imaging of fluid saturations at a resolution of about (2.5×2.5×1)mm 3, thus enabling quantification of sub-core scale capillary pressure curves. These measurements provide independent confirmation that sub-core scale capillary heterogeneity plays an important role in controlling saturation distributions during multiphase flow. © 2011 Elsevier Ltd.
Volume 51, Issue , January 2013, Pages 326-344
Paolo D'Odorico | Abinash Bhattachan | Kyle F. Davis | Sujith Ravi | Christiane W. Runyan
Desertification is a change in soil properties, vegetation or climate, which results in a persistent loss of ecosystem services that are fundamental to sustaining life. Desertification affects large dryland areas around the world and is a major cause of stress in human societies. Here we review recent research on the drivers, feedbacks, and impacts of desertification. A multidisciplinary approach to understanding the drivers and feedbacks of global desertification is motivated by our increasing need to improve global food production and to sustainably manage ecosystems in the context of climate change. Classic desertification theories look at this process as a transition between stable states in bistable ecosystem dynamics. Climate change (i.e., aridification) and land use dynamics are the major drivers of an ecosystem shift to a " desertified" (or " degraded" ) state. This shift is typically sustained by positive feedbacks, which stabilize the system in the new state. Desertification feedbacks may involve land degradation processes (e.g., nutrient loss or salinization), changes in rainfall regime resulting from land-atmosphere interactions (e.g., precipitation recycling, dust emissions), or changes in plant community composition (e.g., shrub encroachment, decrease in vegetation cover). We analyze each of these feedback mechanisms and discuss their possible enhancement by interactions with socio-economic drivers. Large scale effects of desertification include the emigration of " environmental refugees" displaced from degraded areas, climatic changes, and the alteration of global biogeochemical cycles resulting from the emission and long-range transport of fine mineral dust. Recent research has identified some possible early warning signs of desertification, which can be used as indicators of resilience loss and imminent shift to desert-like conditions. We conclude with a brief discussion on some desertification control strategies implemented in different regions around the world. © 2012 Elsevier Ltd.
Global water resources modeling with an integrated model of the social-economic-environmental system
Volume 34, Issue 6, June 2011, Pages 684-700
Evan G R Davies | Slobodan P. Simonovic
Awareness of increasing water scarcity has driven efforts to model global water resources for improved insight into water resources infrastructure and management strategies. Most water resources models focus explicitly on water systems and represent socio-economic and environmental change as external drivers. In contrast, the system dynamics-based integrated assessment model employed here, ANEMI, incorporates dynamic representations of these systems, so that their broader changes affect and are affected by water resources systems through feedbacks. Sectors in ANEMI therefore include the global climate system, carbon cycle, economy, population, land use and agriculture, and novel versions of the hydrological cycle, global water use and water quality. Since the model focus is on their interconnections through explicit nonlinear feedbacks, simulations with ANEMI provide insight into the nature and structure of connections between water resources and socio-economic and environmental change. Of particular interest to water resources researchers and modelers will be the simulated effects of a new water stress definition that incorporates both water quality and water quantity effects into the measurement of water scarcity. Five simulation runs demonstrate the value of wastewater treatment and reuse programs and the feedback-effects of irrigated agriculture and greater consumption of animal products. © 2011 Elsevier Ltd.
The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments
Volume 34, Issue 5, May 2011, Pages 562-576
Satish Bastola | Conor Murphy | John Sweeney
This study attempts to assess the uncertainty in the hydrological impacts of climate change using a multi-model approach combining multiple emission scenarios, GCMs and conceptual rainfall-runoff models to quantify uncertainty in future impacts at the catchment scale. The uncertainties associated with hydrological models have traditionally been given less attention in impact assessments until relatively recently. In order to examine the role of hydrological model uncertainty (parameter and structural uncertainty) in climate change impact studies a multi-model approach based on the Generalised Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods is presented. Six sets of regionalised climate scenarios derived from three GCMs, two emission scenarios, and four conceptual hydrological models were used within the GLUE framework to define the uncertainty envelop for future estimates of stream flow, while the GLUE output is also post processed using BMA, where the probability density function from each model at any given time is modelled by a gamma distribution with heteroscedastic variance. The investigation on four Irish catchments shows that the role of hydrological model uncertainty is remarkably high and should therefore be routinely considered in impact studies. Although, the GLUE and BMA approaches used here differ fundamentally in their underlying philosophy and representation of error, both methods show comparable performance in terms of ensemble spread and predictive coverage. Moreover, the median prediction for future stream flow shows progressive increases of winter discharge and progressive decreases in summer discharge over the coming century. © 2011 Elsevier Ltd.
Volume 51, Issue , January 2013, Pages 367-380
Karl Rittger | Thomas H. Painter | Jeff Dozier
Characterization of snow is critical for understanding Earth's water and energy cycles. Maps of snow from MODIS have seen growing use in investigations of climate, hydrology, and glaciology, but the lack of rigorous validation of different snow mapping methods compromises these studies. We examine three widely used MODIS snow products: the "binary" (i.e., snow yes/no) global snow maps that were among the initial MODIS standard products; a more recent standard MODIS fractional snow product; and another fractional snow product, MODSCAG, based on spectral mixture analysis. We compare them to maps of snow obtained from Landsat ETM+ data, whose 30. m spatial resolution provides nearly 300 samples within a 500. m MODIS nadir pixel. The assessment uses 172 images spanning a range of snow and vegetation conditions, including the Colorado Rocky Mountains, the Upper Rio Grande, California's Sierra Nevada, and the Nepal Himalaya. MOD10A1 binary and fractional fail to retrieve snow in the transitional periods during accumulation and melt while MODSCAG consistently maintains its retrieval ability during these periods. Averaged over all regions, the RMSE for MOD10A1 fractional is 0.23, whereas the MODSCAG RMSE is 0.10. MODSCAG performs the most consistently through accumulation, mid-winter and melt, with median differences ranging from -0.16 to 0.04 while differences for MOD10A1 fractional range from -0.34 to 0.35. MODSCAG maintains its performance over all land cover classes and throughout a larger range of land surface properties. Characterizing snow cover by spectral mixing is more accurate than empirical methods based on the normalized difference snow index, both for identifying where snow is and is not and for estimating the fractional snow cover within a sensor's instantaneous field-of-view. Determining the fractional value is particularly important during spring and summer melt in mountainous terrain, where large variations in snow, vegetation and soil occur over small distances and when snow can melt rapidly. © 2012 Elsevier Ltd.
Volume 34, Issue 4, April 2011, Pages 448-467
Simone Fatichi | Valeriy Y. Ivanov | Enrica Caporali
Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961-2000) and forecasting future (2081-2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario. © 2011 Elsevier Ltd.
Volume 51, Issue , January 2013, Pages 168-196
Philippe Renard | Denis Allard
Understanding the role of connectivity for the characterization of heterogeneous porous aquifers or reservoirs is a very active and new field of research. In that framework, connectivity metrics are becoming important tools to describe a reservoir. In this paper, we provide a review of the various metrics that were proposed so far, and we classify them in four main groups. We define first the static connectivity metrics which depend only on the connectivity structure of the parameter fields (hydraulic conductivity or geological facies). By contrast, dynamic connectivity metrics are related to physical processes such as flow or transport. The dynamic metrics depend on the problem configuration and on the specific physics that is considered. Most dynamic connectivity metrics are directly expressed as a function of an upscaled physical parameter describing the overall behavior of the media. Another important distinction is that connectivity metrics can either be global or localized. The global metrics are not related to a specific location while the localized metrics relate to one or several specific points in the field. Using these metrics to characterize a given aquifer requires the possibility to measure dynamic connectivity metrics in the field, to relate them with static connectivity metrics, and to constrain models with those information. Some tools are already available for these different steps and reviewed here, but they are not yet routinely integrated in practical applications. This is why new steps should be added in hydrogeological studies to infer the connectivity structure and to better constrain the models. These steps must include specific field methodologies, interpretation techniques, and modeling tools to provide more realistic and more reliable forecasts in a broad range of applications. © 2011 Elsevier Ltd.
Volume 36, Issue , February 2012, Pages 36-50
Jens Christian Refsgaard | Steen Christensen | Torben O. Sonnenborg | Dorte Seifert | Anker Lajer Højberg | Lars Troldborg
The geologically related uncertainty in groundwater modeling originates from two main sources: geological structures and hydraulic parameter values within these structures. Within a geological structural element the parameter values will always exhibit local scale heterogeneity, which can be accounted for, but is often neglected, in assessments of prediction uncertainties. Strategies for assessing prediction uncertainty due to geologically related uncertainty may be divided into three main categories, accounting for uncertainty due to: (a) the geological structure; (b) effective model parameters; and (c) model parameters including local scale heterogeneity. The most common methodologies for uncertainty assessments within each of these categories, such as multiple modeling, Monte Carlo analysis, regression analysis and moment equation approach, are briefly described with emphasis on their key characteristics. Based on reviews of previous studies, assessments are made on the relative importance of the three uncertainty categories for different types of model predictions. Furthermore, the strengths, limitations and interactions of these methodologies are discussed and conclusions are made with respect to identifying key subjects for which further research is needed. When all sources of uncertainty are analyzed by exploring model parameter and local scale heterogeneity uncertainty for several plausible geological model structures the joint uncertainties can be assessed by use of model averaging techniques, such as Bayesian Model Averaging (BMA). General challenge in model averaging with respect to choosing mutually exclusive and collectively exhaustive choice models, as well as to assign weights when models are used beyond their calibration base, are discussed. © 2011 Elsevier Ltd.
Volume 34, Issue 3, March 2011, Pages 351-364
Caleb Dechant | Hamid Moradkhani
Estimation of seasonal snowpack, in mountainous regions, is crucial for accurate streamflow prediction. This paper examines the ability of data assimilation (DA) of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center (NWSRFC) are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the ensemble Kalman filter (EnKF) and the particle filter (PF), is made using a coupled SNOW17 and the microwave emission model for layered snow pack (MEMLS) model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the advanced microwave scanning radiometer-earth observing system (AMSR-E) at the 36.5. GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. © 2010 Elsevier Ltd.
The effect of heterogeneity on the character of density-driven natural convection of CO2 overlying a brine layer
Volume 34, Issue 3, March 2011, Pages 327-339
R. Farajzadeh . | P. Ranganathan | P. L J Zitha | J. Bruining
The efficiency of mixing in density-driven natural-convection is largely governed by the aquifer permeability, which is heterogeneous in practice. The character (fingering, stable mixing or channeling) of flow-driven mixing processes depends primarily on the permeability heterogeneity character of the aquifer, i.e., on its degree of permeability variance (Dykstra-Parsons coefficient) and the correlation length. Here we follow the ideas of Waggoner et al. (1992)  to identify different flow regimes of a density-driven natural convection flow by numerical simulation. Heterogeneous fields are generated with the spectral method of Shinozuka and Jan (1972) , because the method allows the use of power-law variograms. In this paper, we extended the classification of Waggoner et al. (1992)  for the natural convection phenomenon, which can be used as a tool in selecting optimal fields with maximum transfer rates of CO2 into water. We observe from our simulations that the rate of mass transfer of CO2 into water is higher for heterogeneous media. © 2010 Elsevier Ltd.
Relative permeability hysteresis and capillary trapping characteristics of supercritical CO2/brine systems: An experimental study at reservoir conditions
Volume 52, Issue , February 2013, Pages 190-206
Morteza Akbarabadi | Mohammad Piri
We present the results of an experimental study on the effects of hysteresis on capillary trapping and relative permeability of CO2/brine systems at reservoir conditions. We performed thirty unsteady- and steady-state drainage and imbibition full-recirculation flow experiments in three different sandstone rock samples, low- and high-permeability Berea and Nugget sandstones. The experiments were carried out at various flow rates with both supercritical CO2 (scCO2)/brine and gaseous CO2 (gCO2)/brine fluid systems. The unsteady-state experiments were carried out with a wide range of flow rates to establish a broad range of initial brine saturations (S wi). This allowed investigation of the sensitivity of residual trapped CO2 saturation (SCO2r) to changes in S wi. The values were successfully compared with those available in the literature. For a given S wi, the trapped scCO2 saturation was less than that of gCO2 in the same sample. This was attributed to brine being less wetting in the presence of scCO2 than in the presence of gCO2. Post-imbibition dissolution of trapped CO2 and formation of dissolution front was also investigated. During the steady-state experiments, scCO2 and brine were co-injected with monotonically increasing or decreasing fractional flows to perform drainage and imbibition processes. We carried out seven sets of steady-state flow tests with various trajectories generating a comprehensive group of relative permeability hysteresis curves. The scanning curves revealed distinct features with potentially important implications for storage of scCO2 in geological formations. For both series of experiments, the ratio of SCO2r to initial CO2 saturation (1- S wi) was found to be much higher for low initial CO2 saturations. The results indicate that very promising fractions (about 49 to 83%) of the initial CO2 saturation can be stored through capillary trapping. © 2012 Elsevier Ltd.
Volume 57, Issue , July 2013, Pages 12-18
Zengchao Hao | Amir AghaKouchak
Defining droughts based on a single variable/index (e.g., precipitation, soil moisture, or runoff) may not be sufficient for reliable risk assessment and decision-making. In this paper, a multivariate, multi-index drought-modeling approach is proposed using the concept of copulas. The proposed model, named Multivariate Standardized Drought Index (MSDI), probabilistically combines the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSI) for drought characterization. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of drought. In this study, the proposed MSDI is utilized to characterize the drought conditions over several Climate Divisions in California and North Carolina. The MSDI-based drought analyses are then compared with SPI and SSI. The results reveal that MSDI indicates the drought onset and termination based on the combination of SPI and SSI, with onset being dominated by SPI and drought persistence being more similar to SSI behavior. Overall, the proposed MSDI is shown to be a reasonable model for combining multiple indices probabilistically. © 2013 Elsevier Ltd.
Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art
Volume 51, Issue , January 2013, Pages 261-279
T. D. Fletcher | H. Andrieu | P. Hamel
Urban hydrology has evolved to improve the way urban runoff is managed for flood protection, public health and environmental protection. There have been significant recent advances in the measurement and prediction of urban rainfall, with technologies such as radar and microwave networks showing promise. The ability to predict urban hydrology has also evolved, to deliver models suited to the small temporal and spatial scales typical of urban and peri-urban applications. Urban stormwater management increasingly consider the needs of receiving environments as well as those of humans. There is a clear trend towards approaches that attempt to restore pre-development flow-regimes and water quality, with an increasing recognition that restoring a more natural water balance benefits not only the environment, but enhances the liveability of the urban landscape. Once regarded only as a nuisance, stormwater is now increasingly regarded as a resource. Despite the advances, many important challenges in urban hydrology remain. Further research into the spatio-temporal dynamics of urban rainfall is required to improve short-term rainfall prediction. The performance of stormwater technologies in restoring the water balance and in removing emerging priority pollutants remain poorly quantified. All of these challenges are overlaid by the uncertainty of climate change, which imposes a requirement to ensure that stormwater management systems are adaptable and resilient to changes. Urban hydrology will play a critical role in addressing these challenges. © 2012.
Volume 34, Issue 9, September 2011, Pages 1102-1112
B. Flemisch | M. Darcis | K. Erbertseder | B. Faigle | A. Lauser | K. Mosthaf | S. Müthing | P. Nuske | A. Tatomir | M. Wolff | R. Helmig
DuMux is a free and open-source simulator for flow and transport processes in porous media, based on the Distributed and Unified Numerics Environment (DUNE). Its main intention is to provide a sustainable and consistent framework for the implementation and application of model concepts, constitutive relations, discretizations, and solvers. The paper provides an overview of DuMux with the focus on software-related aspects. Selected examples highlight the multi-scale and the parallel capabilities. © 2011 Elsevier Ltd.
Volume 34, Issue 7, July 2011, Pages 810-816
Kaixin Wang | Hong Wang
Fractional advection-diffusion equations provide an adequate and accurate description of the movement of solute in an aquifer. However, there are major obstacles that restrict their applications. From a modeling viewpoint, one of the major limitations in the application of fractional advection-diffusion equations to hydrology is the poor predictability of model parameters . From a computational view point, one of the major limitations in numerical solution of fractional advection-diffusion equations in multiple space dimensions is that they generate full coefficient matrices in their numerical approximations, which require O(N3) of computational cost and O(N2) storage for a problem of size N.This paper presents a preliminary step towards the efficient numerical solution of fractional advection-diffusion equations. In this paper we develop a fast characteristic finite difference method for the efficient solution of space-fractional transient advection-diffusion equations in one space dimension. This method generates more accurate solutions than standard implicit methods even if much larger time steps and spatial meshes are used, leading to a discrete system with a greatly reduced size. Furthermore, we explore the structure of the coefficient matrix to come up with an efficient iterative solver which requires only O(N) account of storage and roughly O(Nlog. N) account of computational cost.Our preliminary numerical example runs for some simple one dimensional model problems seem to indicate the following observations: to achieve the same accuracy, the new method uses no more than one thousandth of CPU and about one thousandth of the storage used by the standard method. This demonstrates the strong potential of the method. © 2010.
A robust well-balanced finite volume model for shallow water flows with wetting and drying over irregular terrain
Volume 34, Issue 7, July 2011, Pages 915-932
Lixiang Song | Jianzhong Zhou | Jun Guo | Qiang Zou | Yi Liu
An unstructured Godunov-type finite volume model is developed for the numerical simulation of geometrically challenging two-dimensional shallow water flows with wetting and drying over convoluted topography. In the framework of sloping bottom model, a modified formulation of shallow water equations is used to preserve mass conservation during flooding and recession. The key ingredient of the model is the use of this combination of the sloping bottom model and the modified shallow water equations to provide a robust technique for wet/dry fronts tracking and, together with centered discretization of the bed slope source term, to exactly preserve the static flow on irregular topographies. The variable reconstruction technique ensures nonnegative reconstructed water depth and reasonable reconstructed velocity, and the friction terms are solved by semi-implicit scheme that does not invert the direction of velocity components. The robustness and accuracy of the proposed model are assessed by comparing numerical and reference results of extensive test cases. Moreover, the results of a dam-break flooding over real topography are presented to show the capability of the model on field-scale application. © 2011 Elsevier Ltd.
Volume 34, Issue 5, May 2011, Pages 607-616
Kaveh Madani | Jay R. Lund
Game theory provides a useful framework for studying Multi-Criteria Decision Making problems. This paper suggests modeling Multi-Criteria Decision Making problems as strategic games and solving them using non-cooperative game theory concepts. The suggested method can be used to prescribe non-dominated solutions and also can be used as a method to predict the outcome of a decision making problem. Non-cooperative stability definitions for solving the games allow consideration of non-cooperative behaviors, often neglected by other methods which assume perfect cooperation among decision makers. To deal with the uncertainty in input variables a Monte-Carlo Game Theory (MCGT) approach is suggested which maps the stochastic problem into many deterministic strategic games. The games are solved using non-cooperative stability definitions and the results include possible effects of uncertainty in input variables on outcomes. The method can handle multi-criteria multi-decision-maker problems with uncertainty. The suggested method does not require criteria weighting, developing a compound decision objective, and accurate quantitative (cardinal) information as it simplifies the decision analysis by solving problems based on qualitative (ordinal) information, reducing the computational burden substantially. The MCGT method is applied to analyze California's Sacramento-San Joaquin Delta problem. The suggested method provides insights, identifies non-dominated alternatives, and predicts likely decision outcomes. © 2011 Elsevier Ltd.
Volume 36, Issue , February 2012, Pages 75-85
Shlomo P. Neuman | Liang Xue | Ming Ye | Dan Lu
The rational management of water resource systems requires an understanding of their response to existing and planned schemes of exploitation, pollution prevention and/or remediation. Such understanding requires the collection of data to help characterize the system and monitor its response to existing and future stresses. It also requires incorporating such data in models of system makeup, water flow and contaminant transport. As the collection of subsurface characterization and monitoring data is costly, it is imperative that the design of corresponding data collection schemes be cost-effective, i.e., that the expected benefit of new information exceed its cost. A major benefit of new data is its potential to help improve one's understanding of the system, in large part through a reduction in model predictive uncertainty and corresponding risk of failure. Traditionally, value-of-information or data-worth analyses have relied on a single conceptual-mathematical model of site hydrology with prescribed parameters. Yet there is a growing recognition that ignoring model and parameter uncertainties render model predictions prone to statistical bias and underestimation of uncertainty. This has led to a recent emphasis on conducting hydrologic analyses and rendering corresponding predictions by means of multiple models. We describe a corresponding approach to data-worth analyses within a Bayesian model averaging (BMA) framework. We focus on a maximum likelihood version (MLBMA) of BMA which (a) is compatible with both deterministic and stochastic models, (b) admits but does not require prior information about the parameters, (c) is consistent with modern statistical methods of hydrologic model calibration, (d) allows approximating lead predictive moments of any model by linearization, and (e) updates model posterior probabilities as well as parameter estimates on the basis of potential new data both before and after such data become actually available. We describe both the BMA and MLBMA versions theoretically and implement MLBMA computationally on a synthetic example with and without linearization. © 2011 Elsevier Ltd.