Influence of temporally variable groundwater flow conditions on point measurements and contaminant mass flux estimations

Monitoring of contaminant concentrations, e.g., for the estimation of mass discharge or contaminant degradation rates, often is based on point measurements at observation wells. In addition to the problem, that point measurements may not be spatially representative, a further complication may arise due to the temporal dynamics of groundwater flow, which may cause a concentration measurement to be not temporally representative. This paper presents results from a numerical modeling study focusing on temporal variations of the groundwater flow direction. “Measurements” are obtained from point information representing observation wells installed along control planes using different well frequencies and configurations. Results of the scenario simulations show that temporally variable flow conditions can lead to significant temporal fluctuations of the concentration and thus are a substantial source of uncertainty for point measurements. Temporal variation of point concentration measurements may be as high as the average concentration determined, especially near the plume fringe, even when assuming a homogeneous distribution of the hydraulic conductivity. If a heterogeneous hydraulic conductivity field is present, the concentration variability due to a fluctuating groundwater flow direction varies significantly within the control plane and between the different realizations. Determination of contaminant mass fluxes is also influenced by the temporal variability of the concentration measurement, especially for large spacings of the observation wells. Passive dosimeter sampling is found to be appropriate for evaluating the stationarity of contaminant plumes as well as for estimating average concentrations over time when the plume has fully developed. Representative sampling has to be performed over several periods of groundwater flow fluctuation. For the determination of mass fluxes at heterogeneous sites, however, local fluxes, which may vary considerably along a control plane, have to be accounted for. Here, dosimeter sampling in combination with time integrated local water flux measurements can improve mass flux estimates under dynamic flow conditions.     

Modeling Postfire Response and Recovery using the Hydrologic Engineering Center Hydrologic Modeling System (HEC‐HMS)1

Abstract: This paper investigates application of the Army Corps of Engineers’ Hydrologic Engineering Center Hydrologic Modeling System (HEC‐HMS) to a burned watershed in San Bernardino County, California. We evaluate the HEC‐HMS’ ability to simulate discharge in prefire and postfire conditions in a semi arid watershed and the necessary parameterizations for modeling hydrologic response during the immediate, and subsequent recovery, period after a wildfire. The model is applied to City Creek watershed, which was 90% burned during the Old Fire of October 2003. An optimal spatial resolution for the HEC‐HMS model was chosen based on an initial sensitivity analysis of subbasin configurations and related model performance. Five prefire storms were calibrated for the selected model resolution, defining a set of parameters that reasonably simulate prefire conditions. Six postfire storms, two from each of the following rainy (winter) seasons were then selected to simulate postfire response and evaluate relative changes in parameter values and model behavior. There were clear trends in the postfire parameters [initial abstractions (I_a), curve number (CN), and lag time] that reveal significant (and expected) changes in watershed behavior. CN returns to prefire (baseline) values by the end of Year 2, while I_a approaches baseline by the end of the third rainy season. However, lag time remains significantly lower than prefire values throughout the three‐year study period. Our results indicate that recovery of soil conditions and related runoff response is not entirely evidenced by the end of the study period (three rainy seasons postfire). Understanding the evolution of the land surface and related hydrologic properties during the highly dynamic postfire period, and accounting for these changes in model parameterizations, will allow for more accurate and reliable discharge simulations in both the immediate, and subsequent, rainy seasons following fire.     

Development of a Quantitative Pasture Phosphorus Management Tool Using the SWAT Model1

Abstract: Assessment tools to evaluate phosphorus loss from agricultural lands allow conservation planners to evaluate the impact of management decisions on water quality. Available tools to predict phosphorus loss from agricultural fields are either: (1) qualitative indices with limited applicability to address offsite water quality standards, or (2) models which are prohibitively complex for application by most conservation planners. The purpose of this research was to develop a simple interface for a comprehensive hydrologic/water quality model to allow its usage by farmers and conservation planners. The Pasture Phosphorus Management (PPM) Calculator was developed to predict average annual phosphorus (P) losses from pastures under a variety of field conditions and management options. PPM Calculator is a vastly simplified interface for the Soil and Water Assessment Tool (SWAT) model that requires no knowledge of SWAT by the user. PPM Calculator was validated using 33 months of data on four pasture fields in northwestern Arkansas. This tool has been extensively applied in the Lake Eucha/Spavinaw Basin in northeastern Oklahoma and northwestern Arkansas. PPM Calculator allows conservation planners to take advantage of the predictive capacity of a comprehensive hydrologic water quality model typically reserved for use by hydrologists and engineers. This research demonstrates the applicability of existing water quality models in the development of user friendly P management tools.     

Kinetics of carbon dioxide absorption into mixed aqueous solutions of MDEA and MEA using a laminar jet apparatus and a numerically solved 2D absorption rate/kinetics model

New comprehensive numerically solved 1D and 2D absorption rate/kinetics models have been developed, for the first time, to interpret the experimental kinetic data obtained with a laminar jet apparatus for the absorption of carbon dioxide (CO₂) in CO₂ loaded mixed solutions of mixed amine system of methyldiethanolamine (MDEA) and monoethanolamine (MEA). Three MDEA/MEA weight ratios ranging from 27/03 to 23/07, over a concentration range of 2.316–1.996 kmol/m³ for MDEA and of 0.490–1.147 kmol/m³ for MEA were studied. The models take into account the coupling between chemical equilibrium, mass transfer, and the chemical kinetics of all possible chemical reactions involved in the CO₂ reaction with MDEA/MEA solvent. The partial differential equations of the 1D model were solved by two numerical techniques; the finite difference method (FDM) based on in-house coded Barakat–Clark scheme and the finite element method (FEM) based on COMSOL software. The FEM comprehensive model was then used to solve the set of partial differential equations in the 2D cylindrical coordinate system setting. Both FDM and FEM produced very accurate results for both the 1D and 2D models, which were much better than our previously published simplified model. The reaction rate constant obtained for MEA blended into MDEA at 298–333 K was k_MEA = 5.127 × 10⁸ exp(−3373.8/T). In addition, the 2D model, for the first time, has provided the concentration profiles of all the species in both the radial and axial directions of the laminar jet, thereby enabling an understanding of the correct sequence in which the reaction steps involved in the reactive absorption of CO₂ in aqueous mixed amines occur.     

基于Petri网的铁路车站安全防护系统建模及可靠性分析

采用单一的随机Petri网对车站线路结构、列车运行过程及车站防护系统建模,基于欧洲电工标准委员会定义的安全目标,确定车站安全防护系统的可靠性需求。对无防护系统的列车入站进行建模和分析,然后基于防护系统功能及可靠性特性,结合列车入站动态特性,对防护系统的功能和可靠性进行建模,并根据安全目标与车站事故率之间的数学关系进行系统的安全分析和可用性分析。其结果表明,车站防护系统的可靠性与乘客的个人风险存在着定量的关系,从而可以依据个人风险的标准来确定防护系统可靠性需求。     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

Watershed Regressions for Pesticides (warp) Models for Predicting Atrazine Concentrations in Corn Belt Streams1

Stone, Wesley W. and Robert J. Gilliom, 2012. Watershed Regressions for Pesticides (WARP) Models for Predicting Atrazine Concentrations in Corn Belt Streams. Journal of the American Water Resources Association (JAWRA) 48(5): 970‐986. DOI: 10.1111/j.1752‐1688.2012.00661.x Abstract: Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region‐specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP‐CB) were developed for annual maximum moving‐average (14‐, 21‐, 30‐, 60‐, and 90‐day durations) and annual 95th‐percentile atrazine concentrations in streams of the Corn Belt region. The WARP‐CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model‐development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model‐development sites. The WARP‐CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine‐use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP‐CB models. The WARP‐CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine‐use intensities of 17 kg/km² of watershed area or greater.     

Modeling urban films using a dynamic multimedia fugacity model

A thin film coats impervious urban surfaces that can act as a source or sink of organic pollutants to the greater environment. We review recent developments in the understanding of film and film-associated pollutant behavior and incorporate them into an unsteady-state version of the fugacity based Multimedia Urban Model (MUM), focusing on detailed considerations of surface film dynamics. The model is used to explore the conditions under which these atmospherically-derived films act as a temporary source of chemicals to the air and/or storm water. Assuming film growth of 2.1 nm d⁻¹ (Wu et al., 2008a), PCB congeners 28 and 180 reach air-film equilibrium within hours and days, respectively. The model results suggest that the film acts as a temporary sink of chemicals from air during dry and cool weather, as a source to air in warmer weather, and as a source to storm water and soil during rain events. Using the downtown area of the City of Toronto Canada, as a case study, the model estimates that nearly 1 g d⁻¹ of ∑₅PCBs are transferred from air to film to storm water.     

Multimedia fate modeling and comparative impact on freshwater ecosystems of pharmaceuticals from biosolids-amended soils

This study modeled the impact on freshwater ecosystems of pharmaceuticals detected in biosolids following application on agricultural soils. The detected sulfonamides and hydrochlorothiazide displayed comparatively moderate retention in solid matrices and, therefore, higher transfer fractions from biosolids to the freshwater compartment. However, the residence times of these pharmaceuticals in freshwater were estimated to be short due to abiotic degradation processes. The non-steroidal anti-inflammatory mefenamic acid had the highest environmental impact on aquatic ecosystems and warrants further investigation. The estimation of the solid-water partitioning coefficient was generally the most influential parameter of the probabilistic comparative impact assessment. These results and the modeling approach used in this study serve to prioritize pharmaceuticals in the research effort to assess the risks and the environmental impacts on aquatic biota of these emerging pollutants.     

采煤塌陷地积水对土壤氮素矿化过程的影响

煤炭开采导致大面积的土地塌陷,使大量耕地出现常年积水或季节性积水,对塌陷地土壤氮素矿化过程产生一定影响。采集了某矿采煤塌陷地土壤样品,进行好气和淹水培养条件下间歇淋洗培养实验,研究了塌陷地积水对土壤氮素矿化过程的影响。经过62d的培养,40d左右氮素的矿化过程趋于稳定,淹水培养条件下土壤氮素最终累积矿化量为68.99mg/kg,约为好气培养条件的10倍,且淹水培养条件下土壤氮素矿化势可达69.472mg/kg,均矿化速率为5.210mg/(kg·d),说明淹水对土壤氮素矿化过程有显著的促进作用。将实验所得累积矿化量分别代入简单指数模型及双因子指数模型进行拟合,发现简单指数模型能有效模拟好气和淹水培养条件下土壤氮素矿化过程,并获得了2种培养条件下土壤氮素矿化过程的模型参数。