H21C-0689
A Novel Approach to Parameter Identification for Distributed Hydrologic Modeling
The demands on, and interest in, distributed hydrologic models have increased considerably in recent years. However, most parameters used in hydrologic models (especially those of the "conceptual" type) do not correlate strongly with measurable physical quantities and therefore cannot be directly derived from field observations. Since applying a model in a distributed fashion significantly increases the number of model parameters, the development and implementation of parameter identification strategies represent a real challenge for distributed hydrologic modeling. In this presentation, we discuss a novel methodology for distributed hydrologic parameter identification based on the classification of hydrologic landscapes (HL), which reflect the dominant controls on the fundamental hydrologic processes and therefore arguably the major system parameters. The HL-based approach proposed here is computationally inexpensive and holds promise for making improved predictions in ungaged basins. Applications of this new approach to distributed modeling in river basins in the US Southwest will be presented and discussed.
H21C-0690
Adaptive Parameter Optimization of a Grid-based Conceptual Hydrological Model
Any spatially explicit hydrological model at the mesoscale is a conceptual approximation of the hydrological cycle and its dominant process occurring at this scale. Manual-expert calibration of this type of models may become quite tedious---if not impossible---taking into account the enormous amount of data required by these kind of models and the intrinsic uncertainty of both the data (input-output) and the model structure. Additionally, the model should be able to reproduce well several process which are accounted by a number of predefined objectives. As a consequence, some degree of automatic calibration would be required to find "good" solutions, each one constituting a trade-off among all calibration criteria. In other words, it is very likely that a number of parameter sets fulfil the optimization criteria and thus can be considered a model solution. In this study, we dealt with two research questions: 1) How to assess the adequate level of model complexity so that model overparameterization is avoided? And, 2) How to find a good solution with a relatively low computational burden? In the present study, a grid-based conceptual hydrological model denoted as HBV-UFZ based on some of the original HBV concepts was employed. This model was driven by 12~h precipitation, temperature, and PET grids which are acquired either from satellite products or from data of meteorological stations. In the latter case, the data was interpolated with external drift Kriging. The first research question was addressed in this study with the implementation of nonlinear transfer functions that regionalize most model parameters as a function of other spatially distributed observables such as land cover (time dependent) and other time independent basin characteristics such as soil type, slope, aspect, geological formations among others. The second question was addressed with an adaptive constrained optimization algorithm based on a parallel implementation of simulated annealing (SA). The main difference with the standard SA is the parameter search routine which uses adaptive heuristic rules to improve its efficiency. These rules are based on the relative behavior of the efficiency criteria. The efficiency of the model is evaluated with the Nash-Sutcliffe efficiency coefficient (NS) and the RMSE obtained for various short and long term runoff characteristics such as daily flows; semiannual high and low flow characteristics such as total drought duration frequency of high flows; and annual specific discharge at various gauging stations. Additionally, the parameter search was constrained with the 95% confidence bands of the runoff characteristics mentioned above. The proposed method was calibrated in the Upper Neckar River basin covering an area of approximately 4000~km2 during the period from 1961 to 1993. The spatial and temporal resolutions used were a grid size of (1000 × 1000)~m and 12~h intervals respectively. The results of the study indicate significant improvement in model performance (e.g. Nash-Sutcliffe of various runoff characteristics ~ 0.8) and a significant reduction in computational burden of at least 25%.
H21C-0691
A signature index approach to diagnostic evaluation and parameter estimation of watershed models
With ever increasing complexity of hydrologic models the use of regression based aggregate criteria in the model identification process increasingly limits our ability to identify model components that properly represent the actual hydrologic processes within the watershed and those that do not. The major limitation is that these measures do not provide a strong basis for detecting the causes and resolution of model performance inadequacies. Our presentation will demonstrate a signature index approach in an effort to bring in more hydrological understanding in model evaluation and parameter estimation problems. The approach starts with identification of primary functions of any watershed/model at a hierarchy of timescales and continues with the formulation of signature indices, derived from input-state-output behavior of the watershed, which can be used for proper representation of these functions in the watershed model. Preliminary analysis using the Sacramento Model showed that the approach satisfactorily identifies model parameter/signature index relations; however, secondary effects were unavoidable due to parameter interactions. The solution to this issue is to apply the approach to more parsimonious models first –with fewer interacting parameters- and then increase the model complexity in a stepwise fashion while formulating new signatures if necessary. A Monte Carlo constraining approach is utilized to identify model parameters resulting in improved signature index match when compared to a baseline model run. The advantage of the approach is its effectiveness and efficiency in extracting hydrologically relevant information from observations, which in turn increases its diagnostic power and makes it useful for watersheds with limited historical observations.
H21C-0692
Performance and Probabilistic Verification of Regional Parameter Estimates for Conceptual Rainfall-runoff Models
Identification of appropriate parameter sets for simulation of streamflow in ungauged basins has become a significant challenge for both operational and research hydrologists. This is especially difficult in the case of conceptual models, when model parameters typically must be "calibrated" or adjusted to match streamflow conditions in specific systems (i.e. some of the parameters are not directly observable). This paper addresses the performance and uncertainty associated with transferring conceptual rainfall-runoff model parameters between basins within large-scale ecoregions. We use the National Weather Service's (NWS) operational hydrologic model, the SACramento Soil Moisture Accounting (SAC-SMA) model. A Multi-Step Automatic Calibration Scheme (MACS), using the Shuffle Complex Evolution (SCE), is used to optimize SAC-SMA parameters for a group of watersheds with extensive hydrologic records from the Model Parameter Estimation Experiment (MOPEX) database. We then explore "hydroclimatic" relationships between basins to facilitate regionalization of parameters for an established ecoregion in the southeastern United States. The impact of regionalized parameters is evaluated via standard model performance statistics as well as through generation of hindcasts and probabilistic verification procedures to evaluate streamflow forecast skill. Preliminary results show climatology ("climate neighbor") to be a better indicator of transferability than physical similarities or proximity ("nearest neighbor"). The mean and median of all the parameters within the ecoregion are the poorest choice for the ungauged basin. The choice of regionalized parameter set affected the skill of the ensemble streamflow hindcasts, however, all parameter sets show little skill in forecasts after five weeks (i.e. climatology is as good an indicator of future streamflows). In addition, the optimum parameter set changed seasonally, with the "nearest neighbor" showing the highest skill in the winter months and the "climate neighbor" showing the highest skill in the summer months.
H21C-0693
A Hierarchical Approach to Distributed Parameter Estimation in Rainfall-Runoff Modeling
Distributed rainfall-runoff models intend to account for the heterogeneous characteristics of rainfall distributions and runoff generations thereby, improve the river forecast. In this study, a distributed river forecast model is built on the hierarchy of sub-basins connected through a river–routing system. These hydrologic units (sub-basins) possess a no-flux boundary and traditionally can be simulated by conceptual models with a limited number of parameters. However, calibration is needed to make such a model perform well. In the case of distributed modeling, the lack of streamflow observations inside a river system poses a challenge to estimate the model parameters at sub-basin scales. A hierarchical approach is proposed as follows: First, the study basin (a parent basin) is modeled in lumped mode and calibrated to obtain the optimized parameters. In the next step, the parent basin is divided into three sub-basins (children basins). The same model (with tripled parameters) is applied to the sub-basins driven by the rainfalls over the sub-basins and the model parameters for each sun-basin are calibrated using the parent parameters as their prior values. After obtaining the optimal parameters for the sub-basins, the hydrograph at the outlet of each sub-basin can be generated. Finally, by repeating the similar procedure, each sub-basin can be taken as a parent basin and obtaining the parameters for its children sub-basins. Applying this method to one of the DIMP-2 test basins: the Illinois River basin at south of Siloam Spring, the results show that (1) the streamflow results are improved by using the distributed rainfall and distributed parameters in comparing with the lumped simulation results, and (2) taking the parent basin's parameters as the priors can help to determine reasonable searching ranges when optimizing the parameters of children basins and also reduce the chance of resulting in an optimum which is not physically plausible. Applying this method to one of the DIMP-2 test basins: the Illinois River basin at south of Siloam Spring, the results show that (1) the streamflow results are improved by using the distributed rainfall and distributed parameters in comparing with the lumped simulation results, and (2) taking the parent basin's parameters as the priors can help to determine reasonable searching ranges when optimizing the parameters of children basins and also reduce the chance of resulting in an optimum which is not physically plausible.
H21C-0694
Automatic Calibration Method for a Storm Water Runoff Model
Major metropolitan areas are characterized by continuous increases in imperviousness due to urban development. Increasing imperviousness increases runoff volume and maximum rates of runoff, with generally negative consequences for natural systems. To avoid environmental degradation, new development standards often prohibit increases in total runoff volume and may limit maximum flow rates. Methods to reduce runoff volume and maximum runoff rate are required, and solutions to the problems may benefit from the use of advanced models. In this study the U.S. Storm Water Management Model (SWMM) was adapted and calibrated to the Ballona Creek watershed, a large urban catchment in Southern California. A geographic information system (GIS) was used to process the input data and generate the spatial distribution of precipitation. An optimization procedure using the Complex Method was incorporated to estimate runoff parameters, and ten storms were used for calibration and validation. The calibrated model predicted the observed outputs with reasonable accuracy. A sensitivity analysis showed the impact of the model parameters, and results were most sensitive to imperviousness and impervious depression storage and least sensitive to Manning roughness for surface flow. Optimized imperviousness was greater than imperviousness predicted from landuse information. The results demonstrate that this methodology of integrating GIS and stormwater model with a constrained optimization technique can be applied to large watersheds, and can be a useful tool to evaluate alternative strategies to reduce runoff rate and volume.
H21C-0695
PEST Compatible Software for More Efficient Levenburg-Marquardt Method Based Model Calibration: HEC-HMS / Watershed Model Applications for Demonstration
Our independent Levenberg-Marquardt (LM) implementation accommodates the PEST model independent and input control file protocol. First, to reduce the number of model calls needed to find a local minimum we use a combination of Broyden rank one updates (secant method) and central and forward finite differences to update the model sensitivity matrix at each optimization iteration. The exact combination of Broyden rank one updates and finite differences is easily specified by the user. While PEST Version 11 does include the ability to utilize Broyden updates, that implementation does not realize the complete efficiency gains that are possible. Second, we have added Multi Level Single Linkage (MLSL), a stochastic global optimization algorithm, to our PEST compatible model independent calibration software. MLSL uses the LM algorithm for local search and a minimum distance threshold to avoid repeated visits to the same local minima. The use of our MLSL implementation requires only a minor addition to a PEST input control file. Efficiencies that can be achieved for LM method based model independent calibration from a properly implemented secant version of the LM method will be demonstrated by examining the reduction in the total number of model calls for single HEC-HMS / watershed model inversion runs associated with the use of our independent LM implementation that accommodates the PEST model independent and input control file protocol. Using HEC-HMS / watershed models, we will also compare the efficiencies, in terms of the number of model calls required to achieve a given objective function value, of our implementations of Multistart, Trajectory Repulsion, and MLSL with that of Shuffled Complex Evolution (SCE) and Covariance Matrix Adaption Evolutionary Strategy (CMAES), as interfaced to PEST.
H21C-0696
Estimating Hydraulic Properties of Coastal Aquifers Using Wave Setup
Wave setup is associated with the momentum transfer of breaking waves to the water column, which results in an elevated mean water table at the coast over several days. Groundwater responses to setup were observed as far as 5 km inland in a coastal aquifer in central Maui, Hawaii. Results showed that setup pulses dominate over barometric pressure effects on low-frequency groundwater fluctuations during times of energetic swell events. Matching peak frequencies in setup and observed head were identified. As is the case with tides, the setup propagation through the aquifer shows exponentially decreasing amplitudes and linearly increasing time lags. Setup was used to estimate a mean aquifer diffusivity of 2.3 x 107 m2/d. The results agree with parameters estimated using aquifer tests and tides. A one-dimensional numerical model verified the results of the estimated parameters. The methodology is expected to be applicable to high-permeability coastal environments, such as volcanic islands and atolls.
H21C-0697
Using the Chi-Squared Curve to Assimilate Large Data Sets from Different Sources
We will describe the chi-squared curve method for parameter estimation recently developed by Mead (2007) and Mead and Renaut (submitted). The chi-squared curve method is considerably more efficient, and as accurate as traditional L-curve and cross-correlation methods for parameter estimation. This method involves forming a maximum likelihood estimation problem, and here we will include soil moisture and pressure head data from both in-situ, and laboratory core measurements. We assume all data contain errors, thus this method does not calibrate a model with data, rather parameter estimates are found within a priori data uncertainty ranges. A priori estimates of laboratory errors are given by repeated measurements on cores, while those for in-situ measurements are given by the chi-squared curve method.
H21C-0698
Investigating the Success of Parameter Estimation Routines in Modeling Watershed Behavior under Post-fire Conditions
Post-fire changes in land cover affect water quality as well as alter watershed flow paths. After a drastic land cover change, the parameters of a hydrological model typically change and uncertainty increases. Current operational forecasting by the National Weather Services includes use of the Sacramento Soil Moisture Accounting (SACSMA) model to predict streamflow under post-fire conditions. The goal of our work is to compare the success of various automated optimization techniques in identifying appropriate parameter sets for prediction of both pre- and post-fire watershed behavior. The successful optimization technique will be expected to select model parameters which provide not only the accurate prediction of total discharge but also predict flow from contributing sources, i.e. overland, lateral and base flow components, derived through hydrograph separation using collected geochemical data. We compare performance of the Shuffled Complex Evolution (SCE) (single and multi-step implementation), as well as the Shuffled Complex Evolution Metropolis (SCEM) algorithm coupled to the SACSMA. The pre-fire model runs using SCE-SACSMA (multi-step) and SCEM-SACSMA, using only discharge as a criterion, show reasonable simulation of total runoff and various flow components of the basin. Post-fire model simulations using the SCEM-SACSMA show less accurate simulation of total discharge and unrealistic representation of watershed behavior. Additionally, results from the SCEM parameter probability distributions reveal changes in highest probability parameter sets from pre- to post-fire periods (i.e. optimal values and related distributions are shifted). A multi-step implementation of the SCEM-SACSMA is being tested and will also be presented.
H21C-0699
Distributed Parameter Estimation Using a Regularization Approach
The high dimensionality of the parameter search space can be solved by the introduction of additional information about the parameters. In this research the information contained in the apriori parameter estimates, derived using the soil data and the method developed by the National Weather Service, was used to identify regularization equations. These regularization equations were then used to constrain the parameter variability during the calibration process and reduce the dimension of the calibration problem. The study of spatial variability of apriori parameters with respect to the NRCS based curve numbers and the depth of soil showed some recognizable trends that could be exploited in the form of some simple regression equations. These equations, along with some inter parameter relations, were used as regularization equations. Calibration of the coefficients of the regularization equations instead of the Sacramento Soil Moisture Accounting Model parameters reduced the dimension of the problem from 858 to 33 unknowns and resulted in significant reduction in the objective function values.
H21C-0700
Effective Use of Time-Series Data to Calibrate a Coupled Ground Water/Surface Water Model in a Small Headwater Watershed, Northern Wisconsin
A major focus of the U.S. Geological Survey's Trout Lake Water, Energy and Biogeochemical Budgets (WEBB) project is the development of a watershed model to allow predictions of hydrologic response to future conditions including land-use and climate change. Because of the highly conductive nature of the outwash sand aquifer and the topography of the watershed, stream flow is dominated by groundwater contributions; however, runoff does occur during intense rainfall periods and spring snowmelt. The coupled ground water/surface water model GSFLOW was chosen because it could easily incorporate an existing ground-water flow model and provides for simulation of surface-water processes. Data collected from 1992 to 2006 in the study area include lake levels, ground-water levels and streamflow. The frequency of data collection varies from monthly to daily; in general the more frequent data was collected over a shorter period and during the latter portion of the period monitored. The time-series processing software TSPROC (Doherty, 2003) was used to distill the large time-series data set to a smaller set of observations and summary statistics that captured the salient hydrologic information. The TSPROC software was also used to process model output, thus providing equivalent comparisons of modeled and observed variables. Calibration targets for lake and ground-water levels included the mean and range for the entire simulation period as well as incremental differences in monthly measurements. For wells with daily water levels, the time series was first smoothed with a digital filter and then resampled at the middle of each month. Targets for streamflow included monthly volumes, comparison of points on the flow-duration curve, and total streamflow smoothed with a digital filter and then resampled at specific dates to capture the inherent variability of the observed time series. Baseflow separation was also carried out using a recursive digital filter to separate the quick-flow component from total streamflow; the smoothed baseflow values were resampled at specific dates to capture the seasonal variation in baseflow. The time-series processing reduced hundreds of thousands of observations to less than 5000. This provided a distilled set of calibration targets that effectively represented the components of the system response important for model prediction. Reference: Doherty, J., 2003, TSPROC - A Time-Series Processor Utility: Watermark Numerical Computing, Brisbane, Australia.
H21C-0701
How do primary and secondary information affect the results of parameter identification for groundwater modeling?
Geostatistical inverse problems always utilize both primary information and secondary information to identify subsurface parameters. Many articles investigated inverse algorithms that were used to estimate unknown parameters in a subsurface system, but few of them explore what role the primary and secondary information play and how they affect the results of parameter identification. The main theme of this article is to investigate how the results of identification are affected by head observations quantitatively and qualitatively. The cokriging technique is used as an interpolation tool for estimating unknown parameters in the system. Through several numerical examples the paper demonstrates and answers this question. Keywords: inverse problem, primary information, secondary information, cokriging technique
H21C-0702
Soil and vegetation parameter estimation using chemical and physical unsaturated zone data
Subsurface fluxes can be very sensitive to environmental changes. In particular, the agricultural clearing of native vegetation in semi-arid regions has allowed significant direct groundwater recharge to occur where only negligible amounts of moisture percolated past the root zone before. In order to predict how this small, yet important flux may respond to future adaptations, it is essential to quantify how meteorological forcing, soil conditions, and root extraction interact to effect recharge. Physically-based numerical models provide a means to test the various controls on recharge, yet realistic parameters are needed. We explore the use of physical and chemical unsaturated zone data to estimate the soil and vegetation parameters used in subsurface flux simulations. Soil and vegetation parameters are estimated using an augmented state ensemble approach. The method allows for uncertainty quantification of the estimated parameters. This parameter estimation work is useful because it can reveal the ties between existing recharge conditions and the physical features underlying the parameters. Furthermore, probabilistic ensemble predictions can be made for future recharge scenarios. The feasibility of this parameter estimation approach is first demonstrated using synthetic tests; it is then applied to the Southern High Plains of Texas, where replacement of native grasslands with dryland cotton crops has yielded increased recharge over the past century.
H21C-0703
Spectral Analysis of Borehole Skin and Wellbore Storage Effects
Spectral analysis of water level time series from observation wells can provide unique opportunities to characterize a variety of hydrogeological processes, including identification of system inputs, outputs, and aquifer parameters that control hydraulic responses. This classic technique has provided novel methods for interpreting available information in terms of dynamic systems behavior. This paper explores the transient interconnection between atmospheric pressure and/or solid earth tidal loadings on water level responses in a well with borehole skin effects. A new analytical solution is derived that describes periodic groundwater fluctuations in a well completed in an idealized, two-layer confined aquifer system. The first layer is located immediately adjacent to the wellbore and represents a skin effect of finite thickness. This layer is generally associated with either a filter pack (i.e., a negative skin effect) or to formation damage resulting from mechanical alterations and/or invasion of drilling fluids (i.e., a positive skin effect). The second layer is located radially outside the first layer and represents an undisturbed, idealized, homogeneous aquifer of uniform thickness as characterized in Ritzi et al. (1991, WRR, 27(5), 873-893). The solution presented here also applies to both small and large diameter wells because wellbore storage effects are included. This solution is presented in terms of non-traditional type-curves that are related to the spectral (SRF) and phase (PRF) response functions over a range of dimensionless aquifer transmissivity values. Results from these analyses can be used to characterize the effects of skin type, thickness, and hydraulic properties compared to those in the undisturbed aquifer. Here we focus on positive skin effects since they are of obvious concern in monitoring well applications. These results suggest that positive skin effects may significantly distort both the SRF and PRF over selected ranges of dimensionless transmissivity. However, both of these functions are relatively insensitive to variations in storage coefficient. This approach is illustrated by several real-world examples utilizing hourly water level records from typical monitoring wells.
H21C-0704
Laboratory experiment of the injection test by the Super-critical CO2
Geological storage of carbon dioxide is a one of such methods to reduce the volume of greenhouse gas emission to atmosphere. It is important for geological storage of carbon dioxide to predict the migration of CO2 during the storage. Also, leakage of CO2 is the potential problem. To estimate these problems for geological storage of carbon dioxide, several simulation codes are developed. But, fluid flow of CO2 in the aquifer is not clear. In order to grasp the some parameters of simulation code, we try to measure the some parameters for the fluid flow of CO2 at the laboratory experiment. In these laboratory experiments, Toyoura sand was used to make artificial sediment. The size of sand column was 50 mm in diameter and 200 mm in length. The vessel was first filled with water. The sand was set them in the vessel and make sand column. Next, vessel was placed with water tank. Temperature of water tank was kept the constant. After the set of vessel, super-critical CO2 was injected in the top of the vessel and water was replaced with CO2. Temperature and pressure were measured at point in the top of the column. Gas production rate from the column was also measured. Thus, we apply to grasp the irreducible water saturation and residual gas saturation for this laboratory experiment. Also, we apply to visualize the fluid flow of super-critical CO2 in the water by the packs of glass beads.
H21C-0705
Monitoring of Saltwater Intrusion in a sediment model using Electrical Resistivity Tomography in a High Performance Computing Environment
Numerical and experimental studies have been conducted using electrical resistivity tomography (ERT) to better understand subsurface heterogeneity and saltwater intrusion mechanisms in a sediment model. ERT is a geophysical method which calculates the electrical resistivity distribution in the subsurface environment from a large number of electrical potential measurements made from electrodes. In this study, ERT is used to first characterize the physical sediment model to estimate the resistivity distribution due to heterogeneity. ERT is then used to image the evolution of the saltwater intrusion at various stages in the sediment model. The ERT is formulated as a regularized least-squares (RLS) problem. The inversion of electrical resistivity is conducted through an adjoint-state method. Both numerical and experimental studies are based on a 3D flow flume with dimensions 1m by 1m by 0.06m. The electrode configuration consists of 48 pairs of electrodes with 48 measurements per dipole-dipole pattern. Due to the large number of measurements and unknown parameters, ERT image reconstruction is computationally expensive for 3D inversion. The proposed ERT image reconstruction is implemented in a high performance computing (HPC) environment using PetSc and Cactus Framework to reduce the time for ERT inversion. We demonstrate the applicability of the parallel ERT inversion in both synthetic and laboratory three-dimensional ERT problems.
H21C-0706
A NEW SATURATED ZONE SITE-SCALE FLOW MODEL FOR YUCCA MOUNTAIN
A saturated zone site scale flow model was developed for Yucca Mountain, Nevada, to incorporate new data and analyses including new stratigraphic and water level data from Nye County wells, single and multiple well hydraulic testing data, and new hydrochemistry data. New analyses include use of data from the 2004 transient Death Valley Regional (ground water) Flow System (DVRFS) model, the 2003 unsaturated zone flow model, and the latest hydrogeologic framework model (HFM). This model includes: (1) the latest understanding of SZ flow, (2) enhanced model validation and uncertainty analyses, (3) improved locations and definitions of fault zones, (4) refined grid resolution (500 to 250 m grid spacing), and (5) use of new data. The flow model was completed using the three dimensional, finite element heat and mass transfer computer code, FEHM V2.24. The SZ site scale flow model was calibrated with the commercial parameter estimation code, PEST to achieve a minimum difference between observed water levels and predicted water levels, and also between volumetric/mass flow rates along specific boundary segments as supplied by the DVRFS. 161 water level and head measurements with varied weights were used for calibration. A comparison between measured water level data and the potentiometric surface yielded an RMSE of 20.7 m (weighted RMSE of 8.8 m). The calibrated model was used to evaluate the impact of alternative models on flow paths and specific discharge predictions. Model confidence was built by comparing: (1) calculated to observed hydraulic heads, and (2) calibrated to measured permeabilities (and therefore specific discharge). In addition, flowpaths emanating from below the repository footprint are consistent with those inferred both from gradients of measured head and from independent water chemistry data. Uncertainties in the SZ site scale flow model were quantified because all uncertainty contributes to inaccuracy in system representation and response. Null space and solution space uncertainties were determined.
H21C-0707
Characterization of Transient Transport Behavior During Biostimulation Field Experiments Using Novel Breakthrough Analysis Approaches
Biostimulation experiments were performed to immobilize uranium within a shallow, unconsolidated, and unconfined aquifer at the DOE Integrated Field Challenge Site (IFC) in Rifle, CO. During experiments conducted in 2002 and 2003, twenty wells were used to deliver acetate and bromide to the contaminated aquifer over a 111 and 119 day period, respectively. Due to changes in the injection rate, the injection concentration and the groundwater level during the injection period, the mean concentration of the bromide injected to the aquifer was temporally variable. Although a dense dataset of bromide concentrations was collected from fifteen downgradient monitoring wells, interpretation of the bromide breakthrough datasets in terms of hydrological heterogeneity was difficult using conventional tracer test analysis methods due to the complex bromide input function. We developed two novel approaches for analyzing and interpreting breakthrough curves (BTC) in the presence of a complex tracer injection function. The first approach is based on conventional temporal moment analysis. It estimates the effective velocity and dispersivity based on changes of the first and second moment along the distance between the injection and the monitoring well. The second approach is based on the analytical solution of the one dimensional convection dispersion equation. To account for the complex injection function, here each step in the injection function is represented by an analytical solution of the one dimensional convection dispersion equation. These are weighted by the concentration of each step and combined based on superposition. Fit of this function to the BTCs permits the estimation of the effective velocity and dispersivity. We applied the developed approaches to the bromide BTCs at the IFC to characterize the flow and transport processes in the sense of a stream tube model. This analysis suggested that the novel BTC analysis approach greatly improved our ability to quantitatively interpret the bromide breakthrough datasets, and that velocity and dispersivity varied over space and time in response to the biostimulation treatment.
H21C-0708
Study Of Nonstationari Motion Of Liquid In Pipe-Line At Emergency Breakage
In pipelines none stable movement of liquid is described with no rectilinear differential equations. (1) (2) For long pipelines we can ignore following members and . The system of equations becomes: ; (3) (4) After rectilinearization (3) the equations becomes: . (5) After differentiating the (5) formula, it we equalize its members we get: . (6) If we appeal x straight to the pipeline and for example. If the coordination of rennet is L occurred an emergency hole and its size is . In this case it should be solved the system of differential equations: (7) ; (8) and are pressure in the corresponding sphere and . The expenditure of liquid, flowing through the emergency hole should be taken in to account: . (9) The emergency expenditure could be considered in aquation. In this case, the system of differential equation changes with only equation: , (10) The expenditure from the emergency hole will be: . (11) The 10th equation becomes the following . (12) After designation we get: . (13) There will be added the initial conditions to the 13th equation: (14) If we take into account and and also the time of the wave or getting on the top and at the end of the pipe-line then (15) (16) Where, and -are the function towards Summary Nonstationari motion of at emergency breakage of pipe-line is considered. Linearization of differential, equation is and the solution is found using finite Furie sine-transform. bibl.3.