Geologic heterogeneity and a comparison of two geostatistical models: Sequential Gaussian and transition probability-based geostatistical simulation

A covariance-based model-fitting approach is often considered valid to represent field spatial variability of hydraulic properties. This study examines the representation of geologic heterogeneity in two types of geostatistical models under the same mean and spatial covariance structure, and subsequently its effect on the hydraulic response to a pumping test based on 3D high-resolution numerical simulation and field data. Two geostatistical simulation methods, sequential Gaussian simulation (SGS) and transition probability indicator simulation (TPROGS) were applied to create conditional realizations of alluvial fan aquifer systems in the Lawrence Livermore National Laboratory (LLNL) area. The simulated K fields were then used in a numerical groundwater flow model to simulate a pumping test performed at the LLNL site. Spatial connectivity measures of high-K materials (channel facies) captured connectivity characteristics of each geostatistical model and revealed that the TPROGS model created an aquifer (channel) network having greater lateral connectivity. SGS realizations neglected important geologic structures associated with channel and overbank (levee) facies, even though the covariance model used to create these realizations provided excellent fits to sample covariances computed from exhaustive samplings of TPROGS realizations. Observed drawdown response in monitoring wells during a pumping test and its numerical simulation shows that in an aquifer system with strongly connected network of high-K materials, the Gaussian approach could not reproduce a similar behavior in simulated drawdown response found in TPROGS case. Overall, the simulated drawdown responses demonstrate significant disagreement between TPROGS and SGS realizations. This study showed that important geologic characteristics may not be captured by a spatial covariance model, even if that model is exhaustively determined and closely fits the exponential function.     

Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites

Model simulation and in situ observations are often used to research water and carbon cycles in terrestrial ecosystems, but each of these methods has its own advantages and limitations. Combining these two methods could improve the accuracy of quantifying the dynamics of the water and carbon fluxes of an ecosystem. Data assimilation is an effective means of integrating modeling with in situ observation. In this study, the ensemble Kalman filter(En KF) and the unscented Kalman filter(UKF) algorithms were used to assimilate remotely sensed leaf area index(LAI) data with the Biome-BGC model to simulate water and carbon fluxes at the Harvard Forest Environmental Monitoring Site(EMS) and the Dinghushan site. After MODIS LAI data from 2000–2004 were assimilated into the improved Biome-BGC model using the En KF algorithm at the Harvard Forest site, the R2 between the simulated and observed results for NEE and evapotranspiration increased by 7.8% and 4.7%, respectively. In addition, the sum of the absolute error(SAE) and the root mean square error(RMSE) of NEE decreased by an average of 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration decreased by 24.5% and 25.5%, respectively. MODIS LAI data of 2003 were assimilated into the Biome-BGC model for the Dinghushan site, and the R2 values between the simulated and observed results for NEE and evapotranspiration were increased by 6.7% and 17.3%, respectively. In addition, the SAE values of NEE and ET were decreased by 11.3% and 30.7%, respectively, and the RMSE values of NEE and ET decreased by 10.1% and 30.9%, respectively. These results demonstrate that the accuracy of carbon and water flux simulations can be effectively improved when remotely sensed LAI data are properly integrated with ecosystem models through a data assimilation approach.     

Lessons Learned from 25 Years of Research at the MADE Site

Field studies at well‐instrumented research sites have provided extensive data sets and important insights essential for development and testing of transport theories and mathematical models. This paper provides an overview of over 25 years of research and lessons learned at one of such field research sites on the Columbus Air Force Base in Mississippi, commonly known as the Macrodispersion Experiment (MADE) site. Since the mid‐1980s, field data from the MADE site have been used extensively by researchers around the world to explore complex contaminant transport phenomena in highly heterogeneous porous media. Results from field investigations and modeling analyses suggested that connected networks of small‐scale preferential flow paths and relative flow barriers exert dominant control on solute transport processes. The classical advection‐dispersion model was shown to inadequately represent plume‐scale transport, while the dual‐domain mass transfer model was found to reproduce the primary observed plume characteristics. The MADE site has served as a valuable natural observatory for contaminant transport studies where new observations have led to better understanding and improved models have sprung out analysis of new data.     

Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui River alluvial fan,Taiwan

This work evaluated the spatial variability and distribution of heterogeneous hydraulic conductivity (K) in the Choushui River alluvial fan in Taiwan, using ordinary kriging (OK) and mean and individual sequential Gaussian simulations (SGS). A baseline flow model constructed by upscaling parameters was inversely calibrated to determine the pumping and recharge rates. Simulated heads using different K realizations were then compared with historically measured heads. A global/local simulated error between simulated and measured heads was analysed to assess the different spatial variabilities of various estimated K distributions. The results of a MODFLOW simulation indicate that the OK realization had the smallest sum of absolute mean simulation errors (SAMSE) and the SGS realizations preserved the spatial variability of the measured K fields. Moreover, the SAMSE increases as the spatial variability of the K field increases. The OK realization yields small local simulation errors in the measured K field of moderate magnitude, whereas the SGS realizations have small local simulation errors in the measured K fields, with high and low values. The OK realization of K can be applied to perform a deterministic inverse calibration. The mean SGS method is suggested for constructing a K field when the application focuses on extreme values of estimated parameters and small calibration errors, such as in a simulation of contaminant transport in heterogeneous aquifers. The individual SGS realization is useful in stochastically assessing the spatial uncertainty of highly heterogeneous aquifers. Copyright © 2004 John Wiley & Sons, Ltd.     

Correlation between bacterial attachment rate coefficients and hydraulic conductivity and its effect on field-scale bacterial transport

It has been widely observed in field experiments that the apparent rate of bacterial attachment, particularly as parameterized by the collision efficiency in filtration-based models, decreases with transport distance (i.e., exhibits scale-dependency). This effect has previously been attributed to microbial heterogeneity; that is, variability in cell–surface properties within a single monoclonal population. We demonstrate that this effect could also be interpreted as a field-scale manifestation of local-scale correlation between physical heterogeneity (hydraulic conductivity variability) and reaction heterogeneity (attachment rate coefficient variability). A field-scale model of bacterial transport developed for the South Oyster field research site located near Oyster, Virginia, and observations from field experiments performed at that site, are used as the basis for this study. Three-dimensional Monte Carlo simulations of bacterial transport were performed under four alternative scenarios: (1) homogeneous hydraulic conductivity (K) and attachment rate coefficient (K_f); (2) heterogeneous K, homogeneous K_f; (3) heterogeneous K and K_f with local correlation based on empirical and theoretical relationships; and (4) heterogeneous K and K_f without local correlation. The results of the 3D simulations were analyzed using 1D model approximations following conventional methods of field data analysis. An apparent decrease with transport distance of effective collision efficiency was observed only in the case where the local properties were both heterogeneous and correlated. This effect was observed despite the fact that the local collision efficiency was specified as a constant in the 3D model, and can therefore be interpreted as a scale effect associated with the local correlated heterogeneity as manifested at the field scale.     

The Ensemble Kalman Filter for Groundwater Plume Characterization: A Case Study

The Kalman filter is an efficient data assimilation tool to refine an estimate of a state variable using measured data and the variable's correlations in space and/or time. The ensemble Kalman filter (EnKF) (Evensen 2004, 2009) is a Kalman filter variant that employs Monte Carlo analysis to define the correlations that help to refine the updated state. While use of EnKF in hydrology is somewhat limited, it has been successfully applied in other fields of engineering (e.g., oil reservoir modeling, weather forecasting). Here, EnKF is used to refine a simulated groundwater tetrachloroethylene (TCE) plume that underlies the Tooele Army Depot‐North (TEAD‐N) in Utah, based on observations of TCE in the aquifer. The resulting EnKF‐based assimilated plume is simulated forward in time to predict future plume migration. The correlations that underpin EnKF updating implicitly contain information about how the plume developed over time under the influence of complex site hydrology and variable source history, as they are predicated on multiple realizations of a well‐calibrated numerical groundwater flow and transport model. The EnKF methodology is compared to an ordinary kriging‐based assimilation method with respect to the accurate representation of plume concentrations in order to determine the relative efficacy of EnKF for water quality data assimilation.     

Accounting for Aquifer Heterogeneity from Geological Data to Management Tools

A nested workflow of multiple‐point geostatistics (MPG) and sequential Gaussian simulation (SGS) was tested on a study area of 6 km² located about 20 km northwest of Quebec City, Canada. In order to assess its geological and hydrogeological parameter heterogeneity and to provide tools to evaluate uncertainties in aquifer management, direct and indirect field measurements are used as inputs in the geostatistical simulations to reproduce large and small‐scale heterogeneities. To do so, the lithological information is first associated to equivalent hydrogeological facies (hydrofacies) according to hydraulic properties measured at several wells. Then, heterogeneous hydrofacies (HF) realizations are generated using a prior geological model as training image (TI) with the MPG algorithm. The hydraulic conductivity (K) heterogeneity modeling within each HF is finally computed using SGS algorithm. Different K models are integrated in a finite‐element hydrogeological model to calculate multiple transport simulations. Different scenarios exhibit variations in mass transport path and dispersion associated with the large‐ and small‐scale heterogeneity respectively. Three‐dimensional maps showing the probability of overpassing different thresholds are presented as examples of management tools.     

An isotopic investigation of a near-surface groundwater system

ABSTRACT

The distribution of environmental tritium, deuterium and oxygen-18 in the unsaturated zone and the underlying sandy phreatic aquifer was studied throughout 1981 in an area of high pine forests in the Rhine valley near Heidelberg. The observed vertical distribution of isotopes in the unsaturated zone can be satisfactorily explained by the combined use of a multi-cell model for moisture transport and an evapotranspiration model. The distribution in the underlying aquifer of the tracer input at the water table obtained using this method is found by replacing the total vertical diffusion coefficient in the diffusion equation with the dispersion coefficient. In this way observed tritium profiles are satisfactorily simulated for the period 1966–1981. The stable isotope profile in the unsaturated zone however remains largely unexplained due to inadequate data on the stable isotope content of precipitation over the investigated area.     

Evaluation and modeling of runoff and sediment yield for different land covers under simulated rain in a semiarid region of Brazil

This paper quantifies the runoff and sediment yield for four different land covers in a semiarid region of Brazil. The WESP model, a distributed, event-oriented runoff-erosion model, was applied and its physical parameters, N_s and K_R, were adjusted based on observed runoff and sediment yield data using simulated rainfall with an average intensity of 53 mm h^-1. The sediment yield obtained was 53.02 kg ha^-1 (caatinga vegetation), 231.96 kg ha^-1 (bare soil), 309.75 kg ha^-1 (beans), and 847.38 kg ha^-1(corn). The results showed that caatinga cover yields the lowest erosion and runoff when compared to the other treatments. The results also show that the sediment yield and runoff values simulated with N_s, K_I, and K_R parameters were well calibrated, within acceptable deviations. The caatinga vegetation was more effective in protecting the soil, when compared to the other types of coverage. The beans and corn covers had the highest values of runoff and sediment yield, even higher than those observed for bare soil.     

The factors controlling the partitioning of polybrominated diphenyl ethers and polychlorinated biphenyls in the water-column of the Pearl River Estuary in South China

In this study, the distribution and partition of polybrominateddiphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) in water from the Pearl River Estuary were investigated. A seasonal dependence for dissolved and particle phase PCB and PBDE levels was observed. A high dissolved phase level occurred in the dry season. The positive correlation between POC content in the particles and phase distribution coefficients (K_p) suggested that POC may play an important role in determining the partition of PBDEs and PCBs. A linear correlation between the observed log K_OC and log K_OW for PBDEs and PCBs was found with slopes less than one. The observed log K_OC values were lower than the predicted log K_OC values for PBDEs (except for BDE28). The log K_OC values were higher than the predicted values for PCBs. A combination of sorption to colloids (10-98% for PBDEs and less than 25% for PCBs) and adsorption to BC was responsible for this observation.     

2DV modelling of sediment transport processes over full-scale ripples in regular asymmetric oscillatory flow

Wave-induced, steep vortex ripples are ubiquitous features in shallow coastal seas and it is therefore important to fully understand and model the sediment transport processes that occur over them. To this end, two two-dimensional vertical (2DV) models have been critically tested against detailed velocity and sediment concentration measurements above mobile ripples in regular asymmetric oscillatory flow. The two models are a k–ω turbulence-closure model and a discrete-vortex, particle-tracking (DVPT) model, while the data are obtained in the Aberdeen oscillatory flow tunnel (AOFT). The models and the data demonstrate that the time-dependent velocity and suspended sediment concentration above the ripple are dominated by the generation of lee-side vortices and their subsequent ejection at flow reversal. The DVPT model predicts the positions and strengths of the vortices reasonably well, but tends to overpredict the velocity close to the ripple surface. The k–ω model, on the other hand, underpredicts the height to which the vortices are lifted, but is better able to predict the velocity close to the bed. In terms of the cycle- and ripple-averaged horizontal velocity, both models are able to reproduce the observed offshore flow close to and below the ripple crest and the DVPT model is able to produce the onshore flow higher up. In the vicinity of the vortices, the DVPT model better represents the concentration (because of its better prediction of vorticity). The k–ω model, on the other hand, better represents the concentration close to the ripple surface and higher up in the flow (because of the better representation of the near-bed flow and background turbulence). The measured and predicted cycle- and ripple-averaged suspended sediment concentrations are in reasonable agreement and demonstrate the expected region of exponential decay. The models are able to reproduce the observed offshore cycle- and ripple-averaged suspended sediment flux from the ripple troughs upwards, and as a result, produce net offshore suspended sediment transport rates that are in reasonable agreement. The net measured offshore suspended transport rate, based on the integration of fluxes, was found to be consistent with the total net offshore transport measured in the tunnel as a whole once the onshore transport resulting from ripple migration was taken into account, as would be expected. This demonstrates the importance of models being able to predict ripple-migration rates. However, at present neither of the models is able to do so.     

A dripper-TDR method for in situ determination of hydraulic conductivity and chemical transport properties of surface soils

Field determined hydraulic and chemical transport properties can be useful for the protection of groundwater resources from land-applied chemicals. Most field methods to determine flow and transport parameters are either time or energy consuming and/or they provide a single measurement for a given time period. In this study, we present a dripper-TDR field method that allows measurement of hydraulic conductivity and chemical transport parameters at multiple field locations within a short time period. Specifically, the dripper-TDR determines saturated hydraulic conductivity (K_s), macroscopic capillary length (λ_c), immobile water fraction (θ_im/θ), mass exchange coefficient (α) and dispersion coefficient (D_m). Multiple dripper lines were positioned over five crop rows in a field. Background and step solutions were applied through drippers to determine surface hydraulic conductivity parameters at 44 locations and surface transport properties at 38 locations. The hydraulic conductivity parameters (K_s, λ_c) were determined by application of three discharge rates from the drippers and measurements of the resultant steady-state flux densities at the soil surface beneath each dripper. Time domain reflectometry (TDR) was used to measure the bulk electrical conductivity of the soil during steady infiltration of a salt solution. Breakthrough curves (BTCs) for all sites were determined from the TDR measurements. The K_s and λ_c values were found to be lognormally distributed with average values of 31.4 cm h⁻¹ and 6.0 cm, respectively. BTC analysis produced chemical properties, θ_im/θ, α, and D_m with average values of 0.23, 0.0036 h⁻¹, and 1220 cm² h⁻¹, respectively. The estimated values of the flow and transport parameters were found to be within the ranges of values reported by previous studies conducted at nearby field locations. The dripper TDR method is a rapid and useful technique for in situ measurements of hydraulic conductivity and solute transport properties. The measurements reported in this study give clear evidence to the occurrence of non-equilibrium water and chemical movement in surface soil. The method allows for quantification of non-equilibrium model parameters and preferential flow. Quantifying the parameters is a necessary step toward determining the influences of surface properties on infiltration, runoff, and vadose zone transport.     

Comparison of Hydraulic Tomography with Traditional Methods at a Highly Heterogeneous Site

Over the past several decades, different groundwater modeling approaches of various complexities and data use have been developed. A recently developed approach for mapping hydraulic conductivity (K) and specific storage (S_s) heterogeneity is hydraulic tomography, the performance of which has not been compared to other more “traditional” methods that have been utilized over the past several decades. In this study, we compare seven methods of modeling heterogeneity which are (1) kriging, (2) effective parameter models, (3) transition probability/Markov Chain geostatistics models, (4) geological models, (5) stochastic inverse models conditioned to local K data, (6) hydraulic tomography, and (7) hydraulic tomography conditioned to local K data using data collected in five boreholes at a field site on the University of Waterloo (UW) campus, in Waterloo, Ontario, Canada. The performance of each heterogeneity model is first assessed during model calibration. In particular, the correspondence between simulated and observed drawdowns is assessed using the mean absolute error norm, (L₁), mean square error norm (L₂), and correlation coefficient (R) as well as through scatterplots. We also assess the various models on their ability to predict drawdown data not used in the calibration effort from nine pumping tests. Results reveal that hydraulic tomography is best able to reproduce these tests in terms of the smallest discrepancy and highest correlation between simulated and observed drawdowns. However, conditioning of hydraulic tomography results with permeameter K data caused a slight deterioration in accuracy of drawdown predictions which suggests that data integration may need to be conducted carefully.     

Estimating mean residence time of karst groundwater in mountainous catchments of Western Himalaya,India

The mean residence time (MRT) of karst groundwater in three mountainous catchments of the Western Himalaya was estimated using multiple approaches: the tritium method, the sine wave model and tracer tests. Water samples were collected from precipitation, glacier melt, streams and karst springs for δ²H and tritium analysis during 2012 and 2013. High tritium values were observed in winter precipitation and low values in summer precipitation. The variation of tritium in karst springs was similar to that of the streams, whereas glacier melt showed lower tritium values. The MRT of cold karst springs was shorter than that of warm karst springs. The tracer breakthrough curves (TBC) retrieved for different springs suggested a short travel time for groundwater and possibly conduit flow. Deterioration of water quality and variation in flux magnitude are the two main practical consequences of the short travel time of karst groundwater in the region.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR K. Heal     

Calibration of a Land Subsidence Model Using InSAR Data via the Ensemble Kalman Filter

The application of interferometric synthetic aperture radar (InSAR) has been increasingly used to improve capabilities to model land subsidence in hydrogeologic studies. A number of investigations over the last decade show how spatially detailed time‐lapse images of ground displacements could be utilized to advance our understanding for better predictions. In this work, we use simulated land subsidences as observed measurements, mimicking InSAR data to inversely infer inelastic specific storage in a stochastic framework. The inelastic specific storage is assumed as a random variable and modeled using a geostatistical method such that the detailed variations in space could be represented and also that the uncertainties of both characterization of specific storage and prediction of land subsidence can be assessed. The ensemble Kalman filter (EnKF), a real‐time data assimilation algorithm, is used to inversely calibrate a land subsidence model by matching simulated subsidences with InSAR data. The performance of the EnKF is demonstrated in a synthetic example in which simulated surface deformations using a reference field are assumed as InSAR data for inverse modeling. The results indicate: (1) the EnKF can be used successfully to calibrate a land subsidence model with InSAR data; the estimation of inelastic specific storage is improved, and uncertainty of prediction is reduced, when all the data are accounted for; and (2) if the same ensemble is used to estimate Kalman gain, the analysis errors could cause filter divergence; thus, it is essential to include localization in the EnKF for InSAR data assimilation.     

Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering

Modeling the spread of subsurface contaminants requires coupling a groundwater flow model with a contaminant transport model. Such coupling may provide accurate estimates of future subsurface hydrologic states if essential flow and contaminant data are assimilated in the model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data assimilation into the transport model. This is, however, a crude assumption as flow models can be subject to many sources of uncertainty. If the flow is not accurately simulated, contaminant predictions will likely be inaccurate even after successive Kalman updates of the contaminant model with the data. The problem is better handled when both flow and contaminant states are concurrently estimated using the traditional joint state augmentation approach. In this paper, we introduce a dual estimation strategy for data assimilation into a one-way coupled system by treating the flow and the contaminant models separately while intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals with the estimation of state variables but it can also be used for state and parameter estimation problems. This EnKF-based dual state-state estimation procedure presents a number of novel features: (i) it allows for simultaneous estimation of both flow and contaminant states in parallel; (ii) it provides a time consistent sequential updating scheme between the two models (first flow, then transport); (iii) it simplifies the implementation of the filtering system; and (iv) it yields more stable and accurate solutions than does the standard joint approach. We conducted synthetic numerical experiments based on various time stepping and observation strategies to evaluate the dual EnKF approach and compare its performance with the joint state augmentation approach. Experimental results show that on average, the dual strategy could reduce the estimation error of the coupled states by 15% compared with the joint approach. Furthermore, the dual estimation is proven to be very effective computationally, recovering accurate estimates at a reasonable cost.     

Evaluating spatio-temporal representations in daily rainfall sequences from three stochastic multi-site weather generation approaches

Many hydrological and agricultural studies require simulations of weather variables reflecting observed spatial and temporal dependence at multiple point locations. This paper assesses three multi-site daily rainfall generators for their ability to model different spatio-temporal rainfall attributes over the study area. The approaches considered consist of a multi-site modified Markov model (MMM), a reordering method for reconstructing space–time variability, and a nonparametric k-nearest neighbour (KNN) model. Our results indicate that all the approaches reproduce adequately the observed spatio-temporal pattern of the multi-site daily rainfall. However, different techniques used to signify longer time scale observed temporal and spatial dependences in the simulated sequences, reproduce these characteristics with varying successes. While each approach comes with its own advantages and disadvantages, the MMM has an overall advantage in offering a mechanism for modelling varying orders of serial dependence at each point location, while still maintaining the observed spatial dependence with sufficient accuracy. The reordering method is simple and intuitive and produces good results. However, it is primarily driven by the reshuffling of the simulated values across realisations and therefore may not be suited in applications where data length is limited or in situations where the simulation process is governed by exogenous conditioning variables. For example, in downscaling studies where KNN and MMM can be used with confidence.     

Modelling sediment dynamics due to hillslope–river interactions: incorporating fluvial behaviour in landscape evolution model LAPSUS

Landscape evolution models (LEMs) simulate the three‐dimensional development of landscapes over time. Different LEMs have different foci, e.g. erosional behaviour, river dynamics, the fluvial domain, hillslopes or a combination. LEM LAPSUS is a relatively simple cellular model operating on timescales of centuries to millennia and using annual timesteps that has had a hillslope focus. Our objective was to incorporate fluvial behaviour in LAPSUS without changing the existing model equations. The model should be able to reproduce alternating aggradation and incision in the floodplains of catchments, depending on simulated conditions. Testing was done using an artificial digital elevation model (DEM) and a demonstration of the ability for fluvial simulation was performed for a real landscape (Torrealvilla catchment, southeast Spain). Model equations to calculate sediment dynamics and water routing were similar for both hillslope and fluvial conditions, but different parameter values were used for these domains, defined based on annual discharge. Parameters changing between the domains are convergence factor p, which is used in the multiple flow algorithm to route water, and discharge and gradient exponents m and n, used in transport capacity calculations. Erodibility and ‘sedimentability’ factors K and P were changed between cold (little vegetation, high erodibility) and warm conditions (more vegetation, lower erodibility). Results show that the adapted parameters reproduced alternating aggradation – due to divergent flow in the floodplain and sediment supply under cold conditions – and incision due to reduced sediment supply and resulting clean water erosion during simulated warm conditions. The simulated results are due to interactions between hillslopes and floodplains, as the former provide the sediments that are deposited in the latter. Similar behaviour was demonstrated when using the real DEM. Sensitivity and resolution analysis showed that the model is sensitive to changes in m, n and p and that model behaviour is influenced by DEM resolution. Copyright © 2012 John Wiley & Sons, Ltd.     

Application of a circulation and transport model system to the dispersal of herring larvae in the North Sea

A model system consisting of a three-dimensional circulation and transport model is used to simulate the dispersal of herring larvae in the North Sea. The driving forces of the circulation model are theM₂-tide, time dependent wind stress and air pressure fields, as well as monthly climatological density fields interpolated on a daily basis. The transport model includes advection as well as diffusion and uses a tracer technique to pursue the larvae in the time and space domain. Furthermore, the transport model incorporates a simulation of active vertical movement of the larvae as vertical migration has a marked effect on the drift route of the larvae.Simulated North Sea currents are compared to observed currents gained during the Autumn Circulation Experiment (ACE) which took place from August 1987 to March 1988. The variability in observed currents at the location of the moorings is found to be larger than in the simulated currents. On a larger scale, the simulated circulation in the northern North Sea shows a close correspondence to the inferred circulation from a quasi-synoptic hydrographic survey. The quality of the predictions of larval transport and distribution patterns by the model system is tested using sequential larvae distributions observed during ACE. Common features and discrepancies of observed and simulated distributions are discussed and conclusions for further field investigations and modelling studies are drawn.     

Data modeling and assimilation studies with the MU radar

We report initial results of data modeling and assimilation studies for several MU radar experiments. Various inputs to a one-dimensional ionospheric model are adjusted to provide agreement with observation and also to learn the sensitivity of the model to their variations. Certain observations are also used directly in the model to anchor or constrain its behavior. In particular, studies of the electron density from 100 to 500 km altitude in the ionosphere are carried out with the help of a theoretical model of O⁺, NO⁺, O⁺₂ and N⁺₂ densities and MU radar observations of the power, ion-drift and plasma-temperature profiles. Four typical cases are selected to study quantitatively the effects of the (A) perpendicular-north component of the plasma drift (15 December 1986), (B) atmospheric composition (7 October 1986), (C) solar EUV flux (2 August 1989) and (D) upper-boundary O⁺ density (5 October 1989) on the model N_mF2, h_mF2 and N_e profile, as well as on the neutral wind calculation from h_mF2 and drift data. It is found that the measured vertical ion drift explains quantitatively well the measured h_mF2 (particularly at low solar activity) while the model gives a better match with the measured N_e when it uses the h_mF2-based wind rather than the measured plasma drift. Different model values of the atmospheric O/N₂ ratio and EUV flux and different values of the upper-bound O⁺ density may modify not only N_mF2 markedly but also h_mF2: a lower O/N₂ ratio results in higher h_mF2; the EUVAC model gives higher h_mF2 at high solar activity than does the EUV91 model; with a smaller upper-bound O⁺ density, h_mF2 is lower by day but little changed by night. We specifically note that the meridional wind needed by the model to reproduce the observed h_mF2 differed according to how well the model reproduced the observed N_mF2. The uncertainties in the MSIS86 and EUV model predictions are also discussed. It is found that if the MSIS and EUV91 models are used together, the model gives an N_mF2 higher than that measured at high solar activity. Thus the O/N₂ ratio needs to be reduced from the MSIS value if EUV91 is used. If EUVAC is used, no large modification is required. At equinox for low solar activity, modeling with either EUV model produces N_mF2 values lower than those measured, and so the true O/N₂ ratio may be higher than that given by MSIS model.