Complexity

It is difficult to define complexity in modeling. Complexity is often associated with uncertainty since modeling uncertainty is an intrinsically difficult task. However, modeling uncertainty does not require, necessarily, complex models, in the sense of a model requiring an unmanageable number of degrees of freedom to characterize the aquifer. The relationship between complexity, uncertainty, heterogeneity, and stochastic modeling is not simple. Aquifer models should be able to quantify the uncertainty of their predictions, which can be done using stochastic models that produce heterogeneous realizations of aquifer parameters. This is the type of complexity addressed in this article.     

Significance of conditioning to piezometric head data for predictions of mass transport in groundwater modeling

Transmissivity and head data are sampled from an exhaustive synthetic reference field and used to predict the arrival positions and arrival times of a number of particles transported across the field, together with an uncertainty estimate. Different combinations of number of transmissivity data and number of head data used are considered in each one of a series of 64 Monte-Carlo analyses. In each analysis, 250 realizations of transmissivity fields conditioned to both transmissivity and head data are generated using a novel geostatistically based inverse method. Pooling the solutions of the flow and transport equations in all 250 realizations allows building conditional frequency distributions for particle arrival positions and arrival times. By comparing these fresquency distributions, we can assess the incremental gain that additional head data provide. The main conclusion is that the first few head data dramatically improve the quality of transport predictions.     

Isotope measurements of single ostracod valves and gastropod shells for climate reconstruction: evaluation of within-sample variability and determination of optimum sample size

Sediment cores from Lakes Punta Laguna, Chichancanab, and Petén Itzá on the Yucatan Peninsula were used to (1) investigate “within-horizon” stable isotope variability (δ¹⁸O and δ¹³C) measured on multiple, single ostracod valves and gastropod shells, (2) determine the optimum number of individuals required to infer low-frequency climate changes, and (3) evaluate the potential for using intra-sample δ¹⁸O variability in ostracod and gastropod shells as a proxy measure for high-frequency climate variability. Calculated optimum sample numbers (“n”) for δ¹⁸O and δ¹³C in the ostracod Cytheridella ilosvayi and the gastropod Pyrgophorus coronatus vary appreciably throughout the cores in all three lakes. Variability and optimum “n” values were, in most cases, larger for C. ilosvayi than for P. coronatus for δ¹⁸O measurements, whereas there was no significant difference for δ¹³C measurements. This finding may be explained by differences in the ecology and life history of the two taxa as well as contrasting modes of calcification. Individual δ¹⁸O measurements on C. ilosvayi in sediments from Lake Punta Laguna show that samples from core depths that have high mean δ¹⁸O values, indicative of low effective moisture, display lower variability, whereas samples with low mean δ¹⁸O values, reflecting times of higher effective moisture, display higher variability. Relatively dry periods were thus consistently dry, whereas relatively wet periods had both wet and dry years. This interpretation of data from the cores applies to two important periods of the late Holocene, the Maya Terminal Classic period and the Little Ice Age. δ¹⁸O variability during the ancient Maya Terminal Classic Period (ca. 910–990 AD) indicates not only the driest mean conditions in the last 3,000 years, but consistently dry climate. Variability of δ¹³C measurements in single stratigraphic layers displayed no relationship with climate conditions inferred from δ¹⁸O measurements.     

Predictability of currents on a mesotidal estuary (Ria de Vigo, NW Iberia)

In this contribution, the implementation, validation and sensitivity analysis of an operational forecasting system (three-dimensional hydrostatic model) in the Ría de Vigo is presented. A set of sensitivity tests for different atmospheric and hydrodynamic typical periods was performed. The goal is to determine the relative importance of forcing mechanisms in order to evaluate the source of errors in predictions. Previously, validations for three periods of about 15 days were compared with measured data. Sea-level evolution reveals low errors and correlation values close to 1. Surface velocities were compared with high-frequency radar and horizontal Acoustic Doppler Current Profiler data, showing acceptable results on radar area, where tidal and wind circulation patterns are well reproduced by the model. Conductivity, temperature, and depth profiles were used to validate simulated temperature and salinity. While modelled temperature profiles show good agreement with measured profiles, eventual errors are detected on salinity. The sensitivity analysis took three variables into account: sea level, currents at open boundary conditions, and winds (two different configurations). The results show that the most important source of errors on simulated surface currents is wind. Errors on open boundary conditions seem to be limited on the outermost part of the Ría. Although the results presented mainly focus on the Ría de Vigo, the method and conclusions may be applied to other mesotidal estuaries. Moreover, this work will allow a more effective operational system focused on coastal management to be performed.