Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados

Water regimes in the Brazilian Cerrados are sensitive to climatological disturbances and human intervention. The risk that critical water-table levels are exceeded over long periods of time can be estimated by applying stochastic methods in modeling the dynamic relationship between water levels and driving forces such as precipitation and evapotranspiration. In this study, a transfer function-noise model, the so called PIRFICT-model, is applied to estimate the dynamic relationship between water-table depth and precipitation surplus/deficit in a watershed with a groundwater monitoring scheme in the Brazilian Cerrados. Critical limits were defined for a period in the Cerrados agricultural calendar, the end of the rainy season, when extremely shallow levels

Naturally Rehearsing Passwords

We introduce quantitative usability and security models to guide the design of password management schemes --- systematic strategies to help users create and remember multiple passwords. In the same way that security proofs in cryptography are based on complexity-theoretic assumptions (e.g., hardness of factoring and discrete logarithm), we quantify usability by introducing usability assumptions. In particular, password management relies on assumptions about human memory, e.g., that a user who follows a particular rehearsal schedule will successfully maintain the corresponding memory. These assumptions are informed by research in cognitive science and validated through empirical studies. Given rehearsal requirements and a user's visitation schedule for each account, we use the total number of extra rehearsals that the user would have to do to remember all of his passwords as a measure of the usability of the password scheme. Our usability model leads us to a key observation: password reuse benefits users not only by reducing the number of passwords that the user has to memorize, but more importantly by increasing the natural rehearsal rate for each password. We also present a security model which accounts for the complexity of password management with multiple accounts and associated threats, including online, offline, and plaintext password leak attacks. Observing that current password management schemes are either insecure or unusable, we present Shared Cues--- a new scheme in which the underlying secret is strategically shared across accounts to ensure that most rehearsal requirements are satisfied naturally while simultaneously providing strong security. The construction uses the Chinese Remainder Theorem to achieve these competing goals

De dynamiek van vennen in schijnspiegelsystemen

Op verscheidene plaatsen in Nederland wordt getracht verdroging van vennen tegen te gaan door verwijdering van veel verdampende bomen. Het effect van deze maatregel op de levensgemeenschap in het ven is echter sterk afhankelijk van de ligging van slecht doorlatende lagen en de grootte van het schijnspiegelsysteem, die op hun beurt de reactie van het venpeil op neerslag beïnvloeden. Meer inzicht in de hydrologie van vennen is verkregen via het tijdreeksmodel PIRFICT, met als onderzoekslocatie Beegderheid

Groundwater System Identification through Time Series Analysis

Groundwater, water in the ground. Although it is invisible, it is a vital resource for all terrestrial life (whether direct or indirect). Many processes interact with it. Rain recharges it, as it infiltrates the soil. Plant roots take it up, and their leaves evaporate it. It discharges to rivers and streams, and is abstracted with pumping wells. It is controlled with ditches and drainage means. Such processes and activities leave their traces in the groundwater level fluctuations. Careful analysis of these fluctuations may reveal much of the functioning of systems, and of the effects of individual factors. This is shown by many, but practiced by too few, as traditional time series analysis theory and software are complex. In this thesis, a new method of time series analysis is presented. Its continuous time formulation fits existing physical-hydrologic theory and methods well. It is shown that groundwater level responses generally take the shape of simple distribution functions. This notion, combined with the program Menyanthes that was developed, enable the quick and easy analysis of large numbers of time series. The spatial patterns that emerge in the results of multiple models literally add another dimension to the technique. As time series models are usually accurate also, they may be valuable to every (eco)hydrologist.Water ManagementCivil Engineering and Geoscience

Characterising groundwater dynamics based on a system identification approach

For visual interpretation, mapping or empirical modelling purposes, the amount of information contained in a full spatio-temporal description of the groundwater table dynamics is simply too large. For such purposes, the data has to be compressed without loosing too much information. Methods have been developed to visualise the groundwater regime in overall graphs, or statistically characterise the dynamics with a limited set of parameters. More recently, methods have been sought to identify the properties that determine the dynamics of a groundwater system. In such approaches, it is believed that the spatial differences in the groundwater dynamics are determined by the system properties, while its temporal variation is driven by the dynamics of the input into the system. In this paper, a method is presented that links the dynamics of the input to the spatially variable system properties, and results in a new set of parameters that characterise the groundwater dynamics (GD). While the dynamics of the input are characterised by its mean level and annual amplitude, the functioning of the groundwater system is characterised by its impulse response (IR) function. The IR function can for instance be estimated empirically using a time series model. Subsequently, the input and system characteristics are combined into a set of parameters that describe the output, or GD, using simple analytical expressions. It is shown that these so-called GD characteristics (the mean depth, convexity, annual amplitude and phase shift), can describe the GD in detail (for as far as the time series model can). In the example application, the GD characteristics are compared to other methods for characterising the groundwater regime, using two example series of groundwater level observations. It is shown that the so-called MxGL statistics (Mean Highest, Lowest or Spring Groundwater Level) that are often used have some important drawbacks, as they filter out the low-frequency dynamics of a system and mix-up annual with higher frequencies. Consequently, it is concluded that the capability of MxGL statistics in characterising the GD at different locations is less than that of GD characteristics

Do we really need phytosociological classes to calibrate Ellenberg indicator values?

Wamelink et al. (2002) calibrated Ellenberg indicator values for acidity and water availability against measured soil pH and measured mean spring groundwater level (MSL), respectively. Linear regression between indicator value and measured value of all the observations gave a poor fit. Regression lines per phytosociological vegetation class, on the other hand. generally described the observations well. In this article we demonstrate that this result is, at least partly, an artefact. First. because the data utilized are likely to contain systematic errors, and second, because a wrong regression model was applied. A sigmoid function for the relation between the indicator value for water availability and MSL gives a far better fit than a linear function does. 'Vegetation class' is not an obvious choice as an extra explanatory variable for the regression, as it is only a convenient label for vegetation and should not be used as if it were a real independent environmental variable. In general, indicator values of plant species should be calibrated against environmental variables with great care. This implies that researchers should have knowledge about the ecological demands plants make on their environment, as well as about the spatial and temporal variability of this environment

Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados

Water regimes in the Brazilian Cerrados are sensitive to climatological disturbances and human intervention. The risk that critical water-table levels are exceeded over long periods of time can be estimated by applying stochastic methods in modeling the dynamic relationship between water levels and driving forces such as precipitation and evapotranspiration. In this study, a transfer function-noise model, the so called PIRFICT-model, is applied to estimate the dynamic relationship between water-table depth and precipitation surplus/deficit in a watershed with a groundwater monitoring scheme in the Brazilian Cerrados. Critical limits were defined for a period in the Cerrados agricultural calendar, the end of the rainy season, when extremely shallow levels