Privacy Surrender for Security Theory (PSST)

Undergraduate Theoretical Proposa

Sea-level change and storm surges in the context of climate change

This paper reviews the latest research in New Zealand surrounding the issues of sea-level rise and extreme sea levels in the context of global warming and variability in the Pacific-wide El Nino– Southern Oscillation (ENSO). Past records of climate, sea level (excluding tides) and sea and air temperatures have shown that they are continuously fluctuating over various long-term timescales of years, decades and centuries. This has made it very difficult to determine whether the anthropogenic effects such as increased levels of “greenhouse” gases are having an accelerating effect on global sea levels or an increased incidence of extreme storms. Over the past century, global sea level has risen by 10–25 cm, and is in line with the rise in relative sea level at New Zealand’s main ports of +1.7 mm yr –1. What has become very clear is the need to better understand interannual (year-to-year) and decadal variability in sea-level, as these larger signals of the order of 5–15 cm in annual-mean sea level have a significant “flow-on” effect on the long-term trend in sea level. The paper describes sea level variability in northern New Zealand—both long- and short-term—involved in assessing the regional trends in sea level. The paper also discusses the relative contributions of tides, barometric pressure and wind set-up in causing extreme sea levels during storm surges. Some recent research also looked at a related question—Is there any sign of increased storminess, and hence storm surge, in northern New Zealand due to climate change? The paper concludes that, while no one can be completely sure how sea-level and the degree of storminess will respond in the near future, what is clear is that interannual and decadal variability in sea level is inextricably linked with Pacific-wide ENSO response and longer inter-decadal shifts in the Pacific climate regime, such as the latest shift in 1976

Methodology for estimating detectable change in water quality due to prescribed fire in northern Colorado, A

2001 Spring.Includes bibliographical references.Increases in nutrients and metals in receiving waters have been documented after wildfire. However, water quality impacts from prescribed fire are not well known. This research investigated the design of a post-fire water quality monitoring program using a pre-fire dataset to detect water quality changes from prescribed fire. Since water quality changes due to land use practices are often difficult to detect due to high natural variability, a paired watershed approach was implemented. Two small watersheds were selected in the Cache la Poudre watershed in Northern Colorado and monitored for one year, resulting in 14 pre-fire water quality samples. A single station and paired approach, which consider statistical power are presented and the minimum detectable change is calculated for a range of post-fire sample sizes. Samples from the Bobcat Fire in the Big Thompson Watershed near Drake, Colorado are used to evaluate the results. These results show that with 16 post-fire samples a change of less than 1% of the difference between pre-firewater quality samples and samples from the Bobcat Fire can be detected for most parameters with a statistical power of 80%. The paired watershed approach is shown to reduce the minimum detectable change by half for parameters that are correlated between the two watersheds

Research Note, June 1971

This is issue 9: Bark Thickness, k. Factors for Four Montana Coniferous Tree Specieshttps://scholarworks.umt.edu/montana_forestry_notes/1008/thumbnail.jp

Research Notes, April 1966

This is issue 3: Accuracy of the Topographic Abney in Long-Distance Sightinghttps://scholarworks.umt.edu/montana_forestry_notes/1002/thumbnail.jp

Research Note, April 1973

This is issue 12: Relationship of D.B.H. to Stump Diameter for Four Montana Coniferous Specieshttps://scholarworks.umt.edu/montana_forestry_notes/1011/thumbnail.jp

Learning not to learn: Nature versus nurture in silico

Animals are equipped with a rich innate repertoire of sensory, behavioral and motor skills, which allows them to interact with the world immediately after birth. At the same time, many behaviors are highly adaptive and can be tailored to specific environments by means of learning. In this work, we use mathematical analysis and the framework of meta-learning (or 'learning to learn') to answer when it is beneficial to learn such an adaptive strategy and when to hard-code a heuristic behavior. We find that the interplay of ecological uncertainty, task complexity and the agents' lifetime has crucial effects on the meta-learned amortized Bayesian inference performed by an agent. There exist two regimes: One in which meta-learning yields a learning algorithm that implements task-dependent information-integration and a second regime in which meta-learning imprints a heuristic or 'hard-coded' behavior. Further analysis reveals that non-adaptive behaviors are not only optimal for aspects of the environment that are stable across individuals, but also in situations where an adaptation to the environment would in fact be highly beneficial, but could not be done quickly enough to be exploited within the remaining lifetime. Hard-coded behaviors should hence not only be those that always work, but also those that are too complex to be learned within a reasonable time frame