Accurate Ocean Bottom Seismometer Positioning Method Inspired by Multilateration Technique

The positioning of ocean bottom seismometers (OBS) is a key step in the processing flow of OBS data, especially in the case of self popup types of OBS instruments. The use of first arrivals from airgun shots, rather than relying on the acoustic transponders mounted in the OBS, is becoming a trend and generally leads to more accurate positioning due to the statistics from a large number of shots. In this paper, a linearization of the OBS positioning problem via the multilateration technique is discussed. The discussed linear solution solves jointly for the average water layer velocity and the OBS position using only shot locations and first arrival times as input data.     

Faster growth in warmer winters for large trees in a Mediterranean-climate ecosystem

Large trees (>76 cm breast-height diameter) are vital components of Sierra Nevada/Cascades mixed-conifer ecosystems because of their fire resistance, ability to sequester large amounts of carbon, and role as preferred habitat for sensitive species such as the California spotted owl. To investigate the likely performance of large trees in a rapidly changing climate, we analyzed growth rings of five conifer species against 20th century climate trends from local weather stations. Over the local station period of record, there were no temporal trends in precipitation, but maximum temperatures increased by 0.10 to 0.13 °C/decade (summer and autumn), and minimum temperatures increased by 0.11 to 0.19 °C/decade in all seasons. All species responded positively to precipitation, but more variation was explained by a significant positive response to minimum winter temperatures. High maximum summer temperature adversely affected growth of two species, and maximum spring temperatures in the year prior to ring formation were negatively associated with growth of one species. The strong coherent response to increasing minimum temperatures bodes well for growth of large trees in Sierra/Cascades region mixed conifer forest under continued climatic warming, but these trees will still be under threat by the increased fire intensity that is a indirect effect of warming.     

A Framework for Quantitative Assessment of Impacts Related to Energy and Mineral Resource Development

Natural resource planning at all scales demands methods for assessing the impacts of resource development and use, and in particular it requires standardized methods that yield robust and unbiased results. Building from existing probabilistic methods for assessing the volumes of energy and mineral resources, we provide an algorithm for consistent, reproducible, quantitative assessment of resource development impacts. The approach combines probabilistic input data with Monte Carlo statistical methods to determine probabilistic outputs that convey the uncertainties inherent in the data. For example, one can utilize our algorithm to combine data from a natural gas resource assessment with maps of sage grouse leks and piñon-juniper woodlands in the same area to estimate possible future habitat impacts due to possible future gas development. As another example: one could combine geochemical data and maps of lynx habitat with data from a mineral deposit assessment in the same area to determine possible future mining impacts on water resources and lynx habitat. The approach can be applied to a broad range of positive and negative resource development impacts, such as water quantity or quality, economic benefits, or air quality, limited only by the availability of necessary input data and quantified relationships among geologic resources, development alternatives, and impacts. The framework enables quantitative evaluation of the trade-offs inherent in resource management decision-making, including cumulative impacts, to address societal concerns and policy aspects of resource development.     

Hydrologic simulation approach for El Niño Southern Oscillation (ENSO)-affected watershed with limited raingauge stations

ABSTRACT

Since the performance of hydrological models relies on numerous factors, the selection of an appropriate modeling approach for hydrological study has always been a crucial issue. The major objective of this research is to demonstrate that data-driven models such as the Adaptive Neuro-Fuzzy Inference system (ANFIS) are more suitable in a region where spatially distributed precipitation datasets are not available. Since precipitation has a teleconnection with the El Niño Southern Oscillation (ENSO) in different parts of the world, the sea surface temperatures (SSTs) and sea level pressures (SLPs) of the equatorial Pacific can be expected to act as surrogates for the precipitation if there are insufficient raingauge stations in the watershed. Moreover, in contrast to conceptual and physically-based models, data driven models can incorporate SST and SLP in their input vectors, and hence additional forcing of SST with precipitation has been experimented with in past studies. Therefore, our second objective is to test whether the additional forcing of SST and SLP will improve the hydrologic simulation. For this, various ANFIS models for the winter season were developed considering 10 raingauge stations situated at various locations in the watershed. Rainfall from each raingauge station was considered in the ANFIS model one at a time with and without SST/SLP. The results show that the performance of the ANFIS model improved with the additional fusion of SST and SLP, especially when a raingauge station from a remote location was considered. However, this improvement was observed when the analysis was primarily focused on the winter season which is a period with a strong ENSO signal.

Editor D. Koutsoyiannis Associate editor L. See