Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties

Global change in climate and consequent large impacts on regional hydrologic systems have, in recent years, motivated significant research efforts in water resources modeling under climate change. In an integrated future hydrologic scenario, it is likely that water availability and demands will change significantly due to modifications in hydro-climatic variables such as rainfall, reservoir inflows, temperature, net radiation, wind speed and humidity. An integrated regional water resources management model should capture the likely impacts of climate change on water demands and water availability along with uncertainties associated with climate change impacts and with management goals and objectives under non-stationary conditions. Uncertainties in an integrated regional water resources management model, accumulating from various stages of decision making include climate model and scenario uncertainty in the hydro-climatic impact assessment, uncertainty due to conflicting interests of the water users and uncertainty due to inherent variability of the reservoir inflows. This paper presents an integrated regional water resources management modeling approach considering uncertainties at various stages of decision making by an integration of a hydro-climatic variable projection model, a water demand quantification model, a water quantity management model and a water quality control model. Modeling tools of canonical correlation analysis, stochastic dynamic programming and fuzzy optimization are used in an integrated framework, in the approach presented here. The proposed modeling approach is demonstrated with the case study of the Bhadra Reservoir system in Karnataka, India.

Zero-Crossings Based Spectrum Sensing Under Noise Uncertainties

In this work, the hypothesis testing problem of spectrum sensing in a cognitive radio is formulated as a Goodness-of-fit test against the general class of noise distributions used in most communications-related applications. A simple, general, and powerful spectrum sensing technique based on the number of weighted zero-crossings in the observations is proposed. For the cases of uniform and exponential weights, an expression for computing the near-optimal detection threshold that meets a given false alarm probability constraint is obtained. The proposed detector is shown to be robust to two commonly encountered types of noise uncertainties, namely, the noise model uncertainty, where the PDF of the noise process is not completely known, and the noise parameter uncertainty, where the parameters associated with the noise PDF are either partially or completely unknown. Simulation results validate our analysis, and illustrate the performance benefits of the proposed technique relative to existing methods, especially in the low SNR regime and in the presence of noise uncertainties.

Non-slipping Adhesive Contact between Mismatched Elastic Cylinders

要: We have recently proposed a generalized JKR model for non-slipping adhesive contact between two elastic spheres subjected to a pair of pulling forces and a mismatch strain (Chen, S., Gao, H., 2006c. Non-slipping adhesive contact between mismatched elastic spheres: a model of adhesion mediated deformation sensor. J. Mech. Phys. Solids 54, 1548-1567). Here we extend this model to adhesion between two mismatched elastic cylinders. The attention is focused on how the mismatch strain affects the contact area and the pull-off force. It is found that there exists a critical mismatch strain at which the contact spontaneously dissociates. The analysis suggests possible mechanisms by which mechanical deformation can affect binding between cells and molecules in biology.

Improving reporting of uncertainties in sea level rise assessments

Sea level rise (SLR) assessments are commonly used to identify the extent that coastal populations are at risk to flooding. However, the data and assumptions used to develop these assessments contain numerous sources and types of uncertainty, which limit confidence in the accuracy of modeled results. This study illustrates how the intersection of uncertainty in digital elevation models (DEMs) and SLR lead to a wide range of modeled outcomes. SLR assessments are then reviewed to identify the extent that uncertainty is documented in peer-reviewed articles. The paper concludes by discussing priorities needed to further understand SLR impacts. (PDF contains 4 pages)

Atmospheric nutrient input to coastal areas: reducing the uncertainties

A significant fraction of the total nitrogen entering coastal and estuarine ecosystems along the eastern U.S. coast arises from atmospheric deposition; however, the exact role of atmospherically derived nitrogen in the decline of the health of coastal, estuarine, and inland waters is still uncertain. From the perspective of coastal ecosystem eutrophication, nitrogen compounds from the air, along with nitrogen from sewage, industrial effluent, and fertilizers, become a source of nutrients to the receiving ecosystem. Eutrophication, however, is only one of the detrimental impacts of the emission of nitrogen containing compounds to the atmosphere. Other adverse effects include the production of tropospheric ozone, acid deposition, and decreased visibility (photochemical smog). Assessments of the coastal eutrophication problem indicate that the atmospheric deposition loading is most important in the region extending from Albemarle/Parnlico Sounds to the Gulf of Maine; however, these assessments are based on model outputs supported by a meager amount of actual data. The data shortage is severe. The National Research Council specifically mentions the atmospheric role in its recent publication for the Committee on Environmental and Natural Resources, Priorities for Coastal Ecosystem Science (1994). It states that, "Problems associated with changes in the quantity and quality of inputs to coastal environments from runoff and atmospheric deposition are particularly important [to coastal ecosystem integrity]. These include nutrient loading from agriculture and fossil fuel combustion, habitat losses from eutrophication, widespread contamination by toxic materials, changes in riverborne sediment, and alteration of coastal hydrodynamics. "