Strong correlation and massive spectral-weight redistribution induced spin density wave in a-Fe1.06Te

The electronic structure of a-Fe1.06Te is studied with angle-resolved photoemission spectroscopy. We show that there is substantial spectral weight around Gamma and X, and lineshapes are intrinsically incoherent in the paramagnetic state. The magnetic transition is characterized by a massive spectral-weight transfer over an energy range as large as the band width, which even exhibits a hysteresis loop that marks the strong first order transition. Coherent quasiparticles emerge in the magnetically ordered state due to decreased spin fluctuations, which account for the change of transport properties from insulating behavior to metallic behavior. Our observation demonstrates that Fe1.06Te distinguishes itself from other iron-based systems with more local characters and much stronger interactions among different degrees of freedom, and how a spin density wave is formed in the presence of strong correlation.Comment: 5 pages, 4 figure

A multitemporal remote sensing approach to parsimonious streamflow modeling in a southcentral Texas watershed, USA

International audienceSoil moisture condition plays a vital role in a watershed's hydrologic response to a precipitation event and is thus parameterized in most, if not all, rainfall-runoff models. Yet the soil moisture condition antecedent to an event has proven difficult to quantify both spatially and temporally. This study assesses the potential to parameterize a parsimonious streamflow prediction model solely utilizing precipitation records and multi-temporal remotely sensed biophysical variables (i.e.~from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite). This study is conducted on a 1420 km2 rural watershed in the Guadalupe River basin of southcentral Texas, a basin prone to catastrophic flooding from convective precipitation events. A multiple regression model, accounting for 78% of the variance of observed streamflow for calendar year 2004, was developed based on gauged precipitation, land surface temperature, and enhanced vegetation Index (EVI), on an 8-day interval. These results compared favorably with streamflow estimations utilizing the Natural Resources Conservation Service (NRCS) curve number method and the 5-day antecedent moisture model. This approach has great potential for developing near real-time predictive models for flood forecasting and can be used as a tool for flood management in any region for which similar remotely sensed data are available