Scaling of response spectrum and duration for aftershocks

Aftershocks have the potential to cause collapse of a structure that has been already damaged by the preceding main shock. Seismic safety of a structure should therefore be ascertained through a damage analysis using the anticipated main shock and few larger-aftershock motions. Simulation of aftershock motions needs characterization of the seismic hazard due to aftershocks, and therefore it will be useful to develop a conditional scaling model that can predict the response spectrum of an anticipated aftershock motion consistent with the design spectrum of the main shock motion anticipated at the same station. In this study an attempt is made to develop a conditional scaling model for the pseudo spectral velocity spectrum via linear regression analysis on the aftershock and main shock recordings for the 1999 Chi–Chi earthquake. It is shown that it may be possible to obtain a simpler and approximate version of the conditional model from an unconditional model. Damage-causing potential of a ground motion also depends on its strong motion duration (SMD) and therefore a conditional scaling model is developed for SMD of the aftershock motion in several narrow frequency-bands. The model is developed for the larger-aftershock motions and it is shown that a reasonable replacement of such a model may be obtainable directly from an unconditional model. Finally, a simple weighted averaging scheme is proposed to obtain the composite SMD from the SMDs for different frequency bands by using the pseudo spectral acceleration spectrum of the motion.     

Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution

This article introduces a new classification scheme—head/tail breaks—to find groupings or hierarchy for data with a heavy-tailed distribution. The heavy-tailed distributions are heavily right skewed, with a minority of large values in the head and a majority of small values in the tail, commonly characterized by a power law, a lognormal, or an exponential function. For example, a country's population is often distributed in such a heavy-tailed manner, with a minority of people (e.g., 20 percent) in the countryside and the vast majority (e.g., 80 percent) in urban areas. This new classification scheme partitions all of the data values around the mean into two parts and continues the process iteratively for the values (above the mean) in the head until the head part values are no longer heavy-tailed distributed. Thus, the number of classes and the class intervals are both naturally determined. I therefore claim that the new classification scheme is more natural than the natural breaks in finding the groupings or hierarchy for data with a heavy-tailed distribution. I demonstrate the advantages of the head/tail breaks method over Jenks's natural breaks in capturing the underlying hierarchy of the data.     

Erosion modelling for land management in the Tahoe basin,USA: scaling from plots to forest catchments

Abstract

Recent developments in hydrological modelling of river basins are focused on prediction in ungauged basins, which implies the need to improve relationships between model parameters and easily-obtainable information, such as satellite images, and to test the transferability of model parameters. A large-scale distributed hydrological model is described, which has been used in several large river basins in Brazil. The model parameters are related to classes of physical characteristics, such as soil type, land use, geology and vegetation. The model uses two basin space units: square grids for flow direction along the basin and GRU—group response units—which are hydrological classes of the basin physical characteristics for water balance. Expected ranges of parameter values are associated with each of these classes during the model calibration. Results are presented of the model fitting in the Taquari-Antas River basin in Brazil (26 000 km² and 11 flow gauges). Based on this fitting, the model was then applied to the Upper Uruguay River basin (52 000 km²), having similar physical conditions, without any further calibration, in order to test the transferability of the model. The results in the Uruguay basin were compared with recorded flow data and showed relatively small errors, although a tendency to underestimate mean flows was found.     

CHARACTERIZATION OF ALPINE SOILS,EAGLE CAP,WALLOWA MOUNTAINS,OREGON

This study examines alpine soil development along a toposequence on Eagle Cap, Wallowa Mountains, northeast Oregon. Soils studied are from Wind Blown (WB) and Minimal Snow Cover (MSC) sites in the Ridge-Top Tundra geomorphic province. The soils are predominantly loamy sands, and exhibit minimal structural development. Soil pH ranges from 6.5 to 7.3 increasing with depth, and organic carbon ranges from 3.9% in the A horizon to 0.8% in the C horizon. The soils display significant input from Cascade volcanic ash infall and eolian influx from the weathered marble nearby. It is postulated that the ash content in the soils originates from Mazama Ash deposits. Though Eagle Cap soils have developed for the most part on granodiorite, the strong influence of the volcanic ash on pedogenesis leads to a preliminary classification of Andisols, most probably Typic and Lithic Haplocryands. [Key words: Alpine soils, Andisols, Wallowa Mountains.]     

Alpine Plant Communities: A Statistical Assessment of Their Relation to Microclimatological,Pedological, Geomorphological,and Other Factors

Floristic composition and environmental factors vary widely among plant communities in the alpine belt. Thus far no study has attempted to measure all relevant site conditions in a larger number of alpine communities. Here we show (1) which environmental factors were highly correlated with the floristic composition of the 14 plant communities investigated in the Swiss Alps and (2) which plant communities have similar environmental affinities. In every plant community investigated, the main factors potentially having an impact on plant life were measured and the floristic composition was defined. We used nonmetric multidimensional scaling (NMDS) to determine linkage between plant communities and complex environmental gradients. The first axis of the NMDS corresponds to a climate gradient (temperature/wind speed), and the second axis corresponds to a soil gradient (soil suction/pH/Ca content). With the exception of the Nardus grassland and Carex curvula turf, plant communities belonging to the same phytosociological class are exposed to very similar combinations of environmental factors. Our study shows that the variation between phytosociological classes is much larger than within classes. Still, the variation of environmental factors within individual classes leads to a further differentiation of the floristic composition. Thus, our study reinforces the validity of the phytosociological classification.     

Estimating crop net primary production using national inventory data and MODIS-derived parameters

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917 g C m⁻² yr⁻¹ and 409 g C m⁻² yr⁻¹, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.