Long-range automaton models of earthquakes: Power-law accelerations, correlation evolution, and mode-switching

We introduce a conceptual model for the in-plane physics of an earthquake fault. The model employs cellular automaton techniques to simulate tectonic loading, earthquake rupture, and strain redistribution. The impact of a hypothetical crustal elastodynamic Green's function is approximated by a long-range strain redistribution law with a r(-p) dependance. We investigate the influence of the effective elastodynamic interaction range upon the dynamical behaviour of the model by conducting experiments with different values of the exponent (p). The results indicate that this model has two distinct, stable modes of behaviour. The first mode produces a characteristic earthquake distribution with moderate to large events preceeded by an interval of time in which the rate of energy release accelerates. A correlation function analysis reveals that accelerating sequences are associated with a systematic, global evolution of strain energy correlations within the system. The second stable mode produces Gutenberg-Richter statistics, with near-linear energy release and no significant global correlation evolution. A model with effectively short-range interactions preferentially displays Gutenberg-Richter behaviour. However, models with long-range interactions appear to switch between the characteristic and GR modes. As the range of elastodynamic interactions is increased, characteristic behaviour begins to dominate GR behaviour. These models demonstrate that evolution of strain energy correlations may occur within systems with a fixed elastodynamic interaction range. Supposing that similar mode-switching dynamical behaviour occurs within earthquake faults then intermediate-term forecasting of large earthquakes may be feasible for some earthquakes but not for others, in alignment with certain empirical seismological observations. Further numerical investigation of dynamical models of this type may lead to advances in earthquake forecasting research and theoretical seismology.

An overview of APSIM, a model designed for farming systems simulation

The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Adaptation of rainbow fish to lake and stream habitats

Fish occupy a range of hydrological habitats that exert different demands on locomotor performance. We examined replicate natural populations of the rainbow fishes Melanotaenia eachamensis and M. duboulayi to determine if colonization of low-velocity (lake) habitats by fish from high-velocity (stream) habitats resulted in adaptation of locomotor morphology and performance. Relative to stream conspecifics, lake fish had more posteriorly positioned first dorsal and pelvic fins, and shorter second dorsal fin bases. Habitat dimorphism observed between wild-caught fish was determined to be heritable as it was retained in M. eachamensis offspring raised in a common garden. Repeated evolution of the same heritable phenotype in independently derived populations indicated body shape divergence was a consequence of natural selection. Morphological divergence between hydrological habitats did not support a priori expectations of deeper bodies and caudal peduncles in lake fish. However, observed divergence in fin positioning was consistent with a family-wide association between habitat and morphology, and with empirical studies on other fish species. As predicted, decreased demand for sustained swimming in takes resulted in a reduction in caudal red muscle area of lake fish relative to their stream counterparts. Melanotaenia duboulayi lake fish also had slower sustained swimming speeds (U-crit) than stream conspecifics. In M. eachamensis, habitat affected U-crit of males and females differently. Specifically, females exhibited the pattern observed in M. duboulayi (lake fish had faster U-crit than stream fish), but the opposite association was observed in males (stream males had slower Ucrit than lake males). Stream M. eachamensis also exhibited a reversed pattern of sexual dimorphism in U-crit (males slower than females) relative to all other groups (males faster than females). We suggest that M. eachamensis males from streams responded to factors other than water velocity. Although replication of muscle and U,,it phenotypes across same habitat populations within and/or among species was suggestive of adaptation, the common garden experiment did not confirm a genetic basis to these associations. Kinematic studies should consider the effect of the position and base length of dorsal fins.

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts. Copyright (K) 2003 Royal Meteorological Society.