ALBERT

Description: Practical decision making in spatially distributed environmental assessment and management is increasingly based on environmental process models linked to geographical information systems. Powerful personal computers and Internet-accessible assessment tools are providing much greater public access to, and use of, environmental models and geo-spatial data. However traditional process models, such as the water erosion prediction project (WEPP), were not typically developed with a flexible graphical user interface (GUI) for applications across a wide range of spatial and temporal scales, utilizing readily available geo-spatial data of highly variable precision and accuracy, and communicating with a diverse spectrum of users with different levels of expertise. As the development of the geo-spatial interface for WEPP (GeoWEPP) demonstrates, the GUI plays a key role in facilitating effective communication between the tool developer and user about data and model scales. The GeoWEPP approach illustrates that it is critical to develop a scientific and functional framework for the design, implementation, and use of such geo-spatial model assessment tools. The way that GeoWEPP was developed and implemented suggests a framework and scaling theory leading to a practical approach for developing geo-spatial interfaces for process models. GeoWEPP accounts for fundamental water erosion processes, model, and users needs, but most important it also matches realistic data availability and environmental settings by enabling even non-GIS-literate users to assemble the available geo-spatial data quickly to start soil and water conservation planning. In general, it is potential users' spatial and temporal scales of interest, and scales of readily available data, that should drive model design or selection, as opposed to using or designing the most sophisticated process model as the starting point and then determining data needs and result scales. Copyright © 2003 John Wiley & Sons, Ltd.

Description: For decades, stochastic modellers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated distributions whose mean even diverges from the mean used to generate them, regardless of the length of run. Non-uniform distributions from short sequences of random numbers are a major problem in stochastic climate generation, because truly uniform distributions are required to produce the intended climate parameter distributions. In order to ensure generation of a representative climate with the stochastic weather generator CLIGEN within a 30-year run, we tested the climate output resulting from various random number generators. The resulting distributions of climate parameters showed significant departures from the target distributions in all cases. We traced this failure back to the uniform random number generators themselves. This paper proposes a quality control approach to select only those numbers that conform to the expected distribution being retained for subsequent use. The approach is based on goodness-of-fit analysis applied to the random numbers generated. Normally distributed deviates are further tested with confidence interval tests on their means and standard deviations. The positive effect of the new approach on the climate characteristics generated and the subsequent deterministic process-based hydrology and soil erosion modelling are illustrated for four climatologically diverse sites. Copyright © 2007 John Wiley & Sons, Ltd.