The topography of a catchment has a major impact on the hydrological, geomorphological. and biological processes active in the landscape. The spatial distribution of topographic attributes can often be used as an indirect measure of the spatial variability of these processes and allows them to be mapped using relatively simple techniques. Many geographic information systems are being developed that store topographic information as the primary data for analysing water resource and biological problems. Furthermore, topography can be used to develop more physically realistic structures for hydrologic and water quality models that directly account for the impact of topography on the hydrology. Digital elevation models are the primary data used in the analysis of catchment topography. We describe elevation data sources, digital elevation model structures, and the analysis of digital elevation data for hydrological, geomorphological, and biological applications. Some hydrologic models that make use of digital representations of topography are also considered. K E Y WORDS Basin topography Digital elevation models Terrain analysis Hydrologic models -6087/9and diversity within landscapes. A number of hydrologically-based, topographically-derived indices appear to be particularly powerful and useful for determining this susceptibility to hazard (Moore and Nieber, 1989). Many geographic information systems and resource inventory systems are being developed storing topographic information as primary data for use in analysing water resource and biological problems. This paper reviews the availability of digital elevation data and its accuracy, digital representation of topography, analysis of the digital data for hydrological, geomorphological, and biological applications, and describes some models that make use of digital representations of topography. We use examples from Australia and the United States of America in our discussions because of our familiarity with hydrologic research and practice in these two countries. DIGITAL ELEVATION MODELSA Digital Elevation Model (DEM) is an ordered array of numbers that represents the spatial distribution of elevations above some arbitrary datum in a landscape. I t may consist of elevations sampled at discrete points or the average elevation over a specified segment of the landscape, although in most cases it is the former. DEMs are a subset of Digital Terrain Models (DTMs) which can be defined as ordered arrays of numbers that represent the spatial distribution of terrain attributes.The acquisition. storage, and presentation of topographic information is an area of active research.Generally, raw elevation data in the form of stereo photographs or field surveys, and the equipment to process these data, are not readily available to potential end users of a DEM. Therefore, most users must rely on published topographic maps or DEMs produced by government agencies such as the United States Geological Survey (U.S.G.S.) or the Australian Surveying and Land Information Group (AUSLIG-formerl...

Abstract. In this paper we develop a conceptual and observational case in which soil water patterns in temperate regions of Australia switch between two preferred states. The wet state is dominated by lateral water movement through both surface and subsurface paths, with catchment terrain leading to organization of wet areas along drainage lines. We denote this as nonlocal control. The dry state is dominated by vertical fluxes, with soil properties and only local terrain (areas of high convergence) influencing spatial patterns.We denote this as local control. The switch is described in terms of the dominance of lateral over vertical water fluxes and vice versa. When evapotranspiration exceeds rainfall, the soil dries to the point where hydraulic conductivity is low and any rainfall that occurs essentially wets up the soil uniformly and is evapotranspired before any significant lateral redistribution takes place. As evapotranspiration decreases and/or rainfall increases, areas of high local convergence become wet, and runoff that is generated moves downslope, rapidly wetting up the drainage lines. In the wet to dry transitional period a rapid increase in potential evapotranspiration (and possibly a decrease in rainfall) causes drying of the soil and "shutting down" of lateral flow. Vertical fluxes dominate and the "dry" pattern is established. Three data sets from two catchments are presented to support the notion of preferred states in soil moisture, and the results of a modeling exercise on catchments from a range of climatic conditions illustrate that the conclusions from the field studies may apply to other areas. The implications for hydrological modeling are discussed in relation to methods for establishing antecedent moisture conditions for event models, for distribution models, and for spatially distributing bulk estimates of catchment soil moisture using indices.

Abstract. We analyze the degree of spatial organization of soil moisture and the ability of terrain attributes to predict that organization. By organization we mean systematic spatial variation or consistent spatial patterns. We use 13 observed spatial patterns of soil moisture, each based on over 500 point measurements, from the 10.5 ha Tarrawarra experimental catchment in Australia. The measured soil moisture patterns exhibit a high degree of organization during wet periods owing to surface and subsurface lateral redistribution of water. During dry periods there is little spatial organization. The shape of the distribution function of soil moisture changes seasonally and is influenced by the presence of spatial organization. Generally, it is quite different from the shape of the distribution functions of various topographic indices. A correlation analysis found that ln(a), where a is the specific upslope area, was the best univariate spatial predictor of soil moisture for wet conditions and that the potential radiation index was best during dry periods. Combinations of ln(a) or In(a/tan(/3)), where/3 is the surface slope, and the potential solar radiation index explain up to 61% of the spatial variation of soil moisture during wet periods and up to 22% during dry periods. These combinations explained the , 1995;Willgoose, 1996; Bl6schl, 1999]. This paper examines (1) the degree of spatial organization of soil moisture in a small catchment during different seasons and (2) how well that organization can be predicted using terrain indices.Hydrologic processes can vary in space in an organized way or randomly or in a combination of the two [Gutknecht, 1993; Bl6schl et al., 1993; Bl6schl, 1999]. We use "randomness" to refer to variability that is not predictable in detail but that has predictable statistical properties, and "organization" to refer to regularity or order. Spatial organization implies variation characterized by consistent spatial patterns [Bl6schl, 1999]. In the context of this paper most of the organization is related to topography. Bl6schl [1999] noted that natural systems can vary from completely disorganized (disordered, random) to highly 797

No abstract

Abstract. Many spatial fields exhibit connectivity features that have an important influence on hydrologic behavior. Examples include high-conductivity preferred flow paths in aquifers and saturated source areas in drainage lines. Connected features can be considered as arbitrarily shaped bands or pathways of connected pixels having similar (e.g., high) values. Connectivity is a property that is not captured by standard geostatistical approaches, which assume that spatial variation occurs in the most random possible way that is consistent with the spatial correlation, nor is it captured by indicator geostatistics. An alternative approach is to use connectivity functions. In this paper we apply connectivity functions to 13 observed soil moisture patterns from the Tarrawarra catchment and two synthetic aquifer conductivity patterns. It is shown that the connectivity functions are able to distinguish between connected and disconnected patterns. The importance of the connectivity in determining hydrologic behavior is explored using rainfall-runoff simulations and groundwater transport simulations. We propose the integral connectivity scale as a measure of the presence of hydrologic connectivity. Links between the connectivity functions and integral connectivity scale and simulated hydrologic behavior are demonstrated and explained from a hydrologic process perspective. Connectivity functions and the integral connectivity scale provide promising means for characterizing features that exist in observed spatial fields and that have an important influence on hydrologic behavior. Previously, this has not been possible within a statistical framework.