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

This paper presents a model of the long‐term evolution of catchments, the growth of their drainage networks, and the changes in elevations within both the channels and the hillslopes. Elevation changes are determined from continuity equations for flow and sediment transport, with sediment transport being related to discharge and slope. The central feature of the model is that it explicitly differentiates between the sediment transport behavior of the channels and the hillslopes on the basis of observed physics, and the channel network extension results solely from physically based flow interactions on the hillslopes. The difference in behavior of channels and hillslopes is one of the most important properties of a catchment. The flow and sediment transport continuity equations in the channel and the hillslope are coupled and account for the long‐term interactions of the elevations in the hillslope and in the channels. Sediment transport can be due to fluvial processes, creep, and rockslides. Tectonic uplift may increase overall catchment elevations. The dynamics of channel head advance, and thus network growth, are modeled using a physically based mechanism for channel initiation and growth where a channel head advances when a channel initiation function, nonlinearly dependent on discharge and slope, exceeds a threshold. This threshold controls the drainage density of the basin. A computer implementation of the model is introduced, some simple simulations presented, and the numerics of the solution technique described.

Abstract.The interaction between vegetation and hydrologic processes is particularly tight in water-limited environments where a positive-feedback links soil moisture and vegetation. The vegetation of these systems is commonly patterned, that is, arranged in a two phase mosaic composed of patches with high biomass cover interspersed within a lowcover or bare soil component. These patterns are strongly linked to the redistribution of runoff and resources from source areas (bare patches) to sink areas (vegetation patches) and play an important role in controlling erosion.In this paper, the dynamics of these systems is investigated using a new modeling framework that couples landform and vegetation evolution, explicitly accounting for the dynamics of runon-runoff areas. The objective of this study is to analyze water-limited systems on hillslopes with mild slopes, in which overland flow occurs predominantly in only one direction and vegetation displays a banded pattern. Our simulations reproduce bands that can be either stationary or upstream migrating depending on the magnitude of the runoffinduced seed dispersal. We also found that stationary banded systems redistribute sediment so that a stepped microtopography is developed. The modelling results are the first to incorporate the effects of runoff redistribution and variable infiltration rates on the development of both the vegetation patterns and microtopography. The microtopography for stationary bands is characterized by bare soil on the lower gradient areas and vegetation on steeper gradients areas. For the case of migrating vegetation bands the model generates hillslope profiles with planar topography. The success at generating not only the observed patterns of vegetation, but also patterns of runoff and sediment redistribution suggests that the hydrologic and erosion mechanisms represented in the Correspondence to: P. M. Saco (patricia.saco@newcastle.edu.au) model are correctly capturing some of the key processes driving these ecosystems.

Hypsometry has historically been used as an indicator of geomorphic form of catchments and landforms. Yet there has been litle work aimed at relating hypsometry to landform process and scale. This paper uses the SIBERIA catchment evolution model to explore linkages between catchment process and hypsometry. SIBERIA generates results that are qualitatively and quantitatively similar to observed hypsometric curves for physically realistic parameters. However, we show that not only does the hypsometry reflect landscape runoff and erosion process, but it is strongly dependent on channel network and catchment geometry. We show that the width to length ratio of the catchment has a significant influence on the shape of the hypsometric curve, though little on the hypsometric integral. For landforms dominated by fluvial sediment transport, the classic Strahler 'mature' hypsometric curve is only generated for catchments with roughly equal width and length. Narrow catchments show a hypsometric curve more similar to Strahler's 'monadnock' form. For landscapes dominated by diffusive transport, the simulated hypsometric curve is concave-down everywhere, this being consistent with curves reported for some example catchments in France. Because the transition between diffusive dominance to fluvial is scaledependent, with larger catchments exhibiting greater fluvial dominance, then the hypsometric curve is a scale-dependent descriptor of landforms. Experimental results for simulated landforms from a small-scale rainfall-erosion simulator are reported. It is shown that SIBERIA yields satisfactory fits to the data, confirming its ability to predict the form of the hypsometric curve from a simple model of geomorphic processes.

The Kalman filter assimilation technique is applied to a simplified soil moisture model for retrieval of the soil moisture profile from near-surface soil moisture measurements. First, the simplified soil moisture model is developed, based on an approximation to the Buckingham-Darcy equation. This model is then used in a 12-month one-dimensional field application, with updating at 1-, 5-, 10-, and 20-day intervals. The data used are for the Nerrigundah field site, New South Wales, Australia. This study has identified (i) the importance of knowing the depth over which the near-surface soil moisture measurements are representative (i.e., observation depth), (ii) soil porosity and residual soil moisture content as the most important soil parameters for correct retrieval of the soil moisture profile, (iii) the importance of a soil moisture model that represents the dominant soil physical processes correctly, and (iv) an appropriate forecasting model as far more important than the temporal resolution of near-surface soil moisture measurements. Although the soil moisture model developed here is a good approximation to the Richards equation, it requires a root water uptake term or calibration to an extreme drying event to model extremely dry periods at the field site correctly.