Salt marsh morphodynamics: An investigation of tidal flows and marsh channel equilibrium

Although numerous field studies have evaluated flow and transport processes in salt marsh channels, the overall role of channels in delivering and removing material from salt marsh platforms is still poorly characterised. In this paper, we consider this issue based on a numerical hydrodynamic model for a prototype marsh system and on a field survey of the cross-sectional geometry of a marsh channel network. Results of the numerical simulations indicate that the channel transfers approximately three times the volume of water that would be estimated from mass balance considerations alone. Marsh platform roughness exerts a significant influence on the partitioning of discharge between the channel and the marsh platform edge, alters flow patterns on the marsh platform due to its effects on channel-to-platform transfer and also controls the timing of peak discharge relative to marsh-edge overtopping. Although peak channel discharges and velocities are associated with the flood tide and marsh inundation, a larger volume of water is transferred by the channel during ebb flows, a portion of which transfer takes place after the tidal height is below the marsh platform. Detailed surveys of the marsh channels crossing a series of transects at Upper Stiffkey Marsh, north Norfolk, England, show that the total channel cross-sectional area increases linearly with catchment area in the inner part of the marsh, which is consistent with the increase in shoreward tidal prism removed by the channels. Toward the marsh edge, however, a deficit in the total cross-sectional area develops, suggesting that discharge partitioning between the marsh channels and the marsh platform edge may also be expressed in the morphology of marsh channel systems.

Remote estimation of thermal inertia and soil heat flux for bare soil

A method is presented which allows thermal inertia (the soil heat capacity times the square root of the soil thermal diffusivity, C(h)rootD(h)), to be estimated remotely from micrometeorological observations. The method uses the drop in surface temperature, T-s, between sunset and sunrise, and the average night-time net radiation during that period, for clear, still nights. A Fourier series analysis was applied to analyse the time series of T-s . The Fourier series constants, together with the remote estimate of thermal inertia, were used in an analytical expression to calculate diurnal estimates of the soil heat flux, G. These remote estimates of C(h)rootD(h) and G compared well with values derived from in situ sensors. The remote and in situ estimates of C(h)rootD(h) both correlated well with topsoil moisture content. This method potentially allows area-average estimates of thermal inertia and soil heat flux to be derived from remote sensing, e.g. METEOSAT Second Generation, where the area is determined by the sensor's height and viewing angle. (C) 2003 Elsevier B.V. All rights reserved.

Extraction of tidal channel networks from aerial photographs alone and combined with laser altimetry

Tidal channel networks play an important role in the intertidal zone, exerting substantial control over the hydrodynamics and sediment transport of the region and hence over the evolution of the salt marshes and tidal flats. The study of the morphodynamics of tidal channels is currently an active area of research, and a number of theories have been proposed which require for their validation measurement of channels over extensive areas. Remotely sensed data provide a suitable means for such channel mapping. The paper describes a technique that may be adapted to extract tidal channels from either aerial photographs or LiDAR data separately, or from both types of data used together in a fusion approach. Application of the technique to channel extraction from LiDAR data has been described previously. However, aerial photographs of intertidal zones are much more commonly available than LiDAR data, and most LiDAR flights now involve acquisition of multispectral images to complement the LiDAR data. In view of this, the paper investigates the use of multispectral data for semiautomatic identification of tidal channels, firstly from only aerial photographs or linescanner data, and secondly from fused linescanner and LiDAR data sets. A multi-level, knowledge-based approach is employed. The algorithm based on aerial photography can achieve a useful channel extraction, though may fail to detect some of the smaller channels, partly because the spectral response of parts of the non-channel areas may be similar to that of the channels. The algorithm for channel extraction from fused LiDAR and spectral data gives an increased accuracy, though only slightly higher than that obtained using LiDAR data alone. The results illustrate the difficulty of developing a fully automated method, and justify the semi-automatic approach adopted.

Upwind solution of singular differential equations arising from steady channel flows

We study ordinary nonlinear singular differential equations which arise from steady conservation laws with source terms. An example of steady conservation laws which leads to those scalar equations is the Saint–Venant equations. The numerical solution of these scalar equations is sought by using the ideas of upwinding and discretisation of source terms. Both the Engquist–Osher scheme and the Roe scheme are used with different strategies for discretising the source terms.

Validation of improved TAMANN neural network for operational satellite-derived rainfall estimation in Africa

Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms-a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system.