Abstract
AbstractThis work proposes the analysis of soil moisture conditions based on the use of two recently developed descriptive techniques: (1) wavelet analysis and (2) self‐organizing mapping through Kohonen neural networks. This analysis is applied to soil moisture profiles as well as supporting data, i.e. precipitation, temperature and flow observations, from an experimental site in the Orgeval watershed in France. With wavelet analysis and self‐organizing mapping, a comprehensive description of the structure of soil moisture profile, its evolution over time, and how it relates to observations of precipitation, temperature and flow can be obtained. Soil moisture conditions, particularly in the Orgeval watershed, are an important feature of the hydrologic cycle. There might be a significant advantage to consider soil moisture information in a variety of hydrologic models, such as streamflow models often employed in simulation and prediction modes for operational purposes, and the analysis performed here provides some avenues leading to the consideration of this information. Copyright © 2004 John Wiley & Sons, Ltd.
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