Details zur Publikation
|DOI / URL||Link|
|Titel (primär)||Estimation of catchment-scale soil moisture patterns based on terrain data and sparse TDR measurements using a fuzzy c-means clustering approach|
|Autor||Schröter, I.; Paasche, H.; Dietrich, P.; Wollschläger, U.|
|Journal / Serie||Vadose Zone Journal|
|UFZ Querschnittsthemen||TERENO; RU5;|
We present an efficient method for sampling and spatial estimation of soil moisture at the small catchment scale which is based on terrain data and sparse soil moisture measurements.
Accurate characterization of spatial soil moisture patterns and their temporal dynamics is important to infer hydrological fluxes and flow pathways and to improve the description and prediction of hydrological models. Recent advances in ground-based and remote sensing technologies provide new opportunities for temporal information on soil moisture patterns. However, spatial monitoring of soil moisture at the small catchment scale (0.1–1 km2) remains challenging and traditional in situ soil moisture measurements are still indispensable. This paper presents a strategic soil moisture sampling framework for a low-mountain catchment. The objectives were to: (i) find a priori a representative number of measurement locations, (ii) estimate the soil moisture pattern on the measurement date, and (iii) assess the relative importance of topography for explaining soil moisture pattern dynamics. The fuzzy c-means sampling and estimation approach (FCM SEA) was used to identify representative measurement locations for in situ soil moisture measurements. The sampling was based on terrain attributes derived from a digital elevation model (DEM). Five time-domain reflectometry (TDR) measurement campaigns were conducted from April to October 2013. The TDR measurements were used to calibrate the FCM SEA to estimate the soil moisture pattern. For wet conditions the FCM SEA performed better than under intermediate conditions and was able to reproduce a substantial part of the soil moisture pattern. A temporal stability analysis shows a transition between states characterized by a reorganization of the soil moisture pattern. This indicates that, at the investigated site, under wet conditions, topography is a major control that drives water redistribution, whereas for the intermediate state, other factors become increasingly important.
|Schröter, I., Paasche, H., Dietrich, P., Wollschläger, U. (2015):
Estimation of catchment-scale soil moisture patterns based on terrain data and sparse TDR measurements using a fuzzy c-means clustering approach
Vadose Zone J. 14 (11), 10.2136/vzj2015.01.0008