No abstract

No abstract

Abstract. The central importance of soil for the functioning of terrestrial systems is increasingly recognized.

Measurements of root‐zone soil moisture across spatial scales of tens to thousands of meters have been a challenge for many decades. The mobile application of Cosmic Ray Neutron Sensing (CRNS) is a promising approach to measure field soil moisture noninvasively by surveying large regions with a ground‐based vehicle. Recently, concerns have been raised about a potentially biasing influence of local structures and roads. We employed neutron transport simulations and dedicated experiments to quantify the influence of different road types on the CRNS measurement. We found that roads introduce a substantial bias in the CRNS estimation of field soil moisture compared to off‐road scenarios. However, this effect becomes insignificant at distances beyond a few meters from the road. Neutron measurements on the road could overestimate the field value by up to 40 % depending on road material, width, and the surrounding field water content. The bias could be largely removed with an analytical correction function that accounts for these parameters. Additionally, an empirical approach is proposed that can be used without prior knowledge of field soil moisture. Tests at different study sites demonstrated good agreement between road‐effect corrected measurements and field soil moisture observations. However, if knowledge about the road characteristics is missing, measurements on the road could substantially reduce the accuracy of this method. Our results constitute a practical advancement of the mobile CRNS methodology, which is important for providing unbiased estimates of field‐scale soil moisture to support applications in hydrology, remote sensing, and agriculture.

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.

No abstract

Abstract. Due to the large heterogeneity in the hydraulic properties of natural soils, estimation of field-scale hydraulic parameters is difficult. Past research revealed that data from accurate but small-scale laboratory measurements could hardly ever be transferred to the field scale. In this study, we explore an alternative approach where apparent hydraulic properties of a layered soil profile are directly estimated from hydraulic inverse modelling of a time series of in situ measured soil water contents obtained from time domain reflectometry. The data covered a one-year period with both wet and dry soil conditions. For the time period used for inversion, the model is able to reproduce the general evolution of water content in the different soil layers reasonably well. However, distinct drying and wetting events could not be reproduced in detail which we explain by the complicated natural processes that are not fully represented in the rather simple model. The study emphasises the importance of a correct average representation of the soil-atmosphere interaction.

No abstract