This dataset comprises data of an interdisciplinary pedon-scale irrigation experiment at a grassland site near Karlsruhe, Germany, including pedo-hydrological, geophysical, and remote sensing data. The objective of this experiment is to monitor soil moisture dynamics during a well-defined infiltration process with a combination of direct and non-invasive techniques.
Overall, the quantification of soil water dynamics and, in particular, its spatial distributions is essential for the understanding of land-atmosphere interactions. However, the precise measurement of soil water dynamics and its spatial distribution in a continuous manner is a challenging task. Pedo-hydrological monitoring techniques rely on direct, point-based measurement with buried probes for soil water content and matric potential. Non-invasive remote sensing (RS) and geophysical measurement techniques allow for spatially continuous measurements on different spatial scales and extents. This experiment provides a basis for the analyses of signal coherence between the measurement techniques and disciplines. It contributes to forthcoming developments of monitoring setups and modeling approaches to landscape-water dynamics.
For direct monitoring, an array of time-domain reflectometry (TDR) probes and tensiometers was used. As non-invasive techniques, we applied a ground-penetrating radar (GPR), a hyperspectral snapshot sensor, a long-wave infrared (LWIR) sensor, and a hyperspectral field spectroradiometer. We provide the data in nearly raw format, including information about the site properties and calibration references. The data are organized along with the different sensors and disciplines. Thus, the distinct sensor data can also be used independently of each other. In addition, exemplary scripts for reading and processing the data are included.
Grond is an open source software tool for robust characterization of earthquake sources. Moment tensors and finite fault rupture models can be estimated from a combination of seismic waveforms, waveform attributes and geodetic observations like InSAR and GNSS. It helps you to investigate diverse magmatic, tectonic, and other geophysical processes at all scales.
It delivers meaningful model uncertainties through a Bayesian bootstrap-based probabilistic joint inversion scheme. The optimisation explores the full model space and maps model parameter trade-offs with a flexible design of objective functions.
Rapid forward modelling is enabled by using pre-computed Green's function databases, handled through the Pyrocko software library. They serve synthetic near-field surface displacements and synthetic seismic waveforms for arbitrary earthquake source models and geometries.