Search

The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWSs). However, it is still unclear which source of information - numerical models or in situ measurements - is of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019, using a physically based 1D soil moisture transfer model. A common parameterization set was defined for all sites, except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and receiver operating characteristic analysis (ROC) was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different model parameterizations, to various distances between simulation sites and landslides and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes, and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage., QC 20210915

With Peter Douglas, piano, Caroline Reel, tenor trombone, Amanda Frisch, bass trombone.; This recital is presented in partial fulfillment of the requirements for the degree Bachelor of Music in music performance.; Includes a list of upcoming School of Music events (for April 2021)., Series LXXV, Number 132.; Peter Lehmann is a trombone student of Chris Van Hof., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

With Lorianne Dougall, percussion, Tim McNulty, percussion, Samantha Parker, percussion, Grace Kulow, percussion, Peter Lehmann, trombone, AnnaMarie Kelly, percussion, Nicholas Mark, percussion, Emma Andersen, violin, Benjamin Taubert, percussion, Samantha Adamski, flute, Peter Douglas, piano, Jake Haskins, percussion.; Includes a list of upcoming School of Music events (for April 2021)., Series LXXV, Number 137., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

A total of 15,259 SWCCs from 2,702 sites were assembled from published literature and other sources, standardized, and quality-checked to obtain global database of soil hydraulic properties (GSHP). The GSHP database covers most regions across the globe, with the highest number of curves from North America followed by Africa, Europe, Asia, South America, Australia/Oceania. In addition to SWCCs, other soil variables such as soil texture (12,233 measurements), bulk density (15,125 measurements), and soil organic carbon (2,255 measurements) are also listed in the database.

The saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climate models. Ksat values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of Ksat datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258Ksat measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of Ksat measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition toKsat, other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived Ksat pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these Ksat PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCCD0.49 for tropical and CCCD0.10 for field measurements). These results indicate that there are significant differences between Ksat data collected in temperate and tropical regions and Ksat measured in the laboratory or field. The SoilKsatDB dataset is available at https://doi.org/10.5281/zenodo.3752721 (Gupta e

Saturated hydraulic conductivity (Ksat) is a key soil hydraulic parameter for representing infiltration and drainage in land surface models. For large scale applications, Ksat is often estimated from pedotransfer functions (PTFs) based on easy-to-measure soil properties like soil texture and bulk density. The reliance of PTFs on data from uniform arable lands and the omission of soil structure limits the applicability of texture-based predictions of Ksat in vegetated lands. To include effects of terrain, climate, and vegetation in the derivation of a new global Ksat map at 1 km resolution, we harness technological advances in machine learning and availability of remotely sensed surrogate information. For model training and testing, a global compilation of 6,814 geo-referenced Ksat measurements from the literature was used. The accuracy assessment based on spatial cross-validation shows a concordance correlation coefficient (CCC) of 0.16 and a root mean square error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC = 0.79 and RMSE = 0.72 for non-spatial cross-validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on easy-to-measure soil properties. The validation of the model indicates that Ksat could be modeled without bias using Covariate-based GeoTransfer Functions (CoGTFs) that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data.