ALBERT

Description: In this paper, we present and analyze a global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and USA. In addition to its global spatial coverage, the collected infiltration curves cover a time span of research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use were gathered along with the infiltration data, which makes the database valuable for the development of pedo-transfer functions for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (~76 %) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on the land use is available for 76 % of experimental sites with agricultural land use as the dominant type (~40 %). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for use by public domain only and can be copied freely by referencing it. Supplementary data are available at doi:10.1594/PANGAEA.885492. Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend/update the SWIG by uploading new data to it.

Description: In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type (∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.

Description: In this contribution, different approaches based on the X-parametersTM to model the behavior of mismatched nonlinear transfer systems are examined. The X-parameters based on the PHD1-principle introduced by Verspecht and Root (2006) as an extension of the well-known S-parameters describe nonlinear microwave 2-port-networks under large signal conditions. Using load-pull measurement techniques they can be used for arbitrary load situations. Beside this load-pull approach, in the work of Cai et al. (2015), it is stated that it is sufficient to use one optimized X-parameter set for each value of the load reflection coefficient without introducing a large error. In another contribution of Cai and Yu (2015), this approach is extended to cover the whole smith chart with one optimized X-parameter set instead. In this work, these different approaches are compared and brought into question. 1 polyharmonic distortion; Verspecht and Root (2006).

Description: Finding the number and significant locations of fixed air quality monitoring stations at ground level is challenging because of the complexity of the urban environment and the large number of factors affecting the pollutants concentration. Datasets of urban parameters such as land use, building morphology and street geometry in high resolution grid cells in combination with direct measurements of airborne pollutants in high frequency (1–10 s) along a reasonable number of streets can be used to interpolate concentration of pollutants in a whole gridded domain and determine the optimum number of monitoring sites and best locations for a network of fixed monitors at ground level. In this context, a data-driven modeling methodology is developed based on the application of Self Organizing Map (SOM) to approximate the nonlinear relations between urban parameters (80 in this work) and aerosol pollution data, such as mass and number concentrations measured along streets of a commercial/residential neighborhood of Singapore. Cross-validations between measured and predicted aerosol concentrations based on the urban parameters at each individual grid cell showed satisfying results. The urban parameters used in this case proved to be an appropriate indirect measure of aerosol concentrations within the studied area. The potential locations for fixed air quality monitors are identified through clustering of areas (i.e. group of cells) with similar urban patterns. The typological center of each cluster corresponds to the most representative cell for all other cells in the cluster. In the studied neighborhood four different clusters were identified and for each cluster potential sites for air quality monitoring at ground level are identified.

Description: Finding the number and best locations of fixed air quality monitoring stations at street level is challenging because of the complexity of the urban environment and the large number of factors affecting the pollutants concentration. Data sets of such urban parameters as land use, building morphology and street geometry in high-resolution grid cells in combination with direct measurements of airborne pollutants at high frequency (1–10 s) along a reasonable number of streets can be used to interpolate concentration of pollutants in a whole gridded domain and determine the optimum number of monitoring sites and best locations for a network of fixed monitors at ground level. In this context, a data-driven modeling methodology is developed based on the application of Self-Organizing Map (SOM) to approximate the nonlinear relations between urban parameters (80 in this work) and aerosol pollution data, such as mass and number concentrations measured along streets of a commercial/residential neighborhood of Singapore. Cross-validations between measured and predicted aerosol concentrations based on the urban parameters at each individual grid cell showed satisfying results. This proof of concept study showed that the selected urban parameters proved to be an appropriate indirect measure of aerosol concentrations within the studied area. The potential locations for fixed air quality monitors are identified through clustering of areas (i.e., group of cells) with similar urban patterns. The typological center of each cluster corresponds to the most representative cell for all other cells in the cluster. In the studied neighborhood four different clusters were identified and for each cluster potential sites for air quality monitoring at ground level are identified.