Soil water retention and hydraulic conductivity measured in a wide saturation range
Abstract. Soil hydraulic properties (SHP), particularly soil water retention capacity and hydraulic conductivity of unsaturated soils, are among the key properties that determine the hydrological functioning of terrestrial systems. Some large collections of SHP, such as the UNSODA and HYPRES databases, already exist for more than two decades. They have provided an essential basis for many studies related to the critical zone. Today, SHP can be determined in a wider saturation range and with higher resolution by combining some recently developed laboratory methods. We provide 572 high-quality SHP data sets from undisturbed samples covering a wide range of soil texture, bulk density and organic carbon content. A consistent and rigorous quality filtering ensured that only trustworthy data sets were included. The data collection contains: (i) SHP data: soil water retention and hydraulic conductivity data, determined by the evaporation method and supplemented by retention data obtained by the dew point method and saturated conductivity measurements, (ii) basic soil data: particle size distribution determined by sedimentation analysis and sieving, bulk density and organic carbon content, as well as (iii) meta data including the coordinates of the sampling locations. In addition, for each data set, we provide soil hydraulic parameters for the widely used van Genuchten/Mualem model and for the Peters-Durner-Iden (PDI) model, which accounts for non-capillary retention and conductivity. The data were originally collected to develop and test advanced models of SHP and associated pedotransfer functions. However, we expect that they will be very valuable for various other purposes such as simulation studies or correlation analyses of different soil properties to study their causal relationships.
Tobias Ludwig Hohenbrink et al.
Status: final response (author comments only)
RC1: 'Comment on essd-2023-74', Anonymous Referee #1, 26 Apr 2023
- AC1: 'Reply on RC1', Tobias L. Hohenbrink, 23 May 2023
RC2: 'Comment on essd-2023-74', Anonymous Referee #2, 15 May 2023
- AC2: 'Reply on RC2', Tobias L. Hohenbrink, 23 May 2023
Tobias Ludwig Hohenbrink et al.
Tobias Ludwig Hohenbrink et al.
Viewed (geographical distribution)
The presented dataset stores high quality soil physical data. The description of measurement methods and models applied to compute soil hydraulic parameters by fitting the moisture retention and hydraulic conductivity are detailed and clear. Structure of the manuscript is logical. The main strength of the database is the data on unsaturated hydraulic conductivity. This way the presented work and dataset will attain international interest.
The data could be easily accessed. Organization of the six data tables within the dataset is logical, the tables can be merged by the Sample_ID column.
A paragraph could be added about data quality check under materials and methods, because that could strengthen that the dataset was rigorously checked and the way the check was performed can be very informative for the readers and serve as a guideline. A final data check would be useful to secure that all data is correct. The detailed review can be found under SPECIFIC COMMENTS.
L24-25, L55, L78, L79, L82, L96 and entire text, please specify if you refer to soil profiles or soil samples, the word “data sets” is not enough specific.
L97: … basic soil properties such as soil texture … or something similar
L101-102: please add reference or some examples for the two level texture information, because it is not widely used.
L127: … mixed average soil sample … is it correct?
L134-149: all is clearly described, just a table providing an overview about the methods would be very informative, because for the readers it is a very valuable information what method was used for which soil property. Please add information about the measurement method of N and S, as well – because those are also included in the BasicProp.csv file. Please consider if the method used by soiltexture package can have limitations. Some other methods exist, which might result in a more accurate conversion to USDA silt and sand content. It is possible that in your case there would not be significant difference between different methods, but for other cases there might be. Readers might follow the procedure you published, so it worth to mention other methods, e.g.: Nemes et al (1999) https://doi.org/10.1016/S0016-7061(99)00014-2.
L150: Before “2.4 Fitting models to measured data” subsection could you please add a separate subsection on how quality of the data was secured? Could you shortly describe what rules were applied during checking the data?
L181-182: please add reference and equation used to compute parameter Ks of the PDI model.
L184-186, Table 1: please add meaning of VGM and PDI to have the table self explanatory.
L190-193: would be informative to add 4.1-4.3 tables from 2023-012_Hohenbrink-et-al_Data-Description.pdf file here.
L194: It might worth to consider to create a metadata .xml file following the INSPIRE metadata guidelines (ISO 19115 and ISO 19139) and add it to the dataset.
L216-219 and Figure 2. : please consider to provide this information according to USDA texture classes (based on the USDA sand, silt and clay fractions), because that is internationally used, the German texture classes are not widely known out of Germany. I see that for Figure 3. it might not make sense to use the USDA standard because than you might have only three fractions and Figures 4 and 5 is easier to interpret if meaning of texture classes can be read from Figure 3.
L241: circles on Figure 4 are hardly visible, maybe Figure 4 could be edited somehow to let easier distinguish between circle, triangle and square.
L244: Please shortly add why number of dewpoint measurements ranges between 1 and 8.
L263: … range for coarser texture classes … Do you agree?
L268-271: if th_1_8, th_2_5 and th_4_2 columns of Param table were computed with PDI model, please add “_PDI” as last characters to those column names.
L272-273: please add very short explanation for why filed capacity and wilting point vary widely within texture classes. This is obvious for experts in soil physics but not that trivial for researchers from other environmental fields.
L308: please consider e.g. the work of Twarakavi et al. (2010) (https://doi.org/10.1029/2009WR007939 ) - or possible other papers in this topic – and rephrase the sentence accordingly.
L311: Do authors plan to add soil depth, chemical soil properties - e.g. pH or calcium-carbonate content - or taxonomical information to the dataset in the future? If soil depth is available it might be easy to add to the BasicProp.csv table, it could be an important data column.
Result of checking the database: