Development and analysis of the Soil Water Infiltration Global database
- 1Department of Soil Science and Engineering, Faculty of Agriculture, University of Maragheh, Maragheh, Iran
- 2Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Jülich, Germany
- 3ISMC International Soil Modeling Consortium, Institute of Bio and Geosciences Forschungszentrum Jülich, 52425 Jülich, Germany
- 4USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA
- 5Key Laboratory of Dryland Agriculture, Ministry of Agriculture, Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China
- 6Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Iran
- 7Department of Environmental Sciences, Urmia Lake Research Institute, Urmia University, Urmia, Iran
- 8Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Budapest, Hungary
- 9Environment Agency, Abu Dhabi, UAE
- 10Department of Natural Resources and Environment, Faculty of Agriculture, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan
- 11Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR5023 LEHNA, 69518, Vaulx-en-Velin, France
- 12Universidade Federal de Pernambuco, Centro Acadêmico do Agreste, Núcleo de Tecnologia, Caruaru, Brazil
- 13Hellenic Agricultural Organization, Soil and Water Resources Institute, 57400 Sindos, Greece
- 14Department of Physics (DFI), Federal University of Lavras (UFLA), P.O. Box 3037, CEP 37200-000, Lavras, Brazil
- 15Department of Soil Science, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
- 16Department of Geography, Geo-Informatics and Climatic Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda
- 17UMR 1402 INRA AgroParisTech Functional Ecology and Ecotoxicology of Agroecosystems, Institut National de la Recherche Agronomique, AgroParisTech B.P. 01, 78850 Thiverval-Grignon, France
- 18UMR 8148 IDES CNRS/Université Paris-Sud, XI Bât. 504, Faculté des Sciences 91405, Orsay CEDEX, France
- 19Innovative Groundwater Solutions (IGS), Victor Harbor, 5211, South Australia, Australia
- 20IFSTTAR, GERS, EE, 44344 Bouguenais, France
- 21Freie Universität Berlin, Department of Earth Sciences, Institute of Geographical Sciences, Malteserstr. 74-100, Lankwitz, 12249, Berlin, Germany
- 22Centre for Water Resource Systems, TU Wien, Karlsplatz 13, 1040 Vienna, Austria
- 23Institute of Hydraulic Engineering and Water Resources Management, TU Wien, Karlsplatz 13/222, 1040 Vienna, Austria
- 24Faculty of Agricultural and Environmental Sciences, University of Rostock, Germany
- 25Irstea, UE RiverLy, Lyon-Villeurbanne Center, 69625 Villeurbanne, France
- 26Pyrenean Institute of Ecology-CSIC, AV. Montañana 1005, Av. Victoria s/n. 50059 Zaragoza, 22700 Jaca, Huesca, Spain
- 27Soil Erosion and Degradation Research Group, Department of Geography, University of Valencia, Valencia, Spain
- 28Plant and Food Research, Mount Albert Research Station, Auckland, New Zealand
- 29Czech Technical University in Prague, Faculty of Civil Engineering, Thákurova 7, 166 29 Prague 6, Czech Republic
- 30Plant and Food Research, Palmerston North, New Zealand
- 31ISTO UMR 7327 Université d'Orléans, CNRS, BRGM, 45071 Orléans, France
- 32Department of Soil Management, UNESCO Chair on Eremology, Ghent University, Ghent, Belgium
- 33Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy
- 34UniRedentor University Center. BR 356, 25, Presidente Costa e Silva, Itaperuna, Rio de Janeiro, Brazil
- 35Embrapa Solos, Rua Jardim Botânico, 1.024, CEP 22040-060, Jardim Botânico, Rio de Janeiro, RJ, Brazil
- 36Department of Soil Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
- 37Dryland Agricultural Research Institute, Agricultural Research, Education and Extension Organization Maragheh, East Azerbaijan, Iran
- 38Scientific Member of Soil and Water Research Department, East Azerbaijan Agricultural and Natural Resources Research and Education center, Iran
- 39Agricultural Institute, Centre for Agricultural Research, Hungarian Academy of Sciences, Brunszvik str. 2., 2462 Martonvásár, Hungary
- 40Life and Environmental Sciences, University of California, Merced, USA
- 41Geographisches Institut, Universität Bonn, Bonn, Germany
- 42Institute of Plant Nutrition and Soil Science, Christian-Albrechts-Universität zu Kiel, Olshausenstr. 40, 24118 Kiel, Germany
- 43ARAID Researcher, Instituto Pirenaico de Ecología, Spanish National Research Council (IPE-CSIC), Avda. Llano de la victoria 16, Jaca (Huesca), 22700, Spain
- 44Enginereed and Geosystems Analysis Unit, Belgian Nuclear Research Centre, Mol, Belgium
- 45Soil, Water and Land Use Team, Wageningen Environmental Research, Wageningen UR, 6708PB Wageningen, the Netherlands
- 46Civil, Surveying and Environmental Engineering, the University of Newcastle, Callaghan 2308, Australia
- 47Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran
- 48Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur – 721302, West Bengal, India
- 49State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
- 50Research Soil Scientist, USDA Agricultural Research Service, Mandan, ND, USA
- 51Institute of Hydrology, Slovak Academy of Sciences, Bratislava, Slovakia
- 52Departamento de Suelo y Agua, Estación Experimental de Aula Dei (EEAD), Consejo Superior de Investigaciones Científicas (CSIC), P.O. Box 13034, 50080 Zaragoza, Spain
- 53ICAR-Central Arid Zone Research Institute, Regional Research Station, Kukma – 370105, Bhuj, Gujarat, India
- 54CSIRO Land and Water, Glen Osmond, South Australia, Australia
- 55Soil Science Graduate Program (ufsm.br/ppgcs), Federal University of Santa Maria, state of Rio Grande do Sul, Brazil
- 56Federal Institute of Education, Science and Technology of the Amazonas – IFAM, Campus Center of Manaus, Manaus, Brazil
- 57Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, Gwynedd LL57 2UW, UK
- 58ENGIE Research and Technologies, Simon Bolivardlaan 34, 1000 Brussels, Belgium
- 59Université catholique de Louvain, Earth and Life Institute-Environmental Sciences, Louvain-la Neuve, Belgium
- 60Department of Biological and Agricultural Engineering, 2117 TAMU, Texas A&M Univ., College Station, TX 77843-2117, USA
- 61Aarhus University, Department of Agroecology, Research Centre Foulum, Blichers Allé 20, P.O. Box 50, 8830 Tjele, Denmark
- 62Universidade Federal de Pernambuco (UFPE), Av. Prof. Moraes Rego, 1235-Cidade Universitária, Recife-PE-CEP: 50670-901, Brazil
- 63Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran
- 64Department of Soil Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Kurdistan Province, Iran
- 65Department of Soil Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
- 66Department of Hydrology, Geological Survey of Brazil (CPRM), Av. Pauster, 404. CEP 22290-240, Rio de Janeiro, Brazil
- 67Department of Water Resources and Environment, Federal University of Rio de Janeiro, Avenida Athos da Silveira Ramos, P.O. Box 68570, Rio de Janeiro, RJ, Brazil
- 68Soil and Water Research Department, Sistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zabol, Iran
- 69Department of Soil Science, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany
- 70Department of Earth and Environmental Sciences, Catholic University of Leuven, Geo-Institute, Celestijnenlaan 200E, 3001 Heverlee, Belgium
- 71GeoEnvironmental Research Group, University of Extremadura, Faculty of Philosophy and Letters, Avda. de la Universidad s/n, 10071 Cáceres, Spain
- 72Soil Science Department, Federal University of Santa Maria, state of Rio Grande do Sul, Brazil
- 73Instituto de Geomorfología y Suelos, Department of Geography, University of Málaga, 29071, Málaga, Spain
- 74Department of Physical Geography, Trier University, 54286 Trier, Germany
- 75Civil Engineering Program, Alberto Luiz Coimbra Institute for Postgraduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro, Avenida Athos da Silveira Ramos, Rio de Janeiro, RJ, Brazil
- 76Faculty of Agriculture, Tottori University, 4-101 Koyama-Minami, Tottori 680-8553, Japan
- 77Department of Materials and Life Science, Seikei University, 3-3-1, Kichijoji-kitamachi, Musashino, Tokyo 180-8633, Japan
- 78Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
- 79Plant and Environmental Sciences, New Mexico State University, Las Cruces, New Mexico, USA
- 80Japan International Research Center for Agricultural Science, Rural Development Division, Tsukuba, Japan
- 81Department of Agricultural and Bio-Resources Engineering, Faculty of Engineering, Ahmadu Bello University Zaria, Nigeria
- 82Institute for Land and Water Management Research, Federal Agency for Water Management, Pollnbergstraße 1, 3252 Petzenkirchen, Austria
- 83Department of Water Resources, ITC Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands
- 84Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, Yazd Province, Iran
- 85University of Córdoba, Department of Rural Engineering, 14071 Córdoba, Spain
- 86Soil Physics, Embrapa Soils, Rua Jardim Botânico, 1026, 22460-00 Rio de Janeiro, RJ, Brazil
- 87Department of Soil Science, Agriculture Faculty, University of Zanjan, Zanjan, Iran
- 88Department of Geography, Ruhr University Bochum, 44799 Bochum, Germany
- 89Engineering Faculty, Civil Engineering Department, Munzur University, Tunceli, Turkey
- 90Bureau of Economic Geology, John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin, University Station, Box X, Austin, TX, USA
- 91UFZ Helmholtz Centre for Environment Research, Monitoring and Exploration Technologies, Leipzig, Germany
- 92College of Resources and Environmental Engineering, Ludong University, Yantai 264025, China
- 93Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
- 94University of Pannonia, Georgikon Faculty, Department of Crop Production and Soil Science, Keszthely, Hungary
Correspondence: Mehdi Rahmati (email@example.com)
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.
Infiltration is the process by which water enters the soil surface and it is one of the key fluxes in the hydrological cycle and the soil water balance. Water infiltration and the subsequent redistribution of water in the subsurface are two important processes that affect the soil water balance (Campbell, 1985; Hillel, 2013; Lal and Shukla, 2004; Morbidelli et al., 2011) and influence several soil processes and functions including availability of water and nutrients for plants, microbial activity, erosion rates, chemical weathering, and soil thermal and gas exchange between the soil and the atmosphere (Campbell, 1985). Infiltration plays a definitive role in maintaining soil system functions and as it is a key process that controls several of the United Nations goals for sustainability (Keesstra et al., 2016). The generation of surface runoff, a key factor in controlling floods, is also directly related to the infiltration process. Water that cannot infiltrate in the soil becomes available for surface runoff. Two main mechanisms are responsible for the generation of excess water that produce overland flow: Dunne saturation excess and Hortonian infiltration excess (Sahoo et al., 2008). Dunne overland flow, or saturation excess, occurs when the soil profile is completely saturated and precipitation can no longer infiltrate into soil. The Dunne mechanism is more common to near-channel areas or is generated from partial areas of the hillslope where water tables are shallowest (Sahoo et al., 2008). On the other hand, Hortonian overland flow is characterized by rainfall intensities exceeding the infiltration rate of the soil. In other words, during a rainfall event, water infiltration at the soil surface and runoff are highly dependent on the boundary conditions, namely, the rainfall intensity and the soil hydraulic properties. If the rainfall intensity is less than the soil infiltrability, water will completely infiltrate into the soil without any runoff (Hillel, 2013). In this case, the infiltration rate align with the rainfall intensity. Otherwise, if the precipitation intensity exceeds the soil infiltration rate at a certain moment in time, excess water will be generated even if the soil profile is unsaturated. In this case water will pond on the soil surface and become available for surface runoff. If this occurs, the boundary condition at the soil surface undergoes a shift in the dominant flow process from one governed by capillary action to one governed by pressures of hydraulic head. Assuming that the water pressure heads remain constant at the soil surface, the infiltration rate is described by a decreasing function over time, tending towards the value of the hydraulic conductivity function for the water pressure head imposed at the soil surface (Angulo-Jaramillo et al., 2016; Chow et al., 1988). In the past decades, water infiltration tests, using either ponded or tension infiltrometers, have been developed to quantify the cumulative infiltration at the soil surface. In these cases, the 3-D axisymmetric water infiltration corresponds to an upper boundary defined by a constant water pressure head or a series of constant water pressure heads. The infiltration process is quantified by determining the amount of water which infiltrates, over time, from which the cumulative infiltration, I(t), (L), and the infiltration rate, i(t), (L T−1) can be derived. i(t) and I(t) are related to each other by derivation (Campbell, 1985; Hillel, 2013; Lal and Shukla, 2004):
As stated above, the infiltration rate i(t) is expected to decrease to a plateau defined by the value of the hydraulic conductivity corresponding to the imposed water pressure head plus a term related to radial water infiltration (Angulo et al., 2016). In the case of large rings, the final infiltration rate approaches the value of the hydraulic conductivity corresponding to the imposed water pressure head (gravity flow). Consequently, if water ponding is imposed at the surface, i(t) tends towards the saturated hydraulic conductivity. Infiltration into the soil is controlled by several factors including soil properties (e.g., texture, bulk density, initial water content), layering, slope, cover condition (vegetation, crust, and/or stone), rainfall pattern (Smith et al., 2002; Corradini et al., 2017), and time. As soil texture and soil surface conditions (e.g., cover) are independent of time at the scale of individual infiltration events, these characteristics can be assumed to be constant during the event. On the other hand, soil structure, especially at the soil surface, can rapidly change, for instance, due to tillage, grazing, or the destruction of soil aggregates by rain drop impact. In dry soils, initial infiltration rates are substantially higher than the saturated hydraulic conductivity of the surface layer due to capillary effects which control the sorptivity of the soil. However, as infiltration proceeds, the gradient between the pressure head at the soil surface and the pressure head below the wetting front reduces over time so that the infiltration rate finally reaches a constant value that approximates saturated hydraulic conductivity (Chow et al., 1988).
Infiltration measurements have been largely used to estimate soil saturated hydraulic conductivity. This soil property is a key factor to correctly describe all the components of the soil and land surface hydrological balance and is essential in the appropriate design of irrigation systems. Within the literature it is clear that extensive efforts have been made to estimate this property from basic soil properties using pedotransfer functions (PTFs). PTFs are knowledge-based rules or equations that relate simple soil properties to those properties of soil that are more difficult to obtain (Van Looy et al., 2017). Most of these efforts have been based on measurements made on samples of disturbed or undisturbed soil material. With this infiltration database, data are now made available that may contribute to better predicting the saturated soil hydraulic conductivity and demonstrate the effect of, for example, vegetation and land management on the parameters of interest.
The Richards (1931) equation, Eq. (2), written as a function of soil water content which is often referred to as the Fokker–Planck water diffusion equation, can be used to derive the closed-form expression of the infiltration rate in partially saturated soils.
where θ is the volumetric soil water content (L3 L−3); t is the time (T); z is the vertical depth position (L); K(θ) is the soil hydraulic conductivity (L T−1); and D(θ) is soil water diffusivity (L2 T−1), which is defined by Eq. (3) (Childs and Collis-George, 1950; Klute, 1952):
where h is the matric potential in head units (L). The exact relationships between soil water content, soil matric potential, and soil hydraulic conductivity are necessary to solve the Richards equation. Several solutions of the Richards equation and many empirical, conceptual, semi-analytical, and physically based models – e.g., Green and Ampt (1911), Philip (1957), Smith and Parlange (1978), Haverkamp et al. (1994), and Corradini et al. (2017) – have been introduced to describe the infiltration process over time, even for preferential flows, e.g., Lassabatere et al. (2014). Furthermore, several direct or indirect experimental systems have been introduced to measure soil infiltration in the laboratory or in the field under different conditions (Gupta et al., 1994; McKenzie et al., 2002; Mao et al., 2008a). Data obtained from these systems can also be used to deduce soil saturated hydraulic conductivity directly.
Methods developed to measure and quantify water infiltration in soil are generally time-consuming and costly. Therefore, PTFs have been developed and applied by many researchers – e.g., Jemsi et al. (2013), Parchami-Araghi et al. (2013), Kashi et al. (2014), Sarmadian and Taghizadeh-Mehrjardi (2014), and Rahmati (2017) – in order to easily parameterize infiltration models. However, these PTFs have been developed for specific regions, often limiting their applicability. As already mentioned, a large number of publications reporting soil infiltration data is available, but these data are dispersed in the literature and often difficult to access. Therefore, the aim of this data paper is to present and make available a collection of infiltration data digitized from available literature and from published or unpublished data provided directly by researchers around the world. These data are accompanied by metadata, which provide information about the location of the infiltration measurement, soil properties, and land management. Finally, we will provide some first results highlighting the suitability of the database for further research. The main article is also accompanied by a supplement providing more detailed information about the different methodologies to measure soil infiltration. This is added because many of readers are likely not well versed in soil infiltration and its limitations in measurement and modeling. For more detailed information on this, readers could refer to Smith et al. (2002), Corradini et al. (2017), and Hopmans et al. (2006).
2.1 Data collection
We collected infiltration measurements from different countries or regions by contacting the data owners or by extracting infiltration data from published literature (Fig. 1). To do this, a data request was sent to potential data owners through different forums and email exchanges. The flyer asked data owners to cooperate in the development of the Soil Water Infiltration Global (SWIG) database by providing infiltration data as well as metadata about experimental conditions (e.g., initial soil moisture content at the start of the experiment and method used), soil properties, land use, topography, geographical coordinates of the sites, and any other relevant information to interpret the data and to increase the value of the database. Infiltration data reported in the literature were digitized and included in the database together with additional information provided in these papers. The digitization approach is discussed in Sect. 2.2. In total, 5023 single infiltration curves were collected, of which 510 infiltration curves were digitized from 74 published papers (Table 1) and 4513 were provided by 68 different research teams (Table 2), being published or unpublished data. The references and correspondences for data supplied by direct communications with researchers are also reported in Table 2. Therefore, users may refer to these references for detailed information about the applied methods or procedures.
2.2 Data digitization
In order to digitize infiltration curves reported in the literature, screenshots of the relevant plots were taken, and figures were imported into the plot digitizer 2.6.8 (Huwaldt and Steinhorst, 2015). First, the origin of the axes and the highest x and y values were defined and the diagram plane was spanned. Then, all point values were picked out and an output table with the x–y pairs (time vs. infiltration rate or cumulative infiltration) was generated and stored.
2.3 Database structure
The SWIG database is prepared in *.xlsx with a backup file in *.csv formats containing several datasets. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018) . The first dataset, named “I_cm”, contains cumulative infiltration data in centimeter units and is referred to as “Ixxxx”, whereby “xxxx” is the identifier of the individual infiltration test. The corresponding time intervals in hours for the infiltration data are labeled “T_Hour” and named “Txxxx”. The constant or varying pressure or tension heads (if any) during infiltration measurements are also reported in another dataset named “Tension_cm”. The database also contains additional variables and information relevant to the infiltration data provided by data owners or digitized from articles, as listed in Table 3, and which is labeled “Metadata”. Additional soil properties were determined by different standards; therefore, data harmonization might be needed for some of those, especially in the case of water content at field capacity, pH, or wet-aggregate stability. Further information on measurement methods is available from references of the data. Since the geometric mean diameter (dg) and standard deviation (Sg) of soil particle sizes are rarely measured, both parameters were computed using the following equations (Shirazi and Boersma, 1984):
where fi is the percent of total soil mass having diameters equal to or less than the arithmetic mean of interval limits (Di) that define three main fractions (i) of clay, silt, and sand with mean values of 0.001, 0.026, and 1.025 mm, respectively. For the infiltration data, where the soil texture is unknown, dg and Sg could not be calculated and the data field in the database was left empty. The database also contains the locations of the experimental sites in another dataset named “Locations” that provides the approximate latitude and longitude in decimal degree (dd.dd) format. Table 2 is also provided in the SWIG database in two other worksheets named “Ref. for digitized data” and “Ref. for data provided by owner”.
3.1 Spatial and temporal data coverage
The SWIG database (Rahmati et al., 2018) consists of 5023 soil water infiltration measurements spread over nearly all continents (Fig. 2). Data were derived from 54 countries (Table 4). The largest number of data sources were provided by scientists in Iran (n = 38), China (n = 23), and the USA (n = 15), whereby one data source might contain several water infiltration measurements. The SWIG database covers measurements from 1976 to 2017. A sparse coverage was obtained for the higher latitudes of the Northern Hemisphere (above 60∘) including Norway, Finland, Sweden, Iceland, Greenland, and Russia. The lack of reports with infiltration data from most countries of the former Soviet Union as well as the Sahelian and Saharan countries is also notable, as well as the small number of infiltration data from Australia. Figure 3 shows the number of samples by climatic zone (Rubel et al., 2017; Kottek et al., 2006). The majority of the data are from warm temperate, fully humid climate (49 %); arid steppe climate and warm temperate climate with dry summer are the second and third most represented climate zones with 22 and 12 %, respectively. Figures 4 and 5 show the frequency of experimental sites, respectively, by the World Reference Base (WRB) (IUSS, 2006) and USDA soil taxonomy systems (USDA, 2014) based on the SoilGrids dataset (Hengl et al., 2017). Regarding the WRB classification system (Fig. 4), in total, 35 WRB reference soil subgroups are included among experimental sites, where 55 % of the experimental sites comprised four subgroup classes of Haplic Acrisols (8 %), Haplic Luvisols (11 %), Haplic Calcisols (15 %), and Haplic Cambisols (21 %). A total of 29 soil suborders classes of USDA soil taxonomy are included in this study (Fig. 5) with Udalfs (9 %), Orthents (9 %), and Ustolls (9 %). Thus, the wide spatial and temporal distribution of infiltration data from this database provides a comprehensive view of the infiltration characteristics of many soils in the world which can be used in future studies.
3.2 Analysis of the database using soil properties
Textural information (clay, silt, and sand content) is available for 3842 out of 5023 collected infiltration curves (∼ 76 %). The infiltration measurements cover nearly all soil textural classes according to the USDA classification, except for the sandy clay and silt textural class (Fig. 6), which makes the SWIG database a valuable data source for comprehensive studies. To complete the large dataset, the open-access SWIG database might be amended with information regarding those soils poorly or altogether unrepresented by the existing database, including those not usually considered by infiltration studies, such as soils with extremely high stone content (Poesen, 2018). Loam, sandy loam, silty loam, and clay loam contributed with 19, 18, 14, and 13 % (Table 5) to the infiltration measurements, respectively. Table 5 shows that infiltration measurements are almost equally distributed among textures when these are categorized in three major classes: course- (1092), medium- (1238), and fine- to moderately fine-textured soils (1447). Table 6 reports on the soil properties that are available in the SWIG database and it gives some simple statistics such as mean, minimum, maximum, median, and coefficient of variation. Bulk density (available for 66 % of infiltration measurements) and organic carbon content (available for 62 % of infiltration measurements) are two other soil properties besides texture that have the highest frequency of availability. Saturated hydraulic conductivity, initial soil water content, saturated soil water content, calcium carbonate equivalent, electrical conductivity, and pH are available in 22 to 38 % of infiltration data. The other soil properties have a frequency lower than 10 %.
3.3 Infiltration measurements in the SWIG database
Different instruments were used to measure soil water infiltration (Table 8). About 32 % (1595 out of 5023) of the measurements were carried out using different types of ring infiltrometers. The most frequently used methods are the disc infiltrometer methods (disc, mini-disc, and micro-disc, hood, and tension infiltrometers), which have been used in about 51 % of the experiments. About 5 % of the data were submitted to the database without specifying the measurement method (251 infiltration tests) and around 12 % of the measurements were carried out with other methods not listed above (Table 7).
Fr: frequency (%), Min: minimum, Max: maximum, CV: coefficient of variation.
3.4 Land use classes represented in the SWIG database
Land use is known to potentially impact soil structure and then water infiltration into soils (e.g., Ilstedt et al., 2007; Waterloo et al., 2007). Consequently, we collected information on the type of land use at all experimental sites where available. In general, the type of land use was reported in 3818 out of 5023 infiltration curves (∼ 76 %) and this information is reported in the Metadata dataset. For simplicity, we grouped all reported land use types into 22 major groups (Table 8). A frequency analysis showed that agricultural land use, i.e., cropped land, irrigated land, dryland, and fallow land, is the most frequently reported land use in the database with about 53 % (2019 out of 3818) of all land uses. With 22 %, grasslands are the second most frequently represented land use type. Pasture with 6 % and forest with 5 % are ranked as the third- and fourth-largest reported land use types. The 18 remaining land use types all together cover only 545 experimental sites (less than 15 %).
3.5 Estimating infiltration parameters from infiltration measurements
In order to predict infiltration parameters from infiltration measurements, we classified the SWIG database infiltration curves in two groups: (i) infiltration curves that were obtained under the assumption of 1-D infiltration and (ii) infiltration curves that were obtained under 3-D flow conditions. We fitted the three-parameter infiltration equation of Philip (Kutílek and Krejča, 1987), Eq. (6), to the 1-D experimental data and the simplified form of Haverkamp et al. (1994), Eq. (7), to the 3-D experimental data:
We reduced the number of parameters in Eq. (6) by defining (Philip, 1957) and A2=A where A was assumed to be a constant. In Eq. (7), we put β=0.6 (Angulo-Jaramillo et al., 2000) and the second term between brackets on the right-hand side was assumed to be a constant. Therefore, we simplified the equations as follows:
In our analysis, we assumed that double-ring infiltrometer measurements result in 1-D infiltration conditions, while the different types of disc infiltration and single-ring infiltrometer measurements lead to 3-D flow conditions that can be captured by Eq. (9). As 1-D or 3-D infiltration conditions are not guaranteed for measurements made with rainfall simulators, Guelph permeameters, Aardvark permeameters, linear and point source methods, and hood infiltrometer measurements, these infiltration curves were not considered in our first analysis. By excluding these methods, 596 infiltration curves were excluded from the fitting to Eqs. (8) and (9). In addition, 251 infiltration curves were also excluded from the fitting to Eqs. (8) and (9) as no indication was available on the measurement method used. In total, 4178 infiltration curves were included in our analysis, of which 828 infiltration curves reflected 1-D and 3350 were considered as the results of 3-D infiltration. As no sufficient information was available on the properties of the sand contact layer, we did not correct 3-D infiltration measurements. Finally, the selected infiltration curves were fitted to Eq. (8) or (9) using the lsqnonlin command in Matlab™.
The fitting results of Eq. (8) to the single infiltrometer data are shown in Table 9. R2 values were higher than 0.9 in 97 % of the cases and higher than 0.99 in 77 % of the cases. Fitting Eq. (9) to the 3-D infiltration curves data, R2 values higher than 0.9 and 0.99 were obtained in 94 and 68 % of the cases, respectively. The statistics for the fitting process as well as the fitted parameters of two mentioned models are reported in the SWIG database in an additional dataset labeled “Statistics”. For infiltration curves excluded from the analysis, an empty cell is reported.
a The number soils included in calculation. bns: insignificant; c**: significant at 1 % probability level. SD: standard deviation.
The average values of estimated Ksat and sorptivity (S), using Eq. (8) or (9) as well as measured Ksat for different soil texture classes extracted from the current database, are reported in Table 10. The measured values of Ksat were obtained by other means by the contributors and tabulated in the SWIG database. More detailed information of how Ksat was calculated in individual cases can be found in the references linked to those data points. Comparison between estimated (Ksat-es) and measured (Ksat-m) values of Ksat (Table 10) reveals that there is reasonably good agreement between measurements and estimation, except for loamy sand (with mean Ksat-es=62 cm h−1 vs. Ksat-m=25 cm h−1), sandy loam (with mean Ksat-es=32 cm h−1 vs. Ksat-m=41 cm h−1), silt loam (with mean Ksat-es=27 cm h−1 vs. Ksat-m=3 cm h−1), and silty clay (with mean Ksat-es=26 cm h−1 vs. Ksat-m=45 cm h−1) textural classes. However, the only significant difference between measured and estimated Ksat values was found for the silt loam textural class (Table 10) applying an independent t test.
We also compared our estimated Ksat values from the infiltration measurements from the SWIG database with Ksat values from databases that have been published in the literature (Table 11). The validity of our estimated Ksat values is confirmed by comparing the order of magnitude of the difference between these values, and those tabulated in previous studies, for the various different soil classes. Some of these databases like that of Clapp and Hornberger (1978) and Cosby et al. (1984) have been used to parameterize land surface models. Most of the Ksat values in the listed databases have been obtained from laboratory-scale measurements often performed on disturbed soil samples. In most of the reported databases Ksat is controlled by texture, with the highest mean values obtained for the coarse-textured soils and the lowest mean values for the fine-textured soils. This is not the case for the Ksat values obtained from the SWIG database. Clayey soils have a mean value that is similar to the coarser textured soils. This may be partly explained by the fact that the measurements collected in the SWIG database are obtained from field measurements on undisturbed soils. It was observed that the standard deviation of Ksat in the SWIG database is typically larger than the standard deviations obtained from the databases in the literature. This indicates that texture is apparently not the most important control on Ksat values. However, one would also pose that much of the lack of correlation between soil texture and predicted Ksat from the SWIG database is related to the lack of soil structural information, such as macro porosity quantification or other possible soil attributes. Indeed, many of the datasets presented in our paper on saturated and near-saturated flow can be used to infer the state of the soil's structure, namely its macroporosity, by using the slope of the near-saturated conductivity curve, via Philip's “flow-weighted mean pore-size” analysis. White and Sully (1987) have discussed this in a great detail. Zhang et al. (2015) is another example of where tension infiltrometers can be used to describe the temporal dynamics of the macroporosity which characterizes soil structure. This could inspire researchers to collect such information when conducting additional soil infiltration measurements and include this in the database in the future. This finding indicates that present parameterization in current land surface models, which are mainly based on texture, may severely underestimate the variability of Ksat. In addition, it shows that also mean values are not dominantly controlled by textural properties. Other land surface properties such as land use and crusting may, in fact, be much more important.
3.6 Exploring the SWIG database using principal component analysis
In order to demonstrate the potential of the SWIG database for analyzing infiltration data and for developing pedotransfer functions, principal component analysis (PCA) was performed and biplots were generated to show both the observations and the original variables in the principal component space (Gabriel, 1971).
In a biplot, positively correlated variables are closely aligned with each other and the larger the arrows the stronger the correlation. Arrows that are aligned in opposite directions are negatively correlated with each other and the magnitude of the arrows is again a measure of the strength of the correlation. Arrows that are aligned 90∘ to each other show typically no correlation. Figures 7 and 8 show the results of two PCAs. The first PCA (Fig. 7) shows the relationship between soil textural properties, S and Ksat, based on 3267 infiltration measurements. The first two principal components explain 74.5 % of the variability in the data. Figure 7 shows a positive correlation between Ksat and S (0.527) and the largest values for both variables are found in clay soils. Clay content appears to only be weakly correlated with Ksat and S as is also shown by the correlation coefficients of 0.112 and 0.025, respectively. Figure 8 shows the biplot of soil textural properties with Ksat, S, organic carbon content, and bulk density in the principal component space – based on 1910 infiltration measurements. The first two principal components still explain 55 % of the variability. Neither S nor Ksat showed appreciable correlations with available soil properties. Only Ksat and S are correlated (arrows are aligned but small) with a value of 0.29. Organic carbon and bulk density show a negative correlation with a calculated value equal to −0.51. It also shows that, for example, the sandy clay loam textural class (yellow dots) shows a wide spread in organic matter content and bulk densities. These analyses show that the examined basic soil properties do not contain enough information to properly estimate Ksat and S. However, the SWIG database provides additional information such as land use, initial water content, and slope that might prove to be good predictors. A further analysis in this respect is however beyond the scope of this paper. More importantly, the present analysis in combination with the results provided in Table 11 shows that a texture-dominated derivation of Ksat values, as implemented in most land surface models, does not provide adequate means to estimate Ksat.
3.7 Potential error and uncertainty in the SWIG database
Similar to any other databases, the data presented in the SWIG database may be subject to different error sources and uncertainties. These include (1) transcription errors that occurred when implementing the measurement data into the EXCEL spreadsheets, (2) inaccuracy and uncertainties in determining related soil properties such as textural properties, (3) violation of the underlying assumption when performing the experiments, and (4) uncertainty (variability) in estimated soil hydraulic properties due to the different measurement methods. Unfortunately, none of these errors or uncertainty sources are under the control of the SWIG database authors, and quantification of these sources is often difficult, since the required information is often lacking. The uncertainty and variability related to the applied measurement techniques for estimated soil hydraulic properties may be assessed as information on the applied techniques is available; however, some of these methods may only have been used in few cases, making a statistical analysis difficult.
With respect to the transcription error, a strong effort has been made to double-check data transcription to prevent or at least to minimize any probable error of this nature. Values of soil properties such as textural composition are known to vary strongly between different laboratories and measurement methods. This is especially true for the finer textural classes like clay. Unfortunately, information on the measurement used to determine soil properties is mostly lacking or insufficient to assess the magnitude of errors or biases. Internationally, there are a number of standard methods used to measure soil properties and several methods may have been applied to measure the reported soil properties. In this regard, no conversion has been made and only raw data are reported in the database. However, we have supplied the references for all data (where available) that can be used to ascertain which methodologies were used, if so desired. Although supplying such information for each soil property may facilitate the use of the database, it would have required considerable additional work that could not be performed at this stage of development. Such additions could form the basis of a second version of the database that any readers should feel free to commence.
The uncertainty with respect to the effect of measurement techniques on quantifying the infiltration process itself may be analyzed from the SWIG database as it provides information on the type of measurement technique used. This analysis is again beyond the scope of this paper. Potential error and uncertainty sources with respect to the use of different measurements are discussed in the Supplement. The uncertainty of estimated soil hydraulic properties from infiltration measurements may be strongly controlled by the person performing the experiment but may also be due the different measurement windows of the methods in terms of measurement volume. The SWIG database provides information to quantify uncertainties introduced by difference in measurement volume and this analysis will be closely related to the assessment of the representative elementary volume, REV (see, for example, the work of Pachepsky on the scaling of saturated hydraulic conductivity).
Careful interpretation of the data, with respect to the details of the experimental and soil conditions, is also required when utilizing the SWIG database. For instance, the cases of soils coded 1211–1420 may at first seem odd, as they display very low infiltration rates for soils of a very high (> 95 %) sand content; however, these unusual findings are explained by the soils being recorded as displaying water repellant characteristics. Another example is estimated values of Ksat from clayey soils showing high values of Ksat (e.g., soils coded 3746 to 3833 in the SWIG database). The Ksat values for these soils were obtained using the single-ring infiltrometer method (Gonzalez-Sosa et al., 2010; Braud, 2015; Braud and Vandervaere, 2015) and were conducted in the field under ponded conditions, with vegetation cut but roots left in place. Macropores could have been activated, leading to an infiltration rate much higher than expected for clayey soils. There were also instances of very high values being obtained for forested land uses, and sometimes for grassland, which is probably explained by the visible cracks in the soil surface present in those cases
3.8 Research potentials of the SWIG database
We envision that the SWIG database offers a unique opportunity and information source to (1) evaluate infiltration methods and to assess their value in deriving soil hydraulic properties, (2) test different models and concepts for point-scale and grid-scale infiltration processes, (3) develop pedotransfer functions to estimate soil hydraulic properties such as the Mualem–van Genuchten parameters, (4) identify controls on infiltration processes, (5) validate global predictions of infiltration from land surface models, (6) study more complex processes like preferential flow in soils, and (7) highlight the state-of-the-art understanding of the relationships between infiltration and several soil surface characteristics; for example, the SWIG database has already contributed to the scope of Morbidelli et al. (2018) to advance the knowledge of infiltration over sloping surfaces.
We are confident that the SWIG database is just a first step in collecting and archiving infiltration data and we expect that increasing amounts of data will become available in the near future. These data will be archived in the SWIG database and thus made available to the worldwide research community. In this regard, we are interested in receiving existing or newly measured infiltration curves and for this purpose the corresponding author will serve as point of contact or data can be made available through the International Soil Modeling Consortium, ISMC (https://soil-modeling.org/, last access: 1 July 2018), for further archiving in the SWIG database.
All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference and are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). We add a disclaimer that the database is for public domain use only and can be copied freely by referencing it.
We have collected 5023 infiltration curves from field experiments from all over the world covering a broad range of soils, land uses, and climate regions. We estimated saturated hydraulic conductivity, Ksat, and sorptivity from more than 3000 infiltration curves and compared estimated Ksat values with values from different databases published in the literature. We showed that contrary to the assumption made in many land surface and global climate models, texture is not the main controlling factor for Ksat. In addition, the variability of Ksat derived from these field measurements is considerably larger than reported in the literature. The collected infiltration curves were archived as the SWIG database on the PANGAEA platform and are therefore available worldwide. The data are structured into *.xlsx and *.csv files and include metadata information for further use. Data analysis revealed that infiltration curves are lacking for clayey, sandy-textured, and stony soils. Also infiltration curve data are lacking for the northern and permafrost regions. Here, additional efforts are needed to collect more data as these regions are particularly sensitive to climate change, which will clearly affect the soil hydrology.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-10-1237-2018-supplement.
The idea of globally collecting soil infiltration data was put forward by MR and HV. Published data from literature were digitized by MR, LW, NM, and MK. Data contributors are MR, YAP, LM, SHS, HK, ZH, WAY, AAA, MZA, RAJ, ACDA, GA, RAA, HA, YB, JBA, BB, FB, GB, KB, IB, CC, AC, MC, RC, MC, BC, YC, WC, CC, APC, MBdO, JRdM, MFD, HE, IE, AF, AF, NF, MG, MHG, TAG, SG, EGH, RH, JJJ, DJ, SDK, HK, MKH, MKJ, LL, XL, MAL, LL, MVL, DM, DM, MSM, JDdOM, MRM, JM, FM, MHM, BPM, MPM, SM, RM, DMF, AAM, MRM, SBM, HM, KN, MRN, MVO, TBOF, MRPR, AP, SP, PEP, TP, MP, DJR, SR, MR, FPR, DR, JRC, OCRF, TS, HS, CS, RS, BS, MS, NS, ESM, MS, SS, RS, AAS, PS, ZS, RTM, ET, WGT, ARV, MV, TV, IV, JV, SW, TW, DY, MHY, SZ, YZe, YZh, and HZ. The data were collected by MR, HV, LW, and KVL. The data analysis was conceived, designed, and performed by MR, HV, LW, JV, SHS, CM, KVL, BT, FM, and RTM. The article was written by MR, HV, LW, LM, and HK. The article was actively revised several times by MR, LW, HV, YAP, MHY, SHS, MS, JP, ZH, AC, YC, LL, FM, RM, DMF, RS, WGT, HA, NS, RAA, IB, FPR, and SR. All authors checked the accuracy and/or commented on the contents of the paper.
The authors declare that they have no conflict of interest.
First author thanks the International and Scientific Cooperation Office of the University of Maragheh, Iran, as well as the research committee and board members of the university for their assistance in conducting the current work.
The financial support received from the Forschungszentrum Jülich GmbH is gratefully acknowledged by the first author.
Authors gratefully thank the International Soil Modeling Consortium (ISMC) and the International Soil Tillage Research Organization (ISTRO) for their help in distributing our call for data among researchers throughout the world.
Parts of data were gathered from the work that was supported by the UK–China Virtual Joint Centre for Improved Nitrogen Agronomy (CINAg, BB/N013468/1), which is jointly supported by the Newton Fund, via UK BBSRC and NERC.
The French Claduègne and Yzeron datasets were acquired during the ANR projects FloodScale (ANR-2011-BS56-027) and AVuUR (ANR-07-VULN-01), respectively.
Parts of the database were made available through research work carried out in the framework of LIFE+ projects funded by the EC.
The support of the Spanish Ministry of Economy through project CGL2014-53017-C2-1-R is acknowledged.
The support of the Czech Science Foundation through project no. 16-05665S is acknowledged.
The support of the Slovak Research and Development Agency through project no. APVV-15-0160 is acknowledged.
Authors are grateful to Atilla Nemes, Jan W. Hopmans,
and Marnik Vanclooster for their time and attention in reviewing and
commenting on this article.
Edited by: David Carlson
Reviewed by: Marnik Vanclooster and Attila Nemes
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