Earth Syst. Sci. Data, 10, 1237–1263, 2018
https://doi.org/10.5194/essd-10-1237-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Development and analysis of the Soil Water
Infiltration Global database
Mehdi Rahmati1,2, Lutz Weihermüller2,3, Jan Vanderborght2,3, Yakov A. Pachepsky4, Lili Mao5,
Seyed Hamidreza Sadeghi6, Niloofar Moosavi2, Hossein Kheirfam7, Carsten Montzka2,3,
Kris Van Looy2,3, Brigitta Toth8,94, Zeinab Hazbavi6, Wafa Al Yamani9, Ammar A. Albalasmeh10,
Ma’in Z. Alghzawi10, Rafael Angulo-Jaramillo11, Antônio Celso Dantas Antonino12,
George Arampatzis13, Robson André Armindo14, Hossein Asadi15, Yazidhi Bamutaze16,
Jordi Batlle-Aguilar17,18,19, Béatrice Béchet20, Fabian Becker21, Günter Blöschl22,23, Klaus Bohne24,
Isabelle Braud25, Clara Castellano26, Artemi Cerdà27, Maha Chalhoub17, Rogerio Cichota28,
Milena Císlerová29, Brent Clothier30, Yves Coquet17,31, Wim Cornelis32, Corrado Corradini33,
Artur Paiva Coutinho12, Muriel Bastista de Oliveira34, José Ronaldo de Macedo35,
Matheus Fonseca Durães14, Hojat Emami36, Iraj Eskandari37, Asghar Farajnia38, Alessia Flammini33,
Nándor Fodor39, Mamoun Gharaibeh10, Mohamad Hossein Ghavimipanah6, Teamrat A. Ghezzehei40,
Simone Giertz41, Evangelos G. Hatzigiannakis13, Rainer Horn42, Juan José Jiménez43,
Diederik Jacques44, Saskia Deborah Keesstra45,46, Hamid Kelishadi47, Mahboobeh Kiani-Harchegani6,
Mehdi Kouselou1, Madan Kumar Jha48, Laurent Lassabatere11, Xiaoyan Li49, Mark A. Liebig50,
Lubomír Lichner51, María Victoria López52, Deepesh Machiwal53, Dirk Mallants54,
Micael Stolben Mallmann55, Jean Dalmo de Oliveira Marques56, Miles R. Marshall57, Jan Mertens58,
Félicien Meunier59, Mohammad Hossein Mohammadi15, Binayak P. Mohanty60,
Mansonia Pulido-Moncada61, Suzana Montenegro62, Renato Morbidelli33, David Moret-Fernández52,
Ali Akbar Moosavi63, Mohammad Reza Mosaddeghi47, Seyed Bahman Mousavi1, Hasan Mozaffari63,
Kamal Nabiollahi64, Mohammad Reza Neyshabouri65, Marta Vasconcelos Ottoni66,
Theophilo Benedicto Ottoni Filho67, Mohammad Reza Pahlavan-Rad68, Andreas Panagopoulos13,
Stephan Peth69, Pierre-Emmanuel Peyneau20, Tommaso Picciafuoco22,33, Jean Poesen70,
Manuel Pulido71, Dalvan José Reinert72, Sabine Reinsch57, Meisam Rezaei32,93, Francis Parry Roberts57,
David Robinson57, Jesús Rodrigo-Comino73,74, Otto Corrêa Rotunno Filho75, Tadaomi Saito76,
Hideki Suganuma77, Carla Saltalippi33, Renáta Sándor39, Brigitta Schütt21, Manuel Seeger74,
Nasrollah Sepehrnia78, Ehsan Sharifi Moghaddam6, Manoj Shukla79, Shiraki Shutaro80,
Ricardo Sorando25, Ajayi Asishana Stanley81, Peter Strauss82, Zhongbo Su83,
Ruhollah Taghizadeh-Mehrjardi84, Encarnación Taguas85, Wenceslau Geraldes Teixeira86,
Ali Reza Vaezi87, Mehdi Vafakhah6, Tomas Vogel29, Iris Vogeler28, Jana Votrubova29, Steffen Werner88,
Thierry Winarski11, Deniz Yilmaz89, Michael H. Young90, Steffen Zacharias91, Yijian Zeng83,
Ying Zhao92, Hong Zhao83, and Harry Vereecken2,3
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
Published by Copernicus Publications.
1238 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
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
Earth Syst. Sci. Data, 10, 1237–1263, 2018 www.earth-syst-sci-data.net/10/1237/2018/
M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1239
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
www.earth-syst-sci-data.net/10/1237/2018/ Earth Syst. Sci. Data, 10, 1237–1263, 2018
1240 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
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 (mehdirmti@gmail.com)
Received: 28 January 2018 – Discussion started: 6 March 2018
Revised: 12 June 2018 – Accepted: 13 June 2018 – Published: 10 July 2018
Abstract. 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 con-
tinents in the SWIG database. These data were either provided and quality checked by the scientists who per-
formed 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 mea-
surements (∼ 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 dom-
inant 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 avail-
able 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.
Earth Syst. Sci. Data, 10, 1237–1263, 2018 www.earth-syst-sci-data.net/10/1237/2018/
M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1241
1 Introduction defined by a constant water pressure head or a series of con-
stant water pressure heads. The infiltration process is quan-
tified by determining the amount of water which infiltrates,
Infiltration is the process by which water enters the soil sur- over time, from which the cumulative infiltration, I (t), (L),
face and it is one of the key fluxes in the hydrological cycle and the infiltration rate, i(t), (L T−1) can be derived. i(t) and
and the soil water balance. Water infiltration and the subse- I (t) are related to each other by derivation (Campbell, 1985;
quent redistribution of water in the subsurface are two im- Hillel, 2013; Lal and Shukla, 2004):
portant processes that affect the soil water balance (Camp-
bell, 1985; Hillel, 2013; Lal and Shukla, 2004; Morbidelli et dI (t)
al., 2011) and influence several soil processes and functions i(t)= . (1)dt
including availability of water and nutrients for plants, mi-
crobial activity, erosion rates, chemical weathering, and soil As stated above, the infiltration rate i(t) is expected to de-
thermal and gas exchange between the soil and the atmo- crease to a plateau defined by the value of the hydraulic
sphere (Campbell, 1985). Infiltration plays a definitive role conductivity corresponding to the imposed water pressure
in maintaining soil system functions and as it is a key pro- head plus a term related to radial water infiltration (Angulo
cess that controls several of the United Nations goals for et al., 2016). In the case of large rings, the final infiltration
sustainability (Keesstra et al., 2016). The generation of sur- rate approaches the value of the hydraulic conductivity corre-
face runoff, a key factor in controlling floods, is also directly sponding to the imposed water pressure head (gravity flow).
related to the infiltration process. Water that cannot infil- Consequently, if water ponding is imposed at the surface,
trate in the soil becomes available for surface runoff. Two i(t) tends towards the saturated hydraulic conductivity. In-
main mechanisms are responsible for the generation of ex- filtration into the soil is controlled by several factors includ-
cess water that produce overland flow: Dunne saturation ex- ing soil properties (e.g., texture, bulk density, initial water
cess and Hortonian infiltration excess (Sahoo et al., 2008). content), layering, slope, cover condition (vegetation, crust,
Dunne overland flow, or saturation excess, occurs when the and/or stone), rainfall pattern (Smith et al., 2002; Corradini
soil profile is completely saturated and precipitation can no et al., 2017), and time. As soil texture and soil surface con-
longer infiltrate into soil. The Dunne mechanism is more ditions (e.g., cover) are independent of time at the scale of
common to near-channel areas or is generated from partial individual infiltration events, these characteristics can be as-
areas of the hillslope where water tables are shallowest (Sa- sumed to be constant during the event. On the other hand, soil
hoo et al., 2008). On the other hand, Hortonian overland flow structure, especially at the soil surface, can rapidly change,
is characterized by rainfall intensities exceeding the infiltra- for instance, due to tillage, grazing, or the destruction of
tion rate of the soil. In other words, during a rainfall event, soil aggregates by rain drop impact. In dry soils, initial in-
water infiltration at the soil surface and runoff are highly de- filtration rates are substantially higher than the saturated hy-
pendent on the boundary conditions, namely, the rainfall in- draulic conductivity of the surface layer due to capillary ef-
tensity and the soil hydraulic properties. If the rainfall inten- fects which control the sorptivity of the soil. However, as in-
sity is less than the soil infiltrability, water will completely filtration proceeds, the gradient between the pressure head at
infiltrate into the soil without any runoff (Hillel, 2013). In the soil surface and the pressure head below the wetting front
this case, the infiltration rate align with the rainfall intensity. reduces over time so that the infiltration rate finally reaches
Otherwise, if the precipitation intensity exceeds the soil infil- a constant value that approximates saturated hydraulic con-
tration rate at a certain moment in time, excess water will be ductivity (Chow et al., 1988).
generated even if the soil profile is unsaturated. In this case Infiltration measurements have been largely used to esti-
water will pond on the soil surface and become available for mate soil saturated hydraulic conductivity. This soil property
surface runoff. If this occurs, the boundary condition at the is a key factor to correctly describe all the components of the
soil surface undergoes a shift in the dominant flow process soil and land surface hydrological balance and is essential in
from one governed by capillary action to one governed by the appropriate design of irrigation systems. Within the lit-
pressures of hydraulic head. Assuming that the water pres- erature it is clear that extensive efforts have been made to
sure heads remain constant at the soil surface, the infiltration estimate this property from basic soil properties using pedo-
rate is described by a decreasing function over time, tending transfer functions (PTFs). PTFs are knowledge-based rules
towards the value of the hydraulic conductivity function for or equations that relate simple soil properties to those prop-
the water pressure head imposed at the soil surface (Angulo- erties of soil that are more difficult to obtain (Van Looy et
Jaramillo et al., 2016; Chow et al., 1988). In the past decades, al., 2017). Most of these efforts have been based on mea-
water infiltration tests, using either ponded or tension infil- surements made on samples of disturbed or undisturbed soil
trometers, have been developed to quantify the cumulative material. With this infiltration database, data are now made
infiltration at the soil surface. In these cases, the 3-D axisym- available that may contribute to better predicting the satu-
metric water infiltration corresponds to an upper boundary rated soil hydraulic conductivity and demonstrate the effect
www.earth-syst-sci-data.net/10/1237/2018/ Earth Syst. Sci. Data, 10, 1237–1263, 2018
1242 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
of, for example, vegetation and land management on the pa- a supplement providing more detailed information about the
rameters of interest. different methodologies to measure soil infiltration. This is
The Richards (1931) equation, Eq. (2), written as a func- added because many of readers are likely not well versed in
tion of soil water content which is often referred to as the soil infiltration and its limitations in measurement and mod-
Fokker–Planck water diffusion equation, can be used to de- eling. For more detailed information on this, readers could
rive the closed-form expression of the infiltration rate in par- refer to Smith et al. (2002), Corradini et al. (2017), and Hop-
tially satu(rated soils. ) mans et al. (2006).
∂θ ∂ ∂θ
= Dz (θ ) +Kz (θ ) , (2) 2 Method and materials
∂t ∂z ∂z
where θ is the volumetric soil water content (L3 L−3); t is 2.1 Data collection
the time (T); z is the vertical depth position (L); K(θ ) is the We collected infiltration measurements from different coun-
soil hydraulic conductivity (L T−1); and D(θ ) is soil water tries or regions by contacting the data owners or by extract-
diffusivity (L2 T−1), which is defined by Eq. (3) (Childs and ing infiltration data from published literature (Fig. 1). To do
Collis-George, 1950; Klute, 1952): this, a data request was sent to potential data owners through
∂h different forums and email exchanges. The flyer asked data
Dz (θ )=Kz (θ ) , (3) owners to cooperate in the development of the Soil Water In-
∂θ filtration Global (SWIG) database by providing infiltration
where h is the matric potential in head units (L). The exact data as well as metadata about experimental conditions (e.g.,
relationships between soil water content, soil matric poten- initial soil moisture content at the start of the experiment
tial, and soil hydraulic conductivity are necessary to solve the and method used), soil properties, land use, topography, ge-
Richards equation. Several solutions of the Richards equa- ographical coordinates of the sites, and any other relevant
tion and many empirical, conceptual, semi-analytical, and information to interpret the data and to increase the value of
physically based models – e.g., Green and Ampt (1911), the database. Infiltration data reported in the literature were
Philip (1957), Smith and Parlange (1978), Haverkamp et digitized and included in the database together with addi-
al. (1994), and Corradini et al. (2017) – have been introduced tional information provided in these papers. The digitization
to describe the infiltration process over time, even for pref- approach is discussed in Sect. 2.2. In total, 5023 single infil-
erential flows, e.g., Lassabatere et al. (2014). Furthermore, tration curves were collected, of which 510 infiltration curves
several direct or indirect experimental systems have been in- were digitized from 74 published papers (Table 1) and 4513
troduced to measure soil infiltration in the laboratory or in the were provided by 68 different research teams (Table 2), be-
field under different conditions (Gupta et al., 1994; McKen- ing published or unpublished data. The references and corre-
zie et al., 2002; Mao et al., 2008a). Data obtained from these spondences for data supplied by direct communications with
systems can also be used to deduce soil saturated hydraulic researchers are also reported in Table 2. Therefore, users may
conductivity directly. refer to these references for detailed information about the
Methods developed to measure and quantify water infiltra- applied methods or procedures.
tion in soil are generally time-consuming and costly. There-
fore, PTFs have been developed and applied by many re- 2.2 Data digitization
searchers – e.g., Jemsi et al. (2013), Parchami-Araghi et
al. (2013), Kashi et al. (2014), Sarmadian and Taghizadeh- In order to digitize infiltration curves reported in the liter-
Mehrjardi (2014), and Rahmati (2017) – in order to easily ature, screenshots of the relevant plots were taken, and fig-
parameterize infiltration models. However, these PTFs have ures were imported into the plot digitizer 2.6.8 (Huwaldt and
been developed for specific regions, often limiting their ap- Steinhorst, 2015). First, the origin of the axes and the high-
plicability. As already mentioned, a large number of publi- est x and y values were defined and the diagram plane was
cations reporting soil infiltration data is available, but these spanned. Then, all point values were picked out and an output
data are dispersed in the literature and often difficult to ac- table with the x–y pairs (time vs. infiltration rate or cumula-
cess. Therefore, the aim of this data paper is to present and tive infiltration) was generated and stored.
make available a collection of infiltration data digitized from
available literature and from published or unpublished data 2.3 Database structure
provided directly by researchers around the world. These
data are accompanied by metadata, which provide informa- The SWIG database is prepared in *.xlsx with a backup
tion about the location of the infiltration measurement, soil file in *.csv formats containing several datasets. Supplemen-
properties, and land management. Finally, we will provide tary data are available at https://doi.org/10.1594/PANGAEA.
some first results highlighting the suitability of the database 885492 (Rahmati et al., 2018) . The first dataset, named
for further research. The main article is also accompanied by “I_cm”, contains cumulative infiltration data in centimeter
Earth Syst. Sci. Data, 10, 1237–1263, 2018 www.earth-syst-sci-data.net/10/1237/2018/
M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1243
Figure 1. SWIG flowchart.
units and is referred to as “Ixxxx”, whereby “xxxx” is the Boersma, 1984):
identifier of the individual infiltration test. The correspond-
n
ing time intervals in hours for the infiltration data are la- ∑
d = exp (a), a = 0.01 f lnD , (4)
beled “T_Hour” and named “Txxxx”. The constant or vary- g i i
i=1
ing pressure or tension heads (if any) during infiltration mea- ∑n
surements are also reported in another dataset named “Ten- S exp (b), b2g = = 0.01 f 2 2i ln Di − a , (5)
sion_cm”. The database also contains additional variables i=1
and information relevant to the infiltration data provided by
data owners or digitized from articles, as listed in Table 3, where fi is the percent of total soil mass having diameters
and which is labeled “Metadata”. Additional soil proper- equal to or less than the arithmetic mean of interval limits
ties were determined by different standards; therefore, data (Di) that define three main fractions (i) of clay, silt, and
harmonization might be needed for some of those, espe- sand with mean values of 0.001, 0.026, and 1.025 mm, re-
cially in the case of water content at field capacity, pH, or spectively. For the infiltration data, where the soil texture is
wet-aggregate stability. Further information on measurement unknown, dg and Sg could not be calculated and the data
methods is available from references of the data. Since the field in the database was left empty. The database also con-
geometric mean diameter (dg) and standard deviation (S ) tains the locations of the experimental sites in another datasetg
of soil particle sizes are rarely measured, both parameters named “Locations” that provides the approximate latitude
were computed using the following equations (Shirazi and 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”.
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1244 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
Table 1. References used to extract infiltration curves and metadata.
Dataset Dataset
Number Reference Number Reference
From To From To
1 295 317 Miller et al. (2005) 38 4612 – Wang et al. (2016)
2 318 322 Adindu Ruth et al. (2014) 39 4613 4615 Qian et al. (2014)
3 542 544 Alagna et al. (2016) 40 4617 4619 Fan et al. (2013)
4 545 – Angulo-Jaramillo et al. (2000) 41 4620 – Zhang et al. (2000)
5 546 548 Su et al. (2016) 42 4621 4623 Wang et al. (2015a)
6 549 550 Quadri et al. (1994) 43 4624 4633 Yang and Zhang (2011)
7 551 553 Qi and Liu (2014) 44 4634 4657 Wu et al. (2016)
8 554 558 Huang et al. (2015) 45 4658 4663 Ma et al. (2017)
9 559 568 Al-Kayssi and Mustafa (2016) 46 4664 4681 Thierfelder et al. (2003)
10 1421 1432 Bhardwaj and Singh (1992) 47 4682 4683 Commandeur et al. (1994)
11 1433 1435 Berglund et al. (1980) 48 4684 4686 Di Prima et al. (2016)
12 1436 1443 Wu et al. (2016) 49 4687 4688 Angulo-Jaramillo et al. (2000)
13 1444 1446 Chartier et al. (2011) 50 4689 4691 Machiwal et al. (2006)
14 1447 1456 Sihag et al. (2017) 51 4692 – Ayu et al. (2013)
15 1457 1460 Machiwal et al. (2006) 52 4693 4699 Rei et al. (2016)
16 1461 1466 Igbadun et al. (2016) 53 4700 4702 Omuto et al. (2006)
17 1467 1469 Mohanty et al. (1994) 54 4703 4706 Návar and Synnott (2000)
18 1470 1472 Sauwa et al. (2013) 55 4707 – Scotter et al. (1988)
19 1473 1476 Arshad et al. (2015) 56 4708 4720 Khan and Strosser (1998)
20 1477 1488 Bhawan (1997) 57 4721 4724 Lipiec et al. (2006)
21 1489 1495 Uloma et al. (2013) 58 4725 – Suzuki (2013)
22 1496 – Al-Azawi (1985) 59 4726 4728 Sukhanovskij et al. (2015)
23 1497 1499 Ogbe et al. (2011) 60 4729 4749 Al-Ghazal (2002)
24 1500 1507 Teague (2010) 61 4750 – Sorman et al. (1995)
25 4506 4515 Askari et al. (2008) 62 4751 4764 Bowyer-Bower (1993)
26 4516 – Delage et al. (2016) 63 4765 4788 Medinski et al. (2009)
27 4517 4518 Ruprecht and Schofield (1993) 64 4789 4792 Latorre et al. (2015)
28 4519 4520 Bertol et al. (2015) 65 4793 4795 Biro et al. (2010)
29 4521 4523 Naeth et al. (1991) 66 4796 4799 Mohammed et al. (2007)
30 4524 4529 Huang et al. (2011) 67 4800 4815 Abdallah et al. (2016)
31 4530 4537 van der Kamp et al. (2003) 68 4816 4819 Murray and Buttle (2005)
32 4538 – Jačka et al. (2016) 69 4820 4831 Zhang et al. (2015)
33 4539 4568 Matula (2003) 70 4832 4837 Perkins and McDaniel (2005)
34 4569 4586 Casanova (1998) 71 4838 4841 Arriaga et al. (2010)
35 4587 4593 Holzapfel et al. (1988) 72 4842 4857 Thierfelder et al. (2017)
36 4594 4605 Wang et al. (2015b) 73 4858 4867 Thierfelder and Wall (2009)
37 4606 4611 Mao et al. (2016) 74 4868 4879 Abagale et al. (2012)
3 Results and discussion Greenland, and Russia. The lack of reports with infiltration
data from most countries of the former Soviet Union as well
3.1 Spatial and temporal data coverage as the Sahelian and Saharan countries is also notable, as well
as the small number of infiltration data from Australia. Fig-
The SWIG database (Rahmati et al., 2018) consists of 5023 ure 3 shows the number of samples by climatic zone (Rubel
soil water infiltration measurements spread over nearly all et al., 2017; Kottek et al., 2006). The majority of the data
continents (Fig. 2). Data were derived from 54 countries (Ta- are from warm temperate, fully humid climate (49 %); arid
ble 4). The largest number of data sources were provided by steppe climate and warm temperate climate with dry sum-
scientists in Iran (n = 38), China (n = 23), and the USA mer are the second and third most represented climate zones
(n = 15), whereby one data source might contain several with 22 and 12 %, respectively. Figures 4 and 5 show the
water infiltration measurements. The SWIG database covers frequency of experimental sites, respectively, by the World
measurements from 1976 to 2017. A sparse coverage was ob- Reference Base (WRB) (IUSS, 2006) and USDA soil taxon-
tained for the higher latitudes of the Northern Hemisphere omy systems (USDA, 2014) based on the SoilGrids dataset
(above 60◦) including Norway, Finland, Sweden, Iceland, (Hengl et al., 2017). Regarding the WRB classification sys-
Earth Syst. Sci. Data, 10, 1237–1263, 2018 www.earth-syst-sci-data.net/10/1237/2018/
M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1245
Table 2. References and correspondence for data supplied by data owners.
Dataset
Number Contact person Email for contact Reference
From To
1 1 135 M. Rahmati mehdirmti@gmail.com Rahmati (2017)
2 136 294 A. Farajnia farajnia1966@yahoo.com Unpublished data
3 323 376 M. Shukla shuklamk@nmsu.edu Shukla et al. (2003, 2006)
4 377 426 S. H. R. Sadeghi sadeghi@modares.ac.ir Sadeghi et al. (2014, 2016a, b, c, 2017a, b), Hazbavi and Sadeghi (2016),
Kheirfam et al. (2017a, b), Sharifi Moghaddam et al. (2014), Ghavimi Panah et al. (2017),
Kiani-Harchegani et al. (2017)
5 427 466 M. H. Mohammadi mhmohmad@ut.ac.ir Unpublished data
6 467 505 F. Meunier felicien.meunier@gmail.com Unpublished data
7 506 541 N. Sephrnia n.sepehrnia@gmail.com Sepehrnia et al. (2016, 2017)
8 569 817 D. Moret-Fernández david@eead.csic.es Unpublished data
9 818 940 M. Vafakhah vafakhah@modares.ac.ir Kavousi et al. (2013), Fakher Nikche et al. (2014)
10 941 1060 A. Cerdà artemio.cerda@uv.es Unpublished data
11 1061 1079 J. Rodrigo-Comino rodrigo-comino@uma.es Rodrigo-Comino et al. (2016, 2018)
12 1080 1112 H. Asadi ho.asadi@ut.ac.ir Nikghalpour et al. (2016)
13 1113 1119 K. Bohne klaus.bohne@uni-rostock.de Unpublished data
14 1120 1125 L. Mao leoam@126.com Mao et al. (2008b, 2016)
15 1126 1166 L. Lichner lichner@uh.savba.sk Dušek et al. (2013), Lichner et al. (2011, 2012, 2013)
16 1167 1210 M. V. Ottoni marta.ottoni@cprm.gov.br Oliveira (2005)
17 1211 1420
R. Sándor sandor.rencsi@gmail.com Fodor et al. (2011), Sándor et al. (2015)
18 4476 4485
19 1508 1519 A. Stanley ajayistan@gmail.com Igbadun et al. (2016), Othman and Ajayi (2016)
20 1520 1521 A. R. Vaezi vaezi.alireza@gmail.com Unpublished data
21 1522 1536 A. Albalasmeh aalbalasmeh@just.edu.jo Gharaibeh et al. (2016)
22 1537 1578 D. Machiwal dmachiwal@rediffmail.com Machiwal et al. (2006, 2017), Ojha et al. (2013)
23 1579 1592 H. Emami hemami@um.ac.ir Fakouri et al. (2011a, b)
24 1593 1895 J. Mertens jan.mertens@engie.com Mertens et al. (2002, 2004, 2005)
25 1896 2115 D. Jacques diederik.jacques@sckcen.be Jacques (2000), Jacques et al. (2002)
26 2116 2139 J. Votrubova jana.votrubova@fsv.cvut.cz Votrubova et al. (2017)
27 2140 2143 J. Batlle-Aguilar jorbat1977@hotmail.com Batlle-Aguilar et al. (2009)
28 2144 2179 R. A. Armindo rarmindo@ufpr.br Unpublished data
29 2180 2209 S. Werner steffen.werner@rub.de Unpublished data
30 2210 2255 S. Zacharias steffen.zacharias@ufz.de Unpublished data
31 2256 2281 S. Shutaro sshiraki@affrc.go.jp Unpublished data
32 2282 2304 T. Saito tadaomi@muses.tottori-u.ac.jp Saito et al. (2016)
33 2305 2354 R. Taghizadeh-M. rh_taghizade@yahoo.com Unpublished data
34 2355 2356
W. G. Teixeira wenceslau.teixeira@embrapa.br Teixeira et al. (2014)
35 3644 3647
36 2357 2436 Y. Zhao yzhaosoils@gmail.com Zhao et al. (2011)
37 2437 2475 A. A. Moosavi aamousavi@gmail.com Unpublished data
38 2476 2552 Y. A. Pachepsky yakov.pachepsky@ars.usda.gov Rawls et al. (1976)
39 2553 2643 A. Panagopoulos panagopoulosa@gmail.com Hatzigiannakis and Panoras (2011) and unpublished data
40 2644 2649 B. Clothier brent.clothier@plantandfood.co.nz Al Yamani et al. (2016)
41 2650 2710
C. Castellano ccastellanonavarro@gmail.com Unpublished data
42 3507 3597
43 2711 2756 F. Becker fabian.becker@fu-berlin.de Unpublished data
44 2757 2765 I. Vogeler iris.vogeler@plantandfood.co.nz Vogeler et al. (2006), Cichota et al. (2013)
45 2766 2788 R. Morbidelli renato.morbidelli@unipg.it Morbidelli et al. (2017)
46 2789 2832 S. Giertz sgiertz@uni-bonn.de Giertz et al. (2005)
47 2833 2868 T. Vogel vogel@fsv.cvut.cz Vogel and Cislerova (1993)
48 2869 2948 W. Cornelis wim.cornelis@ugent.be Pulido Moncada et al. (2014), Rezaei et al. (2016a, b)
49 2949 3386
Y. Coquet yves.coquet@univ-orleans.fr Coquet (1996), Coquet et al. (2005), Chalhoub et al. (2009)
50 3705 3709
51 3387 3506 B. Mohanty bmohanty@tamu.edu Dasgupta et al. (2006)
52 3598 3643 D. J. Reinert dalvan@ufsm.br Mallmann (2017)
53 3648 3657 M. R. Pahlavan Rad pahlavanrad@gmail.com Pahlavan-Rad (2017)
54 3658 3680 T. Saito tadaomi@muses.tottori-u.ac.jp Unpublished data
55 3681 3704
X. Li xyli@bnu.edu.cn Li et al. (2013), Hu et al. (2016)
56 4497 4505
57 3710 3745 Y. Bamutaze yazidhibamutaze@gmail.com Unpublished data
58 3746 3833
I. Braud isabelle.braud@irstea.fr Gonzalez-Sosa et al. (2010), Braud (2015), Braud and Vandervaere (2015)
59 3907 4011
60 3834 3874 M. R. Mosaddeghi mosaddeghi@yahoo.com Unpublished data
61 3875 3906 S. B. Mousavi b_mosavi2000@yahoo.com Unpublished data
62 4012 4026 M. Pulido manpufer@hotmail.com Unpublished data
63 4027 4457 Unpublished data
F. P. Roberts frapar@ceh.ac.uk
4458 4475 Robinson et al. (2016, 2017)
64 4486 4496 T. Picciafuoco picciafuoco@hydro.tuwien.ac.at Morbidelli et al. (2017)
65 4880 4886 M. A. Liebig mark.liebig@ars.usda.gov Liebig et al. (2004)
66 4887 4936 Y. Zeng y.zeng@utwente.nl Zhao et al. (2017, 2018)
67 4937 5018 L. Lassabatere laurent.lassabatere@entpe.fr Lassabatere et al. (2010), Yilmaz et al. (2010), Coutinho et al. (2016)
68 5019 5023 I. Eskandari eskandari1343@yahoo.com Unpublished data
www.earth-syst-sci-data.net/10/1237/2018/ Earth Syst. Sci. Data, 10, 1237–1263, 2018
1246 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
Table 3. Description of the variables listed in the database.
Column Supplies Dimension
Code Dataset identifier with 4 digits from 0001 to 5023
Clay Mass of soil particles, < 0.002 mm %
Silt Mass of soil particles, > 0.002 and < 0.05 mm %
Sand Mass of soil particle, > 0.05 and < 2 mm %
Texture 1: sand; 2: loamy sand; 3: sandy loam; 4: sandy clay loam; 5: sandy clay; 6: loam;
7: silt loam; 8: silt; 9: clay loam; 10: silty clay loam; 11: silty clay; 12: clay.
Gravel Mass of particles larger than 2 mm %
dg Geometric mean diameter mm
Sg Standard deviation of soil particle diameter
OC Soil organic carbon content %
Db Soil bulk density g cm−3
Dp Soil particle density g cm−3
Ksat Soil saturated hydraulic conductivity cm h−1
θ Saturated volumetric soil water content cm3 cm−3sat
θi Initial volumetric soil water content cm3 cm−3
FC Soil water content at field capacity cm3 cm−3
PWP Soil water content at permanent wilting point (1500 kPa) cm3 cm−3
θr Residual volumetric soil water content cm3 cm−3
WAS Wet-aggregate stability %
MWD Aggregates mean weight diameter mm
GMD Aggregates geometric mean diameter mm
EC Soil electrical conductivity dS m−1
pH Soil acidity –
Gypsum Soil gypsum content %
CCE Soil calcium carbonate equivalent %
CEC Soil cation exchange capacity Cmolc kg−1
SAR Soil sodium adsorption ratio –
DiscRadius Applied disc radius (if any) mm
Instrument Applied instruments for infiltration measurement:
1: double ring; 2: single ring; 3: rainfall simulator; 4: Guelph permeameter; 5: disc infiltrometer;
6: micro-infiltrometer; 7: mini-infiltrometer; 8: Aardvark permeameter; 9: linear source method;
10: point source method; 11: hood infiltrometer; 12: tension infiltrometer; 13: BEST method.
Vegetation cover %
Land use Dominant land-use or land cover type of the experimental site
Rainfall intensity Simulated rain intensity mm h−1
Slope The mean slope of the soil surface %
Treatment Applied treatment in experimental site
Crust Yes: existence of crust. No: no crust layer.
Sand contact layer Yes: sand contact layer is applied during infiltration measurement. No: no sand contact layer.
tem (Fig. 4), in total, 35 WRB reference soil subgroups are 3.2 Analysis of the database using soil properties
included among experimental sites, where 55 % of the ex-
perimental sites comprised four subgroup classes of Hap- Textural information (clay, silt, and sand content) is available
lic Acrisols (8 %), Haplic Luvisols (11 %), Haplic Calcisols for 3842 out of 5023 collected infiltration curves (∼ 76 %).
(15 %), and Haplic Cambisols (21 %). A total of 29 soil sub- The infiltration measurements cover nearly all soil textu-
orders classes of USDA soil taxonomy are included in this ral classes according to the USDA classification, except for
study (Fig. 5) with Udalfs (9 %), Orthents (9 %), and Ustolls the sandy clay and silt textural class (Fig. 6), which makes
(9 %). Thus, the wide spatial and temporal distribution of in- the SWIG database a valuable data source for comprehen-
filtration data from this database provides a comprehensive sive studies. To complete the large dataset, the open-access
view of the infiltration characteristics of many soils in the SWIG database might be amended with information regard-
world which can be used in future studies. ing those soils poorly or altogether unrepresented by the ex-
isting database, including those not usually considered by
infiltration studies, such as soils with extremely high stone
Earth Syst. Sci. Data, 10, 1237–1263, 2018 www.earth-syst-sci-data.net/10/1237/2018/
M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1247
Figure 2. Global distribution of infiltration measuring sites that were included in the database.
Figure 3. Number of samples by Köppen–Geiger climatic zones (Rubel et al., 2017; Kottek et al., 2006).
content (Poesen, 2018). Loam, sandy loam, silty loam, and 3.3 Infiltration measurements in the SWIG database
clay loam contributed with 19, 18, 14, and 13 % (Table 5)
to the infiltration measurements, respectively. Table 5 shows Different instruments were used to measure soil water infil-
that infiltration measurements are almost equally distributed tration (Table 8). About 32 % (1595 out of 5023) of the mea-
among textures when these are categorized in three major surements were carried out using different types of ring in-
classes: course- (1092), medium- (1238), and fine- to moder- filtrometers. The most frequently used methods are the disc
ately fine-textured soils (1447). Table 6 reports on the soil infiltrometer methods (disc, mini-disc, and micro-disc, hood,
properties that are available in the SWIG database and it and tension infiltrometers), which have been used in about
gives some simple statistics such as mean, minimum, max- 51 % of the experiments. About 5 % of the data were sub-
imum, median, and coefficient of variation. Bulk density mitted to the database without specifying the measurement
(available for 66 % of infiltration measurements) and organic method (251 infiltration tests) and around 12 % of the mea-
carbon content (available for 62 % of infiltration measure- surements were carried out with other methods not listed
ments) are two other soil properties besides texture that have above (Table 7).
the highest frequency of availability. Saturated hydraulic
conductivity, initial soil water content, saturated soil water 3.4 Land use classes represented in the SWIG
content, calcium carbonate equivalent, electrical conductiv- database
ity, and pH are available in 22 to 38 % of infiltration data.
The other soil properties have a frequency lower than 10 %. Land use is known to potentially impact soil structure and
then water infiltration into soils (e.g., Ilstedt et al., 2007; Wa-
www.earth-syst-sci-data.net/10/1237/2018/ Earth Syst. Sci. Data, 10, 1237–1263, 2018
1248 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
Table 4. Countries and the number of data sources (n) contributing
to the database.
Country n Country n
Iran 38 Slovakia 2
China 23 South Africa 2
USA 15 Sudan 2
Brazil 9 Zambia 2
Spain 9 Argentina 1
France 9 Australia 1
Germany 8 Benin 1
India 8 Cameroon 1
Canada 7 Colombia 1
United Kingdom 7 Indonesia 1
Hungary 6 Iraq 1
Nigeria 6 Japan 1
Greece 5 Jordan 1
Belgium 4 Kenya 1
Italy 4 Lebanon 1
Czech Republic 3 Malawi 1
Saudi Arabia 3 Mexico 1
Australia 2 Mozambique 1
Austria 2 Myanmar 1
Chile 2 Netherland 1
Ghana 2 Poland 1 Figure 4. Frequency of WRB reference soil subgroups in experi-
Morocco 2 Scotland 1 mental sites derived from SoilGrids (Hengl et al., 2017).
Namibia 2 Tanzania 1
New Zealand 2 Telangana 1
Pakistan 2 UAE 1
Russia 2 Uganda 1
Senegal 2 Zimbabwe 1
Table 5. Number of soils in each soil USDA textural class for which
infiltration data are included in the database.
Group Soil texture class Availability
Coarse-textured soils 1092
Sand 291
Loamy sand 111
Sandy loam 690
Medium-textured soils 1238
Loam 716
Silt loam 522
Silt 0
Fine- to moderately
1476
fine-textured soil
Clay loam 514
Clay 352
Silty clay loam 253
Sandy clay loam 226 Figure 5. Frequency of USDA soil suborders in experimental sites
Silty clay 131 derived from SoilGrids (Hengl et al., 2017).
Sandy clay 0
Earth Syst. Sci. Data, 10, 1237–1263, 2018 www.earth-syst-sci-data.net/10/1237/2018/
M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1249
Figure 6. Textural distribution of soils (c) and probability density functions of clay (a) and sand (b) particles (plotted on the USDA textural
triangle) for which infiltration data are included in the database. Dots are colored according to their corresponding land use. HCl: highly
clayey; SiCl: silty clay; Cl: clay; SiClLo: silty clay loam; ClLo: clay loam; SaCl: sandy clay; SaClLo: sandy clay loam; L: loam; Si: silty;
SiLo: silty loam; SaLo: sandy loam; LoSa: loamy sand; and Sa: sandy.
Table 6. Soil properties, number of data entries in the database (out of 5023 soil water infiltration curves in total), and their statistical
description.
Soil properties Availability Fr (%) Mean Min Max Median CV (%)
Clay (%) 3842 76 24 0 80 20 64
Silt (%) 3842 76 36 0 82 37 52
Sand (%) 3842 76 41 1 100 38 63
Bulk density (g cm−3) 3295 66 1.32 0.14 2.81 1.35 20
Organic carbon (%) 3102 62 3 0 88 1 200
Saturated hydraulic cond. (cm h−1) 1895 38 41 0 3004 3 426
Initial soil water content (cm3 cm−3) 1569 31 0.17 0 0.63 0.14 68
Saturated soil water content (cm3 cm−3) 1400 28 0.44 0.01 0.87 0.45 24
Carbonate calcium equivalent (%) 1399 28 14 0 56 8 101
Electrical conductivity (dS m−1) 1113 22 25 0 358 1 249
pH 1081 22 7.4 4.7 8.6 7.6 12
Particle density (g cm−3) 438 9 2.52 1.73 2.97 2.56 9
Gypsum (%) 380 8 4 0 49 3 137
Cation exchange capacity (cmolc kg−1) 357 7 17 3 26 18 21
Wet-aggregate stability (%) 309 6 61 5 96 63 37
Residual soil water content (cm3 cm−3) 263 5 0.10 0.001 0.38 0.06 86
Mean weight diameter (mm) 258 5 1 0.10 2.75 1.0 54
Gravel (%) 243 5 18 0 92 15 84
Sodium adsorption ratio 156 3 5 0 89 1 351
Soil water content at FC (cm3 cm−3) 74 1 0.28 0.12 0.54 0.27 34
Soil water content at PWP (cm3 cm−3) 64 1 0.18 0.05 0.36 0.20 47
Geometric mean diameter (mm) 73 1 0.6 0.4 0.8 0.6 18
Fr: frequency (%), Min: minimum, Max: maximum, CV: coefficient of variation.
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1250 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
Table 7. Instruments used to measure soil infiltration curves. maining land use types all together cover only 545 experi-
mental sites (less than 15 %).
Instrument/method used Infiltration curves
Double ring 828 3.5 Estimating infiltration parameters from infiltration
Ring Single ring 570 measurements
Beerkan (BEST) 197
Overall 1595 In order to predict infiltration parameters from infiltration
measurements, we classified the SWIG database infiltration
Disc 607 curves in two groups: (i) infiltration curves that were ob-
Mini-disc 1140
Infiltrometer Micro-disc 36 tained under the assumption of 1-D infiltration and (ii) infil-
Hood 23 tration curves that were obtained under 3-D flow conditions.
Tension 752 We fitted the three-parameter infiltration equation of Philip
Overall 2558 (Kutílek and Krejča, 1987), Eq. (6), to the 1-D experimen-
tal data and the simplified form of Haverkamp et al. (1994),
Guelph 181
Permeameter Eq. (7), to the 3-D experimental data:
Aardvark 50
1 3
Overall 231 I1-D = S t 2 +A[1t +A2t 2 , ] (6)
Rainfall simulator 374 √ 2−β γS2
Linear source method 10 I3-D = S t + Ksat+ t. (7)
Point source method 4 3 RD (θs− θi)
Not reported 251 We reduced the number of parameters in Eq. (6) by defining
Sum 5023 A1 = 0.33×Ksat (Philip, 1957) andA2 = AwhereAwas as-
sumed to be a constant. In Eq. (7), we put β = 0.6 (Angulo-
Jaramillo et al., 2000) and the second term between brackets
Table 8. Number of infiltration curves with a given land use type. on the right-hand side was assumed to be a constant. There-
fore, we simplified the equations as follows:
Land use n Land use n 1 3
I1-D = S t 2 + 0.33Ksatt +At 2 , (8)
Agriculture 2019 Vineyards 22 √
Grassland 821 Upland 11 I3-D = S t + 0.47Ksatt +At. (9)
Pasture 229 Pure sand 10
Forest 204 Brushwood 6 In our analysis, we assumed that double-ring infiltrometer
Garden 152 Road 5 measurements result in 1-D infiltration conditions, while the
Bare 99 Agro-pastoral 4 different types of disc infiltration and single-ring infiltrom-
Urban soils 82 Park 3 eter measurements lead to 3-D flow conditions that can be
Savannah 41 Salt-marsh soil 3 captured by Eq. (9). As 1-D or 3-D infiltration conditions are
Abandoned farms 39 Afforestation 2 not guaranteed for measurements made with rainfall simula-
Idle 32 Campus 2 tors, Guelph permeameters, Aardvark permeameters, linear
Shrub 30 Residential 2 and point source methods, and hood infiltrometer measure-
Available 3818 Unknown 1205 ments, 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
terloo et al., 2007). Consequently, we collected information the fitting to Eqs. (8) and (9) as no indication was available
on the type of land use at all experimental sites where avail- on the measurement method used. In total, 4178 infiltration
able. In general, the type of land use was reported in 3818 out curves were included in our analysis, of which 828 infiltra-
of 5023 infiltration curves (∼ 76 %) and this information is tion curves reflected 1-D and 3350 were considered as the
reported in the Metadata dataset. For simplicity, we grouped results of 3-D infiltration. As no sufficient information was
all reported land use types into 22 major groups (Table 8). available on the properties of the sand contact layer, we did
A frequency analysis showed that agricultural land use, i.e., not correct 3-D infiltration measurements. Finally, the se-
cropped land, irrigated land, dryland, and fallow land, is the lected infiltration curves were fitted to Eq. (8) or (9) using
most frequently reported land use in the database with about the lsqnonlin command in Matlab™.
53 % (2019 out of 3818) of all land uses. With 22 %, grass- The fitting results of Eq. (8) to the single infiltrometer data
lands are the second most frequently represented land use are shown in Table 9. R2 values were higher than 0.9 in 97 %
type. Pasture with 6 % and forest with 5 % are ranked as the of the cases and higher than 0.99 in 77 % of the cases. Fitting
third- and fourth-largest reported land use types. The 18 re- Eq. (9) to the 3-D infiltration curves data, R2 values higher
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M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1251
than 0.9 and 0.99 were obtained in 94 and 68 % of the cases, can be used to infer the state of the soil’s structure, namely
respectively. The statistics for the fitting process as well as its macroporosity, by using the slope of the near-saturated
the fitted parameters of two mentioned models are reported conductivity curve, via Philip’s “flow-weighted mean pore-
in the SWIG database in an additional dataset labeled “Statis- size” analysis. White and Sully (1987) have discussed this
tics”. For infiltration curves excluded from the analysis, an in a great detail. Zhang et al. (2015) is another example
empty cell is reported. of where tension infiltrometers can be used to describe the
The average values of estimated Ksat and sorptivity (S), temporal dynamics of the macroporosity which character-
using Eq. (8) or (9) as well as measured Ksat for different izes soil structure. This could inspire researchers to collect
soil texture classes extracted from the current database, are such information when conducting additional soil infiltration
reported in Table 10. The measured values of Ksat were ob- measurements and include this in the database in the future.
tained by other means by the contributors and tabulated in the This finding indicates that present parameterization in cur-
SWIG database. More detailed information of how Ksat was rent land surface models, which are mainly based on texture,
calculated in individual cases can be found in the references may severely underestimate the variability of Ksat. In addi-
linked to those data points. Comparison between estimated tion, it shows that also mean values are not dominantly con-
(Ksat-es) and measured (Ksat-m) values of Ksat (Table 10) re- trolled by textural properties. Other land surface properties
veals that there is reasonably good agreement between mea- such as land use and crusting may, in fact, be much more
surements and estimation, except for loamy sand (with mean important.
K −1sat-es = 62 cm h vs. Ksat-m = 25 cm h−1), sandy loam
(with mean K −1 −1sat-es = 32 cm h vs. Ksat-m = 41 cm h ), silt 3.6 Exploring the SWIG database using principal
loam (with meanK 27 cm h−1 vs.K −1sat-es = sat-m = 3 cm h ), component analysis
and silty clay (with mean K −1sat-es = 26 cm h vs. Ksat-m =
45 cm h−1) textural classes. However, the only significant In order to demonstrate the potential of the SWIG database
difference between measured and estimated Ksat values was for analyzing infiltration data and for developing pedotrans-
found for the silt loam textural class (Table 10) applying an fer functions, principal component analysis (PCA) was per-
independent t test. formed and biplots were generated to show both the obser-
We also compared our estimated Ksat values from the in- vations and the original variables in the principal component
filtration measurements from the SWIG database with Ksat space (Gabriel, 1971).
values from databases that have been published in the litera- In a biplot, positively correlated variables are closely
ture (Table 11). The validity of our estimated Ksat values is aligned with each other and the larger the arrows the stronger
confirmed by comparing the order of magnitude of the dif- the correlation. Arrows that are aligned in opposite directions
ference between these values, and those tabulated in previ- are negatively correlated with each other and the magnitude
ous studies, for the various different soil classes. Some of of the arrows is again a measure of the strength of the corre-
these databases like that of Clapp and Hornberger (1978) and lation. Arrows that are aligned 90◦ to each other show typi-
Cosby et al. (1984) have been used to parameterize land sur- cally no correlation. Figures 7 and 8 show the results of two
face models. Most of the Ksat values in the listed databases PCAs. The first PCA (Fig. 7) shows the relationship between
have been obtained from laboratory-scale measurements of- soil textural properties, S andKsat, based on 3267 infiltration
ten performed on disturbed soil samples. In most of the re- measurements. The first two principal components explain
ported databases Ksat is controlled by texture, with the high- 74.5 % of the variability in the data. Figure 7 shows a pos-
est mean values obtained for the coarse-textured soils and itive correlation between Ksat and S (0.527) and the largest
the lowest mean values for the fine-textured soils. This is values for both variables are found in clay soils. Clay content
not the case for the Ksat values obtained from the SWIG appears to only be weakly correlated with Ksat and S as is
database. Clayey soils have a mean value that is similar to the also shown by the correlation coefficients of 0.112 and 0.025,
coarser textured soils. This may be partly explained by the respectively. Figure 8 shows the biplot of soil textural prop-
fact that the measurements collected in the SWIG database erties with Ksat, S, organic carbon content, and bulk density
are obtained from field measurements on undisturbed soils. in the principal component space – based on 1910 infiltra-
It was observed that the standard deviation of Ksat in the tion measurements. The first two principal components still
SWIG database is typically larger than the standard devia- explain 55 % of the variability. Neither S nor Ksat showed
tions obtained from the databases in the literature. This indi- appreciable correlations with available soil properties. Only
cates that texture is apparently not the most important con- Ksat and S are correlated (arrows are aligned but small) with
trol on Ksat values. However, one would also pose that much a value of 0.29. Organic carbon and bulk density show a neg-
of the lack of correlation between soil texture and predicted ative correlation with a calculated value equal to −0.51. It
Ksat from the SWIG database is related to the lack of soil also shows that, for example, the sandy clay loam textural
structural information, such as macro porosity quantification class (yellow dots) shows a wide spread in organic matter
or other possible soil attributes. Indeed, many of the datasets content and bulk densities. These analyses show that the ex-
presented in our paper on saturated and near-saturated flow amined basic soil properties do not contain enough informa-
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1252 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
Table 9. Accuracy analysis of empirical models fitted to experimental data of infiltration.
n R2 RMSE (cm)
Infiltration type R2 > 0.90 R2 > 0.99
Mean Min Max SD Mean Min Max SD
1-D 828 0.985 0.529 1 0.049 0.900 1.3× 10−4 69.30 3.31 801 640
3-D 3350 0.975 0.032 1 0.066 0.449 5.5× 10−12 98.95 2.95 3136 2276
All 4178 0.977 0.032 1 0.063 0.538 5.5 10−12× 98.95 3.03 3937 2916
SD: standard deviation.
Table 10. Estimated or measured average values of infiltration parameters for different textural classes extracted from the current database.
Independent t test
Texture class Estimated by Eq. (8) or (9) Measured between measured
and estimated Ksat
S (cm h−0.5) K (cm h−1a sat ) a Ksat (cm h
−1) df t value
n n
Mean Median SD Mean Median SD Mean Median SD
Sand 291 2.3 0.26 4.3 42.2 15 134.5 229 43.6 24 149 518 0.10b
Loamy sand 92 10.6 5.7 17.5 61.4 10 173.2 63 24.6 8.2 72 153 1.59b
Sandy loam 500 9.2 2.95 15.7 32 3.1 94.5 424 41.2 5.7 166 922 1.05b
Silt loam 409 9.4 1.5 19.1 26.5 1.7 61.7 165 2.9 0.96 5.1 572 4.90c
Loam 583 7.9 2.4 12.9 7.8 0.28 26.7 270 4.9 1.18 13.7 851 1.69b
Sandy clay loam 185 5.9 2.1 8.6 7.4 1.4 12.8 84 5.4 2.24 6.9 267 1.35b
Silty clay loam 250 3.2 0.64 12.5 10.6 1.7 24.1 64 12.3 2.42 63.2 312 0.32b
Clay loam 467 6.8 2.1 13.6 8.3 2.3 20 166 7.6 2.97 21.3 631 0.38b
Sandy clay – – – – – – – – – – – – –
Silty clay 121 7.7 2.2 13.4 26.2 7.8 61.5 54 44.8 6.97 88.2 173 1.59b
Clay 333 14.6 1.7 39.5 354.3 1.3 1268.5 79 148.8 2.94 458.4 410 1.42b
Silt – – – – – – – – – – – – –
Total 4179 8.5 2.6 18.2 46 1.8 374.8 1895 41 3.4 174 – –
a The number soils included in calculation. bns: insignificant; c**: significant at 1 % probability level. SD: standard deviation.
tion 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 be-
yond 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.
Figure 7. The relationships between clay, silt, sand contents and
estimated hydraulic parameters (S and Ksat).
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M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1253
www.earth-syst-sci-data.net/10/1237/2018/ Earth Syst. Sci. Data, 10, 1237–1263, 2018
Table 11. Comparison of the estimated Ksat values from current database (SWIG) with measured Ksat values presented in the literature.
Texture class Data Clapp and
Rosetta3 Cosby et al. (1984) Rawls database Ahuja database UNSODA database US soils K data EU-HYDI database
source Hornberger (1978) sat
(Zhang and (Pachepsky and (Weynants et
(Schaap and Leij, 1998)
Schaap, 2017) Park, 2015) al., 2013)
Ksat logKsat (SD) logKsat (SD) logKsat (SD) logKsat (SD) logKsat (SD) logKsat (SD) logKsat (SD)
(cm min−1) (log cm day−1) (log in h−1) (log cm day−1) (log cm day−1) (log cm day−1) (log cm h−1) (log cm day−110 10 10 10 10 10 10 )
Literature 1.056 2.81/0.59 (253) 0.82/0.39 2.71/0.51 (97) 3.01/0.45 (82) 2.70/074 (129) 1.57/0.71 (115) 0.71/1.45 (264)
Sand
SWIG 0.704 3.01/3.51 (291) 1.22/1.73 3.01/3.51 (291) 3.01/3.51 (291) 3.01/3.51 (291) 1.63/2.13 (291) 3.01/3.51 (291)
Literature 0.938 2.02/0.64 (167) 0.30/0.51 1.91/0.61 (135) 2.09/0.69 (19) 2.36/0.59 (51) 1.03/0.42 (76) 0.80/1.41 (234)
Loamy sand
SWIG 1.033 3.17/3.63 (92) 1.39/1.84 3.17/3.63 (92) 3.17/3.63 (92) 3.17/3.63 (92) 1.79/2.25 (92) 3.17/3.63 (92)
Literature 0.208 1.58/0.67 (315) −0.13/0.67 1.53/0.65 (337) 1.73/0.64 (65) 1.58/0.92 (79) 0.66/0.54 (169) 1.17/1.34 (825)
Sandy loam
SWIG 0.534 2.89/3.36 (500) 1.10/1.58 2.89/3.36 (500) 2.89/3.36 (500) 2.89/3.36 (500) 1.51/1.98 (500) 2.89/3.36 (500)
Literature 0.043 1.28/0.74 (130) −0.4/0.55 1.04/0.54 (217) 1.24/0.47 (12) 1.48/0.86 (103) 0.11/0.87 (215) 0.89/1.45 (714)
Silt loam
SWIG 0.442 2.80/3.17 (409) 1.02/1.39 2.80/3.17 (409) 2.80/3.17 (409) 2.80/3.17 (409) 1.42/1.79 (409) 2.80/3.17 (409)
Literature 0.042 1.09/0.92 (117) −0.32/0.63 0.99/0.63 (137) 0.83/0.95 (50) 1.58/0.92 (62) 0.12/0.79 (81) 1.69/1.76 (411)
Loam
SWIG 0.129 2.27/2.81 (583) 0.49/1.02 2.27/2.81 (583) 2.27/2.81 (583) 2.27/2.81 (583) 0.89/1.43 (583) 2.27/2.81 (583)
Literature 0.038 1.14/0.85 (13) −0.2/0.54 1.29/0.71 (104) 0.81/0.80 (36) 0.99/1.21 (41) 0.12/0.94 (139) 0.73/1.45 (128)
Sandy clay loam
SWIG 0.124 2.25/2.49 (185) 0.47/0.70 2.25/2.49 (185) 2.25/2.49 (185) 2.25/2.49 (185) 0.87/1.11 (185) 2.25/2.49 (185)
Literature 0.010 1.04/0.74 (46) −0.54/0.61 0.87/0.55 (47) 1.09/0.78 (21) 1.14/0.85 (21) −0.15/0.75 (83) 0.35/1.50 (364)
Silty clay loam
SWIG 0.178 2.41/2.77 (250) 0.62/0.98 2.41/2.77 (250) 2.41/2.77 (250) 2.41/2.77 (250) 1.03/1.39 (250) 2.41/2.77 (250)
Literature 0.015 0.87/1.11 (58) −0.46/0.59 0.67/0.58 (77) 0.79/1.08 (48) 1.84/0.89 (25) −0.03/0.94 (109) 1.10/1.54 (284)
Clay loam
SWIG 0.139 2.30/2.68 (467) 0.52/0.90 2.30/2.68 (467) 2.30/2.68 (467) 2.30/2.68 (467) 0.92/1.3 (467) 2.30/2.68 (467)
Literature 0.013 1.06/0.89 (10) 0.01/0.33 1.33/0.33 (9) −0.03/1.28 (2) – (–) −0.77/1.22 (21) 0.81/1.56 (5)
Sandy clay
SWIG – –/– (–) –/– –/– (–) –/– (–) –/– (–) –/– (–) –/– (–)
Literature 0.006 0.98/0.58 (14) −0.72/0.69 0.82/0.55 (12) 1.15/0.16 (5) 0.92/0.71 (12) −0.72/0.95 (22) 0.18/1.32 (349)
Silty clay
SWIG 0.439 2.80/3.17 (121) 1.02/1.39 2.80/3.17 (121) 2.80/3.17 (121) 2.80/3.17 (121) 1.42/1.79 (121) 2.80/3.17 (121)
Literature 0.008 1.17/0.92 (60) – 0.94/0.31 (34) 1.03/0.83 (31) 1.41/015 (27) −0.17/0.71 (115) −0.08/1.41 (737)
Clay
SWIG 5.906 3.93/4.48 (333) 2.15/2.70 3.93/4.48 (333) 3.93/4.48 (333) 3.93/4.48 (333) 2.55/3.10 (333) 3.93/4.48 (333)
Literature – 1.64/0.27 (3) – 1.43/– (3) – (–) 1.75/0.20 (3) – (–) −0.29/1.56 (11)
Silt
SWIG – –/– (–) –/– –/– (–) –/– (–) –/– (–) –/– (–) –/– (–)
1254 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
which methodologies were used, if so desired. Although sup-
plying 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 sec-
ond 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 informa-
tion on the type of measurement technique used. This analy-
sis is again beyond the scope of this paper. Potential error and
uncertainty sources with respect to the use of different mea-
surements are discussed in the Supplement. The uncertainty
of estimated soil hydraulic properties from infiltration mea-
Figure 8. The relationships between clay, silt, sand contents, , surements may be strongly controlled by the person perform-Db
and OC and estimated hydraulic parameters (S and K ). ing the experiment but may also be due the different mea-sat
surement windows of the methods in terms of measurement
volume. The SWIG database provides information to quan-
3.7 Potential error and uncertainty in the SWIG tify uncertainties introduced by difference in measurement
database volume and this analysis will be closely related to the assess-
ment of the representative elementary volume, REV (see, for
Similar to any other databases, the data presented in the example, the work of Pachepsky on the scaling of saturated
SWIG database may be subject to different error sources and hydraulic conductivity).
uncertainties. These include (1) transcription errors that oc- Careful interpretation of the data, with respect to the de-
curred when implementing the measurement data into the tails of the experimental and soil conditions, is also required
EXCEL spreadsheets, (2) inaccuracy and uncertainties in de- when utilizing the SWIG database. For instance, the cases
termining related soil properties such as textural properties, of soils coded 1211–1420 may at first seem odd, as they
(3) violation of the underlying assumption when performing display very low infiltration rates for soils of a very high
the experiments, and (4) uncertainty (variability) in estimated (> 95 %) sand content; however, these unusual findings are
soil hydraulic properties due to the different measurement explained by the soils being recorded as displaying water re-
methods. Unfortunately, none of these errors or uncertainty pellant characteristics. Another example is estimated values
sources are under the control of the SWIG database authors, of Ksat from clayey soils showing high values of Ksat (e.g.,
and quantification of these sources is often difficult, since the soils coded 3746 to 3833 in the SWIG database). The Ksat
required information is often lacking. The uncertainty and values for these soils were obtained using the single-ring in-
variability related to the applied measurement techniques for filtrometer method (Gonzalez-Sosa et al., 2010; Braud, 2015;
estimated soil hydraulic properties may be assessed as infor- Braud and Vandervaere, 2015) and were conducted in the
mation on the applied techniques is available; however, some field under ponded conditions, with vegetation cut but roots
of these methods may only have been used in few cases, mak- left in place. Macropores could have been activated, leading
ing a statistical analysis difficult. to an infiltration rate much higher than expected for clayey
With respect to the transcription error, a strong effort has soils. There were also instances of very high values being
been made to double-check data transcription to prevent or obtained for forested land uses, and sometimes for grassland,
at least to minimize any probable error of this nature. Val- which is probably explained by the visible cracks in the soil
ues of soil properties such as textural composition are known surface present in those cases
to vary strongly between different laboratories and measure-
ment methods. This is especially true for the finer textural 3.8 Research potentials of the SWIG database
classes like clay. Unfortunately, information on the measure-
ment used to determine soil properties is mostly lacking or We envision that the SWIG database offers a unique opportu-
insufficient to assess the magnitude of errors or biases. Inter- nity and information source to (1) evaluate infiltration meth-
nationally, there are a number of standard methods used to ods and to assess their value in deriving soil hydraulic prop-
measure soil properties and several methods may have been erties, (2) test different models and concepts for point-scale
applied to measure the reported soil properties. In this regard, and grid-scale infiltration processes, (3) develop pedotrans-
no conversion has been made and only raw data are reported fer functions to estimate soil hydraulic properties such as the
in the database. However, we have supplied the references Mualem–van Genuchten parameters, (4) identify controls on
for all data (where available) that can be used to ascertain infiltration processes, (5) validate global predictions of in-
Earth Syst. Sci. Data, 10, 1237–1263, 2018 www.earth-syst-sci-data.net/10/1237/2018/
M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1255
filtration from land surface models, (6) study more complex The Supplement related to this article is available
processes like preferential flow in soils, and (7) highlight the online at https://doi.org/10.5194/essd-10-1237-2018-
state-of-the-art understanding of the relationships between supplement.
infiltration and several soil surface characteristics; for exam-
ple, the SWIG database has already contributed to the scope
of Morbidelli et al. (2018) to advance the knowledge of infil- Author contributions. The idea of globally collecting soil infil-
tration over sloping surfaces. tration data was put forward by MR and HV. Published data from
We are confident that the SWIG database is just a first literature were digitized by MR, LW, NM, and MK. Data contrib-
step in collecting and archiving infiltration data and we ex- utors are MR, YAP, LM, SHS, HK, ZH, WAY, AAA, MZA, RAJ,
pect that increasing amounts of data will become avail- 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,
able in the near future. These data will be archived in IE, AF, AF, NF, MG, MHG, TAG, SG, EGH, RH, JJJ, DJ, SDK,
the SWIG database and thus made available to the world- HK, MKH, MKJ, LL, XL, MAL, LL, MVL, DM, DM, MSM, JD-
wide research community. In this regard, we are interested dOM, MRM, JM, FM, MHM, BPM, MPM, SM, RM, DMF, AAM,
in receiving existing or newly measured infiltration curves MRM, SBM, HM, KN, MRN, MVO, TBOF, MRPR, AP, SP, PEP,
and for this purpose the corresponding author will serve TP, MP, DJR, SR, MR, FPR, DR, JRC, OCRF, TS, HS, CS, RS, BS,
as point of contact or data can be made available through MS, NS, ESM, MS, SS, RS, AAS, PS, ZS, RTM, ET, WGT, ARV,
the International Soil Modeling Consortium, ISMC (https: MV, TV, IV, JV, SW, TW, DY, MHY, SZ, YZe, YZh, and HZ. The
//soil-modeling.org/, last access: 1 July 2018), for further data were collected by MR, HV, LW, and KVL. The data analysis
archiving in the SWIG database. 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
4 Data availability 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
All collected data and related soil characteristics are provided checked the accuracy and/or commented on the contents of the pa-
online in *.xlsx and *.csv formats for reference and are avail- per.
able at https://doi.org/10.1594/PANGAEA.885492 (Rahmati
et al., 2018). We add a disclaimer that the database is for pub-
lic domain use only and can be copied freely by referencing Competing interests. The authors declare that they have no con-
it. flict of interest.
5 Conclusion
Acknowledgements. First author thanks the International and
We have collected 5023 infiltration curves from field exper- Scientific Cooperation Office of the University of Maragheh, Iran,
as well as the research committee and board members of the univer-
iments from all over the world covering a broad range of sity for their assistance in conducting the current work.
soils, land uses, and climate regions. We estimated saturated The financial support received from the Forschungszentrum
hydraulic conductivity, Ksat, and sorptivity from more than Jülich GmbH is gratefully acknowledged by the first author.
3000 infiltration curves and compared estimated Ksat values Authors gratefully thank the International Soil Modeling Con-
with values from different databases published in the liter- sortium (ISMC) and the International Soil Tillage Research Organi-
ature. We showed that contrary to the assumption made in zation (ISTRO) for their help in distributing our call for data among
many land surface and global climate models, texture is not researchers throughout the world.
the main controlling factor for Ksat. In addition, the variabil- Parts of data were gathered from the work that was supported by
ity of Ksat derived from these field measurements is consid- the UK–China Virtual Joint Centre for Improved Nitrogen Agron-
erably larger than reported in the literature. The collected in- omy (CINAg, BB/N013468/1), which is jointly supported by the
filtration curves were archived as the SWIG database on the Newton Fund, via UK BBSRC and NERC.
The French Claduègne and Yzeron datasets were acquired during
PANGAEA platform and are therefore available worldwide. the ANR projects FloodScale (ANR-2011-BS56-027) and AVuUR
The data are structured into *.xlsx and *.csv files and include (ANR-07-VULN-01), respectively.
metadata information for further use. Data analysis revealed Parts of the database were made available through research work
that infiltration curves are lacking for clayey, sandy-textured, carried out in the framework of LIFE+ projects funded by the EC.
and stony soils. Also infiltration curve data are lacking for The support of the Spanish Ministry of Economy through project
the northern and permafrost regions. Here, additional efforts CGL2014-53017-C2-1-R is acknowledged.
are needed to collect more data as these regions are partic- The support of the Czech Science Foundation through project
ularly sensitive to climate change, which will clearly affect no. 16-05665S is acknowledged.
the soil hydrology. 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
www.earth-syst-sci-data.net/10/1237/2018/ Earth Syst. Sci. Data, 10, 1237–1263, 2018
1256 M. Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database
commenting on this article. Askari, M., Tanaka, T., Setiawan, B. I., and Saptomo, S. K.: Infiltra-
tion characteristics of tropical soil based on water retention data,
Edited by: David Carlson Journal of Japan Society of Hydrology and Water Resources, 21,
Reviewed by: Marnik Vanclooster and Attila Nemes 215–227, 2008.
Ayu, I. W., Soemarno, S. P., and Java, I. I.: Assessment of Infil-
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