Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications

Abstract

Large-scale distributed watershed models are data-intensive, and preparing them consumes most of the research resources. We prepared high-resolution global databases of soil, landuse, actual evapotranspiration (AET), and historical and future weather databases that could serve as standard inputs in Soil and Water Assessment Tool (SWAT) models. The data include two global soil maps and their associated databases calculated with a large number of pedotransfer functions, two landuse maps and their correspondence with SWAT’s database, historical and future daily temperature and precipitation data from five IPCC models with four scenarios; and finally, global monthly AET data. Weather data are 0.5° global grids text-formatted for direct use in SWAT models. The AET data is formatted for use in SWAT-CUP (SWAT Calibration Uncertainty Procedures) for calibration of SWAT models. The use of these global databases for SWAT models can speed up the model building by 75–80% and are extremely valuable in areas with limited or no physical data. Furthermore, they can facilitate the comparison of model results in different parts of the world.

Measurement(s) surface soil • land • weather • evapotranspiration
Technology Type(s) digital curation
Factor Type(s) year
Sample Characteristic - Environment soil environment • climate system
Sample Characteristic - Location Earth (planet)

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.10043090

Background and Summary

Soil and Water Assessment Tool (SWAT)1 is a comprehensive hydrological model for watershed simulation. SWAT is a continuous-time, semi-distributed, and process-based model, which includes coupled upland and river processes. The land phase of SWAT includes hydrology, soil erosion, crop growth, nutrient cycling, algae transport, pesticide fate and transport, crop management, water transfer, snowfall and snowmelt, and soil temperature. The routing phase in the channels and rivers include processes such as flood routing, sediment routing, nutrient routing, pesticide routing, and routing in the reservoirs. The model is now being upgraded and restructured to SWAT+, and also coupled to glacier melt, heavy metal fate and transport, and other watershed-related processes.

SWAT is the most widely used hydrological and water quality model in the world2. SWAT models are used in a variety of applications, including quantification of water resources availability3,4,5,6,7,8,9,10, the impact of climate and landuse changes11,12,13,14,15,16, soil erosion17,18,19, water quality4,20,21,22,23,24,25, and ecosystem services26,27,28. SWAT also contains a modified version of the Environmental Policy Integrated Climate (EPIC) model29,30 for crop yield simulation8,12,31. In total, more than 4,500 ISI publications can be found using SWAT on various watershed-related issues and ecosystem services around the world, which is by far the most extensive collection of such literature in the world with an average of 550 peer-reviewed publications per year in the last 4 years (data gathered from the Web of Science, October 2019).

Lack of data in many parts of the world is a severe impediment to hydrologic modeling. At the same time, much data generated on the global and local scales is also posing a modeling problem creating an additional source of uncertainty. Previous works have shown that the use of different databases for the same region leads to different model outputs and, consequently, different water resources estimates and different estimates of ecosystem variables4,32. Next to model uncertainty, we have previously used the term conditionality4,33,34 to describe another constraint to a so-called calibrated model. All calibrated model parameters are uniquely conditioned on model assumptions, model structure, input data, as well as calibration data, calibration routines, and objective function definition. A calibration program, SWAT-CUP (SWAT Calibration and Uncertainty Procedures)25,35, was developed for the calibration of SWAT models. SWAT-CUP provides five different calibration routines and the option of choosing between 11 different objective functions. We have previously shown that the choice of different routines and objective functions lead to different parameters while producing equally acceptable calibration results36,37. It would be desirable to always obtain unconditional model parameters independent of calibration procedures and objective functions. For this reason, in the new version of the program, we have provided an option for multi-objective calibration, which provides an option of choosing any combination of the objective functions.

Furthermore, data processing and formatting of data for different applications are highly time-consuming and prone to errors, resulting in much of the research time to be spent on data preparation instead of modeling application and analyses. For this reason, we have put together global soil, landuse, and historical and future weather databases for use in SWAT and other similar watershed models (Table 1) as described in the next section. The collection of these data provides a valuable resource for modeling, especially in regions of data scarcity.

Table 1 Sources and resolutions of databases available at the Pangaea and www.2w2e.com website.

Methods

Soil maps of the world

FAO/UNESCO soil map of the world

There is a general lack of reliable soil information for many parts of the world, which has significantly disadvantaged evaluation of soil erosion, land degradation, environmental impact studies, and sustainable land management programs. Two highly-used global soil maps are the FAO/UNESCO Soil Map of the World and Harmonized World Soil Database (HWSD_v121). Both maps provide a limited description of parameters, which are not directly useful for hydrologic models. We have, therefore, used pedotransfer functions developed from soils around the world to create the needed parameters such as hydraulic conductivity, available water capacity, and bulk density. Pedotransfer functions “translate data we have into data we need”38. These functions estimate parameters that are difficult to measure using easily measured soil properties such as texture, color, and structure, that are routinely recorded by soil surveyors39.

The FAO/UNESCO soil map of the world was prepared using the topographic map series of the American Geographical Society of New York at a nominal scale of 1:5,000,000 consisting of a 30 cm topsoil layer, and a 70 cm subsoil layer (Fig. 1). Associated files, which we produced, include “Lookup_Soil_FAO-UNESCO.txt,” which contains the correspondence between soil map and soil database, and the SWAT’s usersoil table in the main SWAT database “SWAT2012.mdb”.

Fig. 1
figure1

Unique soil units in FAO/UNESCO Soil Map of the World.

Initially, in 2004, the first author created the soil database for the FAO/UNESCO 1995 soil map for quantification of water availability and quality in Africa9,10. The soil names were created as a concatenation of the FAO mapping unit (e.g., Af14-3C) and FAO Soil-ID (e.g., 1) to give Af14-3C-1. Soil hydrologic groups were determined according to SWAT Manual40 based on the criteria in Supplementary Table S1. The fraction of anions exclusion (ANION_EXCL) was set to 0.5 according to the SWAT Manual40. The potential or maximum crack volume of the soil profile (SOL_CRK) expressed as a fraction of the total soil volume was set to zero as there was no information available to evaluate this parameter. Other soil properties have initially been calculated9,10 using the program ROSETTA41. In the current study, we have updated this database using a large number of pedotransfer functions, as described below.

Harmonized world soil database (HWSD)

The Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) combined the available regional and national soil information with the data already contained within the 1:5,000,000 scale FAO-UNESCO map, into a new comprehensive Harmonized World Soil Database (HWSD_v121). This map has a resolution of about 1 km (30 arc seconds) and consists of a 30-cm topsoil layer, and a 70-cm subsoil layer (Supplementary Fig. S1).

The soil variables provided in the Harmonized World Soil Database42 and FAO/UNESCO Soil Map of the World included soil texture (%sand, %silt, %clay), organic carbon, pH, and electrical conductivity (EC). However, from a hydrological point of view, we require parameters such as bulk density, water storage capacity, and hydraulic conductivity for different soil layers, which we used pedotransfer functions to estimate. We estimated soil bulk density (Table 2), soil available water capacity (Table 3), soil hydraulic conductivity (Table 4), soil erodibility factor for universal soil loss equation (USLE) (Table 5), and moist soil albedo (Table 6). The used pedotransfer functions are based on the soils from around the world; hence, providing parameters that are more universally applicable. The above variables were calculated for all soil records in the two soil maps.

Table 2 Soil Bulk Density (ρb) pedotransfer function (g cm−3). OC = %organic carbon, C = %clay, T = %silt, S = %sand.
Table 3 Available Water Capacity, AWC( = θ33–θ1500) (cm cm−1) pedotransfer functions. θ33 = soil water content at field capacity, θ1500 = soil water content at wilting point, C = %clay, ρb = bulk density (g cm−3), T = %silt, OC = %organic carbon, S = %sand.
Table 4 Soil Hydraulic Conductivity (cm day−1) pedotransfer functions. θ33 = soil water content at field capacity, θ1500 = soil water content at wilting point, C = %clay, ρb = bulk density (g cm−3), T = %silt, OC = %organic carbon, S = %sand, topsoil = an ordinal variable having the value of 1 for (depth 0–30 cm) or 0 (depth > 30 cm).
Table 5 Soil erodibility factor (cm day−1) pedotransfer function. S = %sand, T = %silt, C = %clay, OC = %organic carbon.
Table 6 Moist Soil Albedo based on the water content at field capacity (θ33).

Furthermore, to account for parameter uncertainty, the soils were sorted by their textural classes based on USDA classification42 that included Clay, Clay-loam, Heavy-clay, Loam, Loamy-sand, Sand, Sandy-clay, Sandy-clay-loam, Sandy-loam, Slit-loam, Silty-clay, and Silty-clay-loam. For each textural class, we pooled the estimates of various pedotransfer functions from both FAO_UNESCO and HWSD databases and calculated their cumulative probability distributions from which we obtained parameter values at the 5%, 50%, and 95% probability levels. Values for bulk density are shown in Table 7 as an example, while other parameters are given in Supplementary Tables S2S6. An example calculation of the 95 percent prediction uncertainty (95PPU) is shown in Supplementary Fig. S2 for the hydraulic conductivity of topsoil sandy loam. The 95PPU parameter range sets a physically meaningful limit on the parameters for different soil textural classes and is instrumental in constraining the respective parameters in model calibration. These ranges can, of course, be modified by the user as needed.

Table 7 Average and uncertainty estimates of bulk density for top and subsoil based on the textural classes. The numbers in the brackets are the number of samples.

In the pre-processing of HWSD database, similar to FAO/UNESCO, we modified the data where necessary by replacing zero values of %sand, %silt, and %clay by 1, and making sure that their summation equals 100%. Also, after applying various pedotransfer functions, we replaced the negative or unreasonable values with the overall averages to avoid model-generated errors. Finally, we should point out that the soil parameters in both databases must still be calibrated for a specific location.

Landcover maps of the world

Global land cover characterization (GLCC)

The GLCC from USGS is a landuse and land cover classification dataset based primarily on the unsupervised classification of the 1-km AVHRR (Advanced Very High-Resolution Radiometer) 10-day NDVI (Normalized Difference Vegetation Index) composites (Supplementary Fig. S3). The AVHRR source imagery dates from April 1992 through March 1993. The GLCC map contains 24 land cover types. We made the correspondence between the GLCC map units and SWAT’s (crop) database in Supplementary Table S7 based on the description of the land covers provided by the maps and the SWAT landuse definitions.

Global landuse GlobCover

The GlobCover is a European Space Agency initiative to develop global composites and land cover maps using observations from the 300-m MERIS sensor onboard the ENVISAT satellite mission (Soolementary Fig. S4). The GlobCover map covers the period of December 2004 to June 2006 and is derived by automatic and regionally-tuned classification of a MERIS full resolution surface reflectance time series. The GlobCover map contains 23 land cover types. We made correspondence between the GlobCover units and SWAT’s (crop) database in Supplementary Table S8 based on the description of the land covers provided by the maps and the SWAT landuse definitions.

The databases for the above two global landuse maps are supported by the table (crop) in the SWAT2012.mdb database and the lookup tables “Lookup_Landuse_GlobCover.txt” and Lookup_Landuse_USGS.txt. However, similar to the soil parameters, landuse parameters must be calibrated for a given location.

Historical weather data

The historical (1970–2005) reanalysis temperature and precipitation data from the Research Unit East Anglia (CRU TS 3.1)43 were reformatted from NetCDF into SWAT-readable text files. The database is daily and has a resolution of 0.5° and covers the entire globe in 67,420 files.

Future weather data

We provide five global climate models (GCM), each with four carbon evolution scenarios supported by ISI-MIP5 (Inter-Sectoral Impact Model Intercomparison Project)44. These daily data cover the period of 1950–2099 and have a resolution of 0.5°. Similar to CRU, they have been reformatted from NetCDF into SWAT-formatted text files.

The five GCM models include HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M, and NorESM1-M (Table 1) with Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5)45. The 0.5° grid WATCH Forcing Data46 for the period of January 1, 1960, to December 31, 1999 (the reference period) was used as observation data to downscale the five GCMs44. WATCH is a combination of the ERA-40 daily data, the 40-year reanalysis of the European Centre for Medium-Range Weather Forecasts, and the Climate Research Unit TS2.1 dataset (CRU)43. The WATCH Forcing Data data combines the daily statistics of ERA-40 with the monthly mean characteristics of CRU and Global Precipitation Climatology Centre (GPCC) datasets and represents a complete gridded observational dataset for bias correction of global climate data44.

The historical and future data can be downloaded for any given geographic location from www.2w2e.com using the template illustrated in Supplementary Fig. S5. The Climate Change Toolkit (CCT) program47 is linked to the above databases and can be used for bias correction if local data is available. CCT uses additive correction for temperature and a multiplicative correction factor for precipitation. The program can also be used for extreme climate analysis48.

Global actual evapotranspiration data

Actual evapotranspiration (AET) from the earth’s land surface is collected by NASA using satellite data from 1982 to 200349,50 (Supplementary Fig. S6). The algorithm calculates canopy transpiration and soil evaporation using a modified Penman-Monteith approach with biome-specific canopy conductance determined from the normalized difference vegetation index (NDVI). Priestley-Taylor approach was used to quantify open water evaporation. The observations from 34 flux network (FEUXNET) tower sites51 were used to parameterize an NDVI-based canopy conductance model to validate the global ET al.gorithm using measurements from 48 additional, independent flux towers49,50.

AET has been used before to calibrate SWAT when other observed data is not available52. It is crucial to have a measure of AET when calibrating a SWAT model with river discharge data. Using river discharge alone, we can confidently estimate runoff and infiltration. However, components of the infiltrated water cannot be estimated with any degree of confidence. These components include soil moisture (S), aquifer recharge (AR), and actual evapotranspiration (AET) (Fig. 2). Using an estimate of AET in calibration can significantly increase our confidence in the other components of infiltrating water.

Fig. 2
figure2

Schematic illustration of the conceptual water balance model in SWAT.

To use the provided MODIS–NASA data for calibration in SWAT-CUP, users, should overlay the MODIS-AET grids with the subbasin map of their ArcSWAT/QSWAT project and average the AET grid points inside each subbasin to one single value to represent the subbasin’s AET.

Data Records

The Global FAO/UNESCO Soil Map of the World and associated SWAT data files (Lookup Table and SWAT2012.mdb)53 are deposited at Pangaea and www.2w2e.com sites. There are 4,931 soil records in this data set.

The Harmonized World Soil map and associated SWAT data files (Lookup Table and SWAT2012.mdb)54 are deposited at Pangaea and www.2w2e.com sites. There are 16,328 soil records in this data set.

The Global Land Cover Characterization (GLCC) map from USGS and SWAT data file (Lookup Table and SWAT2012.mdb)55 are deposited at Pangaea and www.2w2e.com sites. There are 24 landcover types in this database.

The GlobCover from the European Space Agency and associated data files (Lookup Table and SWAT2012.mdb)56 are deposited at Pangaea and www.2w2e.com sites. There are 23 landcover types in this database.

The historical CRU and future GCM weather data57 are deposited at Pangaea and www.2w2e.com.

Finally, the Global Actual Evapotranspiration Data58 in text format is deposited at Pangaea and www.2w2e.com.

Technical Validation

The global soil and landuse databases have been successfully used in many SWAT applications around the world4,6,9,10,16,59,60,61,62. Validation of these maps, which are based on satellite observations, are offered by ground-truth observations conducted by the map developers and also in various literature63,64.

There is a significant variation in the reported values of soil parameters in the literature and by various agencies. In this research, we used a large number of pedotransfer functions and soil samples from around the world to estimate the textural-based soil parameters. In Table 8 we compared our estimated values of bulk density and hydraulic conductivity with values reported by the U.S. Department of Agriculture (USDA), STRUCTx (STRUCTURAL ENGINEERING RESOURCES website, see Table 8), and other reported values. The rest of the parameters could not be found based on textural classes. As evident, there are significant variations in all estimates, especially for saturated hydraulic conductivities. For this reason, it is essential to have a range of estimates, so one can limit the values to a likely range during model calibration.

Table 8 Comparison of the values of bulk density and saturated hydraulic conductivity estimated in this research with values reported by different sources.

Usage Notes

There are 4,931 soil records in the FAO/UNESCO database, and 16,328 records in the HWSD soil map. Both (usersoil) tables are in the SWAT2012.mdb database. The field (Name) is concatenated by using the fields SU-SYM74, SU-SYM90, MU_GLOBAL, and ISSOL as given in the original HWSD database. SU-SYM74 is the soil unit symbol according to the FAO-74 soil classification, SU-SYM90 is the soil unit symbol according to the FAO-90 soil classification, MU_GLOBAL is the Global Mapping Unit identifier, which provides the link between the GIS soil units and the attribute database, and ISSOL is a field indicating if the soil mapping unit is a soil (1) or a non-soil (0). All maps provided have the World WGS-84 Spatial Reference without any projection. The users will have to project these maps as needed before using it in the ArcSWAT or QSWAT models.

Different soil and landuse maps are provided to emphasize the fact that often more than one database is available for building and calibrating a model, and also to encourage the users to use different databases to realize the conditionality of their calibrated models. Calibrated model parameters are always conditioned on the input data, meaning one could obtain a different set of parameters if one had used a different set of available data. This is probably the most disappointing aspect of calibration.

A calibrated model is, therefore, always: non-unique, subjective, conditional, and subsequently limited on the scope of its use. To achieve unconditionality, the calibrated parameters must be integrated over all conditioning factors. Hence, we recommend using different physical inputs and multi-objective calibration procedures.

References

  1. 1.

    Arnold, J. G. et al. SWAT: model use, calibration, and validation. T. ASABE 55, 1491–1508 (2012).

    Article  Google Scholar 

  2. 2.

    Gassman, P. W., Sadeghi, A. M. & Srinivasan, R. Applications of the SWAT Model Special Section: Overview and Insights. J. Environ. Qual 43, 1–8 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  3. 3.

    Cuceloglu, G., Abbaspour, K. C. & Ozturk, I. Assessing the Water-Resources Potential of Istanbul by Using a Soil and Water Assessment Tool (SWAT) Hydrological Model. Water 9, 814 (2017).

    Article  Google Scholar 

  4. 4.

    Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R. & Klöve, B. Modelling hydrology and water quality of the European Continent at a subbasin scale: calibration of a high-resolution large-scale SWAT model. J. Hydrol. 524, 733–752 (2015).

    ADS  Article  Google Scholar 

  5. 5.

    Zuo, D. et al. Simulating spatiotemporal variability of blue and green water resources availability with uncertainty analysis. Hydrol. Process. 29, 1942–1955 (2015).

    ADS  Article  Google Scholar 

  6. 6.

    Rouholahnejad, E., Abbaspour, K. C., Srinivasan, R., Bacu, V. & Lehmann, A. A high resolution spatiotemporal distribution of water resources quantity and quality in the Black Sea Basin. Water Resour. Res. 50, 5866–5885 (2014).

    ADS  Article  Google Scholar 

  7. 7.

    Narsimlu, B., Gosain, A. K. & Chahar, B. R. Assessment of Future Climate Change Impacts on Water Resources of Upper Sind River Basin, India Using SWAT Model. Water Rerour. Manag 27, 3647–3662 (2013).

    Article  Google Scholar 

  8. 8.

    Sun, C. & Ren, L. Assessing crop yield and crop water productivity and optimizing irrigation scheduling of winter wheat and summer maize in the Haihe plain using SWAT model. Hydrol. Process. 28, 2478–2498 (2014).

    ADS  Article  Google Scholar 

  9. 9.

    Schuol, J., Abbaspour, K. C., Sarinivasan, R. & Yang, H. Estimation of freshwater availability in the West African Sub-continent using the SWAT hydrologic model. J. Hydrol. 352, 30–49 (2008).

    ADS  Article  Google Scholar 

  10. 10.

    Schuol, J., Abbaspour, K. C., Srinivasan, R. & Yang, H. Modelling Blue and Green Water Availability in Africa at monthly intervals and subbasin level. Water Resour. Res. 44, W07406 (2008).

    ADS  Article  Google Scholar 

  11. 11.

    Rouholahnejad, E. F., Abbaspour, K. C. & Lehmann, A. Water Resources of the Black Sea Catchment under Future Climate and Landuse Change Projections. Water 9, 598 (2017).

    Article  Google Scholar 

  12. 12.

    Vaghefi, S. A., Abbaspour, K. C., Faramarzi, M., Srinivasan, R. & Arnold, J. G. Modeling Crop Water Productivity Using a Coupled SWAT-MODSIM Model. Water 9, 157 (2017).

    Article  Google Scholar 

  13. 13.

    Vaghefi, S., Mousavi, S. J., Abbaspour, K. C., Srinivasan, R. & Yang, H. Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran. Hydrol. Process. 28, 2018–2032 (2014).

    ADS  Article  Google Scholar 

  14. 14.

    Zuo, D., Xu, Z., Zhao, J., Abbaspour, K. C. & Yang, H. Response of runoff to climate change in the Wei River basin, China. Hydrol. Sci. J 60, 508–522 (2015).

    Article  Google Scholar 

  15. 15.

    Zhang, X., Xu, Y. P. & Fu, G. Uncertainties in SWAT extreme flow simulation under climate change. J. Hydrol. 515, 205–222 (2014).

    ADS  Article  Google Scholar 

  16. 16.

    Abbaspour, K. C., Faramarzi, M., Ghasemi, S. & Yang, H. Assessing the impact of climate change on water resources in Iran. Water Resour. Res. 45, W10434 (2009).

    ADS  Article  Google Scholar 

  17. 17.

    Abdelwahab, O. M., Ricci, G. F., De Girolamo, A. M. & Gentile, F. Modelling soil erosion in a Mediterranean watershed: Comparison between SWAT and AnnAGNPS models. Env. Res 166, 363–376 (2018).

    CAS  Article  Google Scholar 

  18. 18.

    Melaku, N. D. et al. Prediction of soil and water conservation structure impacts on runoff and erosion processes using SWAT model in the northern Ethiopian highlands. J. Soils Sediments 18, 1743–1755 (2018).

    Article  Google Scholar 

  19. 19.

    Rostamian, R. et al. Application of SWAT Model for Estimating Runoff and Sediment in a Mountainous Watershed in Central Iran. Hydrol. Sci 53, 977–988 (2008).

    Article  Google Scholar 

  20. 20.

    Seo, M., Jaber, F., Srinivasan, R. & Jeong, J. Evaluating the Impact of Low Impact Development (LID) Practices on Water Quantity and Quality under Different Development Designs Using SWAT. Water 9, 193 (2017).

    Article  Google Scholar 

  21. 21.

    Merwade, V., Baffaut, C., Bieger, K., Boithias, L. & Rathjens, H. Featured Series Introduction: SWAT Applications for Emerging Hydrologic and Water Quality Challenges. J. Am. Water Resour. As 53, 67–68 (2017).

    Article  Google Scholar 

  22. 22.

    Singh, G. & Saraswat, D. Development and evaluation of targeted marginal land mapping approach in SWAT model for simulating water quality impacts of selected second generation biofeedstock. Environ. Modell. & Softw 81, 26–39 (2016).

    Article  Google Scholar 

  23. 23.

    Rocha, J., Roebeling, P. & Rial-Rivas, M. E. Assessing the impacts of sustainable agricultural practices for water quality improvements in the Vouga catchment (Portugal) using the SWAT model. Sci. Total Enviro 536, 48–58 (2015).

    CAS  Article  Google Scholar 

  24. 24.

    Baffaut, C., Sadler, E. J., Ghidey, F. & Anderson, S. Long-Term agroecosystem research in the Central Mississippi River Basin: SWAT simulation of flow and water quality in the Goodwater Creek Experimental Watershed. J. Environ. Qual 44, 84–96 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  25. 25.

    Abbaspour, K. C. et al. Modelling hydrology and water quality in the pre-ailpine/alpine thur watershed using swat. J. Hydrol. 333, 413–430 (2007).

    ADS  Article  Google Scholar 

  26. 26.

    Zarrineh, N., Abbaspour, K. C., van Griensven, A., Jeangros, B. & Holzkamper, A. Model-Based Evaluation of Land Management Strategies with Regard to Multiple Ecosystem Services. Sustainability 10, 3844 (2018).

    Article  Google Scholar 

  27. 27.

    Karabulut, A. et al. Mapping water provisioning services to support the ecosystem-water-food-energy nexus in the Danube river basin. Ecosys. Serv 17, 278–292 (2016).

    Article  Google Scholar 

  28. 28.

    Notter, B., Hurni, H., Wiesmann, U. & Abbaspour, K. C. Modelling water provision as an ecosystem service in a large East African river basin. Hydrol. Earth Syst. Sc 16, 69–86 (2012).

    ADS  Article  Google Scholar 

  29. 29.

    Kamali, B., Abbaspour, K. C., Lehmann, A., Wehrlid, B. & Yang, H. Uncertainty-based auto-calibration for crop yield – the EPIC+ procedure for a case study in Sub-Saharan Africa. Eur. J. Agron. 93, 57–72 (2018).

    Article  Google Scholar 

  30. 30.

    Williams, J. R. The EPIC Model in Computer Models of Watershed Hydrology Chapter 25 (Water Resources Publications. Highlands Ranch, CO. 1995).

  31. 31.

    Fereidoon, M. & Koch, M. SWAT-MODSIM-PSO optimization of multi-crop planning in the Karkheh River Basin, Iran, under the impacts of climate change. Sci. Total Environ. 630, 502–516 (2018).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32.

    Kamali, B., Abbaspour, K. C. & Yang, H. Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components. Water 9, 709 (2017).

    Article  Google Scholar 

  33. 33.

    Abbaspour, K. C., Sonnleitner, M. & Schulin, R. Uncertainty in Estimation of Soil Hydraulic Parameters by Inverse Modeling: Example Lysimeter Experiments. Soil Sci. Soc. of Am. J 63, 501–509 (1999).

    ADS  CAS  Article  Google Scholar 

  34. 34.

    Abbaspour, K. C., Vaghefi, S. A. & Srinivasan, R. A guideline for successful calibration and uncertainty analysis for soil and water assessment: A review of papers from the 2016 International SWAT Conference. Water 10, 6 (2018).

    Article  Google Scholar 

  35. 35.

    Abbaspour, K. C., Johnson, A. & van Genuchten, M. Th. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J. 3, 1340–1352 (2004).

    Article  Google Scholar 

  36. 36.

    Yang, J., Reichert, P., Abbaspour, K. C. & Yang, H. Comparing uncertainty analysis techniques for a SWAT application to Chaohe Basin in China. J. Hydrol. 358, 1–23 (2008).

    ADS  Article  Google Scholar 

  37. 37.

    Houshmand, D., Esmaili, K., Faridhosseini, A., Sanaei Nejad, H., Khalili, D. & Abbaspour, K. C. Sensitivity of calibrated parameters and water resource estimates on different objective functions and optimization algorithms. Water 9, 384 (2017).

    Article  Google Scholar 

  38. 38.

    Bouma, J. Using soil survey data for quantitative land evaluation. Adv. Soil Sci 9, 177–213 (1989).

    ADS  Article  Google Scholar 

  39. 39.

    Abbaspour, K. C. & Moon, D. E. Relationship between conventional field information and some soil properties measured in the laboratory. Geoderma 55, 119–140 (1992).

    ADS  Article  Google Scholar 

  40. 40.

    Neitsch, S. L., Arnold, J. G, Kiniry, J. R. & Williams, J. R. Soil and Water Assessment Tool User’s Manual, version 2000. (Grassland, Soil and Water Research Laboratory, Ag ricultural Research Service, Blackland Research Center, Texas Agricultural Experiment Station 2001).

  41. 41.

    Schaap, M. G., Leij, F. J. & van Genuchten, M. T. ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. J. Hydrol. 251, 163–176 (2001).

    ADS  Article  Google Scholar 

  42. 42.

    Harmonized World Soil Database. Coordinated by: Nachtergaele, F. et al. (FAO, IIASA, ISRIC, ISSCAS, JRC 2009).

  43. 43.

    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  44. 44.

    Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction - the ISI-MIP approach. Earth Syst. Dyn 4, 219e236 (2013).

    Article  Google Scholar 

  45. 45.

    Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Weedon, G. P. et al. Creation of the watch forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeorol. 12, 823–848 (2011).

    ADS  Article  Google Scholar 

  47. 47.

    Vaghefi, S. A., Abbaspour, N., Kamali, B. & Abbaspour, K. C. A toolkit for climate change analysis and pattern recognition for extreme weather conditions - Case study: California-Baja California Peninsula. Environ. Modell. & Softw. 96, 181–198 (2017).

    Article  Google Scholar 

  48. 48.

    Vaghefi, S. A. et al. The future of extreme climate in Iran. Sci. Rep 9, 1464 (2019).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49.

    Zhang, K. et al. Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005. J. Hydrol. 379, 92–110 (2009).

    ADS  Article  Google Scholar 

  50. 50.

    Zhang, K., Kimball, J. S., Nemani, R. R. & Running, S. W. A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006. Water Resour. Res. 46, W09522 (2010).

    ADS  Google Scholar 

  51. 51.

    Baldocchi, D. Breathing of the terrestrial biosphere: Lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1–26 (2008).

    CAS  Article  Google Scholar 

  52. 52.

    Odusanya, A. E. et al. Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria. Hydrol. Earth Syst. Sci. 23, 1113–1144 (2019).

    ADS  Article  Google Scholar 

  53. 53.

    Abbaspour, K. & Saeid, A. V. Global FAO/UNESCO Soil Map of the World reformatted with SWAT format. PANGAEA, https://doi.org/10.1594/PANGAEA.901313 (2019).

  54. 54.

    Abbaspour, K. & Saeid, A. V. Harmonized World Soil Database in SWAT Format. PANGAEA, https://doi.org/10.1594/PANGAEA.901309 (2019).

  55. 55.

    Abbaspour, K. & Saeid, A. V. Global Land Cover for SWAT “Global Landuse USGS”. PANGAEA, https://doi.org/10.1594/PANGAEA.901311 (2019).

  56. 56.

    Abbaspour, K. & Saeid, A. V. Global Land Cover for SWAT “Global Landuse GlobCover”. PANGAEA, https://doi.org/10.1594/PANGAEA.901310 (2019).

  57. 57.

    Abbaspour, K. & Saeid, A. V. CRU and GCM data for SWAT model. PANGAEA, https://doi.org/10.1594/PANGAEA.901312 (2019).

  58. 58.

    Abbaspour, K., Saeid, A. V., Lehmann, A. & Raghavan, S. Monthly actual evapotranspiration satellite data with SWAT and SWAT-CUP format. PANGAEA, https://doi.org/10.1594/PANGAEA.897170 (2019).

  59. 59.

    Ginanjar, M. R. & Putra, S. S. Sediment trapping analysis of flood control reservoirs in Upstream Ciliwung River using SWAT Model. 2nd transdisciplinary research on environmental problems in southeast Asia. Earth Env. Sci 71, 012014 (2017).

    Google Scholar 

  60. 60.

    Asl-Rousta, B. & Mousavi, S. J. A TOPSIS-based multicriteria approach to the calibration of a basin-scale SWAT hydrological model. Water Resour. Manag. 33, 439–452 (2019).

    Article  Google Scholar 

  61. 61.

    Kangsabanik, S. & Murmu, S. Rainfall-runoff modelling of Ajay river catchment using SWAT model. 7th International Conference on Environment and Industrial Innovation. Earth Env. Sci. 67, 012033 (2017).

    Google Scholar 

  62. 62.

    El-Sadek, A. & İrvem, A. Evaluating the impact of land use uncertainty on the simulated streamflow and sediment yield of the Seyhan River basin using the SWAT model. Turk. J. Agric. For. 38, 515–530 (2014).

    Article  Google Scholar 

  63. 63.

    Nakaegawa, T. Comparison of Water-Related Land Cover Types in Six 1-km Global Land Cover Datasets. Hydrol. Earth Syst. Sci. 13, 649–664 (2012).

    Google Scholar 

  64. 64.

    Frey, K. E. & Smith, L. C. How well do we know northern land cover? Comparison of four global vegetation and wetland products with a new ground-truth database for West Siberia. Global Biogeochem. CY 21, GB1016 (2007).

    ADS  Article  CAS  Google Scholar 

  65. 65.

    Adams, W. A. The effect of organic matter on the bulk and true densities of some uncultivated podzolic soils. Soil Sci 24, 10Y17 (1973).

    Google Scholar 

  66. 66.

    Alexander, E. B. Bulk densities of California soils in relation to other soil properties. Soil Sci. Soc. Am. J 44, 689Y692 (1980).

    Article  Google Scholar 

  67. 67.

    Federer, C. A. Nitrogen mineralization and nitrification: Depth variation in four New England forest soils. Soil Sci. Soc. Am. J 47, 1008Y1014 (1983).

    Article  Google Scholar 

  68. 68.

    Huntington, T. G., Johnson, C. E., Johnson, A. H., Siccama, T. G. & Ryan, D. F. Carbon, organic matter and bulk density relationships in a forested Spodosol. Soil Sci. 148, 380Y386 (1989).

    ADS  Article  Google Scholar 

  69. 69.

    Manrique, L. A. & Jones, C. A. Bulk density of soils in relation to soil physical and chemical properties. Soil Sci. Soc. Am. J 55, 476–481 (1991).

    ADS  CAS  Article  Google Scholar 

  70. 70.

    Federer, C. A., Turcotte, D. E. & Smith, C. T. The organic fractionbulk density relationship and the expression of nutrient content in forest soils. Can. J. For. Res. 23, 1026Y1032 (1993).

    Article  Google Scholar 

  71. 71.

    Bernoux, M., Arrouays, D., Cerri, C., Volkoff, B. & Jolivet, C. Bulk densities of Brazilian Amazon soils related to other soil properties. Soil Sci. Soc. Am. J 62, 743–749 (1998).

    ADS  CAS  Article  Google Scholar 

  72. 72.

    Tomasella, J. & Hodnett, M. G. Estimating soil water retention characteristics from limited data in Brazilian Amazonia. Soil Sci. 163, 190–202 (1998).

    ADS  CAS  Article  Google Scholar 

  73. 73.

    Leonavičiute, N. Predicting soil bulk and particle densities by pedotransfer functions from existing soil data in Lithuania. Geografijos metraštis 33, 7–330 (2000).

    Google Scholar 

  74. 74.

    Post, W. M. & Kwon, K. C. Soil carbon sequestration and land-use change: Processes and potential. Glob Change Biol 6, 317Y327 (2000).

    Article  Google Scholar 

  75. 75.

    Kaur, R., Kumar, S. & Gurung, H. P. A pedo-transfer function (PTF) for estimating soil bulk density from basic soil data and its comparison with existing PTFs. Aust. J. Soil Res. 40, 847Y857 (2002).

    Google Scholar 

  76. 76.

    Tremblay, S., Ouimet, R. & Houle, D. Prediction of organic carbon content in upland forest soils of Quebec, Canada. Can. J. For. Res 32, 1Y12 (2002).

    Article  Google Scholar 

  77. 77.

    Prevost, M. Predicting soil properties from organic matter content following mechanical site preparation of forest soils. Soil Sci. Soc. Am. J 68, 943Y949 (2004).

    Article  Google Scholar 

  78. 78.

    Benites, V. M., Machado, P. L. O. A., Fidalgo, E. C. C., Coelho, M. R. & Madari, B. E. Pedotransfer functions for estimating soil bulk density from existing soil survey reports in Brazil. Geoderma 139, 90Y97 (2007).

    Article  Google Scholar 

  79. 79.

    Perie, C. & Ouimet, R. Organic carbon, organic matter and bulk density relationships in boreal forest soils. Can. J. Soil Sci. 88, 315Y325 (2008).

    Article  Google Scholar 

  80. 80.

    Han, G. Z., Zhang, G. L., Gong, Z. T. & Wang, G. F. Pedotransfer Functions for Estimating Soil Bulk Density in China. Soil Sci. 177, 158–164 (2012).

    ADS  CAS  Article  Google Scholar 

  81. 81.

    Hong, S. Y., Minasny, B., Han, K. H., Kim, Y. & Lee, K. Predicting and mapping soil available water capacity in Korea. Peer J. 1, e71 (2013).

    PubMed  Article  PubMed Central  Google Scholar 

  82. 82.

    Hossain, M. F., Chen, W. & Zhang, Y. Bulk density of mineral and organic soils in the Canada’s arctic and sub-arctic. Inf. Proc. Agric. 2, 183–190 (2015).

    Google Scholar 

  83. 83.

    Petersen, G. W., Cunningham, R. L. & Matelski, R. P. Moisture characteristics of Pennsylvania soils: I. Moisture retention as related to texture. Soil Sci. Soc. Am. J 32, 271–275 (1968).

    ADS  Article  Google Scholar 

  84. 84.

    Hall, D. G. M., Reeve, M. J., Thomasson, A. J. & Wright, V. F. Water retention, porosity and density of field soils. Tech. Monogr. 9, Soil Survey of England and Wales, Harpenden, United Kingdom (1977).

  85. 85.

    Gupta, S. C. & Larson, W. E. Estimating soil water retention characteristics from particle size distribution, organic matter content, and bulk density. Water Resour. Res. 15, 1633–1635 (1979).

    ADS  Article  Google Scholar 

  86. 86.

    Rawls, W. J., Brakensiek, D. L. & Saxton, K. E. Estimation of soil water properties. T. ASAE 25, 1316–1320 (1982).

    Article  Google Scholar 

  87. 87.

    Rawls, W. J., Brakensiek, D. L. & Soni, B. Agricultural management effects on soil water processes. I: Soil water retention and Green-Ampt parameters. T. ASAE 26, 1747–1752 (1983).

    Article  Google Scholar 

  88. 88.

    Rajkai, K. & Varallyay,G. Estimating soil water retention from simpler properties by regression techniques. Proceedings of the International Workshop on Indirect Methods for Estimating the Hydraulic Properties of Unsaturated Soils (eds van Genuchten, M. Th., Leij, F. J. & Lund, L.), University of California, Riverside, 417–426 (1992).

  89. 89.

    Canarache, A. Physical-technological maps - A possible product of soil survey for direct use in agriculture. Soil Technol. 6, 3–16 (1993).

    Google Scholar 

  90. 90.

    Batjes, N. H. Development of a world data set of soil water retention properties using pedotransfer rules. Geoderma 71, 31–52 (1996).

    ADS  Article  Google Scholar 

  91. 91.

    Rawls, W. J., Pachepsky, Y. A., Ritchie, J. C., Sobecki, T. M. & Bloodworth, H. Effect of soil organic carbon on Soilwater retention. Geoderma 116, 61–76 (2003).

    ADS  CAS  Article  Google Scholar 

  92. 92.

    Saxton, K. E. & Rawls, W. J. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Amer. J 70, 1569–1578 (2006).

    ADS  CAS  Article  Google Scholar 

  93. 93.

    Aina, P. O. & Periaswamy, S. P. Estimating available water-holding capacity of Western Nigerian soils from soil texture and bulk density using core and sieved samples. Soil Sci. 140, 55–58 (1985).

    ADS  Article  Google Scholar 

  94. 94.

    Dijkerman, J. An Ustult-Aquult-Tropept catena in Sierra Leone, W. Africa, II. Land qualities and land evaluation. Geoderma 42, 29–49 (1988).

    ADS  Article  Google Scholar 

  95. 95.

    Arruda, F. B., Julio, J. Jr. & Oliveira, J. B. Parametros de solo para calculo de agua disponivel com base natextura do solo. Rev. Bras. Cienc. Solo 11, 11–15 (1987).

    Google Scholar 

  96. 96.

    Cosby, B. J., Hornberger, G. M., Clapp, R. B. & Ginn, T. R. A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res. 20, 682–690 (1984).

    ADS  Article  Google Scholar 

  97. 97.

    Saxton, K. E., Rawls, W. J., Romberger, J. S. & Papendick, R. I. Estimating generalized soil water characteristics from texture. Soil Sci. Soc. Am. J 50, 1031–1036 (1986).

    ADS  Article  Google Scholar 

  98. 98.

    Campbell, G. S. & Shiozawa, S. Prediction of hydraulic properties of soils using particle size distribution and bulk density data. Indirect Methods for Estimating the Hydraulic Properties of Unsaturated Soils, (eds van Genuchten, M. Th., Leij, F. J. & Lund, L. J.), U.S. Salinity Laboratory, Riverside, CA, 317–328 (1992).

  99. 99.

    Rawls, W. J. & Brakensiek, D. L. Prediction of soil water properties for hydrologic modeling. Proceedings of Watershed Management in the Eighties. Denver, CO, ASCE, 293–299 (1985).

  100. 100.

    Woesten, J. H. M., Lilly, A., Nemes, A. & Le Bas, C. Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169–185 (1999).

    ADS  Article  Google Scholar 

  101. 101.

    Weynants, M., Vereecken, H. & Javaux, M. Revisiting Vereecken pedotransfer functions: Introducing a closed-form hydraulic model. Vadose Zone J. 8, 86–95 (2009).

    Article  Google Scholar 

  102. 102.

    Wang, K. C. et al. Variation of surface albedo and soil thermal parameters with soil moisture content at a semi-desert site on the western Tibetan plateau. Boundarey Layer Meteorol 116, 117–129 (2005).

    ADS  Article  Google Scholar 

  103. 103.

    Gascoin, S., Ducharne, A., Ribstein, P., Perroy, E. & Wagnon, P. Sensitivity of bare soil albedo to surface soil moisture on the moraine of the Zongo glacier (Bolivia). Geophys. Res. Lett. 36, L02405 (2009).

    ADS  Article  Google Scholar 

  104. 104.

    Sugathan, N., Biju, V. & Renuka, G. Influence of soil moisture content on surface albedo and soil thermal parameters at a tropical station. J. Earth Syst. Sci. 123, 1115–1128 (2014).

    ADS  Article  Google Scholar 

  105. 105.

    Arkhangelskaya, T. & Lukyashchenko, K. Estimating soil thermal diffusivity at different water contents from easily available data on soil texture, bulk density, and organic carbon content. Biosystem Engineering 168, 83–95 (2018).

    Article  Google Scholar 

  106. 106.

    García-Gutiérre, C., Pachepsky, Y. & Martín, M. A. Technical note: Saturated hydraulic conductivity and textural heterogeneity of soils. Hydrol. Earth Syst. Sci 22, 3923–3932 (2018).

    Article  CAS  Google Scholar 

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Acknowledgements

The authors wish to acknowledge the support of Swiss Federal Institute of Aquatic Science and Technology (Eawag) in publishing this paper.

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K.C.A., H.Y. and R.S. developed the concept, K.C.A. created soil parameters using pedotransfer functions, S.A.V. prepared figures and reformatted climate data in text form, K.C.A. and R.S. interpreted soil and landuse data. All authors edited and participated in manuscript preparation.

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Abbaspour, K.C., Vaghefi, S.A., Yang, H. et al. Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications. Sci Data 6, 263 (2019). https://doi.org/10.1038/s41597-019-0282-4

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