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  • 1
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    Harmonized World Soil Database in SWAT Format (2019)
    PANGAEA
    Details
    Publication Date: 2023-01-30
    Description: 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. The soil variables provided in the Harmonized World Soil Database (2009) and FAO/UNESCO Soil Map of the World included soil texture (%sand, %silt, %clay), organic carbon, pH, and EC. However, from a hydrological point of view, we are in need of parameters such as bulk density, water storage capacity, and hydraulic conductivity for different soil layers. Hence, we have used various pedotransfer functions from the literature to estimate the soil parameters needed in a Soil and Water Assessment Tool (SWAT model). The associated SWAT2012.mdb and lookup table is available at 2w2e GmbH website. The link to the database: https://www.2w2e.com/home/GlobalSoilHwsd
    Keywords: Global Soil Map; Harmonized World Soil Database; SWAT; SWAT-CUP
    Type: Dataset
    Format: application/vnd.rar, 21.2 MBytes
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  • 2
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    Global Land Cover for SWAT "Global Landuse GlobCover " (2019)
    PANGAEA
    Details
    Publication Date: 2023-01-30
    Description: The GlobCover is a European Space Agency initiative to develop global composites and land cover maps using as input observations from the 300-m MERIS sensor on board the ENVISAT satellite mission. The GlobCover map covers the period of December 2004 to June 2006 and was derived by automatic and regionally-tuned classification of a MERIS full resolution surface reflectance time series. The GlobCover map contains 23 land cover types. 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 which could be found in https://www.2w2e.com/.
    Keywords: Globe Cover; Landuse; SWAT
    Type: Dataset
    Format: application/vnd.rar, 396 MBytes
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  • 3
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    CRU and GCM data for SWAT model (2019)
    PANGAEA
    Details
    Publication Date: 2023-01-30
    Description: Historical reanalysis climate data (TMAX, TMIN, PCP) at 0.5o resolution from Climate Research Unit East Anglia (CRU TS 3.1) is reformatted for use in SWAT models. The daily data covers the period of (1970-2005). This data can serve as observed data in regions where observed data is sparse or not available. For future data, we used the ISI-MIP data (Hempel et al., 2013, doi:10.5194/esd-4-219-2013). These were created using five GCM archives as input: HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M, and NorESM1-M. These models were selected because they had daily data for the period of 1950- 2099 for all Representative Concentration Pathway (RCP) scenarios.
    Keywords: Climate data; SWAT
    Type: Dataset
    Format: application/zip, 52.2 MBytes
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  • 4
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    Global FAO/UNESCO Soil Map of the World reformatted with SWAT format (2019)
    PANGAEA
    Details
    Publication Date: 2023-01-30
    Description: 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 include “Lookup_Soil_FAO-UNESCO.txt”, where the map-database correspondence resides, and the SWAT's main database “SWAT2012.mdb” could be found at https://www.2w2e.com.
    Keywords: FAO; Soil Map; SWAT
    Type: Dataset
    Format: application/vnd.rar, 1016.5 kBytes
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  • 5
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    Global Land Cover for SWAT "Global Landuse USGS" (2019)
    PANGAEA
    Details
    Publication Date: 2023-01-30
    Description: 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. The AVHRR source imagery dates from April 1992 through March 1993. The GLCC map contains 24 land cover types. Correspondence is made between the GLCC units and SWAT crop database 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. The SWAT2012.mdb and look up tables could be downloaded in https://www.2w2e.com/
    Keywords: Landuse; SWAT; USGS
    Type: Dataset
    Format: application/vnd.rar, 103.3 MBytes
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  • 6
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    Monthly actual evapotranspiration satellite data with SWAT and SWAT-CUP format (2019)
    PANGAEA
    Details
    Publication Date: 2023-12-23
    Description: Actual Evapotranspiration (AET) consists of evaporation from rainwater intercepted by the canopy before it reaches the ground, from wet and moist soil, and the transpiration through stomata on plant stems and leaves. Evapotranspiration claimed 61% of all rainwater that falls on land. Using the satellite remote sensing data, evapotranspiration from earth land surface have been collected by NASA. Zhang, Kimball et al. (2009) spatially aggregated the global 8-km NASA dataset and published the global monthly AET data with half-degree and one-degree resolutions (Zhang, Kimball et al. 2009). These datasets cover the time period 1983 to 2006. We reformatted the MODIS-AET data, prepared by Zhang et al 2009, in order to be compatible with SWAT-CUP input data. We prepared the MODIS-AET data in two formats (Fig 1). This dataset could be used for calibration and validation of SWAT actual evapotranspiration outputs. SWAT provides monthly actual evapotranspiration at HRU and Subbasin levels in "output.hru" and "output.sub" files, respectively. The SWAT output could be calibrated against observed MODIS-AET data through one of the following approaches: 1) Overlaying the MODIS-AET grids extracted from the 2W2E website with subbasin map of SWAT project and aggregating the located AET grids inside each subbasin to one single grid as the representer of AET of the subbasin. 2) Assessing the individual AET grids files and selecting a proper grid as a representative grid for every subbasin. The dataset could be downloaded entirely or for specific spatial reference through the 2w2e website (Reference 2) -- References: 1. Zhang, K., J. S. Kimball, R. R. Nemani and S. W. Running (2010). "A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006." Water Resources Research 46(9). 2. https://www.2w2e.com/home/ModisNasa
    Type: Dataset
    Format: application/vnd.rar, 264.1 MBytes
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  • 7
    Electronic Resource
    Electronic Resource
    A Bayesian approach for incorporating uncertainty and data worth in environmental projects (1996)
    Springer
    Environmental modeling and assessment 1 (1996), S. 151-158 
    Details
    ISSN: 1573-2967
    Keywords: Data worth model ; risk analysis ; parameter uncertainty ; spatial variability ; Bayesian statistics ; geostatistics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract A data worth model is presented for the analysis of alternative sampling schemes in a special project where decisions have to be made under uncertainty. This model is part of a comprehensive risk analysis algorthm with the acronym BUDA. The statistical framework in BUDA is Bayesian in nature and incorporates both parameter uncertainty and natural variability. In BUDA a project iterates among the analyst, the decision maker, and the field work. As part of the analysis, a data worth model calculates the value of a data campaign before the actual field work, thereby allowing the identification of an optimum data collection scheme. A goal function which depicts the objectives of a project is used to discriminate among different alternatives. A Latin hypercube sampling scheme is used to propagate parameter uncertainties to the goal function. In our example the uncertain parameters are the parameters which describe the geostatistical properties of saturated hydraulic conductivity in a Molasse environment. Our results indicated that failing to account for parameter uncertainty produces unrealistically optimistic results, while ignoring the spatial structure can lead to an inefficient use of the existing data.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01874902
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  • 8
    Electronic Resource
    Electronic Resource
    Uncertainty in Estimation of Soil Hydraulic Parameters by Inverse Modeling: Example Lysimeter Experiments (1999)
    Springer
    Soil Science Society of America journal 63 (1999), S. 501-509 
    Details
    ISSN: 1435-0661
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/
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  • 9
    Electronic Resource
    Electronic Resource
    Inverse Estimation of Parameters in a Nitrogen Model Using Field Data (2000)
    Springer
    Soil Science Society of America journal 64 (2000), S. 533-542 
    Details
    ISSN: 1435-0661
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: conditioned parameter distributions as being a more appropriate alternative to best-fit parameters. Conditioned parameter distributions are quantified within uncertainty domains, and the task of an inverse model then is to reduce or condition this domain through minimization of an appropriate objective function. Propagating the uncertainty in the conditioned parameter distributions will result in simulations where most of the measurements are respected or fall within the 95% confidence interval of the Bayesian distribution (95PCIBD). In this study we used measured pressure heads and NO3 concentrations to estimate 12 hydraulic parameters and up to 14 N turnover–related parameters. Most of the measurements in three soil layers fell within the 95PCIBD. Exceptions were some observed pressure heads corresponding to intense rainfall events and periods of soil freezing, as well as some high NO3 concentrations in the subsoil between 40- and 70-cm depth. We attributed the discrepancies to processes that were not addressed by the simulation model such as freezing and short-circuiting due to macropore flow.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/
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    Paper (German National Licenses)
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