--------------------------------------------- Preferred citation: Qiong Wu; John J. Ramirez-Avila; Jinxi Song (2023). "CUSCN30: the dynamic dataset of Curve Number from 2008 to 2021 over conterminous United States". Mississippi State University Institutional Repository. Dataset. https://doi.org/10.54718/OEOT1989. Corresponding Author: John J. Ramirez-Avila, Mississippi State University, jramirez@cee.msstate.edu Jinxi Song, Northwest University, jinxisong@nwu.edu.cn. License: CC-BY --------------------------------------------- ## Summary The CN dataset (CUSCN30) is proposed to delineate dynamic variations of the CN values over the CONUS from 2008 to 2021, generated at a finer resolution (30m) with abundant types of LULC defined from different advanced datasets. The datasets constructed for CUSCN30 are listed as follows: the National Cropland Data Layer (LCD), the National Forest Type Dataset (NFTD), the National Land Cover Database (NLCD), the Soil Survey Geographic Database (SSURGO), the database Global Hydrologic Soil Groups for USDA-based CN runoff modeling (HYSOGs250m), and Shuttle Radar Topography Mission (SRTM). The CUSCN30 aims to improve the accuracy of distributed hydrologic processing prediction and hydrologic modeling simulation. As the fundamental niche dataset, the CUSCN30 is believed to be valuable for simulating the hydrology processes and will thrive the hydrological science in the new era. --------------------------------------------- ## Files and Folders dataset.zip - contains 14 CUSCN30 files that from 2008 to 2021 based on the ARCII (average) condition. readme.txt (this file) - contains summary information, project specifics, and data specifications filelist.csv - contains a complete list of files included in this dataset TransfertoARCI.py TransfertoARCIII.py File in dataset.zip Only the 14 CN maps of ARCII (average) condition from 2008 to 2021 were included in the dataset. The CN map of ARCI (dry) and ARCIII (wet) is easy to produced by the Python file TransfertoARCI.py and TransfertoARCIII.py based on the same year ARCII map. Spatial Reference Properties for all images: Albers_Conical_Equal_Area Authority: Custom Projection: Albers False_Easting: 0.0 False_Northing: 0.0 central_meridian: -96.0 Standard_Parallel_1: 29.5 Standard_Parallel_2: 45.5 latitude_of_origin: 23.0 Linear Unit: Meter (1.0) Geographic Coordinate System: GCS_WGS_1984 Angular Unit: Degree (0.0174532925199433) Prime Meridian: Greenwich (0.0) Datum: D_WGS_1984 Spheroid: WGS_1984 Semimajor Axis: 6378137.0 Semiminor Axis: 6356752.314245179 Inverse Flattening: 298.257223563 --------------------------------------------- ## Materials & Methods Four steps were implemented to construct CUSCN30. The different datasets were preprocessed (i.e. the NFTD and the HYSOGs250m were resampled into a 30 m-grid) to coordinate with other layers, and the SRTM was mosaiced to get the entire area of the CONUS. Step 1: The hydrologic soil-cover complex. The annual LULC dataset was generated by extracting the cultivated land types from fourteen (14) CDL scenes, one (1) CONUS forest group (CFG) scenario and five (5) NLCD land use scenarios from 2008 to 2021. The priority of LULC datasets decreased from CDL, CFG to NLCD. The classification distribution of the CDL data contains 107 types of crops, predominantly corn, soybean, fallow/idle cropland, winter wheat, and alfalfa. As for the NFTD, 28 groups including the western white pine group, oak/pine group, maple/beech/birch group, and tropical hardwoods were collected. The no-data area was filled with the 11 classes included in the NLCD datasets. Step 2: Tabulation To generate the CNLUT, the hydrologic soil-cover complex (Step1) is required to get their correspondence. The dual HSGs (A/D, B/D, and C/D) were assigned to hydrological group D as recommended by (Jaafar et al. 2019; NRCS 2009; Van Mullem et al. 2002). The table of the merged LULC is listed in Table S1. Finally, a total of 112 classes of hydrologic soil-cover complex represent 28 types of LULC and 4 HSGs (A/B/C/D). The merged LULC detail class, HSG, description of classes and the initial CN value in the NEH-630 and table NLCD are shown in Table 1. Step 3: CN values To generate the annual spatial distribution of the CN for the CONUS, the preprocessed annual LULC and the HSG datasets were merged and CN values were assigned referencing the CNLUT. Although there were 112 classes of hydrologic soil-cover complex, a total of 46 CN values (CNraw) were derived from the CNLUT due to duplication. Step 4: Slope-adjusted Slope potentially influences the determination of the CN value on the finer resolution mapping. To remove the terrain effect of higher resolution estimation of CN values, as proposed by (Huang et al. 2006), the slope-adjusted formulation of CN values (CNslope) is expressed as: 〖CN〗_slope=〖CN〗_raw (322.79+15.63(α))/(α+323.52) where the slope α (m/m) is valid between 0.14 and 1.4 to stay within the bounds of the experimental values. Although the 〖CN〗_slope values should never be above 100, the CN values for open water, initially being 100 in all kinds of HSG, rise above that limit when the slope α was more than 0.05. After slope correction was completed in these areas, CN values above the limit were masked as 100. Step 5: Antecedent Runoff Conditions (ARC) Three ARC scenarios were developed including ARCI (dry), ARCII (average), and ARCIII (wet) to consider climate variability or seasonality within the year. The CN values for the scenario I (〖CN〗_ARCI) and the scenario III (〖CN〗_ARCIII) were calculated based on the determined CN values (〖CN〗_ARCII) as proposed by (Arnold 1994; Arnold et al. 1990). 〖CN〗_ARCI=CN-(20×(100-CN))/(100-CN+exp⁡(2.533-0.0636×(100-CN))) 〖CN〗_ARCII=〖CN〗_slope 〖CN〗_ARCIII=CN×exp⁡(0.00636×(100-CN)) The CUSCN30 improved the accuracy of the dataset of CN map, which supports further studies that require distributed runoff simulation --------------------------------------------- ## Citations Arnold, J., 1994. SWAT-soil and water assessment tool. Arnold, J. G., J. Williams, A. Nicks & N. Sammons, 1990. SWRRB; a basin scale simulation model for soil and water resources management. SWRRB; a basin scale simulation model for soil and water resources management. Boryan, C., Z. Yang, R. Mueller & M. Craig, 2011. Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto International 26(5):341-358. Huang, M., J. Gallichand, Z. Wang & M. Goulet, 2006. A modification to the Soil Conservation Service curve number method for steep slopes in the Loess Plateau of China. Hydrological Processes: An International Journal 20(3):579-589. Jaafar, H. H., F. A. Ahmad & N. El Beyrouthy, 2019. GCN250, new global gridded curve numbers for hydrologic modeling and design. Scientific data 6(1):1-9. NRCS, U., 2009. Chapter 7–Hydrologic Soil Groups in: NRCS–National Engineering Handbook (NEH), Part 630–Hydrology. USDA NRCS, ashington, DC:7.1-7.5. Van Mullem, J., D. Woodward, R. Hawkins, A. Hjelmfelt & Q. Quan, Runoff curve number method: Beyond the handbook. In: Proceedings of Second Federal Interagency Hydrologic Modeling Conference, Las Vegas, Nevada, 2002.