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Influence on Runoff: STATSGO vs. SSURGO: Soil Dataset Effect in Small Semiarid Watershed


  • Department of Civil Engineering, N.I.T. Kurukshetra, Thanesar – 136119, Haryana, India


This paper highlights the impact of the spatial varaiablity of soil data having different resolution on runoff estimated by Soil Conservation Service Curve Number (SCS-CN) methodology by taking an example of small Sub-watershed (16.59 SQ Km) in Cochise County, Arizona, USA. For this purpose watershed was delineated using DEM (Digital Elevation Model). Input data such as soil maps were prepared using GIS software. Data used is satellite imagery of LANDSAT satellite for the study area along with STATSGO and SSURGO soil maps of different resolutions along with observed (gauged) discharge & rainfall data. Anderson –Darling normality test and 1 sample T-Test was used to compare the proximity between observed discharge values(gauged data) and estimated discharge values using soil datasets of different (Higher & lower) Resolution. The normality test and the time series plot indicate that the data meet the t-test’s assumptions of normality and randomness. Results highlight the importance of dataset scale and influence is found comparable; Trend of computed runoff is better reflected using STATSGO data in comparison to SSURGO data (Figure 4, Table 3, 4 & 5). One-sample T-test is applied, and value of P (Table 4 & 5) is supporting that STATSGO data mean of estimated discharge is more close to observed discharge data mean in comparison to SSURGO data. Anderson-Darling normality test shows STATSGO data is following normal distribution (Figure6 & p-value 0.488(Table4).1-Sample T-Test p-value 0.488 is greater than significance level α (alpha) 0.05. While in the case of SSURGO dataset 1-sample t-test is failed (Table (5), P (0.00) <α (0.05). The 95% confidence interval (Table 4)indicates STATSGO data set (Figure 6 & Table 4). is better reflecting observed discharge trend in comparison to SSURGO dataset (Figure 6 & Table 5) for Study Area (Figure 1). In General, higher resolution datasets are considered better, but it is concluded and depicted that it might not be true always. The model may perform in a better way by accounting for other factors such as AMC conditions and intensities of rainfall. The methodology is easy and can be extended to bigger areas for runoff simulation along with application in all simulation software used in watershed modelling.


STATSGO, SSURGO, Curve Number (CN), Runoff, Soil Data Resolution.

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  • Wu TH, Hall JA , Bonta JV. Evaluation of runoff and erosion models. J. Irrig. Drain. Eng. 1993,119(2), pp.364–82.
  • Kothyari UC, Jain SK. Sediment yield estimation using GIS. Hydrol. Sci. J. 1997, 42(6),pp. 833–84.
  • Greene RG, Cruise JF. Urban watershed modeling using geographic information system. J. Water Resour. Plann.Manage. 1995,121(4),pp. 318–325.
  • Tsihrintzis VA , Hamid R. Urban stormwater quantity/quality modeling using the SCS method and empirical equations.J. Am. Water Resour. Assoc. 1997,33(1),pp. 163–176.
  • Lewis D, Singer MJ, Tate KW. Applicability of SCS curve number method for a California oak woodlands watershed.J. Soil Water Conserv. 2000,55(2),pp. 226–230.
  • Chandrmohan T, Durbude DG. Estimation of runoff using small watershed models. Hydrol. J. 2001, 24(2), pp.45–53.
  • He C. Integration of geographic information systems and simulation model for watershed management. Environ.Model Softw. 2003 18(8–9), pp.809–813.
  • Liu XZ, Kang SZ, Liu LD , Zhang XP. SCS model based on geographic information and its application to simulate rainfall-runoff relationship at typical small watershed level in Loess Plateau. Trans. CSAE (in Chinese) 2005, 21(5), pp.93–97.
  • Mishra SK, Tyagi JV, Singh VP and Singh R. SCS-CN-based modeling of sediment yield. J. Hydrol. 2006,324(1–4),pp.301–322.
  • Liu XZ, Li JZ. Application of SCS model in the estimation of runoff from small watershed in Loess Plateau of China.Chin. Geogra. Sci. 2008, 18(3), pp.235–241.
  • Sahu RK, Mishra SK , Eldho TI. Comparative evaluation of SCS-CN-inspired models in applications to classified datasets.Agr. Water Manage. 2010,97(5),pp. 749-756.
  • Pandey A , Sahu AK. Generation of curve number using remote sensing and Geographic Information System.1996,1(1),pp.11-19.
  • Nayak TR, Jaiswal RK. Rainfall-runoff modelling using satellite data and GIS for Bebas river in Madhya Pradesh. IE (I) Journal 2003, 84, pp. 47-50.
  • Zhan Z , Huang H . ArcCN-Runoff: An ArcGIS tool for generating curve number and runoff maps, 2004, 19(10),pp.375-379.
  • Gandini ML , Usunoff EJ. SCS Curve Number Estimation Using Remote Sensing NDVI in A GIS Environment. J. Env.Hyd. 2004, 12,pp. 1-9.
  • KniselW G.CREAMS: A Field Scale Model for Chemicals, Runoff, and Erosion from Agricultural Management Systems.United States Department of Agriculture Conservation Research Report No Washington D.C, 1980, 21(6),pp.1131-1135.
  • Beasley DB, Huggins LF , Monke EJ. ANSWERS: a model for watershed planning. Tran. ASABE 1980,23(4), pp.938– 944.
  • Young RA, Onstad CA, Bosch DD , Anderson WP. AGNPS: a nonpoint-source pollution model for evaluating agricultural watersheds. J. Soil Water Conserv. 1989, 44(2), pp.168–173.
  • Sharpley AN , Williams JR. EPIC, Erosion/ Productivity Impact Calculator: 1. Model Documentation. US Department of Agriculture Technical Bulletin: Washington, D.C.1990,pp.235.
  • Arnold JG, Williams JR, Srinivasan R, King KW and Griggs RH. SWAT: Soil Water Assessment Tool: Texas A&M University, 1995.
  • Water balance model. Date accessed: 12/04/2014.
  • Ajmal M, Waseem M, Ahn J-H, Kim T-W. Improved runoff estimation using event-based rainfall-runoff models. Water Resour. Manag. 2015, 29(6), pp.1995–2010.
  • Water management sources. WMS_Overview/wms_overview.html. Date accessed: 02/05/2015.
  • Arnold JG, Williams JR, Srinivasan R ,King KW. SWAT: Soil and Water Assessment Tool: USDA-ARS, Grassland, Soil and Water Research Laboratory, Temple, TX, 1996, 53(5),pp.1423-1431.
  • Williams JR. The EPIC model. In: Singh, V.P. (Ed.) Computer Models of Watershed Hydrology: Water Resources Publications, USA,1995,pp.1130.
  • Young RA, Onstad CA, Bosch DD & Anderson WP. AGNES, Agricultural Non-Point Source Pollution Model: A Watershed Analysis Tool. USDA Conservation Report 35: Washington D.C, 1987, pp.1-6.
  • Akhondi S. An investigation of curve number model in flood estimation using Geographical information System (GIS): MSc. Thesis, Tarbiat Modares University, Tehran, 2001.
  • Mahboubeh M. Application of natural resources conservation service-curve number method for runoff estimation with GIS in the Kardeh Watershed, Iran. Eur. J. Sci. Res.2009,34,pp. 575-590.
  • Goodrich DC, Lane LJ, Shillito RM, Miller SN, Syed KH, Woolhiser DA. Linearity of basin response as a function of scale in a semiarid watershed. Water Resour. Res. 1997, 33(12),pp. 2951–2965.
  • Simanton JR, Hawkins RH, Mohseni-Saravi M and Renard KG. Runoff curve number variation with drainage area, Walnut Gulch, Arizona. Trans. ASAE 1996, 39(4), pp.1391–1394.
  • Nichols MH, Renard KG , Osborn HB. Precipitation changes from 1956 – 1996 on the alnut Gulch experimental watershed.J. Amer. Wat. Res. Assoc. 2002, 38(1),pp. 161-172.
  • Skirvin S . Vegetation data, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resour. Res.2008, 44, pp. 1-6.
  • Heilman P, Nichols MH, Goodrich DC, Miller S, Guertin P. Geographic information systems database, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resour. Res. 2008, 44(8),pp. 1-6.
  • U.S. Department of Agriculture. Date accessed 02/05/2015.
  • M. Ranjit Kumar, T. Meenambal, V. Kumar ,“Simulation Model for Predicting the Effects of Changes in Land Use on Watershed Hydrology “,Indian Journal of Science and Technology,2016 Jan, 9(2), Doi no:10.17485/ijst/2016/ v9i2/86356
  • M. Kavitha Mayilvaganan, P. Mohana, K. B. Naidu,“Delineating Groundwater Potential Zones in Thurinjapuram Watershed Using Geospatial Techniques”,Indian Journal of Science and Technology,2011 Nov, 4(11), Doi no:10.17485/


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