Runoff and Sediment Yield Prediction Using Agriculture Non-Point Source (AGNPS) Model in Ata-Gad Watershed, Uttarakhand, India

Deepa Naik, Pramod Kumar, Aniruddha Deshmukh, (doi: 10.23953/cloud.ijarsg.346)


The present study was undertaken to predict the runoff and sediment loss from Ata-gad watershed, Chamoli district, Uttarakhand, India. The land use/land cover (LULC) map was prepared using IRS-P6 LISS-III data. Digital Elevation Model (DEM) from ASTER and soil information from Soil and Land Use Survey of India (SLUSI) was used for runoff and sediment yield prediction. It was observed that large part of the watershed is forested (71.9%) and agricultural activity is ongoing in lower reaches of the valley (18%). The watershed area is mostly under moderately steep (15-35%) to very steep slope (50-75%). LULC, Soil, DEM and other inputs were fed into Agriculture Non-point Source (AGNPS) model through AGNPS Data Generator (ADGen) interface of image processing software. The AGNPS model helps to visualize the effect of slope, rainfall, LULC, etc. on runoff and sedimentation characteristics of a watershed. It was observed that nearly fifty percent area of the watershed produced 2.54 cm of runoff corresponding to 17.8 cm of rainfall. As large part of the watershed is under forest and consequently 64.24% of its area produced less than 1.42 cumec and only 0.11% of the area showed more than 49.55 cumec of peak runoff. Twenty-one percent area of the watershed is having steep slope (slope>75%) and showed the maximum rate of erosion as 48.67 tons/ha. Erosional characteristics vis-à-vis other properties of the landscape were also analyzed. It was also observed that with the increase in slope, though the soil erosion has increased but the slope factor solely does not affect erosional characteristics.


AGNPS; GIS; Remote sensing; Runoff; Soil erosion

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (