APPLICATION OF US-SCS CURVE NUMBER METHOD AND GIS FOR DETERMINING SUITABLE LAND COVER OF SMALL WATERSHED

https://doi.org/10.22146/ijg.1578

Christanti Nana Widiyati(1*), Sudibyakto Sudibyakto(2)

(1) Serayu Opak Progo Watershed Management Centre, Ministry of Forestry
(2) Faculty of Geography, Gadjah Mada University
(*) Corresponding Author

Abstract


This study aims to reveal the appropriate land cover which can reduce runoff
using US-SCS Curve Number method with GIS. Four land cover scenarios are
developed to reveal which one of existing land cover types is appropriate for the
area. To make a validation of the application US-SCS Curve Number Method,
calculating observational run-off is required. Statistical analysis is than used to
test those two run-off data. The result of this study shows that actual run-off depth
is 2143, 0 mm and peak discharge is 91, 76 m3/s. The result also reveals that forest
coverage can reduce dramatically surface run-off until 48, 38 percent. Potato
increases surface run-off 1, 59 percent, on the contrary, applying cacica papaya
can reduce surface run-off 24, 6 percent. Scenario 4 is developed based on the
result of the previous scenario on run-off yield. Run-off yield result from scenario 4
is 1690, 40 mm (decrease 21, 14 percent from actual run-off). Statistical analysis
shows that there is no difference between observed run-off depth and estimated
run-off depth in the level of significance 5 %.


Keywords


US-SCS curve number method, GIS, suitable land cover

Full Text:

PDF



DOI: https://doi.org/10.22146/ijg.1578

Article Metrics

Abstract views : 1686 | views : 1409

Refbacks

  • There are currently no refbacks.




Copyright (c) 2010 Indonesian Journal of Geography

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web
Analytics IJG STATISTIC