Soil Erosion Prediction and Its Control Measures Analysis Using GIS and RUSLE Method – Case Study: Gansal Sub-Watershed, Bukit Tigapuluh National Park

By Eko Desi Sularso, M.Sc. – (Thesis M.Sc.  Unesco-IHE, Delft, Netherland, 2014), Supervised by Prof. Charlotte de Fraiture Ph.D, M.Sc., Prof. Dr. Ir. Robiyanto H. Susanto, M.Agr.Sc. and F.X. Suryadi, Ph.D, M.Sc.

Gansal River that is located on the downstream part of Bukit Tigapuluh National Park is one of the most interesting ecotourism sites that are rich both in cultural, sports and environmental tourism. While this river is clean most of the time, it still turns brown during and after a rain. This brown water is spoiling its beautiful image of a green and clean tropical rain forest. The major source of sediments is from land use change in the upstream area of the river. This research aim to determine the soil erosion rate as a basis data information that can be used and considered by people living there and in the decision making process to conserve the Gansal sub-watershed.

A methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) techniques was adapted to measure soil erosion. GIS data layer include rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C) and conservation practice (P). Watershed delineation using the hydrologic analysis tools has been done in order to create basis map of study site. In this scenario, monthly precipitation were collected from the three nearest meteorological station and used as input data in calculating R-factor. Inverse Distance Weighted (IDW) technique was used to calculate spatial distribution of R. K-factor has been drawn from the soil map of BTNP. LS-factor has been computed and performed with the lsfac_c.exe C++ program by using 30-m DEM data. C-factor has been derived by processing Landsat 8 Thematic Mapper using classification tools available at ArcGIS 9.3. P-factor has been assumed as equal to 1.

Scenario II implemented several conservation support practices to reduce P-factor values. Contouring method will be implemented to the land covered by jungle rubber meanwhile terrace will be applied to the open area, settlements and crop land. The major changes in this scenario were implementing contouring method which consists of 5 values, those are 0.5 for the area with 0-3% slope, 0.6 (3-5% slope), 0.7 (5-8% slope), 0.8 (8-15% slope) and 0.9 for the area with more than 15% slope. Land use change was applied in scenario III by converting the existing land use into forest at riverside as far as 50 m width for river and 20 m width for tributary that was expected to reduce soil erosion. Scenario IV was assumed considering a conversion of 40,774 ha forest caused by a development of settlement.

The result show that under current land use, predicted soil losses ranged from 0.173 ton ha-1 y-1 to 15 ton ha-1 y-1 in the forested area to more than 480 ton ha-1 y-1 in the steep slope without any vegetation covered on it. It also gave the information that about 98.1% of Gansal sub-watershed can be classified as very low potential erosion risk (<15 ton ha-1 y-1), while rest of the area is under moderate to very high erosion risk. The spatial pattern of classified soil erosion risk zones indicates that the areas with high and severe erosion risk are located in the middle regions of the study area in which Talang Mamak community living in BTNP doing some daily activities, while the areas with low erosion risk are in the forested area.

By implementing scenario II, the area experiencing very high soil erosion rate would reduce by 6,000 ton ha-1 y-1 ranging from near zero in flat areas covered by forest, to just under 1,300 ton ha-1 y-1 in parts of open-steep area. The areas with mainly decreased soil erosion rate were located in the central parts of sub-watershed near the Gansal River. Meanwhile, under recommended land use treatments in scenario III, the maximum values of soil loss remain the same as the first scenario. This scenario only gave more effect in reducing the area that have soil erosion rate between near zero to 15 ton ha-1 y-1. In the scenario IV due to losing a great amount of forest area caused a strong increase of soil erosion rate in the whole area ranging from 70 ton ha-1 y-1 to 9,000 ton ha-1 y-1. All of the regions were subject to severe erosion including 68 % of very high erosion rate class (> 480 ton ha-1 y-1) that were occurred on the steep and very steep hill slope and 31 % of high erosion rate class (60 – 180 ton ha-1 y-1) that mostly happened on the gently sloping ridge.

It can be concluded that the C-factor and LS-factor are the most important factor influencing soil erosion rate in hilly area. Altering forest cover to other purposes in this area can increase runoff velocity and soil erosion rate especially in steep hill slope.

Keywords: RUSLE, GIS, Soil Erosion, BTNP, Gansal sub-watershed3

By | 2014-02-18T13:58:14+00:00 February 18th, 2014|Penelitian S2|Comments Off on Soil Erosion Prediction and Its Control Measures Analysis Using GIS and RUSLE Method – Case Study: Gansal Sub-Watershed, Bukit Tigapuluh National Park

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