EXPERIMENTAL METHOD 1 Description of the Study Area
Bogor, 21-22 October 2015
319 The Universal Soil Loss Equation USLE is an emperical model widely apllied to predict soil
erosion Dutta, 2014 due to its simple structure and easy application Amore et al., 2004 and its results showed a reasonable accurate for a certain purpose Bagarello, Ferro, Pampalone,
2013. The USLE was developed by Wischmeier and Smith in 1965 as mentioned in Wischmeier Smith, 1978. This model was developed based on required parameters from
field measurements at various regions in the USA, however the parameters have been expanded using data from researchers who have used the model from different countries with
different biophysical conditions Amore et al., 2004. Therefore, accuracy of the prediction is depend on the required parameters which are the index of length and steepness of slope LS,
rainfall erosivity R, soil erodibility, management of crop cover and soil conservation practices CP which can be derived from different methods.
As a tropical country with high rainfall intensity, the erosivity of the rainfall is an important parameter in determining soil erosion Lee Heo, 2011. Rainfall erosivity is the erosive force
of rainfall Diodato, Verstraeten, Bellocchi, 2014 or the potential of rainfall causing erosion Huang, Zhang, Zhang, Xu, 2013. In the USLE, rainfall erosivity is denoted as R which
indicates the total storm energy Etimes the maximum 30-minutes intensity I30 or EI30 and it reflects the duration peak rates of detachment and runoff Wischmeier Smith, 1978.
To calculate the EI30, it needs temporally continuous rainfall data from automatic raingauge recorders Huang et al., 2013 which are hardly found in many developing countries
Capolongo, Diodato, Mannaerts, Piccarreta, Strobl, 2008, including Indonesia. Therefore, rainfall data recorded from regular or conventional raingauge have been used to estimate R
value. Rainfall erosivity, in Indonesia, is commonly calculated from Utomo and Mahmud, Utomo, or
Bols equations. The rainfall erosivity based on the equation of Bols is developed using mean monthly rainfall, mean rainfall day per month, and mean maximum rainfall within 24 hours
for every month Utomo, 1989. Equation of Utomo an
d Mahmud and Utomo’s equation Utomo, 1989 are more simple, these equations are based on monthly rainfall. Although,
those equations have been widely applied, however from the study literatures, validation of the methods using direct field measurement is hardly found. Therefore, the purpose of the
study is to compare soil erosion from prediction by USLE using three types formulas of rainfall erosivity with direct measurement from sediment yield.
2. EXPERIMENTAL METHOD 2.1 Description of the Study Area
The study was undertaken in Keduang sub-watershed which is the highest sediment contributor for Gajah Mungkur reservoir. Administratively, the area is belongs to Wonogiri
District, Central Java Province, Indonesia. Geographycally the Keduang sub-Watershed located at 7
4’30” - 7 55’26” South Latitude and 110
59’23” - 111 13’26” East Longitude. The
Keduang sub-Watershed occupies an area 36709 ha. The administratif map of the Keduang sub-Watershed is provided in Figure 1.
Field data collection was conducted in 2013. Rainfall data were collected from Balai Besar Wilayah Sungai BBWS Bengawan Solo, which consisted of several rainfall station including
Girimarto, Sidoharjo, Slogohimo, Jatisrono, Jatipurno, and Jatiroto. Among these six rainfall stations, Jatipurno has the highest annual rainfal which was 4194 mm, in contrast, annual
rainfall in Sidoharjo was only 1242 mm. Dystropets is the dominant soil in the study area, followed by Tropudals, and Dystrandepts. Land cover from Landsat 7 ETM+ imagesas
presented in Figure 2 was obtained from Balai Pemantapan Kawasan Hutan BPKH
Bogor, 21-22 October 2015
320 Jogyakarta. Dry land agriculture is the dominant land cover which is around 87 of the sub-
watershed.
Figure 1: The administratif map of the Keduang sub-Watershed
Figure 2: Landcover of the study area Source: BPKH Jogyakarta
Bogor, 21-22 October 2015
321
2.2 Methods and Data Analysis 2.2.1
Prediction of soil erosion
Soil erosion was predicted by USLE equation Wischmeier Smith, 1978 as presented in formula 1.
A = f R, K, LS, CP..............................................................................................1 Where:
A = Soil loss per ha per year, R = Rainfal erosivity,
K = Soil erodibility, LS = Index of Slope length,
C = Plant management, P = Soil conservation practice
Three methods for estimating rainfall erosivity were applied. The three methods are Utomo and Mahmud, Utomo, and Bols equations Utomo, 1989.
The equation of Utomo and Mahmud is:
EI
30
= 10,80 + 4,15 Rm...............................................................................2 Where: EI
30
= Rainfall erosivity Rm= Mean monthly rainfall in cm
The equation of Utomo is: EI
30
= -8,79 + 7.01Rm........................................................................................3 Where: EI
30
= Rainfall erosivity Rm= Mean monthly rainfall in cm
The equation of Bols is: E1
30
= 6.119 Rm
1.21
D
-0.47
M
0.53
....................................................................4 Where: EI
30
= Rainfall erosivity Rm= Monthly rainfall in cm
M = Maximum rainfall cm during 24 hours within the corresponding month Soil erodibility is defined as the sensitivity of soil to erosion and it reflected as K in the USLE
equation Auerswald, Fiener, Martin, Elhaus, 2014; Wang et al., 2009. In this regards, soil erodibility determined by a series of processes which are detachment and transport of soil
particles, infiltration rate, breakdown of soil agregate when soil erosion occurs Auerswald et al., 2014; Borselli, Torri, Poesen, Iaquinta, 2012 estimated using soil erodibility monograph
Wischmeier Smith, 1978 or can be calculated using silt plus very sand content, clay content, soil organic matter, an aggregation index, and permeability index. However do to
limtitation of data, soil erodbility can be estimated from soil texture Paimin, Sukresno, 2010 or soil type. In this study the K variable is obtained from secondary data using soil type
Kurnia, Suwardjo, 1984. The slope variable was derived from DEM ASTER which has spatial resolution 30 x 30 m.
DEM ASTER data were converted into slopes S and classified into five classes, that are 0
– 8, 8 -15, 15 -25, 25 -45, and 45. To obtain slope length LS variable, formula
Bogor, 21-22 October 2015
322 developed by Arnoldus Tresnawati, 1991 was applied. The formula is presented in equation
5.
LS = L22,1
0,6
x S9
1.4................................................................................................................
5 Crop management C values and soil conservation practices P were integrated into CP
values. The values were determined based on monitoring and evaluation of watershed performance handbook Dirjen RLPS, 2009 and Java Erosion Model USLE Ministry of
Public Work, 2012. Besides these, the CP values were validated from ground check.
Soil loss by erosion was calculated for every land unit. To obtain the average soil erosion per unit area ha, it was conducted by weighted average basis. The predicted soil loss was
classified into 5 classes and evaluated its level of erosion hazard. Table 1 presents the classification of erosion hazard level according to decree No. P.4V-SET2013 Dirjen
BPDAS-PS, 2013. Table 1: Erosion hazard classification
Solum depth [cm] Erosion class
I II
III IV
V Erosion [tonhayear]
15 15-60
60-180 180-480
480
Deep 90 Very low
Low Moderate
Severe Very severe
Moderate 60 - 90 Low
Moderate Severe
Very severe Very severe
Shallow 30 – 60
Moderate Severe
Very severe Very severe
Very severe Very shallow 30
Severe Very severe
Very severe Very severe
Very severe Source: Decree No. P.4V-SET2013 Dirjen BPDAS-PS, 2013