Monitoring, Prediction and Mitigation of Disasters
by Satellite Remote Sensing (MPMD), pp. 39-45, 2004

Application of Remotely Sensed Data to Sediment Load Estimation
by A Distributed Rainfall-Sediment-Runoff Catchment Model
Takahiro Sayama1, Kaoru Takara2 and Yasuto Tachikawa2
1

Graduate School of Engineering, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto 611-0011, Japan

2

Abstract: This paper describes a physically-based distributed rainfall-sediment-runoff model for floods andsediment movement in a catchment scale. A grid-cell based kinematic wave runoff (KWR) model simulates
surface and subsurface flows on each grid-cell, while soil transportation capacities of sheet erosion are calculated
based on the unit stream power theory to model sheet erosion and deposit processes. Constructed model isapplied to the Lesti River basin, which is located in the upper Brantas River basin, East Java, Indonesia. Land
cover condition in the basin is studied with three different kinds of remotely sensed data including
ADEOS/AVNIR, LANDSAT7/ETM+, and TERRA/MODIS. The verification indicated that the model well
reproduces the sedimentation record during a rainy season from November 1995 to April 1996, which suggests
the potential ability of the model.

1.

INTRODUCTION

The integrated sediment management (ISM) in river basins, from upper streams to estuaries, has become
increasingly important. The sediment problems have been dealt with independently in the areas, such as
mountains, alluvial areas, plains, and estuaries. Local managements independently conducted affect sedimentenvironment in other areas. In addition, human activities, such as land use change and dam construction, also
affect the sediment movement in large scales over long terms. Therefore, the concept of integrated management
dealing with sediment movement in whole basins has become recognized more important and realistic.
For ISM, it is necessary to establish methods to predict sediment movement both in basins and in streams. The
advanced research on sediment transportation in a river stream has been conducted from a viewpoint of sediment
hydraulics. In a catchment scale, however, there are still uncertainties in the prediction of sediment movement.
The distributed runoff model, which has been studied in hydrology, makes water movement in basins clear byusing spatially distributed information. This paper constructs a rainfall-sediment-runoff model by combining
sediment yield, deposit, and transportation processes with the grid-cell based Kinematic Wave Runoff (KWR)
model1). Geographical Information System (GIS) and Remote sensing (RS) are useful tools in the modeling.
The constructed model is applied to the upper Brantas river basin, East Java, Indonesia. Covered with large
amounts of volcanic ash, this area yields much sediment from cultivated lands, forests, and even urban regions.
Surface flow in the areas covered by volcanic ash is the main cause that yields the sediment in the areas. The
transportation capacity of the surface flow on each grid-cell must be considered to model the sediment yield anddeposit processes. The transportation capacity is calculated based on the Unit Stream Power (USP) theory2). The
sediment yielded in slope cells follows a path dictated by the flow direction to the ‘River’ grid-cells on which
bed load and suspended load are calculated.
2. LANDCOVER CLASSIFICATION BY REMOTELY SENSED DATALand cover condition in the Lesti River basin is studied using three kinds of remotely sensed data including
ADEOS/AVNIR3) (16m resolution, July 1997), LANDSAT7/ETM+ (30m resolution, May 2002), and multiple
scenes of TERRA/MODIS NDVI4) (250m resolution 16 days composite, from 2002 to 2003). Figure 1 shows
two land cover classification maps based on ADEOS/AVNIR and LANDSAT7/ETM+. There is no significant
difference between two scenes except for some erroneous area due to cloud cover and some area outside theLesti River basin. Large-scale landuse change was not found inside the basin from 1997 to 2002. On the other
hand, TERRA/MODIS NDVI (see Fig. 2) captured in different seasons indicate seasonal variability of vegetation
cover. Figure 3 shows average NDVI values based on TERRA/MODIS images for each land cover class, which

Figure 1. Land cover classification maps based on ADEOS/AVNIR captured in 1997 (left) and
LANDSAT7/ETM+ captured in 2002 (right).

1.0

May2002

July 2002

August 2002

October 2002

December 2002

March 2003

0.0

Figure 2. TERRA/MODIS NDVI images in different months

is obtained from LANDSAT7/ETM+.
NDVI
becomesthein lowest
in theend of a dry season especially in is
Figure 3. Seasonal
trendsof NDVIland coverclasses

1/100000 River channel,
Boundary Map

1/50000 Topography Map

Land classification map

DEM
Slope

Precipitation

ADEOS/AVNIR

Equivalent roughness Coefficients

Flow direction map

Grid-cell based KWR model
Sediment yield/deposit model on slope cells
USP model

Sediment transport modelin river channel

River or slope
YES
Bed load

Unit Stream Power

Suspended load

NO

Surface flow

TC=0

Transportation capacity (TC)TC > Sediment inflow
Sediment yield (TC – Sediment inflow

Sediment deposit (Sediment inflow – TC)

Down stream

NO

Downstream cell

YES
Sediment runoff at the outlet

Figure 4 The flow of analysis
obtained from LANDSAT7/ETM+. NDVI becomes the lowest in the end of a dry season especially in cultivatedland and in tree crops area, while forest areas maintain relatively high NDVI for all seasons. Though the interand inner annual variability of vegetation activity as seen in remotely sensed data (Fig. 3) should be consideredwith the understanding of the effect of vegetation on sediment runoff processes, the authors have not addressed it
yet for the rainfall-sediment-runoff modeling.
3.

RAINFALL SEDIMENT RUNOFF MODEL

3.1 Application of GIS
Data sets of geographic information such as flow direction, slope and land cover on each grid-cell are needed fordeveloping distributed hydrological models. In Indonesia, however, Digital Elevation Models (DEM) are notavailable. To overcome this disadvantage, this research uses GIS for generating a DEM. Digitizing contour lines
on a 1:50,000 topographical map for the area of interest, GIS has generated a Triangulated Irregular Network(TIN) and transformed it to a 250 m-DEM. In this research, equivalent roughness indices are assigned to eachgrid-cell based on the land cover classification map from the ADEOS/AVNIR image (Fig. 1 left).
3.2 Sediment yield and deposit process model on slope cells
The grid-cell based KWR model simulates rainfall-runoff on each grid-cell, which has the same resolution as
DEM (250 m). This model assumes that sediment is yielded when surface flow occurs. Surface flow has
limitation for transporting sediment on each grid-cell, so that this research considers the transportation capacityof surface flow on each grid-cell in each time step to model sediment yield process as well as sediment deposit

Rainfall(mm/hr)

D Y1

Yield

Discharge(m^3/sec)

YieldTC > Q sin

Yield

DepositTC < Q sin

Figure 5. The concept of USP

Dec.3
0
10
20
30
40
50
60
400
350
300
250
200
150
100
50
0
Dec.3

Dec.4

Dec.5

Dec.6

Dec.7

observed
calculated

Dec.4

Dec.5

Dec.6

Dec.7

Figure 6. Simulation result in December

process physically. The transportation capacity is calculated by equation (1)2), which is based on the USP theory.
The grid-cell based KWR model simulates the mean velocity V and the discharge Q on each grid-cell with
1-hour time step. Transportation capacity (TC) is the product of the Ct and Q as in equation (2).

⎛ VS Vcr S ⎞
−
⎟
ω ⎠
⎝ω

log C t = I + J ⎜(1)

TC = C t Q(2)

If the volume of sediment supplied from upper grid-cells (Qsin) is bigger than the TC, the volume of sediment of
TC-SI will be yielded. On the other hand, the sediment from the upper grid-cells will be deposited with thevolume of SI-TC when SI is bigger than TC because surface flow cannot move all of sediment from upper
grid-cells5).
3.3 Sediment transportation model in river channels
A single channel is taken into account in each grid-cell of ‘River’ land cover class. The channel width is assumed
to be 1 m to 25 m, which depends on the distance from the catchment’s outlet. Rainfall over the `River’ grid-cellis directly input to the channel as lateral inflow. The sediment that reaches to the `River’ grid-cell is transported
in the channel to downstream as bed load and suspended load. The transportation depends on the discharge in thechannel computed by the KWR model for channels.
The bed load qbb is computed by equation (3) proposed by Ashida and Michiue6).
3 ⎛
τ ⎞⎛
u ⎞
3
qbb = 17 sgd τ *e2 ⎜ 1 − *c ⎟⎜ 1 − *c ⎟(3)⎜
τ * ⎟⎠⎜⎝
u* ⎟⎠⎝where s : specific gravity of sediment in water ; g : gravity acceleration ; d : grain size ; τ*e : non-dimensional
effective shear stress (=0.05) ; τ*c : non-dimensional critical shear stress ; τ* : non-dimensional shear stress ; u*c :critical shear velocity ; and u* : shear velocity.
The suspended load transport rate qbs is expressed as the depth-integration of the product of the mean velocity
and the concentration distribution as equation (4)7).

7000

Yield ratio

1.4

Deposit ratio
Yield peak ratio1

Deposit peak
ratio

6000
5000
ppm

1.2

0.8

4000
3000

0.6

2000

0.4

1000

0.2

0

0
Normal

A layer * 2

K / 10

0

N*2

Z =

40

60

80

100

120

d isch a rge (m ^ 3 /sec)

Figure 7. Comparison of the effects ofparameters on sediment yield/deposit

qbs = uC

20(

Figure 8. Sediment concentration hysteresish1 − e − 6Z e 6Za / h
a 6Z

)

ω

(4)

βκu*

where u : depth averaged velocity ; Ca : concentration at the reference level ; h : water depths ; a : reference level(0.05h) ; ω : fall velocity ; β : constant (= 1.2) ; and κ : Karman’s constant (= 0.4).

4. APPLICATION TO THE LESTI RIVER BASIN
4.1 The Lesti River basin
The model is applied to the Lesti River basin (625 km2) located in the upper Brantas River basin (12,000 km2),
which is the second largest river in Java Island, Indonesia. The Lesti River basin is covered with volcanic soil
because of Mt. Semeru eruption. The Sengguruh dam, which is located at the junction of the Lesti River and theBrantas River, was constructed in 1988 for water resources and power generation, but the 85 % of gross storage(21.5 million m3) has been filled with the sedimentation in 8 years after the dam construction8). Since thesedimentation at the Senggruh dam and river channels is one of the major cause for decreasing the dam life andincreasing the risk of flood, the prediction of sediment movement in a catchment scale such as sediment depositat the reservoir and changes in river morphology caused by sediment transport is most important task before
planning any mitigation measures.
4.2 Rainfall runoff analysis
To verify the result, the simulated rainfall runoff is compared with the observed data, measured from December
3 to December 5 in 1995. The surface soil (volcanic ash) layer depth and permeability (or hydraulicconductivity) are assumed to be uniform and constant in the whole basin. The runoff coefficient and equivalentroughness coefficient have been decided according to the land cover classification. Let the condition be referred
to as “Normal”. Figure 6 shows an example of simulated hydrograph, which agrees well with the observed one.
4.3 The sediment yields and deposits differences according to the parameters
According to the simulation, 334 million m3 of sediment was yielded under the condition of “Normal”. The
surface soil layer depth, permeability and equivalent roughness coefficient represent the land cover and thegeological conditions of each grid-cell in distributed hydrological modeling. Here, total sediment yields anddeposits, peak sediment yields and deposits from the whole basin are examined with the different conditions insurface layer depth, permeability, and roughness coefficient. Figure 7 shows the results when the surface layer
depth is twice as thick as the “Normal” condition. The amount of sediment yield becomes 0.83 of “Normal”
condition. Less amount of sediment will be yielded under the assumption of doubled surface layer depth becausesurface flow seldom occurs on thicker surface layers. When the permeability is one tenth of the “Normal”
condition, both total amount of sediment yield and peak sediment yield increase because slower subsurface flow
increases the possibility of occurring the surface flow. This result explains that on the field covered with volcanicash (small k value), surface flow is easy to occur and more soil to be eroded.
4.4 Sediment concentration hysteresis
Figure 8 shows the sediment concentration hysteresis at the outlet with a triangular precipitation (max 11 mm/h,
total 134 mm). The figure explains that the sediment concentration is higher when the precipitation increases
than when it decreases. The model can express the physical process that if high concentrated surface flow occurs
once, the sediment yield rate decrease, and, finally decreasing precipitation makes the yielded sediment depositon the fields.
4.5 Sediment runoff analysis in a rainy season from 1995 to 1996

Cumulative Sediment (m^3)
Cumulative Sediment (m^3)
Rainfall(mm/hr)

Sediment runoff analysis is conducted with the “Normal” soil condition by inputting observed precipitation in arainy season from November 1995 to April 1996. The upper two panels in Figure 9 show the hyetograph, thecumulative sediment runoff at the outlet, and the cumulative total sediment yield. Roedjito and Harianto8)
reported that annual sedimentation at the Sengguruh dam reservoir is around 1 million m3, while simulated
sediment runoff at the outlet in the rainy season is about 65 million m3. The simulation is relatively proper, if thefollowing reasons are considered. The simulation period is not a year but 6 months, the outlet decided in thismodel application is located about 10 km upstream along the Lesti River from the Sengguruh dam, and thesimulated sedimentation does not include the sedimentation coming from the Brantas river. The lower panel in
Figure 9 shows the cumulative sediment yield and deposit, explaining that the sediment volume of 428 million
m3 had been yielded and 148 million m3 had been deposited after the rainy season in the whole basin; therefore,
the sediment of about 300 million m3 had entered into river channel. The volume of eroded material at thecultivated hillslope of Mt. Semeru is found to be larger than other places, which is analogous phenomenon to thenatural physical process of sediment erosion.

95Nov
0
10
20
30
40
50
60
700000600000500000400000300000200000100000095Nov
4e+0063e+0062e+0061e+006
0
95Nov

95Dec

96Jan

96Feb

96Mar

96Apr

Simulated sediment runoff at the outlet

95Dec

96Jan

96Feb

96Mar

96Apr

Simulated sediment yield
Simulated sediment deposit
95Dec

96Jan

96Feb
Time(month)

96Mar

96Apr

Figure 9. Rainfall sediment runoff analysis in a rainy season from 1995 to5

CONCLUSIONSWith the aid of GIS and remote sensing, this paper applied the distributed rainfall-sediment-runoff model to theBrantas River basin, Indonesia. The model calculated sediment movement on each grid-cell based on waterenergy, which contributed to sediment transportation, without introducing any sediment movement parameters.
Simulating water movement can simultaneously express the spatially distributed sediment movement processes
depending upon land cover and geology. The sensitivity of model parameters, such as equivalent roughness
coefficient, surface layer depth, and permeability to the sediment yields was also investigated. As a result, theplaces, with small permeability, thin surface layer, and small equivalent roughness, yielded larger amounts of
sediment. These conditions would often be found in the deforested mountain areas covered with fine volcanic
ash as in the Lesti River basin. The verification indicated the model’s good performance that reproduced theactual sedimentation record during a rainy season. Further, it was noted by the simulations that the erosion rate at
the cultivated hillslope of Mt. Semeru was higher than that in other places. As mentioned in Section 2, further
research is necessary to understand variability of vegetation activity, which would significantly affect sediment
runoff processes.
References
1) Kojima, T.: Application of remote sensing and GIS to hydrological analysis, Dr. of Engineering Dissertation,
Kyoto University, 1997 (in Japanese).
2) Yang, C. T.: Sediment Transport--Theory and Practice, The McGraw-Hill Companies, Inc., 1996.
3) NASDA: ADEOS Reference Handbook, 1997.
4) Huete, A., Didan, K., Miura T., Rodriguez, E. P., Gao, X. and Ferreira, L.G.: Overview of the radiometricand biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 83, pp.
195-213, 2002.
5) Moore, I. D. and Burch, G. J.: Sediment transport capacity of sheet and rill flow, Water Resources Research,
Vol. 22, No. 8, pp. 1350-1360, 1986.
6) Ashida, K. and Michiue, M.: Study on hydraulic resistance and bed-load transport rate in alluvial streams,
Proc. JSCE, No. 206, and, pp. 59-69, 1972 (in Japanese).
7) Lane, E. W. and Kalinske, A. A.: Engineering calculation of suspended sediment, Trans. A.G.U., 22, pp.
603-607, 1941.
8) Roedjito, D. M. and Harianto: Controlling reservoir sedimentation in Sengguruh Reservoir, Brantas Riverbasin, Reservoirs in River Basin Development, Proceedings of the ICOLD Symposium Oslo Norway, pp.
281-292, 1995.