DELINEATION OF FLOOD-PRONE AREAS THROUGH THE PERSPECTIVE OF RIVER HYDRAULICS

DELINEATION OF FLOOD-PRONE AREAS
1
THROUGH THE PERSPECTIVE OF
2
RIVER HYDRAULICS
3
Stevanus Nalendra Jati, Dasapta Erwin Irawan, Rusmawan Suwarman,
4
Deny Juanda Puradimaja
5
Fakultas Ilmu dan Teknologi Kebumian, Institut Teknologi Bandung
6
7
8
Highlight:
9
• The baseline period 2011-2020 rainfall intensity analysis shows minimum
10
rainfall occurs in August and maximum rainfall intensity occurs in November
11
and April.
12
• The runoff coefficient shows that 53% of the water will be covered in the
13
Komering Ulu Sub Watershed (KUSW) and has implications for increased
14
flow peak discharge with an average of 11.6%.
15
• HEC-RAS simulation results showed 174.4 km2 of areas potentially flooded in
16
the five years of return period and 200 km2 in the ten years of the return period.
17
• Flood prone area map shows four districts and 51 villages potentially flooded
18
in 5 and 10 years of return periods.
19
Abstract. Abstract. Flash floods in the Saka River (part of the KUSW) struck
20
Muara Dua District with a population of 177.47 people/km2 on May 8th, 2020,
21
due to increased rainfall intensity and land cover changes upstream. Based on this
22
incident, this research will examine hydraulic parameters that directly implications
23
for potential flooding. The rainfall intensity analysis was based on calculations
24
from the Gumbel-Sherman equation in the baseline period 2011-2020. Then the
25
parameters of the runoff coefficient consisting of the slope, land cover, and type
26
of lithology are analyzed by the Hassing method. The results of the rainfall
27
intensity analysis showed that the lowest intensity occurred in August while the
28
highest power occurred in November and April. The runoff coefficient of 53% has
29
implications for peak flow discharge which has an average increase of 11.6%.
30
Flood simulation in KUSW modeled with Hydrologic Engineering Center-River
31
Analysis System (HEC-RAS) software shows 174.4 km2 potential flooding in the
32
five years of the return period and 200 km2 in the ten years of the return period.
33
This analysis model is used as a preventive effort and reduces the negative impact
34
around KUSW.
35
Keywords: Flood-prone areas, rainfall, runoff coefficient, KUSW.
36

2 Author, et al.
1. Introduction
37
Hydrometeorological disasters that often occur in Indonesia are marking by the
38
high rainfall that occurs. Floods, landslides, tornadoes, tidal waves, and drought
39
are disasters caused by climate change included in hydrometeorological disasters
40
[1]. The implications of intense climate change on rivers will undoubtedly affect
41
the discharge and speed of river flow in an area. The impact of climate change
42
can continuously cause changes to waterways due to evaporation and
43
precipitation processes. Hydraulics is an applied science that studies the
44
mechanical properties of fluids, both macro and micro, used for fluid properties
45
engineering by considering hydraulic parameters [2].
46
In this study, four parameters are using to identify and delineate areas prone to
47
flooding, including changes in land cover, rainfall intensity, soil infiltration, and
48
peak runoff discharge estimates. Hydraulic analysis modeled in statistical and
49
numerical form can help in flood prevention and guidance in flow control. Then
50
the numerical model in the form of statistics can be used in simulating floods
51
around the river canal [3]-[4]. The statistical approach in estimating the peak
52
runoff discharge in the return period of 5 and 10 years can help in providing an
53
overview of the likely future runoff discharge [5].
54
The Komering Ulu Sub-Watershed is part of the Musi River Watershed
55
on the south side which flows relative to the northeast. The flood incident
56
on the Komering River on May 8th
, 2020, which hit several districts in
57
Ogan Komering Ulu Timur Regency, is suspected to be due to an
58
increase in rainfall intensity and changes in land cover and directly
59
implies a significant increase in river flow. Based on this problem, this
60
study aims to calculate the estimated peak runoff discharge at the return
61
period of 5 and 10 years and identify the flood-prone areas in the
62
Komering Ulu sub-watershed. Therefore, the results of this study can be
63
used as a preventive effort and reduce the negative impact of floods in
64
the Komering Ulu sub-watershed.
65
2. Method
66
2.1. Rainfall Analysis
67
Analysis of the average annual rainfall is calculated by taking the maximum value
68
of precipitation every month for one year in the ten-year baseline period, namely
69
2011-2020 [6]. The statistical approach used in determining the maximum rain
70
with a return period of 5 and 10 years is the statistical approach to the Gumbel
71
equation. This Gumbel equation aims to analyze the possible rainfall intensity
72
with the desired return period [7]. This method is generally used for maximum
73

Delineation of FPA through the Perspective of 3
River Hydraulic Analysis in KUSW, South Sumatra
data analysis, for example, for analysis of flood frequency [8]. According to Faqih
74
in [6], [9]-[10], the Gumbel equation as follows:
75
𝑋 = 𝑋̈ + 𝐾. 𝑆 𝐾 =
𝑌𝑇𝑟−𝑌𝑛
𝑆𝑛
𝑌𝑇𝑟 = −ln [−ln
𝑇𝑟−1
𝑇𝑟
] (1)
76
Rainfall intensity analysis calculates the results of the rainfall analysis with the
77
Gumbel equation, which is then transformed into an epochal rainfall intensity
78
using the Mononobe method. This method aims to analyze the rainfall by dividing
79
it into several durations. The Mononobe equation is written as follows:
80
𝐼 =
𝑅24
24
[
24
𝑡
]
2 3
⁄
(2)
81
The results of the analysis of the two approaches above were used to construct
82
the Intensity Duration Frequency (IDF) curve of rainfall with a return period of 5
83
and 10 years. The IDF curve was built using a statistical approach with three
84
equations: the Talbot, Sherman, and Ishiguro equations [5]. One of the three
85
equations is selected based on the minor difference between the calculation of
86
rainfall intensity using the Mononobe method. The following are three equations
87
used in the construction of the IDF curve:
88
The Talbot equation:
89
𝐼 =
𝑎
𝑡+𝑏
(3)
90
91
𝑎 =
∑[𝐼.𝑡]∑[𝐼2]−∑[𝐼2.𝑡]∑[𝐼]
𝑁∑[𝐼2]−∑[𝐼][𝐼]
𝑏 =
∑[𝐼]∑[𝐼.𝑡]−𝑁∑[𝐼2.𝑡]
𝑁∑[𝐼2]−∑[𝐼][𝐼]
92
93
The Sherman equation:
94
𝐼 =
𝑎
𝑡𝑛 (4)
95
log 𝑎 =
∑[log 𝐼]∑[(log 𝑡)2]−∑[log 𝑡.log 𝐼]∑[log 𝑡]
𝑁∑[(log 𝑡)2]−∑[log 𝑡][log 𝑡]
96
97
𝑛 =
∑[log 𝐼]∑[log 𝑡]−𝑁∑[log 𝑡.log 𝐼]
𝑁∑[(log 𝑡)2]−∑[log 𝑡][log 𝑡]
98
99
The Ishiguro equation:
100
𝐼 =
𝑎
√𝑡+𝑏
(5)
101
𝑎 =
∑[𝐼.√𝑡]∑[𝐼2]−∑[𝐼2.√𝑡]∑[𝐼]
𝑁∑[𝐼2]−∑[𝐼][𝐼]
102
103
𝑏 =
∑[𝐼]∑[𝐼.√𝑡]−𝑁∑[𝐼2.√𝑡]
𝑁∑[𝐼2]−∑[𝐼][𝐼]
104

4 Author, et al.
2.2. Runoff Coefficient Analysis
105
Determining the runoff coefficient as a parameter in this study consists of
106
calculating the pattern of land cover change, slope, and rock types found in the
107
study area. The three components are computed using the Hassing method to
108
obtain a runoff coefficient value that will be used for calculating the estimated
109
runoff discharge [11]. The coefficient of each component in the calculation using
110
the Hassing method can be seen in Table 1.
111
Table 1 Runoff coefficient
112
No Land Cover (CL) CL Value
1 Shrubs 0.07
2 Rice Field 0.15
3 Swamp bush 0.07
4 Residential 0.6
5 Open Space 0.2
6 Plantation 0.4
7 Dryland farming 0.1
8 Secondary dryland forest 0.2
9 Dryland farming mixed with shrubs 0.1
No Lithology (CS) CS Value
1 Qs (loose material: sand, silt, clay) 0.16
2 Qa (loose material: bolder, gravel) 0.04
3 QTk (pyroclastic rock layers) 0.26
4 Tmpm (fine-grained sedimentary rock layer) 0.26
5 Tma (fine-grained sedimentary rock layer) 0.26
No Slope Classification (CT) CT Value
1 Flat (< 1%) 0.03
2 Very Ramps (2- 10%) 0.08
3 Ramps (11-20%) 0.16
113
2.3. Estimating Peak Discharge
114
The estimated peak runoff discharge was analyzed using a statistical approach
115
using a rational method. This method has a multiplying factor consisting of
116
rainfall intensity, runoff coefficient value, and research area. The Rational
117
Method chosen in this study is since the size does not exceed 1000 km2
. The
118
mathematical equation in calculating the estimated peak runoff discharge is
119
written as follows [12]:
120
𝑄𝑝 = 0.278. 𝐶. 𝐼. 𝐴 (6)
121

2.4. HEC-RAS Simulation
122
Analysis and identification of flood-prone areas in the study area were carried out
123
using the Hydrologic Engineering Center-River Analysis System (HEC-RAS)
124
software. This GIS-based application is an application that is used to model water
125
flow, both steady flow, and unsteady flow. The data that must be inputted to run
126
the flow simulation include DEMNAS data (tides.big.go.id). The flow rate has
127
been calculated using the rational method, the slope coefficient, and the runoff
128
coefficient that has been calculated in the previous stage. Then the data collection
129
is processed using the "performance an unsteady flow simulation" tool to simulate
130
runoff discharge in the study area [9], [12]. So that from this simulation, it can be
131
identified which areas can be affected by the estimated peak discharge size.
132
3 Result and Discussion
133
3.1 Rainfall Analysis
134
Rainfall analysis in this study was carried out by accumulating five daily rainfall
135
data. The five daily rainfall data calculation is carried out every month, namely
136
in the baseline period 2011 - 2020. This is because the chance of flooding is more
137
significant if there is extreme rainfall for five consecutive days [13]-[14]. Flood
138
events can cause various things, such as the process of erosion, landslides, or
139
changes in the shape of the river to the occurrence of flooding [15].
140
141
Figure 1. Baseline period rainfall data chart for 2011-2020
142

6 Author, et al.
Rainfall data used comes from 4 rainfall observation points scattered around the
143
study area. Figure 1 shows that low rainfall intensity occurs in June, July to
144
August. Meanwhile, high rainfall starts from November to May. In the min
145
baseline chart in the range 2011-2020, the maximum value is in April with a
146
weight of 361.69 mm. Meanwhile, the minimum value occurs in August with a
147
value of 17.15 mm. The max baseline chart shows the maximum rainfall intensity
148
in April with a value of 539.54 mm, while the minimum value occurs in August
149
at 118.82 mm. Then the average baseline graph shows that the maximum rainfall
150
intensity occurs in April with a value of 95.91 mm and the minimum rainfall
151
intensity occurs in August with a value of 28.69 mm.
152
The dynamics of changes in rainfall intensity in the study area can cause changes
153
in river flow discharge. Figure 2 shows that the study area has a high distribution
154
of rainfall intensity each month. The implication of increased rainfall is an
155
increase in the erosion process that continues in the study area. Color gradations
156
from yellow to orange indicate that the site has the most significant potential for
157
an increase in erosion rate and changes in river flow discharge.
158
159
Figure 2 Baseline period rainfall map for 2011-2020.
160
The Intensity Duration Frequency (IDF) curve shows that high rainfall intensity
161
occurs for a short duration (Figure 3). This indicates that rain with high intensity
162
or heavy rain generally lasts for a short period. However, it is different from
163
rainfall with low power, which usually lasts for quite a long duration. The
164

calculation of rainfall intensity becomes a multiplying factor in calculating the
165
peak runoff discharge using the Rational Method.
166
167
168
Figure 3 Intensity Duration Frequency Curve with a return period of 5 and 10 years.
169
Rainfall intensity is one of the parameters used to calculate the estimated peak
170
runoff discharge in the study area. Changes in rainfall intensity in a neighborhood
171

8 Author, et al.
can affect erosional processes and increase flow rates. Based on the rainfall
172
intensity map in the Komering Ulu Sub Watershed, there are several areas
173
downstream that have moderate to high rainfall intensity. This can increase the
174
potential for flooding in the downstream part, which is generally used as a
175
residential area. The high intensity of rainfall harms the area around the river
176
flow. The increase in discharge and flow velocity implies a higher erosional rate
177
on the side of the river. It causes the road foundations located along the river to
178
be damaged or landslide (Figure 4). The occurrence of landslides on the side of
179
the river is indicated by vehicle loading and human activities.
180
181
Figure 4. Road cracks and landslides are caused by high rainfall in Madang Suku Satu
182
District.
183
3.2 Runoff Coefficient Analysis
184
In this study, the making of land cover maps was carried out using the ArcGIS
185
application, which aims to identify changes around each type of land cover in a
186
room. The land cover map that is processed is a map derived from the Ministry
187
of Environment and Forestry (KLHK) for ten years, namely 2010-2019 (Figure
188
5).
189
The map of land cover changes over ten years shows that there are nine types of
190
land cover included in the study area, including shrubs, swamp scrub, rice fields,
191
open land, plantations, dryland agriculture, dryland farming mixed with shrubs,
192
land forest. Dry secondary and residential. In those ten years, there was no
193
addition or reduction in the type of land cover. However, there was a sweeping
194
change in 10 years in every kind of land cover each year. Changes around each
195
type of land cover will affect the value of the calculated runoff coefficient
196
calculated using the Hassing method.
197

198
Figure 5 Map of land cover changes within ten years (2010-2019)
199
Based on the graph of the percentage of slope area in the Komering Ulu Sub
200
Watershed, it shows that the slope class that dominates sequentially, namely, the
201
slope class is very ramps with a percentage of 53.8%, the slope class is flat with
202

10 Author, et al.
a ratio of 29.4%, and the slope class is ramping which has a spread percentage of
203
16.8% in the study area (Figure 6). The slope map also shows that most of the
204
residential areas are on a flat slope class. This condition can increase the risk of
205
flooding in this residential area if there is an increase in discharge on the
206
Komering River.
207
208
Figure 6 Map of slope class area in Komering Ulu subwatershed
209
The parameter in determining the next coefficient value is the lithology type
210
parameter in the study area. The geological map and the percentage area chart of
211
the Komering Ulu Sub Watershed formation show that the most dominant form
212
is the Kasai Formation (QTk), with a percentage of 74.3%. They were then
213
followed by Alluvium Deposits (Qa) which occupied 14.1% of the total area of
214
the study area. Swamp Deposits (Qs) with an area percentage of 8.4%, the
215
Muaraenim Formation (Tmpm) with an area percentage of 3.2%, and the
216
Airbenakat Formation, which has the smallest area percentage of only 0.000043%
217
scattered in the Komering Ulu Sub Watershed (Figure 7).
218
Based on the calculation of the runoff coefficient using the Hassing method, it
219
can be identified that the total potential overtopping water in the Komering Ulu
220
Sub Watershed reaches 53% (Table 2). With this relatively large percentage, it
221
will negatively impact if the water discharge increases from time to time. The
222
results of the runoff coefficient calculation will be used as a multiplying factor in
223
calculating the estimated peak runoff discharge using the Rational Method.
224

225
Figure 7 Geological map of Komering Ulu Sub Watershed
226
Table 2 Calculation results of runoff coefficient using Hassing method.
227
Land Cover Coefficient
Type C
Area (A) (km2)
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Shrubs 0.07 87 90 91 91 94.6 88 89 93.09 92 90
Rice Field 0.15 69 70 65 61 51.3 54 55 57.3 57 57
Swamp bush 0.07 64 63 63 65 71.2 70 71 64.6 71 71
Residential 0.6 35 35 35 32 30.85 30 30 29.21 30 31
Open Space 0.2 13 12 12 12.3 12.43 11 8 4.48 4.5 5
Plantation 0.4 172 170 172 175 30 30 29 30 29.5 30
Dryland farming 0.1 7 8 7 9.7 9.52 7 7 6.07 7 8
2nd dryland forest 0.2 21 22 22 20 19.64 20 21 21.94 22 23
Dryland farming w/ shrubs 0.1 222 220 223 224 370.5 380 380 383.3 377 375
CL = 0.23 0.204 0.203 0.203 0.20 0.227 0.14 0.14 0.135 0.135 0.137
Slope Coefficient Coefficient of Lithology
Type A C C x A Type A C C x A
Flat 203 0.03 6.09 Qs (sand, silt, clay) 58 0.16 9.28
Very Ramps 371 0.08 29.68 Qa (Boulder & Gravel) 97 0.04 3.88
Ramps 116 0.16 18.56 QTk (Pyroclastic rock layer) 513 0.26 133.38
CT 0.08 Tmpm (Sedimentary rock layer) 22 0.26 5.72
Runoff Coefficient Tma (Sedimentary rock layer) 3E-04 0.26 8E-05
C= CL+CT+CS 0.23 0.08 0.22 0.53 CS 0.22
3.3 Estimating Peak Discharge
228
Based on the results of the calculation of the peak runoff discharge with the
229
Rational Method of 5 and 10 years return period, which is made based on the
230

12 Author, et al.
division of the duration of the Intensity Duration Frequency (IDF) curve, it can
231
be identified that the shorter the time and high intensity of rainfall, the greater the
232
potential discharge that will occur at research area. After the estimated debit data
233
is accumulated based on the return period, it can be seen in the chart that the peak
234
debit at the return period of 5 years increases by 11,6% to the peak discharge in
235
the ten years return period (Figure 8). This shows that the increase in flow rate
236
has implications for the more significant potential for flooding in the study area.
237
238
Figure 8 Peak discharge change chart with a return period of 5 and 10 years
239
Based on the map of the simulation results of the HEC-RAS application, it can
240
be seen that flood runoff in the study area is mainly located around the Komering
241
River with an area of the five years of return period simulation of 174.4 km2,
242
while fortunately, the ten years of the return period of the flood-prone site covers
243
200 km2 (Figure 9). The discharge simulation model that has been carried out
244
can be a reference in analyzing potential flood areas in the KUSW.
245
The map of a flood-prone zone is utilized to identifying areas (Figure 10), both
246
districts and villages, that could be affected by flooding. Based on the map of the
247

location prone to flooding in the KUSW, several areas are potentially affected by
248
flooding, including:
249
a) Madangsuku Satu District: 7 villages have the potential for flooding.
250
b) Madangsuku Dua District: 25 villages have the potential for flooding.
251
c) Madangsuku Tiga District: 3 villages have the potential for flooding.
252
d) Buay Pemuka Bangsa Raja District: 16 villages have the potential for
253
flooding.
254
255
Figure 9 HEC-RAS simulation map with a return period of 5 (A) and 10 (B) years
256

14 Author, et al.
257
Figure 10 Flood-prone area map in KUSW.
258
4 Conclusions
259
The simulation model of the peak runoff discharge estimation using the HEC-
260
RAS application shows that the flood-prone areas in the five years of return
261
period are 174.4 km2
, while 200 km2
in the ten years of the return period. Then
262
the result of identification of zones prone to flooding was carried out by
263
overlaying the administrative map of Ogan Komering Ulu Timur Regency with
264
a simulation model of the peak runoff discharge estimation from the HEC-RAS
265
application. The flood-prone area shows four districts and 51 villages that have
266
the potential for flooding in the 5 and 10 years of the return period.
267
Acknowledgment
268
269
References
270
[1] Qodriyatun, S.N., Hydrometeorological Disasters, and Climate Change
271
Adaptation Efforts, Journal of Social Issues, 5(10), pp. 9-12, 2013.
272
[2] Bomers, A., Schielen, R.M.J., & Hulscher, SJMH, The influence of grid
273
shape and grid size on hydraulic river modeling performance,
274
Environmental Fluid Mechanics, 19(5), pp. 1273–1294, Feb. 2019.
275

[3] Wang, L., Hydraulic Analysis for Strategic Management of Flood Risk
276
Along the Illinois River, Journal Environmental Earth Sciences, 78(80),
277
2019.
278
[4] Di Curzio, D., Rusi, S., & Semeraro, R., Multi-scenario numerical
279
modeling applied to groundwater contamination: the Popoli Gorges
280
complex aquifer case study (Central Italy), Acque Sotterranee - Italian
281
Journal of Groundwater, AS27(361), pp. 49-58, Dec. 2018.
282
[5] Yuan, J., Emura, K., Farnham, C., Alam, Md.A., Frequency analysis of
283
annual maximum hourly precipitation and determination of best-fit
284
probability distribution for regions in Japan, Journal Urban Climate, 7(8),
285
pp. 276–286, Jul. 2017.
286
[6] Faqih, A., A Statistical Bias Correction Tool for Generating Climate
287
Change Scenarios in Indonesia Based on CMIP5 Datasets, in IOP Conf.
288
Ser.: Earth and Environmental Science, 58(1), pp. 1-11, 2017.
289
[7] Oosterbaan, R.J., Frequency and Regression Analysis, Drainage Principles
290
and Applications, H.P. Ritzema, 2nd
Edition., International Institute for
291
Land Reclamation and Improvement, pp. 175-223, 1994.
292
[8] Verrina, G.P., Anugrah D.D., & Sarino, Upstream Lematang
293
Subwatershed runoff analysis, Journal of Civil and Environmental
294
Engineering, 5(10), pp. 9-12, 2013.
295
[9] Khosravi, G., Majidi, A., & Nohegar, A., Determination of Suitable
296
Probability Distribution for Annual Mean and Peak Discharges
297
Estimation (Case Study: Minab River- Barantin Gage, Iran), International
298
Journal of Probability and Statistics, 1(5), pp. 160-163, 2012.
299
[10] Baghel, H., Mittal, H.K., Singh, P.K., Yadav, K.K., & Jain, S., Frequency
300
Analysis of Rainfall Data Using Probability Distribution Models,
301
International Journal of Current Microbiology and Applied Sciences, 8(6),
302
pp. 1390-1396, 2019.
303
[11] Moraru, A., Usman, Kh.R., Bruland, O., & Alfredsen, K., River
304
idealization for identification of critical locations in steep rivers using 2D
305
hydrodynamic modeling and GIS, 22nd Northern Research Basins
306
Workshop and Symposium, pp. 145–154. Aug. 2019.
307
[12] Ahn, J., Cho, W., Kim, T., Shin, H., & Heo, J.H., Flood frequency analysis
308
for the annual peak flows simulated by an event-based rainfall-runoff
309
model in an urban drainage basin, Journal Water, 6(12), pp. 3841-3863,
310
Dec. 2014.
311
[13] Wardoyo, W., & Jayadi, R., Analysis of Extreme Hydrology Parameters
312
on Mt Merapi Area to Justify the Effect of Climate Change, Proceeding of
313
International Conference on Climate Change Impact on Water Resources
314
and Coastal Management in Developing Country, 2005.
315
[14] Cristian, G., Beilicci, R., & Beilicci, E., Advance Hydraulic Modelling of
316
Maciovita River, Caras Severin County, Romania, IOP Conf. Ser.:
317
Materials Science and Engineering, 471(4), pp. 1-6, 2019.
318

16 Author, et al.
[15] Grenfell, S. E., Grenfell, M. C., Rowntree, K. M., & Ellery, W. N., Fluvial
319
Connectivity and Climate: A Comparison of Channel Pattern and Process
320
in Two Climatically Contrasting Fluvial Sedimentary Systems in South
321
Africa, Journal Environmental Earth Sciences, 205, pp. 142-154, 2012.
322

DELINEATION OF FLOOD-PRONE AREAS THROUGH THE PERSPECTIVE OF RIVER HYDRAULICS

Recommended

Recommended

More Related Content

Similar to DELINEATION OF FLOOD-PRONE AREAS THROUGH THE PERSPECTIVE OF RIVER HYDRAULICS

Similar to DELINEATION OF FLOOD-PRONE AREAS THROUGH THE PERSPECTIVE OF RIVER HYDRAULICS (20)

More from Dasapta Erwin Irawan

More from Dasapta Erwin Irawan (20)

Recently uploaded

Recently uploaded (20)

DELINEATION OF FLOOD-PRONE AREAS THROUGH THE PERSPECTIVE OF RIVER HYDRAULICS