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MODEL KESESUAIAN POLA RUANG BERBASIS GEOLOGI TERINTEGRASI SOSIOEKONOMI DI KAW...

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Flash floods in the Saka River (part of the KUSW) struck Muara Dua District with a population of 177.47 people/km2 on May 8th, 2020, due to increased rainfall intensity and land cover changes upstream. Based on this incident, this research will examine hydraulic parameters that directly implications for potential flooding. The rainfall intensity analysis was based on calculations from the Gumbel-Sherman equation in the baseline period 2011-2020. Then the parameters of the runoff coefficient consisting of the slope, land cover, and type of lithology are analyzed by the Hassing method. The results of the rainfall intensity analysis showed that the lowest intensity occurred in August while the highest power occurred in November and April. The runoff coefficient of 53% has implications for peak flow discharge which has an average increase of 11.6%. Flood simulation in KUSW modeled with Hydrologic Engineering Center-River Analysis System (HEC-RAS) software shows 174.4 km2 potential flooding in the five years of the return period and 200 km2 in the ten years of the return period. This analysis model is used as a preventive effort and reduces the negative impact around KUSW.

Lecturer at Institut Teknologi Bandung
at
Institut Teknologi Bandung

- 1. 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. 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
- 3. 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. 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
- 5. Delineation of FPA through the Perspective of 5 River Hydraulic Analysis in KUSW, South Sumatra 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. 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
- 7. Delineation of FPA through the Perspective of 7 River Hydraulic Analysis in KUSW, South Sumatra 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. 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
- 9. Delineation of FPA through the Perspective of 9 River Hydraulic Analysis in KUSW, South Sumatra 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. 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
- 11. Delineation of FPA through the Perspective of 11 River Hydraulic Analysis in KUSW, South Sumatra 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. 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
- 13. Delineation of FPA through the Perspective of 13 River Hydraulic Analysis in KUSW, South Sumatra 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. 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
- 15. Delineation of FPA through the Perspective of 15 River Hydraulic Analysis in KUSW, South Sumatra [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. 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

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