________________________________
*Corresponding author: Department of Water Resource Engineering, Universitas Brawijaya, Malang 65145, Indonesia E-mail address: [email protected] (Devi Nurcahyaningtyas)
doi: https://doi.org/10.21776/ub.pengairan.2024.015.01.4
Received: 2023-03-30; Revised: 2023-08-01; Accepted: 2024-03-30.
P-ISSN: 2086-1761 | E-ISSN: 2477-6068 © 2024 [email protected]. All rights reserved.
Vol. 15 No. 1 (2024)
Jurnal Teknik Pengairan: Journal of Water Resources Engineering
Journal homepage: https://jurnalpengairan.ub.ac.id/index.php/jtp
Original research article
Flood Prone Mapping based on Surface Runoff Analysis using the SWAT Model at the Upstream Side of Brantas
Devi Nurcahyaningtyas, Donny Harisuseno*, Jadfan Sidqi Fidari
Department of Water Resource Engineering, Universitas Brawijaya, Malang 65145, Indonesia
A R T I C L E I N F O A B S T R A C T Keywords:
ArcSWAT;
Flood hazard mapping;
Mitigation;
Surface runoff
Currently, Batu City is experiencing rapid development both in terms of population and the amount of land building. Where development is uncontrolled and not balanced by reasonable conservation efforts, it will cause water resource problems such as flooding. So, it is necessary to understand flow patterns better as an actual effort in effective water management and flood hazard mitigation. This study aims to obtain a map of flood-prone areas in the Brantas sub- watershed upstream of Batu City. The primary methodology adopted in this research entails the examination of surface runoff through the utilization of the ArcSWAT program, followed by the analysis of pertinent parameters, including rainfall, land use, soil type, land slope, river density, and surface runoff. Then, scoring and weighting are done before overlaying each parameter to get a flood vulnerability map at the research location. The results of this study indicate that the mapping of flood-prone areas at the most significant research location is at a high level of flood vulnerability of 78.31 km2 or 52.22% of the total area of the watershed.
1. Introduction
Population growth significantly contributes to changes in land use. People cannot avoid the changes and development of land due to the demand for a better life. The availability of land that is not balanced with population growth has opened up new land for agriculture, housing, industry, roads, and other uses [1]. Unplanned changes and development can result in an imbalance in the hydrological cycle. The soil's capacity to absorb and hold water decreases when land cover changes [2]. This change also affects rainfall water that becomes runoff. The runoff quantity will increase yearly if land use is not well-managed. Rainwater will flow directly into downstream rivers, causing flooding due to overflowing rivers.
Floods are natural disasters that are difficult to predict because they occur suddenly and irregularly unless the affected area has a history of flooding [3]. Based on data on flood disaster recaps by the Regional Disaster Management Agency of Batu City, there were 15 flood events in 2019 and 25 flood events in 2020. This figure indicates that floods in Batu City from 2019 to 2020 increased by 67%. Past flood event data shows that floods in Batu City only occur during the rainy season. The inability of drainage systems, the amount of waste in the gutters, and the collapse of river protection buildings are the most significant contributors to flooding in Batu City. This condition can also occur due to the lack of
infiltration areas due to changes in land use. Rainwater that falls will become surface runoff.
Regarding this matter, providing flood-prone area maps that can be used for planning and early warning systems is one way to reduce the negative impact of floods. The suitable method for quickly mapping flood-prone areas in a region is GIS or Geographic Information System [4].
The Flood Hazard Map is created based on the results of overlaying each parameter used. These parameters include rainfall, land use, soil type, slope, river density [3], and surface runoff. Surface runoff analysis is conducted using ArcSWAT.
ArcSWAT is one of the hydrological models often used to study the impact of physical changes in the watershed [1]. In this study, ArcSWAT will produce the distribution of surface runoff in each sub-basin formed based on its analysis.
2. Materials and Methods 2.1. Study Location
This research took place in the Upper Brantas Sub Watershed area with the Basin Block Upper Brantas, which in the upstream area is a conservation forest area around the Arboretum and in the downstream area is Sengkaling Dam [5]. The Basin Block Upper Brantas area comprises 37 villages spread across two regions, Malang Regency and Batu City.
However, this study is limited to the area analyzed in Batu City.
2.2. Data Collection and Analysis
The data used in this study were daily rainfall data from 2012-2021 from 7 rainfall stations in the Upper Brantas Sub Watershed in Batu City (Junggo, Ngaglik, Ngujung, Pendem, Temas, Tinjumoyo, and Tlekung), DEM data, and shapefile data (land cover, soil type, and land use). The steps in this research are:
a. Research idea
b. Preparation involves identifying problems and conducting literature studies to support the validity of solving the issues raised in this study.
c. Data collection includes both non-spatial data (rainfall, climate, and meteorological data) and spatial data (shapefiles: soil type, rivers, land use, land cover).
d. ArcSWAT Modeling, model simulation with a start date of January 1, 2021, and an end date of December 31, 2021, and the simulation output is monthly form. Calibration of the model is conducted using the Manual Calibration Helper, which involves attempting to adjust the parameters. After calibration, the result is evaluated using the statistical parameters (NSE, RMSE, and Correlation tests). Then, a SWAT re-simulation with maximum rainfall input and a surface runoff distribution map is created using the surface runoff (SURQ) output.
e. Scoring and weighting are given to each selected parameter or map [6], are:
1) Rainfall Map 2) Land Use Map 3) Soil Type Map 4) Slope Map 5) River Density Map 6) Surface Runoff Map
f. Overlaying all parameters to obtain a flood map.
3. Results and Discussion 3.1. Rainfall Consistency Test
Hydrological analysis is an important step before conducting further analysis. The preliminary analysis used is the consistency test of rainfall data to compare the data being tested with the surrounding data. The method used is the double-mass curve on seven rain gauge stations (Junggo, Ngaglik, Ngujung, Pendem, Temas, Tinjumoyo, and Tlekung) [7]. The corrected rainfall data is obtained from the consistency test, as shown in Table 1.
3.2. Wathershed Boundary
The watershed boundary was formed using the Automatic Watershed Delineation tool in ArcSWAT software [8], [9]. The resulting watershed has an area of approximately 146 km2, as shown in Figure 1. Table 2 shows the land use area in the watershed, and Table 3 shows the soil types.
Table 1. Rainfall data
Year Annual Rainfall Data (mm)
Tlekung Tinjumoyo Temas Pendem Ngujung Ngaglik Junggo
2012 1785 1667 1290 1577 1807 1523 1687
2013 2720 2301 1989 1885 3261 2588 2073
2014 1491 1579 1346 1750 1897 1473 1564
2015 1612 1301 1183 1759 1553 1375 1235
2016 2082 2005 2594 2946 2022 2678 2186
2017 1862 1896 1918 1977 1872 1949 2038
2018 1303 1699 645 854 1434 1197 1456
2019 1634 1870 1211 1603 1559 1772 1261
2020 2314 1869 1340 1773 2080 2012 2237
2021 2500 1931 1786 2364 1842 2180 2208
Table 2. Area based on land use type
Land Use Type Area
km2 %
Scrub 2.29 1.57%
Secondary Dryland Forest 35.15 24.04%
Plantation Forest 45.32 30.99%
Urban area 17.03 11.65%
Dryland Agriculture 23.95 16.38%
Fields 22.45 15.35%
Open Land 0.04 0.03%
146.23 100%
Table 3. Area based on soil type
Soil Type Area
km2 %
Brown Regosol 30 20.52%
Association of Brown Latosol and Grey Regosol 12.17 8.32%
Association of Brown Andosol and Humic Gley 34.23 23.41%
Grey Regosol 4.51 3.08%
Complex of Brown Andosol and Litosol 34.50 23.59%
Association of Grey Andosol and Grey Regosol 30.82 21.08%
146.23 100%
Figure 1. Watershed boundary map
3.3. Simulation Results
After all the necessary data inputs were made, the first simulation was conducted without changing the parameters in ArcSWAT. These parameters remained as they were to test the model's reliability and the conditions in the area being studied. If the results did not meet the criteria, some discharge parameters were adjusted [10]. The ArcSWAT program provides convenience for users to perform manual calibration using the Manual Calibration Helper in the SWAT Simulation menu. The parameters modified in this study are presented in Table 4, while the comparison results between the estimated model discharge and observation discharge before and after calibration are presented in Table 5 and Figure 2.
The observation and calibrated model discharge data were tested using Nash-Sutcliffe (Table 6) and Root Mean Square Error (RMSE) (Table 7) [11].
The Nash-Sutcliffe method showed that the comparison value between the observation and model discharge had a satisfactory relationship overall, with an NSE of 0.6.
Meanwhile, the RMSE method resulted in a difference in the value between the recording and model discharge of 4.5. From the two test methods, it can be concluded that the model discharge is sufficiently accurate and can be used as a benchmark to determine the distribution of runoff in the study area.
Figure 2. Graph of observation discharge and model discharge (a) Before calibration (b) After calibration
Jan Feb Mar Apr May Jun Jul Augst Sep Oct Nov Dec 0
10 20 30 40 50 60 70
Jan Feb Mar Apr May Jun Jul Augst Sep Oct Nov Dec 0
5 10 15 20 25 30 35 40
Table 4. Calibration input parameters
Parameters Description Lower bound Upper bound Calibration Value
Epco Compensation factor of plant absorption 0 1 0.48
Esco Groundwater evaporation factor 0 1 0.32
Alpha_Bf Baseflow alpha value 0 1 0.275
Gw_Delay Groundwater return flow delay time 0 450 127.5
Gw_Revap Groundwater evaporation coefficient 0.02 0.2 0.025
GWQMN Aquifer shallow depth threshold for backflow 0 1000 215
Ch_K2 River hydraulic conductivity 0 500 225
Ch_N2 Manning coefficient 0.01 0.3 0.22
CN SCS Curve Number 35 90 56
Table 5. Comparison of model discharge and observation discharge before and after calibration
Month Discharge (m3/s)
Observation Discharge Model Before Calibration Model After Calibration
Jan 13.458 35.372 7.716
Feb 18.121 46.103 17.428
March 10.928 40.619 17.354
April 10.97 28.686 16.157
May 8.914 12.633 17.000
June 7.863 6.097 9.802
July 4.599 2.344 7.720
August 6.953 4.153 6.752
Sept 6.382 1.182 5.479
Oct 5.78 1.485 4.610
Nov 20.897 66.652 26.456
Dec 28.431 60.488 34.559
Table 6. The result of the Nash-Sutcliffe model discharge on the measured data after calibration.
Month Total Discharge (m3/s)
(X-Y)2 (X-Xmean)2 Observed Discharge (X) Model Discharge (Y)
Jan 13.458 7.716 32.967 2.301
Feb 18.121 17.428 0.481 38.188
March 10.928 17.354 41.303 1.028
April 10.970 16.157 26.906 0.944
May 8.914 17.000 65.390 9.167
June 7.863 9.802 3.757 16.629
July 4.599 7.720 9.740 53.912
August 6.953 6.752 0.041 24.880
Sept 6.382 5.479 0.816 30.901
Oct 5.780 4.610 1.367 37.966
Nop 20.897 26.456 30.893 80.213
Dec 28.431 34.559 37.552 271.895
Total 251.213 568.022
Average 11.941
NSE 0.6
Table 7. Calculation result of Root Mean Square Error (RMSE)
Month Total Discharge (m3/s)
(X-Y)2 Observed Discharge (X) Model Discharge (Y)
Jan 13.458 7.716 32.967
Feb 18.121 17.428 0.481
March 10.928 17.354 41.303
April 10.970 16.157 26.906
May 8.914 17.000 65.390
June 7.863 9.802 3.757
July 4.599 7.720 9.740
August 6.953 6.752 0.041
Sept 6.382 5.479 0.816
Oct 5.780 4.610 1.367
Nov 20.897 26.456 30.893
Dec 28.431 34.559 37.552
Total 251.213
n 12
RMSE 4.5
3.4. Surface Runoff by ArcSWAT
The surface runoff used is the output of the ArcSWAT program for the 2021 simulation year. After calibration, it was re-inputted using maximum rainfall data at seven rainfall stations. This condition is intended to account for the possibility of floods caused by maximum or extreme rainfall.
The surface runoff is distributed over 37 sub-basins, as shown in Figure 3. The results of the surface runoff are presented in Table 8.
3.5. Rainfall
The rainfall used is the total annual rainfall in 2021. The higher the rainfall, the greater the potential for floods, and
vice versa [12]. The rainfall map was obtained by analyzing rainfall data using the Polygon Thiessen method. The rainfall map is classified in Table 9 and shown in Figure 4.
3.6. Land Use
Land use plays a role in the amount of runoff resulting from rainfall that exceeds the infiltration rate. Heavily vegetated land will allow more rainfall to infiltrate and take more time for the runoff to reach the river, reducing the likelihood of flooding compared to areas with few or no vegetation [13]. Table 10 and Figure 5 show the land use map's classification.
Table 8. Surface runoff in each subbasin for the year 2021
Surface Runoff (mm)
Subbasin/Month Jan Feb March April May June July August Sept Oct Nov Dec 1 647.59 1022.26 728.59 640.78 532.89 138.58 115.32 318.52 141.15 133.68 954.81 1230.61 2 560.13 920.43 625.18 552.18 440.50 91.32 70.03 280.01 119.58 100.95 844.91 1110.70 3 454.04 764.64 496.58 447.08 348.62 71.94 58.27 233.99 90.55 78.96 687.31 915.31 4 453.62 762.93 496.23 446.80 348.83 71.56 60.02 221.77 83.97 75.60 683.66 912.46 5 627.04 995.89 702.37 620.72 512.93 131.87 109.37 313.40 137.75 132.19 932.11 1198.16 6 627.04 995.89 702.37 620.73 512.95 131.91 109.57 313.69 137.99 132.44 932.21 1198.17 7 586.25 949.12 657.67 581.24 472.30 116.44 96.74 301.74 128.42 115.90 879.53 1144.33 8 636.15 1017.54 721.61 628.83 517.43 122.97 101.20 316.11 145.39 124.40 946.63 1229.78 9 494.00 846.76 554.82 491.50 377.61 77.29 60.34 268.07 100.19 75.17 767.46 1028.58 10 555.35 939.27 638.62 553.95 434.26 87.14 69.56 280.90 111.33 81.14 859.91 1148.24 11 590.93 967.00 673.05 587.07 474.21 109.68 89.92 303.31 133.80 108.03 894.64 1172.76 12 431.95 733.96 482.67 428.36 335.85 76.47 62.92 239.32 92.60 74.31 658.63 881.81 13 433.74 734.75 474.15 427.44 332.20 69.86 57.26 226.03 85.09 73.71 656.98 878.50 14 467.00 785.80 510.11 458.89 356.45 65.93 53.15 221.48 86.30 75.70 704.69 940.88 15 460.44 800.13 511.42 455.42 343.54 67.30 51.15 256.08 91.81 65.00 718.46 969.84 16 405.53 726.75 441.47 395.85 285.97 48.39 33.80 234.08 75.99 44.24 639.90 878.99 17 630.51 1025.26 726.12 628.15 512.03 117.50 97.74 311.76 143.52 119.02 951.75 1244.92 18 591.52 984.68 683.60 591.19 471.83 99.59 81.38 293.22 127.20 101.46 905.59 1199.23 19 598.81 966.26 671.64 590.81 480.51 110.13 88.51 311.44 144.31 121.03 896.72 1164.92 20 392.05 681.59 423.68 385.78 290.89 53.38 39.63 209.85 73.61 60.12 599.36 814.10 21 717.37 1116.05 821.11 710.86 601.86 157.58 134.60 353.42 180.26 158.18 1054.76 1347.19 22 466.73 791.57 511.73 460.93 355.87 75.21 57.29 270.10 107.50 84.74 715.69 951.98 23 554.85 920.89 624.61 550.05 436.51 96.76 80.45 293.84 127.45 102.42 844.03 1113.26 24 605.95 973.95 671.41 590.35 477.33 99.90 89.16 318.57 144.94 116.15 905.06 1173.21 25 618.65 993.29 691.07 607.86 494.67 110.55 98.03 324.36 150.88 123.45 923.89 1197.12 26 427.10 747.12 463.56 423.86 315.79 65.31 50.38 260.97 96.73 72.91 666.98 898.37 27 424.67 743.70 453.82 410.49 301.08 54.80 39.77 246.14 90.41 61.39 656.91 891.50 28 664.88 1050.11 751.23 654.95 543.75 130.80 113.52 337.10 163.96 138.48 984.63 1266.53 29 519.13 864.18 567.48 504.63 393.16 78.45 65.65 283.94 119.03 89.94 786.44 1038.98 30 637.27 1014.73 714.01 625.00 512.86 116.67 102.33 328.38 155.07 128.11 947.47 1223.17 31 423.95 739.58 452.73 408.93 301.21 54.68 40.72 247.32 90.04 60.24 655.19 886.89 32 437.23 756.21 469.34 423.28 315.64 59.19 45.06 253.94 94.61 65.41 673.87 907.37 33 336.85 625.64 351.41 328.23 224.50 37.13 22.58 221.92 67.10 38.63 538.24 748.49 34 518.62 863.97 570.93 509.50 399.01 84.90 67.15 285.99 119.74 94.01 786.52 1040.19 35 424.45 739.25 458.54 418.76 312.50 61.97 47.94 260.15 96.71 72.04 660.07 887.89 36 521.12 870.11 584.20 518.62 409.62 91.94 74.74 287.58 122.10 98.01 794.76 1050.24 37 454.20 777.72 495.24 449.75 343.14 72.15 58.25 273.18 106.75 83.71 701.48 935.11
Table 9. Rainfall classification
Rainfall Gauge Annual Rainfall (mm) Classification Value Weight Score
Junggo 2236 Moist/ Wet 3
2
6
Tinjumoyo 2313 Wet 4 8
Ngaglik 1339 Dry 2 4
Ngujung 1869 Dry 2 4
Temas 1773 Dry 2 4
Pendem 2011 Moist/ Wet 3 6
Tlekung 2080 Moist/ Wet 3 6
Figure 3. Subbasin map
Figure 4. Rainfall classification map
Table 10. Land use classification
Land use type Value Weigth Score
Forest 1 1.5 1.5
Shrubland 2 3
Grassland, Cultivation, Plantation 3 4.5
Bare Soil, Field 4 6
Urban area 5 7.5
Figure 5. Land use classification map
3.7. Soil Type
The soil type in an area significantly affects the process of water absorption, or what we commonly refer to as infiltration [3]. The greater the water absorption or infiltration capacity, the lower the vulnerability to flooding, and vice versa. The classification of the Soil Type map is shown in Table 11 and Figure 6.
3.8. Land Slope
Land slope or incline is the percentage ratio between the vertical distance (height of land) and the horizontal distance (length of flat land) [14]. The steeper the slope, the greater the surface runoff of water, as the steepness of the slope also
increases the energy of water transport and vice versa. The classification of the land slope map is shown in Table 12 and Figure 7.
3.9. River Density
River density is the length of a river channel per square kilometer of a watershed [15]. River density describes surface water storage capacity in basins such as lakes, swamps, and river channels that flow in a watershed. The higher the density of the river channel in a watershed, the faster the runoff rate for the same rainfall. Table 13 and Figure 8 present the classification of the river density map.
Table 11. Soil type classification
Soil type Area (km2) Value Weight Score
Brown Regosol 30.00 1
1.5
1.5
Association of Brown Latosol and Grey Regosol 12.17 4 6
Association of Brown Andosol and Humic Gley 34.23 2 3
Grey Regosol 4.51 1 1.5
Complex of Brown Andosol and Litosol 34.50 2 3
Association of Grey Andosol and Grey Regosol 30.82 2 3
Figure 6. Soil type map
Figure 7. Land slope map
Table 12. Land slope classification
Slope Description Value Weight Score
0 - 8 Flat 5
1.5
7.5
8 - 15 Ramps 4 6
15 - 25 Somewhat Steep 3 4.5
25 - 45 Steep 2 3
> 45 Very Steep 1 1.5
Table 13. River density classification
Classification Value Weight Score
Sparse 5
1.5
7.5
Somewhat Sparse 4 6
Moderate 3 4.5
Dense 2 3
Very Dense 1 1.5
Figure 8. River density map
3.10.Surface Runoff
The surface runoff is the output of surface runoff using the maximum rainfall analyzed with the ArcSWAT program [16], [17]. The larger the surface runoff that occurs in an area, the higher the level of flood vulnerability in the area, and vice versa. The classification of the surface runoff map is presented in Table 14 and Figure 9.
3.11.Overlay All Parameters
After all the parameters were analyzed by assigning values, weights, and scores, they were overlaid on each other [6]. The overlay result is presented in Figure 10, and the classification of flood vulnerability levels in the study location
is presented in Figure 11.
Based on Figure 11, it can be seen that the high flood vulnerability level covers an area of 78.50 km2, or about 53.7%
of the total area of the study site. Meanwhile, the other flood vulnerability classifications cover 6.63 km2 or 4.5% for low vulnerability level, 51.13 km2 or 35% for medium vulnerability level, and 10.01 km2 or 6.8% for very high vulnerability level.
3.12. Validation
Validation was carried out directly using flood coordinates from previous events [18] obtained from the Regional Disaster Management Agency (BPBD) of Batu City.
The results of the validation point distribution are presented in Figure 12.
Figure 9. Surface runoff distribution map
Figure 10. Flood hazard mapping
Table 14. Surface runoff classification
Runoff Height (mm) Value Weight Score
< 350 1
2
2
351 – 415 2 4
416 – 480 3 6
481 – 545 4 8
> 546 5 10
Figure 11. Diagram of flood vulnerability coverage area
Figure 12. Points historical location flood events map 6.63
51.13
78.5
10.01 0
10 20 30 40 50 60 70 80 90
low medium high very high
La n d A re a ( km² )
Flood Vulnerability Level
As indicated on the spatial map of flood vulnerability levels generated from this study, most of the historical flood occurrence points are located in areas with high and very high flood vulnerability levels out of 30 validated data, as shown in Figure 12. Thus, the validity level of the validation process is sufficiently accurate, and the results of this spatial analysis can be used in mapping flood vulnerability levels in the research location.
The floods in 2019 and 2020 mostly happened in residential areas due to the lack of infiltration areas and low- elevation topography. The floods in 2019 and 2020 were also caused by heavy rainfall and damage to river structures.
Therefore, in the mapping results conducted by researchers, some areas classified as having a high and very high flood vulnerability do not show any previous flood history. This condition is because the land use in those areas has changed over the years, as confirmed by field surveys conducted by the researchers and validated by the Batu City Disaster Management Agency (BPBD).
4. Conclusion
Based on the SWAT (Soil & Water Assessment Tools) analysis results, surface runoff is high and scattered across 37 sub-basins with an average runoff height of 621.77 mm. The land use and soil type of the Upper Brantas Sub Watershed area are dominated by settlement land cover and the Brown Andosol and Glei Humus soil association. The parameters in mapping flood-prone areas are rainfall, land use, soil type, land slope, river density, and surface runoff. After overlaying, the highest level of flood vulnerability in the research location was high, covering an area of 78.50% or 53.7% of the total watershed area. The mapping results for flood vulnerability areas were validated using historical flood occurrence points obtained from the Regional Disaster Management Agency (BPBD) of Batu City. Out of 30 flood points, most were located in areas with high and very high flood vulnerability levels.
Thus, it can be concluded that the spatial analysis results of flood vulnerability levels have sufficient accuracy to be used in mapping flood vulnerability areas in the research location.
Author Declaration
Authors' contributions and responsibilities
The authors made substantial contributions to the conception and design of the study. The authors took responsibility for data analysis, interpretation, and discussion of results. The authors read and approved the final manuscript.
Funding
No funding information from the authors.
Availability of data and materials All data are available from the authors.
Competing interests
The authors declare no competing interest.
Additional information
No additional information from the authors.
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