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Analysis of Placement Pattern and Number of Rain Stations Based on the Equation of Kagan Rodda in Ciliwung Watershed

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Analysis of Placement Pattern and Number of Rain Stations Based on the Equation of Kagan Rodda in Ciliwung Watershed VOLUME 5 │ NUMBER 2 │ NOVEMBER 2020 ADRI http://adri.journal.or.id/index.php/ijens/index INTERNATIONAL JOURNAL ISSN: 2656-1174 (online) OF ENGINEERING AND Attribution 4.0 International (CC BY 4.0) NATURAL SCIENCE You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially Analysis of Placement Pattern and Number of Rain Stations Based on The Equation of Kagan Rodda in Ciliwung Watershed Jantiara Eka Nandiasa, Masnia, Fathiya Amatullah Civil Engineering Department Mercu Buana University Bekasi, Indonesia [email protected] , [email protected], [email protected] Abstract The accuracy of hydrological data is influenced by the number of rain stations in a watershed, the density and distribution patterns as well as the accuracy in recording the data itself. The rain gauge network system must be planned in accordance with the needs of the rainfall data to be collected. The placement of a rain station in a watershed (DAS) can be found using the Kagan Rodda method equation. The Ciliwung Watershed is a watershed in an area that has been developed with a high population density with a large number of rain gauges, currently there are 7 rain stations. After looking for the analysis of the placement pattern and the number of rain stations using the Kagan Rodda method in the Ciliwung watershed, it was found that 4 stations were recommended, so that the number of stations in the Ciliwung watershed became 11 stations, and the placement pattern with the Kagan Rodda triangle, the side length of the triangle is the same as the distance between rain stations. L = 7,947km. Keywords Ciliwung Watershed, Kagan Rodda, Length, Rain Station 1. Introduction Accurate hydrological data will increase the accuracy of the analysis results. The accuracy of hydrological data is influenced by the number of rain stations in a watershed, the density and distribution patterns as well as the accuracy in recording the data itself. Hydrological data is a collection of information or facts regarding hydrological phenomena such as the magnitude of: rainfall, temperature, evaporation, duration of sun exposure, flow velocity, river sediment concentration will always change with time (Soewarno, 1995). The rain gauge network system must be planned according to the need for the utilization of the rainfall data to be collected. In an area that has been developed (intensive development) with a high population density, the number of rain gauges needed should also be greater. This is because the level of development that is taking place in that place demands more accurate information about rainfall compared to less or less developed areas with low population density (Asdak, 2018). The Ciliwung Watershed is a watershed with a large category and includes several cities and includes the capital city of Indonesia. The placement pattern and the number of stations in this watershed affect the hydrological analysis needed for the surrounding area, especially the D.K.I Province area of Jakarta, which is the capital city of Indonesia and has many government offices and business areas, and often experiences flooding. 1.1 Thiessen Method This method is obtained by making a polygon that cuts perpendicularly in the middle of the connecting line of two rain captive posts. With each captive post the Pn rain will be located in a closed polygon area with an area of An.The average rainfall is obtained by adding up all the products of rainfall at the Pn rain breeder post with a closed polygon area with an area An for all areas located in the catchment area and then divided by the total area At, with the following equation: A1.P1+ A2.P2+...+An.Pn P = At Where : P = average rainfall (mm) P1-Pn = Rainfall at each station (mm) A1-An = Area delimited by polygon line (km2) At = total catchment area 27 VOLUME 5 │ NUMBER 2 │ NOVEMBER 2020 ADRI http://adri.journal.or.id/index.php/ijens/index INTERNATIONAL JOURNAL ISSN: 2656-1174 (online) OF ENGINEERING AND Attribution 4.0 International (CC BY 4.0) NATURAL SCIENCE You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially 1.2 Kagan Rodda Method Determination of the rain station network is not only limited to determining the number of stations required in a watershed, but also the location and distribution pattern. Qualitative guidance is provided by (Rodda, 1972), namely by utilizing the rainfall correlation coefficient (Sri Harto Br, 1993). This must still be related to the surrounding conditions concerning the availability of observers and their distribution patterns. In the research conducted by (Rodda, 1972), for tropical areas where local rainfall with a very limited spread area has a variety of spaces for rain with a certain period, it is very uncertain even though it actually shows a relationship to some degree (Sri Harto Br, 1993). 2. Methodology Figure 1. Methodology Based on the research flowchart, this research methodology is as follows: 1. Collecting and preparing rain data and rain stations. a. Rain Data: Maximum daily and monthly rainfall. Rainfall data for 10 years b. Rain Station Data: area of influence of rain stations, distance between rain stations, and the number of rain stations. 2. Calculating the maximum monthly average rainfall in the watershed using the Thiessen Polygon method. 3. Calculate the distance between rain stations. 4. Calculating the coefficient of variation (Cv) from the calculation of the regional average rainfall. 5. Calculate the correlation between rainfall stations, for both daily and monthly rainfall, as needed. 6. The relationship obtained is depicted in an exponential curve graph, from this graph it can be obtained the magnitude of d (0) using the mean values of d and r (d). 7. With this quantity, the smoothing error and interpolation error can be calculated using the equations Z1 and Z3, after the high accuracy is determined. 8. After the number of stations has been determined for the said watershed, the determination of rain stations can be carried out using the equation r (d) and depicting an equilateral triangle net. 28 VOLUME 5 │ NUMBER 2 │ NOVEMBER 2020 ADRI http://adri.journal.or.id/index.php/ijens/index INTERNATIONAL JOURNAL ISSN: 2656-1174 (online) OF ENGINEERING AND Attribution 4.0 International (CC BY 4.0) NATURAL SCIENCE You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially 2. Result and Discussion 2.1 Rainfall Analysis The data used to calculate the rainfall analysis comes from BBWS Ciliwung Cisadane and the BMKG Online Data Website, in the following table there is data that has been processed into maximum daily rainfall data which is then used to calculate the regional average rainfall analysis. Table 1. Maximum Daily Rainfall No Year Annual Maximum Daily Rainfall (mm) 1 2010 86.86 2 2011 98.04 3 2012 99.80 4 2013 122.21 5 2014 124.69 6 2015 144.24 7 2016 114.29 8 2017 126.16 9 2018 118.49 10 2019 110.94 To calculate the regional average rainfall using the Thiessen Method, the area of each rain station is required. The data on the area of the rain station are as follows : Table 2. Rain Station Area Coordinates and Areas Rainfall Station Y X Area (km2) Kemayoran -6.15601 106.84168 64.62056 Kampus UI -6.36121 106.82375 65.87182 Cibinong -6.46048 106.85636 75.22758 Gadog -6.65343 106.86918 56.41886 Cilember -6.65287 106.91492 33.81965 Citeko -6.69797 106.93513 37.83580 Perkebunan Gunung Mas -6.70908 106.96738 52.30826 The following are the results of the analysis of the regional mean rainfall sought by the Thiessen method : Table 3. Average Rainfall No Year Thiessen Average Rainfall (mm) 1 2010 29103.04 2 2011 32637.62 3 2012 34942.53 4 2013 41556.27 5 2014 42213.66 6 2015 50818.97 7 2016 40891.14 8 2017 42489.41 9 2018 36656.32 10 2019 35734.14 2.2 Correlation Coefficient and Correlation Radius From the available rain station locations, you can search for the distance between rain stations and from the rainfall data for each rain station, the correlation between rain stations can be found with a regression graph. 29 VOLUME 5 │ NUMBER 2 │ NOVEMBER 2020 ADRI http://adri.journal.or.id/index.php/ijens/index INTERNATIONAL JOURNAL ISSN: 2656-1174 (online) OF ENGINEERING AND Attribution 4.0 International (CC BY 4.0) NATURAL SCIENCE You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially The correlation between the rain stations is then graphed with the exponential distance between the rain stations in order to obtain the Correlation Coefficient and Correlation Radius values. The following is a table of distances between rain stations in Km: Table 4. Distance Between Stations Kam Rainfall Kemayo Cibin Gad Cilem Cite pus Stastion ran ong og ber ko UI Kemayo 55.1 60.9 0 22.81 33.77 55.61 ran 5 1 Kampus 32.7 39.2 0 11.56 33.8 UI 1 3 Cibinon 21.3 27.6 0 22.24 g 9 9 Gadog 0 5.06 8.82 Cilembe 0 5.48 r Citeko 0 P.Gunu ng Mas The following is an example of a regression graph between two stations. The following graph shows the 10-year average rainfall between each station: Figure 2.
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