Determination of Optimal Rain Gauge on The Coastal Region Use Coefficient Variation: Case Study in Makassar

https://doi.org/10.22146/jcef.58378

Giarno Arno(1*), Muflihah Muflihah(2), Mujahidin Mujahidin(3)

(1) Department of Climatology, Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Tangerang Selatan, INDONESIA
(2) Balai Besar Meteorologi, Klimatologi, dan Geofisika, Wilayah IV, Makassar, INDONESIA
(3) Stasiun Meteorologi Maritim Paotere, Badan Meteorologi, Klimatologi, dan Geofisika, Makassar, INDONESIA
(*) Corresponding Author

Abstract


The quality of rainfall data is highly significant in disaster analysis, ecology, and water resource management. However, the accuracy and quantity of rain gauges are often inadequate, especially for analyzing extreme events, including the Makassar City flood, in 2019. This inadequacy is due to several reasons, including rain gauges’ inadequacy and insufficient distribution. This study, therefore, aims to analyze the requirements of optimal rain gauges, using coefficients of variation in various error levels, based on the latest rainfall data in several locations within Makassar City. Monthly and yearly rainfall observation data from 2010 to 2019 obtained at 5 locations were used to calculating the optimal rain gauge number. According to the results, the existing station has a 10% and 15% monthly and annual error, respectively. This region has 3 groups causing highly optimal rain gauges, and these are the first group comprising Paotere, Panaikang, as well as Biring Romang, while the second and third groups comprise Sudiang and Barombong. The northwest wind blows towards the coast and crosses these three places in a line, thus, causing rainfall intensity with a slight disparity, between the first group. Furthermore, the combination of these places resulted in low optimal rain gauge. However, the combination of the first group with the second and third lead to an increase in the optimal rain gauge number. The low elevation, proximity, and location of the first group’s three locations in line with the rain-causing wind results in low optimal rain gauge. In the combination of the first, second, and third groups, additional gauges are required to obtain a 5% or 10% error. The rainfall intensity and position greatly influence the rain catchment in Makassar, and consequently, the optimal rain gauge number. In addition, the distance, topographical aspects, and the combined land-sea and monsoonal winds’ factors must also be analyzed, in deploying equipment.

Keywords


Optimum rain gauge, Uncertainty, Coefficient of variation, Rainfall, Makassar

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References

Ahuja, P. R., 1960. Planning of precipitation network for water resources development in India. WMO flood control series No 15, 106–112.

Adhikary, S. K., Yilmaz, A. G. and Muttil, N., 2015. Optimal design of rain gauge network in the Middle Yarra River catchment, Australia, Hydrol. Process, 29, 2582–2599.

Al-Abadi, A. M. and Al-Aboodi, A. H. D., 2014. Optimum rain-gauges network design of some cities in Iraq, Journal of Babylon University/Engineering Sciences, 22(4), 946-958.

Al Fahmi, F., Boer, R., Hidayat, R., Perdinan and Sopaheluwakan, A., 2019. The impact of concave coastline on rainfall offshore distribution over, Indonesian Maritime Continent, The Scientific World Journal, 2019, Article ID 6839012.

Cameron, K, and Hunter P., 2002. Using spatial models and kriging techniques to optimize long‐term groundwater monitoring networks: a case study, Environmetrics, 13, 629– 656.

Church, R. L., 2002. Geographical information systems and location science, Computers and Operations Research, 29(6): 541– 562.

Church, R. L. and Murray, A. T., 2009. Business site selection, location analysis and GIS, Wiley: New York.

Ciach, G. J., 2003. Local random errors in tipping-bucket rain gauge measurements, Journal of Atmospheric and Oceanic Technology, 20, 752-759.

Ciach, G. J., Krajewski, W. F. and Villarini, G., 2007. Product error driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data, Journal of Hydrology, 8, 1325–1347.

Daskin, M. S., 1995. Network and discrete location—models, algorithms and applications, Wiley: New York.

Ganguli, M. K., Rangarajan, R. and Panchang, G. M., 1951. Accuracy of mean rainfall estimates – Data of Damodar catchment, Journal of Irrigation and Power, 8.

Giarno, Zadrach L. D. and Mustofa, M. A., 2012. Kajian awal musim hujan and awal musim kemarau di Indonesia, Jurnal Meteorologi and Geofisika, 1, 1–8.

Giarno Hadi, M. P., Suprayogi, S. and Murti, S. H., 2018. Distribution of accuracy of TRMM daily rainfall in Makassar Strait, Forum Geografi, 32, 38-52.

Hu, Q.F., Yang, D.W., Wang, Y.T. and Yang, H.B., 2013. Accuracy and spatio-temporal variation of high-resolution satellite rainfall estimate over the Ganjiang River Basin, China Technology and Science, 56, 853–865.

Ichikawa, H. and Yasunari, T., 2006. Time-space characteristics of diurnal rainfall over Borneo and surrounding oceans as observed by TRMM-PR, Journal of Climate, 19(7), 1238–1260.

Liberti, G. L., Ch´eruy, F. and Desbois, M., 2001. Land effect on the diurnal cycle of clouds over the TOGA COARE area, as observed from GMS IR Data, Monthly Weather Review, 129(6), 1500–1517.

McGranahan, G., Balk, D. and Anderson, B., 2007. The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones, Environment and Urbanization, 19: 17-37.

Ngene, B. U., Agunwamba, J. C., Nwachukwu, B. A. and Okoro, B. C., 2015. The Challenges to Nigerian raingauge network improvement, Res. J. Environ. Earth Sci., 7(4), 68-74.

Oliver, J. E., 2004. Encyclopedia World Climatology, Springer

Patel, A. D., Dholakia, M. B., Patel, D. P. and Prakash, I., 2016. Analysis of optimum number of rain Gauge in Shetrunji River Basin, Gujarat - India, International Journal of Science Technology & Engineering, 2(11), 380-384.

Prakash, M. R. and Singh, V. S., 2000. Network design for groundwater monitoring—a case study, Environmental Geology, 39, 628– 632.

Putthividhya, A. and Tanaka, K., 2012. Optimal rain gauge network design and spatial precipitation mapping based on geostatistical analysis from colocated elevation and humidity Data, International Journal of Environmental Science and Development, 3(2), 124-129.

Ramage, C. S., 1968. Role of Tropical ‘Maritime Continent’ in the atmospheric circulation, Monthly Weather Review, 96, 365-370.

Rycroft, H.B., 1949. Random sampling of rainfall, J. South African Forestry Assoc. 18.

Small, C. and Nicholls, R.J., 2003. A Global Analysis of Human Settlement in Coastal Zones. Journal of Coastal Research, 19(3): 584-599.

WMO, 1994. Guide to hydrological practices: Data acquisition and processing, analysis, forecasting, and other apllications, WMO-No.168.

Wu, H., Chen, Y., Chen, X., Liu, M., Lu Gao, L. and Deng, H., 2020. A new approach for optimizing rain gauge networks: A case study in the Jinjiang Basin, Water, 12, 2252.

Xie, P.P. and Arkin, P.A., 1997. Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions, Journal of Climatology, 9, 840–858.

Yang, G. Y. and Slingo, J., 2001. The diurnal cycle in the tropics, Monthly Weather Review, 129(4), 784–801, 2001.



DOI: https://doi.org/10.22146/jcef.58378

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