Development of a Hydrological Telemetry System in Bago River
Ralph Allen Acierto*1,†, Akiyuki Kawasaki*1, Win Win Zin*2, Aung Than Oo*3, Khon Ra*3, and Daisuke Komori*4
*1The University of Tokyo
7 Chome-3-1 Hongo, Bunkyo, Tokyo 113-8654, Japan
*2Yangon Technological University, Yangon, Myanmar
*3Irrigation and Water Utilization Management Department, Yangon, Myanmar
*4Tohoku University, Miyagi, Japan
Hydrological monitoring is one of the key aspects in early warning systems that are vital to flood disaster management in flood-prone areas such as Bago River Basin in Myanmar. Thousands of people are affected due to the perennial flooding. Owing to the increasing pressure of rapid urbanization in the region and future climate change impacts, an early warning system in the basin is urgently required for disaster risk mitigation. This paper introduces the co-establishment of the telemetry system by a group of stakeholders. The co-establishment of the system through intensive consultations, proactive roles in responsibility sharing, and capacity building efforts, is essential in developing a base platform for flood forecasting and an early warning system in the basin. Herein, we identify the key challenges that have been central to the participatory approach in co-establishing the system. We also highlight opportunities as a result of the ongoing process and future impact on the disaster management system in the basin. We also highlight the potential for scientific contributions in understanding the local weather and hydrological characteristics through the establishment of the high-temporal resolution observation network. Using the observation at Zaung Tu Weir, Global Satellite Mapping of Precipitation (GSMaP) and Global Precipitation Measurement (GPM) satellite estimates were assessed. Near real-time and standard versions of both satellite estimates show potential utility over the basin. Hourly aggregation shows slightly higher than 40% probability of detection (POD), on average, for both satellite estimates regardless of the production type. Thus, the hourly aggregation requires correction before usage. The results show useful skills at 60% POD for standard GSMaP (GSMAP-ST), 55% POD for near real-time GSMaP (GSMAP-NR), and 46% POD for GPM, at 3-hourly aggregations. Six-hourly aggregations show maximum benefit for providing useful skill and good correspondence to gauge the observation with GSMAP-ST showing the best true skill score (TSS) at 0.54 and an equitable threat score (ETS) at 0.37. While, both final run GPM and GSMAP-NR show lower POD, TSS, and ETS scores. Considering the latency of near real-time satellite estimates, the GSMAP-NR shows the best potential with a 4-hour latency period for monitoring and forecasting purposes in the basin. The result of the GSMAP-NR does not vary significantly from the GSMAP-ST and all GPM estimates. However, it requires some correction before its usage in any applications, for modeling and forecasting purposes.
-  IPCC, “Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,” IPCC, Geneva, Switzerland, 2014.
-  S. Blenkinsop, E. Lewis, S. C. Chan, and H. J. Fowler, “Quality-control of an hourly rainfall dataset and climatology of extremes for the UK,” International Journal of Climatology, Vol.37, No.2, pp. 722–740, 2017.
-  W. W. Zin, A. Kawasaki, and S. Win, “River flood inundation mapping in the Bago River Basin, Myanmar,” Hydrological Research Letters, Vol.9, No.4, pp. 97–102, 2015.
-  S. S. Bhagabati and A. Kawasaki, “Consideration of the rainfall-runoff-inundation (RRI) model for flood mapping in a deltaic area of Myanmar,” Hydrological Research Letters, Vol.11, No.3, pp. 155–160, 2017.
-  A. Kawasaki, N. Ichihara, Y. Ochii, R. A. Acierto, A. Kodaka, and W. W. Zin, “Disaster response and river infrastructure management during the 2015 Myanmar floods: A case in the Bago River Basin,” International Journal of Disaster Risk Reduction, Vol.24 (Supplement C), pp. 151 – 159, 2017.
-  Y. Tian and C. D. Peters-Lidard, “A global map of uncertainties in satellite-based precipitation measurements,” Geophysical Research Letters, Vol.37, No.24, 2010. L24407.
-  Z. Liu, D. Ostrenga, B. Vollmer, B. Deshong, K. Macritchie, M. Greene, and S. Kempler, “Global Precipitation Measurement Mission Products and Services at the NASA GES DISC,” Bulletin of the American Meteorological Society, Vol.98, No.3, pp. 437–444, 2017.
-  G. Huffman, D. Bolvin, D. Braithwaite, K. Hsu, and R. Joyce, “Algorithm Theoretical Basis Document (ATBD) NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG),” Nasa, December 29, 2013.
-  T. Kubota, S. Shige, H. Hashizume, T. Ushio, K. Aonashi, M. Kachi, and K. Okamoto, “Global Precipitation Map using Satelliteborne Microwave Radiometers by the GSMaP Project : Production and Validation,” In 2006 IEEE MicroRad, pp. 290–295, 2006.
-  T. Ushio, K. Sasashige, T. Kubota, S. Shige, K. Okamoto, K. Aonashi, T. Inoue, N. Takahashi, T. Iguchi, M. Kachi, R. Oki, T. Morimoto, and Z.-I. Kawasaki, “A Kalman Filter Approach to the Global Satellite Mapping of Precipitation (GSMaP) from Combined Passive Microwave and Infrared Radiometric Data,” Journal of the Meteorological Society of Japan. Ser. II, Vol.87A, pp. 137–151, 2009.
-  W. McKinney and P. D. Team, “Pandas – Powerful Python Data Analysis Toolkit,” Pandas – Powerful Python Data Analysis Toolkit, p. 1625, 2015.
-  J. D. Hunter, “Matplotlib: A 2D graphics environment,” Computing in Science and Engineering, Vol.9, No.3, pp. 99–104, 2007.
-  M. Waskom, O. Botvinnik, P. Hobson, J. B. Cole, Y. Halchenko, S. Hoyer, A. Miles, T. Augspurger, T. Yarkoni, T. Megies, L. P. Coelho, D. Wehner, cynddl, E. Ziegler, diego0020, Y. V. Zaytsev, T. Hoppe, S. Seabold, P. Cloud, M. Koskinen, K. Meyer, A. Qalieh, and D. Allan, “seaborn: v0.5.0 (November 2014),” November 2014.
-  E. Jones, T. Oliphant, P. Peterson et al. “SciPy: Open source scientific tools for Python,” 2001 – [online; accessed Jan. 19, 2018]