Research Article, Geoinfor Geostat An Overview Vol: 6 Issue: 4

Projection of Riverine Flooding on Government Healthcare Facilities in Peninsular Malaysia Due To Climate Change

Kamesh R^1*, Bala S¹, Rafiza S¹, Nadia M¹, Zaki M² ,Marini M², Amri M², Nurul M², Huang YF³, Anis K⁴, Norlen M⁴, Norbizura A⁴, Rohaida I⁴, Thahirahtul Z⁴ and Yazid K⁴

¹Environmental Health Research Centre, Institute for Medical Research (IMR), Ministry of Health Malaysia, Jalan Pahang, Kuala Lumpur, Malaysia

²National Hydraulic Research Institute of Malaysia (NAHRIM) Jalan Putra Permai, Selangor, Seri Kembangan, Selangor, Malaysia

³Lee Kong Chian Faculty of Engineering and Science, Department of Civil Engineering, University Tunku Abdul Rahman, Malaysia

⁴Ministry of Health Malaysia (MOH), Block E1, E6,E7 & E10, Complex E, Federal Government Administration Centre, Putrajaya, Malaysia

*Corresponding Author : Kamesh Rajendran
Medical Officer, Environmental Health Research Centre, Institute of Medical Research, Ministry of Health Malaysia, Jalan Pahang, Kuala Lumpur, Malaysia
Tel: +603-26162313
E-mail: kameshr297@hotmail.com

Received: July 19, 2018 Accepted: August 10, 2018 Published: October 31, 2018

Citation: Kamesh R, Bala S, Rafiza S, Nadia M, Zaki M, et al. (2018) Projection of Riverine Flooding on Government Healthcare Facilities in Peninsular Malaysia Due To Climate Change. Geoinfor Geostat: An Overview 6:4. doi: 10.4172/2327-4581.1000192

Abstract

Keywords: Flood; Flood vulnerability; Riverine flood projection; River basin; Healthcare facilities; Climate change; HEC-RAS; Geographic Information System (GIS); Malaysia

Introduction

Malaysia is a country prone to flood risks, mostly by nature of its physical (topography and drainage) as well as its human geography (settlement and land use patterns). The combination of natural (heavy monsoon rainfall, intense convection rain storms) and human factors has produced different types of floods, namely monsoon, flash and tidal [1]. Monsoon and flash floods are the most severe climaterelated natural disasters in Malaysia. The leading cause of floods is heavy rainfall of long duration or of high intensity, creating high runoff in rivers or a build-up of surface water in areas of low relief. Rainfall over long periods may produce a gradual but persistent rise in river levels that in turn causes rivers to inundate surrounding land for days or weeks at a time.

Past flood events are based on statistical descriptors (i.e. flood severity), spatiotemporal descriptors (i.e. flood magnitude) and also on socio-economical descriptors (i.e. the extent of flood damage, human casualties, psychological impact). There are basically four types of flood events that can be generally characterized as extreme flood events, including dam-break floods [2-5] storm surges [6,7], flash floods [8-11], and extreme/large river floods [12-15]. Among these types of extreme flood events, flash floods and large river floods are the most common and generally the most serious extreme events [16,17] which pose the greatest flood risk to the general population.

River flow forecasting is an important yet difficult task in the field of hydrology because predicting future events involves a decisionmaking process where the ability to predict future river flow will provide the right edge and assist the engineers in terms of flood control management, and provide some benefits in the areas of water supply management [18-22]. Accurate continuous data collections on the catchment area are needed to produce a good river flow forecast.

There have been many studies on flood vulnerability and threat mapping using Remote Sensing Data and Geographical Information System (GIS) tools [23]. Flood monitoring across the globe has been done extensively by radar remote sensing data [2,24] and several of these studies have applied probabilistic methods [2,25,26]. Various case studies have also applied GIS and neural network methods for flood vulnerability mapping [24,27]. However, studies involving the application of computer based models utilizing risk assessment software for the projection of flood in relation to climate change are still scarce and lacking in Malaysia [28].

Overall this study aimed to identify vulnerability of healthcare facilities to riverine flooding which would assist in further adaptations, for providing continuous healthcare services even during the times of the flood. The findings in this study should be of particular interest to government departments (national and regional) and regulatory and planning authorities, as the findings presented herein should help to improve on the general flood hazard assessment data, and procedures used for emergency health services, planning and infrastructure assessment.

Study Area

Malaysia is located in South East Asia which lies between the latitude 2º N and 7º N of the equator and longitude 99.5º E and 120º E. It also covers an area of approximately 329,750 km², comprising of Peninsula Malaysia (West Malaysia) and the states of Sabah and Sarawak (East Malaysia) which is located along the northwest coast of Borneo Island. Malaysia is generally influenced by convective rain and the rainfall distribution is greatly influenced by topography and the monsoon winds [29-35]. There are 191 major river basins in Malaysia, which 144 river basins are prone to flood. In view of the effects of climate change, NAHRIM has conducted detailed studies in the 15 most socio-economically vulnerable river basins in Peninsular Malaysia, namely Muda, Kedah, Kerian, Selangor, Kesang, Muar, Batu Pahat, Johor, Pulai, Skudai, Tebrau, Pahang, Setiu, Dungun and Kelantan River basins (Figure 1). The coordinates of the government Hospitals (H), Primary health clinics (PHC’s) and Community health clinics (CHC’s) were overlaid with the flood extent maps associated with 100-year Return Period for the time horizons of 2030 and 2050 produced by NAHRIM.

Methodology

The National Hydraulic Research Institute of Malaysia (NAHRIM) has conducted comprehensive studies to assess the impact of climate change on the hydrologic conditions of Peninsular Malaysia [33,35] as well as for Sabah and Sarawak [34]. The latest study conducted in 2014 was based on 15 climate projections for the 21^st century by 3 different coupled land-atmosphere-ocean Global Climate Models (GCMs), that are ECHAM5 of the Max Planck Institute of Meteorology of Germany, CCSM3 of the National Center for Atmospheric Research (NCAR) of USA, and MRI-CGCM2.3.2 of the Meteorological Research Institute of Japan, based on four different IPCC AR4 greenhouse gas emission scenarios (A1FI, A2, A1B, B1). The climate projections were dynamically downscaled at hourly intervals and a fine 6 km × 6 km spatial resolution by the Regional Hydroclimate Model of Peninsular Malaysia (RegHCM-PM), which is a combination of mesoscale atmospheric model and regional land surface hydrology model that is also able to capture the impact of steep topography on local climatic conditions [35].

Ensemble average of the 15 scenarios of bias-corrected downscaled future rainfall was utilized for design flood modelling simulations. The current (baseline) and future flood assessments were carried out based on 100-year Return Period for the current and future time horizons of 2030 and 2050. The 100-year Return Period was used in the assessments since it is the adopted standard design protection level mandatory for all the major water-related infrastructures in the country.

The flood modelling for the 15 river basins was carried out by combination of projected river reaches flow data generated from NAHRIM Watershed Environmental Hydrology (N-WEHY) Model and 2D-hydrodynamic flood modelling data using US Army Corps of Engineer, Hydrologic Engineering Centre’s River Analysis System (HEC-RAS). Figure 2 summarizes the workflow of the flood modelling process by NAHRIM that comprises of four (4) main activities which are downscaled climate change projections and scenarios, preparation of model input, flood modelling and generation of flood maps, and vulnerability assessment of the healthcare facilities carried out by IMR in this study.

The results of generated future flood maps from the hydrodynamic models in Geographic Information System (GIS) format. This data was then applied to prepare inputs to Aeronautical Reconnaissance Coverage View (ArcView), Version 3.2 which was used for visualisation. All outputs were transferred into ArcView and a flood project file (FLOOD.APR) was created for easy query, visualisation and mapping for the end user and research outcomes. The healthcare facilities reported in this study were all government healthcare facilities registered as of the year 2015.

Results

2D flood inundation and extent maps for the 15 river basins were produced based on current condition (baseline) and future climate condition for 2030 and 2050 by NAHRIM. In general, increasing trends of flood magnitudes and inundated areas are simulated in the future compared to current design floods. The total flood areal extent in these 15 basins are most likely to increase from the current 3,918 km² (6% of total basin area) to 6,007 km² (9.1%) and 6,210 km² (9.4%) in the periods of 2030 and 2050, respectively. However, potential flooding in certain areas within 15 river basins are identified to be more severe in 2030 and 2050. The healthcare facilities around Muda, Kedah, Kelantan, Pahang, Muar and Batu Pahat river basins were more vulnerable to potential flooding as shown in Tables 1 and 2.

Table 1: Healthcare facilities Affected by Projected Flooding and Inundation in 15 Vulnerable River Basins studied by NAHRIM and IMR at Baseline, 2030 and 2050 with Water Depth Levels of 0.01 m-0.50 m, 0.50-1.2 m and>1.2 m.

Table 2: Summary of Healthcare facilities affected by Projected Flooding and Inundation in 15 Vulnerable River Basins studied by NAHRIM and IMR based on water depth levels at Baseline, 2030 and 2050.

The most number of CHC’s (24.4%) were affected in Pahang river basin, followed by 47% PHC’s and 62.5% hospitals in Kelantan river basin. Overall healthcare facilities situated around Kelantan river basin was affected the most (30.5%). It is clear from the hazard map prepared that the number of healthcare facilities affected by flood especially with water depth level of more than 1.2m will increase to 163 (58%) and 213 (76%) healthcare facilities in 2030 and 2050, respectively, due to higher projected future extreme rainfall and flood magnitude and extent.

This information should be utilized to manage flood warning activities such as voidances and road closures to ensure uninterrupted health services even during floods and as adaptation plan for flood damage control.

Discussion and Conclusion

Many healthcare facilities have not been designed with extreme weather events like riverine flooding in consideration. This is especially the case with facilities which are situated in areas where the weather was moderate prior to anthropogenic climate change and the resulting phenomena such as floods, heavy storms, landslides and mudslides occur due to reasons such as land use. New healthcare facilities will need to be designed and sited accordingly. Modifications may need to be made to existing facilities and their environments as well as innovative healthcare facilities to deal with extreme weather events such as flooding. Nowadays, potential flood damage is generally assessed using flood simulation models. Even though developments in the field of computer science have enabled the generation and application of high-resolution flood prediction models, difficulties still remain in recreating actual extreme flood events, which in turn has a direct impact on model predictions of flood elevations, inundation extent and hazard risk.

This paper highlighted the use of GCM’s dynamic downscaling projection data, and applied to 2D-hydrodynamic flood modelling to provide basic framework for generating information to public health physicians, hydrologists, engineer, policy makers and planners on the anticipated climate change impact on riverine flooding using an integrated approach.

However early warning systems have been developed in many areas to prevent negative health impacts through alerting public health authorities and the general public about climate-related health risks. Besides that, in order to ensure uninterrupted health services in Malaysia, current resources have been upgraded and able to hold essential utilities and resources (medical gases, Liquefied petroleum gas, food, water supply and electricity) up to 3-5 days in health clinics and 2 weeks in hospitals. Health services are also provided at relief centres. Basic medical treatments as well as prevention of communicable diseases are provided at relief centres. At relief centres with more than 1000 evacuees, 24 hours static medical services are provided. Registries and early evacuation plans to designated health facilities are in place, established specifically for the less resilient groups.

There is also capacity building for crisis preparedness and disaster management during flood which is enhanced by technical training of staff in terms of survival training and flood simulation exercises. Post disaster psycho-social support services are also enhanced to minimize the impact of disasters on the affected communities and healthcare workers. Furthermore, flood adaptation programmes and strengthening of disaster risk management and resilience of infrastructure would be further enhanced in the Eleventh Malaysia Plan and beyond.

Results of this study were reported by the Institute for Medical Research (IMR), Ministry of Health, in Malaysia’s commitment to Vulnerability and Adaptation Assessment for the Third National Communication to United Nations Framework Convention from Climate Change (UNFCCC) and Second Biennial Update Reporting for flood vulnerability impact assessment.

Acknowledgements

We would like to thank the Ministry of Health (MOH) of Malaysia through the Director-General of Health Malaysia, Deputy Director General of Health (Research & Technical Support) and the Director of Institute for Medical Research for ensuring the availability of the healthcare facilities data for use in this study. We also would like to thank NAHRIM for the development of the projected riverine flood inundation and extent map and compiling the data as well as all MOH staff and officers who were involved in this study. This study was approved by the National Institutes of Health, Ministry of Health, Malaysia for publication with NMRR ID: NMRR-16-1422-31929 (Project Code: NIH/IMR16- 051). This project was initiated under the Malaysia 3^rd National Communication (NC3)-Vulnerability and Adaptation Working Group (VA).

Competing Interests

The authors have declared that no competing interests exist.

References

1. Allan RP, Soden BJ (2008) Atmospheric warming and the amplification of precipitation extremes. Science 321: 1481-1484.
2. Amengual A, Homar V, Jaume O (2015) Potential of a probabilistic hydrometeorological forecasting approach for the 28 September 2012 extreme flash flood in Murcia, Spain. Atmos Res 166: 10-23.
3. Androulidakis YS, Kombiadou KD, Makris CV, Baltikas VN, Krestenitis YN (2015) Storm surges in the Mediterranean Sea: Variability and trends under future climatic conditions. Dyn Atmos Oceans 71: 56-82
4. Antico A, Torres ME, Diaz HF (2016) Contributions of different time scales to extreme Parana´ floods. Clim Dyn 46: 3785-3792
5. Ashley ST, Ashley WS (2008) Flood fatalities in the United States. J Appl Meteorol Climatol 47:805-818
6. Bergman N, Sholker O, Roskin J, Greenbaum N (2014) The Nahal Oz Reservoir dam-break flood: Geomorphic impact on a small ephemeral loess-channel in the semi-arid Negev Desert, Israel. Geomorphology 210: 83-97.
7. Bernstein L, Bosch P, Canziani O, Chen Z, Christ R, et al. (2008) Climate change 2007: Synthesis report: An assessment of the intergovernmental panel on climate change, IPCC
8. Breilh JF, Bertin X, Chaumillon E, Giloy N, Sauzeau T (2014) How frequent is storm-induced flooding in the central part of the Bay of Biscay? Glob Planet Change 122: 161-175.
9. Bruwier M, Erpicum S, Pirotton M, Archambeau P, Dewals B (2015) Assessing the operation rules of a reservoir system based on a detailed modelling chain. Nat Hazards Earth Syst Sci 15: 365-379.
10. Christensen JH, Christensen OB (2007) A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Clim Change 81: 7-30.
11. Di Baldassarre G, Montanari A, Lins H, Koutsoyiannis D, Brandimarte L (2010) Flood fatalities in Africa: From diagnosis to mitigation. Geophys Res Lett 37: L22402.
12. Farajzadeh M (2001) The flood modeling using multiple regression analysis in Zohre & Khyrabad Basins. In the Proceedings of 5th International Conference of Geomorphology, August, Tokyo, Japan.
13. Foulds SA, Macklin MG, Brewer PA (2014) The chronology and the hydrometeorology of catastrophic floods on Dartmoor, South West England. Hydrol Process 28: 3067-3087.
14. Gasim MB, Toriman ME, Idris M, Lun PI, Kamarudin MK, et al (2013) River flow conditions and dynamic state analysis of Pahang River. Am J App Sci 10: 42-57.
15. Ghani AA, Zakaria NA, Falconer RA (2009) River modeling and flood mitigation: Malaysian perspectives. Water Manage 162: 1-2.
16. Herget J, Kapala A, Krell M, Rustemeier E, Simmer C, et al. (2015) The millennium flood of July 1342 revisited. Catena 130: 82-94.
17. Horritt MS, Bates PD (2002) Evaluation of 1D and 2D numerical models for predicting river flood inundation. J hydrol 268: 87-99.
18. Islam MM, Sado Kimiteru, Owe M, Brubaker K, Ritchie J, et al. (2001) Flood damage and management modelling using satellite remote sensing data with GIS: Case study of Bangladesh. IAHS Publ 267: 455-457.
19. Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman, WNA, et al. (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental EarthSciences 67: 251-264
20. Keizrul A, Chong SF (2002) Flood forecasting and warning systems in Malaysia. DID, Hydrology and Water Resources Division, Department of Irrigation and Drainage, Malaysia.
21. Kundzewicz ZW, PiÃ?Â?skwar I, Brakenridge GR (2013) Large floods in Europe, 1985-2009. Hydrol Sci J 58: 1-7.
22. Kvocka D, Falconer RA, Bray M (2015) Appropriate model use for predicting elevations and inundation extent for extreme flood events. Nat Hazards 79: 1791-1808.
23. MartÃ?±´nez Ibarra E (2012) A geographical approach to post-flood analysis: The extreme flood event of 12 October 2007 in Calpe (Spain). Appl Geogr 32:490-500.
24. NAHRIM (2006) Study of the impact of climate change on the hydrologic regimes and water resources of peninsular Malaysia, Malayisa.
25. NAHRIM (2010) Study of the impact of climate change on the hydrologic regimes and water resources of Sabah and Sarawak, Malayisa.
26. NAHRIM (2014) Extension study of the impact of climate change on the hydrologic regimes and water resources of peninsular Malaysia, Malayisa.
27. Pall P, Aina T, Stone DA, Stott PA, Nozawa T, et al. (2011) Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470: 382-385.
28. RasÃ?Â?ka P, Emmer A (2014) The 1916 catastrophic flood following the BÃ?±´la´ Desna´ dam failure: The role of historical data sources in the reconstruction of its geomorphologic and landscape effects. Geomorphology 226: 135-147.
29. Rojas R, Feyen L, Watkiss P (2013) Climate change and river floods in the European Union: socio-economic consequences and the costs and benefits of adaptation. Global Environ Change 23: 1737-1751.
30. Schro¨ter K, Kunz M, Elmer F, Mu¨hr B, Merz B (2015) What made the June 2013 flood in Germany an exceptional event? A hydro-meteorological evaluation. Hydrol Earth Syst Sci 19: 309-327.
31. IPCC (2013) Climate Change 2013: The physical science basis; Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, et al. (eds) Cambridge University Press, Cambridge, UK.
32. Wang B, Chan JC (2002) How strong ENSO events affect tropical storm activity over the western North Pacific. J Climate 15: 1643-1658.
33. Weng Chan N (1998) Environmental hazards associated with hill land development in Penang Island, Malaysia: Some recommendations on effective management. Disas Prev Management: An Int J 7: 305-318.
34. Youssef AM, Pradhan B, Hassan AM (2011) Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environmental Earth Sciences 62: 611-623.
35. Viessman W, Lewis GL, Knapp JW (1989) Introduction to hydrology (5th edn), New York Harper &� Row, USA.