Hasegawa, A., Gusyev, M., and Iwami, Y.
Paper:
Meteorological Drought and Flood Assessment Using the Comparative SPI Approach in Asia Under Climate Change Akira Hasegawa† , Maksym Gusyev, and Yoichi Iwami International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI) 1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan † Corresponding author, E-mail:
[email protected] [Received June 16, 2016; accepted October 2, 2016]
The standardized precipitation index (SPI) has been used to monitor and analyze meteorological droughts using long-term monthly precipitation from national meteorological and hydrological services on multiple timescales. Instead of evaluating climatic impacts with separately-computed SPI for present and future climates, we introduced the comparative SPI (cSPI) computed using target (future) datasets on the basis of a reference (present) dataset. The cSPI approach evaluates standardized precipitation change in one dataset for different periods and for different datasets in a common period. Using 12-month cSPI, we investigate the change in central conditions and in the probabilities of dry and wet conditions between present and future climates. Meteorological drought and flood hazards in Asia are examined with MRI-AGCM3.2S, a 20km mesh global atmospheric model, time-slice experiments of the present (1979–2003) and future (2075– 2099) with four different sea surface temperature patterns. As one result indicates, the median of the 12month cSPI shifts to severely dry around the Mediterranean Sea to the Persian Gulf, and to extremely wet in the Tibetan Plateau, North and South India, and around the Yellow Sea. Therefore, we conclude that the cSPI approach is a useful way to characterize both future drought and flood hazards under climate change. Keywords: comparative SPI (cSPI), precipitation, meteorological drought, climate change, Asia
1. Introduction As water-related hazards may be increasing in Asia under climate change, the population of the Asian region, which was about 60% of the world’s population in 2013, will be the most vulnerable to these increased hazards in future climates [1]. The Intergovernmental Panel on Climate Change (IPCC) Working Group (WG) II 5th Assessment Report (AR5) Part B [1] emphasized increased risk of drought-related water and food shortages causing malnutrition as well as increased flooding leading to widespread damage to infrastructure, livelihoods, and settlements in Asia. Therefore, a robust tool is required to characterize and assess drought and flood hazards under 1082
future climates. Many precipitation indices have been used in climate change studies, e.g., pav (averaged precipitation), RX1day (wettest day), RX5day (wettest consecutive five days), SDII (simple daily intensity index), R95p (precipitation from very wet days), and CDD (consecutive dry days). A short list of available indices can be found in Box 2.4 Table 1 of IPCC WG I AR5 [2]. Generally, these indices are computed separately for present and future climates and then compared to provide policymakers with valuable information about mitigation and adaptation plans in response to global warming [2, 3]. The standardized precipitation index (SPI) [4, 5] has also been used to assess the meteorological drought and flood hazards in future climates projected with climate models [6–12]. However, it is difficult to assess climatic impacts by comparing separately-computed SPI values between present and future climates, as conducted with the precipitation indices listed in Box 2.4 Table 1 [2]. This difficulty arises from the definition and computation of standardized indices. Even if the precipitation median increases or decreases dramatically due to climate change, the SPI median of future climate remains about zero, for separately-computed SPI, making the statistical differences in the SPI values small between present and future climates. Although some studies were confused by this evaluation problem of the separately-computed SPI for present and future climates, other studies computed the future climate SPI on the basis of the present climate to overcome this difficulty of standardized index evaluations. We independently proposed a fixed procedure to calculate SPI as the comparative SPI (cSPI) of target datasets on the basis of a reference dataset [12]. When reference and target datasets are assigned to present and future climates, we can assess climatic impacts with the cSPI of the future climates on the basis of the present climate. Dubrovsky et al [8] called this approach the relative SPI (rSPI), which is calibrated using a reference precipitation series in the first step and is then applied to the target series in a different time and space. While the combinations of reference and target datasets for the different periods at the same location are useful for climatic impact assessments, the inter-station comparison at the different locations in a common period has limited applicability due to
Journal of Disaster Research Vol.11 No.6, 2016
Meteorological Drought and Flood Assessment Using the Comparative SPI Approach in Asia Under Climate Change
climatic variability across regions. The other cSPI-like studies did not name the approach in discussing the usefulness of standardized indices, except for the studies following to [8]. Therefore, we investigate the meteorological drought and flood hazards on the land surface in Asia around the end of the 21st century using the cSPI due to climate change under a business-as-usual (BAU) scenario in this study.
Table 1. The SPI classification. 2.0 ≤ 1.5 ≤ 1.0 ≤ −1.0 < −1.5 < −2.0
0 β α Γ(α )
where x is the amount of precipitation, α and β are shape and scale parameters, respectively, and Γ(α ) is the ordinary gamma function of α . Note that the precipitation and both the parameters should be positive. The shape and scale parameters, α and β , of the gamma PDF are estimated for each timescale of interest with the maximum likelihood solutions: ( ) √ 1 4A x α= 1+ 1+ and β = . . . (1) 4A 3 α where A = ln(x) − ∑ ln(x)/n, x = ∑ x/n is the mean precipitation, and n is the number of values in the aggregated precipitation dataset [27]. The cumulative distribution function (CDF) of the gamma distribution is obtained Journal of Disaster Research Vol.11 No.6, 2016
with the resulting parameters as follows: ∫ x
G(x) =
g(x) dx = 0
1 α β Γ(α )
∫ x
xα −1 e−x/β dx (2)
0
When the aggregated precipitation dataset contains zero values, the gamma distribution in Eq. (2) is undefined. To avoid this problem, the G(x) is modified as H(x): H(x) = q + (1 − q) G(x) . . . . . . . . . (3) where q is the probability of zero precipitation values. The standard score, Z, of the standard normal distribution is converted from the modified CDF, H(x): { −z for 0 < H(x) ≤ 0.5 Z = SPI = . . (4) +z for 0.5 < H(x) < 1 where z = t − (c0 + c1t + c2t 2 )/(1 + d1t + d2t 2 + d3t 3 ), with { √ ln ((H(x))−2 ) for 0 < H(x) ≤ 0.5 t= √ ln ((1 − H(x))−2 ) for 0.5 < H(x) < 1 and approximation coefficients as c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, d1 = 1.432788, d2 = 0.189269, and d3 = 0.001308 [28].
Name: Akira Hasegawa
Affiliation: Research Specialist, ICHARM, PWRI
Address: 1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan
Brief Career: 2000 PhD. (Science), University of Tsukuba 2000 JSPS Research Fellow, Institute of Geoscience, Univ. of Tsukuba 2003 Researcher, Center for Climate System Research, Univ. of Tokyo 2004 Researcher, Japan Agency for Marine-Earth Science Technology 2006 Postdoctoral Fellow, National Institute for Environmental Studies 2010 Research Specialist, ICHARM, PWRI
Selected Publications:
• A. Hasegawa, M. Gusyev, T. Ushiyama, J. Magome, and Y. Iwami, “Drought assessment in the Pampanga River basin, the Philippines – Part 2: A comparative SPI approach for quantifying climate change hazards,” in “MODSIM2015, 21st International Congress on Modeling and Simulation,” T. Weber, M. J. McPhee, and R. S. Anderssen (Eds.), Modelling and Simulation Society of Australia and New Zealand, pp. 2388–2394, ISBN: 978-0-9872143-5-5, 2015. • A. Hasegawa, and S. Emori, “Effect of air-sea coupling in the assessment of CO2 -induced intensification of tropical cyclone activity,” Geophysical Research Letters, doi:10.1029/2006GL028275, 2007. • A. Hasegawa, and S. Emori, “Tropical cyclones and associated precipitation over the western North Pacific: T106 atmospheric GCM simulation for present-day and doubled CO2 climates,” SOLA, doi:10.2151/sola.2005-038, 2005.
Academic Societies & Scientific Organizations: • Meteorological Society of Japan (MSJ)
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Hasegawa, A., Gusyev, M., and Iwami, Y.
Name:
Name:
Maksym Gusyev
Yoichi Iwami
Affiliation:
Affiliation:
Lecturer/Research Specialist, GRIPS/ICHARM, PWRI
Chief Researcher on Hydrology and Hydraulics, ICHARM, PWRI
Address:
Address:
1-6, Minamihara, Tsukuba, Ibaraki 305-8516, Japan
1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan
Brief Career:
Brief Career:
1999 B. Eng., National Technical University of Ukraine, Kiev, Ukraine 2001 Dip. Eng., National Technical University of Ukraine, Kiev, Ukraine 2004 MSES, School of Public and Environmental Affairs, Indiana University, Bloomington, USA 2010 Groundwater Scientist I and II, Hydrogeology Department, Crown Research Institute of Geological & Nuclear Sciences, New Zealand 2011 Ph.D., Indiana University, Bloomington, USA 2013 Lecturer/Research Specialist, National Graduate Institute for Policy Studies (GRIPS)/ICHARM, PWRI, Tsukuba, Japan
2004 Senior Advisor on River Management, Mekong River Commission Secretariat 2006 Director of River & National Road Office in Kochi, Shikoku D.B., Ministry of Land, Infrastructure, Transport and Tourism (MLIT) 2010 Director of 3rd Research Department, Water Resources Environment Center 2012 Head of River Environment Research Division, National Institute for Land and Infrastructure Management, MLIT 2013 Chief Researcher on Hydrology and Hydraulics, ICHARM, PWRI
Selected Publications:
Selected Publications:
• M. A. Gusyev, U. Morgenstern, M. K. Stewart, Y. Yamazaki, K. Kashiwaya, T. Nishihara, D. Kuribayashi, H. Sawano, and Y. Iwami, “Application of tritium in precipitation and baseflow in Japan: a case study of groundwater transit times and storage in Hokkaido watersheds,” Hydrol. Earth Syst. Sci., Vol.20, pp. 3043–3058, doi:10.5194/hess-20-3043-2016, 2016. • M. A. Gusyev, M. Toews, U. Morgenstern, M. Stewart, P. White, C. Daughney, and J. Hadfield, “Calibration of a transient transport model to tritium data in streams and simulation of groundwater ages in the western Lake Taupo catchment, New Zealand,” Hydrol. Earth Syst. Sci., Vol.17, pp. 1217–1227, doi:10.5194/hess-17-1217-2013, 2013. • M. A. Gusyev, H. M. Haitjema, C. P. Carlson, and M. A. Gonzalez, “Use of nested flow models and interpolation techniques for science-base management of the Sheyenne National Grassland, North Dakota, USA,” Groundwater, Vol.51, No.3, pp. 414–420, doi:10.1111/j.1745-6584.2012.00989.x, 2012. • H. M. Haitjema, D. T. Feinstein, R. J. Hunt, and M.A. Gusyev, “A hybrid finite difference and analytic element model for detailed surface – groundwater modeling on a regional scale,” Groundwater, Vol.48, No.4, pp. 538–548, 2010.
• Y. Kwak, A. Yorozuya, Y. Iwami, “Disaster risk reduction using image fusion of optical and SAR data before and after tsunami,” IEEE Aerospace2016, IEEE, doi:978-1-4673-7676-1/16, 2016. • A. Hasegawa, M. Gusyev, T. Ushiyama, J. Magome, and Y. Iwami, “Drought assessment in the Pampanga River basin, the Philippines – Part 2: A comparative SPI approach for quantifying climate change hazards,” in “MODSIM2015, 21st International Congress on Modeling and Simulation,” T. Weber, M. J. McPhee, and R. S. Anderssen (Eds.), Modelling and Simulation Society of Australia and New Zealand, pp. 2388–2394, ISBN: 978-0-9872143-5-5, 2015. • A. Sugiura, S. Fujioka, S. Nabesaka, M. Tsuda, Y. Iwami, “Development of a flood forecasting system on upper Indus catchment using IFAS,” Proceeding of 6th International Conference on Flood Management (ICFM6), ICFM6, 2014.
Academic Societies & Scientific Organizations: • Japan Society of Civil Engineers (JSCE)
Academic Societies & Scientific Organizations: • American Geophysical Union (AGU) • European Geophysical Union (EGU) • The Japan Geoscience Union (JpGU)
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