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Strengthening Climate and Disaster Resilience of Investments in the Pacific Stochastic Rainfall Modelling Analysis for Funafuti, Tuvalu

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Strengthening Climate and Disaster Resilience of Investments in the Pacific Stochastic Rainfall Modelling Analysis for Funafuti, Tuvalu Technical Assistance Consultant’s Report ___________________________________________________________________________________________ Project Number: 48488-001 September 2020 Regional: Strengthening Climate and Disaster Resilience of Investments in the Pacific Stochastic Rainfall Modelling Analysis for Funafuti, Tuvalu Prepared by Dr Andrew Magee (Climate Change Scientist) For Asian Development Bank This consultant’s report does not necessarily reflect the views of ADB or the Government concerned, and ADB and the Government cannot be held liable for its contents. (For project preparatory technical assistance: All the views expressed herein may not be incorporated into the proposed project’s design. ABBREVIATIONS ADB – Asian Development Bank AEP – Annual Exceedance Probability AR1 – Autoregressive lag-1 BOM – Australian Bureau of Meteorology CC – Clausius-Clapeyron CMIP5 – Coupled Model Intercomparison Project (Phase 5) CMIP6 – Coupled Model Intercomparison Project (Phase 6) DDF – Depth-Duration-Frequency EEZ – Exclusive Economic Zone ENSO – El Niño-Southern Oscillation GCM – Global Climate Model (or General Circulation Model) IPCC – Intergovernmental Panel on Climate Change IPO – Interdecadal Pacific Oscillation LB – Lower bound MJJASO – May-October (dry season) MJO – Madden Julian Oscillation MOF – Method of fragments NDJFMA – November-April (wet season) PACCSAP – Pacific-Australia Climate Change Science and Adaptation Planning PACRAIN – Pacific Rainfall Database PARD – Pacific Regional Department PIR – Pacific Islands Region R95p – 95th percentile rainfall R99p – 99th percentile rainfall RCP – Representative Concentration Pathway SCC – Super Clausius-Clapeyron SCL – Stochastic Climate Library SPCZ – South Pacific Convergence Zone SPI – Standardized Precipitation Index TC – Tropical cyclone TMS – Tuvalu Meteorological Service UB – Upper bound 20CRv3 – 20th Century Reanalysis Version 3 CONTENTS EXECUTIVE SUMMARY ................................................................................................ 5 I. INTRODUCTION ................................................................................................. 8 I-A. Brief project description............................................................................ 8 I-B. Objectives and scope of this report .......................................................... 9 II. CLIMATE CHARACTERISTICS OF FUNAFUTI, TUVALU: CURRENT AND FUTURE PROJECTIONS .................................................................................. 10 II-A. Tuvalu’s current climate ......................................................................... 10 II-B. Future projections of climate change in Tuvalu ...................................... 11 II-B-1. Temperature .............................................................................. 11 II-B-2. Rainfall....................................................................................... 12 II-B-3. Drought ...................................................................................... 13 III. GENERATING A LONG-TERM HISTORICAL DAILY RAINFALL TIME SERIES FOR FUNAFUTI ................................................................................................ 14 III-A. Observed rainfall datasets ..................................................................... 14 III-B. Reanalysis rainfall datasets ................................................................... 14 III-C. Comparing observed and reanalysis rainfall datasets ............................ 15 III-C-1. Daily data ................................................................................... 15 III-C-2. Annual data ............................................................................... 16 III-D. Selecting an appropriate daily rainfall dataset for stochastic modeling ... 17 III-E. Infilling missing rainfall data ................................................................... 18 IV. STOCHASTIC MODELING – INSTRUMENTAL DATA ...................................... 20 IV-A. Stochastic rainfall modeling methodology .............................................. 20 IV-B. Validating performance of the stochastic model on instrumental rainfall data........................................................................................................ 20 IV-B-1. Number and distribution of rain days .......................................... 20 IV-B-2. Rainfall depth ............................................................................. 21 IV-B-3. Intensity, frequency and duration of extreme events .................. 22 IV-B-4. Severity and duration of below-average rainfall .......................... 22 IV-B-5. Variability over multiple time scales (interannual to multidecadal) ............................................................................. 22 V. STOCHASTIC MODELING – CLIMATE CHANGE SCENARIOS ...................... 24 V-A. Climate change scaling factors .............................................................. 24 V-A-1. Rainfall....................................................................................... 24 V-A-2. Maximum daily temperature ....................................................... 25 V-B. Applying climate change scaling factors to instrumental data ................. 26 V-C. Daily and sub-daily Depth-Duration-Frequency (DDF) statistics for Funafuti (instrumental and climate change scenarios) ........................... 28 V-D. Stochastic modeling outputs .................................................................. 30 V-D-1. Daily .......................................................................................... 30 V-D-2. Monthly ...................................................................................... 30 V-D-3. Annual ....................................................................................... 31 VI. UNCERTAINTIES AND LIMITATIONS .............................................................. 33 VII. CONCLUSIONS AND RECOMMENDATIONS .................................................. 35 VIII. REFERENCES .................................................................................................. 37 APPENDIX I - COMPARISON OF STOCHASTIC RAINFALL MODELING METHODOLOGIES ........................................................................................... 39 APPENDIX II - CMIP5 GCM OUTPUTS – MAXIMUM DAILY TEMPERATURE ............ 43 APPENDIX III - BOXPLOTS OF DAILY AND MONTHLY FUNAFUTI RAINFALL STATISTICS CONSIDERING CLIMATE CHANGE SCENARIOS ...................... 47 APPENDIX IV - DAILY AND MONTHLY RAINFALL CHANGE STATISTICS ................ 51 APPENDIX V - DEPTH-DURATION-FREQUENCY (DDF) PLOTS ............................... 52 5 EXECUTIVE SUMMARY 1. The objective of the stochastic rainfall modeling analysis is to inform the preparation of the proposed Funafuti Water and Sanitation Project. To achieve this objective, daily rainfall and drought time series are needed to inform the design of a rainwater harvesting system to increase the freshwater availability for the population. As such, this study must evaluate how flood and drought risk might change in the future, considering all plausible climate change scenarios. 2. Stochastic modeling has been used to generate long-term rainfall time series for Funafuti for the current climate, as well as future climates, including Representative Concentration Pathway (RCP) scenarios 8.5 for the 20451 and 20702 time horizons. Global Climate Model (GCM) projections for Tuvalu are analyzed from the Coupled Model Intercomparison Project 5 (CMIP5). Both rainfall change and maximum daily temperature change variables are considered for the RCP scenarios and time horizons above. Uncertainty around GCM rainfall projections for Tuvalu resulted in the calculation of a lower bound (LB) and upper bound (UB) for each RCP scenario and time horizon. 3. A comparison of stochastic modeling techniques highlighted that running an autoregressive lag-1 (AR1) stochastic model at the annual scale and disaggregating to daily using the method of fragments (MOF) technique resulted in replication of all necessary statistical properties of the observed rainfall time series. This same methodology was applied to derive stochastically generated time series for Funafuti rainfall scaled for climate change. 4. RCP8.5 was chosen as the most precautionary climate change pathway for the following reasons: (i) because there is large uncertainty about what emission scenarios for the future will be as we do not know what mitigation measures will be taken at national and international levels over the coming century, (ii) rainfall and temperature-related scaling factors for RCP8.5 are larger, compared to RCP4.5 and RCP6.0, and therefore more conservative for both flood and drought events, (iii) RCP8.5 is a commonly adopted future scenario and is still very much a plausible future. 5. Up to 33 CMIP5 GCMs have been considered in this study. By 20451, daily mean rainfall change will range from -17.3% (RCP8.5 – lower bound (RCP8.5-LB)) to +23.7% (RCP8.5 – upper bound (RCP8.5-UB)), depending on month. Maximum daily rainfall is expected to increase by between 1.2% and 35.4%, compared to the historical record. A 1-in- 100 year rainfall event increases from 268 mm (instrumental) up to 352 mm (RCP8.5-UB). For annual rainfall, and depending on LB and UB, mean rainfall is expected to range from between ~7% less (RCP8.5-LB) to 13% more (RCP8.5-UB). For RCP8.5-UB, much wetter conditions are anticipated overall, including a ~14% and ~16% increase in annual maximum
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