Climate Change Impact on Rainfall and Temperature in Muda Irrigation Area Using Multicorrelation Matrix and Downscaling Method
Total Page：16
File Type：pdf, Size：1020Kb
647 © IWA Publishing 2015 Journal of Water and Climate Change | 06.3 | 2015 Climate change impact on rainfall and temperature in Muda irrigation area using multicorrelation matrix and downscaling method Nurul Nadrah Aqilah Tukimat and Sobri Harun ABSTRACT Statistical downscaling model was used to generate 30-year climate trend of Kedah – the state which Nurul Nadrah Aqilah Tukimat Department of Water Resources & Environmental, has the largest cultivation area in Malaysia, resulting from climate changes. To obtain a better Universiti Malaysia Pahang, Gambang, predictors set, multicorrelation matrix analysis was added in the climate model as a screening tool to Pahang, Malaysia explain the multiple correlation relationship among 26 predictors and 20 predictands. The Sobri Harun (corresponding author) performance of the predictor set was evaluated statistically in terms of mean absolute error, mean Department of Hydraulic and Hydrology, square error, and standard deviation. The simulation results depict the climatic changing trend in this Universiti Teknologi Malaysia, Skudai, region in terms of temperature, rainfall, and wet and dry length compared to historical data captured Johor, Malaysia W from 1961 to 2008. Annual temperature and rainfall depth are expected to increase 0.2 C per decade E-mail: [email protected] and 0.9% per year, respectively, from the historical record. The months of November and January are expected to receive the highest and lowest rainfall depth, respectively, because of the two monsoon seasons. The wet spell is estimated to be from May to November in the middle of Kedah. The annual dry spell shall be from January to March, and is expected to shorten yearly. Key words | climate projection, multiple correlation, rainfall, statistical downscaling, temperature INTRODUCTION Climate refers to the average weather recorded in a year; it is In Malaysia, global warming has a serious impact on the generally affected by geographic factors such as the oceans land and water resources, agricultural activities, and hydro- and the altitude of the region. Changes in climate occur when logical cycle. As claimed by the Department of Irrigation the accumulated heat absorbed by the existing greenhouse and Drainage, Malaysia, more than 70% of water use in gases, such as nitrous oxide, oxygen, methane, water vapor, Malaysia is allocated for irrigation purposes and the remain- carbon dioxide, and tropospheric ozone, increases and acceler- ing 30% is for domestic, industry, and other demands. ates global warming. As reported by the Intergovernmental Unfavorable calamitous events caused by climate changes Panel on Climate Change AR4 (IPCC ), the global average jeopardize the agricultural sectors by, for example, affecting surface temperature for the past 100 years has increased from the cultivation growth, destroying machinery, inducing 0.6 W C (1901–2000) to 0.74 W C (1906–2005). The World Meteor- losses, and affecting agricultural production. Data have ological Organization claimed the year 2010 as the warmest shown that a previous calamity on the irrigation area year where an increase of 1.2–1.4 W C had been recorded particu- during years 1989–2010 destroyed thousands hectares of larly in Africa, parts of Asia, and parts of the Arctic. Since 1961, paddies, rubbers, and vegetables in several states in Malaysia more than 80% of the heat had been added to the climate such as Perlis, Kedah, and Penang which then escalated into system, causing the average temperature of the ocean to food scarcity problems and millions Ringgit losses. increase at 3,000 m depth; the rate of observed sea level rise In terms of annual precipitation, Bates et al. () is estimated to be 0.17 m in the 20th century. have proved that the volume of water resources for our doi: 10.2166/wcc.2015.015 Downloaded from https://iwaponline.com/jwcc/article-pdf/6/3/647/374571/jwc0060647.pdf by guest on 28 May 2020 648 N. N. A. Tukimat & S. Harun | Climate projection using multicorrelation matrix and downscaling method Journal of Water and Climate Change | 06.3 | 2015 society and ecosystems is strongly correlated to climate et al. (), Guttman et al. (), Lopes (), and Spak change. Unusual warms change the water volume stored et al. (). in reservoirs due to unpredictable rain availability, timing, and water quality. In the United States, Izuka et al. () stated the storage of the Garlinghouse Tunnel had METHODOLOGY decreased around 50% since the 1980s; the storage depends fully on the precipitation and river runoff during Climate model wet periods. When global warming escalates, it increases the water evaporation rate and may easily lead to water Statistical downscaling is analogous to the model output storage loss of more than 20% of the average annual statistic and perfect program approach used for short- runoff. This becomes especially worrying since the valley range numerical weather prediction (Wilby & Dawson width is impounded and has larger water open area ). The model was developed by Robert L. Wilby and (FAO ). This also means that water storage has Christian W. Dawson from the United Kingdom and uses turned into an uncontrollable factor when rainfall patterns a weather generator method to produce a multiple realiz- become unpredictable, and the compounded effects are felt ation of the synthetic daily weather sequence. This by all sectors. software calculates the statistical relationship based on mul- For the agriculture sector, more uncertainties in climate tiple regression techniques between large scale (predictor) make it increasingly difficult to predict and plan the irriga- and local climate (predictand). Table 1 gives the list of pre- tion demand even for the following month. New dictor and predictand combinations used in this study. approaches are therefore needed to gauge changes in rain- These relationships were developed using observed fall directions, duration, trending, and depth. This has weather data and previously captured relationships among been largely addressed using various climate models devel- GCM-derived predictors. This produces the maximum, oped to primarily understand the changes in present/ mean, and minimum temperature, the precipitation and future climatic conditions with due consideration on green- humidity of site-specific daily scenarios for a selected house gases and aerosols emission (Goosse et al. ). Such region, and the range of statistical parameters such as var- models also assist in evaluating regional surface water iance and frequencies of extremes. The SDSM downscaling response and reservoir system capacity to meet local used two types of data, viz. predictand and predictor at the demands (Yano et al. ). grid box of 28X × 33Y. This case study introduces a statistical downscaling The historical rainfall data (1961–2008) recorded at 20 model (SDSM) which has been used to generate the cur- locations were used as the predictands while the National rent/future local climate trend. SDSM models have been Center for Environmental Prediction (NCEP) reanalysis data widely applied for hydrological issues caused by climate and GCM outputs of the Hadley Center General Circulation scenarios because it provides station-scale climate infor- Model (HadCM3) under A2 scenario were used as the predic- mation at general circulation model (GCM) scale between tor to simulate climate trend. The HadCM3 model was a atmospheric circulation pattern (predictors) and local-scale modified version of HadCM2, done to improve the accuracy parameters (predictands) using multiple regression tech- of the climate projection results without the application of niques. It is a popular tool among researchers since it can flux adjustments. It has wider coarse spatial resolutions of portray an easily understandable relationship pattern 2.5W × 3.75W (latitude by longitude) which can be applied in between predictor and predictand. Besides, the model does many climatic regional studies. Samadi et al. () consider not require high computational demand to view the simu- HadCM3 to be the best GCM model, i.e., superior to other lation result because the output is presented in finer models such as CCSNIES, CSRIO (Australian Government), resolutions. To conclude, it is cost-effective and gives satis- and Geophysical Fluid Dynamics Laboratory (GFDL). In factory climate simulation with its capability and reliability this study, the A2 scenario had been chosen to give an upper proven by researchers such as Sharma et al. (), Khan bound on future emissions, and was selected from an Downloaded from https://iwaponline.com/jwcc/article-pdf/6/3/647/374571/jwc0060647.pdf by guest on 28 May 2020 649 N. N. A. Tukimat & S. Harun | Climate projection using multicorrelation matrix and downscaling method Journal of Water and Climate Change | 06.3 | 2015 Table 1 | List of predictor and predictand combinations No. Predictor variables Predictor description Predictand variables (station) Predictor description 1 Mlsp Mean sea level pressure GM Gajah Mati 2 p_f Surface airflow strength IBT Ibu Bekalan Tupah 3 p_u Surface zonal velocity KP Kedah Peak 4 p_v Surface meridional velocity KT Keretapi Tokai 5 p_z Surface vorticity Kod Kodiang 6 p_th Surface wind direction KSS Kota Sarang Semut 7 p_zh Surface divergence Pen Pendang 8 p5_f 500 hPa airflow strength SL Sungai Limau 9 p5_u 500 hPa zonal velocity TC Telok Chengai 10 p5_v 500 hPa meridional velocity LTP Ladang Tanjung Pauh 11 p5_z 500 hPa vorticity KN Kuala Nerang 12 p500 500 hPa geopotential height Kg.T Kg. Terabak 13 p5th 500 hPa wind