Impact of climate change on rainfall in Northwestern Bangladesh using multi-gcm ensembles

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: (2014) Published online 26 June 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.3770 Impact of climate change on rainfall in Northwestern Bangladesh using multi-gcm ensembles Dipak Kumar, D. S. Arya,* A. R. Murumkar and M. M. Rahman Department of Hydrology, Indian Institute of Technology, Roorkee, India ABSTRACT: Teesta River basin, located in the northwest of Bangladesh, is more vulnerable to floods if compared to other parts of the country. In this contet, daily rainfall data of ten raingauge stations located in the catchment of the Jamuneswari River, part of the Teesta River basin, were analysed to study the impact of climate change on rainfall. Length of wet and dry series and mean monthly rainfalls along with their variances were used for validating Long Ashton Research Station Weather Generator (LARS-WG). The analysis was carried out for A1B, A2 and B1 emission scenarios using 15 Global Climate Models GCMs simulations for the periods centred at 2020, centred at 2055 and centred at The analysis of the data shows that the uncertainty in the prediction increases with the timescale. It was also found that the variability in the predictions is smaller in annual values followed by seasonal. Ensemble of seasonal analysis shows that most of the GCM are in agreement for changes in monsoon season. The LARS-WG has reasonable skill to downscale the point rainfall data and the results obtained so are useful to analyse the impact of climate change on the hydrology of the basin. KEY WORDS rainfall; GCM; statistical downscaling; ensemble; LARS-WG; climate change Received 15 October 2012; Revised 16 May 2013; Accepted 19 May Introduction Atmosphere ocean coupled Global Climate Models (GCMs) simulate the present and future climate of Earth under different climate change scenarios (SRES, 2000). The computational grid of GCMs is very coarse thus they are unable to skillfully model the sub-grid scale climate features like topography or clouds (Wilby et al., 2002). Hence, there is a need for downscaling from coarse resolution of a GCM to a very fine resolution or even at the station scale. The available downscaling methodologies are broadly grouped into statistical and dynamical categories. Among the statistical downscaling methods, the use of stochastic weather generators is very popular. They are computationally less demanding, simple to apply and provide station scale information (Coulibaly et al., 2005; Kilsby et al., 2007). The weather generators are basically statistical models which are used to generate a long synthetic time series, fill in missing data and produce different realizations of the same data (Wilby, 1999). They employ random number generators and use the observed time series of a station as input. Stochastic weather simulation is not new and has a history starting from 1950s, as reported by Racsko et al. (1991). Among some researchers who contributed to its evolution are Bruhn et al. (1980), Nicks and Harp (1980), Richardson (1981), Richardson and Wright (1984) and * Correspondence to: D. S. Arya, Department of Hydrology, Indian Institute of Technology, Roorkee, India. dsarya@gmail.com Schoof et al. (2005). Wilby (1999) has presented a comprehensive review of its theory and evolution over time. Weather generators have been employed to obtain long time series of hydro-meteorological variables which are then used by crop growth model to forecast agricultural production (Riha et al., 1996; Hartkamp et al., 2003) and assessment of risk associated with climate variability (Semenov, 2006; Bannayan and Hoogenboom, 2008). When the climate scientists started looking for low cost, computationally less epensive and less demanding and quick methods for impact assessment, the weather generator emerged as the most viable solution (e.g. Wilks, 1992; Wilks and Wilby, 1999). Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator specially designed for climate change impact studies (Semenov and Barrow, 1997). It has been tested for diverse climates and found better than many others (Semenov et al., 1998). A recent study by Semenov (2009) tested the LARS-WG for rainfall modelling at different sites across the world and has shown its ability to model rainfall etremes with reasonable skill. Over the past 15 years, ensemble forecasting became established in Numerical Weather Prediction Centers as a response to the limitations imposed by the inherent uncertainties in the prediction process. The ultimate goal of ensemble forecasting is to predict quantitatively the probability of the state of the atmosphere in future. This article also presents a multi-gcm and multi-scenarios (A1B, A2 and B1) ensemble forecast of rainfall during 2013 Royal Meteorological Society

2 1396 D. KUMAR et al , and using LARS- WG statistical downscaling tool to study the changes in rainfall pattern on seasonal and annual scale. 2. Study area and data used The Jamuneswari Basin, part of Teesta River basin lies between to N latitude and to E longitude in Northwestern region of Bangladesh (Figure 1). The elevation of the area ranges from 24 to 72 m above mean sea level. The area of Jamuneswari catchment is about km 2 and the average rainfall for the Northwestern region is about 1900 mm. Maimum temperatures range from about 25 C in January to 35 C in April May. Evapotranspiration reaches to a maimum in April when temperature, sunshine and wind touch their maimum, and humidity is almost the minimum. Evapotranspiration is eceeded by average rainfall from May to October (ODA and JICA, 1993). Ten rainfall stations namely: Badraganj, Debiganj, Ghoraghat, Mahipur, Mithapukur, Nawabganj, Nilphamari, Phulbari, Pirganj and Saidpur (Figure 1) were selected for analysis from the Jamuneswari River catchment. The daily rainfall data of these stations were obtained from Flood Forecasting and Warming Centre, Bangladesh Water Development Board, Dhaka. The daily data were available from 1966 to 2008, i.e. for 43 years. 3. Methodology Stochastic weather generators are widely used by many researchers world-wide (Racsko et al., 1991; Semenov and Porter, 1994; Qian et al., 2004, Semenov, 2007). Stochastic weather generators may be site-specific, i.e. they generate weather time series for a single site, or spatial, i.e. they generate weather for a number of locations simultaneously, reflecting the spatial correlation of the different climate variables (Bardossy and Plate, 1991; Hutchinson, 1995) Description of LARS-WG weather simulator LARS-WG is a stochastic weather generator which can be used for the simulation of weather data at a single site (Racsko et al., 1991; Semenov et al., 1998; Semenov and Brooks, 1999), under both current and future climate conditions. These data are in the form of daily time series for a suite of climate variables, namely, precipitation (mm), maimum and minimum temperature ( C) and solar radiation (MJ m 2 d 1 ). It generates a suite of climate variables namely precipitation, maimum and minimum temperatures, solar radiation, etc. Precipitation is considered as the primary variable and the other three variables on a given day are conditioned on whether the day is wet or dry. The WGs use observed daily weather for a given site to compute a set of parameters for fitting probability distributions correlations between observed and generated time series. This set of parameters is then used to generate synthetic weather time series of arbitrary length by randomly selecting values from the appropriate distributions. By perturbing parameters of distributions for a site with the predicted changes of climate derived from global or regional climate models, a daily climate scenario for this site is generated. LARS-WG model has been tested in diverse climates and demonstrated a good performance in reproducing various weather statistics including etreme weather events (Semenov et al., 1998, Semenov, 2008). LARS-WG uses a semi-empirical distribution (SED) to approimate probability distributions of dry and wet series, daily precipitation, minimum and maimum temperatures and solar radiation. SED is defined as the cumulative probability distribution function (PDF). The number of intervals (n) used in SED is 23, which offers more accurate representation of the observed distribution compared with the 10 used in the previous version. For each climatic variable v, a value of a climatic variable v i corresponding to the probability p i is calculated as: v i = min {v : P (v obs v) p i }, i = 0,..., n (1) where P( ) denotes probability based on observed data {v obs }. For each climatic variable, two values, p 0 and p n, are fied as p 0 = 0andp n = 1, with corresponding values of v 0 = min{v obs } and v n = ma{v obs }. To approimate the etreme values of a climatic variable accurately, some p i are assigned close to 0 for etremely low values of the variable and close to 1 for etremely high values; the remaining values of p i are distributed evenly on the probability scale (Semenov and Stratonovitch, 2010). The process of generating synthetic weather data using LARS- WG is divided into three distinct steps: Model calibration (SITE ANALYSIS) observed weather data are analysed to determine their statistical characteristics. Model validation (QTEST) the statistical characteristics of the observed and synthetic weather data are analysed to determine if there are any statistically significant differences or not. Synthetic data generation (GENERATOR) the parameter derived from observed weather data during the model calibration process are used to generate synthetic weather data having the same statistical characteristics as the original observed data, but differing on a day-today basis. Synthetic data corresponding to a particular climate change scenario may also be generated by applying GCM-derived changes in precipitation, temperature and solar radiation to the LARS-WG parameter files. LARS- WG incorporates 15 GCMs (as given in Table 1) and three scenarios namely A1B, A2 and B1 in three time spans, i.e , and

3 IMPACT OF CLIMATE CHANGE ON RAINFALL IN NORTHWESTERN BANGLADESH 1397 Figure 1. Jamuneswari River Basin. Table 1. Global Climate Models from IPCC AR4 incorporated into the LARS-WG stochastic weather generator version 5.0. Centre Centre acronym Country GCM Grid resolution Australia s Commonwealth Scientific and Industrial Research Organization CSIRO Australia CSIRO-MK Canadian Centre for Climate Modelling and Analysis CGCM3 (T47) CCCma Canada CGCM3 (T63) Beijing Climate Centre BCC China BCC-CM Institute of Atmospheric Physics LASG China FGOALS-g Centre National de Recherches Meteorologiques CNRM France CNRM-CM Institute Pierre Simon Laplace IPSL France IPSL-CM Ma-Planck Institute for Meteorology MPI-M Germany ECHAM5-OM Meteorological Institute, University of Bonn MIUB Germany ECHO-G Model and Data Group at MPI-M M&D Germany ECHO-G National Institute of Geophysics and Volcanology INGV Italy SXG Meteorological Research Institute, Japan MIROC3.2 (hires) MRI Japan MIROC3.2 (medres) National Institute for Environmental Studies NIES Japan MRI-CGCM Meteorological Research Institute of KMA METRI Korea ECHO-G Bjerknes Centre for Climate Research BCCR Norway BCM Institute for Numerical Mathematics INM Russia INM-CM UK Met. Office HadCM3 UKMO UK HadGEM Geophysical Fluid Dynamics Laboratory GFDL-CM2.0 GFDL USA GFDL-CM Goddard Institute for Space Studies GISS-AOM 3 4 GISS USA GISS-E-H 4 5 GISS-E-R 4 5 National Centre for Atmospheric Research PCM NCAR USA CCSM Application The observed daily rainfall at each site was used by LARS-WG to compute the statistical properties of each site. The probability distributions of the length of wet and dry series and daily rainfall in each month, and the monthly means and standard deviations were computed. Subsequently, the parameters needed for modelling the synthetic data were derived from the observed data. The Kolmogorov Smirnov goodness-of-fit test is used to compare the probability distributions for the length of wet and dry series for each season (the year is divided into quarters starting on first December) and

4 1398 D. KUMAR et al. Table 2. Summary of the results from the comparison of observed and generated monthly mean rainfalls along their variance. Stations Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Badraganj Debiganj Ghoraghat Mahipur Mithapukur Nawabganj Nilphamari Phulbari Pirganj Saidpur Table 3. Summary of the results from the comparison of observed and generated monthly mean rainfalls by K S test. Stations Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Badraganj Debiganj Ghoraghat Mahipur Mithapukur Nawabganj Nilphamari Phulbari Pirganj Saidpur Table II represents t- and and Table III represents the K S test for months at each stations, and passed tests are represented by whereas the failed tests are represented by. Table 4. List of GCM s for each span , and SRA1B SRA2 SRB1 SRA1B SRA2 SRB1 SRA1B SRA2 SRB1 BCM2 CNCM3 BCM2 BCM2 CNCM3 BCM2 BCM2 CNCM3 BCM2 CGMR GFCM21 CSMK3 CGMR GFCM21 CSMK3 CGMR GFCM21 CSMK3 CNCM3 HADCM3 FGOALS CNCM3 HADCM3 FGOALS CNCM3 HADCM3 FGOALS CSMK3 HADGEM GFCM21 CSMK3 HADGEM GFCM21 CSMK3 INCM3 GFCM21 FGOALS INCM3 GIAOM FGOALS INCM3 GIAOM FGOALS IPCM4 GIAOM GFCM21 IPCM4 HADCM3 GFCM21 IPCM4 HADCM3 GFCM21 MPEH5 HADCM3 GIAOM MPEH5 INCM3 GIAOM MPEH5 INCM3 GIAOM NCCCSM INCM3 HADCM3 NCCCSM IPCM4 HADCM3 NCCCSM IPCM4 HADCM3 IPCM4 HADGEM NCPCM MIHR HADGEM NCPCM MIHR INCM3 MIHR INCM3 MPEH5 INCM3 MPEH5 IPCM4 MPEH5 IPCM4 NCCCSM IPCM4 NCCCSM MIHR NCCCSM MIHR MIHR MPEH5 MPEH5 MPEH5 NCCSM NCCSM NCCSM NCPCM NCPCM

5 IMPACT OF CLIMATE CHANGE ON RAINFALL IN NORTHWESTERN BANGLADESH 1399 (a) (b) (c) Figure 2. (a) Annual changes centred at (b) Annual changes centred at (c) Annual changes centred at daily rainfall distributions for monthly data. The monthly mean of the observed series with that of the synthetic series are compared using Student s. The usually measures the inter-annual variability of observed and generated monthly rainfall means. The p-value associated to indicates the probability that monthly mean rainfalls are derived from the same population. In this study, p-values of less than 0.05 were considered as indicating the likelihood of a substantial difference between the true and simulated climate for that particular variable. All GCMs predictions with three emission scenarios were used for generation of downscaled rainfall over three time span at every site. 4. Results and discussion 4.1. Calibration and validation of the model Daily rainfall data were used for calibration and validation of LARS-WG. The data were imported into the

6 1400 D. KUMAR et al. (a) (b) (c) Figure 3. (a) Seasonal changes for MAM centred at (b) Seasonal changes for MAM centred at (c) Seasonal changes for MAM centred at weather generating module and the statistical properties of SED were computed for length of wet and dry series for each season and monthly rainfall means for each month along with variances. Numbers of runs were given with different random seed value to generate the synthetic data having the same statistical properties as of the observed data. Table 2 shows the results of t- and for each month for all the stations. The tests failed are marked with in the table. The table shows that mean and variance of observed and synthetic data for most of the months are found statistically significant. The table also reveals that the test mostly failed during the nonmonsoon seasonal months (i.e. Nov, Dec and Jan). It means that the model could not reproduce the same probability distributions for the synthetic length of wet and dry series for failed months. This is also important to mention that only 2 3% of the annual rainfall is received during these months in this region. The results of the comparison of probability distributions of observed and synthetic data for wet and dry series for each month are given in Table 3.

7 IMPACT OF CLIMATE CHANGE ON RAINFALL IN NORTHWESTERN BANGLADESH 1401 (a) (b) (c) Figure 4. (a) Seasonal changes for JJA centred at (b) Seasonal changes for JJA centred at (c) Seasonal changes for JJA centred at The majority of the months have ehibited a p-value of Kolmogorov Smirnov test greater than 0.05 (marked as in Table 3). This means that the SED was able to reproduce correctly the shapes of the observed probability distributions Generation of climatic scenarios Table 4 shows the list of GCM s and emission scenarios used in various time spans. The time spans used are , centred at 2020; , centred at 2055; and , centred at Accordingly, ensembles of future climate projections were generated by choosing different combinations of GCM, scenarios and time span. This data were analysed further to study the changes on annual and seasonal scales. Ensemble mean and standard deviation (SD) were computed using the GCMs available for a particular emission scenario in a particular time span. All GCMs were given equal

8 1402 D. KUMAR et al. (a) (b) (c) Figure 5. (a) Seasonal changes for SON centred at (b) Seasonal changes for SON centred at (c) Seasonal changes for SON centred at weightage while computing the ensemble mean and SD. Maps showing the spatial distribution of ensemble mean and SD on annual and seasonal scales are also prepared using spline interpolation technique in Arc GIS Changes in annual rainfall The changes in annual rainfall are shown in Figure 2(a) (c) for three time spans using ensemble mean and standard deviation of the projected future climate. The change in annual rainfall in the Jamuneswari catchment for the time span shows an increment by 1 5% (with SD 4 24%), 3 9% for the time span (with SD 8 14%) and 4 13% (with SD 8 22%) for It is concluded that the rainfall in the catchment is epected to increase on annual scale from one time span to another time span in future. The analysis of the data also shows that the uncertainty in the prediction increases with the timescale.

9 IMPACT OF CLIMATE CHANGE ON RAINFALL IN NORTHWESTERN BANGLADESH 1403 Critical analysis of the Figure 2(a) (c) also reveals that the epected maimum changes in the rainfall are shifting from Badraganj (in 2020) to Nawabganj (in 2090). The minimum changes are observed at Saidpur station in almost all the three time spans Changes in seasonal rainfall Seasonal rainfall changes were shown in Figure 3(a) (c) for March, April and May (MAM) months, in Figure 4(a) (c) for June, July and August (JJA) months, and for September, October and November (SON) months in Figure 5(a) (c) for various time spans. The maps for MAM months represent pre-monsoon season, JJA months represent the monsoon season and SON months are post-monsoon season in Jamuneswari catchment. December, January and February (DJF) months accounting only 2 3% of the annual rainfall was not considered important from the point of view of flood estimation. Hence, the maps of rainfall changes for DJF months were not prepared. The pre-monsoon seasonal (MAM) rainfall is epected to increase from 1 3% in 2020 to 1 9% in 2090 in the entire catchment, whereas monsoon rainfall is epected to increase from 2 5% in 2020 to 3 18% in The percent change in rainfall is epected to increase by 3 5% (SD 10 15% for time span and SD 20 25% for time span ) for the post-monsoon season (SON). The highest percent change in rainfall is observed in post-monsoon season during the time span which is approimately 3 15% with a SD of 20 25%. A decreasing pattern over the time spans for premonsoon (MAM) and monsoon seasons (JJA) and increasing pattern in the post-monsoon season (SON) is epected at station Badraganj. Stations Mithapukur and Nawabganj show decreasing rainfall pattern in time span and increasing in other two time spans. 5. Conclusions Bangladesh is one of the most flood-prone countries in world. Because of its unique geographical location and topography, different types of floods with different magnitudes occur every year. Climate change is adding more vigour to the situation as the scientists are epecting more and more etreme events. In this contet, a study of impact on rainfall in Jamuneswari basin having ten raingauge stations, Bangladesh was carried out using a weather generator LARS-WG. After satisfactory validation of the model, weather corresponding to available GCM s for three scenarios namely A1B, A2 and B1 in three time spans as (centred at 2020), (centred at 2055) and (centred at 2090) were generated on seasonal and annual scale. The results show that the annual rainfall in Jamuneswari catchment is epected to increase from one time span to another time span in future. Seasonal analysis of the projected rainfall data shows increasing pattern during all the seasons in all the time spans. Ensemble of seasonal and monthly analysis show that most of the GCM are in agreement for changes in monsoon season (JJA). Maimum changes in the rainfall are observed at station Badraganj, Nawabganj, Saidpur and Mithapukur. The LARS-WG has reasonable skill to simulate the daily rainfall values and the results obtained in this study are useful to analyse the impact of climate change on hydrological processes including flood estimation in the Jamuneswari catchment. Acknowledgements The authors would like to epress their gratitude to Bangladesh Water Development Board for providing the data used in the study. We would also like to thank Mr M. Semenov for providing us the license to run LARS weather generator. References Bannayan M, Hoogenboom G Predicting realizations of daily weather data for climate forecasts using the non-parametric nearestneighbour re-sampling technique. International Journal of Climatology 28(10): Bardossy A, Plate EJ Modelling daily rainfall using a semi- Markov representation of circulation pattern occurrence. 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