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Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan

Abstract

Downscaling rainfall in an arid region is much challenging compared to wet region due to erratic and infrequent behaviour of rainfall in the arid region. The complexity is further aggregated due to scarcity of data in such regions. A multilayer perceptron (MLP) neural network has been proposed in the present study for the downscaling of rainfall in the data scarce arid region of Baluchistan province of Pakistan, which is considered as one of the most vulnerable areas of Pakistan to climate change. The National Center for Environmental Prediction (NCEP) reanalysis datasets from 20 grid points surrounding the study area were used to select the predictors using principal component analysis. Monthly rainfall data for the time periods 1961–1990 and 1991–2001 were used for the calibration and validation of the MLP model, respectively. The performance of the model was assessed using various statistics including mean, variance, quartiles, root mean square error (RMSE), mean bias error (MBE), coefficient of determination (R 2) and Nash–Sutcliffe efficiency (NSE). Comparisons of mean monthly time series of observed and downscaled rainfall showed good agreement during both calibration and validation periods, while the downscaling model was found to underpredict rainfall variance in both periods. Other statistical parameters also revealed good agreement between observed and downscaled rainfall during both calibration and validation periods in most of the stations.

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References

  • Ahmadi A, Moridi A, Lafdani E and Kianpisheh G 2014 Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study; J. Earth Syst. Sci. 123 (7) 1603–1618.

    Article  Google Scholar 

  • Ahmed K, Shahid S and Harun S B 2014 Spatial interpolation of climatic variables in a predominantly arid region with complex topography; Environment Systems and Decisions 34 555–563.

    Article  Google Scholar 

  • Akaike H 1974 Information theory and an extension of the maximum likelihood principle; In: Proceedings 2nd International Symposium on Information Theory (eds) Petrov and Caski, pp. 267–281.

  • Akhtar M, Ahmad N and Booij M 2008 The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios; J. Hydrol. 355 (1) 148–163.

    Article  Google Scholar 

  • Alamgir M, Shahid S, Hazarika M K, Nashrrullah S, Harin S B and Shamsudin S 2015 Analysis of meteorological drought pattern during different climatic and cropping seasons in Bangladesh; J. Am. Water Resourc. Assoc., doi: 10.1111/jawr.12276.

    Google Scholar 

  • Alexandersson H 1986 A homogeneity test applied to precipitation data; J. Climatol. 6 (6) 661–675.

    Article  Google Scholar 

  • Anandhi A, Srinivas V V, Nanjundiah R S and Nagesh Kumar D 2008 Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine; Int. J. Climatol. 28 (3) 401–420.

    Article  Google Scholar 

  • Ashiq M W, Zhao C, Ni J and Akhtar M 2010 GIS-based high-resolution spatial interpolation of precipitation in mountain–plain areas of Upper Pakistan for regional climate change impact studies; Theoret. Appl. Climatol. 99 (3–4) 239–253.

    Article  Google Scholar 

  • Cannon A J 2008 Probabilistic multisite precipitation downscaling by an expanded Bernoulli–Gamma density network; J. Hydrometeor. 9 (6) 1284–1300.

    Article  Google Scholar 

  • Chadwick R, Coppola E and Giorgi F 2011 An artificial neural network technique for downscaling GCM outputs to RCM spatial scale; Nonlin. Process. Geophys. 18 1013–1028.

    Article  Google Scholar 

  • Chen Y N and Xu Z X 2005 Plausible impact of global climate change on water resources in the Tarim River Basin; Sci. China Ser D, Earth Sci. 48 (1) 65–73.

    Article  Google Scholar 

  • Chiew F H S 2006 Estimation of rainfall elasticity of streamflow in Australia; Hydrol. Sci. J. 51 (4) 613–625.

    Article  Google Scholar 

  • Chu J T, Xia J, Xu C Y and Singh V P 2010 Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China; Theoret. Appl. Climatol. 99 (1–2) 149–161.

    Article  Google Scholar 

  • Dawson C W and Wilby R L 2001 Hydrological modelling using artificial neural networks; Progr. Phys. Geogr. 25 (1) 80–108.

    Article  Google Scholar 

  • Firat M, Dikbas F, Koç A C and Gungor M 2010 Missing data analysis and homogeneity test for Turkish precipitation series; Sadhana 35 (6) 707–720.

    Article  Google Scholar 

  • Gaitan C, Hsieh W and Cannon A 2014 Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada; Clim. Dyn. 43 (12) 3201–3217.

    Article  Google Scholar 

  • Gaitan C, Hsieh W, Cannon A and Gachon P 2013 Evaluation of linear and non-linear downscaling methods in terms of daily variability and climate indices: Surface temperature in southern Ontario and Quebec, Canada; Atmos.-Ocean. 52 (3) 211–221.

    Article  Google Scholar 

  • Gardner M W and Dorling S R 1998 Artificial neural networks (the multilayer perceptron) – A review of applications in the atmospheric sciences; Atmos. Environ. 32 (14–15) 2627–2636.

    Article  Google Scholar 

  • Goyal M K and Ojha C S P 2012 Downscaling of surface temperature for lake catchment in an arid region in India using linear multiple regression and neural networks; Int. J. Climatol. 32 (4) 552–566.

    Article  Google Scholar 

  • Goyal M, Ojha C and Burn D 2011 Nonparametric statistical downscaling of temperature, precipitation, and evaporation in a semiarid region in India; J. Hydrol. Eng. 17 (5) 615–627.

    Article  Google Scholar 

  • Goyal M, Burn D and Ojha C S P 2012 Evaluation of machine learning tools as a statistical downscaling tool: Temperatures projections for multi-stations for Thames River Basin, Canada; Theoret. Appl. Climatol. 108 (3–4) 519–534.

    Article  Google Scholar 

  • Groisman P Y, Karl T R, Easterling D R, Knight R W, Jameson P F, Hennessy K J, Suppiah R, Page C M, Wibig J, Fortuniak K, Razuvaev V N, Douglas A, Forland E and Zhai P M 1999 Changes in the probability of heavy precipitation: Important indicators of climatic change; Clim. Change 42 243–283.

    Article  Google Scholar 

  • Guo J, Chen H, Xu C -Y, Guo S and Guo J 2012 Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling; Stochastic Environ. Res. Risk Assess. 26 (2) 157–176.

    Article  Google Scholar 

  • Hannachi A, Jolliffe I T and Stephenson D B 2007 Empirical orthogonal functions and related techniques in atmospheric science: A review; Int. J. Climatol. 27 (9) 1119–1152.

    Article  Google Scholar 

  • Harpham C and Dawson C W 2006 The effect of different basis functions on a radial basis function network for time series prediction: A comparative study; Neurocomputing 69 (16–18) 2161–2170.

    Article  Google Scholar 

  • Hashmi M Z, Shamseldin A Y and Melville B W 2011 Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP); Environmental Modelling & Software 26 (12) 1639–1646.

    Article  Google Scholar 

  • Haylock M R, Cawley G C, Harpham C, Wilby R L and Goodess C M 2006 Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios; Int. J. Climatol. 26 (10) 1397–1415.

    Article  Google Scholar 

  • Hosseinzadeh Talaee P, Kouchakzadeh M and Shifteh B 2014 Homogeneity analysis of precipitation series in Iran; Theoret. Appl. Climatol. 118 (1–2) 297–305.

    Article  Google Scholar 

  • Hsieh W W 2009 Machine learning methods in the environmental sciences: Neural networks and kernels; Cambridge University Press.

  • Hu Y, Maskey S and Uhlenbrook S 2013 Downscaling daily precipitation over the Yellow River source region in China: A comparison of three statistical downscaling methods; Theoret. Appl. Climatol. 112 (3–4) 447–460.

    Article  Google Scholar 

  • Huth R, Kliegrova S and Metelka L 2008 Non-linearity in statistical downscaling: Does it bring an improvement for daily temperature in Europe? Int. J. Climatol. 28 (3) 465–477.

    Article  Google Scholar 

  • Kajornrit J, Wong K and Fung C 2012 Estimation of missing precipitation records using modular artificial neural networks; In: Neural Information Processing (eds) Huang T, Zeng Z, Li C and Leung C, Springer–Berlin Heidelberg, pp. 52–59.

  • Kannan S and Ghosh S 2013 A nonparametric kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin; Water Resourc. Res. 49 (3) 1360–1385.

    Article  Google Scholar 

  • Kannan S and Ghosh S 2011 Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output; Stoch. Environ. Res. Risk Assess. 25 (4) 457–474.

    Article  Google Scholar 

  • Lins H, Shiklomanov I and Stakhiv E 1990 Hydrology and water resources; In: Climate Change, the IPCC Scientific Assessment (eds) McTegart W J G and Griffiths D C, IPCC WG Report, WMO/UNEP. Ch. 4, pp. 1–42.

  • Liu Z, Xu Z, Charles S P, Fu G and Liu L 2011 Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China; Int. J. Climatol. 31 (13) 2006–2020.

    Article  Google Scholar 

  • Mahmood R and Babel M 2013 Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India; Theoret. Appl. Climatol. 113 (1–2) 27–44.

    Article  Google Scholar 

  • Malinowski E R 1977 Determination of the number of factors and the experimental error in a data matrix; Anal. Chem. 49 (4) 612–617.

    Article  Google Scholar 

  • Maraun D, Wetterhall F, Ireson A M, Chandler R E, Kendon E J, Widmann M, Brienen S, Rust H W, Sauter T, Themeßl M, Venema V K C, Chun K P, Goodess C M, Jones R G, Onof C, Vrac M and Thiele-Eich I 2010 Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user; Rev. Geophys. 48 (3) RG3003.

    Article  Google Scholar 

  • Maurer E P and Hidalgo H G 2008 Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods; Hydrol. Earth Syst. Sci. 12 551–563.

    Article  Google Scholar 

  • McLachlan G J and Krishnan T 1997 The EM algorithm and extensions; Wiley, New York.

    Google Scholar 

  • Mehrotra D and Mehrotra R 1995 Climate change and hydrology with emphasis on the Indian subcontinent; Hydrol. Sci. J./Journal Des Sciences Hydrologiques 40 (2) 231–242.

    Article  Google Scholar 

  • Mendes D and Marengo J 2010 Temporal downscaling: A comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios; Theoret. Appl. Climatol. 100 (3–4) 413–421.

    Article  Google Scholar 

  • Middelkoop H, Daamen K and Gellens D 2001 Impact of climate change on hydrological regimes and water resources management in the Rhine basin; Climatic Change 49 105–128.

    Article  Google Scholar 

  • Najafi M R, Moradkhani H and Wherry S A 2010 Statistical downscaling of precipitation using machine learning with optimal predictor selection; J. Hydrol. Eng. 16 (8) 650–664.

    Article  Google Scholar 

  • Ng S K and McLachlan G J 2004 Using the EM algorithm to train neural networks: Misconceptions and a new algorithm for multiclass classification; Neural Networks, IEEE Trans. 15 (3) 738–749.

    Article  Google Scholar 

  • Pervez M S and Henebry G M 2014 Projections of the Ganges–Brahmaputra precipitation – Downscaled from GCM predictors; J. Hydrol. 517 120–134.

    Article  Google Scholar 

  • Pour S H, Harun S B and Shahid S 2014 Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia; Atmosphere 5 (3) 914–936.

    Article  Google Scholar 

  • Rissanen J 1978 Modelling by shortest data description; Automatica 14 465–471.

    Article  Google Scholar 

  • Rodrigo F S 2002 Changes in climate variability and seasonal rainfall extremes: A case study from San Fernando (Spain), 1821–2000; Theor. Appl. Climatol. 72 193–207.

    Article  Google Scholar 

  • Sachindra D A, Huang F, Barton A and Perera B J C 2014 Statistical downscaling of general circulation model outputs to precipitation – part 1: Calibration and validation; Int. J. Climatol. 34 (11) 3264–3281.

    Article  Google Scholar 

  • Salvi K, Kannan S and Ghosh S 2013 High-resolution multisite daily rainfall projections in India with statistical downscaling for climate change impacts assessment; J. Geophys. Res.: Atmos. 118 (9) 3557–3578.

    Google Scholar 

  • Samadi S, Carbone G J, Mahdavi M, Sharifi F and Bihamta M R 2012 Statistical downscaling of climate data to estimate streamflow in a semi-arid catchment; Hydrol. Earth Syst. Sci. Discuss. 9 4869–4918.

    Article  Google Scholar 

  • Samadi S, Carbone G, Mahdavi M, Sharifi F and Bihamta M R 2013 Statistical downscaling of river runoff in a semi-arid catchment; Water Resour. Manag. 27 (1) 117– 136.

    Article  Google Scholar 

  • Santos M and Fragoso M 2013 Precipitation variability in northern Portugal: Data homogeneity assessment and trends in extreme precipitation indices; Atmos. Res. 131 34–45.

    Article  Google Scholar 

  • Şen Z, Alsheikh A L, Alamoud A S M, Al-Hamid A A, El-Sebaay A S and Abu-Risheh A W 2012 Quadrangle downscaling model and water harvesting in arid regions: Riyadh case; J. Irrig. Drain. Eng. 138 (10) 918– 923.

    Article  Google Scholar 

  • Shahid S 2011 Trends in extreme rainfall events of bangladesh; Theoret. Appl. Climatol. 104 (3–4) 489–499.

    Article  Google Scholar 

  • Sohn S J, Ahn J B and Tam C Y 2013 Six month-lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble; Geophys. Res. Lett. 40 (3) 579–583.

    Article  Google Scholar 

  • Souvignet M and Heinrich J 2011 Statistical downscaling in the arid central Andes: Uncertainty analysis of multi-model simulated temperature and precipitation; Theoret. Appl. Climatol. 106 (1–2) 229–244.

    Article  Google Scholar 

  • Su B D, Jiang T and Jin W B 2006 Recent trends in observed temperature and precipitation extremes in the Yangtze River basin, China; Theor. Appl. Climatol. 83 139–151.

    Article  Google Scholar 

  • Syed F S, Iqbal W, Syed A and Rasul G 2014 Uncertainties in the regional climate models simulations of South- Asian summer monsoon and climate change; Clim. Dyn. 42 (7–8) 2079–2097.

    Article  Google Scholar 

  • Tareghian R and Rasmussen P F 2013 Statistical downscaling of precipitation using quantile regression; J. Hydrol. 487 122–135.

    Article  Google Scholar 

  • Tripathi S, Srinivas V V and Nanjundiah R S 2006 Downscaling of precipitation for climate change scenarios: A support vector machine approach; J. Hydrol. 330 (3–4) 621–640.

    Article  Google Scholar 

  • Tsidu G M 2012 High-resolution monthly rainfall database for ethiopia: Homogenization, reconstruction, and gridding; J. Climate 25 (24) 8422–8443.

    Article  Google Scholar 

  • Valle S, Li W and Qin S J 1999 Selection of the number of principal components: The variance of the reconstruction error criterion with a comparison to other methods; Ind. Eng. Chem. Res. 38 4389–4401.

    Article  Google Scholar 

  • Von Storch H 1999 On the use of “inflation” in statistical downscaling; J. Climate 12 (12) 3505–3506.

    Article  Google Scholar 

  • Wang X J, Zhang J Y, Shahid S, Guan E H, Wu Y X, Gao J and He R M 2014 Adaptation to climate change impacts on water demand; Mitigation and Adaptation Strategies for Global Change, doi: 10.1007/s11027-014-9571-6.

  • Wang X J, Zhang J Y and Yang Z F 2013 Historic water consumptions and future management strategies for Haihe River basin of northern China; Mitigation and Adaptation Strategies for Global Change, doi: 10.1007/s11027-013-9496-5.

  • Wetterhall F, Bárdossy A, Chen D, Halldin S and Xu C Y 2006 Daily precipitation-downscaling techniques in three Chinese regions; Water Resour. Res. 42 (11) W11423.

    Article  Google Scholar 

  • Wilby R 1998 Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection indices; Clim. Res. 10 (3) 163–178.

    Article  Google Scholar 

  • Wilby R L, Dawson C W and Barrow E M 2002 SDSM – a decision support tool for the assessment of regional climate change impacts; Environmental Modelling & Software 17 (2) 145–157.

    Article  Google Scholar 

  • Wilby R, Charles S, Zorita E, Timbal B, Whetton P and Mearns L 2004 Guidelines for use of climate scenarios developed from statistical downscaling methods; www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf.

  • Zhang L, Ma Z M and Kang S Z 2008 Analysis of impacts of climate variability and human activity on stream flow for a river basin in arid region of northwest China; J. Hydrol. 352 239–249.

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to Ministry of Higher Education (MOHE)–Malaysia and Universiti Teknologi Malaysia (UTM) for providing financial support for this research through FRGS research project (Vote No. R.J130000.7822.4F541). Authors are also grateful to the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.

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Ahmed, K., Shahid, S., Haroon, S.B. et al. Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan. J Earth Syst Sci 124, 1325–1341 (2015). https://doi.org/10.1007/s12040-015-0602-9

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  • DOI: https://doi.org/10.1007/s12040-015-0602-9

Keywords

  • Multilayer perceptron neural network
  • rainfall
  • arid region
  • downscaling