Comparison of Skill of Two Spatial-temporal Approaches of Daily Rainfall Simulation Across Iran

Document Type : Original Article

Authors

1 M.Sc Graduate, University of Tehran

2 Associate Professor, Dept. of Irrigation and Reclamation Engineering, University of Tehran

3 Ph.D. student of Agrometeorology, University of Tehran

Abstract

  The aim of this study is a comparison among two multi-site stochastic weather generators for simulation of winter rainfall occurrence across Iran using data of a selected network consisting of 130 rain gauge stations with a historical data of 21 years. The applied approaches included Hidden Markov Model (HMM) as a parametric approach and K-nearest neighbor (KNN) as non-parametric approach. Six stations namely, Bandar Anzali, Sari, Gharakhil Ghaemshahr, Gorgan, Shiraz and Zahedan were chosen respectively as the representative of different climates including very humid, humid, semi humid, Mediterranean, semi dry and dry climates. In comparison of first and second order momentums, results indicated that HMM performed well in almost every station. Data dispersion was examined using box plot and confidence interval analysis. The results revealed better performance for HMM. Regarding probabilities spaces, HMM showed a better performance in simulation of extreme events and higher percentiles of empirical distribution but KNN approach provided better estimations for middle percentiles values. LEPS Score index was used for comparison of cumulative distribution of observed and simulated series which showed more agreement in case of HMM. The spatial correlation was evaluated using Log-odds ratio index, which indicated that KNN model did better. Both approaches performed well in estimation of duration of wet and dry spells though a tendency to overestimate was observed at HMM and a tendency to underestimate viewed at KNN in simulating of wet spells. In general, HMM has more skill in simulation of daily rainfall series which might be attributed to its complex mathematical structure, however relatively good results of KNN approach showed that it can be recommended for less complicated applications.

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References

 
Ailliot P, Thompson C, Thompson P (2009) Space time modeling of precipitation using a hidden Markov model and censored Gaussian distributions. Journal of the Royal Statistical Society 58(3):405-426.
Beersma JJ, Buishand TA (2003) Multi-site simulation of daily precipitation and temperature conditional on the atmospheric circulation. Climate Research 25(2):121-133.
Buishand TA, Brandsma T (2001) Multisite simulation of daily precipitation and temperature in the Rhine basin by nearest-neighbor resampling. Water Resources Research 37(11):2761-2776.
Ejlali N, Pezeshk H. (2009) A Bidirectional hidden markov model in linear memory. Statistical Sciences. 2(2):131-148 (In Farsi)
Ghamghami M, Ghahreman N,Bazrafshan J (2015) Spatial-Temporal modeling of occurrence and amount of winter rainfall using hidden Markov model. Watershed Management Research 6(12):139-153.
 Ghamghami M, Ghahreman N, Araghinejad Sh (2010) Application of a non parametric approach for simulation of rainfall and temperature in terms of climate change. Journal of Climate Research 1(3,4):75-94 (In Farsi).
Ghamghami M, Ghahreman N Araghinejad Sh (2011) An evaluation of the performance of an advanced approach of the K-nearest neighbor in simulating the daily meteorological data. Iranian Journal of Soil and Water Research 42(1):45-54 (In Farsi).
Hejazi Zadeh Z Fattahi E (2004) Synoptic patterns analysis of winter precipitation in Iran. Iranian Journal of Geography 3:89-107 (In Farsi).
Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resources Research 30(5):1535-1546.
Lall U, Sharma A (1996) A nearest neighbor bootstrap for time series resampling. Water Resources Research 32(3):679-693.
Maruddani B, Kurniawan A, Sugihartono, Munir A (2010) Rain fade modeling using hidden markov model for tropical area. In: Proc. of PIERS, 5-8 July, Cambridge, USA, 96-100.
Mehrotra R, Srikanthan R, Sharma A (2006) A comparison of three stochastic multisite precipitation occurrence generators. Journal of Hydrology 331(1-2):280-292.
Rajagopalan B, Lall U (1999) A K-nearest-neighbor simulator for daily precipitation and other weather variables. Water Resources Research 35(10):3089-3101.
Robertson AW, Kirshner S, Smyth P (2004) Downscaling of daily rainfall occurrence over Northeast Brazil using a hidden markov model. Journal of climate 17(22):4407-4424.
Sharif M, Burn DH (2007) An improved K-nearest neighbor weather generating model. Hydrologic Engineering 12(1):42-51.
Sharif M, Burn DH (2006) Simulating climate change scenarios using an improved k-nearest neighbor model. Journal of Hydrology 325:179-196.
Thyer M, Kuczera G (2003) A hidden markov model for modeling long-term persistence in multi-site rainfall time series, 2. Real data analysis. Journal of Hydrology 275(1-2):27-48.
Wilks DS, Wilby RL (1999) The weather generator game: A review of stochastic weather models. Progress in Physical Geography 23(3):329-357.
Yates D, Gangopadhyay S, Rajagopalan B, Strzepek K (2003) A technique for generating regional climate scenarios using a nearest neighbor algorithm. Water Resources Research 39(7):SWG7-1-7-15.
Young KC (1994) A multivariate chain model for simulating climate parameters with daily data. Journal of Applied Meteorology 33(6):661-671.
Zucchini W, Guttorp P (1991) A hidden markov model for space-time precipitation. Water Resources Research 27(8):1917-1923.
 
 

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History

  • Receive Date: 13 February 2016
  • Revise Date: 23 March 2016
  • Accept Date: 23 March 2016

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