Development of Daily Rainfall Simulation Model by Using Markove Chain and Preserve Spatial Correlation (Case Study: Khozestan Province)

Document Type : Research Paper

Authors

1 Ph.D. Student on Water Resources Engineering, Department of Science and Water Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.

2 Professor on Water Resource Engineering, Department of Science and Water Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.

3 Assistant Professor on Graduate University of advanced technology, Kerman, Iran.

Abstract

The prediction of precipitation and dispersion of its time are the applied ways in the agricultural sector. Appropriate management of using rainwater and prediction of the occurrence or nonoccurrence of daily rainfall have significant role for agricultural planning and water resources management. In this study 4 synoptic stations of arid moderate and semi-arid moderate climates of Khozestan province of Iran which have daily 30 years rainfall dataset have been used in order to modeling occurrence of daily rainfall. The model is made just for rainy periods. To this aim, a stochastic rainfall time series consisting of first, second and third-order Markov chain (MC) models have been used for reproducing rainfall occurrence. To detect the best order of MC models, the Akaike information criterion (AIC) has been used. After identification of the best order of MC model, due to the importance of the spatial correlation among the study stations, the Wilks approach has also been used for the rainfall events modeling. The performance of the Wilks approach has verified using coefficient of determination (R2). The results show the first-order MC model has the best generation results for daily rainfall events. Based on this criterion, the average of preference first order Markov chain compared with second and third order was 61 and 74% for all study stations, respectively. Also, based on the R2, the result illustrate that Wilks approach can accurately simulate the occurrence of rainfall in a regional manner.

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History

  • Receive Date: 23 April 2018
  • Revise Date: 24 May 2019
  • Accept Date: 01 June 2019
  • First Publish Date: 30 December 2019

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