Investigation of Markov Chain Uncertainty for Forecasting Hydrological Status Based on Meteorological Status

Document Type : Research Paper

Authors

1 Water Engineering Department, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran

2 Professor, Water Engineering Department, Imam Khomeini International University, Qazvin, Iran

Abstract

Examining and quantifying the degree of uncertainty in the prediction results of the models is the most important step before using the results of the models in water resources decisions making. In this research, the Markov chain technique was used to predict the hydrological status of the basin according to the amount of precipitation in the previous time step. The present study aims to determine the degree of uncertainty of prediction using the confidence interval of the probability of occurrence of events in different meteorological and hydrological conditions. To assess the uncertainty of the predictions by the 2D Markov chain transfer probability matrix, 20 periods with similar characteristics to historical conditions were simulated by the Monte Carlo method. To determine the uncertainty in estimating the hydroclimatology transfer matrix components, the nonparametric method of ratios for large samples and the exact method based on the sign test for the median of the predicted matrix components were used. The results of the long-term hydroclimatology matrix showed that the hydrological conditions tended to remain normal. The results of uncertainty analysis also indicated that the sign test, as a non-parametric method, in assessing the uncertainty of the Markov Chain acts better, and because the range of probabilities of transfer from different meteorological states to hydrological states is almost the same for all stations, the study basin is considered meteorologically and hydrologically homogeneous.

Keywords


Abdi B, Bozorg-Haddad O, Loáiciga, H.A. (2020) Analysis of the effect of inputs uncertainty on riverine water temperature predictions with a Markov chain Monte Carlo (MCMC) algorithm. Environ Monit Assess, 192, 100. https://doi.org/10.1007
Abedini M,  Ziai A, Shafiei M, Ghahraman B,  Ansari H, Meshkini J. (2017) Uncertainty assessment of groundwater flow modeling by using the generalized likelihood uncertainty estimation method (case study: Bojnourd plain). Iranian Journal of Irrigation and Drainage, 10(6): 755-769. (In Farsi)
Amirmoradi K, Shokoohi A, Azizian A. (2020) Evaluating the risk of economic loss due to river flood in urban areas(study area: Kan watershed, 50(9): 2239-2259. (In Farsi)
Bonakdari H, Zaji AH, Binns AD, Gharabaghi B (2019) Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals. Journal of Hydrology, 572: 75-95. https://doi.org/10.1016/j.jhydrol.2019.02.027 
Cao, C. (1993) Time serials of rainfall and their stochastic simulation’, urban storm drainage. Italy, 25-28 July, 45-62.
Delafkar H, Zareie T, Roohian M.(2012). Surface water resources Management using the Markov chain method (Case study: Occurrence of different hydrological states in Shapoor river of Bushehr province). The Second National Conference on Modern Management Sciences. (In Farsi)
Gasm. A. M. (1987) An application of Markov chain model for wet and dry spells probabilities at Juba in Southern Sudan.  Geo Journal, 15.4, 420-424.
Habibnejad R, Shokoohi A. (2020) Uncertainty analysis of IDF curves simulation under climate change scenarios using a weather generator model (case study: Tehran). Iran-Water Resources Research, 16(2): 164-177. (In Farsi)
Mahavarpour Z. (2015) The analyze occurrences daily precipitation probability in Iran by using the Markov Chain model. GEOGRAPHICAL RESEARCH, 4(115); 229-240.
Martin- Vide J, Gomez, L. (1999) Regionalization of peninsular Spain based on the length of dry spells. Int. J. of Climatology, 19: 537-555.
McKee, T.B., Doesken, N.J. and Kleist, J. (1995) Drought monitoring with multiple timescales. 9th conference on Applied Climatology, TX. The USA. 233-236.
Nalbantis I, Tsakiris G. (2009) Assessment of hydrological drought revisited. Water resources management, 23(5): 881-897.
Nalbantis, I. (2008) Evaluation of hydrological drought index. Journal of European water, 23/24: 67-77.
Nazaripoor H, Karimi Z, Sedaghat M. (2016). Hydro-meteorological drought assessment based on integrated drought index and its forecast with Markov chain in Sarbaz river basin (southeast of Iran). Journal of Soil and Water Sciences. 20(1). 151-169. (In Farsi)
Noorali M, Ghahraman B, Poorreza M, Davari K. (2017). Estimation of HEC-HMS Flood Simulation Model Uncertainty Using Markov Chain Monte Carlo Algorithm. Watershed Management Research Journal.15. 235-249. (In Farsi)
Rashtabri M, taleie M. (2019) Prediction of land use changes and its hydrological effects using Markov chain and SWAT model in Sarab Zayandehrood catchment area. Journal of Spatial Information Technology Engineering. 7(4). 41-59. (In Farsi)
Razi F, Shokoohi A, Eslami A. (2020) Forecasting Hydrological Regime Based on Rainfall Regime Using Two-dimensional Markov Chain in Anzali Watershed. Journal of ECOHYDROLOGY, 7(3): 663-674. (In Farsi)
Razi F, Shokoohi A. (2020) Determining and Estimating the Lag time between Meteorological and Hydrological Drought Using a Water Balance Model. Journal of Watershed Engineering and Management, 12(3): 712-724. (In Farsi)
Razi F, Shokoohi A. (2021) Determining the Effect of Intensity and Duration of Drought on the Lag Time Between Meteorological and Hydrological Drought and Examining Uncertainties (Case Study: Anzali Basin). Journal of ECOHYDROLOGY, 7(4): 843-854. (In Farsi)
Sadeghi Tabas S, Pourreza bilondi M. (2015) Comparison of optimization and uncertainty analysis methods in hydrological modeling. Journal of Range and Watershed Management, 68(3): 533-552. (In Farsi)
Shafiei M, Ghahraman B, Saghafian B. (2018) A review on hydrological model-ling concepts, part2: uncertainty assessment concepts. Journal of Water and Sustainable Development, 6(1): 35-40. (In Farsi)
Tavanpour N, Ghaemi A, Honar T, Shirvan A. (2018) Investigation the occurrence probability and persistence of rainy days by using Markov Chain model (case study: Lamerd city). Iran-Water Resources Research,14(2): 89-99. (In Farsi)
Teimouri M. Fathzadeh A. (2014) Monitoring of Surface water resources availability in the Atrak River using the modified SWSI index and Markov Chain model case study: Atrak basin. GEOGRAPHY AND DEVELOPMENT, 12(34): 99-107. (In Farsi)
Vafakhah M. Bashiri seghale M.(2011).Investigation of the probability of occurrence of the wet season and hydrological drought using Markov chain in Kashfarood watershed. Journal of Watershed Management Research. 94. 1. (In Farsi)
Yaghobi M, Massah Bavani A. (2016) Comparison and evaluation of different sources of uncertainty in the study of climate change impact on runoff in semi-arid basins (case study: Azam Harat river basin, 11(3); 113-130 (In Farsi)
Zareie A, Moghimi M, Bahrani M. (2017). Monthly Drought Monitoring and Prediction Using Standard Precipitation Index and Markov Chain (Case Study: Southeast Iran). Geography and Environmental Sustainability Journal.23. 39-51. (In Farsi)