Document Type : Research Paper

Authors

1 Dams and Water Resources Engineering Department, Collage of Engineering, University of Mosul, Mosul, Iraq

2 Department of Dams and Water Resources Engineering,Collage of Engineering,University of Mosul, Mosul,Iraq

3 Department of Dams and Water Resources, College of Engineering, University of Mosul, Mosul, Iraq

Abstract

The study of drought and its forecasting plays an important role in planning and managing water resource systems, especially in extreme climatic periods. This study aims to analyze and forecast drought characteristics, through the use of the Reconnaissance Drought Index (RDI) in order to analyze temporal and spatiotemporal climatic drought in nine climate stations in the Kurdistan Region of Iraq for the period (1973-2020) to detect the beginning and end of the drought period, as well as forecasting future droughts using two types of artificial neural networks: Recursive Multi-Step Neural Networks (RMSNN) and Direct Multi-Step Neural Network (DMSNN). The results revealed that the driest years were in the years (1998-99) for Amadiyah, Erbil and Sulaymaniyah stations, and the years (2007-08) for the rest of the stations in the study area. Moreover, the results of the two models depending on the simulation methods adopted have shown the ability of these models with regard to the forecasting for the last six years, and the ability of both models to forecast with an increase in the amount of error as we go forward. However, the (DMSNN) model was more accurate, as shown by the results of the statistical tests.

Keywords

Main Subjects

[2]     H. Wu, , M. J., Hayes, D. A., Wilhite, & Svoboda, M. D.. "The effect of the length of record on the standardized precipitation index calculation". International Journal of Climatology: A Journal of the Royal Meteorological Society, 25(4), 505-520 (2005).
[3]     A. K., Mishra, V. R., Desai, & V. P.  Singh,. "Drought forecasting using a hybrid stochastic and neural network model". Journal of Hydrologic Engineering, 12(6), 626-638 (2007).
[4]     T. A., Awchi, & M. M. Kalyana,. Meteorological drought analysis in northern Iraq using SPI and GIS. Sustainable Water Resources Management, 3(4), 451-463 (2017).
[5]     S. M., Kassim, A. M., Younis, O. M. Agha. Temporal and Spatial Analysis of Drought Using the Standard Precipitation Index for the Northwestern Region of Iraq. AREJ, No.1, Vol.26, pp115-127 (2021).   
[6]     A. I., Jasim, & T. A. Awchi, Regional meteorological drought assessment in Iraq. Arabian Journal of Geosciences, 13(7), 1-16 (2020)..
[7]     S. M., Kassim, Analysis of Meteorological Drought using standardized precipitation index (SPI) for different time scale -a case study of  Iraq. M. Sc. Thesis, College of Engineering, University of Mosu (2021)  l.
[8]     K.Subramanya, Engineering hydrology, 4e. Tata McGraw-Hill Education (2013). .
[9]     O. M. A., Agha, & N. Sarlak,. Analysis of Meterological Drought in Iraq Using The Reconnassaince Drought Index (RDI). International Journal of Advanced Research (IJAR) Vol, 5(3), 473-479  (2017).
[10]  G., Tsakiris, D., Pangalou, & H. Vangelis,. Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water resources management, 21(5), 821-833 (2007)..
[11]  A. Al-Mohseen, K., & RM Towfeeq, A.. Artificial Neural Network for Single Reservoir Operation. Al-Rafidain Engineering Journal (AREJ), 22(2), 29-37 (2014).
[12]  F. K., Saeed, K. A.,Almohseen, & A. M. Younis, .”The Use of Artificial Neural Networks in the Analysis of Seepage and Slope Stability for the Proposed Qaim Dam on the Khosar River”. Al-Rafidain Engineering Journal (AREJ), 26(1), 96-104 (2021)..
[13]  S., Pashiardis, & S. Michaelides,. Implementation of the standardized precipitation index (SPI) and the reconnaissance drought index (RDI) for regional drought assessment: a case study for Cyprus. European Water, 23(24), 57-65 (2008) .
[14]  M. A. A., Zarch, H., Malekinezhad, M. H., Mobin, Dastorani, M. T., & Kousari, M. R.. Drought monitoring by reconnaissance drought index (RDI) in Iran. Water resources management, 25(13), 3485 (2011).
[15]  O. M. A., Agha, Climate Trends and Behavior of Drought Indices: Case study of Iraq. Ph.D. Thesis, Civil Engineering, University of Gaziantep .
[16]  G., Tsakiris, I., Nalbantis, D., Pangalou,  D., Tigkas, & H. Vangelis,. "Drought meteorological monitoring network design for the reconnaissance drought index (RDI). In Proceedings of the 1st International Conference “Drought management: scientific and technological innovations” ". Zaragoza, Spain: option Méditerranéennes, series A (No. 80, p. 2008).
[17]  D. Tigkas,. Drought characterisation and monitoring in regions of Greece. European Water, 23(24), 29-39 (2008).
[18]  D.,  Tigkas, H., Vangelis, & G. Tsakiris, (2013). The RDI as a composite climatic index. Eur Water, 41, 17-22.
[19]  H., Vangelis, D., Tigkas, & G. Tsakiris,. The effect of PET method on Reconnaissance Drought Index (RDI) calculation. Journal of Arid Environments, 88, 130-140  (2013).
[20]  F. A., Al-Faraj, M., Scholz, D., Tigkas, & M. Boni,. Drought indices supporting drought management in transboundary watersheds subject to climate alterations. Water Policy, 17(5), 865-886 (2015).
[21]  S., Barua, Ng, A. W. M., & B. J. C. Perera, Artificial neural network–based drought forecasting using a nonlinear aggregated drought index. Journal of Hydrologic Engineering, 17(12), 1408-1413 (2012)..
[22]  A. S. Y., AL-Dabbagh, K. A., AL-Mohseen, & I. A AL-Aani,. Estimating Daily Reference Evapotranspiration for Mosul Area Using Artificial Neural Networks. Al-Rafidain Engineering Journal (AREJ), 15(4), 16-27 (2007).
[23]  A. K., Mishra, & V. R.  Desai,. Drought forecasting using feed-forward recursive neural network. ecological modelling, 198(1-2), 127-138 (2006).
[24]  H., Razmkhah, E., Rostami, A. R., Ravari, & A. Fararouie, . “Evaluation and Forecasting Meteorological Drought, Case Study: Kohgilooyeh and Boyer Ahmad” (2021).
[25]  M.M. Kiliana, . “Modeling and Analysis of Drought in the North of Iraq”. M. Sc Thesis , College of Engineering , University of Mosu (2013)  l.
[26]  S. M., Kassim, Analysis of Meteorological Drought using standardized precipitation index (SPI) for different time scale -a case study of  Iraq. M. Sc. Thesis, College of Engineering, University of Mosul (2021) .
[27]  N. F.,  MUSTAFA, H. M.,  RASHID, & H. M. IBRAHIM,. Aridity index based on temperature and rainfall data for Kurdistan region-Iraq. Journal of Duhok University, 21(1), 65-80 (2018).
[28]  R. M., Qasab Bashi, A. M., Younes, & O. M.  Mahmood Agha,. Testing of the Homogeneity of Rain and Temperature Data: in an area Kurdistan Region – Iraq. Al-Rafidain Engineering Journal (AREJ), 26(2), 227-236 (2021). doi:10.33899/rengj.2021.130076.1095.
[29]  D., Tigkas, H., Vangelis, & G. Tsakiris, . “DrinC: a software for drought analysis based on drought indices”. Earth Science Informatics, 8(3), 697-709 (2015).
[30]  M. T. Jones,. Artificial Intelligence: A Systems Approach: A Systems Approach. Jones & Bartlett Learning (2008).
[31]  Sandhu, R., & Irmak, S.. Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation. Agricultural Water Management223, 105687 (2019).