Abstract
Frequency domain analysis using an additive decomposition method is proposed to reconstruct the missing hydrometeorological data of selected sites in Lake Urmia basin in Iran. Precipitation, evaporation, streamflow and groundwater time series are used for this aim. Trends, within- and multi-year cycles, and randomness are taken into account to reconstruct each of the time series for which models are developed, calibrated and validated separately. Statistical similarity between the observed and reconstructed time series is checked. Statistical characteristics including the average, standard deviation, skewness, and the first-order autocorrelation coefficient are well preserved at the reconstructed time series. A conceptual water budget model is also established to check for the consistency between the reconstructed and the observed datasets. The water budget model is taken as a quantitative way to confirm that the frequency domain analysis using the additive decomposition is an effective method for the reconstruction of the missing hydrometeorological data based on the case study performed for the Lake Urmia basin in Iran.
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References
Alipour S (2006) Hydrogeochemistry of seasonal variation of Urmia Salt Lake, Iran. Saline Systems 2(1):193–194
Andrew R, Guan H, Batelaan O (2017) Estimation of GRACE water storage components by temporal decomposition. J Hydrol 552:341–350
Arkian F, Nicholson SE, Ziaie B (2018) Meteorological factors affecting the sudden decline in Lake Urmia’s water level. Theor Appl Climatol 131(1–2):641–651
Bayazit M, Aksoy H (2001) Using wavelets for data generation. J Appl Stat 28(2):157–166
Box GE, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, Oakland
Bradshaw GA, McIntosh BA (1994) Detecting climate-induced patterns using wavelet analysis. Environ Pollut 83(1–2):135–142
Chaudhari S, Felfelani F, Shin S, Pokhrel Y (2018) Climate and anthropogenic contributions to the desiccation of the second largest saline lake in the twentieth century. J Hydrol 560:342–353
Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: A seasonal-trend decomposition procedure based on loess. J Off Stat 6(1):3–73
Domenico PA, Schwartz FW (1998) Physical and Chemical Hydrogeology, vol 506. Wiley, New York
Duffaut Espinosa LA, Rosales F, Posadas A (2018) Embedding spatial variability in rainfall field reconstruction. Int J Remote Sens 39(9):2884–2905
Edelman JH (1983) Groundwater hydraulics of extensive aquifers (No. 13). International Livestock Research Institute, Wageningen University, Netherland
Hashemi M (2008) An independent review: the status of water resources in the Lake Uromiyeh Basin. UNDP/GEF “Conservation of Iranian Wetlands” Project, Pp, 37–38
He L, Huang GH, Zeng GM, Lu HW (2008) Wavelet-based multiresolution analysis for data cleaning and its application to water quality management systems. Expert Syst Appl 35(3):1301–1310
Hirsch RM (1979) An evaluation of some record reconstruction techniques. Water Resour Res 15(6):1781–1790
Iranian Marine Industrial Company (IMIC) (2003) Shahid-Kalantary Causeway and Lake Urmia Bridge Project. Reports and Documentations. SADRA, Iran
Iranian Water Resource Management Company (IWRMCo.) (2016). Daily rainfall report of Iran based on seconder catchment areas. Retrieved from http://wrs.wrm.ir/m3/gozaresh.asp
Jakovovic D, Werner AD, de Louw PG, Post VE, Morgan LK (2016) Saltwater upcoming zone of influence. Adv Water Resour 94:75–86
Jeihouni M, Toomanian A, Alavipanah SK, Hamzeh S (2017) Quantitative assessment of Urmia Lake water using spaceborne multisensor data and 3D modeling. Environ Monit Assess 189(11):572
Kang E, Min J, Ye JC (2017) A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med Phys 44(10):e360–e375
Karbassi A, Bidhendi GN, Pejman A, Bidhendi ME (2010) Environmental impacts of desalination on the ecology of Lake Urmia. J Great Lakes Res 36(3):419–424
Kashyap RL, Ramachandra Rao A (1976) Dynamic Stochastic Models from Empirical Data. Academic Press, New York
Khaki M, Forootan E, Kuhn M, Awange J, van Dijk AIJM, Schumacher M, Sharifi MA (2018) Determining water storage depletion within Iran by assimilating GRACE data into the W3RA hydrological model. Adv Water Resour 114(1–18)
Lewis MA, Cheney CS, Dochartaigh BE (2006) Guide to permeability indices. CR/06/160N, British Geological Survey, Natural Environmental Council, London
Lohman SW (1972) Groundwater hydraulics, p. 70. US Government Printing Office. Washington DC
Matalas NC (1967) Mathematical assessment of synthetic hydrology. Water Resour Res 3(4):937–945
Mohajjel M, Taghipour K (2014) Quaternary travertine ridges in the Lake Urmia area: active extension in NW Iran. Turk J Earth Sci 23(6):602–614
Nourani V, Molajou A, Tajbakhsh AD, Najafi H (2019) A Wavelet Based Data Mining Technique for Suspended Sediment Load Modeling. Water Resour Manag 33(5):1769–1784
Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water Resour Manag 23(14):2877
Pankratz A (2012) Forecasting with dynamic regression models (Vol. 935). John Wiley & Sons. Canada
Post V, Kooi H, Simmons C (2007) Using hydraulic head measurements in variable-density ground water flow analyses. Ground Water 45(6):664–671
Rohli RV, Andrew Joyner T, Reynolds SJ, Shaw C, Vázquez JR (2015) Globally extended Kӧppen–Geiger climate classification and temporal shifts in terrestrial climatic types. Phys Geogr 36(2):142–157
Ruiming F (2018) Wavelet based relevance vector machine model for monthly runoff prediction. Water Quality Research Journal. https://doi.org/10.2166/wcc.2018.196
Salas JD, Delleur V, Yevjevich V, Lane WL (1980) Applied modeling of hydrologic time series. Water Resources Publication, Chelsea
Shadkam S, Ludwig F, Van Oel P, Kirmit C, Kabat P (2016) Impacts of climate change and water resources development on the declining inflow into Iran's Urmia Lake. J Great Lakes Res 42(5):942–952
Shoaib M, Shamseldin AY, Khan S, Sultan M, Ahmad F, Sultan T, Dahri ZH, Ali I (2019) Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling. Water Resour Manag 33(3):955–973
Sinha S, Routh PS, Anno PD, Castagna JP (2005) Spectral decomposition of seismic data with continuous-wavelet transform. Geophysics 70(6):P19–P25
Sivakumar B, Jayawardena AW, Li WK (2007) Hydrologic complexity and classification: a simple data reconstruction approach. Hydrol Process 21:2713–2728
Su L, Miao C, Duan Q, Lei X, Li H (2019) Multiple-wavelet coherence of world's large rivers with meteorological factors and ocean signals. J Geophys Res-Atmos. https://doi.org/10.1029/2018JD029842
Tencaliec P, Favre AC, Prieur C, Mathevet T (2015) Reconstruction of missing daily streamflow data using dynamic regression models. Water Resour Res 51(12):9447–9463
Thomas HA, Fiering MB (1962) Mathematical synthesis of streamflow sequences for the analysis of river basins by simulation. Design of Water Resource Systems. Harvard University Press, Cambridge, pp 459–493
Vaheddoost B, Aksoy H (2017) Structural characteristics of annual precipitation in Lake Urmia basin. Theor Appl Climatol 128(3–4):919–932
Vaheddoost B, Aksoy H (2018) Groundwater interaction with Lake Urmia, Iran. Hydrol Process 32(21):3283–3295
Vaheddoost B, Aksoy H, Abghari H (2016) Prediction of water level using monthly lagged data in Lake Urmia, Iran. Water Resour Manag 30(13):4951–4967
WWA/Yekom (2005) The Environmental Impact Assessment and study (quality and quantity) of the Development Projects in the Lake Uromiyeh Basin, The West Azerbaijan Water Authority (WWA), Ministry of Energy (MoE), I.R. Iran
Yekom Consulting Engineers (2002) Management plan for the Lake Uromiyeh ecosystem. 1st Report- EC-IIP, environmental management project for Lake Uromiyeh. Tehran, Iran
Zarghami M (2011) Effective watershed management; case study of Urmia Lake, Iran. Lake and Reservoir Management 27(1):87–94
Acknowledgments
This study is based on the PhD thesis of the leading author. It was supported by Research Fund of Istanbul Technical University, Project Number: 39016 - Modelling Studies with Statistical Approaches for Lake Urmia.
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Vaheddoost, B., Aksoy, H. Reconstruction of Hydrometeorological Data in Lake Urmia Basin by Frequency Domain Analysis Using Additive Decomposition. Water Resour Manage 33, 3899–3911 (2019). https://doi.org/10.1007/s11269-019-02335-3
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DOI: https://doi.org/10.1007/s11269-019-02335-3