Abstract
The comprehensive recognition of groundwater level, especially in arid and semiarid areas, is important in many hydrogeological and hydrological studies. This research aims at predicting and decomposing groundwater level using dynamic panel-data (D-PD) method in an arid hardrock–alluvium Al-Buraimi region, Oman–UAE border. The D-PD method incorporates temporal and spatial variations to predict groundwater level and detects effects that are not simply computable in pure cross-sectional or pure time-series methods. Monthly rainfall, potential evapotranspiration and artificial abstraction, as independent variables, were calculated for 39 Thiessen polygons. The D-PD model was developed to predict groundwater level for the calibration (October 1996 to September 2008) and validation (October 2008 to September 2012) periods. The calibrated D-PD model was then used to decompose the groundwater level hydrograph and to quantify the influence of climate, artificial abstraction, “unobservable spatial-specific effect” and “unobservable time-specific effect” components. The results show significant effect of rainfall on the second time lag, while artificial abstraction and potential evapotranspiration as surrogate of natural groundwater abstraction have influence on the fourth and eighth time lags on the groundwater level prediction. It is found that “unobservable spatial-specific effects” and “unobservable time-specific effects” components of the D-PD model highly influence groundwater level prediction in the study area. The proper match obtained from D-PD method between computed and observed groundwater levels in the study area indicates that the method is efficient and robust and proposes its application to predict and decompose groundwater level variations in regions with complex hydrogeology.
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References
Abdalla OA (2009) Groundwater recharge/discharge in semi-arid regions interpreted from isotope and chloride concentrations in north White Nile Rift, Sudan. Hydrogeol J 17(3):679–692
Alsaaran NA (2005) Experimental performance of spatial interpolators for groundwater salinity. Arabian J Sci Eng 30(1A):3–15
Arellano M (2003) Panel data econometrics. Oxford University Press, Oxford, United Kingdom, Advanced texts in econometrics
Arellano M, Bond S (1998) Dynamic panel data estimation using DPD98 for GAUSS: a guide for users. Institute for Fiscal Studies, working Paper
Bakker M, Maas K, Von Asmuth JR (2008) Calibration of transient groundwater models using time series analysis and moment matching. Water Resour Res 44:W04420. doi:10.1029/2007wr006239
Baltagi B (2005) Econometric analysis of panel data, 3rd edn. Wiley, New York, pp 1–77
Bierkens MFP (1998) Modeling water table fluctuations by means of a stochastic differential equation. Water Resour Res 34(10):2485–2499
Christmann D, Sonntag C (1987) Groundwater evaporation from East-Saharan depressions by means of deuterium and oxygen-18 in soil moisture. In: Isotope techniques in water resources development (Proceedings of the serious symposium IAEA, Vienna). IAEA, Vienna (pp 189–204)
Ciarreta A, Zarraga A (2010) Economic growth-electricity consumption causality in 12 European countries: a dynamic panel data approach. Energ Policy 38(7):3790–3796
Çoban S, Topcu M (2013) The nexus between financial development and energy consumption in the EU: a dynamic panel data analysis. Energ Econ 39:81–88
Covas FB, Rump B, Zakrajšek E (2014) Stress-testing US bank holding companies: a dynamic panel quantile regression approach. Int J Forecast 30(3):691–713
Davison WD (1982) Results of test drilling in the Buraimi area, Sultanate of Oman. Public Authority for Water Resources (PAWR), Sultanate of Oman, Water Supply Paper 1
Gaud P, Jani E, Hoesli M, Bender A (2005) The capital structure of Swiss companies: an empirical analysis using dynamic panel data. Eur Financ Manag 11(1):51–69
Hansen L (1982) Large sample properties of generalized method of moments estimation. Econometrica 50(3):1029–1054
Hargreaves GH, Samani ZA (1982) Estimating potential evapotranspiration. J Irrigation Drainage Division 108(3):225–230
Hsiao C (2003) Analysis of panel data, 2nd edn. Cambridge University Press, Cambridge, pp 1–67
Izady A, Davary K, Alizadeh A, Ghahraman B, Sadeghi M, Moghaddamnia A (2012) Application of “panel-data” modeling to predict groundwater levels in the Neishaboor Plain, Iran. Hydrogeol J 20(3):435–447. doi:10.1007/s10040-011-0814-2
Izady A, Davary K, Alizadeh A, Moghaddam Nia A, Ziaei AN, Hasheminia SM (2013) Application of NN-ARX model to predict groundwater levels in the Neishaboor Plain, Iran. Water Resour Manag 27(14):4773–4794
Izady A, Davary K, Alizadeh A, Ziaei AN, Akhavan S, Alipoor A, Joodavi A, Brusseau ML (2015) Groundwater conceptualization and modeling using distributed SWAT-based recharge for the semi-arid agricultural Neishaboor plain, Iran. Hydrogeol J 23(1):47–68. doi:10.1007/s10040-014-1219-9
Kaczmarek MB (1988) Hydrogeology and groundwater availability Buraimi production well-field. Director General Water, water projects, Hydrogeology Section, Sultanate of Oman, Part I - Final report
Kaczmarek MB, Brook M, Haig T, Read RE, George D (1993a) Water resources assessment and hydrogeology of wadi Safwan and wadi Sharm, Northern Oman. Ministry of Water Resources, Sultanate of Oman
Kaczmarek MB, Brook M, Haig T, Read RE, George D (1993b) Water resources assessment and hydrogeology of the Mahdah watershed, Northern Oman. Ministry of Water Resources, Sultanate of Oman
Kaczmarek MB, Brook M, Haig T, Read RE, George D (1993c) Water resources assessment and hydrogeology of the Zarub Gap watershed, wadi Musayliq and wadi Al Ayn, Northern Oman. Ministry of Water Resources, Sultanate of Oman
Kasman A, Duman YS (2015) CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis. Econ Model 44:97–103
Knotters M, Van Walsum PE (1997) Estimating fluctuation quantities from time series of water table depths using models with a stochastic component. J Hydrol 197:25–46
Lehsten D, Von Asmuth JR, Kleyer M (2011) Simulation of water level fluctuations in kettle holes using a time series model. Wetlands 31:511–520. doi:10.1007/s13157-011-0174-7
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Modeling Softw 15:101–124
Manzione RL, Knotters M, Heuvelink GBM, Von Asmuth JR, Camara G (2010) Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water table levels in the Brazilian Cerrados. Hydrogeol J 18:1927–1937. doi:10.1007/s10040-010-0654-5
MRMEWR: Ministry of Regional Municipalities and Water Resources (2004) Consultancy for drilling and aquifer testing program of Buraimi at Ad Dhairah Region. Ministry of Regional Municipalities, Environment and Water Resources, Sultanate of Oman
Obergfell C, Bakker M, Zaadnoordijk WJ, Maas K (2013) Deriving hydrogeological parameters through time series analysis of groundwater head fluctuations around well fields. Hydrogeol J 21:987–999. doi:10.1007/s10040-013-0973-4
Onanda M, Price V, Jolley T, Stuck A, Hall R (2013) Water balance computation for the Sultanate of Oman. Final Report. Ministry of Regional Municipalities and Water Resources, Sultanate of Oman
Pearl J (2003) Causality: models, reasoning, and inference. Econom Theory 19:675–685
Peeters L, Fasbender D, Batelaan O, Dassargues A (2010) Bayesian data fusion for water table interpolation: incorporating a hydrogeological conceptual model in kriging. Water Resour Res 46:W08532. doi:10.1029/2009WR008353
Peterson TJ, Cheng X, Western AW, Siriwardena L, Wealands SR (2011) Novel indicator geostatistics for water table mapping that incorporate elevation, land use, stream network and physical constraints to provide probabilistic estimation of heads and fluxes. Paper presented at the 19th international congress on modelling and simulation, Perth, Australia 12–16 December 2011
Peterson TJ, Western AW (2014) Nonlinear time series modeling of unconfined groundwater head. Water Resour Res 50:8330–8355. doi:10.1002/2013WR014800
Sequeira TN, Maçãs Nunes P (2008) Does tourism influence economic growth? A dynamic panel data approach. Appl Econ 40(18):2431–2441
Shapoori V, Peterson TJ, Western AW, Costelloe JF (2015a) Top-down groundwater hydrograph time-series modeling for climate-pumping decomposition. Hydrogeol J 23(4):819–836
Shapoori V, Peterson TJ, Western AW, Costelloe JF (2015b) Decomposing groundwater head variations into meteorological and pumping components: a synthetic study. Hydrogeol J 23(7):1431–1448. doi:10.1007/s10040-015-1269-7
Siriwardena L, Peterson TJ, Western AW (2011) A state-wide assessment of optimal groundwater hydrograph time series models. Paper presented at the 19th international congress on modelling and simulation, Perth, Australia 12–16 December 2011
Tabios GQ, Salas JD (1985) A comparative analysis of techniques for spatial interpolation of precipitation. J Am Water Resour Assoc 21(3):365–380
Tankersley CD, Graham WD, Haltfield K (1993) Comparison of uni-variate and transfer function models of groundwater fluctuations. Water Resour Res 29(10):2517–3533
Turner WR, Forbes AS, Rapp JR (1986) Groundwater resources of the wadi Safwan area. Public Authority for Water Resources (PAWR), Sultanate of Oman, Report: PAWR I-86-9
Van Geer FC, Zuur AF (1997) An extension of Box-Jenkins transfer/noise from time series of groundwater head series. J Hydrol 192:65–80
Von Asmuth JR, Bierkens MFP, Maas K (2002) Transfer function-noise modeling in continuous time using predefined impulse response functions. Water Resour Res 38:23. doi:10.1029/2001WR001136
Von Asmuth JR, Maas K, Bakker M, Petersen J (2008) Modeling time series of ground water head fluctuations subjected to multiple stresses. Groundwater 46:30–40. doi:10.1111/j.1745-6584.2007.00382.x
Yi MJ, Lee KK (2004) Transfer function-noise modeling of irregularly observed groundwater heads using precipitation data. J Hydrol 288:272–287. doi:10.1016/j.jhydrol.2003.10.020
Yihdego Y, Webb JA (2011) Modeling of bore hydrographs to determine the impact of climate and land-use change in a temperate subhumid region of southeastern Australia. Hydrogeol J 19:877–887. doi:10.1007/s10040-011-0726-1
Zhang C, Xu J (2012) Retesting the causality between energy consumption and GDP in China: evidence from sectoral and regional analyses using dynamic panel data. Energ Econ 34(6):1782–1789
Acknowledgments
Authors would like to thank the Sultan Qaboos University (SQU) for the financial support under grant # CL/SQU-UAEU/14/04. Thanks are due to the Ministry of Regional Municipalities and Water Resources for providing the data. We also wish to acknowledge Prof. Manuel Arellano from CEMFI (Madrid, Spain) for his precious help.
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Izady, A., Abdalla, O. & Mahabbati, A. Dynamic panel-data-based groundwater level prediction and decomposition in an arid hardrock–alluvium aquifer. Environ Earth Sci 75, 1270 (2016). https://doi.org/10.1007/s12665-016-6059-6
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DOI: https://doi.org/10.1007/s12665-016-6059-6