Probabilistic forecast of climate change effects on Hamadan-Bahar aquifer

Document Type : Research Paper

Authors

1 Ph.D. Student, Earth Science Faculty, Department of Geology, University of Shahid Beheshti (SBU), Tehran, Iran

2 Associate Professor, Earth Science Faculty, Department of Geology, Shahid Beheshti University (SBU), Tehran, Iran

3 Associate Professor, Department of Irrigation and Drainage Engineering, Abourayhan Campus, University of Tehran, Pakdasht, Iran

Abstract

This study will evaluates climate change impacts on groundwater resources in Hamadan-Bahar alluvial aquifer in the west of Iran. Different climate models are weighted in the basis of their ability in predicting monthly observed climate data in the base study period (1970-2000). With respect to climate models weights and their predictions, precipitation and temperature changes in 10, 50 and 90 probability percentile are estimated. Daily observation data of Hamadan synoptic station and ΔP, Δt under A2 emission scenario at 90 probability percentile, as a critical condition in groundwater recharge, have been imported to an stochastic weather generator, named LARS-WG, and future precipitation and temperature data are produced for the study period (2015– 2045). Multi layer perceptron artificial neural network and visual MODFLOW are used for simulating daily run off and groundwater table respectively. Simulated groundwater table indicates a significant depletion in groundwater table around 38 meters specially in the south-southwest of aquifer and at the end of modeling period aquifer saturated thickness will be less than 12 meters.

Keywords


  1. امیدوار ک. و اژدرپور م (1391) استفاده از مدل شبکه عصبی مصنوعی در برآورد بارش – رواناب در حوضه آبریز رودخانه اعظم هرات. فصلنامه تحقیقات جغرافیایی. 27 (4): 640-620.
  2. مساح بوانی ع.ر  (1385) ارزیابی ریسک تغییر اقلیم و تأثیر آن بر منابع آب، مطالعه موردی حوضه زاینده‌رود اصفهان. پژوهشکده مهندسی آب دانشگاه تربیت مدرس. تهران. پایان‌نامه دکتری.
  3. دفتر مطالعات پایه منابع آب (1389) گزارش تمدید ممنوعیت دشت همدان-بهار. شرکت آب منطقه‌ای استان همدان. 40 صفحه.
  4. Abrahart RJ and  See L (2000) Comparing neural network (ANN) and Auto Regressive Moving Average (ARMA) techniques for the provision of continuous river flow forecasts in two contrasting catchment. Hydrological Process. 14:2157-2172.
  5. Allen DM, Cannon AJ, Toews MW and Scibek J (2010) Variability in simulated recharge using different GCMs. Water Resource Research. 46 (10): 1-18.
  6. Alley WM, Healy RW and LaBaugh JW (2002) Flow and storage in groundwater systems. Science 296:1985–1990.
  7. Block PJ, Souza Filho FA, Sun L and Kwon HH (2009) A stream flow forecasting framework using multiple climate and hydrological models. Journal of American Water Resource Association. 45(4): 828–43.
  8. Cannon AJ (2008) Probabilistic multisite precipitation downscaling by an expanded Bernoulli-gamma density network. Journal of Hydrometeorology. 9 (6): 1284–1300.
  9. Changnon SA,  Huff FA and Hsu CF (1988) Relations between precipitation and shallow groundwater in Illinois. Journal of Climate. 1: 1239– 1250.
  10. Holman IP, Allen DM, Cuthbert MO and Goderniaux P (2012) Towards best practice for assessing the impacts of climate change on groundwater. Hydrogeology Journal. 20: 1-4. 
  11. Holman IP (2006) Climate change impacts on groundwater recharge: uncertainty, shortcomings and the way forward? Hydrogeol Journal. 14:637–647.
  12. Ines AVM and Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology. 138(1–4):44–53.
  13. Jyrkama MI and Sykes JF (2007) The impact of climate change on spatially varying groundwater recharge in the Grand River Watershed (Ontario). Journal of Hydrology. 338:237–250.
  14. Kundzewicz ZW, Mata LJ, Arnell NW, Döll P, Kabat P, Jiménez B, Miller KA, Oki T, Sen Z and Shiklomanov IA (2007) Freshwater resources and their management. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Parry ML, Canziani OF, Palutikof JP, van der Linden PJ and Hanson CE (Eds.), Cambridge University Press, Cambridge, UK, Pp: 173-210.
  15. Luoma S and Okkonen J (2014) Impact of future climate change and Baltic sea level rise on groundwater recharge, groundwater levels, and surface leakage in the Hanko aquifer in southern Finland. Journal of Water. 6 (12): 3671– 3700. doi:10.3390/w6123671
  16. McDonald MG and Harbaugh AW (1988) Techniques of water resources investigations reports, Book 6: Modeling techniques, U.S. Geological Survey, Reston, Virginia, 258 p.
  17. Morris BL, Lawrence ARL and Chilton PJC (2003) Groundwater and its susceptibility to degradation: a global assessment of the problem and options for management. Early Warning and Assessment Report Series, RS. 03-3. United Nations Environment Program, Nairobi, Kenya. 140 p.
  18. Nourani V, Komasi M and Mano A (2009) A multivariate ANN-Wavelet approach for rainfall– runoff modeling. Water Resource Management. 23: 2877–2894.
  19. Piani C, Haerter JO and Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology Journal. 99(1):187–92.
  20. Pindyck RS (2012) Uncertain outcomes and climate change policy. Journal of Environmental Economics and Management. 63(3):289–303.
  21. Russell S. Crosbie, Bridget R. Scanlon, Freddie S. Mpelasoka, Robert C. Reedy, John B. Gates and Zhang L (2013) Potential climate change effects on groundwater recharge in the high plains aquifers, USA. Water Resources Research Journal. 49: 1–16. doi:10.1002/wrcr.20292.
  22. Schnur R and Lettenmaier DP (1998) A case study of statistical downscaling in Australia using weather classification by recursive partitioning. Journal of Hydrology. 212–213: 362–379.
  23. Scibek J and Allen DM (2006) Modeled impacts of predicted climate change on recharge and groundwater levels. Water Resource Research Journal, 42, W11405. doi:10.1029/2005WR004742.
  24. Scibek J, Allen DM and Cannon A (2007) Groundwater–surface water interaction under scenarios of climate change using a high-resolution transient groundwater model. Journal of Hydrology. 333:165–181
  25. Semenov MA and Barrow EM (2002) LARS-WG: a stochastic weather generator for use in climate impact studies. Version 3.0 user manual, 28 p.
  26. Shah T, Burke J and Villholth K (2007) Groundwater: a global assessment of scale and significance. In: Molden D (Eds.), Water for food, water for Life: A comprehensive assessment of water management in agriculture. Earthscan London, and International Water Management Institute, Colombo, pp: 395-424.
  27. Teutschbein C and Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. Journal of Hydrology. 456–457:12–29.
  28. Toews MW and Allen DM (2009) Simulated response of groundwater to predicted recharge in a semi-arid region using a scenario of modeled climate change. Environmental Research Letters Journal. 4:035003. doi.org/10.1088/1748-9326/4/3/035003.
  29. van Roosmalen L, Christensen BSB and Sonnenborg TO (2007) Regional differences in climate change impacts on groundwater and stream discharge in Denmark. Vadose Zone Journal. 6(3):554–571.
  30. Wilby RL, Dawson CW and Barrow EM (2002) A decision support tool for the assessment of regional climate change impacts. Environmental Modelling and Software. 17 (2): 145–157.
  31. Yates D, Gangopadhyay S, Rajagopalan B and Strzepek K (2003) A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resource Research Journal. 39 (7), 1199. doi:10.1029/2002WR001769.
  32. Zektser IS and Loaiciga HA (1993) Groundwater fluxes in the global hydrologic cycle: Past, present, and future. Journal of Hydrology. 144: 405– 427.
  33. Zorita E and von Storch H (1999) The analog method – a simple statistical downscaling technique: comparison with more complicated methods. Journal of Climate. 12: 2474–2489.