The Effect of Climate Change on Volume of Water Resources and Transfer of Inter-Basin Water

Document Type : Research Paper

Authors

1 Associate Professor, Department of Physical Geography, University of Isfahan, Iran.

2 Ph.D. Student, Department of Physical Geography, University of Isfahan, Iran.

Abstract

Climate change is a serious challenge to human interest by its adverse effect on various sectors, such as water sources, agriculture, and energy. According to IPCC report, the average annual temperature of the earth has been raised between 0.3 to 0.6º because of the spread of the greenhouse gases, and this report predicts this amount will increase between 1 to 3.5º until 2100(Boberg et al ,2010).
To study the effect of greenhouse gases in the atmosphere and the oceanic-atmospheric, general circulation model in regional scale is the most efficient vehicle. These models have been developed to simulate the current climate. They also performed well in predicting future changes in the climate and simulating interactions of soil, atmosphere, and oceans (IPCC, 2007). The effect of climate change on water sources is assessed using rainfall-runoff models by simulating hydrological processes. Studying future climate change and its likely events will help planners and water sources administrators to cope with the future challenge. Considering these likely changes will contribute to objective planning toward optional operations. Predicting future runoff value is one of the most important factors about dam construction, water transferring, agricultural growth and industrial activities.
 Semenov (2008) assessed LARS-WG performance by data from 20 representative stations. Babaeian  et al, (2004) and Khaliliaqdam, et al( 2013), studied the effect of climate change on the hydroelectric reservoir of Jor Dam by the microclimate model LARS-WG and scenarios B1, B2, and A1B. Output results of the model showed that Tmin and Tmax will increase to the amount of o.3-0.6 degrees. As a result, available water reservoirs of behind the dams for hydroelectric generation are affected by the decreased rainfall. BaniHabib et al, (2016), simulated the input flow of Shahcheraghi Dam using the generator LARS-WG, data downscaling, and the function of artificial neural networks on output of LARS. They found that nightly and daily temperatures rise 1.1 and 1.2 degrees from 2015 to 2040, and rainfall will decrease by 9% during January. By simulating artificial neural networks, it was determined that the input flow will experience 2.4-4.1 % decrease based on different scenarios.

Keywords

References

1-    Abaurrea, J. and Cebrián, A.C., 2001. Trend and variability analysis of rainfall series and their extreme events. In Detecting and modelling regional climate change (pp. 191-201). Springer, Berlin, Heidelberg.
 
2-    Ashofteh, P. and A. R. Massah Bouani. 2010. Impact of Climate Change on Maximum Discharges: Case Study of Aidoghmoush Basin, East Azerbaijan. Journal of Science and Technology of Agriculture and Natural Resources; 14 (53) :28-38). (In Persian).
 
3-    Ashraf, B. Mousavi Baygi, M. Kamali G.A.  and  Davari, K., 2011. Prediction of Seasonal Variations of Climatological Parameters over Next 20 Years by Using Statistical Downscaling Method of HADCM3 Data (Case 4-Study: Khorasan Razavi Province), Journal of Water and Soil . 25(4): 940-952. (In Persian).
 
4-    Babaeian, I., Kwon, W.T. and Im, E.S., 2004. Application of weather generator technique for climate change assessment over Korea. Korea Meteorological Research Institute. Climate Research lab., 98pp.
 
5-    Balling, R.C., Kiany, K., Sadegh, M., Sen Roy, S. and Khoshhal, J., 2016. Trends in extreme precipitation indices in Iran: 1951–2007. Advances in Meteorology. Vol. 2016.pp.1-8.
 
6- Boberg, F., Berg, P., Thejll, P., Gutowski, W.J. and Christensen, J.H., 2010. Improved confidence in climate change projections of precipitation further evaluated using daily statistics from ENSEMBLES models. Climate dynamics, 35(7-8), pp.1509-1520.
 
7- Booij, M.J., Tollenaar, D., van Beek, E. and Kwadijk, J.C., 2011. Simulating impacts of climate change on river discharges in the Nile basin. Physics and Chemistry of the Earth, Parts A/B/C, 36(13), pp.696-709.
 
8- Dousti, M., Habibnezhad, M., Shahedi, K. and Yaghoubzadeh, M.H., 2013. Study of climate indices of Tamar River basin Golestan Province in terms of climate change using by LARS-WG model. Journal Earth Space Phys, 39(4), pp.177-189. (In Persian).
 
9- Driessen, T.L.A., Hurkmans, R.T.W.L., Terink, W., Hazenberg, P., Torfs, P.J.J.F. and Uijlenhoet, R., 2010. The hydrological response of the Ourthe catchment to climate change as modelled by the HBV model. Hydrology and Earth System Sciences, 14(4), pp.651-665.
 
10- Farajzadeh, M., 2013. Climate change effects on river discharge-case study sheshpir river, Geography and Environmental Planning Journal. 49(1): 5-8. (In Persian).
 
11- Hewitson, B.C. and Crane, R.G., 1996. Climate downscaling: techniques and application. Climate Research, 7(2), pp.85-95.
 
12- IPCC, 2007. Climate Change, The Scientific Basis, In Contribution of Working Group Ito the Third Assessment Report of Intergovernmental Panel of Climate Change; Editor by J.T.
 
13- Jamieson, P.D., Porter, J.R. and Wilson, D.R., 1991. A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research, 27(4), pp.337-350.
 
14- Johnson, G.L., Hanson, C.L., Hardegree, S.P. and Ballard, E.B., 1996. Stochastic weather simulation: Overview and analysis of two commonly used models. Journal of Applied Meteorology, 35(10), pp.1878-1896.
 
15- Kamal, A.R. and Massah Bavani, A.R., 2010. Climate Change and Variability Impact in Basin’s Runoff with Interference of tow hydrology models uncertainty, Journal of Water and Soil, 24(5), pp. 920-931. (In Persian).
 
16- Khaliliaqdam, N. Mosaedi, A. Soltani, A. and Kamkar, B., 2013. Evaluation of ability of LARS-WG model for simulating some weather parameters in Sanandaj. Journal of  Water and Soil Conservation. 19(4), pp. 85-102. (In Persian).
 
17- Bavani, A.M. and Morid, S., 2006. Impact of Climate Change on the Water Resources of Zayandeh Rud Basin. Journal of Water and Soil Science, 9(4), pp.17-28.. (In Persian).
 
18- Parajuli, P.B., 2010. Assessing sensitivity of hydrologic responses to climate change from forested watershed in Mississippi. Hydrological Processes, 24(26), pp.3785-3797.
 
19- Rahimi, D. 2009. 'The effect of single thunders on flood's management (Case study: Farsan Basin)', Geography and Environmental Planning, 20(3), pp. 85-100. (In Persian).
 
20- Semenov, M.A, and Barrow, E. M, 2002. LARS-WG a stochastic weather generator for use in climate impact studies, User’s manual; Version3.0, 27p.
 
21- Semenov, M.A., 2008. Simulation of extreme weather events by a stochastic weather generator. Climate Research, 35(3), pp.203-212.
 
22- Soleymani Nanadegani, M. Parsinejad, M. Araghinejad, Sh. and  Massah Bavani, A., 2011. Study on climate change effect on net irrigation requirement and yield for rain fed wheat (Case study: Behshahr), Journal of Water and Soil . 25(2), pp.  389-397. (In Persian).
 
23- Weichert, A. and Bürger, G., 1998. Linear versus nonlinear techniques in downscaling. Climate Research, 10(2), pp.83-93.
 
24- Wilby, R.L. and Harris, I., 2006. A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UK. Water Resources Research, 42(2), pp.  121- 134.
 
25- Wilby, R.L., Wigley, T.M.L., Conway, D., Jones, P.D., Hewitson, B.C., Main, J. and Wilks, D.S., 1998. Statistical downscaling of general circulation model output: A comparison of methods. Water Resources Research, 34(11), pp.2995-3008.
Irrigation Sciences and Engineering
Volume 42, Issue 3
October 2019
Pages 61-74

Files

History

  • Receive Date: 24 April 2017
  • Revise Date: 17 October 2017
  • Accept Date: 21 October 2017
  • Publish Date: 23 September 2019

Share

How to cite

Statistics