Climate Change Effects on Soil Freezing Depth in a Mountainous Region and a Semi-Arid Climate on the Malayer Plain | ||
| پژوهش های آبخیزداری | ||
| Article 5, Volume 31, Issue 3 - Serial Number 120, September 2018, Pages 40-55 PDF (910.72 K) | ||
| Document Type: Research | ||
| DOI: 10.22092/wmej.2018.121655.1113 | ||
| Authors | ||
| Elham Kalhor1; Hamid Nouri* 2; Alireza Ildoromi3 | ||
| 1M.Sc. Department of Range and Watershed Management, Faculty of Natural Resources and Environments, Malayer University, Malayer, Iran | ||
| 2Assistant Professor, Department of Range and Watershed Management, Faculty of Natural Resources and Environments, Malayer University, Malayer, Iran | ||
| 3Associate Professor, Department of Range and Watershed Management, Faculty of Natural Resources and Environments, Malayer University, Malayer, Iran | ||
| Abstract | ||
| Soil freezing depth is an important parameter in the management of soil water availability in agriculture and horticulture. The purpose of this study was to investigate the effect of global warming and climate change on the freezing depth by using different scenarios on the Malayer Plain, which is the center of grape and raisin production in the country. Daily minimum and maximum air temperatures, sunshine hours, and precipitation data were collected from the Malayer Synoptic Station from 1995 to 2013 and used to simulate the near future climate using the HADCM3 and LARS-WG models based on the optimistic, pessimistic, and moderate scenarios. Performances of these models were evaluated by comparing observation and simulation data using the R2, NSE, RMSE, and MAE indices. The results showed that these models have the necessary efficiency to predict the production of daily data in the region. The air freezing index was estimated by using the Norwegian, Finnish, and American methods based on the mean daily air temperature. Soil freezing depth was estimated by the McKeown and Standard model based on the soil texture at different depths and air temperatures .Annual precipitation, maximum and minimum temperatures will rise under the three scenarios A1B, A2, and B1. The air temperature will increase in winter more than the other seasons, while the absolute value of minimum temperature changes will be higher than that of the maximum temperature. The RSME and CRM indicators in the base and future periods indicate that the Norwegian method, and more, than that the Finnish method, are suitable for estimating the soil freezing depth (at the 5% level) in this semi-arid and mountainous Zagros Region. Application of this method overestimates a deepe freezing depth for the future. Due to the climate change, the depth of freezing will be reduced by the end of 2030 in this important agricultural plain. | ||
| Keywords | ||
| freezing depth; LARS-WG model; Malayer; McKeown method; standard method | ||
| References | ||
|
Babaian A, Najafi Nik Z. 2007. Introduction and evaluation of LARS-WG model for modeling meteorological parameters of Khorasan province, statistical period (1961–2003). Nivar: Scientific and Technical Journal of the Iran Meteorological Organization, 62–63(2): 50–64. Barnett T, Malone R, Pennell W, Stammer D, Semtner B, Washington W. 2004. The effects of climate change on water resources in the West: Introduction and overview. Climatic Change, 62: 1–11. Cary JW, Campbell GS, Papendick RI. 1978. Is the soil frozen or not?. An algorithm using weather records. Water Resour. Res, 14(6): 1117–1122. Firozan M. 2010. Statistical analysis of glaciers and minimum temperatures with emphasis on penetration depth. Case study: Bojnourd City. Master's thesis, Tarbiat Moallem University, Faculty of Geography, Sabzevar, Iran. Fridleifsson IB, Bertani R, Huenges E. Lund JW, Ragnarsson A, Rybach L. 2008. The possible role and contribution of geothermal energy to the mitigation of climate change. IPCC scoping meeting on renewable energy sources, pp. 59–80. 6. Gaetano T, Cameron M, Wilks DS. 2010. Physical simulation of Maximum Seasonal Soil Freezing Depth in the United States Using Routine weather observations. Journal of Applied Meteorology, 40(1): 546–555. Golshan S, Raeini Sirjaz M, Norouz Velashedi R. 2014. Investigation and detection of global warming effects on changes in soil temperature and its estimation by regression correlation method. Case study: Kerman. Water and Soil Conservation Research Journal, 22 (4):121–138. Goodarzi M, Soltani M. 2018. The prediction of the effect of climate change on the temperature and precipitation of the decade of 2020. Journal of Rainfall Systems, 3 (2):31–38. Goodarzi M, Khosravaniyan J, Hejazi S. 2015. Application of the LARS-WG model for forecasting the meteorological parameters in Gharosso Basin. Geographical Space Scientific-Research Quarterly, 5 (51): 263–279. Iwata Y, Nemoto M, Hasegawa S, Yanai Y, Kuwao K, Tomoyoshi H. 2011. Influence of rain, air temperature, and snow cover on subsequent spring-snowmelt infiltration into thin frozen soil layer in northern Japan. Journal of Hydrology, 401(3–4): 165– 176. Khalili Aghdam N, Assadidi A, Soltani A, Kamkar B. 2012. Validity of the LARS-WG model to predict some Sanandaj atmospheric parameters. Journal of Soil and Water Conservation, 19(4):12–26. Koewn M, Klark M, Matheson D. 1988. Frost penetration and thermal regim in dry gravel. Cold Reg. Eng, 2(3):111–123. Najafi Mood M, Alizadeh A, Mohammadian A, Mousavi J. 2007. Investigating the relationship between air temperature and soil temperature in different depths and also, estimating the depth of frost. Case study: Razavi Khorasan Province. Journal of Water and Soil Science (Agricultural Sciences and Technology), 22 (2): 456–466. Nazari Pouya H, Kardavani P, Faraji Rad A. 2017. Investigation and evaluation of climate change effects on hydroquilithic parameters of Ekbatan Dam Basin (Hamadan Province). Ecocheidrology, 3(2): 181–194. Reddy KS, Kumar M, Maruthi V, Umesha B, Vijayalaxmi, Nageswar Raocvk. 2014. Climate change analysis southern Telangana region, Andhra Pradesh using LARS- WG moel. Jornal of Currebt Science, 107(1): 54– 62. Salajeghe M, Sana'e Nejad H, Bakhtiari B, Araghizadeh M. 2013. Glacial depth zoning and determination of the most suitable estimation method using screen temperature in Khorasan Razavi. Second International Conference on Plant, Water, Soil and Air Modeling, Kerman: Advanced Industrial and Advanced Studies University, pp. 154–167. Semenov MA, Barrow EM. 2002. LARS-WG a Stochastic weather generator for use in climate impact studies", User Manual, Version 3.0, p 28. Sobhani B, Fatiminia F. 2014. Modeling of climate factors in South Khorasan Province. Natural Geography Researches, 46(3): 313–332. Steurer P, Crandell J. 1995. Comparison of Methods used to create estimate of air-freezing index. Journal of Cold Regions Engineering, 9(2): 64– 75. Tsilingiridis G, Papakostas K. 2014. Investigating the relationship between air and ground temperature variations in shallow depths in northern Greece.Energy, 73(3): 1007– 1016. Xu, HN, Spitler JD. 2014. The relative importance of moisture transfer, soil freezing and snow cover on ground temperature predictions. Renewable Energy, 72: 1–11 Yazdani V, Ghahraman B, Farahi G, Nouri H. 2011. Modeling the depth of soil temperature with the help of meteorological parameters. Journal of Soil and Water Conservation Research, 19 (4): 23–39. Zabul Abbasi F, Melbosi Sh, Babayan A, Barani R. 2009. Prediction of climate change in Southern Khorasan in 2010–2010 by using the ECHO-G Output Statistical Exponential Scale. Journal of Water and Soil (Agricultural Science and Technology), 24 (2): 218–233. Zhou Y, Guo D. 2000. The zonal and regional conditions for development of frozen ground. Science Press of China. Beijing, 3(2): 56–69. | ||
|
Statistics Article View: 868 PDF Download: 1,044 |
||
Journal management system. designed by sinaweb