پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 360 |
| تعداد مقالات | 3,762 |
| تعداد مشاهده مقاله | 4,963,404 |
| تعداد دریافت فایل اصل مقاله | 3,331,394 |
Evaluation of Changes in Meteorological Parameters in Atrak Basin Considering Climate Change Status | ||
| Water Harvesting Research | ||
| دوره 4، شماره 2 - شماره پیاپی 6، اسفند 2021، صفحه 256-267 اصل مقاله (1.81 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2022.5287.1061 | ||
| نویسنده | ||
| Massoud Goodarzi* | ||
| Associate professor Soil Conservation & Watershed Management Research Institute SCWMRI, AREEO, Tehran, Iran | ||
| چکیده | ||
| Projection of climate change in basins is very important for determining water capacity, water resources management as well as watershed management studies and environmental hazards. Therefore, in this study, temperature and precipitation changes were projected in Atrak basin, in the northern Khorasan province. For this purpose, the output projection of canESM2 global model were used considering three scenarios of Representative Concentration Pathway (RCP) 2.6, RCP 4.5 and RCP 8.5 using SDSM as downscaling model and temperature and precipitation changes in the period (2021-2050) were compared with the base period (1995-2019). For calibration and validation of SDSM model, station observational data and NCEP/NCAR data were evaluated. MAE, MSE, RMSE and R2 correlation indices were determined to clarify the performance of the model. The results showed that the SDSM model has a high ability to simulate temperature and precipitation changes in the study area. According to the results of the CanESM2 model, in the future periods, temperature and precipitation would be increased compared to the base period, which might be from 0.9 and 1.0 degree Celsius for minimum and maximum temperatures, respectively. Most of the temperature changes would be related to the eastern parts of the study area while, the amount of precipitation in the basin would be increased from 1.5 up to 11.7 percent, the largest increase of which would be related to the central and northern regions of the basin. The highest and lowest temperature and precipitation changes in the basin are predicted based on RCP 8.5 and RCP 2.6 scenarios. | ||
| کلیدواژهها | ||
| Atrak؛ Climate change؛ RCP؛ SDSM | ||
| مراجع | ||
|
Shaemi, A., & Habibinokhandan, M. (2009). Global warming and its ecological consequences, Ferdowsi university press. Sedaqatkerdar, A., & Fatahi, E. (2008). Drought early warning indices in Iran, Geography and development, 6(11), 59-76. Samadi, S. Z., Massah Bavani, A., (2008), "Introducing artificial naural network methods and SDSM model for statistical downscaling of temperature & precipitation", 3rd conference on water resources management of Iran, Tabriz university, 14th October. Azizi, Q., Ahmadi, M., & Kardavani, P. (2019). Future outlook of temperature changes using CMIP5 and RCP scenarios: a case study on selected stations in Ilam, journal of physical geography, 43(12), 71-88. Karamouz, M., Ramezani, F., & Razavi, S. (2006). Long-term prediction of precipitation using meteorologic signals using ANN, 7th international congress on civil engineering, Tehran, pp. 11. Kasiri, M., Goodarzi, M., Janbaz Qobadi, G., & Motovali, S. (2020). Future Projection of temperature and precipitation changes in the southern coast of Caspian Sea. Physical Geography Quarterly, 13(47), 35-51. Kamal, A., & Massah Bavani, A. (2010). Climate variability and change impacts on basin runoff considering Hydrological models Uncertainty, journal of water and soil, 5, 920-931. Goodarzi, M., Hosseini, A., & Mesgari E. (2016). Climate models, Azarkelk press, Zanjan, Iran. Goodarzi, Massoud, Hosseini, Seyed Asaad, Hosseini, Majid (2017), Prediction of long-term rainfall changes in Saqez using general circulation models of barley, 6th Conference on Rainwater Catchment Surface Systems, Islamic Azad University, Khomeinishahr University, 10p. Goodarzi, M., Salahi, B., & Hosseini, A. (2018). Future outlook of temperature and precipitation changes using statistical downscaling model: a case study Urmia lake basin. Geographical space, Islamic Azad university Ahar branch, 64(18), 41-60. Malmir, M., Rezapour, M., Sharif Azari, S., & Qandehari, Q. (2016). Evaluation of climate change impacts on Qaresu basin runoff using statistical downscaling of HadCM3 and dynamic neural network, journal of water and soil conservation, 3(3), 317-326. Naderi, S., Goodarzi, M., & Qadami Dehno, M. (2017). Climate change impacts on climatic parameters in Seymareh basin, science and engineering watershed management journal, 39(11), 69-76. Houshyar, M., Sobhani, B., Hosseini, S. A. (2018). Future projection of Maximum Temperature in Urmia through Downscaling output of CanESM2 Model. Geography and Planning, 22(63), 305-325. Bates, B., Kundzewicz, Z.W., Wu, S., & Palutikof, J. (2008). Climate Change and Water. Intergovernmental Panel on Climate Change (IPCC). 200 p. Beecham, S., Rashid, M., & Chowdhury R.K. (2014). Statistical downscaling of multi-site daily rainfall in a South Australian catchment using a Generalized Linear Model. International Journal of Climatology, 34(14): 3654-3670. Dibike, Y.B., & Coulibaly, P. (2005). Hydrologic impact of climate change in the Saguenay Watershed: Comparison of down-scaling Methods and Hydrologic Models. Journal of Hydrologic, 307: 145–163. Dimri, A.P., Kumar, D., Choudhary, A., & Maharana, P. (2018). Future changes over the Himalayas: Maximum and minimum temperature, Global and Planetary Change, 162, 212-234. Goodarzi, M., Jahanbakhsh, S., Rezaee, M., Ghafouri, A., & Mahdian, M H. (2011). Assessment of Climate Change Statistical Downscaling Methods in a Single Site in Kermanshah, Iran, American-Eurasian Journal. Agric. & Environ. Sci, 6 (5), 564-572. Hardy, J.T. (2003). Climate Change: Causes, Effects, and Solutions. John Wiley & Sons, Ltd. 247 P. Heydari, Sh., Hosseini, S.A., & Heydari, A. (2020). Investigating the effects of climate change on stream flows of Urmia Lake basin in Iran, Modeling Earth Systems and Environment, 1, 329-339. Hu, T.S., Lam, K.C., & Ng, S.T. (2001). River flow time series prediction with a range dependent neural network. Hydrological Science Journal, 46, 729-745. Cha, D. H., Lee, D. K., Jin, C. S., Kim, G., Choi, Y., Suh, M. S., ... & Kang, H. S. (2016). Future changes in summer precipitation in regional climate simulations over the Korean Peninsula forced by multi-RCP scenarios of HadGEM2-AO. Asia-Pacific Journal of Atmospheric Sciences, 52(2), 139-149. Kilsby, C.G., Jones, P.D., Burton, A., Ford, A.C., Fowler, H.J., Harpham, C., James, P., Smith, A., & Wilby, R.L. (2007). A daily weather generator for use in climate change studies. Environmental Modelling and Software, 22: 1705–1719. Lane, M. E., Kirshen, P. H., & Vogel, R. M. (1999). Indicators of impact of global climate change on U.S. water resources. ASCE, J. Water Resour. Planning and Manag. 125, 194-204. Leong Tan, M. L., Yusop, Z., Chua, V. P., & Chan, N. W. (2017). Climate change impacts under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia. Atmospheric Research, 189, 1-10. Lin, J. Y., Cheng, C. T., & Chau, K. W. (2006). Using support vector machines for long-term discharge prediction. Hydrological sciences journal, 51(4), 599-612. Nilawar, A. P., & Waikar, M. L. (2019). Impacts of climate change on streamflow and sediment concentration under RCP 4.5 and 8.5: A case study in Purna river basin, India. Science of the total environment, 650, 2685-2696. Panol, T.J., & Loret, F. (1998). Climatic warning hazard and wildfire occurrence in coastal eastern Spain. Climate Change, 38, 345-357. Tatsumi, K., Oizumi, T., & Yamashiki, Y. (2013). Introduction of daily minimum and maximum temperature change signals in the Shikoku region using the statistical downscaling method by GCMs. Hydrological Research Letters, 7(3), 48-53. Wilby, R. L., & 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). Wilby, R., Dawson, C.W., Barrow, E. M., 2002, SDSM – A Decision Support Tool for the Assessment of Regional Climate Change Impacts, Environmental Modelling and Software, 17(2): 145-157. Wilby, R.L., & Dawson. W.C. (2007). SDSM 4.2- A decision support tool for the assessment of regional climate change impacts, SDSM manual version 4.2, Environment Agency of England and Wales: 94pp Wilby, R.L., Tomlinson, O, J., & Dawson, C.W. (2007). Multi-site simulation of precipitation by condition resampling. Journal of climate Research, 23, 183-194. | ||
|
آمار تعداد مشاهده مقاله: 481 تعداد دریافت فایل اصل مقاله: 1,066 |
||
نماد الکترونیک
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب