ارزیابی روش‌های توسعه مدل اقلیمی چندگانه برمبنای CMIP5 برای بررسی پتانسیل استحصال آب از رطوبت هوا

Ahmed, K., Sachindra, D. A., Shahid, S., Iqbal, Z., Nawaz, N., & Khan, N. (2020). Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmospheric Research, 236, 104806. https://doi.org/10.1016/j.atmosres.2019.104806.

Alizadeh, A. (2010). Principle of Applied Hydrology. Emam Reza University Press. (In Persian).

Bisht, D. S., Sridhar, V., Mishra, A., Chatterjee, C., & Raghuwanshi, N. S. (2019). Drought characterization over India under projected climate scenario. International Journal of Climatology, 39(4), 1889–1911. https://doi.org/10.1002/joc.5922.

Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R. K., Kwon, W. T., Laprise, R., MagañaRueda, V., Mearns, L., Menéndez, C. G., Räisänen, J., Rinke, A., Sarr, A., & Whetton, P. (2007). Regional Climate Projections. In Climate Change 2007, The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.

Das, S., Das, J., & V.Nanduri, U. (2021). Identification of Future Meteorological Drought Hotspots over Indian Region: A Study Based on NEX‐GDDP Data. International Journal of Climatology. https://doi.org/https://doi.org/10.1002/joc.7145.

Dunn, R. J. H., Willett, K. M., Ciavarella, A., & Stott, P. A. (2017). Comparison of land surface humidity between observations and CMIP5 models. Earth System Dynamics, 8(3), 719–747. https://doi.org/10.5194/esd-8-719-2017.

Gaitán, E., Monjo, R., Pórtoles, J., & Pino-Otín, M. R. (2020). Impact of climate change on drought in Aragon (NE Spain). Science of the Total Environment, 740. https://doi.org/10.1016/j.scitotenv.2020.140094.

Giorgi, F., & Gutowski, W. J. (2016). Coordinated experiments for projections of regional climate change. Current Climate Change Reports, 2, 202–210.

Haile, G. G., Tang, Q., Hosseini-Moghari, S. M., Liu, X., Gebremicael, T. G., Leng, G., Kebede, A., Xu, X., & Yun, X. (2020). Projected Impacts of Climate Change on Drought Patterns Over East Africa. Earth’s Future, 8(7), 1–23. https://doi.org/10.1029/2020EF001502.

Holland, J. H. (1975). Adaptations in Natural and Artificial Systems. University of Michigan Press.

Hosseinabadi, S., Yaghoobzadeh, M., Amirabadizadeh, M., & Foroozanmehr, M. (2020). Meteorological Drought Assessment in Future Periods by Using of the Data of the Fifth Report of Climate Change (Case Study: Zabol and Shiraz Cities). Journal of Arid Regions Geographics Studies, 10(40), 78–87. (In Persian).

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.

IPCC. (2013). Climate change 2013: The physical science basis IPCC working group I contribution to AR5. Retrieved October 18, 2018. http://www.ipcc.ch/report/ar5/wg1/.

Jain, S., Salunke, P., Mishra, S. K., & Sahany, S. (2019). Performance of CMIP5 models in the simulation of Indian summer monsoon. Theoretical and Applied Climatology, 137, 1429–1447. https://doi.org/10.1007/s00704-018-2674-3.

Jiang, R., Liang, J., Zhao, Y., Wang, H., Xie, J., Lu, X., & Li, F. (2021). Assessment of vegetation growth and drought conditions using satellite-based vegetation health indices in Jing-Jin-Ji region of China. Scientific Reports, 11(1), 1–18. https://doi.org/10.1038/s41598-021-93328-z.

KarimpourReihan, M., Alizadeh, M., & Kamalian, S. (2016). Selection of suitable sites for water harvesting from air humidity in Bushehr province using GIS. Quarterly of Geography & Regional Planning, 6(25), 43–51. (In Persian).

Khan, J. U., Islam, A. K. M. S., Das, M. K., Mohammed, K., Bala, S. K., & Islam, G. M. T. (2020). Future changes in meteorological drought characteristics over Bangladesh projected by the CMIP5 multi-model ensemble. Climatic Change, 162(2), 667–685. https://doi.org/10.1007/s10584-020-02832-0.

Kharuk, V. I., Ranson, K. J., & Dvinskaya, M. L. (2007). Evidence of evergreen conifer invasion into larch dominated forests during recent decades in central Siberia. Eurasian Journal of Forest Research, 10(2), 163–171. https://doi.org/http://hdl.handle.net/2115/30308.

Koohi, S., & Ramezani Etedali, H. (2022). Future meteorological drought conditions of southwestern Iran based on the NEX-GDDP climate dataset. Journal of Arid Land.

Mahmoudi, P., KhajehAmiriKhaledi, C., & SalariFanodi, M. R. (2016). Examining the feasibility of water harvesting from air humidity in the Southern province of Sistan and Baluchestan. Journal of Water and Soil Conservation, 23(2), 253-263. https://doi.org/10.22069/JWFST.2016.3067. (In Persian).

Mirakbari, M., Mesbahzadeh, T., MohseniSaravi, M., Khosravi, H., & MortezaieFarizhendi, G. (2018). Performance of Series Model CMIP5 in Simulation and Projection of Climatic Variables of Rainfall, Temperature and Wind Speed (Case Study: Yazd). Physical Geography Research Quarterly, 50(3), 593–609. https://doi.org/10.22059/JPHGR.2018.248177.1007156. (In Persian).

Mohan, S., & Bhaskaran, P. K. (2019). Evaluation of CMIP5 climate model projections for surface wind speed over the Indian Ocean region. Climate Dynamics, 53(9–10), 5415–5435. https://doi.org/10.1007/s00382-019-04874-2.

Pearson, K. (1896). Mathematical Contributions to the Theory of Evolution.–On a Form of Spurious Correlation Which May Arise When Indices Are Used in the Measurement of Organs. Proceedings of the Royal Society of London, 60, 489–498.

Raghavan, S. V., Liu, J., Nguyen, N. S., Vu, M. T., & Liong, S. Y. (2018). Assessment of CMIP5 historical simulations of rainfall over Southeast Asia. Theoretical and Applied Climatology, 132(3–4), 989–1002. https://doi.org/10.1007/s00704-017-2111-z.

Rathore, P., Roy, A., & Karnatak, H. (2019). Modelling the vulnerability of Taxus wallichiana to climate change scenarios in South East Asia. Ecological Indicators, 102(November 2017), 199–207. https://doi.org/10.1016/j.ecolind.2019.02.020

Shagega, F. P., Munishi, S. E., & Kongo, V. M. (2019). Prediction of future climate in Ngerengere river catchment, Tanzania. Physics and Chemistry of the Earth, 112, 200–209. https://doi.org/10.1016/j.pce.2018.12.002

Shen, M.-L., Keenlyside, N., Selten, F., Wiegerinck, W., & Duane, G. S. (2016). Dynamically combining climate models to “supermodel” the tropical Pacific. Geophysical Research Letters, 43, 359–366. https://doi.org/10.1002/2015GL066562

Sood, A., & Smakhtin, V. (2015). Global hydrological models: a review. Hydrological Sciences Journal, 60(4), 549–565. https://doi.org/10.1080/02626667.2014.950580

Stanski, H. R., Wilson, L. J., & Burrows, W. R. (1989). Survey of Common Verification Methods in Meteorology (2nd ed.). World Meteorological Organization.

Su, B., Jian, D., Li, X., Wang, Y., Wang, A., Wen, S., Tao, H., & Hartmann, H. (2017). Projection of actual evapotranspiration using the COSMO-CLM regional climate model under global warming scenarios of 1.5° C and 2.0° C in the Tarim River basin, China. Atmospheric Research, 196, 119–128.

Swain, S., & Hayhoe, K. (2015). CMIP5 projected changes in spring and summer drought and wet conditions over North America. Climate Dynamics, 44(9–10), 2737–2750. https://doi.org/10.1007/s00382-014-2255-9

Wang, B., Zheng, L., Liu, D. L., Ji, F., Clark, A., & Yu, Q. (2018). Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia. International Journal of Climatology, 38(13), 4891–4902. https://doi.org/10.1002/joc.5705

Wilhite, D. A., Sivakumar, M. V. K., & Pulwarty, R. (2014). Managing drought risk in a changing climate: The role of national drought policy. Weather and Climate Extremes, 3(March 2013), 4–13. https://doi.org/10.1016/j.wace.2014.01.002

Yousefi, B., & BoroomandNasab, S. (2015). Desalination Using the Condensation Irrigation System, A Case Study of the Research Farm of Shahid Chamran University of Ahvaz. Water and Wastewater Consulting Engineers, 26(3), 127–133. (In Persian).

Zhang, S., Chen, F., He, X., & Liu, B. (2017). A new downscaling approach and its performance with bias correction and spatial disaggregation as contrast. Journal of Water and Climate Change, 8(4), 675–690. https://doi.org/10.2166/wcc.2017.010