3702. Climate change science
| Title | 3702. Climate change science |
|---|---|
| Parent | 37. Earth Sciences |
Latest research outputs
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Return period and Pareto analyses of 45 years of tropical cyclone data (1970-2014) in the Philippines
Espada, Rudolf. 2018. "Return period and Pareto analyses of 45 years of tropical cyclone data (1970-2014) in the Philippines." Applied Geography. 97, pp. 228-247. https://doi.org/10.1016/j.apgeog.2018.04.018Article
Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition
Prasad, Ramendra, Deo, Ravinesh C., Li, Yan and Maraseni, Tek. 2018. "Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition." Geoderma. 330, pp. 136-161. https://doi.org/10.1016/j.geoderma.2018.05.035Article
Non-tuned data intelligent model for soil temperature estimation: a new approach
Sanikhani, Hadi, Deo, Ravinesh C., Yaseen, Zaher Mundheer, Eray, Okan and Kisi, Ozgur. 2018. "Non-tuned data intelligent model for soil temperature estimation: a new approach." Geoderma. 330, pp. 52-64. https://doi.org/10.1016/j.geoderma.2018.05.030Article
Australian rainfall trends and their relation to the southern oscillation index
Chowdhury, R. K. and Beecham, S.. 2010. "Australian rainfall trends and their relation to the southern oscillation index." Hydrological Processes. 24 (4), pp. 504-514. https://doi.org/10.1002/hyp.7504Article
Temporal characteristics and variability of point rainfall: a statistical and wavelet analysis
Beecham, S. and Chowdhury, R. K.. 2010. "Temporal characteristics and variability of point rainfall: a statistical and wavelet analysis." International Journal of Climatology. 30 (3), pp. 458-473. https://doi.org/10.1002/joc.1901Article
Influence of SOI, DMI and Niño3.4 on South Australian rainfall
Chowdhury, Rezaul K. and Beecham, Simon. 2013. "Influence of SOI, DMI and Niño3.4 on South Australian rainfall." Stochastic Environmental Research and Risk Assessment. 27 (8), pp. 1909-1920. https://doi.org/10.1007/s00477-013-0726-xArticle
Drought modelling based on artificial intelligence and neural network algorithms: a case study in Queensland, Australia
Dayal, Kavina, Deo, Ravinesh and Apan, Armando A.. 2017. "Drought modelling based on artificial intelligence and neural network algorithms: a case study in Queensland, Australia." Filho, Walter Leal (ed.) Climate change adaptation in Pacific countries:fostering resilience and improving the quality of life. Springer. pp. 177-198Edited book (chapter)
Statistical downscaling of multi-site daily rainfall in a South Australian catchment using a Generalized Linear Model
Beecham, Simon, Rashid, Mamunur and Chowdhury, Rezaul 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), pp. 3654-3670. https://doi.org/10.1002/joc.3933Article
Statistical downscaling of rainfall: a non-stationary and multi-resolution approach
Rashid, Md. Mamunur, Beecham, Simon and Chowdhury, Rezaul Kabir. 2016. "Statistical downscaling of rainfall: a non-stationary and multi-resolution approach." Theoretical and Applied Climatology. 124 (3-4), pp. 919-933. https://doi.org/10.1007/s00704-015-1465-3Article
Statistical downscaling of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment
Rashid, Md. Mamunur, Beecham, Simon and Chowdhury, Rezaul K.. 2015. "Statistical downscaling of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment." Science of the Total Environment. 530-531, pp. 171-182. https://doi.org/10.1016/j.scitotenv.2015.05.024Article
Decadal scale relationship between indices of climate variability and Australian rainfall
Stilgoe, Bernard. 2016. Decadal scale relationship between indices of climate variability and Australian rainfall. Masters Thesis Master of Science (Research). University of Southern Queensland.Masters Thesis
Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model
Rashid, Md. Mamunur, Beecham, Simon and Chowdhury, Resaul Kabir. 2017. "Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model." Theoretical and Applied Climatology. 130 (1-2), pp. 453-466. https://doi.org/10.1007/s00704-016-1892-9Article
Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region
Mouatadid, Soukayna, Raj, Nawin, Deo, Ravinesh C. and Adamowski, Jan F.. 2018. "Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region." Atmospheric Research. 212, pp. 130-149. https://doi.org/10.1016/j.atmosres.2018.05.012Article
Re-imagining standard timescales in forecasting precipitation events for Queensland’s grazing enterprises
McCarthy, E., Deo, R. C., Li, Y. and Maraseni, T.. 2017. "Re-imagining standard timescales in forecasting precipitation events for Queensland’s grazing enterprises." Syme, G., Hatton MacDonald, D., Fulton, B. and Piantadosi, J. (ed.) 22nd International Congress on Modelling and Simulation (MODSIM2017). Hobart, Australia 03 - 08 Dec 2017 Australia. Modelling and Simulation Society of Australia and New Zealand . https://doi.org/10.36334/modsim.2017.L1.mccarthyPaper
Wavelet analysis–artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China
Wen, Xiaohu, Feng, Qi, Deo, Ravinesh C., Wu, Min and Si, Jianhua. 2017. "Wavelet analysis–artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China." Hydrology Research: an international journal. 48 (6), pp. 1710-1729. https://doi.org/10.2166/nh.2016.396Article
Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors
Prasad, Ramendra, Deo, Ravinesh C., Li, Yan and Maraseni, Tek. 2018. "Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors." Soil and Tillage Research. 181, pp. 63-81. https://doi.org/10.1016/j.still.2018.03.021Article
Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons
Yaseen, Zaher Mundher, Fu, Minglei, Wang, Chen, Mohtar, Wan Hanna Melini Wan, Deo, Ravinesh C. and El-Shafie, Ahmed. 2018. "Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons." Water Resources Management. 32 (5), pp. 1883-1899. https://doi.org/10.1007/s11269-018-1909-5Article
Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, northwest China
Yang, Linshan, Feng, Qi, Yin, Zhenliang, Wen, Xiaohu, Si, Jianhua, Li, Changbin and Deo, Ravinesh C.. 2017. "Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, northwest China." Hydrological Processes. 31 (5), pp. 1100-1112. https://doi.org/10.1002/hyp.11098Article
Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey
Ghorbani, Mohammad Ali, Deo, Ravinesh C., Karimi, Vahid, Yaseen, Zaher Mundher and Terz, Ozlem. 2018. "Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey." Stochastic Environmental Research and Risk Assessment. 32 (6), pp. 1683-1697. https://doi.org/10.1007/s00477-017-1474-0Article
Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran
Ghorbani, M. A., Deo, Ravinesh C., Yaseen, Zaher Mundher, Kashani, Mahsa H. and Mohammadi, Babak. 2018. "Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran." Theoretical and Applied Climatology. 133 (3-4), pp. 1119-1131. https://doi.org/10.1007/s00704-017-2244-0Article
Statistical evaluation of rainfall time series in concurrence with agriculture and water resources of Ken River basin, Central India (1901–2010)
Meshram, Sarita Gajbhiye, Singh, Sudhir Kumar, Meshram, Chandrashekhar, Deo, Ravinesh C. and Ambade, Balram. 2018. "Statistical evaluation of rainfall time series in concurrence with agriculture and water resources of Ken River basin, Central India (1901–2010)." Theoretical and Applied Climatology. 134, pp. 1231-1243. https://doi.org/10.1007/s00704-017-2335-yArticle
Carbon dioxide fluxes and their environmental controls in a riparian forest within the hyper-arid region of Northwest China
Ma, Xiaohong, Feng, Qi, Yu, Tengfei, Su, Yonghong and Deo, Ravinesh C.. 2017. "Carbon dioxide fluxes and their environmental controls in a riparian forest within the hyper-arid region of Northwest China." Forests. 8 (10), pp. 1-17. https://doi.org/10.3390/f8100379Article
Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
Yaseen, Zaher Mundher, Ebtehaj, Isa, Bonakdari, Hossein, Deo, Ravinesh C., Mehr, Ali Danandeh, Mohtar, Wan Hanna Melini Wan, Diop, Lamine, El-Shafie, Ahmed and Singh, Vijay P.. 2017. "Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model." Journal of Hydrology. 554, pp. 263-276. https://doi.org/10.1016/j.jhydrol.2017.09.007Article
Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA
Yaseen, Zaher Mundher, Ghareb, Mazen Ismaeel, Ebtehaj, Isa, Bonakdari, Hossein, Siddique, Ridwan, Heddam, Sali, Yusif, Ali A. and Deo, Ravinesh. 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA." Water Resources Management. 32 (1), pp. 105-122. https://doi.org/10.1007/s11269-017-1797-0Article
Improving the use of climate information in decision-making
Hewitt, Chris D., Stone, Roger C. and Tait, Andrew B.. 2017. "Improving the use of climate information in decision-making." Nature Climate Change. 7 (9), pp. 614-616. https://doi.org/10.1038/nclimate3378Notes or commentaries
Separation of the Climatic and Land Cover Impacts on the Flow Regime Changes in Two Watersheds of Northeastern Tibetan Plateau
Yang, Linshan, Feng, Qi, Yin, Zhenliang, Deo, Ravinesh C., Wen, Xiaohu, Si, Jianhua and Li, Changbin. 2017. "Separation of the Climatic and Land Cover Impacts on the Flow Regime Changes in Two Watersheds of Northeastern Tibetan Plateau." Advances in Meteorology. 2017, pp. 1-15. https://doi.org/10.1155/2017/6310401Article
Changes in climatic elements in the Pan-Hexi region during 1960–2014 and responses to global climatic changes
Wei, Liu, Feng, Qi and Deo, Ravinesh C.. 2018. "Changes in climatic elements in the Pan-Hexi region during 1960–2014 and responses to global climatic changes." Theoretical and Applied Climatology. 133 (1-2), pp. 405-420. https://doi.org/10.1007/s00704-017-2194-6Article
Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones
Nguyen-Huy, Thong, Deo, Ravinesh C., An-Vo, Duc-Anh, Mushtaq, Shahbaz and Khan, Shahjahan. 2017. "Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones." Agricultural Water Management. 191, pp. 153-172. https://doi.org/10.1016/j.agwat.2017.06.010Article
Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm
Prasad, Ramendra, Deo, Ravinesh C., Li, Yan and Maraseni, Tek. 2017. "Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm." Atmospheric Research. 197, pp. 42-63. https://doi.org/10.1016/j.atmosres.2017.06.014Article
Spatial variation in springtime temperature index values during ENSO and IOD events shows non-equivalent phase response for viticultural regions in Australia
Jarvis, C., Darbyshire, R., Goodwin, I., Barlow, E. and Eckard, R.. 2018. "Spatial variation in springtime temperature index values during ENSO and IOD events shows non-equivalent phase response for viticultural regions in Australia." Agriculture and Forest Meteorology. 250-251, pp. 217-225. https://doi.org/10.1016/j.agrformet.2017.12.261Article
Influence of El Niño-Southern Oscillation and the Indian Ocean Dipole on winegrape maturity in Australia
Jarvis, C., Darbyshire, R., Eckard, R., Goodwin, I. and Barlow, E.. 2018. "Influence of El Niño-Southern Oscillation and the Indian Ocean Dipole on winegrape maturity in Australia ." Agriculture and Forest Meteorology. 248, pp. 502-510. https://doi.org/10.1016/j.agrformet.2017.10.021Article
Food systems and climate change: impact and adaptation in cropping and livestock
Ghahramani, Afshin and Seneweera, Saman. 2018. "Food systems and climate change: impact and adaptation in cropping and livestock." Zeunert, Joshua and Waterman, Tim (ed.) Routledge handbook of landscape and food. Milton Park, United Kingdom. Routledge. pp. 271-277Edited book (chapter)
Connecting the dots - climate information has no value unless it changes a management decision: Some illustrative cases
Stone, Roger C.. 2009. "Connecting the dots - climate information has no value unless it changes a management decision: Some illustrative cases." Wesley, Michael, Xinsheng, Wang and McMillen, Don (ed.) Non-traditional security in PRC - Australia relations: GLOBAL issues of common concern (2009). Guangzhou, China 02 - 04 Jul 2009 Brisbane, Australia.Paper
Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq
Yaseen, Zaher Mundher, Jaafar, Othman, Deo, Ravinesh C., Kisi, Ozgur, Adamowski, Jan, Quilty, John and El-Shafie, Ahmed. 2016. "Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq." Journal of Hydrology. 542, pp. 603-614. https://doi.org/10.1016/j.jhydrol.2016.09.035Article
Quantitative definition and spatiotemporal distribution of little water season (LIWAS) in Korea
Kim, Su-Jeong, Byun, Hi-Ryong and Deo, Ravinesh C.. 2016. "Quantitative definition and spatiotemporal distribution of little water season (LIWAS) in Korea." Asia-Pacific Journal of Atmospheric Sciences. 52 (4), pp. 379-393. https://doi.org/10.1007/s13143-016-0012-1Article
The early 20th century warming: Anomalies, causes, and consequences
Hegerl, Gabriele C., Bronnimann, Stefan, Schurer, Andrew and Cowan, Tim. 2018. "The early 20th century warming: Anomalies, causes, and consequences." WIREs Climate Change. 9 (4). https://doi.org/10.1002/wcc.522Article
Economic and policy implications of relocation of agriculturalproduction systems under changing climate: example of Australian rice industry
Mushtaq, Shahbaz. 2016. "Economic and policy implications of relocation of agriculturalproduction systems under changing climate: example of Australian rice industry." Land Use Policy: the international journal covering all aspects of land use. 52, pp. 277-286. https://doi.org/10.1016/j.landusepol.2015.12.029Article
An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland
Deo, Ravinesh C. and Sahin, Mehmet. 2016. "An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland." Environmental Monitoring and Assessment. 188 (90). https://doi.org/10.1007/s10661-016-5094-9Article
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model
Deo, Ravinesh C., Tiwari, Mukesh K., Adamowski, Jan F. and Quilty, John M.. 2017. "Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model." Stochastic Environmental Research and Risk Assessment. 31 (5), pp. 1211-1240. https://doi.org/10.1007/s00477-016-1265-zArticle
Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model
Deo, Ravinesh C., Kisi, Ogzur and Singh, Vijay P.. 2017. "Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model." Atmospheric Research. 184, pp. 149-175. https://doi.org/10.1016/j.atmosres.2016.10.004Article