Article
Water Resources
Santiago Mendoza Paz, Patrick Willems
Summary: This study focuses on assessing the strengths and weaknesses of quantile mapping in climate downscaling based on global climate model projections in Southern Africa. Different methods, including parametric and non-parametric transformations, are used and validated using cross-validation. The results reveal that non-parametric methods and parametric methods using exponential-type transformation have generally good skill in correcting biases. The uncertainty contribution analysis shows that the climate models are the largest contributors to overall uncertainty, while in some cases the methods have the highest uncertainty share. The stationary assumptions of quantile mapping are found to be robust. The projections indicate a tendency towards dryer conditions and intensified precipitation events in the region, with strong intra-regional variations.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2022)
Article
Water Resources
Cristian Chadwick, Jorge Gironas, Fernando Gonzalez-Leiva, Sebastian Aedo
Summary: Standard quantile mapping (QM) is a bias adjustment method that effectively removes historical climate biases but alters the global climate model (GCM) signal. Quantile delta mapping (QDM) explicitly preserves relative changes in quantiles but might have biases in preserving GCM changes in standard deviation. Unbiased quantile mapping (UQM) method proposed in this study preserves GCM changes of both mean and standard deviation. Comparisons using synthetic experiments and four Chilean locations show that UQM outperforms QDM, QM, detrended quantile mapping, and scale distribution mapping. A tradeoff exists between preserving the relative changes in GCM quantiles (QDM recommended) or changes in GCM moments (UQM recommended).
HYDROLOGICAL SCIENCES JOURNAL
(2023)
Article
Meteorology & Atmospheric Sciences
Julia Mindlin, Carolina S. Vera, Theodore G. Shepherd, Marisol Osman
Summary: This article explores the response of summer rainfall in southeastern South America to greenhouse warming and identifies the combination of remote drivers that lead to extreme drying and wetting scenarios. The study also highlights the possibility of drying in southeastern South America and emphasizes the impact of regional box definition on the results.
JOURNAL OF CLIMATE
(2023)
Article
Green & Sustainable Science & Technology
Aida Hosseini Baghanam, Vahid Nourani, Ehsan Norouzi, Amirreza Tabataba Vakili, Hueseyin Gokcekus
Summary: This study focuses on bias correction of climate models using multi-resolution wavelet transform and assessing the correlation between climate signals. The results show that the bias correction method based on discrete wavelet transform outperforms the quantile mapping method. The combination of wavelet transform and artificial neural network can identify the most important predictors in climate models.
Article
Geosciences, Multidisciplinary
William Ingram, Andrew C. Bushell
Summary: Recent studies show that some current GCMs exhibit climate sensitivity beyond accepted ranges, with increasing vertical resolution not necessarily leading to higher climate sensitivity. Further research is needed to test this result in other GCMs and across a broader range of resolutions.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Article
Astronomy & Astrophysics
Chandra Rupa Rajulapati, Simon Michael Papalexiou
Summary: Bias correction methods are used to adjust simulations from climate models and make them suitable for decision-making. In this study, a semi-parametric quantile mapping (SPQM) method is introduced to correct bias in daily precipitation. The SPQM method corrects simulations based on observations and assumes that the detrended simulations have the same distribution as the observations. The results show that the SPQM method performs well in reproducing observed statistics and wet and dry spells.
EARTH AND SPACE SCIENCE
(2023)
Article
Ecology
Duy Thao Nguyen, Saqib Ashraf, Minhhuy Le, Le Quang Trung, Mustajab Ali
Summary: Climate change impacts hydrological cycle and environment. This study evaluates the key climate parameters in Lahore, Pakistan using GCM and LSTM models, and finds that LSTM model outperforms GCM in climate forecasting with significant improvement in statistical parameters.
ECOLOGICAL INFORMATICS
(2023)
Article
Water Resources
Nadav Peleg, Nikolina Ban, Michael J. Gibson, Albert S. Chen, Athanasios Paschalis, Paolo Burlando, Joao P. Leitao
Summary: Synthetic design storms are commonly used for planning new drainage systems or assessing flood impacts. However, the current methods apply the same changes in rainfall intensities in space, which limits some hydrological applications. To address this issue, researchers explored the potential use of rainfall data from climate models and introduced a spatial quantile mapping method. The results suggest that spatial storm profiles may need to be readjusted when assessing flood impacts.
ADVANCES IN WATER RESOURCES
(2022)
Article
Green & Sustainable Science & Technology
H. C. Bloomfield, D. J. Brayshaw, A. Troccoli, C. M. Goodess, M. De Felice, L. Dubus, P. E. Bett, Y. -M. Saint-Drenan
Summary: Climate change will have a significant impact on future European power systems, especially in the energy balance after 2030. Under different power system scenarios, national power systems may be significantly affected by climate change, particularly in terms of seasonal variations in renewable resources.
Article
Agronomy
Thiago A. Spontoni, Thiago M. Ventura, Rafael S. Palacios, Leone F. A. Curado, Widinei A. Fernandes, Vinicius B. Capistrano, Clovis L. Fritzen, Hamilton G. Pavao, Thiago R. Rodrigues
Summary: Meteorological elements have significant impacts on the environment, cultures, and climate change. By studying the meteorological variables in the Brazilian Pantanal, this research demonstrated the effectiveness of artificial intelligence in improving environmental modeling, reducing costs, and increasing reliability. Various machine learning techniques were compared, resulting in a new model that accurately describes reference evapotranspiration with fewer climatic variables.
Article
Water Resources
Maedeh Enayati, Omid Bozorg-Haddad, Javad Bazrafshan, Somayeh Hejabi, Xuefeng Chu
Summary: This study compared the performance of quantile mapping (QM) techniques as a bias correction method for raw outputs from GCM/RCM combinations, finding that the results varied depending on transformation functions, parameter sets, and topographic conditions. The QUANT and RQUANT methods were identified as excellent options for correcting bias in rainfall data.
JOURNAL OF WATER AND CLIMATE CHANGE
(2021)
Article
Multidisciplinary Sciences
Liying Liu
Summary: Using MIV-BP neural network and GA-BP neural network, this study assessed the water resource security in the Guizhou karst area, China. The results revealed that the water security level was at a moderate warning to critical safety level from 2001 to 2015, with a moderate warning level in 2011. It is suggested to understand the modes of water resources development and the impact of engineering water shortage for future planning and regional ecological restoration.
SCIENTIFIC REPORTS
(2021)
Article
Green & Sustainable Science & Technology
J. Y. He, P. W. Chan, Q. S. Li, H. W. Tong
Summary: This study investigates the future offshore wind resources under climate change in the South China Sea. It is projected that the wind power density will increase in the northern SCS but decrease in the southern SCS in the future warmer world.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Geosciences, Multidisciplinary
Xin Wang, Yilun Han, Wei Xue, Guangwen Yang, Guang J. Zhang
Summary: This paper investigates the application of machine learning parameterizations in climate simulations. A model is designed using deep neural networks to emulate a super-parameterization and is able to produce stable simulations under real-world conditions. The study finds that ML parameterizations can improve the accuracy of certain climate phenomena, but further improvements are still needed.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Geosciences, Multidisciplinary
Peter Nojarov
Summary: This study reveals changes in atmospheric circulation and wind conditions over the western part of the Black Sea from 1979 to 2019, impacting the active tourist season in Bulgaria from June to September. The increase in wind from the northeastern quarter has led to higher sea waves, with the most significant changes observed in August and September. These trends pose an immediate threat to tourism development along the Bulgarian Black Sea coast.