Article
Meteorology & Atmospheric Sciences
Dasol Kim, Doo-Sun R. Park, Corene J. Matyas
Summary: This study investigates the spatial variations in rainfall accompanying tropical cyclones over the western North Pacific from June to October during 1998-2019, based on the phases of El Nino-Southern Oscillation (ENSO). The study finds that the rainfall characteristics, including rainfall strength, total rainfall area, and total rainfall volume, show spatial variations that are closely related to the maximum wind speed and environmental conditions. The results suggest that the variation in rainfall strength is mainly influenced by the maximum wind speed, while the variations in rainfall area and rainfall volume are strongly controlled by the environmental conditions.
JOURNAL OF CLIMATE
(2023)
Article
Geosciences, Multidisciplinary
Dino Collalti, Eric Strobl
Summary: The study highlights the significant impact of maximum precipitation during storms on parish-level damage risk, even after controlling for local wind speed. For example, the estimated damage risk for a 20-year rainfall event in Jamaica is at least 238 million USD, approximately 1.5% of the country's annual GDP.
Article
Environmental Sciences
Jie Wu, Yang Chen, Zhen Liao, Xuejie Gao, Panmao Zhai, Yamin Hu
Summary: A study suggests that compound events of tropical cyclones and heatwaves will become more frequent in coastal Southeast China and migrate to the interior. The increase in heatwaves contributes the most to the projected increase in frequency of these compound events. In addition to the unprecedented frequency and intensity, the emergence of unseasonal compound events will overwhelm local adaptive capacities in South and Southeast China.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Environmental Sciences
Balaji Kumar Seela, Jayalakshmi Janapati, Chirikandath Kalath Unnikrishnan, Pay-Liam Lin, Jui Le Loh, Wei-Yu Chang, Utpal Kumar, K. Krishna Reddy, Dong-In Lee, Mannem Venkatrami Reddy
Summary: This study summarizes the raindrop size distributions of North Indian Ocean tropical cyclones observed at coastal and inland stations in south India. The results show that coastal stations have more mid- and large-size drops, while small and mid-size drops contribute primarily to the total number concentration and rainfall rate at both inland and coastal stations. The study also highlights the differences in rain retrieval algorithms for remote sensing radars and rainfall erosivity studies between coastal and inland stations.
Article
Meteorology & Atmospheric Sciences
Kexin Song, Jiuwei Zhao, Ruifen Zhan, Li Tao, Lin Chen
Summary: Previous studies rarely evaluated the confidence and uncertainty issues of simulations, despite the widespread use of climate models. This study examines the performance of CMIP6-HighResMIP simulations in representing long-term variability of tropical cyclone (TC) activity and quantifies the contributions of internal and external forcing. The models show overall poor performance in simulating long-term changes in TC activity, but perform well in capturing some specific regional interdecadal variations and linear trends. These results highlight the importance of understanding the confidence and uncertainty in future TC changes projected by models.
JOURNAL OF CLIMATE
(2022)
Article
Multidisciplinary Sciences
Adam H. Sobel, Chia-Ying Lee, Steven G. Bowen, Suzana J. Camargo, Mark A. Cane, Amy Clement, Boniface Fosu, Megan Hart, Kevin A. Reed, Richard Seager, Michael K. Tippett
Summary: Recent research shows that climate models incorrectly simulate the equatorial Pacific response to greenhouse gas warming, leading to a discrepancy between model predictions and observations of a more La Nina-like state. This could result in incorrect projections of regional tropical cyclone activity and other perils such as severe convective storms and droughts. While these errors may be transient, the transient response is important for climate adaptation in the next several decades. Therefore, it is desirable to develop projections that represent a broader range of possible future tropical Pacific warming scenarios, even if current coupled earth system models cannot produce such projections.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Meteorology & Atmospheric Sciences
Yumin Moon, Daehyun Kim, Allison A. Wing, Suzana J. Camargo, Ming Zhao, L. Ruby Leung, Malcolm J. Roberts, Dong-Hyun Cha, Jihong Moon
Summary: This study evaluates the performance of a global climate model in simulating tropical cyclone rainfall structures. The results show that the model tends to overproduce rainfall around cyclones compared to satellite observations, both in terms of maximum rainfall intensity and average rainfall rates. Increasing the model's horizontal resolution leads to higher peak rainfall intensity but lower average rainfall rates. Ocean coupling reduces rainfall rates by affecting moisture flux convergence and surface latent heat flux. The model is able to reproduce the rainfall asymmetries induced by vertical wind shear in observed cyclones. Additionally, there is a positive relationship between the average inner-core rainfall and the likelihood of cyclone intensification, consistent with previous studies.
JOURNAL OF CLIMATE
(2022)
Article
Agronomy
Feng Chen, Haibo Hu, Defeng Pan, Junyi Wang, Hua Zhang, Zheng Pan
Summary: The issue of regional soil and water loss caused by human activity is severe in coastal regions. This study analyzes rainfall data in Dongtai City, Jiangsu Province between 2011 and 2017 to understand the distribution and impact of rainfall erosion. The annual average erosive rainfall frequency was 37.7, accounting for 51.6% of the total, and the annual erosive rainfall was 1082.0 mm on average, accounting for 90.6% of the total. The study suggests using a composite model based on rainfall amount and intensity to accurately assess soil erosion risk.
Article
Meteorology & Atmospheric Sciences
Gabriele Villarini, Wei Zhang, Paul Miller, David R. Johnson, Lauren E. Grimley, Hugh J. Roberts
Summary: This study developed a probabilistic rainfall generator for tropical cyclones affecting Louisiana, by analyzing 12 storms and developing a data-driven model to relate observed rainfall to parametric models. The study addressed biases and stochastic nature in rainfall processes, characterizing random errors and proposing a methodology to generate ensembles with specified statistical properties. The results are applicable beyond Louisiana and the IPET model, representing a tool for quantifying risk associated with TC rainfall.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2022)
Article
Meteorology & Atmospheric Sciences
Ying Li, Dajun Zhao
Summary: This study analyzes the spatiotemporal distribution of tropical cyclone extreme rainfall (TCER) in China, finding significant regional differences in the TCER threshold and high intensity in the northern region. TCER has shown a slight increasing trend over time and is most likely to occur in August. The duration of TC precipitation with extreme rainfall lasts for four to six days, mainly occurring on the third to fourth days. The prevailing tracks of TCER with wide areas exhibit a northwestward and westward trend.
ADVANCES IN ATMOSPHERIC SCIENCES
(2022)
Article
Geosciences, Multidisciplinary
Elizabeth J. Wallace, Sylvia G. Dee, Kerry A. Emanuel
Summary: The study found a significant correlation between the number of compiled hurricanes from paleohurricane records and TCs in the basin-wide for the past millennium. However, the skill of compilation is limited by the proxy temporal resolution, with current paleohurricane proxy networks predominantly capturing storms moving in the Caribbean/Gulf of Mexico.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Article
Environmental Sciences
Sadya Islam, Gabriele Villarini, Wei Zhang
Summary: This study finds that urban areas have a noticeable impact on the rainfall of stronger and wetter tropical cyclones by increasing urban surface roughness and surface warming. However, the urban modification is not prominent for weaker storms associated with lower total rainfall. This research expands the understanding of how urban factors affect tropical cyclone rainfall and provides critical information for future adaptation and mitigation strategies.
Article
Meteorology & Atmospheric Sciences
So-Hee Kim, Joong-Bae Ahn, Jianqi Sun
Summary: This study developed a new statistical-dynamical seasonal typhoon forecast model that showed significant predictability in predicting typhoon landfall in East Asia and its sub-domains from July to September. The model was able to capture the interannual variability of typhoon landfall with high confidence levels in different regions of East Asia.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2022)
Article
Environmental Sciences
Cameron Do, Yuriy Kuleshov
Summary: This study assessed and mapped the risk from tropical cyclones (TCs) nationwide in Australia, finding that the highest risk was in coastal areas of eastern Queensland and New South Wales, followed by medium risk in the Northern Territory and north-western Western Australia.
Article
Environmental Sciences
Sarah Hulsen, Robert Mcdonald, Rebecca Chaplin-Kramer, David N. Bresch, Richard Sharp, Thomas Worthington, Chahan M. Kropf
Summary: Coastal ecosystems have the potential to contribute to disaster risk reduction and climate change adaptation, but changes in the ecosystem can lead to a decrease in protection. For future coastal protection and adaptation policies, the impact of climate change on coastal protection services should be taken into consideration.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Engineering, Environmental
Andreas Langousis, Alin Andrei Carsteanu
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2020)
Article
Computer Science, Artificial Intelligence
Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis
Summary: Traditional daily streamflow forecasting using a single machine learning algorithm is limited to few case studies. This study proposes super learning, combining 10 machine learning algorithms, and demonstrates superior predictive performance over other methods using a large dataset.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Environmental Sciences
Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis, Simon Michael Papalexiou
Summary: This study utilizes statistical boosting to predict 12 hydrological signatures in 667 basins in the contiguous US using 28 attributes, with a formal assessment of probabilistic predictions using quantile scores. The results show that climatic indices and topographic characteristics are the most important attributes for predicting hydrological signatures.
Article
Engineering, Environmental
Roberto Deidda, Matteo Hellies, Andreas Langousis
Summary: This study investigates the limitations of the regional approach in describing the frequency distribution of annual rainfall maxima and compares it with a boundaryless approach. It emphasizes that the boundaryless approach is superior in describing local precipitation features and avoids abrupt changes in distribution parameters induced by splitting the study area into contiguous homogeneous regions. Through cross-validation, the study clearly demonstrates the advantages of the boundaryless approach in accurately representing precipitation characteristics.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Engineering, Environmental
Nikolaos P. Bakas, Vagelis Plevris, Andreas Langousis, Savvas A. Chatzichristofis
Summary: An optimization algorithm driven by probability theory provides a stable, parameter-free method for optimizing black-box objective functions, with theoretical proof. Experimental results confirm the effectiveness of the algorithm in reaching the optimal value.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Engineering, Environmental
Athanasios V. Serafeim, George Kokosalakis, Roberto Deidda, Irene Karathanasi, Andreas Langousis
Summary: This study introduces two probabilistic approaches for minimum night flow estimation in water distribution networks, which are particularly suited to minimize noise effects and provide confidence interval estimation of observed MNFs. Both methods show good performance in real-world applications, making them suitable for engineering applications and beyond.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Environmental Sciences
Stergios Emmanouil, Andreas Langousis, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou
Summary: The study focuses on the importance of spatiotemporal characteristics of rainfall for hydrological modeling, hydroclimatic risk estimation, and impact assessment. By bias-correcting and downscaling the ERA5 rainfall dataset using the Stage IV precipitation product, the authors developed a high-resolution precipitation product over the CONUS on a 4-km grid with records back to 1979. The developed product demonstrates good performance and robust behavior, making it suitable for hydroclimatic risk applications and frequency analysis, as well as distributed hydrologic modeling.
WATER RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
Athanasios V. Serafeim, George Kokosalakis, Roberto Deidda, Irene Karathanasi, Andreas Langousis
Summary: Quantification of water losses in water distribution networks is crucial for developing strategies to reduce them. This study compares two widely used water loss estimation approaches and concludes that they can effectively converge, providing reliable estimates under certain conditions.
Article
Environmental Sciences
Stergios Emmanouil, Andreas Langousis, Efthymios Nikolopoulos, Emmanouil N. Anagnostou
Summary: Given the rapidly changing climate, accurate spatiotemporal information on the evolution of extreme rainfall events is required for flood risk assessment and infrastructure design. This study investigates the spatiotemporal evolution of extreme rainfall using a parametric approach and high-resolution precipitation data. Results show that the intensification of precipitation extremes due to climate change can have severe impacts on existing infrastructure, with the observed trends being influenced by topography and rainfall climatology.
Article
Engineering, Environmental
Athanasios Serafeim, George Kokosalakis, Roberto Deidda, Irene Karathanasi, Andreas Langousis
Summary: The study develops a probabilistic model to estimate the minimum night flow (MNF) in water distribution networks, considering the network characteristics and the effect of inlet/operating pressures on leakages in PMAs. The model is tested in a real-world application and proves to be accurate and reliable, providing water experts and officials with a useful tool for selecting appropriate leakage reduction techniques.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Environmental Sciences
Georgia Papacharalampous, Andreas Langousis
Summary: This study fills the gap in urban water demand forecasting by proposing a new family of probabilistic algorithms based on quantile regression. Seven algorithms were compared, with linear boosting algorithm showing the best performance and mean and median combiners also being skillful in forecasting.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Environmental
Anastasios Perdios, George Kokosalakis, Nikolaos Th Fourniotis, Irene Karathanasi, Andreas Langousis
Summary: This study develops a statistical framework for detecting malfunctions in pressure reducing valves (PRVs) in water supply and distribution networks. The framework uses root mean squared error as a metric to monitor PRV performance and the hazard function concept to identify the duration of events and issue alerts. The results show that this statistical approach effectively detects major PRV malfunctions.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Environmental Sciences
Athanasios Serafeim, George Kokosalakis, Roberto Deidda, Nikolaos Th Fourniotis, Andreas Langousis
Summary: This study proposes a hierarchical clustering approach enriched with topological proximity constraints to optimize the sizing and allocation of PMAs (or DMAs) in water distribution networks. The approach aims to minimize water leakages while maintaining sufficient hydraulic resilience. The method is advantageous as it uses the original pipeline grid as a connectivity matrix, is statistically rigorous and user unbiased, and requires minimal processing power.
Article
Environmental Sciences
Stergios Emmanouil, Andreas Langousis, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou
Summary: This study aims to evaluate the effects of future climate scenarios on intensity-duration-frequency (IDF) curves over the entire Contiguous United States, taking into account the nonstationary nature of rainfall across fine spatiotemporal resolutions. Our results show significant downward trends in return period estimates for most regions, which are more pronounced at lower exceedance probability levels. Therefore, future infrastructure design should be strategically tailored to account for a wide range of potential outcomes.
Review
Green & Sustainable Science & Technology
Dionisios Koutsantonis, Konstantinos Koutsantonis, Nikolaos P. Bakas, Vagelis Plevris, Andreas Langousis, Savvas A. Chatzichristofis
Summary: In this review paper, a computational analysis of a large number of published articles in the field of Adaptive Learning was conducted. Bibliometric maps for keywords, authors, and references were constructed using a multidimensional scaling algorithm. Significant patterns and inter-item associations in the field of adaptive learning were revealed, and the findings were interpreted based on the current literature. The time-series evolution of research terms, their trends over time, and prevalent statistical associations were also demonstrated.