Article
Engineering, Marine
Wei-Shiun Lu, Chi-Hsiang Tseng, Shih-Chun Hsiao, Wen-Son Chiang, Kai-Cheng Hu
Summary: Taiwan's coastal hazards may worsen due to climate change. Analyzing wave climate characteristics at different time scales provides a reference for understanding the impact of climate change on coastal environments.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Environmental Sciences
Erik Kusch, Richard Davy
Summary: Advances in climate science have made widely used observation data obsolete, prompting the development of a workflow to integrate improved data into biological analyses. The ERA5 product family offers high-resolution climate variables and can be downscaled using Kriging. KrigR provides a user-friendly tool for obtaining tailored climate data at high resolutions.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Geosciences, Multidisciplinary
Yong-Tak Kim, Hyun-Han Kwon, Carlos Lima, Ashish Sharma
Summary: This study introduces a novel approach that expands the existing QDM by incorporating Kriging and a Bayesian framework to address spatial bias. The proposed model is validated to effectively simulate bias-corrected daily rainfall sequences over large regions at fine resolutions. The potential use of this approach in the field of hydrometeorology is discussed.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Article
Environmental Sciences
Xianghua Niu, Xikun Wei, Wei Tian, Guojie Wang, Wenhui Zhu
Summary: Land evaporation is an important variable in climate change, water cycle, and water resources management. Using a deep learning-based model, researchers found that future land evaporation is projected to increase, with more significant changes in high emission scenarios and larger increases in spring and summer compared to autumn and winter.
Article
Meteorology & Atmospheric Sciences
Jose Gonzalez-Abad, Jorge Bano-Medina, Jose Manuel Gutierrez
Summary: This study evaluates deep downscaling models using explainable artificial intelligence techniques, introduces two new diagnostic methods, and demonstrates their role in design and evaluation. The results show the usefulness of incorporating explainable artificial intelligence techniques into statistical downscaling evaluation frameworks, especially when working with large regions and/or under climate change conditions.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Article
Meteorology & Atmospheric Sciences
Jose Gonzalez-Abad, Jorge Bano-Medina, Jose Manuel Gutierrez
Summary: This study compares multiple deep learning models extracted from the literature for downscaled temperature prediction under changing climatic conditions. The researchers introduce two novel explainable artificial intelligence techniques and demonstrate their applications in designing and evaluating deep learning models.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Review
Meteorology & Atmospheric Sciences
Emma Gaitan, M. Rosa Pino-Otin
Summary: Cultivating grapevines in Mediterranean regions like Spain is a traditional practice. The current climate in this region is ideal for grape cultivation, but future climate changes may pose challenges. Therefore, it is crucial to conduct accurate local studies on future projections.
ATMOSPHERIC RESEARCH
(2023)
Article
Engineering, Civil
Shadi Arfa, Mohsen Nasseri, Hassan Tavakol-Davani
Summary: This study assessed and compared the effects of different downscaling methods on an urban network in Tehran, Iran. The findings suggest that DMDM outperforms other techniques in daily downscaling, and the GEV distribution method is more effective in sub-daily disaggregation. Simulation results indicate a higher risk of urban flooding under the RCP 8.5 scenario compared to RCP 4.5 and RCP 2.6 scenarios.
WATER RESOURCES MANAGEMENT
(2021)
Article
Meteorology & Atmospheric Sciences
Sebastian G. Mutz, Samuel Scherrer, Ilze Muceniece, Todd A. Ehlers
Summary: Local scale estimates of temperature change are crucial for decision making, especially in Chile. Utilizing weather station data, empirical-statistical models were constructed based on large-scale predictors to estimate local temperature changes. The models showed high prediction skill scores and supported the main drivers of Chilean climate.
Article
Water Resources
Parthiban Loganathan, Amit Baburao Mahindrakar
Summary: This study adapted an improved PCR downscaling technique to downscale historical outputs of 26 CMIP5 GCMs, focusing on regional climate impacts in the Cauvery river basin. The PCR model performed remarkably well with significantly reduced computational time and small variance in validation results. Additionally, strategically chosen GCMs such as CCSM4, inmcm4, and EC-EARTH demonstrated exceptional performance in reproducing precipitation statistics over the study area.
JOURNAL OF WATER AND CLIMATE CHANGE
(2021)
Article
Meteorology & Atmospheric Sciences
Christopher Jung, Dirk Schindler
Summary: This study assessed the future development of winter storm intensity in Central Europe, finding a significant increase in wind gust intensity towards the end of the 21st century under different concentration pathways. The methodology proposed in the study allows for quantifying uncertainty associated with winter storm projections and developing climate-sensitive storm damage models.
WEATHER AND CLIMATE EXTREMES
(2021)
Article
Agronomy
Francesco Zignol, Erik Kjellstrom, Kristoffer Hylander, Biruk Ayalew, Beyene Zewdie, Alejandro Rodriguez-Gijon, Ayco J. M. Tack
Summary: Climate change affects crop production and food security worldwide, especially for smallholder farmers. This study focuses on modeling the microclimate below forest canopies using remote sensing data, due to constraints related to model development, lack of high-resolution datasets, and limited in-situ measurements. The research examines the contribution of in-situ field measurements and GIS estimates to explain microclimate variation, and provides spatiotemporal microclimate projections for a landscape in Ethiopia. The findings show the potential of using remote sensing data for microclimate mapping and suggest the use of these projections for climate-resilient agriculture.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Article
Environmental Sciences
Xiaohu Zhao, Guohe Huang, Yongping Li, Qianguo Lin, Junliang Jin, Chen Lu, Junhong Guo
Summary: Future changes in meteorological droughts in Henan Province, China show increased duration and intensity while decreased frequency. This study also finds differences in drought changes among different emission scenarios, with the SSP2-4.5 scenario showing lower magnitudes of changes in duration and intensity relative to other scenarios.
JOURNAL OF CONTAMINANT HYDROLOGY
(2021)
Article
Environmental Sciences
Saif Haider, Muhammad Umer Masood, Muhammad Rashid, Tauqeer Ali, Chaitanya B. Pande, Fahad Alshehri, Ismail Elkhrachy
Summary: This study explores the potential of rainwater harvesting for Lahore city, using rainfall data and land use information. The findings show that rainwater harvesting can help address urban flooding and meet a significant portion of Lahore's water demand.
Article
Agricultural Engineering
X. C. Zhang, M. X. Shen, J. Chen, J. W. Homan, P. R. Busteed
Summary: This study evaluated nine statistical downscaling methods in simulating daily precipitation distribution, frequency, and temporal sequence at four Oklahoma weather stations, finding that SWG methods had certain advantages in simulating precipitation for non-stationary climate changes.
TRANSACTIONS OF THE ASABE
(2021)
Article
Engineering, Industrial
Sina Mohammadi, Mehdi Tavakolan, Banafsheh Zahraie
Summary: This paper proposes an innovative intelligent simulation-based construction planning framework that integrates an ontological inference engine to configure construction processes, activities, and resources. The framework successfully generates construction processes, activities, and required resources based on the construction product, available resources, and planning rules, and is capable of automatically generating 4D BIM models for a better understanding of the construction plan.
ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
(2022)
Article
Geosciences, Multidisciplinary
Mercedeh Taheri, Milad Shamsi Anboohi, Rahimeh Mousavi, Mohsen Nasseri
Summary: This study investigates the performance of multi-source Global Gridded Snow Products (GGSPs) in hydrological modeling using multi-stage calibration strategies. The results show that using GGSPs as complementary information can improve the accuracy of the modeling compared to traditional calibration methods.
FRONTIERS OF EARTH SCIENCE
(2023)
Article
Environmental Sciences
Amir Reza Azarnivand, Masoud Sadrinasab, Mohsen Nasseri
Summary: Climate change affects global atmospheric circulation patterns and intensifies extreme weather events. This study investigates the impact of climate change and meteorological variables on water circulation patterns in the Persian Gulf, leading to changes in salinity, temperature, and density of water mass. The findings project significant alterations in physical properties, which can have detrimental effects on the aquatic ecosystem in the Gulf. The study highlights the importance of developing adaptation management plans in line with sustainable development goals.
ESTUARIES AND COASTS
(2023)
Article
Water Resources
Mercedeh Taheri, Milad Shamsi Anboohi, Mohsen Nasseri, Abdolmajid Mohammadian
Summary: This study developed a water balance model to estimate evapotranspiration and established a correct dynamic relationship between evapotranspiration and soil moisture using physical concepts. The evaluation results showed that the model had better performance in simulating streamflow and groundwater level, with higher accuracy compared to alternative models, and the estimated spatiotemporal distribution of evapotranspiration agreed well with climatic-vegetation conditions.
HYDROLOGICAL SCIENCES JOURNAL
(2023)
Article
Meteorology & Atmospheric Sciences
Omid Zandi, Mohsen Nasseri, Banafsheh Zahraie
Summary: This article investigates the potential of using large-scale precipitation products and land surface characteristics to improve the accuracy of an elevation-based spatial non-stationary regression method. A two-step approach of downscaling and merging is proposed and assessed in an orographically complex region in Iran. The results show that the proposed framework improves the accuracy of precipitation predictions and has better extrapolation ability and robustness compared to the benchmark model.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2023)
Article
Geochemistry & Geophysics
Rahimeh Mousavi, Mohsen Nasseri, Saeed Abbasi, Mercedeh Taheri, Milad Shamsi Anboohi
Summary: This study investigates the impact of precipitation and evapotranspiration (ET) products on hydrological model performance, specifically water balance in two basins in Iran. The results show that using large-scale products as model inputs in mountainous and highland watersheds, where ground measurements are not possible, can effectively maintain model performance. Simultaneously calibrating ET and streamflow, the use of ET products improves ET simulation but decreases the accuracy of streamflow simulation.
Article
Computer Science, Interdisciplinary Applications
Rahimeh Mousavi, Mohsen Nasseri, Saeed Abbasi
Summary: The study proposes a statistical blending method that combines five large-scale and satellite precipitation and evapotranspiration products in three modeling scenarios. The blending procedures, organized using a conceptual water balance model, improve the performance of the model and show conformity with the observed precipitation patterns and behavior in the study area.
JOURNAL OF HYDROINFORMATICS
(2023)
Article
Meteorology & Atmospheric Sciences
Hesam Barkhordari, Mohsen Nasseri, Hamidreza Rezazadeh
Summary: Previous studies have shown that global gridded hydroclimatic products lack precision and consistency. This study evaluates the efficiency of eight streamflow datasets in two large-scale watersheds with different climate conditions. Two tuning procedures are used to correct the products, resulting in improved accuracy in terms of statistical metrics and streamflow simulation.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Neda Dolatabadi, Mohsen Nasseri, Banafsheh Zahraie
Summary: Radar satellite imagery is widely used for accurate estimation of soil moisture. This study investigated the contribution of vegetation canopy to the accuracy of retrieved soil moisture and used the Integral Equation Model (IEM) coupled with the Water Cloud Model (WCM) to estimate surface soil moisture. Data-driven models (Support Vector Machine and Regression Tree) were used to obtain soil moisture estimates at measurement stations based on radar signal and vegetation indices. The Regression Tree model showed the best performance and was used to calculate regionalized estimates for the watershed. The results demonstrated the feasibility of using data-driven models for regionalized soil moisture measurements.
EARTH SCIENCE INFORMATICS
(2023)
Article
Geochemistry & Geophysics
Maryam Khodadadi, Tarokh Maleki Roozbahani, Mercedeh Taheri, Fatemeh Ganji, Mohsen Nasseri
Summary: This study explores the relationship between groundwater withdrawal and the uncertainty effects of actual evapotranspiration (ET) by incorporating the uncertainty of calculated ET values into a comprehensive interval-based water balance model. The study area is the Ghorveh-Dehgolan basin in Northern Iran. The proposed approach improves the statistical metrics of the model responses and decreases the uncertainty level tied to simulated streamflow and groundwater levels.
Article
Environmental Sciences
Yasaman Mohammadi, Omid Zandi, Mohsen Nasseri, Yousef Rashidi
Summary: The aim of this paper is to use machine learning models (Random Forest and Gaussian Process Regression) to characterize the spatiotemporal patterns of daily PM10 in Tehran, Iran, for policy-makers. The performance of these models was compared to a benchmark interpolator called Inverse Distance Weighting using statistical metrics. The results showed that the machine learning models performed well in spring and summer, while Inverse Distance Weighting and machine learning models performed better in winter and autumn, respectively. Additionally, the results of the Correlated Triple Collocation analysis suggested that machine learning techniques provided a more accurate spatial distribution. Overall, the Inverse Distance Weighting method may not provide a realistic estimation of pollutant levels in the study region.
Article
Engineering, Environmental
Arash Ghomlaghi, Mohsen Nasseri, Bardia Bayat
Summary: Precipitation is crucial for hydroclimatic studies, and accurate measurement is essential. While satellite data is available, gauge measurements remain the most reliable. Current research focuses on redesigning rain gauge networks globally, but few have explored the potential of using global gridded precipitation products for network optimization.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Environmental Sciences
Mohammad Masoud Mohammadpour Khoie, Mohsen Nasseri, Mohammad Ali Banihashemi
Summary: This study examines the impacts of human activities and climate change on streamflow and sediment transport in the Gorganroud watershed in northern Iran. The results suggest that changes in land use have contributed more than 60% to the changes in streamflow and sediment regime, with the increase in orchard land use being the primary driver.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Engineering, Civil
Arfan Arshad, Ali Mirchi, Javier Vilcaez, Muhammad Umar Akbar, Kaveh Madani
Summary: High-resolution, continuous groundwater data is crucial for adaptive aquifer management. This study presents a predictive modeling framework that incorporates covariates and existing observations to estimate groundwater level changes. The framework outperforms other methods and provides reliable estimates for unmonitored sites. The study also examines groundwater level changes in different regions and highlights the importance of effective aquifer management.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Lihua Chen, Jie Deng, Wenzhe Yang, Hang Chen
Summary: A new grid-based distributed karst hydrological model (GDKHM) is developed to simulate streamflow in the flood-prone karst area of Southwest China. The results show that the GDKHM performs well in predicting floods and capturing the spatial variability of karst system.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Faruk Gurbuz, Avinash Mudireddy, Ricardo Mantilla, Shaoping Xiao
Summary: Machine learning algorithms have shown better performance in streamflow prediction compared to traditional hydrological models. In this study, researchers proposed a methodology to test and benchmark ML algorithms using artificial data generated by physically-based hydrological models. They found that deep learning algorithms can correctly identify the relationship between streamflow and rainfall in certain conditions, but fail to outperform traditional prediction methods in other scenarios.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yadong Ji, Jianyu Fu, Bingjun Liu, Zeqin Huang, Xuejin Tan
Summary: This study distinguishes the uncertainty in drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty. The results show that the estimation of total uncertainty reaches a minimum in the mid-21st century and that model uncertainty is dominant in tropical regions.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Z. R. van Leeuwen, M. J. Klaar, M. W. Smith, L. E. Brown
Summary: This study quantifies the effectiveness of leaky dams in reducing flood peak magnitude using a transfer function noise modelling approach. The results show that leaky dams have a significant but highly variable impact on flood peak magnitude, and managing expectations should consider event size and type.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Zeda Yin, Yasaman Saadati, M. Hadi Amini, Linlong Bian, Beichao Hu
Summary: Combined sewer overflows pose significant threats to public health and the environment, and various strategies have been proposed to mitigate their adverse effects. Smart control strategies have gained traction due to their cost-effectiveness but face challenges in balancing precision and computational efficiency. To address this, we propose exploring machine learning models and the inversion of neural networks for more efficient CSO prediction and optimization.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Qimou Zhang, Jiacong Huang, Jing Zhang, Rui Qian, Zhen Cui, Junfeng Gao
Summary: This study developed a N-cycling model for lowland rural rivers covered by macrophytes and investigated the N imports, exports, and response to sediment dredging. The findings showed a considerable N retention ability in the study river, with significant N imports from connected rivers and surrounding polders. Sediment dredging increased particulate nitrogen resuspension and settling rates, while decreasing ammonia nitrogen release, denitrification, and macrophyte uptake rates.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Xue Li, Yingyin Zhou, Jian Sha, Man Zhang, Zhong-Liang Wang
Summary: High-resolution climate data is crucial for predicting regional climate and water environment changes. In this study, a two-step downscaling method was developed to enhance the spatial resolution of GCM data and improve the accuracy for small basins. The method combined medium-resolution climate data with high-resolution topographic data to capture spatial and temporal details. The downscaled climate data were then used to simulate the impacts of climate change on hydrology and water quality in a small basin. The results demonstrated the effectiveness of the downscaling method for spatially differentiated simulations.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Tongqing Shen, Peng Jiang, Jiahui Zhao, Xuegao Chen, Hui Lin, Bin Yang, Changhai Tan, Ying Zhang, Xinting Fu, Zhongbo Yu
Summary: This study evaluates the long-term interannual dynamics of permafrost distribution and active layer thickness on the Tibetan Plateau, and predicts future degradation trends. The results show that permafrost area has been decreasing and active layer thickness has been increasing, with an accelerated degradation observed in recent decades. This has significant implications for local water cycle processes, water ecology, and water security.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Chi Zhang, Xu Zhang, Qiuhong Tang, Deliang Chen, Jinchuan Huang, Shaohong Wu, Yubo Liu
Summary: Precipitation over the Tibetan Plateau is influenced by systems such as the Asian monsoons, the westerlies, and local circulations. The Indian monsoon, the westerlies, and local circulations are the main systems affecting precipitation over the entire Tibetan Plateau. The East Asian summer monsoon primarily affects the eastern Tibetan Plateau. The Indian monsoon has the greatest influence on precipitation in the southern and central grid cells, while the westerlies have the greatest influence on precipitation in the northern and western grid cells. Local circulations have the strongest influence on the central and eastern grid cells.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Manuel Almeida, Antonio Rodrigues, Pedro Coelho
Summary: This study aimed to improve the accuracy of Total Phosphorus export coefficient models, which are essential for water management. Four different models were applied to 27 agroforestry watersheds in the Mediterranean region. The modeling approach showed significant improvements in predicting the Total Phosphorus diffuse loads.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yutao Wang, Haojie Yin, Ziyi Wang, Yi Li, Pingping Wang, Longfei Wang
Summary: This study investigated the distribution and transformation of dissolved organic nitrogen (DON) in riverbed sediments impacted by effluent discharge. The authors found that the spectral characteristics of dissolved organic matter (DOM) in surface water and sediment porewater could be used to predict DON variations in riverbed sediments. Random forest and extreme gradient boosting machine learning methods were employed to provide accurate predictions of DON content and properties at different depths. These findings have important implications for wastewater discharge management and river health.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Saba Mirza Alipour, Kolbjorn Engeland, Joao Leal
Summary: This study assesses the uncertainty associated with 100-year flood maps under different scenarios using Monte Carlo simulations. The findings highlight the importance of employing probabilistic approaches for accurate and secure flood maps, with the selection of probability distribution being the primary source of uncertainty in precipitation.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Janine A. de Wit, Marjolein H. J. van Huijgevoort, Jos C. van Dam, Ge A. P. H. van den Eertwegh, Dion van Deijl, Coen J. Ritsema, Ruud P. Bartholomeus
Summary: The study focuses on the hydrological consequences of controlled drainage with subirrigation (CD-SI) on groundwater level, soil moisture content, and soil water potential. The simulations show that CD-SI can improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Constantin Seidl, Sarah Ann Wheeler, Declan Page
Summary: Water availability and quality issues will become increasingly important in the future due to climate change impacts. Managed Aquifer Recharge (MAR) is an effective water management tool, but often overlooked. This study analyzes global MAR applications and identifies the key factors for success, providing valuable insights for future design and application.
JOURNAL OF HYDROLOGY
(2024)
