Article
Geography, Physical
Daniel Andrade Maciel, Claudio Clemente Faria Barbosa, Evlyn Marcia Leao de Moraes Novo, Rogerio Flores Junior, Felipe Nincao Begliomini
Summary: The study evaluated the use of Machine Learning and Semi-Analytical algorithms for Z(sd) retrieval in Brazilian inland waters, generating Z(sd) maps using Random Forest on the Google Earth Engine platform. Machine Learning methods performed well, with Random Forest showing errors lower than 22%, and Semi-Analytical approaches yielding results closer to the Machine Learning methods. This indicates high accuracy of Machine Learning methods for Z(sd) retrieval.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Optics
Chong Fang, Pierre-Andre Jacinthe, Changchun Song, Chi Zhang, Kaishan Song
Summary: Secchi disk depth is a reliable indicator of lake clarity and aquatic ecosystem health. This study explored the effects of climatic factors and suspended particulate matter (SPM) concentration on SDD variability in Lake Khanka. The novel SPM index (SPMI) was developed and applied to remote sensing data to estimate SPM concentration. The study found that SPM concentration was the dominant driver of SDD variation, and speculated that wind speed variations may contribute to sediment resuspension.
Article
Environmental Sciences
Miaomiao Chen, Fei Xiao, Zhou Wang, Qi Feng, Xuan Ban, Yadong Zhou, Zhengzheng Hu
Summary: This study improves the QAA model for water clarity measurement and proposes a model that combines QAA_clear and QAA_turbid. By applying the model to Honghu Lake data, it is found that the model performs better in water quality measurement and produces results consistent with in situ measurements.
Article
Remote Sensing
Ziyao Yin, Junsheng Li, Yao Liu, Ya Xie, Fangfang Zhang, Shenglei Wang, Xiao Sun, Bing Zhang
Summary: This study utilized satellite images to track the changes in water quality in Lake Taihu over 36 years, identified the driving factors of water quality changes, and developed a model to estimate water quality, providing data support for local water resource conservation.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Eliza S. Deutsch, Marie-Josee Fortin, Jeffrey A. Cardille
Summary: This study assesses the water clarity of lakes across southern Canada using remote sensing technology and field measurements. It identifies factors influencing water clarity and provides insights into the relationship between optical properties of lakes and their health and vulnerability status.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
Hannah J. Rubin, David A. Lutz, Bethel G. Steele, Kathryn L. Cottingham, Kathleen C. Weathers, Mark J. Ducey, Michael Palace, Kenneth M. Johnson, Jonathan W. Chipman
Summary: This study compared the performance of random forest machine learning algorithm and a simple linear model in modeling lake water clarity, and found that the random forest model outperformed in both the single-date approach and complete dataset analysis.
Article
Engineering, Environmental
Yuan He, Zheng Lu, Weijia Wang, Dong Zhang, Yunlin Zhang, Boqiang Qin, Kun Shi, Xiaofan Yang
Summary: In this study, a novel and transferrable hybrid deep-learning-based recurrent model (DGRN) was developed to accurately map the water clarity of global lakes using Landsat 8 OLI images. The model demonstrated good performance and revealed that water depth was the most important factor determining the spatial distribution of water clarity. Furthermore, it was found that the water clarity of global lakes improved during the COVID-19 period due to the reduction of anthropogenic activities.
Article
Engineering, Environmental
Yibo Zhang, Yunlin Zhang, Kun Shi, Yongqiang Zhou, Na Li
Summary: This study established a general model based on Landsat data to accurately derive the Secchi disk depth (SDD) of various inland waters across China. By using in situ reflectance measurements and satellite images, models for estimating SDD in different bands were developed and successfully applied to investigate the spatial distribution of SDD in lakes across China.
Article
Environmental Sciences
Eliza S. Deutsch, Jeffrey A. Cardille, Talia Koll-Egyed, Marie-Josee Fortin
Summary: The study used Landsat 8 images and water clarity datasets from southern Canada to evaluate the relationship between in situ Secchi disk depth and Landsat 8 Blue/Red band ratio, finding that a global algorithm effectively represents this relationship across diverse lake types. Improved model fit was achieved by applying a median filter to remove outliers caused by atmospheric artifacts in available imagery. The findings suggest that large datasets and temporal averaging methods can help better elucidate the true relationships between in situ water clarity and satellite reflectance data.
Article
Environmental Sciences
John Gardner, Tamlin Pavelsky, Simon Topp, Xiao Yang, Matthew R. Ross, Sagy Cohen
Summary: Humans have significantly disrupted the global sediment cycle, affecting river morphology and ecosystems. Based on satellite observations from 1984 to 2018, the RivSed database provides a spatially explicit view of river sediment, revealing declining trends in sediment concentration in 32% of US rivers. Most rivers show decreasing sediment concentration downstream, primarily due to large dams. Comparing observations with models, there are differences in longitudinal sediment concentration patterns. RivSed has important implications for river geomorphology and ecology, illustrating human impacts on US river corridors.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Astronomy & Astrophysics
Sarah E. Lang, Kelly M. A. Luis, Scott C. Doney, Olivia Cronin-Golomb, Max C. N. Castorani
Summary: Understanding and attributing changes to water clarity is crucial for studying and managing coastal ecosystems. However, variability in space and time limits the ability to describe patterns of water clarity. Regional satellite algorithms can provide a more comprehensive understanding of these changes.
EARTH AND SPACE SCIENCE
(2023)
Article
Environmental Sciences
Teng Li, Bozhong Zhu, Fei Cao, Hao Sun, Xianqiang He, Mingliang Liu, Fang Gong, Yan Bai
Summary: This study predicted the Secchi Disk Depth (SDD) in Qiandao Lake using Landsat8/OLI data and obtained pixel-by-pixel changing rates from satellite remote sensing. The results showed seasonal and spatial variations in SDD, as well as a significant impact of heavy rainfall on lake transparency.
Article
Environmental Sciences
Harshit Khanna, Y. W. Fan, S. N. Chan
Summary: Water transparency is important to water quality processes, and an artificial intelligence-based object detection algorithm was used for automatic detection of a Secchi disk in water, opening up opportunities for predicting short-term water quality changes.
MARINE POLLUTION BULLETIN
(2022)
Article
Astronomy & Astrophysics
Sarah E. Lang, Kelly M. A. Luis, Scott C. Doney, Olivia Cronin-Golomb, Max C. N. Castorani
Summary: Understanding and attributing changes to water quality is crucial for coastal ecosystem study and management. In this study, open-access satellite data and low-cost in situ methods were used to improve estimates of water clarity in a complex coastal water body. This approach increases the spatiotemporal coverage of in situ water clarity data and improves estimates from bio-optical algorithms.
EARTH AND SPACE SCIENCE
(2023)
Article
Chemistry, Analytical
Feng Lin, Libo Gan, Qiannan Jin, Aiju You, Lei Hua
Summary: This study proposes a method to measure the transparency of water using image processing and deep learning algorithms. The experiments show that this method is more accurate and objective than personal observation.
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)