The Hydrometeorological Sandbox - École de technologie supérieure (HYSETS) is a rich, comprehensive and large-scale database for hydrological modelling covering 14425 watersheds in North America. The database includes data covering the period 1950-2018 depending on the type and source of data. The data include a wide array of hydrometeorological data required to perform hydrological and climate change impact studies: (1) watershed properties including boundaries, area, elevation slope, land use and other physiographic information; (2) hydrometric gauging station discharge time-series; (3) precipitation, maximum and minimum daily air temperature time-series from weather station records and from (4) the SCDNA infilled gauge meteorological dataset; (5) the NRCan and Livneh gridded interpolated products' meteorological data; (6) ERA5 and ERA5-Land reanalysis data; and (7) the SNODAS and ERA5-Land snow water equivalent estimates. All data have been processed and averaged at the watershed scale, and provides a solid basis for hydrological modelling, climate change impact studies, model calibration assessment, regionalization method evaluation and essentially any study requiring access to large amounts of spatiotemporally varied hydrometeorological data.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371877 | PMC |
http://dx.doi.org/10.1038/s41597-020-00583-2 | DOI Listing |
Water Res X
May 2025
Institute for Artificial Intelligence R&D of Serbia, Fruškogorska 1, Novi Sad 21000, Serbia.
This study evaluates three Machine Learning (ML) models-Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. We adopt a data-centric approach, utilizing large, validated datasets to train the models, and apply SHapley Additive exPlanations (SHAP) to enhance the interpretability and reliability of the ML models. The results show that TKAN outperforms LSTM but slightly lags behind TCN in streamflow forecasting.
View Article and Find Full Text PDFSci Data
January 2025
Department of Earth and Environmental Engineering, Columbia University, New York, USA.
The Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions have provided estimates of Terrestrial Water Storage Anomalies (TWSA) since 2002, enabling the monitoring of global hydrological changes. However, temporal gaps within these datasets and the lack of TWSA observations prior to 2002 limit our understanding of long-term freshwater variability. In this study, we develop GRAiCE, a set of four global monthly TWSA reconstructions from 1984 to 2021 at 0.
View Article and Find Full Text PDFSci Total Environ
January 2025
CNRS, SIGMA Clermont, Institut de Chimie de Clermont-Ferrand, Université Clermont Auvergne, F-63000 Clermont-Ferrand, France.
Climate change affects groundwater availability and residence times, necessitating a thorough understanding of aquifer characteristics to define sustainable yields, particularly in regions where water is heavily exploited. This study focuses on the Volvic volcanic aquifer (Chaîne des Puys, France), where groundwater recharge has decreased due to climate change, raising concerns about water use sustainability. To address these challenges, this work proposes a multi-tracer approach, based on hydrogeological monitoring, including the estimation of groundwater ages, major elements chemistry and water stable isotopes to better characterise this resource decrease and more peculiarly its origin and its impact on the environment that has never been addressed.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Department of Geography, Hong Kong Baptist University, Hong Kong SAR, China.
Land use changes profoundly affect hydrological processes and water quality at various scales, necessitating a comprehensive understanding of sustainable water resource management. This paper investigates the implications of land use alterations in the Gap-Cheon watershed, analyzing data from 2012 and 2022 and predicting changes up to 2052 using the Future Land Use Simulation (FLUS) model. The study employs the Hydrological Simulation Program-FORTRAN (HSPF) model to assess water quantity and quality dynamics.
View Article and Find Full Text PDFAppl Microbiol Biotechnol
January 2025
Key Laboratory of Marine Ranching, Ministry of Agriculture and Rural Affairs, China, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou, 510300, China.
The construction of artificial reefs (ARs) is an effective way to restore habitats and increase and breed fishery resources in marine ranches. However, studies on the impacts of ARs on the structure, function, and assembly patterns of the bacterial community (BC), which is important in biogeochemical cycles, are lacking. The compositions, diversities, assembly patterns, predicted functions, and key environmental factors of the attached and free-living microbial communities in five-year ARs (O-ARs) and one-year ARs (N-ARs) in Fangchenggang, China, were analyzed via 16S rRNA gene sequencing.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!