AI Article Synopsis
- Researchers are using stable water isotopes (18O and 2H) to better understand the hydrology of the Mackenzie River basin in northern Canada, particularly in wetland areas near Fort Simpson.
- The study compares estimates of groundwater contributions to streamflow from the WATFLOOD hydrological model with conventional isotope hydrograph separation in five catchments along the lower Liard River, showing a strong alignment between the two methods.
- The sensitivity analysis also suggests that runoff from channelized fens might contribute less to overall streamflow than earlier studies indicated.
Article Abstract
Monitoring of stable water isotopes (18O and 2H) in precipitation and surface waters in the Mackenzie River basin of northern Canada has created new opportunities for researchers to study the complex hydrology and hydroclimatology of this remote region. A number of prior studies have used stable isotope data to investigate aspects of the hydrological regime of the wetland-dominated terrain near Fort Simpson, Northwest Territories, Canada. The present paper compares estimates of groundwater contributions to streamflow derived using the WATFLOOD distributed hydrological model, equipped with a new water isotope tracer module, with the results of conventional isotope hydrograph separation for five wetland-dominated catchments along the lower Liard River. The comparison reveals highly promising agreement, verifying that the hydrological model is simulating groundwater flow contributions to total streamflow with reasonable fidelity, especially during the crucial snowmelt period. Sensitivity analysis of the WATFLOOD simulations also reveals intriguing features about runoff generation from channelized fens, which may contribute less to streamflow than previously thought.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/10256010500053730 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multi-scenario prediction of ecosystem services value and mechanism of its trade-offs under the township scale-evidence from Liaoning province.
Environ Monit Assess
January 2025
College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, 100083, China.
Land use changes alter the capacity for the stable provisioning of regional ecosystem services, and the rational integration of ecological and economic benefits has become a critical challenge. The values of 11 specific ecosystem service functions and ecosystem service trade-off degrees were estimated. The Pearson correlation coefficient method and spatial autocorrelation were then utilized to investigate the relationship between these variables.
View Article and Find Full Text PDFEffectiveness of three machine learning models for prediction of daily streamflow and uncertainty assessment.
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 PDFGRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks.
Sci 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 PDFUnderstanding complex volcanic hydrosystems using a multi-tracer approach.
Sci 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 PDFLand use dynamics and their impact on hydrology and water quality of a river catchment: a comprehensive analysis and future scenario.
Environ 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 PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!