Results 241 to 250 of about 131,130 (311)

Combining the Underwater Vision Profiler 6 with sediment traps to measure in situ velocity of marine particles

Limnology and Oceanography: Methods, EarlyView.
Abstract Particles sinking from the surface to the deep ocean play a key role in the biological carbon pump, whose efficiency depends partly on sinking velocities. Over the last decade, in situ imaging has enabled critical advances in our understanding of particle dynamics in the ocean.
Manon Laget, Alexandre Accardo, Marc Picheral, Camille Catalano, Lionel Guidi, Tristan Biard   +5 more
wiley   +1 more source

Quantitative assessment of water-related ecological security patterns based on ecosystem service flows: A case study of Jilin Province, China. [PDF]

iScience
Ru Q, Yang X, Liu Y, Wang X, Dai C, Shang Y.   +5 more
europepmc   +1 more source

Uncertainty assessment for a novel optically shallow water radiometric acquisition platform

Limnology and Oceanography: Methods, EarlyView.
Abstract We present a novel motorized platform, a custom‐built jet‐ski designed for acoustic and radiometric measurements in optically shallow coastal waters. This platform integrates in‐water radiometers with single‐beam and multi‐beam acoustic sensors, along with a suite of active and passive instruments (conductivity‐temperature‐depth, fluorescence,
Raphaël Mabit, Simon Bélanger, Pascal Bernatchez   +2 more
wiley   +1 more source

Future climate change impacts on carbon dynamics and ecohydrological risks in the West Liao river Basin, China: implications for carbon management. [PDF]

Carbon Balance Manag
Qiao Z, Ma L, Xu Y, Yang D, Liu T, Chen Y.   +5 more
europepmc   +1 more source

Uncertainty in aquatic greenhouse gas flux estimates arises from subjective processing of floating chamber time series

Limnology and Oceanography: Methods, EarlyView.
Abstract Accurate quantification of greenhouse gas (GHG) fluxes from aquatic systems is essential for constraining regional and global carbon budgets. Closed floating chambers are widely used to measure carbon dioxide (CO2) and methane (CH4) fluxes at the water–air interface, yet large uncertainties persist due to subjective processing of chamber time ...
Camille Minaudo, Miguel Cabrera‐Brufau, Jorge Juan Montes‐Pérez, Katrin Attermeyer, Antonio Camacho, Alba Camacho‐Santamans, Constantin Cazacu, Relu Giuca, Ana Lillebø, Benjamin Misteli, Daniel Morant, Biel Obrador, Bruna R.F. Oliveira, Jolita Petkuviené, Antonio Picazo Mozo, Carlos Rochera, Daniel von Schiller   +16 more
wiley   +1 more source

Design storm estimation for flood risk assessment in the temperate Himalayan basin using hydrological modelling. [PDF]

Sci Rep
Attar MI, Khan JN, Altaf Y, Alsubih M, Naseer S, Kumar R, Bhat OA, Bangroo SA, Sharma MK.   +8 more
europepmc   +1 more source

A machine learning assisted method for rapidly annotating benthic megafauna in large volumes of marine imagery

Limnology and Oceanography: Methods, EarlyView.
Abstract Recent technological advancements have rapidly expanded our capacity for collecting image data in the marine environment, but processing images into meaningful ecological metrics remains a manual, time‐consuming, and biased process. This is particularly challenging with electro‐optical cabled imaging systems which generate images at a rate ...
Katharine T. Bigham, Ada Carter
wiley   +1 more source

Analysis the impact of hydrological flow patterns on hydropower generation in Haditha dam reservoir. [PDF]

Sci Rep
Tayyeh HK, Mohammed R, Hussein AM, Benzougagh B, Alasqah A, Khedher KM, Almutairi MK.   +6 more
europepmc   +1 more source

A simple index for assessing cumulative human impacts on mountain lakes

Limnology and Oceanography: Methods, EarlyView.
Abstract Mountain lakes are highly sensitive ecosystems and effective sentinels of environmental change, yet the exposure and magnitude of the human footprint remain poorly quantified. In this study, we develop a simple and non‐invasive abiotic index to assess cumulative pressures on mountain lakes. The proposed index integrates eight variables grouped
Clara Sáez, Ignasi Arranz, Guillermo García‐Gómez, Javier Sánchez‐Hernández   +3 more
wiley   +1 more source

Surface Variable-Based Machine Learning for Scalable Arsenic Prediction in Undersampled Areas. [PDF]

Geohealth
Azad S, Stahl MO, Erickson M, DeYoung BA, Connolly C, Chillrud L, Schilling K, Navas-Acien A, Basu A, Mailloux B, Bostick BC, Chillrud SN.   +11 more
europepmc   +1 more source