Adding Pierard article

src/data/papers-citing-parcels.ts

@@ -2557,6 +2557,16 @@ export const papersCitingParcels: Paper[] = [
     abstract:
       'Marine plastic pollution predominantly originates from rivers, yet the extent of its return to shorelines remains uncertain. This study employs a Lagrangian particle tracking model to simulate the trajectories of plastic debris discharged from the Ganges, Brahmaputra, Godavari, and Krishna rivers along India’s east coast. Seasonal simulations were conducted for four periods pNEM (January–February), PreM (March–May), SWM (June–September), and NEM (October–December) using windage factors of 1%, 3%, and 5% to represent various plastic particle types. Results indicate that the highest deposition of riverine plastic occurs along the east coast of India during NEM (28.2–30%), highlighting its vulnerability to plastic accumulation. Increased windage led to greater deposition, underscoring the role of wind-driven transport. Krishna and Godavari rivers exhibited peak deposition (46% and 42%) during PreM, while the Ganges contributed ~ 42% during NEM. The Brahmaputra had the lowest deposition rates. Ocean currents transported plastic from the Godavari and Krishna rivers northward during PreM and SWM, while winds and Stokes drift pushed Ganges and Brahmaputra particles southwestward in pNEM and NEM. These findings emphasize seasonal variations in plastic transport and inform coastal management strategies to mitigate pollution along the east coast of India.',
   },
+  {
+    title:
+      'Quantifying variability in Lagrangian particle dispersal in ocean ensemble simulations: an information theory approach',
+    published_info: 'Nonlinear Processes in Geophysics, 32, 411-438',
+    authors:
+      'Pierard, CM, S Rühs, L Gómez-Navarro, MC Denes, F Meirer, T Penduff, E van Sebille (2025)',
+    doi: 'https://doi.org/10.5194/npg-32-411-2025',
+    abstract:
+      'Ensemble Lagrangian simulations aim to capture the full range of possible outcomes for particle dispersal. However, single-member Lagrangian simulations are most commonly available and only provide a subset of the possible particle dispersal outcomes. This study explores how to generate the variability inherent in Lagrangian ensemble simulations by creating variability in a single-member simulation. To obtain a reference for comparison, we performed ensemble Lagrangian simulations by advecting the particles from the surface of the Gulf Stream, around 35.61° N, 73.61° W, in each member to obtain trajectories capturing the variability of the full 50-member ensemble. Subsequently, we performed single-member simulations with spatially and temporally varying release strategies to generate comparable trajectory variability and dispersal and also with adding Brownian motion diffusion to the advection. We studied how these strategies affected the number of surface particles connecting the Gulf Stream with the eastern side of the subtropical gyre. We used an information theory approach to define and compare the variability in the ensemble with the single-member strategies. We defined the variability as the marginal entropy or average information content of the probability distributions of the position of the particles. We calculated the relative entropy to quantify the uncertainty of representing the full-ensemble variability with single-member simulations. We found that release periods of 12 to 20 weeks most effectively captured the full ensemble variability, while spatial releases with a 2.0° radius resulted in the closest match at timescales shorter than 10 d. We found that adding relatively high amounts of Brownian motion diffusion (Kh=1000 m2 s−1) captures the entropy aspects of the full ensemble variability well but leads to an overestimation of connectivity. Our findings provide insights to improve the representation of variability in particle trajectories and define a framework for uncertainty quantification in Lagrangian ocean analysis.',
+  },
   {
     title:
       'Dispersion monitoring services in the Mediterranean Sea: A multi-model statistical approach',