ArticleScientific reports2026
Enhancing reservoir water quality simulation through machine learning-driven remote sensing integration with EFDC: a coupled framework for eutrophication management in data-scarce regions.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Hydrological model accuracy is often constrained by limited in-situ data. This study develops a coupled framework integrating machine-learning-based remote sensing retrievals with the Environmental Fluid Dynamics Code (EFDC) to improve reservoir water quality simulations. Landsat imagery (2013-2023) and multiple algorithms (Random Forest, Gradient Boosting, AdaBoost, etc.) were used to derive spatiotemporal distributions of total nitrogen (TN), total phosphorus (TP), and chlorophyll-a (Chl-a), which were incorporated as dynamic boundary conditions in EFDC. The coupled model reduced simulation errors by 0.13-5.28% and increased mean R² from 0.70 to 0.81. Compared with standalone EFDC, retrieval-based estimates showed lower mean relative errors for TN (20.61%), TP (28.95%), and Chl-a (26.08%). Seasonal analysis revealed Chl-a peaks in June (14.6 µg/L) and TN/TP accumulation in summer. Scenario simulations indicated that external load reduction (5-30%) effectively decreased TN and TP but had limited influence on Chl-a due to threshold effects. The optimal integrated strategy (30% external load reduction + 1.0% outflow reduction, scenario f-1) achieved concurrent reductions of 27.4% (TN), 23.7% (TP), and 13.2% (Chl-a), averaging 21.4% across all indicators. Critically, scenario analysis revealed that reducing external inflow loads alone produced limited suppression of algal biomass due to threshold effects and internal nutrient buffering, whereas combined reductions in inflow loading and moderate adjustments to hydrodynamic outflow regulation were necessary to achieve meaningful Chl-a control. These findings demonstrate that alterations to both nutrient inflow and reservoir hydrodynamics are essential levers for eutrophication management, with implications for operational decision-making in data-scarce reservoir systems worldwide.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.