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Urban expansion, altered precipitation regimes, and the legacy of rigid infrastructure have intensified flash flooding, pollutant loading, and ecological fragmentation in riverine and coastal zones. Simultaneously, advances in analytical chemistry, satellite observation, numerical hydraulics, and spatial optimization now permit multi-scale investigation of coupled hydro-environmental processes. The Research Topic of “
In Singapore’s Kallang River and the Murderkill River in the USA, DOM was characterized using a suite of optical and chemical tools. UV–visible absorbance (e.g.,
On Tsushima Island, Japan, researchers used 10 years of Landsat 8 imagery, Normalized Difference Vegetation Index (NDVI), and Digital Elevation Model (DEM) layers to classify land cover and estimate runoff coefficients. Runoff trends were extrapolated from 2014 to 2034, using hydrological modeling integrated into ArcGIS. Flood risk was zoned by overlaying the runoff potential with slope and proximity to urban centers (
Using three epochs of Landsat imagery (2000, 2015, and 2020), NDVI differencing, and the InVEST Habitat-Quality model, wetland loss and habitat degradation in the Greater Amanzule Wetland (Ellembelle District) were quantified under accelerating community and industrial expansion (
The cost for fencing, planting, and maintaining a 40-m riparian buffer across 75,500 km of NSW streams was estimated. Marxan (100 runs; boundary-length modifier = 0.6) compared
A common thread is the dominance of event-scale hydrological pulse-storms in DOM dynamics, vegetation loss in runoff escalation, and vortex shedding at bridge piers-in driving system instability. Vegetation is a dual regulator: it shapes DOM composition and runoff attenuation and can enhance pollutant deposition when incorporated into riparian rehabilitation. Economic optimization (Marxan) demonstrates that geomorphic and ecological information can be monetized, achieving large-scale rehabilitation at a fraction of the
Event-driven hydrology, land-cover conversion, and engineered nature-based structures jointly determine river system resilience. Molecular analytics refine pollutant fingerprints; satellite hydrology quantifies runoff trajectories; and optimization frameworks translate geomorphic insights into cost-efficient action. The Research Topic has shed light on future research trends, which should couple machine learning approaches with real-time sensor networks, embed habitat quality metrics in participatory planning, and improve the practicality of environmental–hydraulics knowledge in scalable nature-based solutions.
JP: Project administration, Methodology, Writing – review and editing, Conceptualization, and resources. NI: Writing – original draft, Writing – review and editing, and Conceptualization. PH: Writing – review and editing and Conceptualization. MM: Writing – review and editing and Conceptualization. RH: Writing – review and editing and Conceptualization. RS: Conceptualization and Writing – review and editing. EA-Q: Conceptualization and Writing – review and editing.
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