Xiao Shen

Catchment transit time, the time water takes to travel from precipitation to streamflow, is a key diagnostic of how catchments store, route, and transform water and solutes. Its spatial variability reflects interactions among hydroclimatic forcings, landscape structure, flow‐path organization, and storage dynamics, yet understanding its environmental drivers remains fragmented across scales, regions, and methodological frameworks. This review examines how environmental drivers shape transit‐time spatial variability, focusing on two widely used metrics: mean transit time (MTT) and young water fraction (Fyw). We first clarify the mathematical and physical distinctions between these two metrics, showing that MTT is not well suited for cross‐study comparisons because it is highly sensitive to methodological choices, tracer types, and sampling design. MTT is therefore presented mainly as a historical summary, whereas Fyw is identified as a more robust and transferable metric for comparative synthesis. Integrating primarily Fyw‐based evidence, we identify a possible scale‐dependent shift in environmental controls of transit time variability: from soil and hillslope–riparian processes in small catchments to hydrological connectivity and network organization at intermediate scales, to broader groundwater–geologic influences in larger basins. However, this framework remains tentative because available evidence is uneven across spatial scales and climate regimes. Future progress requires broader geographic coverage, stronger reliance on robust metrics such as Fyw, clearer quantification of uncertainty in MTT estimates, and more process‐based frameworks linking environmental drivers, hydrological processes, and tracer‐derived response metrics. These are essential for improving transferable understanding and prediction of water and solute transport, especially in ungauged regions. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water and Environmental Change Science of Water > Methods

Researchers and environmental managers need big datasets spanning long time periods to accurately assess current and historical water quality conditions in fresh and estuarine waters. Using remote sensing data, we can survey many water bodies simultaneously and evaluate water quality conditions with greater frequency. The combination of existing and historical water quality data with remote sensing imagery into a unified database allows researchers to improve remote sensing algorithms and improves understanding of mechanisms causing blooms. We report on the development of a water quality database “EstuarySAT” which combines data from the Sentinel-2 multi-spectral instrument (MSI) remote sensing platform and water quality data throughout the coastal USA. EstuarySAT builds upon an existing database and set of methods developed by the creators of AquaSat, whose region of interest is primarily larger freshwater lakes in the USA. Following the same basic methods, EstuarySAT utilizes open-source tools: R v. 3.24+ (statistical software), Python (dynamic programming environment), and Google Earth Engine (GEE) to develop a combined water quality data and remote sensing imagery database (EstuarySAT) for smaller coastal estuarine and freshwater tidal riverine systems. EstuarySAT fills a data gap that exists between freshwater and estuarine water bodies. We are able to evaluate smaller systems due to the higher spatial resolution of Sentinel-2 (10 m pixel image resolution) vs. the Landsat platform used by AquaSat (30 m pixel resolution). Sentinel-2 also has a more frequent revisit (overpass) schedule of every 5 to 10 days vs. Landsat 7 which is every 17 days. EstuarySAT incorporates publicly available water quality data from 23 individual water quality data sources spanning 1984–2021 and spatially matches them with Sentinel-2 imagery from 2015–2021. EstuarySAT currently contains 299,851 matched observations distributed across the coastal USA. EstuarySAT’s primary focus is on collecting chlorophyll data; however, it also contains other ancillary water quality data, including temperature, salinity, pH, dissolved oxygen, dissolved organic carbon, and turbidity (where available). As compared to other ocean color databases used for developing predictive chlorophyll algorithms, this coastal database contains spectral profiles more typical of CDOM-dominated systems. This database can assist researchers and managers in evaluating algal bloom causes and predicting the occurrence of future blooms.