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Satellite derived sea surface temperatures (SSTs) are often used as a proxy for in situ water temperatures, as they are readily available over large spatial and temporal scales. However, contamination of satellite images can prohibit their use in coastal areas. We compared in situ temperatures to SST foundation (~10 m depth) at 31 sites inshore of the East Australian Current (EAC), the dynamic western boundary current of the south Pacific gyre, using an area averaging approach to overcome coastal contamination. Varying across- and along-shelf distances were used to area average SST measurements and de-correlation time scales were used to gap fill data. As the EAC is typically anisotropic (dominant along-shore flow) the choice of across-shelf distances influenced the correlation with in situ temperatures more than along-shelf distances. However, the “optimal” distances for both measurements were within known de-correlation length scales. Incorporating both SST area and time averaging (based on de-correlation time scales) produced data for an average of 96% of days that in situ loggers were deployed, compared to 27% (52%) without (with) area averaging. Temperature differences between the in situ data and SSTs varied depending on time of year, with higher differences in the austral summer when daily in situ temperatures can range by up to 4.20°C. The differences between the in situ and SST measurements were, however, significant with or without area averaging (t-test: p-values < 0.05). Nevertheless, when using the area averaging approaches SSTs were only an average of ~1.05°C different from in situ temperatures and less than in situ temperature fluctuations. Linear mixed models revealed that latitude, distance to the coast and nearest estuary did not influence the difference between the in situ and satellite data as much as the water depth. This study shows that using de-correlation length and time scales to inform how to process satellite data can overcome contamination and missing data thereby greatly increasing the coverage and utility of SST data, particularly in coastal areas.

In inland settings, groundwater discharge thermally modulates receiving surface water bodies and provides localized thermal refuges; however, the thermal influence of intertidal springs on coastal waters and their thermal sensitivity to climate change are not well studied. We addressed this knowledge gap with a field- and model-based study of a threatened coastal lagoon ecosystem in southeastern Canada. We paired analyses of drone-based thermal imagery with in situ thermal and hydrologic monitoring to estimate discharge to the lagoon from intertidal springs and groundwater-dominated streams in summer 2020. Results, which were generally supported by independent radon-based groundwater discharge estimates, revealed that combined summertime spring inflows (0.047 m3 s−1) were comparable to combined stream inflows (0.050 m3 s−1). Net advection values for the streams and springs were also comparable to each other but were 2 orders of magnitude less than the downwelling shortwave radiation across the lagoon. Although lagoon-scale thermal effects of groundwater inflows were small compared to atmospheric forcing, spring discharge dominated heat transfer at a local scale, creating pronounced cold-water plumes along the shoreline. A numerical model was used to interpret measured groundwater temperature data and investigate seasonal and multi-decadal groundwater temperature patterns. Modelled seasonal temperatures were used to relate measured spring temperatures to their respective aquifer source depths, while multi-decadal simulations forced by historic and projected climate data were used to assess long-term groundwater warming. Based on the 2020–2100 climate scenarios (for which 5-year-averaged air temperature increased up to 4.32∘), modelled 5-year-averaged subsurface temperatures increased 0.08–2.23∘ in shallow groundwater (4.2 m depth) and 0.32–1.42∘ in the deeper portion of the aquifer (13.9 m), indicating the depth dependency of warming. This study presents the first analysis of the thermal sensitivity of groundwater-dependent coastal ecosystems to climate change and indicates that coastal ecosystem management should consider potential impacts of groundwater warming.

The latest ECMWF global reanalysis, ERA5, is able to provide hourly surface pressure and water vapor-weighted mean temperature (Tm), which are two key factors in GPS precipitable water vapor (PWV) retrieval. Performance of surface pressure, surface air temperature, and Tm derived from ERA5 and its predecessor ERA-Interim (ERAI) are evaluated by comparing with more than 2000 meteorological stations and 89 radiosonde stations in the year of 2016 over China. Average pressure error RMS is 0.7 hPa for ERA5, compared to 1.0 hPa for ERAI, and ERA5 pressure diurnal variations agree much better than ERAI with in situ measurements. Temperature and Tm differences between ERA5 and ERAI are relatively smaller, with error RMS of 1.8 K and 1.6 K for ERA5-derived temperature and Tm, respectively. PWV error contributed by reanalysis-derived parameters is also estimated. The ERA5-induced PWV error is generally less than 1 mm, with smaller errors (< 0.4 mm) in eastern China but larger errors (can exceed 0.6 mm) in northwestern China and in the southeast of the Tibetan Plateau. Diurnal variations of PWV retrieved using pressure and Tm from meteorological measurements (MET) and reanalysis products are compared. Good agreements are found between ERA5-based PWV and MET-based PWV in diurnal variations, while artificial diurnal signals are introduced in ERAI-based PWV, especially in the Tibetan Plateau. This study indicates that ERA5 can support high-accuracy hourly GPS PWV retrieval over China without contaminating the diurnal cycles, which is of great importance for historical GPS PWV retrieval at stations without collocated meteorological sensors equipped.

Most of Earth’s fresh surface water is consolidated in just a few of its largest lakes, and because of their unique response to environmental conditions, lakes have been identified as climate change sentinels. While the response of lake surface water temperatures to climate change is well documented from satellite and summer in situ measurements, our understanding of how water temperatures in large lakes are responding at depth is limited, as few large lakes have detailed long-term subsurface observations. We present an analysis of three decades of high frequency (3-hourly and hourly) subsurface water temperature data from Lake Michigan. This unique data set reveals that deep water temperatures are rising in the winter and provides precise measurements of the timing of fall overturn, the point of minimum temperature, and the duration of the winter cooling period. Relationships from the data show a shortened winter season results in higher subsurface temperatures and earlier onset of summer stratification. Shifts in the thermal regimes of large lakes will have profound impacts on the ecosystems of the world’s surface freshwater. This study presents hourly data from a thermistor string in Lake Michigan, inspecting its response at depth to surface warming. Based on the data, the study suggests bottom lake temperatures respond to changes in turnover and re-stratification, with the ultimate possibility of the lake shifting from dimictic to monomictic.

Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

Pozzolanic reaction of volcanic ash with hydrated lime is thought to dominate the cementing fabric and durability of 2000-year-old Roman harbor concrete. Pliny the Elder, however, in first century CE emphasized rock-like cementitious processes involving volcanic ash (pulvis) \"that as soon as it comes into contact with the waves of the sea and is submerged becomes a single stone mass (fierem unum lapidem), impregnable to the waves and every day stronger\" (Naturalis Historia 35.166). Pozzolanic crystallization of Al-tobermorite, a rare, hydrothermal, calcium-silicate-hydrate mineral with cation exchange capabilities, has been previously recognized in relict lime clasts of the concrete. Synchrotron-based X-ray microdiffraction maps of cementitious microstructures in Baianus Sinus and Portus Neronis submarine breakwaters and a Portus Cosanus subaerial pier now reveal that Al-tobermorite also occurs in the leached perimeters of feldspar fragments, zeolitized pumice vesicles, and in situ phillipsite fabrics in relict pores. Production of alkaline pore fluids through dissolution-precipitation, cation-exchange and/or carbonation reactions with Campi Flegrei ash components, similar to processes in altered trachytic and basaltic tuffs, created multiple pathways to post-pozzolanic phillipsite and Al-tobermorite crystallization at ambient seawater and surface temperatures. Long-term chemical resilience of the concrete evidently relied on water-rock interactions, as Pliny the Elder inferred. Raman spectroscopic analyses of Baianus Sinus Al-tobermorite in diverse microstructural environments indicate a cross-linked structure with Al3+ substitution for Si4+ in Q3 tetrahedral sites, and suggest coupled [Al3++Na+] substitution and potential for cation exchange. The mineral fabrics provide a geoarchaeological prototype for developing cementitious processes through low-temperature rock-fluid interactions, subsequent to an initial phase of reaction with lime that defines the activity of natural pozzolans. These processes have relevance to carbonation reactions in storage reservoirs for CO2 in pyroclastic rocks, production of alkali-activated mineral cements in maritime concretes, and regenerative cementitious resilience in waste encapsulations using natural volcanic pozzolans.

Frost damage is a complex and critical issue for canal construction in seasonally frozen regions, posing a challenge to agriculture and industry. This study combines in-situ monitoring and numerical simulation in field-scale to investigate the freeze depth, frost heave, and the effect of accumulated water on slope deformation in a canal in Jilin Province, China. The results reveal that the canal experiences intense frost heave of approximately 15 cm during winter due to low temperatures and sufficient water supply. The shallowly buried groundwater and seepage from accumulated water were the dominant water sources driving migration during frost heave. While accumulated water insulated the canal lining below the water surface and reduced frost heave, the combined effect of ice pressure and subsoil frost heave also caused damage to the canal. It is necessary to enhance protection measures for canal slopes at locations, where ice cover occurs. In addition, the freezing process differs between the sunny and shady slopes of canal. Shady slopes experience longer and more intense frost heave, leading to asymmetrical damage to the canal. This study presents the development and characteristics of frost damage for canal with high water table in seasonally frozen areas. It can serve and guide the design and management of water conveyance infrastructure in cold regions.

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system generates global, daily, gap-filled foundation sea surface temperature (SST) fields from satellite data and in situ observations. The SSTs have uncertainty information provided with them and an ice concentration (IC) analysis is also produced. Additionally, a global, hourly diurnal skin SST product is output each day. The system is run in near real time to produce data for use in applications such as numerical weather prediction. Data production is monitored routinely and outputs are available from the Copernicus Marine Environment Monitoring Service (CMEMS; marine.copernicus.eu). As an operational product, the OSTIA system is continuously under development. For example, since the original descriptor paper was published, the underlying data assimilation scheme that is used to generate the foundation SST analyses has been updated. Various publications have described these changes but a full description is not available in a single place. This technical note focuses on the production of the foundation SST and IC analyses by OSTIA and aims to provide a comprehensive description of the current system configuration.

Global nuclear power is surging ahead in its quest for global carbon neutrality, eyeing an anticipated installed capacity of 436 GW for coastal nuclear power plants by 2040. As these plants operate, they emit substantial amounts of warm water into the ocean, known as thermal discharge, to regulate the temperature of their nuclear reactors. This discharge has the potential to elevate the temperature of the surrounding seawater, potentially influencing the marine ecosystem in the discharge vicinity. Therefore, our study area is on the Qinshan and Jinqimen Nuclear Power Plants in China, employing a blend of Landsat 8/9, and unmanned aerial vehicle (UAV) imagery to gather sea surface temperature (SST) data. In situ measurements validate the temperature data procured through remote sensing. Leveraging these SST observations alongside hydrodynamic and meteorological data from field measurements, we input them into the MIKE 3 model to prognosticate the three-dimensional (3D) spatial distribution and temperature elevation resulting from thermal discharge. The findings reveal that (1) satellite remote sensing can instantly acquire the horizontal distribution of thermal discharge, but with a spatial resolution much lower than that of UAV. The spatial resolution of UAV is higher, but the imaging efficiency of UAV is only 1/40,000 of that of satellite remote sensing. (2) Numerical simulation models can predict the 3D spatial distribution of thermal discharge. Although UAV and satellite remote sensing cannot directly obtain the 3D spatial distribution of thermal discharge, using remotely sensed SST as the temperature field input for the MIKE 3 model can reduce the quantity of measured temperature data and lower the cost of numerical simulation. (3) In the process of monitoring and predicting the thermal discharge of nuclear power plants, achieving an effective balance between monitoring accuracy and cost can be realized by comprehensively considering the advantages and costs of satellite, UAV, and numerical simulation technologies.

This study analyzed how external forcings, such as meteorological conditions and inflows, influence the average water surface temperature (WST) of the urban Lake Paranoá, Brasília-Brazil, using both in situ measurements and remote sensing estimates over a 40-year period. The temperature model calibrated for Lake Paranoá with no time lag (0-day delay) presented the following metrics: R2 = 0.92, RMSE = 0.59 °C, demonstrating the feasibility of obtaining reliable thermal estimates from remote sensing even in urban water bodies. Simple and multiple regression analyses were applied to identify the main external drivers of WST across different temporal scales. A warming trend of 0.036 °C/yr in lake surface temperature was observed, higher than the concurrent increase in air temperature (0.026 °C/yr), suggesting enhanced thermal stratification that may impact water quality. The most influential variables on WST were air temperature, relative humidity, and wind speed, with varying degrees of influence depending on the time scale considered (daily, monthly, annual or seasonal). Remote sensing proved to be essential for overcoming the limitations of traditional monitoring, such as temporal gaps and limited spatial coverage, and allowed detailed mapping of thermal patterns throughout the lake. Integrating these data into hydrodynamic models enhances their diagnostic, predictive, and decision-support capabilities in the context of climate change.