Wildfires are becoming larger and more frequent across much of the US due to a combination of climate change and land use activities. Increasing wildfires have begun to raise concerns about effects on fresh waters, including water quality and other ecosystem services. Despite this, previous research mostly consists of short-term case studies and focuses on streams and rivers rather than lakes and reservoirs (hereafter, lakes). Using the Monitoring Trends in Burn Severity (MTBS) database, we show that 4.5% of lakes ≥ 1 ha in the continental US experienced at least one watershed wildfire from 1984-2016. Interestingly, lake watershed fires are not restricted to the western US. Of all the lower 48 states, Florida, Texas and Kansas were the top 3 states with the most lakes experiencing wildfire, whereas Idaho, Arizona and Nevada were the top 3 states by percentage of lakes in respective states experiencing wildfire. Using the LAGOS-US database, we present new regional-scale findings demonstrating effects of large wildfires on lake water quality. For example, we found a negative correlation between post-fire lake water clarity and the proportion of a lake’s watershed burned in 11 Minnesota and Wisconsin lakes (r = -0.61). We highlight the urgent need for more broad-scale studies that encompass an ecologically diverse set of waterbodies, landscapes and fire regimes, particularly in landscapes in which humans depend on lakes for fresh water. Finally, we emphasize that growing data sources such as MTBS and continental-scale water quality databases (e.g., LAGOS-US) offer prime opportunities for research advances that can help scale up findings from local case studies.

In this presentation, we report on a comprehensive and balanced assessment of the relationship between climate and conflict risks and its implications for future directions of research. Research findings on the relationship between climate and conflict are diverse and contested. Based on the judgments of experts representing a broad range of disciplines and analytical approaches, we have assessed current understanding. The assessment is structured around the importance of climate as a driver of organized armed conflict within countries, changes in conflict risk across climate futures, and implications for conflict risk reduction and climate change adaptation. Across experts, best estimates are that 3–20% of conflict risk over the last century has been influenced by climate, and none of their individual ranges excludes a role of climate in 10% of conflict risk to date. There is agreement that climate variability and change shape the risk of organized armed conflict within countries. However, other drivers are judged substantially more influential for conflict overall, and the mechanisms of climate–conflict linkages are a key uncertainty. Intensifying climate change is estimated to increase future conflict risk as additional linkages become relevant, although uncertainties also expand. Synoptic understanding of the climate–conflict relationship is important even if climate’s role is relatively minor among the drivers of conflict. Given that conflict has pervasive detrimental human, economic, and environmental consequences, climate–conflict linkages, even if minor, would significantly influence the social costs of carbon and decisions to limit future climate change. The assessment has pointed to the different ways climate may interact with the major drivers of conflict risk. Crosscutting priorities for future directions of research include (1) deepening insight into climate–conflict linkages and conditions under which they manifest, (2) ambitiously integrating research designs, (3) systematically exploring future risks and response options, responsive to ongoing decision-making, and (4) evaluating the effectiveness of interventions to manage climate–conflict links. The implications of this expanding scientific domain unfold in real time.

Mean monthly climatological mixed layer depth (MLD) combined with temperature, dissolved oxygen, and apparent oxygen utilization (AOU) are used to produce global estimates of the seasonal variability of ocean heat content anomaly (OHCA), O2 content anomaly (O2CA), and AOU content anomaly (ACA) in the surface mixed layer. Linear regression analyses show that the highest correlation occurs when O2CA lags OHCA by one month, whereas the highest correlation occurs when ACA lags OHCA by 2-3 months. The O2CA is negatively correlated, while the ACA is positively correlated with the OHCA in the mixed layer. The O2-heat ratio in the surface mixed layer is about -1.85 nmol/J in the subtropical and subpolar regions, which is on the same order of magnitude due to the O2 solubility effect alone. The solubility effect is the primary driver for the seasonal cycle of the O2 inventory in the mixed layer, and thus subject to changes in ocean warming. The 1-month lag between O2CA and OHCA suggests the O2 inventory quickly responds to heat content changes on seasonal time scales due to strong mixing in the mixed layer. The 2-3 month lag between ACA and OHCA suggests oxygen changes through biological activities take a longer time following OHC changes in relation to physical changes through O2 solubility. Our analysis indicates that the deoxygenation rate in the mixed layer, estimated from the regression analysis, is approximately -2.2 Tmol/year based on the O2-heat ratio in the mid-latitudes, accounting for 6±2% of the global deoxygenation for the time period 1955-2019.

The representations of clouds in climate model MRI-ESM2 (Yukimoto et al. 2019, JMSJ) used in CMIP6 simulations are significantly improved from the previous version MRI-CGCM3 used in CMIP5 simulations. The score of the spatial pattern of 2m temperature for MRI-ESM2 is better than any of the 47 CMIP6 models. The score for radiative fluxes at the top of the atmosphere for MRI-ESM2 is ranked in the top seven models among these CMIP6 models. We will introduce comprehensively various modifications related to clouds, which contribute to the dramatically improved cloud representation, and their main impacts (Kawai et al. 2019, GMD). The modifications cover various schemes and processes including the cloud scheme, turbulence scheme, cloud microphysics processes, the interaction between cloud and convection schemes, resolution issues, cloud radiation processes, aerosol properties, and numerics. In addition, we would like to mention what data we used during the model development, how we evaluated our model, how useful the intercomparison studies were, and what data we need for further model development.

Forest canopy structure is influenced by tree attributes and processes such as forest generation, growth, and mortality. Structural complexity of a tree or a stand has, however, been challenging to assess as comprehensive and quantitative measurements have practically been impossible to produce. Thus, we utilized 3D information provided by terrestrial laser scanning (TLS) in assessing structural complexity of individual Scots pine (Pinus sylvestris L.) trees to better understand of forest systems and especially relationships between structural complexity and crown shape and size. Additionally, we investigated the effects of forest management (i.e. thinning) on structural complexity of individual Scot pine trees. We applied fractal analysis (i.e. box dimension) to provide a measure for structural complexity of individual trees and investigated its relationship between crown dimensions (i.e. width, volume, and projection area). There was a positive relationship between crown characteristics and structural complexity indicating an increased structural complexity when crown shape and size increased. The strongest relationship (correlation coefficient of 0.4-0.7) was found between structural complexity and crown projection area and crown volume. The relationship between structural complexity and all crown attributes was stronger in denser forests (~900 stems/ha) with correlation coefficient 0.6-0.7 compared to sparse forests (~400 stems/ha) with correlation coefficient 0.6. Additionally, it was shown that structural complexity of individual Scots pine trees increased with forest management intensity. Crown characteristics can be considered as drivers of structural complexity of individual trees. Crown shape and size can be expected to characterize vitality of trees. Thus, this study provides an example how crown characteristics can be related to structural complexity of individual trees and how they can be quantitatively assessed. Furthermore, the study affirms the possibilities of TLS as a tool for characterizing forest canopy structure and dynamics.

Plate reconstructions show that the plume feeding today’s Icelandic volcanism passed beneath the continental lithosphere of Greenland between 50 and 100 million years ago. While there has been ample volcanism on the margins of Greenland, both on the west and the east coasts, the thickness of the Greenland craton prevented surface eruptions within Greenland, leaving details of the plume track unclear. However, the passage of the plume is expected to leave a scar in the lower continental lithosphere of Greenland, where the hot plume material interacts with and erodes the stiff cratonic root. This interaction should cause increased surface heat flux and reduced lithosphere thickness along the plume track, and can potentially be constrained by observations that constrain heat flux (e.g., magnetic data and ice flow rates) and lithospheric structure (e.g., seismic tomography and magnetotelluric modeling). While most seismic tomography models indicate that there might be an east-west trending corridor of reduced lithosphere thickness, recent heat flux maps inferred from magnetic data show a northwest-southeast trending anomaly of high heat flux, suggesting an alternative direction of the passage of the Iceland plume. In order to resolve the discrepancy between the suggested hotspot tracks, we use numerical models to investigate how the surface heat flux would evolve in response to the passage of a plume beneath thick cratonic lithosphere. Our work focuses both on the temporal evolution (e.g. the onset and duration), and the shape of the heat flux anomaly, as well as lithospheric thinning related to convective erosion of the cratonic root by hot plume material. We show that both the onset and the duration of the heat flux anomaly, as well as the degree of lithosphere thinning, depend on various parameters, especially the viscosity structure of lithosphere and asthenosphere and the plume strength. A comparison between observations from Greenland and our model predictions should provide new and better constraints on the subsurface structure of Greenland, and how it was modified by its interaction with the Iceland plume.

The presence of vegetation in riverine habitats gathered the attention of scientific community during the last decades. Aquatic and riparian plants interact with sediment and affect transport of bed material, besides the additional drag provided by stems and foliage. At the same time, water flows and bed erosion have an impact on the growth and survival of vegetation species. While the interactions of fluvial vegetation with river hydro- and morpho-dynamics are conceptually known, their modelling and the quantification of their effects are still object of research. Particularly, it is widely documented in literature that the mutual interactions between the presence of aquatic plants and the morphodynamic equilibrium of a river reach result in an organised disposition (patches of different size and geometry) of vegetation within the channel itself. In this work, we investigated the critical conditions for the formation of such organised arrangements in a straight channel of constant width characterised by an erodible bed of uniform sediment with uniform vegetation density. A 2D stability analysis was performed on shallow water equations (SWE) for the water flow, 2D Exner equation for bed elevation dynamics and sediment continuity and an additional equation for vegetation dynamics. Closure relations for drag and sediment transport in vegetated conditions were involved, as well. Results reveal the particular conditions of dimensionless parameters leading either to the suppression of vegetation or to the survival of aquatic plants. In the latter, the analysis suggests that multiple patches (order higher than 1) of vegetation may exist as aa result of instability, which is contrast to the findings prenset in literature.

In Washington State, climate change will reshape the Puget Sound marine ecosystem through bottom-up and, top-down processes, directly affecting species at all trophic levels. We applied analytical approaches to better understand future climate change effects on temperature and salinity in Puget Sound. We used empirical downscaling techniques to derive high resolution time series of future sea surface temperature and salinity, based on scenario outputs of two coarse resolution General Circulation Models, GFDL-CM4 and CNRM-CM6-1-HR, which were created as part of the CMIP6 - Coupled Model Intercomparison Project Phase 6. We calculated long-term averages for historical simulations, calculated anomalies for future years, and applied a delta-downscaling approach to a Regional Ocean Modeling System (ROMS) time series, yielding short (2020–2050) and long-term (2070–2100) forecasts. Downscaled output for Puget Sound showed temperature and salinity variability between scenarios and models, but overall there was strong model agreement. Model variability and uncertainty was higher for long-term projections. Spatially, we found regional differences for both temperature and salinity: including higher temperatures in the South Basin and higher salinity in the North Basin. Caveats to our methodology include the assumption that variable relationships are static and cannot represent interactions between large scale and local change, but this study is a first step to translating CMIP6 outputs to higher resolution predictions of future conditions in Puget Sound. The climate projections for Puget Sound oceanography will be used to drive the Atlantis ecosystem model for Puget Sound, an end-to-end ecosystem modeling approach that represents all trophic levels and evaluates the species-level impacts of climate change. This project is part of a Washington State Sea Grant funded project, “Evaluating the effects of Southern Resident orcas recovery actions and external threats in the marine ecosystem of Puget Sound.”

The Anomalous Transport Experiment (ATREX) comprised seven quasi-simultaneous observations of lower thermospheric winds, horizontally separated by up to 600 km, on 27 March 2012, 0900 UT. Zonal and meridional wind profiles were obtained with five sounding rockets launched from Wallops Island (38 N, 75.5 W) releasing seven trimethyl aluminum trails which were observed from three cameral sites. The wind profiles exhibit deep coherent wave structures between 85 and 140 km, dominated by very strong diurnal and semidiurnal tides below 120 km. The total wind amplitude exceeds 100 m/s, three times the value from the Global Scale Wave Model. In addition, a large scale, vertically propagating and growing, inertia-gravity wave, most visible above 120 km, has been isolated by empirical orthogonal function analysis. The region between 100 and 110 km shows the largest mesoscale variability of winds and wind shears, often exceeding 40 m/s/km. We will present an estimation of the structure function for wind fluctuations and possible evidence for stratified mesoscale turbulence.

A large number of General Circulation Models (GCMs) are currently available for modelling the atmospheric conditions over the Earth. However, there is a large variability in the future climate predicted by the available set of climate models. Hence, the climate data introduces the most amount of uncertainty in the climate change impact assessment. Regional-scale climate change impact studies based on these models may produce a wide range of possible impacts that becomes unusable for policymakers. A robust GCM selection procedure is introduced in the current study to bring the uncertainty to a realistic range. The proposed approach takes into account the process representation in the climate models by checking teleconnections in data along with their ability to predict the regional climate in spatial and temporal scale. The interdependence between the climate models are also accounted for in the proposed approach to avoid underestimation of uncertainty. The procedure is validated in the Bharathapuzha River Basin, Kerala, India. The study considers 22 GCMs that participated in the Coupled Model Inter-comparison Project-5 and 6 Regional Climate Models (RCMs) that are recommended for the Indian subcontinent. The climate models BNU-ESM, CMCC-CM, GFDL-ESM2G, GFDL-ESM2M and MPI-ESM-MR are found to be performing well for the prediction of both precipitation and temperature. The proposed climate model selection procedure can bring down the band width of uncertainty from 376 mm to 162 mm in monthly rainfall prediction with a containing ratio of 44%. The downscaling of the climate predictions can further increase the containing ratio by removing the systematic error. The bandwidth of uncertainty has reduced from 10.82 K to 3.83 K in the prediction of minimum temperature and from 8.35 K to 4.52 K for maximum temperature. The proposed GCM selection procedure provides more confidence in the predicted future climate since regionally significant correlations between climate variables are preserved in the selected models. The model selection procedure is validated for the period 2006-2018 with the observed climatic variables, and the selected models are found to be performing well.

As noted in the session description any research object produced should be made ’FAIR’; but ‘FAIR’ objects require attribution. What does that mean for the vast variety of research objects? What roles need to be acknowledged, how and where? These are questions the Federation of Earth Science Data Partners (ESIP) Research Object Citation cluster have been addressing. In particular, we have been applying the lens of the CREDiT taxonomy roles in these discussions. Here our conclusions to date will be described.

An ENSO event can transition from one event to another in complex ways. An El Niño (La Niña) event can be preceded by a La Niña (El Niño) event to become a cyclic ENSO, by a neutral event to become an episodic ENSO, or by another El Niño (La Niña) event to become a multi-year ENSO. We find that the observed El Niño and La Niña exhibit different complexities in their event-to-event transition patterns. The El Niño is dominated in order by episodic, cyclic, and multi-year transitions, but the reversed order is found in the La Niña. This difference in the transition complexity arises from a subtropical Pacific forcing mechanism that triggers ENSO events. Using observational analyses and forced atmospheric model experiments, we show that a preceding ENSO event can activate a subtropical Pacific forcing mechanism to trigger another ENSO event during the following year. These tropical-subtropical Pacific interactions result in a cyclic ENSO transition if the two ENSO events are of opposite signs or a multi-year ENSO transition if they are of the same sign. The preceding ENSO event should excite deep convections in the tropical Pacific in order to activate the subtropical Pacific mechanism. This requirement enables mean temperatures in the cold tongue and warm pool to respectively control how easily the cyclic and multi-year transitions can occur. This mean state control enables the subtropical Pacific forcing mechanism to result in more multi-year transitions for La Niña than El Niño and more episodic transitions for El Niño than La Niña. Furthermore, a future warmer tropical Pacific is projected to decrease the frequency of occurrence of multi-year ENSO transitions but increase the occurrence of cyclic ENSO transitions.. We find that the CMIP5 and CMIP6 models can reproduce the transition complexity for El Niño but not for La Niña. The models tend to produce too many episodic La Niña events and too few multi-year La Niña events. We are able to link the former deficiency to a weaker than observed subtropical Pacific forcing mechanism in the CMIP5/6 models and the latter to a cold bias in mean state SSTs in the equatorial Pacific in the CMIP5/6 models. To achieve better simulations of ENSO transition complexity, further efforts are to improve the model deficiencies in simulating the SP-onset mechanism and mean SSTs in the equatorial Pacific.

Depressions are topographic areas that have no outward flow and occur in portions of landscapes with little to no elevation change, or areas with negative relief in relation to surrounding areas. While these depressions are an important part of the hydrological system, they have historically been filled in or ignored during flow routing and other hydrological processing calculations. With the increased prevalence of high-resolution topography data, understanding and evaluating how topographic depressions can impact overland flow is vital for improving hydrological analyses, specifically in the context of flood inundation mapping. Flooding caused from the filling of depressions (pluvial flooding) can have compounding effects when simultaneously occurring with river (fluvial) or coastal flooding. Our goal is to consider both pluvial and fluvial flooding in flat urban environments to identify areas that are significantly more vulnerable to inundation as compared to flood mapping from only one particular source. Our approach relies on a multiplex network that utilizes the Height Above Nearest Drainage (HAND) method as well as a hydraulic head equalization algorithm to estimate inundation patterns. Social vulnerability data are integrated in this framework to identify urban hot spots, defined as areas with a lower relative socioeconomic status in conjunction with a higher probability of inundation. Combining technical and social information leads to the identification of communities that are more vulnerable to the physical, economical, and social components of floods. This approach can help urban flood planners associate social disparities in relationship to flood preparedness and response.

When will a particular city on the Eastern Seaboard have a 70% chance of flooding at least one hour on at least 70 days of the year? This jam-packed question drives this study, as coastal flooding is becoming increasingly frequent in many East Coast U.S. cities. The frequency of floods that were once categorized as high tide or “nuisance” flooding is rapidly increasing, and many events are escalating to the moderate and major/severe flooding thresholds, putting life and property at risk. This study focuses on flooding events prone to causing property damage and hazardous conditions to make improvements for the subseasonal and seasonal outlooks that are most impactful. A number of East Coast cities are employed as case studies, such as Key West, FL, Charleston, SC, and Annapolis, MD, to analyze current and projected flooding rates. The spatial extent of flooding is considered by using high-resolution topographical data in combination with National Ocean Service tidal datums. NOAA sea level rise scenarios are used to determine the probable time period at which a portion of each city will be experiencing flooding for at least one hour per day frequently throughout the year. Additionally, tide gauge data from the case study cities is decomposed to understand which physical components are contributing to the regional coastal flooding on timescales from subseasonal to seasonal and beyond.

Morphodynamic evolution of coastal barriers and the way it is recorded in the internal structure are still revealing many uncertainties. Coastal spits have a high potential of preservation due to their fast elongation under littoral drift, and are thus suitable for this kind of study. Compared to sandy spits, gravel and mixed spits are understudied. Yet these coarse-grained coastal barriers are sensitive sedimentary bodies due to the high energy conditions they are usually associated with and to often scarce sediment supply. The present study focus on the relationships between multi-decadal morphological evolution of a mixed sand and gravel spit in the bay of Somme (Northern France) and its sedimentary architecture. A geophysical survey using a 400 MHz GPR GSSI antenna have been performed on the Molliere spit, a fast-growing secondary spit developing along the main 15 km-long Somme barrier spit. The development of this sedimentary body starts in the 1940s and allows a morpho-stratigraphical approach, thanks to the cross analysis of aerial photographs and GPR profiles. The spit is 5 km long and is composed of several ridges, sometimes with hooks at the tip. A synthetic depositional and stratigraphical model specific to gravel spits is proposed, based on radar profiles and aerial photographs analysis, differentiating three contrasting morpho-sedimentary units. The Unit 1, at the spit root, is characterized by mainly progradating structures that can be assimilated to a beach ridge. The Unit 2, in the central part of the spit, is characterized by individual ridges with a core structure reflecting a longshore development, topped by an aggradating unit associated to a cross-shore-dominated dynamics. The third unit, at the spit terminus, is also mainly characterized by longshore dynamics, but interfering with sand bank dynamics due to the proximity of the Somme estuary and the development of the spit platform. Larger-scale GPR prospecting, especially in older coast sections, will be necessary to understand how this fast secondary spit development (decadal to centennial time scale) is integrated into the longer-term (millennial) construction dynamics of the main Somme spit.

To assess spatial correlation between field-surveyed, discontinuous occurrences of surface water during base-flow conditions on Santa Rosa Island, Channel Islands National Park with geologic substrate, we compared mapped geology to the corresponding permeability at GPS-located sites. We used a portable, handheld air permeameter (NER TinyPerm II) to measure in situ rock permeability on outcrops, under ambient moisture conditions, at the core scale. Sites represent a range of surface water conditions, from wet to dry, and mapped geologic features such as rock type or structure, including unit contacts and Quaternary faults. Along the island-spanning Santa Rosa Island fault and other subsidiary faults, we measured permeability within fault cores and damage zones, and in adjacent rock protolith. Permeability estimates at 31 sites (>600 measurements) reveal four trends associated with rock type and geologic structure consistent with presence or absence of surface-water occurrence: i) Volcanic rocks and intact, indurated sandstones and shales express lower and intermediate permeabilities (~10^-1 – 10^2 mD), correlating with more continuous surface water presence. ii) Similar rocks, but with secondary fractures induced by tectonic or topographic stresses, express intermediate to higher permeabilities (~10^3 – 10^6 mD) and little correlation with surface water leading to generally dry valley floors. iii) Faults may act as both conduits and barriers to flow with clay-rich cores exhibiting low permeability (~10^-1 – 10^2 mD) whereas adjacent damage zones contain broken rock with intermediate to higher permeabilities (~10^2 – 10^7 mD). Outside the damage zones, non-fractured rock protoliths generally express lower permeability (~10^0 – 10^3 mD). Laterally continuous fault-core gouge, and juxtaposition of rock types with contrasting permeabilities, likely act as effective barriers to cross-fault flow, forcing groundwater to the surface. iv) Bedding, parting, and fracture planes appear to impart local permeability anisotropy as reflected in observed reduced permeability in directions perpendicular to such planes. This research could benefit vegetation restoration strategies in water-limited landscapes where ecosystems are reliant on surface water and where mapped geology can be used to infer permeability.

The visual representation of data is at the heart of science. From weather forecasts, to hazard maps, to the topography of planets, the choice of colors is critical to conveying information. Yet, largely due to historical usage, default software options, and an apparent attraction to multiple bright colors, color maps such as rainbow-like “jet” are still widely used. These color maps are problematic from both a scientific and societal perspective. For instance, they can distort data because they use uneven color gradients, which lose meaning when printed in black and white, and color combinations are often applied that are unintuitive to the data they are trying to represent. From an inclusivity standpoint, such rainbow maps are also unreadable for the population with some form of color-vision deficiency. Here, we present the work that has been accomplished by the scientific (inc. visualization) community, as well as the readily available solution - “Scientific Colour Maps” (Crameri 2018, Zenodo; Crameri et al. (2020; Nature Coms); www.fabiocrameri.ch/colourmaps). This initiative features freely available, citable color map downloads for an extensive suite of software programs, and handy how-to guide, and discussion around data types and coloring options. There is a pot of scientific gold at the end of every rainbow. Crameri, F. (2018). Scientific colour-maps. Zenodo. http://doi.org/10.5281/zenodo.1243862 Crameri, F., Shephard, G.E. Heron, P.J. The misuse of colour in science. (2020 v11; Nature Communications) https://doi.org/10.1038/s41467-020-19160-7

Waterways such as the Hudson River play an integral role in agriculture, health, transportation, recreation, energy, and sustaining biodiversity. Although water pollution in New York Harbor has been extensively studied, the reduction of millions of commuters during the COVID-19 lockdown presents an unprecedented opportunity to study human impact on water quality. We used remote sensing data to assess how the COVID-19 lockdown impacted water quality in New York Harbor, particularly in areas near Combined Sewer Outfalls (CSOs). This technique has previously been used to measure water quality in the Hudson River. We used ACOLITE to process Landsat-8 and Sentinel-2 images from 2015-2020. The algorithms “t_nechad”, “spm_nechad”, and “kdpar_qaasw” were used to measure turbidity, and “chl_oc2”, “chl_oc3”, “chl_re_moses3b”, “chl_re_moses740”, and “chl_re_mishra” to measure chlorophyll concentration. After uploading processed images into SeaDAS, we extracted values from pixels corresponding to Department of Environmental Protection (DEP) field sites. At these sites, the DEP measures Total Suspended Solids and Chlorophyll A Concentration using optical turbidity sensors and fluorometers, respectively. By comparing pixel values with DEP data we determined that the chlorophyll algorithms did not produce accurate readings of chlorophyll concentration in New York Harbor. We focused on analyzing turbidity at five DEP sites, four of which were located around wastewater treatment plants, to assess any CSO-induced changes in water quality. The frequency of usable satellite data from 2020 was severely limited by cloudiness, so we combined Landsat-8 and Sentinel-2 turbidity measurements (R = 0.8685) to form time series for each site. We expected to see a decrease in turbidity during the lockdown period, due to a decrease in sewage from office buildings. However, turbidity strongly fluctuated throughout all years with no discernable temporal pattern, and we could not distinguish between 2020 measurements and seasonal patterns. Thus, preliminary analysis shows that there was no significant variation in water turbidity due to the COVID-19 lockdown.