The rivers in the Peruvian Amazon basin are the veins that feed the largest rainforest on the planet. They connect habitats and ecosystems and are considered a source of high biodiversity. Since before the conquest, many indigenous peoples have settled along riverbanks to be supplied with water, food, and many other ecosystem services. Furthermore, the Amazonian rivers have been the basis for the sociability of those population groups. For many years, the indigenous riverine people, such as the Kukama and the Shipibo-Conibo, have developed an empirical knowledge about the rivers from their coexistence and lifestyle. Therefore, indigenous communities have learned how to be adapted under the physical dynamics by shaping their culture. It evidenced the great understanding of rivers that communities developed over the years, endowed with affective and spiritual links, which has allowed them to be preserved for many years. As part of a scientific study of the hydrogeomorphological characteristics of the main Peruvian Amazonian rivers, it was considered important to involve indigenous perceptions and knowledge. Recognizing these views is necessary for a holistic understanding of rivers; however, these have been absent from the plans and projects developed by government institutions. “River Stories” (https://www.historiasdelrio.com) is a digital storytelling platform that supports the dissemination of indigenous cosmovision in terms of experiences, in which the knowledge, memory and spirituality are displayed. It hosts more than 100 stories from indigenous people (including children and teenagers), students, local authorities, and scientists that reveal the importance of rivers in their daily life. In this way, different perspectives of the rivers from science, civil society, and the indigenous worldview are exchanged. Integrating this knowledge is essential to highlight the vital role that rivers play at the social, cultural and environmental areas. This, especially in the current context, where multiple threats, such as infrastructure and energy projects, put Amazonian rivers at risk.

It is important to perform the quantitative interpretation of the continental margin lithosphere for a more accurate and comprehensive understanding of its tectonic behavior. In this study, we derived the seismic and elastic properties from the 2D seismic data recorded by the Korea Meteorological Administration (KMA) in 2014. In general, most of the previous researches have been based on travel time tomography or Full Waveform Inversion (FWI) methods. However, these methods are not robust to directly apply to the seismic ambient noise data due to its low signal-to-noise ratio (SNR). Therefore, we conducted L2-norm model-based impedance inversion to not only delineate the local geological structures but also suppress the meaningless footprints in the observed data. Moreover, we used FWEA18 models (Tao et al., 2018) as initial velocity and density models to create the inversion result more stable and accurate. Then, we interpreted the lithosphere and asthenosphere from the inverted P-impedance model, which is more obvious than the interpretation of the pre-existing data. The average depths of Moho and Lithosphere-Asthenosphere-Boundary (LAB) are 30 km and 80km, respectively. Furthermore, we estimated the change of bulk density as well as P- and S-wave velocities of the crust, lithospheric mantle, and asthenospheric mantle. Also, we predicted four elastic properties of each layer from the inverted seismic properties, such as bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio. These model results can help to understand the physical state and elastic behavior variations of the lithospheric and asthenospheric mantle as well as local lithospheric structures beneath the peninsula.

Department of Water Resources (DWR), in partnership with the Merced Irrigation District (MID), is conducting a reconnaissance level study to assess the potential of implementing Flood-MAR in the Merced River watershed within the San Joaquin Valley. One aspect of the Flood-MAR study focuses on reservoir operations using synthetic hydrology generated based on the Paleoclimate (Tree-Ring) Study methodology. The MID HEC-ResSim model was calibrated to simulate water supply and flood control operations over the water year. The model was modified to estimate water available for Flood-MAR based on flow at a downstream location, conveyance capacity and groundwater recharge capacity. Results include changes in storage at Lake McClure, flow in Merced River, water supply deliveries, and Flood-MAR deliveries due to Flood-MAR implementation. The study also assesses the vulnerability of reservoir operations due to changing hydrology by evaluating the effects of climate change on flood management, water supply reliability, and drought resiliency. The flood management aspect of the vulnerability analysis assesses change in peak flow, flow volume, and duration of flooding events. The water supply reliability assesses change in carryover storage and deliveries for irrigation demands. The drought resiliency assessment includes change in magnitude and duration of the historical and synthetic drought events. The results of the reservoir operations vulnerability analysis will be used to determine the multiple benefits of Flood-MAR implementation.

Improved monitoring of inundation area variations in lakes and reservoirs is crucial for assessing surface water resources in a growing population and a changing climate. Although long-record optical satellites, such as Landsat missions, provide sub-monthly observations at fairly fine spatial resolution, cloud contamination often poses a major challenge for producing temporally continuous time series. We here proposed a novel method to improve the temporal frequency of usable Landsat observations for mapping lakes and reservoirs, by effectively recovering inundation areas from contaminated images. This method automated three primary steps on the cloud-based platform Google Earth Engine. It first leveraged multiple spectral indices to optimize water mapping from archival Landsat images acquired since 1992. Errors induced by minor contaminations were next corrected by the topology of isobaths extracted from nearly cloud-free images. The isobaths were then used to recover water areas under major contaminations through an efficient vector-based interpolation. We validated this method on 428 lakes/reservoirs worldwide that range from ~2 km2 to ~82,000 km2 with time-variable levels measured by satellite altimeters. The recovered water areas show a relative root-mean-squared error of 2.2%, and the errors for over 95% of the lakes/reservoirs below 6.0%. The produced area time series, combining those from cloud-free images and recovered from contaminated images, exhibit strong correlations with altimetry levels (Spearman’s rho mostly ~0.8 or larger) and extended the hypsometric (area-level) ranges revealed by cloud-free images alone. The combined time series also improved the monthly coverage by an average of 43%, resulting in a bi-monthly water area record during the satellite altimetry era thus far (1992–2018). Given such performance and a generic nature of this method, we foresee its potential applications to assisting water area recovery for other optical and SAR sensors (e.g., Sentinel-2 and SWOT), and to estimating lake/reservoir storage variations in conjunction with altimetry sensors.

Remote sensing observations are our primary method of studying planetary surfaces, and in the inner solar system, in situ exploration quickly provided ground truth to these remote sensing observations. Our view of the surface appearance of worlds like the Moon, Mars, and even Venus has grown in tandem with our understanding of the large-scale structure from remote sensing. However, our knowledge of the icy worlds of the outer solar system is based solely on decades of remote sensing observations without any in situ surface data to help understand how geological processes are manifest on these worlds. The surfaces of icy worlds like Europa are likely to be truly alien in appearance, dominated by processes such as impact gardening, sputtering, sintering, and other types of physical and chemical weathering that act together in ways we have never yet observed in situ. Remote sensing has revealed that Europa’s surface consists of an icy layer, exposed to the vacuum of space at cryogenic temperatures. The airless rocky Moon may be the best landed analog for Europa’s surface, but the Moon is an old, battered world covered with impact craters, which have gardened the surface to a highly-mixed regolith depth of 5-15 meters overlying kilometers of broken-up megaregolith. Europa’s young surface, approximately tens of millions of years old, likely has a gardening depth on the scale of centimeters up to a meter (Costello et al., AGU Fall Meeting, 2019). The rocky Moon is also compositionally different from icy Europa, and the thermal and radiolytic processes that shape the texture of the uppermost surface of an icy body have no rocky analog. As study of icy worlds has continued on the basis of remote sensing data only, multiple competing models exist for the formation of various surface features. Follow-up flyby and orbital missions may not be able to resolve these situations even with higher-resolution remote sensing data and digital elevation models. Images taken by an in situ surface lander on an icy world such as Europa, coupled with ground truth compositional and other measurements, will be essential to our understanding of how geologic processes work on these worlds. A mission such as a Europa Lander is the necessary next step, and will revolutionize our ability to interpret remote sensing data from myriad other bodies in the outer solar system.

The high computational cost of large-scale, process-based hydrological simulations can be approached using variable resolution meshes, where only the region around significant topographic features is refined. However, generating quality variable resolution meshes from digital elevation data is non-trivial. In literature, usually a slope or curvature-based criterion is defined to detect regions of refinement. These techniques often involve a number of free parameters that control the finest and coarsest resolutions, and the transition between fine resolution to coarse resolution. The influence of these parameters on the resulting mesh is usually not well-understood. In order to overcome the large number of free parameters involved, we propose to carry out the Mallat decomposition of the digital elevation data using the Haar wavelet. This gives a nested multilevel representation of the elevation data, split into average coefficients and detail coefficients. Applying hard-thresholding to these detail coefficients assigns a required level of refinement to each data point. This reduces the number of free parameters to exactly one: the acceptable error threshold. In this presentation, we focus on identifying which geomorphometric parameter(s) should be used to steer mesh refinement. We compare zero-inertia model simulation runs on meshes generated by decomposing elevation and slope. We hypothesize that because of the form of the Haar wavelet, the first mesh refinement essentially is using the gradient information, while the latter is using the curvature as refinement criterion. Our results suggest that in high-elevation catchments the curvature of the topography is a far better indicator for refinement than the slope. Using the Mallat decomposition on the tensor of the first derivative of the bed elevation (i.e., bed slope) for mesh refinement yields better agreement in the hydrograph compared to the decomposition of the bed elevation. We present surface runoff results for the Lower Triangle catchment, CO, USA, to illustrate the performance of the wavelet-based local mesh refinement.

In this study, the authors explore simple concepts of persistence in streamflow forecasting based on the real-time streamflow observations. The authors use 15-minute streamflow observations from the year 2002 to 2018 at 140 U.S. Geological Survey (USGS) streamflow gauges monitoring the streams and rivers over the State of Iowa. The spatial scale of the basins ranges from about 7 km2 to 37,000 km2. Motivated by the need for evaluating the skill of real-time streamflow forecasting systems, the authors perform quantitative skill assessment of different persistence schemes across spatial scales and lead-times. They show that temporal persistence forecasts skill has strong dependence on basin size and weaker, but non-negligible, dependence on geometric properties of the river network of the basin. The authors show that anomaly persistence forecasting can serve as a good reference for the evaluation of real-time streamflow forecasts at scales of order 100 km2. Building on results from this temporal persistence, they extend the streamflow persistence to space through flow-connected river network. It simply assumes that streamflow at a station in space will persist to another station which is flow-connected, and refer to it as pure spatial persistence forecasts (PSPF). The authors show that skill of PSPF derived streamflow forecasts is strongly dependent on basin area-ratio and lead-times, and weakly related to the downstream flow distance between stations. They show that the skill depicted in terms of Kling-Gupta efficiency (KGE) > 0.5 can be achieved for basin area ratio > 0.5 and lead-time up to three days. Adding complexities of hydrologic routing and rainfall QPF to the PSPF further improves the skill. The authors discuss the implications of their findings for improvements of rainfall-runoff models as well as data assimilation schemes.

The consensus is that global warming must be limited to 1.5°C to prevent profound environmental harms, and to do so will require reaching net zero CO2 emissions globally by 2050. This sets the imperative to rapidly phase out fossil fuel production and use, since continued fossil fuel use is inconsistent with emissions reduction goals. Meanwhile, it has been found that living in close proximity to fossil fuel infrastructure can have adverse impacts on human and environmental health, with these impacts often falling disproportionately on low-income and communities of color. Such environmental injustice further sets the imperative for fossil fuel phase out. California serves as an example: based on data from the CA Division of Oil, Gas, and Geothermal Resources, from 2011 through 2018, 77% of the permits for oil and gas wells issued in California were in communities with a higher-than-average percentage of residents living in poverty and/or communities with a majority non-white population. In analyzing data from the South Coast Air Quality Management District, it was found that the use of air toxics, or chemicals known to cause serious health effects in humans, occurs regularly at oil and gas drilling sites, with tens of thousands of tons of such chemicals used in Los Angeles County alone over a four year period. Finally, using CalEnviroscreen data, it was found that the heaviest air toxics usage occurred in communities already considered “disadvantaged” by the California Environmental Protection Agency. Such communities are generally prone to poorer health outcomes and less economic opportunity. Specifically, of the 15 zip codes in which oil companies used the most air toxic chemicals in Los Angeles County, 11 of them were labeled as disadvantaged. It is proposed that, in order to alleviate the environmental justice concerns around oil and gas extraction, low-income communities and communities of color be prioritized in enacting measures to phase out fossil fuel extraction and use. For example, a key policy recommendation is enacting a health and safety setback buffer of no less than half a mile protecting homes, schools, and other sensitive sites within which existing wells are shut down first.

Tectonic models as a universal outcome generate predictions regarding the travel time paths of rocks as they are displaced due to the application of particular input parameters and boundary conditions. A need for most of these models, either as a constraint for realistic input conditions or to gauge their relevance to a particular natural system, is pressure‐temperature‐time (P‐T‐t) paths from individual rock samples that track the conditions they experienced during displacement. Although arguments can be made that P‐T paths and absolute peak P‐T conditions may not necessarily be diagnostic of processes involved, this type of information is clearly a valuable addition to other types of data, such as timing and microstructural information regarding strain recorded during rock deformation. Low‐resolution P‐T paths can be limited in their ability to test ideas regarding lithospheric response to perturbations, including motion within fault zones. Here we apply advances in thermodynamic modeling to acquire high‐resolution P‐T paths that show the conditions responsible for garnet growth within one of the Himalayas’ major fault systems. The approach we outline can be applied to any garnet‐bearing assemblage using bulk rock and mineral compositions and have the potential to significantly increase the understanding of the dynamics of field areas that contain garnet, from the mineral’s crystallization to erosion‐driven or tectonically-driven exhumation. Overall, high-resolution garnet-based P-T paths were generated for two transects across the Himalayan Main Central Thrust (MCT) spaced ~850 km apart (along the Bhagirathi and Marsyangdi drainages) and monazite grains were dated in situ to help constrain crystallization time. Rocks collected at equivalent structural positions to the MCT along both transects show similar paths and a shear zone imbrication model suggest the MCT zone has very high exhumation rates, up to 12 mm/yr since the Pliocene.

Natural soil systems usually exhibit complex properties such as fractal geometry, resulting in complex dynamics for the movement of pollutants. This study developed a fractional advection-dispersion-reaction equation (fADRE) to quantify nitrate transport in soil systems. Applications showed that the fADRE model can quantify both hydrological and biogeochemical impacts on the fate and transport of nitrate in various saturated soils. The qualitative relationship between model parameters and the target system properties was also explored in detail.

Capturing watershed-scale runoff response remains difficult, in part because of heterogeneous land surface characteristics in mountainous regions. This challenge has impacted our progress in understanding soil moisture role in modulating rainfall-runoff process. Situated in Northern California, the Russian River watershed is frequented by atmospheric rivers (ARs) that bring most of the significant rainfall events to the area and are associated with almost all of the floods. To observe the precipitation in this watershed, NOAA Hydrometeorology Testbed has installed 14 telemetered stations across the watershed since 2005, each with 2-minute soil moisture volumetric water content (VWC) sensors at 6 depths. The Center for Western Weather and Water Extremes at the University of California San Diego has installed 6 more stations since 2017. Understanding soil moisture variability is crucial for hydrologic modeling and operations, particularly flood prediction. This high resolution soil moisture observation network allows comprehensive analysis of soil moisture variability. For instance, correlation analysis of 2-minute VWC at 10-cm depth reveals a uniform shallow-layer soil moisture behavior with correlations of >0.8 at most locations and across different seasons, demonstrating the network’s utility in capturing spatial and temporal soil moisture variabilities. Following this result, we investigate how antecedent soil moisture condition modulates the rainfall-runoff process. We include precipitation and stream discharge records from the same stations and nearby USGS gauges. A series of AR events in February 2019 offers a prime example. The February 2nd and Valentine’s Day ARs saturated the soil in most parts of the watershed and resulted in minor flooding. Percentile rank analysis indicated the subsequent February 26th-27th ARs recorded the highest event total rainfalls since 2017 at most gauges. Consequently, the February 26th-27th ARs resulted in rapid runoff responses and widespread flooding. This example also reveals the spatial variation in antecedent soil moisture VWC “threshold” where runoff generation becomes efficient. Work is ongoing to profile this threshold variation within the watershed, and preliminary analysis suggests a range from <0.2 to >0.5 at 10-cm depth.

High-resolution viscous-plastic (VP) sea ice models reproduce the narrow deformations lines observed in the Arctic sea ice, called the Linear Kinematic Features (LKFs). Recent studies showed that standard VP models overestimate the intersection angles between the LKFs when compared to observations. We investigate fracture angles in a uniaxial compression test and two different VP rheology. The first one uses an elliptical yield curve and a normal flow rule. In contrast, the second rheology uses a different elliptical plastic potential that creates a non-normal flow rule. Results show that the non-normality of the flow rule changes the angles of fracture. This new rheology can create fracture angles as low as 22º when the rheology with normal flow rule is limited to angles above 30º. A newly adapted theory – based on one developed from granular material observations – predicts the modeled fracture angles accurately. Using a non-normal flow rule takes longer to solve numerically, but allow reductions of the fracture angle to values within the range of satellite observations and decouples the angle of fracture from the shape of the yield curve.

The seismic structure of the Earth’s lowermost mantle is characterized by two large low shear provinces (LLSVPs) beneath the Pacific and Africa. Surrounding the LLSVPs are regions with generally higher-than-average seismic velocities which are likely caused by ancient subducted slabs. However, seismic observations have also revealed relatively low-velocity structures in the subducting regions outside of the LLSVPs in the lowermost mantle. The question is what cause these low velocity structures? Here, three-dimensional, high-resolution geodynamic models are performed to study the thermal structure and dynamics of the Earth’s lowermost mantle outside of the LLSVPs. Widespread hot thermal anomalies, or thermal ridges as named here, are found in the relatively cold, downwelling regions of the lowermost mantle in our models, with linear, ridge-like morphology, elongated in the lateral mantle flow directions. A systematic parameter-space exploration shows that thermal ridges only form when there are gaps and thermal heterogeneities in the cold downwellings, and their formation is promoted by an increase of the core-mantle boundary (CMB) heat flux and Rayleigh number and by a reduction of the lowermost mantle viscosity. The low seismic velocity anomalies outside of the LLSVPs above the CMB may be related to the formation of thermal ridges in the lowermost mantle.

Modeling relative permeability in multi-scale rocks and fractured networks has broad applications to understanding oil production and recovery in reservoir formations. Natural porous media are typically composed of two domains; one incorporates macropores, while the other contains micropores. In the literature, numerous theoretic models have been developed based on the series-parallel tubes approach (Mualem, 1976; van Genuchten, 1980) to estimate wetting-phase relative permeability (krw) from pore size distribution or capillary pressure curve. In this study, we, however, invoke concepts from critical path analysis (CPA), a theoretical technique from statistical physics. CPA has been successfully used to model flow and transport in porous media (Hunt, 2001; Ghanbarian-Alavijeh and Hunt, 2012; Hunt et al., 2013; 2014; Ghanbarian et al., 2016; Ghanbarian and Hunt, 2017). We estimate the wetting-phase relative permeability from the measured capillary pressure curve using two methods: (1) critical path analysis (CPA), and (2) series-parallel tubes (vG-M). To evaluate these models, we use 26 experiments from the literature for which capillary pressure and wetting-phase relative permeability data were measured at 500 data point over a wide range of wetting-phase saturation (Sw). Results demonstrate that CPA estimates krw more precisely than vG-M. We show that accurate krw estimation by the CPA-based model needs precise characterization of capillary pressure curve and accurate calculation of the crossover point (Swx) separating the two domains.

The Australian seasonal snowpack can be classified as a marginal maritime snowpack with a temperature near 0 °C throughout the snow season. Subtle changes in atmosphere – snow energetics therefore result in rapid change in snowpack properties, which occur against a background of a warming climate. This has been attributed to a 40% decline in spring snow depths in the past 40 yrs. and geologic records suggest the seasonal snowpack is now near a 2000 yr. minimum. Modelled future snow cover predicts further decline by 57 % to 78 % of current maximum snow depth by the 2040s. Such research primarily attributes this decline in snow cover to global warming. However, the past decline in Australian snow cover can also be attributed to change in synoptic wintertime precipitation patterns that have resulted in a dramatic increase in proportional winter and spring precipitation of tropical origin since the 1950s. Tropical moisture is predominantly transported into southeast Australia during negative phases of the Indian Ocean Dipole (IOD) by northwest cloud bands – visible expressions of atmospheric rivers coupling tropical moisture sources northwest of Australia to the Australian Alps. Here we present a case study of one such event that occurred from the 21 to 23 July 2016 when 118 mm of rain-on-snow over a 12 hr period led to near complete ablation of the snowpack. While predictions of future variability of the IOD due to global warming remain uncertain, we suggest that warming atmospheric temperatures increase the risk of such extreme rain–on-snow events during negative IOD events. Combined with reduced snow cover in response to warmer ambient wintertime temperatures, such rain-on-snow events may further accelerate the reduction in seasonal snow cover in the Australian Alps, possibly on occasions after which the snowpack does not recover before spring. These conditions would present significant challenges to the Australian snow sports industry which is worth $2 billion annually and lead to change in snow dependent ecosystems and alpine hydrology.

Bedrock weathering and soil production set the rate that hillslope colluvium is produced, while material properties determine whether sediment can fail under a given set of conditions. Together these factors generally control both the frequency and magnitude of landslides. In 2017, Puerto Rico (USA) experienced widespread landsliding across a range of lithologies due to Hurricane Maria, making it an ideal setting to explore the role of sediment generation in landslide response to storms. Based on an inventory of >70,000 landslides island-wide and detailed field mapping from a subset of source areas, we estimate that 0.01-0.1 km3 of material was evacuated from the hillslopes (approximately 1-10 mm of lowering). Focusing on the high-density landslide area of Utuado, we estimate an average lowering of 5-50 mm. From past inventories and records of storm events, a watershed is impacted by a hurricane every ~25 years with enough rain for widespread landslides every ~5 years. Assuming a similar density, the landslide contribution to hillslope lowering could be on the order of 1-10 mm/yr. In a humid-tropical environment, where weathering rates are likely high, can hillslopes continue to produce material at this pace? Elsewhere on the island, soil production rates are on the order of 0.1 mm/yr leading to soil residence times of approximately 10 ky. However, to keep pace with landslide events like Hurricane Maria, soil production likely needs to be at least an order of magnitude faster to maintain soil-mantled hillslopes in this study area. For our study area, we ask: has the large-magnitude rainfall from Hurricane Maria caused an abnormally high density of landsliding, resetting the clock on the material availability for areas like Utuado? Here we relate measures of material properties, bedrock weathering intensity, denudation, and land-use history to begin answering this question.

Bottomland hardwood forests are a historically dominant ecosystem in the Southeastern United States, with 5.3 million-acres remaining in the Lower Mississippi Alluvial Valley. Despite the importance of forested wetlands in the global carbon cycle, not much is known about CO2 exchanges in such periodically flooded ecosystems. To address this, CO2 fluxes were measured over a mature, predominantly oak bottomland hardwood forest canopy in Northeast Louisiana. Measurements were carried out from a 37m tower, between August 2013 to October 2018, using the eddy covariance technique. Like other deciduous broadleaf forests, the site exhibited distinct diurnal and seasonal variability and was a net carbon sink during the spring and summer months, but unlike the majority of such forests and inland wetlands, the site was a net source of carbon annually. The cumulative annual net ecosystem exchange (NEE) ranged from 95.99 g C m-2 in 2014, to 267.91 g C m-2 in 2017, with an average annual NEE of 127.75 g C m-2. To better understand the variability, the relationship between the environmental factors and the components of NEE were observed. Diurnally, GPP increased with increasing global radiation and exceeded Reco from around 830 till around 1800 hrs. Seasonally, nighttime Reco increased with temperature. GPP increased and exceeded Reco from late March to early August, with a corresponding increase in the length of daylight, temperature, and leaf area index. GPP values showed more variability than Reco on a daily scale, thereby having a more distinct effect on the NEE. Research is on-going to better understand the effects of the characteristic flooding on carbon fluxes in this unique ecosystem.

During the global COVID-19 pandemic, anthropogenic emissions of air pollutants and greenhouse gases, especially traffic emissions in urban areas, have declined significantly. Long-term measurements of trace gas concentrations in urban areas can be used to quantify the impact of emission reductions on local air quality. Open-path Fourier transform infrared (OP-FTIR) spectroscopy is a non-intrusive technique that can be used to simultaneously measure multiple atmospheric trace gases in the boundary layer. This study investigates the reduction of surface CO, CO2 , and CH4 mole fractions during the lockdown in downtown Toronto, Canada, which is the fourth largest city in North America. The mean daily CO mole fraction anomaly (ΔCO) for the period from March 14 to May 18, 2020 declined by 46 ± 16% compared to the period before lockdown from January 13 to March 13, 2020. The mean daily ΔCO during the lockdown also declined relative to the same period in previous years: by 50 ± 20% relative to 2019 and by 44 ± 25% relative to 2018. Changes in the diurnal variations of CO, CO2 and CH4 during the lockdown are also investigated and compared to 2019 and 2018. Both CO and CO2 show early morning maxima on weekdays corresponding to rush hour. The change of the amplitude of the diurnal variation in CO during the lockdown is significant, compared to the period before lockdown. The differences in the diurnal variation in CO during the same two periods in 2019 and 2018 are not significant. Ratios of CO/CO2 anomalies show seasonal variations, which are also likely due to seasonal changes of emissions from local sources. These results show that the COVID-19 lockdown in Toronto modified surface mole fractions, diurnal variations, and ratios of air pollutants monitored by OP-FTIR. In addition, measured CO mole fractions are compared with simulated CO mole fractions by WRF-STILT to assess the relationship between atmospheric measurements and urban emissions from Toronto.