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.

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.

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.

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.

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.

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.

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.

Recent increasing wildfire activities in California have raised widespread public concerns. This raises a question as to whether the increase of of fire weather this year and in recent decades is mainly due to weather pattern change dominated by natural variability or anthropogenic warming. We observed that vapor pressure deficit (VPD), an important fire weather risk index, in the warm season (May to September) of California, has increased significantly since 1979, with a positive trend of 0.70 hPa or 0.50 σ per decade, and this trend is also significant for VPD in large fire days; trend analysis of saturated and actual vapor pressure suggests that direct warming and drying contributes to about 70% and 30%, respectively, to the positive VPD trend. This increase of VPD and warming for such a short period cannot be simply linked to anthropogenic factors as it can also be affected by circulation changes. Our self-organizing maps analysis reveals the close relationship between VPD and circulation. Using a flow analogue approach, we estimated circulation changes contribute to about half (54%) of the observed VPD trend. Since circulation changes represent the upper limit of internal variability of the climate system because both the internal climate variability and anthropogenic forcings can contribute to such a change, our results suggest that anthropogenic warming must explain at least the other half of the observed VPD trend.

On 16 December 2017 a chain mass movement, started as a rockslide, produced a debris and mud flow which channelized in the Burritos River. The flow reached and flooded the Santa Lucia village (-43.41°/-72.37°) destroying 50% of the urban area and killing 22 people. This fatal event resulted as a conjunction of geologic, meteorological and glaciological variables: Highly altered volcanic rocks with deep vertical fractures and steep slopes in the Yelcho Range; an intense precipitation event 30 hours prior the rockslide of up to 124.8 mm in one day with a high zero-degree isotherm elevation; an ice-cored lateral moraine deposit under the headwall plus hyper saturated soils downhill near the Burritos River. The role of a proglacial lake has been discussed, nonetheless, current studies argue that the main liquid inputs could have been pre-existing water in saturated soil. In this study we analyzed satellite imagery available on the web, including Sentinel 2 and Landsat scenes. Based on the Public Chilean Glacier Inventory, we quantify the retreat of glaciers laying on the eastern flank of the Yelcho Range, west of the Santa Lucía village. On a time-span of 33 years (1987 to 2020), we estimate over 650 m of glacial retreat plus an accelerated retreat rate up to 30 m/yr for the last decade. Such glacier shrinkage coupled with increasing local temperatures may lead to the genesis of more proglacial lakes, enhancing the odds of chain mass movements, favoring the interaction between rockslides and glacial lakes. Our results show an example of a relatively simple analysis that can be performed with basic tools and with no further expenses. The proper understanding of glacial landscape evolution with time and its response to extreme climatic events is essential to prevent human loses, as these events will be more frequent due to the current climate change context. For that purpose, further work aims to clarify more hidden variables in the region, such as the number of proglacial lakes, steep slopes on headwalls, highly eroded slopes, among others, in order to enlarge the local knowledge linked to one of many components involved on a natural disaster, thus, increasing the resilience of communities whom are still occupying the affected area after the Santa Lucía landslide disaster.

One global-scale dust storm and two larger-than-average regional-scale dust storms have been recorded by spacecraft observations within the dust seasons of the past two Mars years. Each of these storms began near times when the orbit-spin coupling torques on Mars were changing most rapidly. While a clear association between Martian planet-encircling dust events and orbital torque episodes has been established (JGR-Planets 125, e2019JE006077), possible relationships between orbital/dynamical variability and regional-scale storm occurrence have not previously been investigated. In this study we compare the initiation periods and early development of the MY 34 “C” storm and the MY 35 “A” storm with prior findings obtained for the planet-encircling dust event of MY 34. We employ observations by MRO’s Mars Climate Sounder to compare dust loading, atmospheric temperatures, dynamical heating indices, and dust layer peak altitudes, to illuminate the similarities and differences between these events, in juxtaposition with a discussion of the concurrent variability of the planetary orbital angular momentum and the resulting torque on Mars.

The Undergraduate Student Instrumentation Project (USIP) was a NASA program to engage undergraduate students in rigorous scientific research, for the purposes of innovation and developing the next generation of professionals in space research. The program is student led and executed from initial ideation of research objectives to the design and deployment of scientific payloads. The University of Houston was selected twice to participate in the USIP programs. The first program (USIP_UH I) ran from 2013 to 2016. USIP_UH II ran from 2016 to 2019. USIP_UH I (USIP_UH II) at the University of Houston was composed of eight (seven) research teams developing six (seven), distinct, balloon-based scientific instruments. This project was a for-credit course two years in duration. The program has been so successful in terms of improved student career outcomes the University has decided to continue the project with purely local funding. The pandemic has produced a substantial instructional challenge since this project is a lab class! The virtual classroom that we designed to meet this need provides tools for ongoing collaboration, revisions, storage, project planning, systems engineering, and a tool to request immediate feedback from faculty and fellow researchers. Additionally, the classroom provides an ongoing place to store data from different students for many years. New students can use this continuity in a consistent and secure way. We also provided tools for conferencing and communication. A combination of several tools were selected and customized to meet this need. These tools include Google Classroom, Microsoft Teams, Slack, Git, Groupme, and Zoom.

Conceptual models are among the most frequently used type of models in watershed modelling studies, due to their low computational requirements and ease of interpretation. Model selection requires the comparison of model alternatives, which is complicated when the models present in the literature differ in many uncontrolled aspects, such as conceptualization, implementation, and source code availability. To overcome this limitation, several model-building frameworks have been introduced in the last decade, which facilitate model comparisons by enabling different model alternatives within the same software and numerical architecture. Building on the decennial experience with the development and usage of Superflex, a flexible modeling framework for conceptual model building, so far implemented in FORTRAN language and not available as open source, we propose SuperflexPy, an open source Python framework for building conceptual hydrological models. Compared to other existing models or flexible frameworks, SuperflexPy is designed to be extremely easy to modify or extend, allowing scientists to build models that reflect their processes understanding; thanks to its object-oriented architecture and its complete integration with the Python programming environment, SuperflexPy can be seen as a high-level programming language for constructing hydrological models that are extremely flexible both in terms of elements configuration (i.e. how the elements are connected into a structure) and spatial organization. By design, SuperflexPy is not limited to water quantity but can be extended to simulate transport processes (water quality). In this presentation, we will illustrate the principles behind the design of the framework and showcase some applications.

Amazonian rivers are highly interconnected and dynamic systems. Their behavior depends, to a large extent, on their geomorphology, being classified in 1) meandering rivers (MR), characterized by high rates of migration and sinuosity, and 2) anabranching rivers (AR), known for forming several permanent channels and islands. A planimetric characterization of the main rivers of the Peruvian Amazon (Huallaga, Ucayali, Marañon, and Amazonas), spanning from the Andes to the Amazon lowland region, was carried out to understand their physical dynamics. By a multi-temporal analysis from 1987 to 2017 using Landsat images, a segmentation was made for each river based on 1) the characterization of the geological valley, 2) the confluence of important tributaries, 3) changes of the main channel through the years, and 4) planimetric variables such as confinement, bend length, amplitude, sinuosity, and asymmetry. As a result, a total of 160 sections were obtained, in which a new set of 25 metrics was applied, filtered from an initial set of 31 variables and their statistics (i.e. mean, variance, kurtosis, and skewness), calculated through different approaches (i.e. half-meander, full-meander, and full-river). The variables were standardized and principal component analysis (PCA) was performed. The resulting biplot showed a distinction between AR and MR, with a shared area consisting predominantly of Marañon and Huallaga sections. The average value of sinuosity was found more associated with the MR, while higher length and asymmetry variance values were more oriented to the AR. This study also indicated the similarity in the behavior of some river sections of different types, based exclusively on their morphometric characteristics. At the same time, revealed how some sections could not be differentiated from others despite being nominally different. In this scenario, the PCA highlighted the need for a complete set of statistics that can recognize different features of these rivers, capturing greater complexity. Thus, the evaluation and segmentation of these planimetric variables, according to their planform characteristics, allows a better understanding of their dynamics, providing accurate information for coherent decision-making.

Bangladesh, part of Indo-Gangetic-Brahmaputra Plain, is frequently affected by floods and droughts. As the economy of Bangladesh is still agriculture based, effective measurement of soil moisture will not only strengthen the irrigation management but also improve the hydrological modelling and drought prediction. But only nine agro-meteorological stations of Bangladesh measure the soil moisture four times a month which creates a vacuum to scientifically manage her water resources. SMAP (Soil Moisture Active Passive) satellite of NASA provides an unprecedented opportunity for full scale measurement of soil moisture over this region. Field measurements of soil moisture from April 2015 were used to assess the effectiveness of the SMAP’s measurement over the North West Region of Bangladesh which suffers from frequent dry spells. Initially the Root mean squared error (RMSE) between the SMAP and observed soil moisture were found to vary between 12.28 to 16.72% for the available stations. The results showed a bias in SMAP data and it was significantly reduced using bias correction. Later multiple linear regression, based on supplementary climate data in addition to SMAP observations, was applied to obtain an improved estimate of soil moisture and the RMSE were reduced to 1.19 to 3.18%. Lastly, different machine learning techniques (i.e. ANN, SVR, XGBoost etc.) were used to reduce the bias further. This study demonstrates a promising potential of using the SMAP data in soil moisture estimation over Bangladesh for its effective water resources management.

Cloud-top heights (CTH) from the Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra constitute our longest-running single-platform CTH record from a stable orbit. Here, we provide the first evaluation of the Terra Level 2 CTH record against collocated International Space Station Cloud-Aerosol Transport System (CATS) lidar observations between 50ºN - 50ºS. Bias and precision of Terra CTH relative to CATS, calculated from the normality of CTH error histograms, are shown to be strongly tied to cloud horizontal and vertical heterogeneity and altitude. For single-layered, unbroken, optically thick clouds observed for all altitudes, the uncertainty in MODIS and MISR CTH are -540±690 m and -280±370 m, respectively. The uncertainties are generally smaller for lower altitude clouds and larger for optically thinner clouds. For multi-layered clouds, errors are summarized herein using both absolute CTH and CATS-layer-altitude proximity to Terra CTH. We show that MISR detects the lower cloud in a two-layered system, provided top-layer optical depth < ~0.3, but MISR low-cloud errors are unaltered by the presence of thin cirrus. Systematic and random errors are propagated to explain inter-sensor disagreements, as well as to provide the first estimate of MISR stereo-opacity bias. For MISR, altitude-dependent wind-retrieval bias (-90 to -110 m) and stereo-opacity bias (-110 to -240 m) and for MODIS, bias due to low opacity near cloud-top lead to overall negative CTH bias. MISR’s precision is largely driven by wind-speed uncertainty (3.7 m s-1), whereas MODIS precision is driven by forward-modeling uncertainty.

Cloud microphysics plays important role on the storm dynamics. To investigate the impact of advanced microphysics schemes, using single and double moment (WSM6/WDM6) schemes, numerical simulations are conducted for a severe convective system that formed over the Korean Peninsula. Spatial rainfall distribution and pattern correlation associated with the convective system are improved in WDM6. During developing stage of the system, the distribution of total hydrometeors is larger in WDM6 compared to WSM6. Along with mixing ratio of hydrometeors (cloud, rain, graupel, snow and ice),number concentration of cloud and rainwater are also predicted in WDM6. To understand the differences in vertical representation of cloud hydrometeors between the schemes, rain number concentration (Nr) from WSM6 is also computed using particle density to compare with Nr readily available in WDM6.Varied vertical distribution, and large differences in rain number concentration, rain particle mass are evident between the schemes. Inclusion of number concentration of rain and cloud, CCN along with mixing ratio of different hydrometers have improved the storm morphology in WDM6. Inorder to investigate the cloud-aerosol interactions, numerical simulation has been conducted using an increase in CCN(aerosol) in WDM6 which has shown an improved rainfall distribution with intense hydrometer distribution. The latent heating (LH) rates of different phase change processes (condensation, evaporation, freezing, melting, sublimation and deposition) are also computed using various transformation rate terms in the microphysics modules. It is inferred that the change in aerosol has increased the LH of evaporation and freezing and affected the warming and cooling processes, cloud vertical distribution and subsequent rainfall.

Precise understanding of complex physical mechanisms of mesoscale process require high resolution observations of temperature, moisture, wind, precipitation, clouds. Using all collocated observations of microwave radiometer, wind profilers, electric field mill, weather radars over South-East India an observational analysis is conducted for the first time. Analysis suggests that these systems developed in warm, moist environment associated with large-scale low-level convergence. Passage of system is accompanied by convective regions with intense upward motion and towers extending up to higher levels indicating developing phase and presence of upward/downward motion comprising of heavy precipitation representing mature phase of the system followed by stratiform regions with prominent downdraft motion and less precipitation related to decay phase. Large (small) values of reflectivity and cloud liquid water values represent presence of deep (shallow)convective (stratiform) regions. Cloud to Ground (CG) lightning activity associated with storm electrification processes showed the existence of both +CG and –CG flashes in convective and dominant –CG in stratiform regions. Presence of different sized cloud liquid hydrometers in convective regions resulted in bipolar nature due to their collisions however in stratiform regions their distribution is mostly uniform and resulted in single polarity. Combination of different observations has provided the unique opportunity to examine interrelations of different physical mechanisms in storm environment. Inspection of reflectivity, CG lightning and cloud liquid water measurements have demonstrated the relationship of lightning mechanism with storm dynamics and cloud microphysics. Combined investigation of temperature, moisture and wind measurements have given considerable insight of thetae ridge formations resulting from thermal and moisture advections. Isentropic upgliding and downgliding facilitated the unique way to visualize the vertical transport of temperature and moisture through ascent and descent of air parcel. Blend of observations presented considerable insight of synoptic and complex mesoscale processes and their mutual interactions in the storm environment and provided encouraging results in explaining MCS structure.

SubSahara Africa has been the last continent to experience a significant number of cases in the novel Coronavirus (COVID-19). Several studies have suggested that air pollution is related to COVID-19 mortality; poor air quality has been linked to cardiovascular, cerebrovascular, and respiratory disease, which are considered co-morbidities linked to COVID-19 death. We examine potential connections between country-wide COVID-19 mortality and environmental conditions in Senegal, Cabo Verde, Nigeria, Cote D’Ivoire, and Angola. We analyze PM2.5 concentrations from cost-effective in situ measurements, aerosol optical depth (AOD), and fire count from space-borne platforms during the dry season when dust and biomass burning aerosols are present in Southern Africa. In addition, we examine the COVID-19 mortality during the wet season using space-borne rain measurements to determine potential linkages which might occur as a result of time spent indoors and concurrent cases of flu, waterborne and vector-borne diseases in West Africa. Results are presented from March through December of 2020.