South Turkish Neogene basins record post-collisional exhumation/erosion of Neotethyan basins. Facies and paleocurrent data indicate general northerly derivation of siliciclastic sediments. Provenance is further indicated by integrated detrital zircon U-Pb geochronology (29 samples; 2748 grains) of 7 mapped late Oligocene-Miocene basins (from E-W): 1. Hatay, 2. K.Maraş, 3. Adana, 4. Misis, 5. Mut, 6. Manavgat and 7. Kopru. 1-4 and 6-7 represent two separate tectonically active depocenters, whereas 5 was a relatively stable platform. The early-mid Miocene of Hatay Basin contains sparse zircons with Precambrian (570-900 Ma) and Cretaceous (73-99 Ma) populations that become prominent in the mid-late Miocene. The early Miocene of K.Maraş Basin has abundant Pan-African, Grenvillian and Eocene zircons; mid-Miocene exhibits scarce Neoproterozoic-Mesoproterozoic but abundant Cretaceous-Neogene zircons. The late Oligocene-early Miocene of Adana Basin has minor Cretaceous-Neogene zircons, becoming more abundant in the early Miocene. The Miocene of Misis Basin has abundant Precambrian grains, mainly restricted to the NE. The Miocene of Mut Basin has major zircon populations of 544-710 Ma, 730-990 Ma, and two subordinate 1700-2000 Ma and 2400-2700 Ma clusters. Early Miocene (17-19 Ma) zircons occur in some samples. The Manavgat Basin samples are similar to the Mut Basin ones but with more evidence of mid-late Miocene (8-14 Ma) zircons. Lastly, the Kopru Basin (E Isparta Angle) has Pan-African (540-640 Ma) and Grenvillian (810-1000 Ma) populations, and small Paleozoic clusters. The Pan-African and Grenvillian-aged material was probably supplied directly and/or recycled from rifted Gondwanan Cadomian ‘basement’ and its Paleozoic passive margin cover, as partly exposed in south/central Anatolia. Sparse Permian-Triassic zircons relate to Neotethyan rifting. Late Cretaceous-early Cenozoic grains were derived from adjacent Late Cretaceous ophiolitic, magmatic arc and related metamorphic units. Early Miocene zircons relate to local basaltic volcanism. The variable zircon abundances hint at differential exhumation/erosion. Two regional drainage system dominated, one through the K.Maraş basin system (E) and the other through the Isparta Angle basin system (W).

Urbanization increases runoff, sediment, and nutrient loadings to freshwaters, causing flooding, harmful algal blooms, and increased costs for drinking water filtration. Most watershed models use runoff quality data from specific land uses; coupled with a variety of methods for quantifying loads, the most common being Build-up and Wash-off (BUWO) equations for which loading is a function of antecedent dry period (ADP). However, several studies have shown there is no significant correlation between urban runoff quality and ADP, suggesting predictions based solely on landuse and ADP are questionable. The objective of this study is to discover which parameters, climatological or catchment characteristics, are most significant and should be included in the BUWO relationship. Stormwater quality data was obtained from the National Stormwater Quality Database (NSQD), which is the largest and most recent database of its type in the U.S. Bayesian Network Structure Learner (BNSL) was used to assess the relationships between catchment characteristics, climatological information, and runoff quality for each land use. Given the optimal BN structure, it was determined which parameters affect water quality event mean concentrations the most, and which the least. The results demonstrated that for some constituents (i.e. particulate nitrogen, particulate phosphorous, and sediments), other factors (such as rainfall depth and duration, surface slope) exert a more important influence on urban runoff quality than ADP.

Urbanization and climate change are exacerbating stress on aging urban critical infrastructure systems, including water, energy, mobility, and telecommunication networks. Simulation tools and scenario analyses able to capture the interdependencies among these different infrastructure systems are crucial to support decision making and realize sustainable and resilient development. Yet, existing simulation tools are mostly developed within the boundaries of individual application sectors and information often remains siloed, despite the increasing data and computational opportunities offered by the digital transformation of many infrastructure sectors. In this work, we present how the ide3a project (international alliance for digital e-learning, e-mobility and e-research in academia – https://ide3a.net) addresses this research gap. ide3a is building a digital campus to support digital learning, research, and mobility in collaboration within a network of six European partner universities. Several senior and early career researchers with multidisciplinary backgrounds in water management, IT systems, mobility, energy, urban planning, sustainability, and psychology, work together to integrate state-of-the-art research on critical infrastructure and digitalization into traditional higher education curricula. As part of the ide3a portfolio of digital tools for learning and research, we present a prototype of “ConnectiCity”, an open-source simulation-based serious game that integrates multi-sectoral models to perform simulations of interconnected critical infrastructure systems and quantify cascading effects under various climate, social, and technical scenarios. Along with other ide3a activities, it is used to train early career researchers and students alike to enrich their transdisciplinary knowledge, foster critical system thinking, drive research on urban critical infrastructure dynamics, and ultimately working across disciplines to tackle contemporary urban challenges.

Modeling atmospheric chemistry across scales with the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) leverages a new regional refinement capability within the NCAR Community Earth System Model (CESM). This framework allows for simultaneous simulation at human-relevant and policy-relevant scales at the same time as hemispheric and global-scales. Here, we present the development of a regional refined grid over the Australian region and assess initial simulations for the 2019/2020 wildfire season. The Australian wildfire events of 2019/2020 saw large local emissions of pollution with wide-scale impacts across the southeast of the continent, where smoke and haze degraded air quality for many days for millions of people. Hemispheric transport of pollution at low and lofted altitudes also occurred, creating an atmospheric signature over New Zealand and South America, and had a direct influence on climate. These multiple-scale impacts from wildfire in the Australian region make it ideal for testing the new capability of multi-scale modeling.

Exoplanets exhibit properties suggestive of a diversity of worlds far exceeding that observed within our own solar system. This diversity, combined with limited data, poses challenges for future exoplanet characterization, especially regarding life detection: not only is the diversity planets unprecedented, but the low resolution and s/n data available to current and near-future technology demand we improve our ability to infer properties of planetary atmospheres, surfaces and potential signs of life from very little data. For this reason, recent consensus recommendations from both within the exoplanet science community, and without, are directing the field to move from searching from specific products of life to developing probabilistic frameworks for inferring the presence of life that encompass entire planetary systems. Here, we demonstrate a new framework based on statistical characterization of planetary atmospheric chemistries with the goal to provide the quantitative tools required by this approach. We validate these tools against current observational constraints available for Jovian worlds by constructing chemical reaction networks (CRNs) from the atmospheres of hot jupiters simulated over a wide range of temperatures and metallicities using VULCAN. For each model, we calculated measures of the CRN topology and more traditional measures of disequilbrium. To model the uncertainty in observations, these properties were then used as the basis for an interpolation function, which was then fed a series of 10,000 point Gaussian distributions of possible initial conditions to simulate the likelihood distribution of possible atmospheric models centered around a specific observable such as T or metallicity. We present results demonstrating how our multivariate, statistical approach permits quantifying distance from disequilibrium in Jovian atmospheres. We discuss implications for inferring the presence of life as a driver of atmospheric disequilibrium on terrestrial worlds, and how technologically produced molecules could influence CRN topology.

Groundwater is the largest source of Earth’s liquid freshwater and plays a critical role in global food security. With the rising global demand for drinking water and increased agricultural production, overuse of groundwater resources is a major concern. Because groundwater withdrawals are not monitored in most regions with the highest use, methods are needed to monitor withdrawals at a scale suitable for implementing sustainable management practices. In this study, we combine publicly available datasets into a machine learning framework for estimating groundwater withdrawals over the state of Arizona. This extends a previous study in which we estimated groundwater withdrawals in Kansas, where the climatic conditions and aquifer characteristics are significantly different. Datasets used in our model include energy-balance (SSEBop) and crop coefficient evapotranspiration estimates, precipitation(PRISM), and land-use (USDA-NASS Cropland Data Layer), and a watershed stress metric. Random forests, a widely popular machine learning algorithm, are employed for predicting groundwater withdrawals from 2002-2018 at 5 km spatial resolution. We used in-situ groundwater withdrawals available over the Arizona Active Management Area (AMA) and Irrigation Non-Expansion Area (INA) from 2002-2010 for training and 2011-2018 for validating the model respectively. The results show high training (R2 ≈ 0.98) and good testing (R2 ≈ 0.82) scores with low normalized mean absolute error ≈ 0.28 and root mean square error ≈ 1.28 for the AMA/INA region. Using this method, we are able to spatially extend estimates of groundwater withdrawals to the whole state of Arizona. We also observed that land subsidence in Arizona is predominantly occurring in areas having high yearly groundwater withdrawals of at least 100 mm per unit area. Our model shows promising results in sub-humid and semi-arid (Kansas) and arid regions (Arizona), which proves the robustness and extensibility of our integrated approach combining remote sensing and machine learning into a holistic, automated, and fully-reproducible workflow. The success of this method indicates that it could be extended to areas with more limited groundwater withdrawal data under different climatic conditions and aquifer properties.

We present a fast model for stratospheric ozone chemistry based on a neural network approach. The model is intended to replace the detailed chemistry schemes of chemistry and transport models (CTMs), general circulation models (GCMs) or Earth system models (ESMs), which are computationally very expensive. The neural network (NN) model estimates the rate of change of ozone in 24 hours at a grid point and is trained on data of the detailed full chemistry model of the ATLAS chemistry and transport model (CTM). The benefit of this surrogate models is a much lower computation time (minutes instead of days) while the same level of accuracy is achieved. This represents a necessary step from understanding the chemistry and building sophisticated CTMs towards the usage of this knowledge in climate models, which is only feasible if much lower computation times can be achieved. Modelling of the Earth system is a complex task and models usually contain a large number of sub-modules and parameterizations. This applies for example to the atmosphere, hydrosphere, solid earth and the ice sheets. Atmospheric chemistry is complex and usually involves dozens of species and hundreds of reactions with a wide range of concentrations and lifetimes. This project concentrates on the estimation of the rate of change of ozone in the extrapolar stratosphere. The dynamics from the polar regions and from other layers of the atmosphere regarding the ozone change are not treated within this work. The ATLAS model is a Lagrangian CTM for stratospheric chemistry. It solves a coupled differential equation system using a stiff solver and a variable time-step. The stratospheric chemistry scheme of ATLAS has 46 active species, 171 reactions and heterogeneous chemistry on polar stratospheric clouds. It is not using the concept of chemical families. The application of the ATLAS CTM has high requirements on computational power. This is the reason why the coupling of full chemical models to climate models is generally not feasible with respect to the computation time of a global climate model. However, the incorporation of detailed chemistry is often desirable, in order to account for various feed-backs between chemistry, atmosphere and ocean. These complex chemical models motivate the formulation of faster but still accurate surrogate models, that are tailored to the coupling into earth climate models. This project builds on the SWIFT project, which has a polar and an extrapolar surrogate model for the stratospheric ozone chemistry. We investigate an alternative approach to the polynomial approach used by extrapolar SWIFT by exploiting the improved approximation capability of NN with respect to nonlinear contexts.

The NEXUS area covers approximately 30% of the Brazilian territory. In order to assist preservation and sustainable development policies in that region, this study proposes to replicate the work done by Yeh et al in Africa , in which a convolutional neural network estimates indicators through satellite images, each covering a region of approximately 45 km². This work compares the size and distribution of Brazil’s census tracts with those in Africa to define if the scale of images can be maintained and to define the clusters that will be used. To avoid biasing the model, special care must be taken in selecting clusters, such as keeping a balance between urban and rural sectors and, most importantly, making sure that there is little to no overlap of clusters. To do so, two approaches were proposed. The first one samples tracts in each municipality as centroids for clusters, the second merges neighboring urban tracts into a single group and fits clusters to these groups.

We present an integrated image of the lithosphere of Alaska and its western Chukchi and Bering seas shelves based on joint modeling of potential field data constrained by thermal analysis and seismic data. We also perform 3D forward modelling and inversion of Bouguer anomalies to analyze crustal density heterogeneities. The obtained crustal model shows NW regional thickening (32 to 36 km), with localized trends of thicker crust in the Brooks Range (40 km) and in the Alaska and St. Elias ranges (50 km). Offshore, 28–30 km thick crust is obtained near the Bearing slope break and 36–38 km in the northern Chukchi Shelf. In interior Alaska, the crustal thickness changes abruptly across the Denali fault, from 34-36 to the N to above 30 km to the S, that agrees with the presence of a crustal tectonic buttress guiding block motion W and S to the subduction zone. The average crustal density is 2810 kg∙m-3. Denser crust (2910 kg∙m-3) is found S of the Denali Fault related to the oceanic nature of the Wrangellia Composite Terrane rocks. Offshore, less dense crust (< 2800 kg∙m-3) is found along the basins of the Chukchi and Beaufort shelves. At LAB levels, there is a regional SE–NW trend that coincides with the Pacific plate motion, with a lithospheric root beneath the Brooks Range, Northern Slope, and Chuckchi Sea, that may be a relic of the Chukotka-Artic Alaska microplate. The lithospheric root (> 180 km) agrees with the presence of a boundary of cold, strong lithosphere that deflects the strain to the south. South of the Denali Fault the LAB topography is quite complex. East of 150 °W, below Wrangellia and the eastern side of Chugach terranes, the LAB is much shallower than it is west of this meridian. The NW trending limit separating thinner lithosphere in the East and thicker in the West agrees with the two–tiered slab shape of the subducting Pacific Plate. This research has been funded by the We–Me project (PIE–CSIC–201330E111), AGUR 2017–SGR–847, Alpimed (PIE-CSIC-201530E082), Subtetis (PIE-CSIC-201830E039) and funds from the University of Houston. This is a contribution within the PolarCSIC platform. Ref: M. Torne, I. Jiménez–Munt, J. Vergés, M. Fernàndez, A. Carballo, M. Jadamec. Geophysical Journal International, Volume 220, Issue 1, January 2020, Pages 522–540, https://doi.org/10.1093/gji/ggz424.

Nearly isotropic comets with very long orbital period are supposed to come from the Oort Cloud. Recent observational and theoretical studies have greatly revealed the dynamical nature of this cloud and its evolutionary history. However, many issues are yet to be known. Our goal is to understand current structure of this cloud as well as its dynamical origin. For estimating the current structure of the Oort Cloud, key information lies in the original orbit of the Oort Cloud new comets (OCNCs) that are defined at a distance where these objects do not receive gravitational perturbation from major planets (such as at rg = 250 au from the Sun before comets enter into the planetary region). There have been several attempts to obtain OCNC’s original orbits, but it never has been an easy task. This requires numerical orbit propagation of the observed comets with high accuracy including perturbation from major disturbing bodies. In addition, non-gravitational forces often play significant roles here. First and foremost, the orbit determination of OCNC includes substantially large uncertainty because of limited number of observational arcs and very large eccentricity of the comets (~1). Here we show our preliminary result of comparison of various catalogues of OCNCs’ original orbital elements at rg = 250 au: So-called the Warsaw catalogues by Krolikowska, the ephemeris given by MPC (Minor Planet Center), that given by Horizons/JPL, and others calculated by a few individuals (Marsden, Kinoshita, and Nakano). The resulting orbits that these catalogues yield are overall similar, but sometimes they are starkly different by reasons yet to be known. Through a series of plots with a help of our own orbit propagation using numerical and analytic methods, we give considerations on which catalogue yields the information that is the most significant (or the most fundamental) for understanding structure, origin, and evolution of the Oort Cloud.

Solar transients impinging on Earth’s magnetosphere often present a larger velocity than the surrounding solar wind, leading to a different response of the bow shock-magnetosheath system. As such, we intend to provide a systematic study of the global effects of different solar wind Mach numbers in a pure quasi-perpendicular configuration with a realistic three-dimensional terrestrial-like curved bow shock. Simulations have been performed with the hybrid code LatHyS, which is based on the widely used CAM-CL scheme. In particular, we have studied the interaction with the terrestrial magnetosphere of a solar wind at different Alfvénic Mach numbers and low-beta (less than unity, ratio between the thermal to the magnetic pressures). One of most noteworthy outcome is the generation of an intense rippling phenomenon propagating along the bow shock surface as the incoming Mach number increases. A similar rippling has been observed in-situ with satellites, as well as studied with computer simulations. However, the latter have mainly addressed by adopting ad-hoc planar-shock initial configurations, which still leaves poor knowledge of the possible effects on a global three-dimensional curved interaction. Our analysis then is expected to provide further insights into both the macroscopic and kinetic effects of different incoming solar wind conditions on the overall planetary bow-shock and magnetosheath structure.

We analyzed correlations between solar, interplanetary-medium parameters, and geomagnetic-activity proxies in 27-day averages (a Bartels rotation) for the 2009 – 2016 time interval. We considered two new proxies: I) Bzs GSM (Geocentric Solar Magnetic), calculated as the daily percentage of the IMF southward component along the GSM z-axis and then averaged every 27 days; ii) four magnetospheric indices (T-indices), calculated from the local north-south (X) contributions of the magnetosphere’s cross-tail (TAIL), the symmetric ring current (SRC), the partial ring current (PRC), and the Birkeland current (FAC), derived from the Tsyganenko and Sitnov (J. Geophys. Res. 110, A03208, 2005: TS05) semi-empirical magnetospheric model. Our results suggest, among the parameters tested here, solar facular areas, interplanetary-magnetic field intensity and new proxies derived here could be taken into account in an empirical model, with a 27-day resolution, to explain geomagnetic activity felt on the Earth’s surface in terms of solar surface features and the IMF condition. We further retrieve a clear annual oscillation in series of 27-day-mean values of toward/away asymmetries of geomagnetic-activity indices, which can be interpreted in the light of the Russell–McPherron hypothesis for the semiannual variation of geomagnetic activity (Russell, C.T., and R. L. McPherron (1973), J. Geophys. Res., 78, 92 – 108).

Agricultural water resources are threatened by climatic variability and increased competition for available freshwater resources. In order to mitigate the effect of climate change on cotton production, breeders are increasing their efforts on improving drought tolerance in this essential fiber crop. To achieve this, effective screening of diverse germplasm is needed to identify useful genetic variation that can be utilized for crop improvement. Within the last decade, unmanned aerial vehicles (UAVs) have led to the ability to quickly and reliably image large areas while simultaneously decreasing temporal effects associated with a large time window for data collection. This technology allows researchers to scale their phenotyping efforts, enabling studies that utilize mapping and monitoring efforts such as plant water stress detection. In this study, we used UAV-based thermal imagery to screen a diverse population of over 350 different genotypes of cotton in order to locate varieties that exhibit cooler canopies. This diversity panel was grown under two contrasting levels of irrigation, well-watered and water-limited, with data collection flights occurring weekly for three months during the season. The thermal images were clipped to plot boundaries, soil and plant pixels were segmented, and average temperatures were extracted to identify potential drought tolerant varieties. The objectives of this study were to (i) demonstrate that UAV-based thermal imagery, along with our calibration methods, can be used to render accurate plant canopy temperature values and (ii) identify cotton genotypes that outperform others in a drought-stressed environment.

Stomata, microscopic pores on leaf surfaces, regulate the uptake of carbon dioxide and the simultaneous loss of water vapor by leaves. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which in turn have been applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models, and their ability to accurately detect epidermal structures is constrained to morphologies found within the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, while also providing a roadmap for translation of a machine learning to analyze stomatal patterning in new plant species.

Hyperspectral based prediction of nutrient content in maize leaves Hyperspectral imaging is a promising method to predict crop traits in a high-throughput manner and unlock quantitative genetic studies. A single hyperspectral image can be used to predict several unrelated traits at once using spectral data from 350nm - 2500nm. Researchers have successfully modeled different physiological traits in maize such as vegetative Nitrogen content but the effect of different development stages, genotypes, and treatments on modelling power remains unclear. Here, I explore the ability to model leaf macro- and micro- nutrient content and leaf water content from hyperspectral transmittance data collected with a LeafSpec imaging device. I will compare three different machine learning algorithms; Partial Least Squares Regression, Random Forest and a Convolutional Neural Net to model nutrient content collected from twenty hybrids throughout the 2020 field season in fertilized and not fertilized blocks. Genotypes and development stages excluded from model training are used to externally validate models. Sulfur, Nitrogen, Calcium, Copper, and Iron leaf concentrations were the most amenable nutrients to prediction with coefficient of determination scores from 0.78 - 0.73, respectively. Models trained on samples from a collection of time points were able to accurately predict new time points and genotypes. The findings demonstrate the ability to predict nutrient content in field grown maize over a variety of developmental stages, genotypes, and treatments from a handheld hyperspectral imaging device.

Nitrous oxide (N20) is a greenhouse gas that is three hundred times more potent than carbon dioxide. The majority of N20 emissions worldwide are the result of excess soil nitrogen being metabolized by microbes. It has been hypothesized that crops with better nitrogen uptake efficiency and more roots will reduce excess soil nitrogen therefore reducing N20 emissions. To test this hypothesis, a pilot study was performed in 2021 in collaboration with Iowa State University in which root growth dynamics were captured using RootTracker ™ technology in four commercial maize hybrids. This preliminary study showed a correlation between increased root growth and reduced N20 emissions. Further, we find genetic differences in root growth that is consistent across reps, suggesting that i) cultivar choice impacts N2O emissions and ii) that it is possible to breed for root system architecture to limit N2O emissions. It was also observed that the hybrid with the fastest rate of root growth (lowest N20 emissions) did not reach the greatest soil depth, suggesting early root establishment could be pivotal to more efficient nitrogen uptake. These preliminary results suggest there are differences in root growth by variety that could be exploited to reduce agricultural N20 emissions at scale.

Subduction zones are essential for mantle convection through the recycling of oceanic lithosphere, however, asthenospheric flow at convergent margins is not uniform. Deformation of the asthenosphere can be driven by subduction processes such as, viscous entrainment to plate motions, slab rollback-induced toroidal flow, and mantle wedge dynamics. These mechanisms are critical to understanding volcanism, margin evolution, and lithosphere-asthenosphere coupling. The easternmost Alaska subduction zone has been extensively studied showing evidence from seismic anisotropy for large-scale toroidal flow around the slab edge. Westward however, near the Shumagin Gap, few observations have been made. Along-strike changes in oceanic plate fabric, steepening slab dip, proximity to the slab edge, plate motion, and hydration of the mantle may all influence anisotropy and mantle flow in this region. Here, we evaluate models using independent offshore shear-wave splitting measurements acquired using data from the Alaska Amphibious Community Seismic Experiment (AACSE). We compare our splitting observations to forward models that consider the distribution of anisotropy and the backazimuthal dependence of observations. The models we test include viscously entrained flow due to oceanic plate motion (~310° CW North), anisotropic fabric variations, anisotropy related to bending faults and mantle serpentinization, and changes in frozen anisotropy in the oceanic lithosphere. Onshore shear-wave splitting observations show fast-axis directions ~55° CW North, inconsistent with 2D mantle wedge flow, assuming A-type olivine, but it is consistent with B-type fabric or trench-parallel flow as suggested by previous studies. Offshore splitting observations appear to vary along-strike. Here, two distinct oceanic plate fabrics exist, one developed from a northeast-spreading direction and the other from an east-spreading direction. Frozen anisotropy in the oceanic lithosphere may play a significant role in the splitting signal produced offshore and may be an important contributor to the distribution of seismic anisotropy. By synthesizing onshore and offshore observations here with our understanding of flow at the eastern slab edge, we can build a more complete model of mantle dynamics for the Alaskan subduction zone.

Flood events are influenced by terrestrial factors including land cover, land and water management, watershed physiographic features, and hydro-climatic components including snowmelt and precipitation. In Canada, flooding is a frequent and prominent natural disaster, which is modulated by different flood-generating mechanisms. In this study, we assess the intensity and frequency of three flood-generating mechanisms including Rain on Snow (ROS), intense rainfall, and snowmelt-driven flood events over the Assiniboine-Red River basin, which is one of the most flood-prone regions in Canada and located in the Lake Winnipeg watershed. We downscale and bias correct seven Global Climate Models (GCMs) that participated in CMIP6 using two methods of Bias Correction/Constructed Analogues with Quantile mapping (BCCAQ) (BCCAQ) and Multivariate Bias Correction (MBC). The observed and downscaled climate variables (precipitation and temperature) are used to drive a process-based distributed snow model to evaluate the changes in flood-generation mechanisms in the historical and future periods. The projected future changes are analyzed under policy-relevant global mean temperature (GMT) increases from 1.0 °C to 3.0 °C above the pre-industrial period. Overall, all models project higher regional temperature increases compared to the global mean with warmer and wetter winters. The snow model results indicate future decreases in the snow cover duration, snowmelt rate, and snow water equivalent (SWE), and earlier shifts in the maximum SWE timing. Moreover, both the intensity and frequency of ROS events increase in all seasons except summers. However, the increases in the rain and snowmelt events are mostly projected to occur in the spring.