Current machine learning methods for discharge prediction often employ aggregated basin-wide hydrometeorological data (lumped modeling) for parametric and non-parametric training. This approach may overlook the spatial heterogeneity of river systems and their impact on discharge patterns. We hypothesize that integrating temporal-spatial hydrologic knowledge into the data modeling process (distributed/disaggregated modeling) can improve the performance of discharge prediction models. To test this hypothesis, we designed experiments comparing the performance of identical Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models forced with either lumped or distributed features. We gather meteorological forcing and static attributes for the Mackenzie basin in Canada- a large and unique basin. Importantly, discharge performance is assessed out-of-sample with k-fold replication across gauges. Results reveal a 9.6% improvement in the mean Nash-Sutcliffe Efficiency (NSE) and a 4.6% improvement in mean Kling-Gupta Efficiency (KGE) when LSTMs are trained with distributed information. Notably, the models exhibit consistently unbiased predictions, with a negligible relative bias (RBias ≈ 0.0) across all predictions. These experiments and results demonstrate the importance of integrating topologically guided geomorphologic and hydrologic information (distributed modeling) in data-driven discharge predictions.

In recent decades, climate change has lengthened wildfire seasons globally and doubled the annual area burned. Thus, capturing fire dynamics is critical for projecting Earth system processes in warmer, drier, more fire prone future. Recent advances in fire regime modeling have linked land surface and Earth system models with fire behavior models. Such models often rely on fine surface fuels to drive fire spread, and while many models can simulate processes that control how these fuels change through time (i.e., fine fuel succession), fuel loading estimates remain highly uncertain. Uncertainties are amplified in climate change forecasts when initial conditions and feedbacks are not well represented. The goal of this review is to highlight fine fuel succession as a key uncertainty in model systems. We review the current understanding of mechanisms controlling fine fuel succession (with an emphasis on decomposition), describe how these mechanisms are incorporated into models, and evaluate the strengths and uncertainties associated with different approaches. We also use three state-of-the-art fire regime models to demonstrate the sensitivity of decomposition projections to both parameter and model structure uncertainty and show that sensitivity increases dramatically under future climate warming. Given that many of the governing decomposition equations are hard-coded in models and often based on individual case studies, substantial uncertainties are currently ignored. To understand future climate-fuel-fire feedbacks, it is essential to be transparent about model choices and uncertainty. This is particularly critical as the domain of Earth system models is expanded to include evaluation of future wildfire regimes.

A document by Qiurun Yu. Click on the document to view its contents.

The Angstrom-Prescott (A-P) model is widely suggested for estimating solar radiation (Rs) in areas without measured or deficiency of data. The coefficients of this model must be locally calibrated, to calculate evapotranspiration (ET) correctly. The aim of this research was calibration and validation of the coefficients of the A-P model at six meteorological stations across arid and semi-arid regions of Iran. This model was improved by adding the air temperature and relative humidity terms. Besides, the coefficients (’a’ and ‘b’) of the A-P model and improved models was calibrated using some optimization algorithms including Harmony Search (HS) and Shuffled Complex Evolution (SCE). Performance indices, i.e., Root Mean Square Error (RMSE), Mean Bias Error (MBE), and coefficient of determination (R2) were used to analyze the models ability in estimating Rs. The results indicated that the performance of the A-P model had more precision and less error than improved models in all the stations. In addition, the best results were obtained for the A-P model with the SCE algorithm. The RMSE varies between 0.82 and 2.67 MJ m-2 day-1 for the A-P model with the SCE algorithm in the calibration phase. In the SCE algorithm, the values of RMSE had decreased about 4% and 7% for Mashhad and Kerman stations in the calibration phase compared to the HS algorithm, respectively. In other words, the highest decrease of RMSE is related to Kerman station.

An acceleration of the background seismicity and a shortening of the slow slip events on the Boso peninsula (Japan) recurrence intervals suggest a slow decoupling of the Philippine Sea-North America (PHS-NAM) subduction interface from 1990 to 2011. Motivated by these observations, we used GPS (Global Positioning System) time series to study the 14-year evolution of interface coupling offshore Honshu with a specific focus on the Kanto region. We processed the GPS data in double difference and analyze them with a trajectory model that accounts for seismic and aseismic variations, and that includes an inter-seismic acceleration term. We inverted the surface acceleration obtained, on both the Pacific-North America (PAC-NAM) and the PHS-NAM interfaces. The inverted slip rate changes over time compares well with previous studies: we observe slip deceleration between 39$^o$-41$^o$ N and slip acceleration between 37$^o$-39$^o$ N, with a maximum amplitude of 3.45 mm/yr$^2$ corresponding to an equivalent geodetic coupling change of 0.64. Our analysis reveals a novel and robust slip acceleration South of 36.5$^o$ N that we interpret as a decoupling of the PAC-NAM interface. It is located noticeably far from the 2011 Tohoku earthquake rupture and is therefore unlikely connected to it. We link the slip rate changes to the background seismicity changes and retrieve the slip acceleration from either the seismicity rate or the surface displacement. Our results further demonstrate that inter-seismic slip rate can significantly evolve over years to decades, and suggest a simple relationship between the background seismicity and the slip on the subduction interface.

Numerical mixing, defined here as the physically spurious diffusion of tracers due to the numerical discretization of advection, is known to contribute to biases in ocean circulation models. However, quantifying numerical mixing is non-trivial, with most studies utilizing specifically targeted experiments in idealized settings. Here, we present a precise, online water-mass transformation-based method for quantifying numerical mixing that can be applied to any conserved variable in global general circulation models. Furthermore, the method can be applied within individual fluid columns to provide a spatially-resolved metric. We apply the method to a suite of global ocean-sea ice model simulations with differing grid spacings and sub-grid scale parameterizations. In all configurations numerical mixing drives across-isotherm heat transport of comparable magnitude to that associated with explicitly-parameterized mixing. Numerical mixing is prominent at warm temperatures in the tropical thermocline, where it is sensitive to the vertical diffusivity and resolution. At colder temperatures, numerical mixing is sensitive to the presence of explicit neutral diffusion, suggesting that much of the numerical mixing in these regions acts as a proxy for neutral diffusion when it is explicitly absent. Comparison of equivalent (with respect to vertical resolution and explicit mixing parameters) $1/4^\circ$ and $1/10^\circ$ horizontal resolution configurations shows only a modest enhancement in numerical mixing at $1/4^\circ$. Our results provide a detailed view of numerical mixing in ocean models and pave the way for future improvements in numerical methods.

Agricultural managed aquifer recharge (AgMAR) is a proposed management strategy whereby surface water flows are used to intentionally flood croplands with the purpose of recharging underlying aquifers. However, legacy nitrate (NO3-) contamination in agriculturally-intensive regions poses a threat to groundwater resources under AgMAR. To address these concerns, we use a reactive transport modeling framework to better understand the effects of AgMAR management strategies (i.e., by varying the frequency, duration between flooding events, and amount of water) on N leaching to groundwater under different stratigraphic configurations and antecedent moisture conditions. In particular, we examine the potential of denitrification and nitrogen retention in deep vadose zone sediments using variable AgMAR application rates on two-dimensional representations of differently textured soils, soils with discontinuous bands/channels, and soils with preferential flow paths characteristic of typical agricultural field sites. Our results indicate that finer textured sediments, such as silt loams, alone or embedded within high flow regions, are important reducing zones providing conditions needed for denitrification. Simulation results further suggest that applying recharge water all-at-once, rather than in increments, increases denitrification within the vadose zone, but transports higher concentrations of NO3- deeper into the profile. This transport into deeper depths can be aggravated by wetter antecedent soil moisture conditions. We conclude that ideal AgMAR management strategies can be designed to enhance denitrification in the subsurface and reduce N leaching to groundwater, while specifically accounting for lithologic heterogeneity, antecedent soil moisture conditions, and depth to the water table.

Although iron and light are understood to regulate the Southern Ocean biological carbon pump, observations have also indicated a possible role for manganese. Low concentrations in Southern Ocean surface waters suggest manganese limitation is possible, but its spatial extent remains poorly constrained and direct manganese limitation of the marine carbon cycle has been neglected by ocean models. Here, using available observations, we develop a new global biogeochemical model and find that phytoplankton in over half of the Southern Ocean cannot attain maximal growth rates because of manganese deficiency. Manganese limitation is most extensive in austral spring and depends on phytoplankton traits related to the size of photosynthetic antennae and the inhibition of manganese uptake by high zinc in Antarctic waters. Importantly, manganese limitation expands under the increased iron supply of past glacial periods, reducing the response of the biological carbon pump. Overall, these model experiments describe a mosaic of controls on Southern Ocean productivity that emerge from the interplay of light, iron, manganese and zinc, shaping the evolution of Antarctic phytoplankton since the opening of the Drake Passage.

A document by Lei Li. Click on the document to view its contents.

The $b$-value in earthquake magnitude-frequency distribution quantifies the relative frequency of large versus small earthquakes. Monitoring its evolution could provide fundamental insights into temporal variations of stress on different fault patches. However, genuine $b$-value changes are often difficult to distinguish from artificial ones induced by temporal variations of the detection threshold. A highly innovative and effective solution to this issue has recently been proposed by van der Elst (2021) through the b-positive method, which is based on analyzing only the positive differences in magnitude between successive earthquakes. Here, we provide support to the robustness of the method, largely unaffected by detection issues due to the properties of conditional probability. However, we show that the b-positive method becomes less efficient when earthquakes below the threshold are reported, leading to the paradoxical behavior that it is more efficient when the catalog is more incomplete. Thus, we propose the b-more-incomplete method, where the b-method is applied only after artificially filtering the instrumental catalog to be more incomplete. We also present other modifications of the b-method, such as the b-more-positive method, and demonstrate when these approaches can be efficient in managing time-independent incompleteness present when the seismic network is sparse. We provide analytical and numerical results and apply the methods to fore-mainshock sequences investigated by van der Elst (2021) for validation. The results support the observed small changes in $b$-value as genuine foreshock features.

Export of sinking particles from the surface ocean is critical for carbon sequestration and for providing energy to the deep-ocean biosphere. The magnitude and spatial patterns of this flux have been estimated in the past by satellite-based algorithms and ocean biogeochemical models; however, these estimates remain uncertain. Here, we present a novel analysis of a global compilation of \textit{in situ} ocean particle size spectra from Underwater Vision Profiler 5 (UVP5) measurements, from which we determine particulate carbon fluxes. Using a machine learning algorithm, we extrapolate sparse observations of particle abundance by size to the global ocean from oceanographic variables that are more commonly observed. We reconstruct global maps of particle size distribution parameters for large sinking particles (80 \textmu{}m to 2.6 cm), and combine them with empirical relationships to calculate the sinking carbon flux from the euphotic zone and the wintertime mixed layer depth. Our flux reconstructions are comparable to other estimates, but suggest a less variable seasonal cycle in the tropical ocean, and a more continuous export in the Southern Ocean than previously thought. Because our estimates are not bounded by a specific depth horizon, we reconstruct export at multiple depths, and find that export from the wintertime mixed layer globally exceeds that from the euphotic zone. Our estimates provide a baseline for more accurate understanding of particle cycles in the ocean, and open the way to fully three-dimensional global reconstructions of particle size spectra and fluxes in the ocean, supported by the growing database of UVP5 observations.

Metal compounds abundant in Early Earth are thought to play an important role in the origins of life. Certain iron-sulfur minerals, for example, are proposed to have served as primitive metalloenzyme cofactors for their ability to catalyze organic synthesis processes and facilitate electron transfer reactions. An inherent difficulty with studying the catalytic potential of many metal compounds is the wide range of data and parameters to consider when searching for individual minerals and ligands of interest. Detecting mineral-ligand pairs that are structurally analogous enables more relevant selections of data to study, since structural affinity is a key indicator of comparable catalytic function. However, current structure-oriented approaches tend to be subjective and localized, and do not quantify observations or compare them with other potential targets. Here, we present a mathematical approach that compares structural similarities between various minerals and ligands using molecular similarity metrics. We use an iterative substructure search in the crystal lattice, paired with benchmark structural similarity methods. This structural comparison may be considered as a first stage in a more advanced analysis tool that will include a range of chemical and physical factors when computing mineral-ligand similarity. This approach will seek relationships between the mineral and enzyme worlds, with applications to the origins of life, ecology, catalysis and astrobiology.

Warm, subsurface ocean waters that access ice shelves in the Amundsen Sea are likely to be a key driver of high meltrates and ice shelf thinning. Numerical models of the ocean circulation have been essential for gaining understanding of the mechanisms responsible for heat delivery and meltrate response, but a number of challenges remain for simulations that incorporate this region. Here, we develop a suite of numerical experiments to explore how sub ice shelf cavity circulation and meltrate patterns are impacted by parameterization schemes for (1) subgrid-scale ocean turbulence, and (2) ice-ocean interactions. To provide a realistic context, our experiments are developed to simulate the ocean circulation underneath the Pine Island ice shelf, and validated against mooring observations and satellite derived meltrate estimates. Each experiment is forced with data-informed open boundary conditions that bear the imprint of the gyre in Pine Island Bay. We find that even at a ~600 m grid resolution, flow aware ocean parameterizations for subgrid-scale momentum and tracer transfer are crucial for representing the circulation and meltrate pattern accurately. Our simulations show that enhanced meltwater diffusion near the ice-ocean interface intensifies near wall velocities via thermal wind, which subsequently increases meltrates near the grounding line. Incorporating a velocity dependent ice-ocean transfer coefficient together with a flow aware ocean turbulence parameterization therefore seems to be necessary for modelling the ocean circulation underneath ice shelves in the Amundsen Sea at this resolution.

A document by Xinlong Liu. Click on the document to view its contents.

Much information about the North American lithosphere has been gained by imaging seismic wave velocities. Additional constraints on the state of the subsurface can be gained by studying seismic attenuation, which has different sensitivity to physical properties. We produce a model of lateral variations in attenuation across the conterminous U.S. by analyzing P waveforms from deep earthquakes recorded by the EarthScope Transportable Array using a time-domain waveform matching approach. We divide the study area into 12 overlapping tiles and differential attenuation is measured in each tile independently; with analysis being repeated independently for 4 of the tiles. Measurements are combined into a smooth map using a linear inversion. Comparing results for adjacent tiles and for repeated tiles shows that the imaged features are robust. The final map is produced by combining all the measurements and shows generally higher attenuation west of the Rocky Mountain Front than east of it, with significant small length scale variations superimposed on that broad pattern. In general, there is a strong anticorrelation between differential attenuation and shear wave velocities at 90 km depth. However, a given change in velocity may correspond to large or small change in attenuation, depending on the area; suggesting that different physical mechanisms are operating. In some cases, most notably in the Snake River Plain, attenuation and velocity do not show the expected anticorrelation. The southern Intermountain Seismic Belt coincides with a high gradient in the attenuation signal, but even larger gradients further inland do not show any association with seismicity.

Health and environmental hazards related to high pollutant concentrations have become a serious issue from the perspectives of public policy and human health. The objective of this research is to improve the estimation of grid-wise PM2.5, a criteria pollutant, by reducing systematic bias in estimating PM2.5 empirically from speciation provided by MERRA-2 using a ML approach. We present a unique application of machine learning (ML) for estimating hourly PM2.5 concentrations at grid points of Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). The model was trained using various meteorological parameters and aerosol species simulated by MERRA-2 and ground measurements from Environmental Protection Agency (EPA) air quality system (AQS) stations. monitors. The ML approach significantly improved performance and reduced mean bias in the 0-10 µg m-3 range. We also used the Random Forest ML model for each EPA region using one year of collocated datasets. The resulting ML models for each EPA region were validated and the aggregate data set has a Pearson correlation of 0.88 (RMSE = 4.8 µg m-3) and 0.82 (RMSE = 5.8 µg m-3) for training and testing, respectively. The correlation (and RMSE) increased to 0.89 (4.0), 0.95 (1.6), 0.94 (1.1) for daily, monthly, and yearly average comparisons. The results from initial implementation of the ML model for global region are encouraging but require more research and development to overcome challenges associated with data gaps in many parts of the world.

The Rolling Deck to Repository (R2R; www.rvdata.us) program is entering its second decade of managing underway data from US-operated academic research vessels to ensure preservation of, and access to, these national oceanographic research assets. Reflecting on the move from decentralized data submission by chief scientists to an operational centralized facility has brought insights that may inform other communities with distributed networks of data acquisition providers with diverse practices and resources. 4,000 cruises and 100+TB of data later, here are lessons R2R has learned. - Managing data via a central aggregating system where both curation and domain data expertise can be optimally leveraged promotes more complete and efficient data preservation. - Identifying key organizing elements for the data, and implementing persistent identifiers and metadata for those elements, facilitates management and usability. R2R developed authoritative DOIs and standard metadata for cruises to organize R2R data for discoverability and access, and facilitate reciprocal linking to related data in external repositories. When data submissions from diverse providers are heterogeneous, standardizing data at ingest supports data aggregation and synthesis that promote broad data re-use. - Providing tools and expertise to assist with standardization, such as recommended data structures and best practice guidance for data acquisition, reduces heterogenoeus practices over time even when compliance is voluntary. - Developing organized and persistent communication mechanisms with all main stakeholders is central to success. R2R has annual community-level meetings, as well as more frequent individual interactions, with vessel operators/technicians, the NOAA National Centers for Environmental Information staff, and oceanographic research scientists. These communications have been critical to informing high level priorities, overall approaches, and specific technical details and decisions.

The 2018 eruption of Kīlauea volcano produced the largest and most destructive lava flows in the lower East Rift Zone (LERZ) in the past 200 years. Average effusion rates exceeded 100 m3 s-1 (DRE) for more than two months as lava covered > 30 km2 of land area. The largest and longest-lived lava flow was produced by fissure 8 and had flow advance rates exceeding 100 m hr-1 and a run-out length of 13 km. While residents were able to safely evacuate from this rapidly advancing flow, hundreds of structures were destroyed. We integrate observed eruption parameters from the fissure 8 flow with numerical models for lava flows to investigate how eruption rate, topography, and rheology affect the initial path, advance rate, and extent of a lava flow. Many numerical models have been created to represent the advance and/or extent of lava flows. We apply both 1D and 2D, rules-based and physics-based models to explore the advantages and limitations of these model types. First, we validate the models for fissure 8 flow parameters using existing datasets from field observations and sample analysis. Second, we vary the eruption rate and lava rheology to test the influence of these parameters on the advance rate and flow extent. This analysis demonstrates the level of confidence that can be associated with modeling results when estimating difficult-to-constrain parameters during an eruption. Third, using input digital elevation models (DEM) of different resolutions, we examine the sensitivity of model accuracy to DEM resolution, with a specific focus on the influence on flow advance of smaller-scale topographic features that may not be resolved in coarse-resolution DEMs. Through better understanding of how different parameters control flow emplacement, and how to best apply the models describing that emplacement, we aim to improve the ability to estimate advance rate and flow path during (and prior to) the initial stages of flow emplacement and provide more detailed hazard assessments for future eruptions.