Hydrological alterations, which can be represented by the extent of the changes in the flow patterns resulting from anthropogenic factors, can reduce aquatic biodiversity by disrupting the life cycles of organisms. However, past studies have faced difficulties in quantifying the impacts of dams and climate change, which are major drivers of hydrological alterations. Here, we aimed to evaluate and compare the hydrological alterations caused by dams and climate change throughout the Omaru River catchment, Japan, using a distributed hydrological model. First, to assess the impacts of dam and climate change independently, we performed runoff analyses using either dam discharge or future climatic data (two future periods × three representative concentration pathways; RCPs). Subsequently, we derived indicators of hydrologic alterations (IHA) to quantify changes in flow alterations by comparing them to IHA under natural conditions (i.e., without dam or climate change data). The runoff analyses showed high reproducibility throughout the study period (Nash-Sutcliffe efficiency = 0.921–0.964). We found that dams altered IHAs more than climate change. However, on a catchment-scale standpoint, climate change induced wider ranges of flow alterations, such as low flow metrics along the tributaries and uppermost main stem, suggesting a watershed-level shrinkage in important corridors of aquatic organisms by reducing upstream length and water level. We also observed that the altered flow by water withdrawals were ameliorated by the confluence of tributaries and downstream hydropower outflows. Our approach, which used a distributed hydrological model, developed a better understanding of flow alterations by dams and climate change.

Understanding climate change impacts on global marine ecosystems and fisheries requires complex marine ecosystem models, forced by global climate projections, that can robustly detect and project changes. The Fisheries and Marine Ecosystems Model Intercomparison Project (FishMIP) uses an ensemble modelling approach to fill this crucial gap. Yet FishMIP does not have a standardised skill assessment framework to quantify the ability of member models to reproduce past observations and to guide model improvement. In this study, we apply a comprehensive model skill assessment framework to a subset of global FishMIP models that produce historical fisheries catches. We consider a suite of metrics and assess their utility in illustrating the models’ ability to reproduce observed fisheries catches. Our findings reveal improvement in model performance at both global and regional (Large Marine Ecosystem) scales from the Coupled Model Intercomparison Project Phase 5 and 6 simulation rounds. Our analysis underscores the importance of employing easily interpretable, relative skill metrics to estimate the capability of models to capture temporal variations, alongside absolute error measures to characterise shifts in the magnitude of these variations between models and across simulation rounds. The skill assessment framework developed and tested here provides a first objective assessment and a baseline of the FishMIP ensemble’s skill in reproducing historical catch at the global and regional scale. This assessment can be further improved and systematically applied to test the reliability of FishMIP models across the whole model ensemble from future simulation rounds and include more variables like fish biomass or production.

Sustainable river management often requires long-term morphological simulations. As the future is unknown, uncertainty needs to be accounted for, which may require probabilistic simulations covering a large parameter domain. Even for one-dimensional models, the simulation times can be long. One of the strategies to speed up simulations is simplification of models by neglecting terms in the governing hydrodynamic equations. Examples are the quasi-steady model and the diffusive wave model, both widely used by scientists and practitioners. Here, we establish under which conditions these simplified models are accurate. Based on the results of linear stability analyses of the St. Venant-Exner equations, we assess migration celerities and damping of infinitesimal, but long riverbed perturbations. We did this for the full dynamic model, i.e. no terms neglected, as well as for the simplified models. The accuracy of the simplified models was obtained from comparison between the characteristics of the riverbed perturbations for simplified models and the full dynamic model. We executed a spatial-mode and a temporal-mode linear analysis and compared the results with numerical modelling results for the full dynamic and simplified models. The numerical results match best with the temporal-mode linear stability analysis. The analysis shows that the quasi-steady model is highly accurate for Froude numbers up to 0.7, probably even for long river reaches with large flood wave damping. Although the diffusive wave model accurately predicts flood wave migration and damping, key morphological metrics deviate more than 5% (10%) from the full dynamic model when Froude numbers exceed 0.2 (0.3).

Upper mantle anisotropy has been mapped beneath continents at high spatial resolution. Beneath the oceans, however, shear wave splitting constraints on upper mantle anisotropy are sparse, due to the paucity of seismic receivers. The technique that does not require the availability of seismic stations close to the region under study is differential PS-SKS splitting. Here, we use global wavefield simulations to investigate circumstances under which PS-SKS splitting can be applied, and then use this technique to measure upper mantle anisotropy beneath the Pacific Ocean basin. Our results demonstrate that upper mantle anisotropy in our study region mostly reflects shearing due to the Pacific plate. North of Fiji, we observe a rotation of fast polarization directions, away from the direction of absolute plate motion of the Pacific plate. We attribute this to far-field mantle flow effects associated with the subduction of the Australian plate beneath the Pacific.

The Budyko framework consists of a curvilinear relationship between the evaporative ratio (i.e., actual evaporation over precipitation) and the aridity index (potential evaporation over precipitation) and defines evaporation’s water and energy limits. A basin’s movement within the Budyko space illustrates its hydroclimatic change and can help identify the main drivers of change. Basins are expected to move along their Budyko curves when only long-term changes in the aridity index drive changes in the evaporative ratio. We hypothesize that the increasing effects of global warming on the hydrological cycle will cause basins to move along their Budyko curves. To test our hypothesis, we quantify the movement in Budyko space of 353 river basins from 1901 to 2100 based on the outputs of nine models from the Coupled Model Intercomparison Project - Phase 5 (CMIP5). We find that significant increases in potential evaporation due to global warming will lead to basins moving primarily horizontally in Budyko space accompanied by minor changes in the evaporative ratio. However, 37% of the basins will still deviate from their Budyko curve trajectories, with less evaporation than expected by the framework. We elaborate on how land-use change, vegetation changes, or shifts in precipitation or snow to rain ratios can explain these deviations.

A blanket of sedimentary and regolith material covers approximately three-quarters of the Australian continent, obscuring the crustal geology below and potential mineral resources within. Sedimentary basins also trap seismic energy increasing seismic hazard and generating noisy seismograms that make determining deeper crustal and lithospheric structure more challenging. The most fundamental question that can first be asked in addressing these challenges is how thick are the sediments? Borehole drilling and active seismic experiments using a controlled seismic source (e.g. vibroseis) provide excellent constraints, but they are limited in geographical coverage due to their expense, especially when operating in remote areas. On the other hand, passive-seismic experiments that involve the deployment of seismic receivers only (i.e. seismometers) are relatively low-cost and portable, providing a practical alternative for initial surveys. Here we utilize receiver functions obtained for both temporary and permanent seismic stations in South Australia, covering regions with a diverse sediment distribution. We present a straightforward method to determine the basement depth based on the arrival time of the P-converted-to-S phase generated at the boundary between the crustal basement and sedimentary strata above. Utilizing the available borehole data, we establish a simple predictive relationship between Ps arrival time and the basement depth, which could then be applied to other sedimentary basins with some consideration. The method is found to work best for Phanerozoic sediments and offers a way to determine the sediment-basement interface in unexplored areas requiring only temporary seismic stations deployed for < 6 months.

Distributed sensor networks empower real-time in-situ computing and seismic imaging without transmitting the raw data to the remote data center. This attribute is valuable for planet exploration that has stringent bandwidth. The previously distributed ambient noise imaging algorithm computes results using data from near neighbors, while the information from distant neighbors is not utilized, hence the image quality is compromised. To overcome this problem, this work proposes an innovative common-receiver-based decentralized ambient noise imaging algorithm. In this algorithm, the basic imaging algorithm is still Eikonal tomography, but the distant neighbors are also used to computing the seismic image so that the quality of the output image can be preserved. An in-situ computing and clustering algorithm is created to optimize the data transition and computation while meeting the bandwidth constrains. The experiments were performed on both synthetic data from Enceladus and real data from the USArray archives. The new algorithm generates higher-resolution images under the same bandwidth constraints, comparing to previous algorithms, and the quality of the output image is satisfactorily preserved. The communication cost reduction over the raw data collection is in several orders of magnitude (e.g., 1: 1600). It meets the desired bandwidth constraint in planetary exploration applications.

The rupture process of earthquakes at intermediate depth (~70-300 km) have been rarely illuminated by a joint analysis of geodetic and seismic observations, hindering our understanding on their dynamic rupture mechanisms. Here we present detailed rupture process of the 2019 Mw8.0 Peru earthquake at the depth of 122 km with a holistic approach reconciling InSAR and broadband seismological waveform data. The joint inversion of InSAR observations and teleseismic body waves results in a finite rupture model that extends ~200 km along strike, with unilateral rupture towards north that lasted for ~60 s. There are four major asperities in the finite fault model which are well corresponding to position and timing of the sources in back-projection (BP) and multiple points source (MPS) results. The largest asperity, which occurred ~40 s after the rupture initiation, was featured with longer and smoother risetime, and radiated much weaker high-frequency seismic waves compared to other smaller asperities. This distinct frequency-dependent rupture requires a strong dynamic weakening mechanism, likely thermal pressurization of pore free water rather than thermal runaway. Our frequency content analysis could be generalized to study other earthquakes including those deeper than 300 km.

Atmospheric gravity waves (GWs) play a key role in determining the thermodynamical structure of the Earth’s middle atmosphere. Despite the small spatial and temporal scales of these waves, a few high-top general circulation models (GCMs) that can resolve them explicitly have recently become available. This study compares global GW characteristics simulated in one such GCM, the Japanese Atmospheric GCM for Upper-Atmosphere Research (JAGUAR), with those derived from three-dimensional (3-D) temperatures observed by the Atmospheric Infrared Sounder (AIRS) aboard NASA’s Aqua satellite. The target period is from 15 December 2018 to 8 January 2019, including the onset of a major sudden stratospheric warming (SSW). The 3-D Stockwell transform method is used for GW spectral analysis. The amplitudes and momentum fluxes of GWs in JAGUAR are generally in good quantitative agreement with those in the AIRS observations in both magnitude and distribution. As the SSW event progressed, the GW amplitudes and eastward momentum flux increased at low latitudes in the summer hemisphere in both the model and observation datasets. Case studies demonstrate that the model is able to reproduce comparable wave events to those in the AIRS observations with some differences, especially noticeable at low latitudes in the summer hemisphere. Through a comparison between the model results with and without the AIRS observational filter applied, it is suggested that the amplitudes of GWs near the exits and entrances of eastward jet streaks are underestimated in AIRS observations.

Capability of TEC ’s CoRrelation Analysis (CRA) (Iwata and Umeno, JGR-Space Physics, 2016) for detecting preseismic anomalies is explained from the view point of the increase in signal-to-noise ratio to amplify preseismic TEC’s small anomaly signals with multiple sensor data synchronization and correlation. Furthermore, deceleration at propagation velocities of MSTID before the 2016 Kumamoto earthquake firstly observed by CRA (Iwata and Umeno, JGR-Space Physics, 2017) as velocity reduction of MSTID propagation in the F Layer of the ionosphere is then elucidated as a candidate of preseismic anomalies. A new physical model (Umeno, Nakabayashi, Iwata, Kao, 2021,DOI: 10.4236/ojer.2021.104008 ) which is recently constructed from the first principle to explain such ionospheric anomaly (ΔV=αΔE, Linear response theory of deceleration ΔV in propagation velocities of MSTID before large earthquakes with electric field change ΔE) is also presented to characterize preseismic ionospheric TEC anomalies by associating deceleration, acceleration, moving to the inverse direction of macroscopic ionic velocities before various large earthquakes such as our existing findings on ionospheric TEC anomaly before 2016 Tainan earthquake (Goto, Uchida, Igarashi, Chen, Kao, Umeno, JGR-Space Physics, 2019). In particular, this physical model predicts that the 35 m/s change in MSTID propagation velocities estimated by TEC’s CRA for 2016 Kumamoto earthquake requires 0.58 mV/m electric field change in the F Layer ionosphere, which is almost consistent with the estimation (Kelley et. al. JGR-Space Physics, 2017) in that the E✖︎B/B^2 drift of 12 m/s for dislocations of electrons requires 0.5 mV/m electric field in the E Layer to explain Heki’s finding of TEC anomaly behavior before the Tohoku-Oki earthquake. The 10000 times amplification effect of weak signals such as 0.5-0.58 mV/m in electrical field to affect MSTID propagation velocity change as is firstly observed by Iwata and Umeno, 2017 by CRA which means a significant signal amplification capability in this multi-sensor TEC correlation analysis (CRA). To summarize, various interrelation between physical models to exhibit TEC anomalies and observed TEC anomalies as above will be presented to understand preseismic ionospheric anomaly before large earthquakes.

The Glacier Energy and Mass Balance (GEMB) module of NASA’s Ice-sheet and Sea-level System Model (ISSM) can simulate the evolution of firn density profiles in the ice sheet snowpack. It is a column model that can give detailed subsurface parameters such as layer depth, snow grain growth, depth dependent albedo, layer density etc. These parameters are model simulation outputs, and lack of observations makes them difficult to evaluate on a broader scale. In this work, we take advantage of remotely-sensed snow conditions in the top ~15m of the firn; more specifically, the layer depth vs density profiles, measured through the Snow Radar sensor from NASA’s Operation IceBridge mission. We use these observations to calibrate the GEMB module over the Greenland Ice Sheet. The module optimized with the help of actual observations will become more reliable for predicting glacial mass loss and associated global sea level rise over time.

The East Asian monsoon system is generated by land-ocean thermal contrast between Asia and NW Pacific, altering the hydroclimate variability in the region. Suspended particulate matter (SPM) consists of a mixture of geochemical pools with distinct origins, evolutionary paths, behaviors and isotopic signatures. The characteristics of SPM reflect the physical processes occurring within a river catchment, including erosion, sediment transport, nutrient and pollutant movement, and organic matter dynamics. The Changjiang is the second largest supplier of global SPM, discharges the largest fresh water and suspended sediment discharge into the East China Sea, significantly influencing the oceanic mass balance. The Changjiang basin is subject to the Indian Monsoon in its headwaters and the East Asian Monsoon downstream, which dominates the chemical weathering patterns in the basin.A widespread inherited feature of the Changjiang basin is its heavy Cu signatures in dissolved and particulate loads. However, the SPM samples conversely displays light 𝛿65Cu values in Lower Reaches, ranging from −0.26 to +0.45 ‰ (2SD, n=31), with almost constant CuSPM concentrations (average ~49 µg/g). Heavy and light Cu isotopic signatures are naturally sourced, albeit with considerable fluctuations in daily samples. The flood event in July 2020 elucidate the effect of intense erosion over Cu concentrations and isotopes, showing larger variations from −0.10 to +0.45 ‰ (2SD, n=10). We infer that the temporal and spatial variations in Cu isotopes of Changjiang SPMs with low enrichment metrics is the combined effects of monsoon-driven weathering and soil erosion in the large catchment. This study provides new insights into Cu geochemical behavior during earth surface processes, which will contribute to a holistic understanding of the global Cu cycle.

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation during the 2009-2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

Vast quantities of marine debris have beached at remote islands in the western Indian Ocean such as Seychelles, but little is known about where this debris comes from. To identify these sources and temporal patterns in accumulation rate, we carried out global Lagrangian particle tracking experiments incorporating surface currents, waves, and variable windage, beaching, and sinking rates, taking into account both terrestrial (coastal populations and rivers) and marine (fisheries and shipping) sources of debris. Our results show that, whilst low-buoyancy terrestrial debris may originate from the western Indian Ocean (principally Tanzania, Comoros, and Seychelles), most terrestrial debris beaching at remote western Indian Ocean islands drifts from the eastern and northern Indian Ocean, primarily Indonesia and, to a lesser extent, India and Sri Lanka. Purse-seine fragments beaching at Seychelles are likely associated with fishing activity in the western Indian Ocean, but longline fragments may also be swept from the southeastern Indian Ocean. The entire of Seychelles is at very high risk from waste discarded from shipping routes transiting the Indian Ocean, and comparison with observations suggests that many bottles washing up on beaches may indeed originate from these routes. Our analyses indicate that marine debris accumulation at Seychelles (and the Outer Islands in particular) is likely to be strongly seasonal, peaking during February-April, and this pattern is driven by local monsoonal winds. This seasonal cycle may be amplified during positive Indian Ocean Dipole phases and El-Ni\~{n}o events. These results underline the vulnerability of small island developing states to marine plastic pollution, and are a crucial first step towards improved management of the issue. The Lagrangian trajectories used in this study are available for download, and our analyses can be rerun under different parameters using the associated scripts.

The IRIS Data Management Center (DMC) maintains a large assemblage of pre-computed and dynamically generated quality assurance metrics for seismic data by means of its MUSTANG system. Freely accessible through web services at http://service.iris.edu/mustang, this collection of measurements includes basic statistics, data latency, data availability, miniSEED flag counts, Power Spectral Densities (PSD), Probability Density Functions (PDF), and more. The metrics produced are suitable for a wide range of uses such as data selection for research projects, identification of data and metadata problems, assessment of station health and upgrades, and characterization of environmental noise, among others. Currently, MUSTANG measurements for seismic channels span our entire primary data repository from the year 1972 to the present. We are expanding MUSTANG’s utility to the broader geosciences community by calculating measurements on our active source, PH5 data repository and by working towards including other types of data such as infrasound. We will also present improvements to metric visualization and quality assessment tools like the MUSTANG Databrowser (basic metric plots and boxplots), MUSTANGular (map-based metric plots), and ISPAQ (a stand-alone Python utility for calculating metrics for miniSEED data stored locally or at any FDSN data center).

Over the last two decades, several datasets have been developed to assess flood risk at the global scale. In recent years, some of these datasets have become detailed enough to be informative at national scales. The use of these datasets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using global datasets and methodologies. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global datasets nationally. To assess national flood risk, we use 6 datasets of global flood hazard, 7 datasets of global population, and 3 different methods for calculating vulnerability that have been used in previous global studies of flood risk. We find that the datasets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global datasets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global datasets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation.

We design a new strategy to load-balance high-intensity sub-grid atmospheric physics calculations restricted to a small fraction of a global climate simulation’s domain. We show why the current parallel load balancing infrastructure of CESM and E3SM cannot efficiently handle this scenario at large core counts. As an example, we study an unusual configuration of the E3SM Multiscale Modeling Framework (MMF) that embeds a binary mixture of two separate cloud-resolving model grid structures that is attractive for low cloud feedback studies. Less than a third of the planet uses high-resolution (MMF-HR; sub-km horizontal grid spacing) relative to standard low-resolution (MMF-LR) cloud superparameterization elsewhere. To enable MMF runs with Multi-Domain CRMs, our load balancing theory predicts the most efficient computational scale as a function of the high-intensity work’s relative overhead and its fractional coverage. The scheme successfully maximizes model throughput and minimizes model cost relative to precursor infrastructure, effectively by devoting the vast majority of the processor pool to operate on the few high-intensity (and rate-limiting) HR grid columns. Two examples prove the concept, showing that minor artifacts can be introduced near the HR/LR CRM grid transition boundary on idealized aquaplanets, but are minimal in operationally relevant real-geography settings. As intended, within the high (low) resolution area, our Multi-Domain CRM simulations exhibit cloud fraction and shortwave reflection convergent to standard baseline tests that use globally homogenous MMF-LR and MMF-HR. We suggest this approach can open up a range of creative multi-resolution climate experiments without requiring unduly large allocations of computational resources.

The construction sector serves as a vital pillar from both social and economic standpoints, fulfilling essential housing needs and contributing significantly to economic growth. However, its environmental impact is notable, manifesting through the release of substantial greenhouse gas emissions, ultimately leading to detrimental environmental consequences, such as global warming and ozone depletion. The global construction industry is a significant contributor, accounting for a substantial 39% of the world's greenhouse gas emissions. Civil construction, particularly in developing countries like India, holds a pronounced economic role. To address environmental concerns, the adoption of sustainable construction practices becomes imperative, not only to curtail greenhouse gas emissions but also to conserve finite resources. Developing economies are compelled to embark on large-scale civil construction and energy production ventures, accentuating the criticality of transitioning to a low-carbon economy, though this poses challenges in the process. To identify potential mitigation opportunities, comprehending the contributions of the construction industry and related activities is vital. Thus, the present study seeks to quantify the overall carbon material footprint of the construction sector in India, with a focus on identifying key contributing supply chains, industries, and products. To achieve this, extensive economy-wide input-output analysis is conducted utilizing economic input-output transaction tables jointly by the Central Statistical Organization (CSO) and Planning Commission. This comprehensive approach enables detailed sector disaggregation refined analysis, and facilitates benchmarking. To assess GHG emissions, the study develops a model based on input-output and process-based life cycle assessment principles. The significance of this in-depth analysis is to provide critical insights for reducing construction-related greenhouse gas emissions in developing countries. The outcome will aid in prioritizing future emissions reduction endeavors, directing targeted solutions toward improving the overall environmental performance of the construction industries.