Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models — particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process can be improved via coupled NNs. We present a novel differentiable routing model that mimics the classical Muskingum-Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning’s roughness (n) and channel geometries from raw reach-scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real-world data, the trained differentiable routing model produced more accurate long-term routing results for both the training gage and untrained inner gages for larger subbasins (>2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework’s potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national-scale flood simulations.
The elastic property of asteroids is one of the paramount parameters for understanding their physical nature. For example, the rigidity enables us to discuss the asteroid’s shape and surface features such as craters and boulders, leading to a better understanding of geomorphological and geological features on small celestial bodies. The sound velocity allows us to construct an equation of state that is the most fundamental step to simulate the formation of small bodies numerically. Moreover, seismic wave velocities and attenuation factors are useful to account for resurfacing caused by impact-induced seismic shaking. The elastic property of asteroids thus plays an important role in elucidating the asteroid’s evolution and current geological processes. The Hayabusa2 spacecraft brought back the rock samples from C-type asteroid (162173) Ryugu in December 2020. As a part of the initial analysis of returned samples, we measured the seismic wave velocity of the Ryugu samples using the pulse transmission method. We found that P- and S-wave velocities of the Ryugu samples were about 2.1 km/s and 1.2 km/s, respectively. We also estimated Young’s modulus of 6.0 – 8.0 GPa. A comparison of the derived parameters with those of carbonaceous chondrites showed that the Ryugu samples have a similar elastic property to the Tagish Lake meteorite, which may have come from a D-type asteroid. Both Ryugu and Tagish Lake show a high degree of aqueous alteration and few high-temperature components such as chondrules, indicating that they formed in the outer region of the solar system.
The aim of this work is to understand the formation of primary evaporites—sulfates, borates, and chlorides—in Gale crater using thermochemical modeling to determine constraints on their formation. We test the hypothesis that primary evaporites required multiple wet-dry cycles to form, akin to how evaporite assemblages form on Earth. Starting with a basalt-equilibrated Mars fluid, Mars-relevant concentrations of B and Li were added, and then equilibrated with Gale lacustrine bedrock. We simulated cycles of evaporation followed by groundwater recharge/dilution to establish an approximate minimum number of wet-dry cycles required to form primary evaporites. We determine that a minimum of 250 wet-dry cycles may be required to start forming primary evaporites that consist of borates and Ca-sulfates. We estimate that ~14,250 annual cycles (~25.6 k Earth years) of wet and dry periods may form primary borates and Ca-sulfates in Gale crater. These primary evaporites could have been remobilized during secondary diagenesis to form the veins that the Curiosity rover observes in Gale crater. No LiCl salts form after 14,250 cycles modeled for the Gale-relevant scenario (approximately 106 cycles would be needed) which implies Li may be leftover in a groundwater brine after the time of the lake. No major deposits of borates are observed to date in Gale crater which also implies that B may be leftover in the subsequent groundwater brine that formed after evaporites were remobilized into Ca-sulfate veins.
Using ICON and GOLD satellite observations, the response of the thermospheric daytime horizontal winds and neutral temperature to the 2020/2021 major sudden stratospheric warming (SSW) is studied at low- to middle latitudes (0° - 40°N). Comparison with observations during the non-SSW winter of 2019/2020 and the pre-SSW period (December 2020) clearly demonstrates the SSW-induced changes. The northward and westward thermospheric winds are enhanced during the warming event, while temperature around 150 km drops by up about 50 K compared to the pre-SSW phase. Changes in the horizontal circulation during the SSW can generate upwelling at low-latitudes, which can contribute to the adiabatic cooling of the low-latitude thermosphere. The observed changes during the major SSW are a manifestation of long-range vertical coupling in the atmosphere.
Previous research showed that radiative feedbacks are essential to the spontaneous development of convective aggregation (CSA) in idealized atmosphere models. We find that the contribution of radiative feedbacks decreases with warming and that, in warm climates, CSA occurs without radiative feedbacks. We perform 2D simulations in different climates using a cloud-resolving model and use a local moist static energy (LMSE) framework to quantify the contribution of radiative feedbacks to the increase of LMSE variance, which characterizes the development of CSA. The result shows that radiative contribution dominates the LMSE variance production when SST is less than 300 K; when SST is higher than 300 K, adiabatic variance production becomes more important than radiative production. Then we turn off radiative feedbacks by horizontally homogenizing radiative heating rates at all model levels. CSA still occurs in warmer climates (310–320 K). This result agrees with the LMSE diagnosis and additional 3D simulations.
It is recapitulated that the gravitational potential energy that is conserved along the neutral surfaces needs two terms, one from buoyancy and the other from gravity. I also show a mathematical identity for the time change of this gravitational potential energy which can be interpreted as exchange of energy amongst kinetic, internal, and gravitational potential forms. Movements along the neutral surface conserve the gravitational potential energy and it is shown that not only conversions into and out of the gravitational potential energy balance, but that each of the conversion terms is zero.
There has always been a question about what transmits electromagnetic waves and/or gravitational forces. Furthermore, the nature of dark matter and dark energy remains elusive. This paper is introducing a new simple theory that may explain many unexplained phenomena in physics. This theory should stimulate many physicists and mathematicians for a new way of thinking about our universe and unsolved mysteries in physics. In this theory, the presence of new particles in the universe called "Spacetime or Gravilon (Gravi for gravity and L for light and on for transmission)", are proposed as the replacement for the space time fabric / field described by Einstein. The fact that gravity and light have the same speed for their propagation can be explained by the presence of Spacetime particles as they utilize the same vehicle (Spacetime particles) for their effect. The presence of Spacetime particles in the universe may explain the unknown phenomena called dark matter. The maximal possible contraction of time particles between space particles can explain why light or any matter cannot surpass the maximal speed of light and why time nearly stops with the light speed. These particles are simplified in this paper but are most likely complicated structures that could have more dimensions than the known four dimensional space time and could be very diverse in nature with multiple subunits than simple illustration in this paper. However, in the core, these particles are made from two fundamental parts. One part contains space particles connected to each other via other part time particles. Time particles could have particle quality but could also be pure energy. Basically, these Gravilon particles introduced in this paper have two major components. One is space and the other one is time. Space particles connected to the time particles could explain Spacetime effect known in the relativity theory. The main difference between this theory and the Einstein relativity, is the fact that this theory is introducing Spacetime particles instead of Space-time fabric. Connection of space with time particles can also explain relativity. Furthermore, interaction of these particles with matter can give matters their masses as an explanation for why matter has mass. Higgs bosons and fields could be also a part of Spacetime particles. This simple theory could also explain limitations of light speed and why light and gravity have the same speed for their effect. Furthermore, probably the distance between Spacetime particles can be stretched or contracted to fit small propagating particles in between based on their sizes. However, these stretching, and contraction properties should have limits. Those particles that can fit between the Spacetime particles before reaching the maximal stretching capability of Spacetime particles will behave mostly as quanta following quantum mechanics rules. Once propagating particles are too
Regions of ECH occurrence in different planetary magnetospheres. Two distinct regions of ECH waves are present in Saturn and Jupiter. ECH waves are seen in the equatorial regions outside the high density plasmasphere / plasma torus and also at intermediate latitude in the magnetospheres, where plasma is confined in a thin disc near centrifugal equator.
Ecosystems around the globe are experiencing increased variability due to land use and climate change. In response, ecologists are increasingly using near-term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near-term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost-prohibitive or impossible for forecasting ecological variables that lack high-frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in-situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using FLARE (Forecasting Lake And Reservoir Ecosystems), an open-source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1 to 35-day-ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1-day-ahead forecast root mean square error (RMSE) of 0.94°C, mean 7-day RMSE of 1.33°C, and mean 35-day RMSE of 2.15°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1-7-day-ahead horizons, weekly data assimilation resulted in the most skillful forecasts at 8-35-day-ahead horizons. Within a year, daily to fortnightly data assimilation substantially outperformed monthly data assimilation in the stratified summer period, whereas all data assimilation frequencies resulted in skillful forecasts across depths in the mixed spring/autumn periods for shorter forecast horizons. Our results suggest that lower-frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high-frequency sensor data.
Energetic electron precipitation (EEP) associated with pulsating aurora can transfer greater than 30 keV electrons from the outer radiation belt region into the upper atmosphere and can deplete atmospheric ozone via collisions that produce NOx and HOx molecules. Our knowledge of exactly how EEP occurs is incomplete. Previous studies have shown that pitch angle scattering between electrons and lower-band chorus waves can cause pulsating aurora associated with EEP and that substorms play an important role. In this work, we quantify the timescale of chorus wave decay following substorms and compare that to previously determined timescales. We find that the chorus decay e-folding time varies based on magnetic local time (MLT), magnetic latitude, and wave frequency. The fastest decay occurs for lower-band chorus in the 21 to 9 MLT region and compares well to the timescale of Troyer et al. (2022) for energetic pulsating aurora. We are able to further support this connection by modelling our findings in a quasi-linear diffusion simulation. These results provide observations of how chorus waves behave after substorms and add additional statistical evidence linking energetic pulsating aurora to substorm driven lower-band chorus waves.
Insufficient in-situ observations from the Antarctic marginal ice zone limit our understanding and description of relevant mechanical and thermodynamic processes that regulate the seasonal sea ice cycle. Here we present high-resolution thermal images of the ocean surface and complementary measurements of atmospheric variables that were acquired underway during one austral winter and one austral spring expedition in the Atlantic and Indian sectors of the Southern Ocean. Skin temperature data and ice cover images were used to estimate the partitioning of the heterogeneous surface and calculate the heat fluxes to compare with ERA5 reanalyses. The winter marginal ice zone was composed of different but relatively regularly distributed sea ice types with sharp thermal gradients. The surface-weighted skin temperature compared well with the reanalyses due to a compensation of errors between the sea ice fraction and the ice floe temperature. These uncertainties determine the dominant source of inaccuracy for heat fluxes as computed from observed variables. In spring, the sea ice type distribution was more irregular, with alternation of sea ice cover and large open water fractions even 400 km from the ice edge. The skin temperature distribution was more homogeneous and did not produce substantial uncertainties in heat fluxes. The discrepancies relative to reanalysis data are however larger than in winter and are attributed to biases in the atmospheric variables, with the downward solar radiation being the most critical.
Dome Fuji, inland East Antarctica is one of only few regions where 1.5-Ma old ice can be preserved for investigating the mid-Pleistocene Transition. We used stochastic simulation and various radar datasets to generate a bed topography ensemble with the continuous, realistic roughness necessary to assess basal conditions. Ensemble analysis reveals the magnitude and spatial distribution of topographic uncertainty, facilitating uncertainty-constrained assessments of subglacial drainage and topographic adjustments to geothermal heat flow. We find that topographic variability can lead to widespread local geothermal heat flow variations of ± 20% the background value, which aggregate to raise the regional value and suggest previously underestimated distributions and rates of basal melting. We also find that survey profile spacing has an increasing influence on topographic uncertainty for rougher bed, deriving an empirical relationship that could guide future survey planning based on uncertainty tolerance.
The water storage capacity of the root zone determines whether plants survive dry periods and controls the partitioning of precipitation into streamflow and evapotranspiration. It is currently thought that top-down, climatic factors are the primary control on this capacity via their interaction with plant rooting adaptations. However, it remains unclear to what extent bottom-up, geologic factors can provide an additional constraint on storage capacity. Here we use a machine learning approach to identify regions with lower than climatically expected apparent storage capacity. We find that in seasonally dry California these regions overlap with particular geologic substrates. We hypothesize that these patterns reflect diverse mechanisms by which substrate can limit storage capacity, and highlight case studies consistent with limited weathered bedrock extent (melange in the Northern Coast Range), toxicity (ultramafic substrates in the Klamath-Siskiyou region), nutrient limitation (phosphorus-poor plutons in the southern Sierra Nevada), and low porosity capable of retaining water (volcanic formations in the southern Cascades). The observation that at regional scales climate alone does not ‘size’ the root zone has implications for the parameterization of storage capacity in models of plant dynamics (and the interrelated carbon and water cycles), and also underscores the importance of geology in considerations of climate-change induced biome migration and habitat suitability.
Drought and floods affect the structure, composition, and the function of global environments and thus human societies. Although several studies exist on both droughts and floods, studies on whether droughts can be a means to cause floods or vice versa are lacking in the literature. However, it has been repeatedly said that after a severe drought season, there is heavy rainfall and thus flooding. Using different global terrestrial ecosystems from across the globe, understanding the underlying mechanisms, evolutions, and drivers of how droughts can abruptly cause flooding or vice versa on a global scale representing drought-flood hotspot regions from both the Northern and Southern Hemispheres is indispensable. Considering drought hotspot areas across the globe such as the 2000s Australia’s Millennium drought, and the 2010/11 Horn of Africa drought that experienced large-scale flooding in the aftermath of drought is crucial. Subject to analysis and interpretations, the study findings of drought-flood underlying interactions reveal major contributions to the growing field of drought hydrology for future policymaking.
Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi-site basin context. The proposed model is a multi-site extension of the modified Fractional Gaussian Noise (mFGN) model which is well-known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted-mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice-versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto-curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to Bio-bio River basin (Chile), where the results show that the WmFGN maintains the qualities of the single-site mFGN, while significantly improving spatial correlation.
We assess the Southern Ocean CO2 uptake (1985-2018) using data sets gathered in the REgional Carbon Cycle Assessment and Processes Project phase 2 (RECCAP2). The Southern Ocean acted as a sink for CO2 with close agreement between simulation results from global ocean biogeochemistry models (GOBMs, 0.75±0.28 PgCyr-1) and pCO2-observation-based products (0.73±0.07 PgCyr-1). This sink is only half that reported by RECCAP1. The present-day net uptake is to first order a response to rising atmospheric CO2, driving large amounts of anthropogenic CO2 (Cant) into the ocean, thereby overcompensating the loss of natural CO2 to the atmosphere. An apparent knowledge gap is the increase of the sink since 2000, with pCO2-products suggesting a growth that is more than twice as strong and uncertain as that of GOBMs (0.26±0.06 and 0.11±0.03 PgCyr-1 decade-1 respectively). This is despite nearly identical pCO2 trends in GOBMs and pCO2-products when both products are compared only at the locations where pCO2 was measured. Seasonal analyses revealed agreement in driving processes in winter with uncertainty in the magnitude of outgassing, whereas discrepancies are more fundamental in summer, when GOBMs exhibit difficulties in simulating the effects of the non-thermal processes of biology and mixing/circulation. Ocean interior accumulation of Cant points to an underestimate of Cant uptake and storage in GOBMs. Future work needs to link surface fluxes and interior ocean transport, build long overdue systematic observation networks and push towards better process understanding of drivers of the carbon cycle.
New Zealand atmospheric river (AR) lifecycles are analyzed to examine the synoptic conditions that produce extreme precipitation and regular flooding. An AR lifecycle tracking algorithm, novel to the region, is utilized to identify the genesis location of New Zealand ARs: the location where moisture fluxes enhance and become distinct synoptic features capable of producing impactful weather conditions. Genesis locations of ARs that later impact New Zealand cover a broad region extending from the Southern Indian Ocean (90°E) into the South Pacific (170°W) with the highest genesis frequency being in the Tasman Sea. The most impactful ARs, associated with heavy precipitation, tend to originate from distinct regions based on landfall location. Impactful North Island ARs tend to originate from subtropical regions to the northwest of New Zealand, while impactful South Island ARs are associated with genesis over southeast Australia. The synoptic conditions of impactful AR genesis are identified with North Island ARs typically associated with a cyclone in the central Tasman Sea along with a distant, persistent low pressure off the coast of West Antarctica. South Island AR genesis typically occurs in conjunction with moist conditions over Australia associated with a zonal synoptic-scale wavetrain. The Madden–Julian oscillation (MJO) is examined as a potential source of variability that modulates New Zealand AR lifecycles. It appears that the MJO modulates AR characteristics, especially during Phase 5, typically bringing more frequent, slow moving ARs with greater moisture fluxes to the North Island of New Zealand.