Interannual and seasonal variation of the Huanghe (Yellow River) water discharge over the past 50 years: Connections to impacts from ENSO events and dams

The Huanghe, the second largest river in China, is now under great pressure as a water resource. Using datasets of river water discharge, water consumption and regional precipitation for the past 50 years, we elucidate some connections between decreasing water discharges, global El Niño/Southern Oscillation (ENSO) events and anthropogenic impacts in the drainage basin. Global ENSO events, which directly affected the regional precipitation in the river basin, resulted in approximately 51% decrease in river water discharge to the sea. The degree of anthropogenic impacts on river water discharge is now as great as that of natural influences, accelerating the water losses in the hydrological cycle. The large dams and reservoirs regulated the water discharge and reduced the peak flows by storing the water in the flood season and releasing it in the dry season as needed for agricultural irrigation. Thus, as a result, large dams and reservoirs have shifted the seasonal distribution patterns of water discharge and water consumption and finally resulted in rapidly increasing water consumption. Meanwhile, the annual distribution pattern of water consumption also changed under the regulation of dams and reservoirs, indicating that the people living in the river basin consume the water more and more to suit actual agricultural schedule rather than depending upon natural pattern of annual precipitation. The combination of the increasing water consumption facilitated by the dams and reservoirs and the decreasing precipitation closely associated with the global ENSO events over the past half century has resulted in water scarcity in this world-famous river, as well as in a number of subsequent serious results for the river, delta and coastal ocean.     

Primary proton spectrum around the knee deduced from the emulsion-chamber data obtained at Mts. Fuji and Kanbala

We have done extensive Monte Carlo simulations using the new simulation codes of CORSIKA and COSMOS to compare with the gamma-family data obtained at Mts. Fuji (3750 m above sea level) and Kanbala (5500 m above sea level). Then, we estimated the primary proton and helium spectra around the knee energy region using a multiple-layered feed-forward neural network as a classifier of primary particle kind. The selection efficiency of proton-induced family events is estimated to be 82%. The flux value of protons at 2×10¹⁵ eV is (5.5±1.5)×10⁻¹⁴ (m⁻² s⁻¹ sr⁻¹ GeV⁻¹). The result suggests heavy-enriched primary composition around the knee region.     

Fluctuations of electron precipitation to the dayside auroral zone modulated by compression and expansion of the magnetosphere

Concurrent variations of CNA fluctuations and geomagnetic fluctuations are classified into Type 1 (substorm-type), Type 2 (Pc5-type), and Type 3 which is the object of the present study. Type 3 apparently has peculiar characteristics in that CNA fluctuations at a certain auroral-zone station show a pronounced positive correlation with magnetic fluctuations at distant low-latitude stations. The magnetic fluctuations of this type are identified to generalized Si which tends to take place much more frequently than the so-called Si. The CNA fluctuations of this type are found to take place on the dayside auroral zone only when preceded by a magnetic substorm on the night side. Considering the change of the growth rate of electron cyclotron instability and enhancement of pitch angle diffusion due to the change of magnetic field intensity, the following model is proposed to explain the occurrence mechanism of Type 3 concurrent variations. The CNA fluctuations take place only when two conditions are satisfied; generation of the accelerated electrons in association with substorm onset and modulation of the precipitation of the electrons by compression and expansion of the magnetosphere, in other words, by generalized Si.     

Verification of Carbon Sink Assessment: Can We Exclude Natural Sinks?

Any human-induced terrestrial sink is susceptible to the effects of elevated atmospheric CO₂ concentration, nitrogen deposition, climate variability and other natural or indirect human-induced factors. It has been suggested in climate negotiations that the effects of these factors should be excluded from estimates of carbon sequestration used to meet the emission reduction commitments under the Kyoto Protocol. This paper focuses on the methodologies for factoring out the effects of atmospheric and climate variability/change. We estimate the relative magnitude of the non-human induced effects by using two biosphere models and discuss possibilities for narrowing estimate uncertainty.     

A survey of possible impact structures on 25143 Itokawa

We determined the morphologies and dimensions of possible impact craters on the surface of Asteroid 25143 Itokawa from images taken by the Hayabusa spacecraft. Circular depressions, circular features with flat floors or convex floors, and circular features with smooth surfaces were identified as possible craters. The survey identified 38 candidates with widely varying morphologies including rough, smooth and saddle-shaped floors, a lack of raised rims and fresh material exposures. The average depth/diameter ratio was 0.08±0.03: these craters are very shallow relative to craters observed on other asteroids. These shallow craters are a result of (1) target curvature influencing the cratering process, (2) raised rim not being generated by this process, and (3) fines infilling the craters. As many of the crater candidates have an unusual appearance, we used a classification scheme that reflects the likelihood of an observed candidate's formation by a hypervelocity impact. We considered a variety of alternative interpretations while developing this scheme, including inherited features from a proto-Itokawa, spall scars created by the disruption of the proto-Itokawa, spall scars following the formation of a large crater on Itokawa itself, and apparent depressions due to random arrangements of boulders. The size-frequency distribution of the crater candidates was close to the empirical saturation line at the largest diameter, and then decline with decreasing diameter.     

The structure of monomeric and dimeric uranyl adsorption complexes on gibbsite: A combined DFT and EXAFS study

We investigated the structure of uranyl sorption complexes on gibbsite (pH 5.6-9.7) by two independent methods, density functional theory (DFT) calculations and extended X-ray absorption fine structure (EXAFS) spectroscopy at the U-L_III edge. To model the gibbsite surface with DFT, we tested two Al (hydr)oxide clusters, a dimer and a hexamer. Based on polarization, structure, and relaxation energies during geometry optimization, the hexamer cluster was found to be the more appropriate model. An additional advantage of the hexamer model is that it represents both edges and basal faces of gibbsite. The DFT calculations of (monomeric) uranyl sorption complexes show an energetic preference for the corner-sharing versus the edge-sharing configuration on gibbsite edges. The energy difference is so small, however, that possibly both surface species may coexist. In contrast to the edge sites, sorption to basal sites was energetically not favorable. EXAFS spectroscopy revealed in all investigated samples the same interatomic distances of the uranyl coordination environment (R_U-Oax ≈ 1.80 Å, R_U-Oeq ≈ 2.40 Å), and towards the gibbsite surface (R_U-O ≈ 2.87 Å, R_U-Al ≈ 3.38 Å). In addition, two U-U distances were observed, 3.92 Å at pH 9.7 and 4.30 Å at pH 5.6, both with coordination numbers of ∼1. The short U-U distance is close to that of the aqueous uranyl hydroxo dimer, UO₂(OH)₂, reported as 3.875 Å in the literature, but significantly longer than that of aqueous trimers (3.81-3.82 Å), suggesting sorption of uranyl dimers at alkaline pH. The longer U-U distance (4.30 Å) at acidic pH, however, is not in line with known aqueous uranyl polymer complexes. Based on the EXAFS findings we further refined dimeric surface complexes with DFT. We propose two structural models: in the acidic region, the observed long U-U distance can be explained with a distortion of the uranyl dimer to form both a corner-sharing and an edge-sharing linkage to neighboring Al octahedra, leading to R_U-U = 4.150 Å. In the alkaline region, a corner-sharing uranyl dimer complex is the most favorable. The U-O path at ∼2.87 Å in the EXAFS spectra arises from the oxygen atom linking two Al cations in corner-sharing arrangement. The adsorption structures obtained by DFT calculations are in good agreement with the structural parameters from EXAFS analysis: U-Al (3.394 Å), U-U (3.949 Å), and U-O (2.823 Å) for the alkaline pH model, and U-Al (3.279 Å), U-U (4.150 Å), and U-O (2.743 Å) for the acidic pH model. This work shows that by combining EXAFS and DFT, consistent structural models for uranyl sorption complexes can be obtained, which are relevant to predict the migration behavior of uranium at nuclear facilities.     

Bayesian and Neural Network Approaches to Estimate Deep Temperature Distribution for Assessing a Supercritical Geothermal System: Evaluation Using a Numerical Model

The temperature distribution at depth is a key variable when assessing the potential of a supercritical geothermal resource as well as a conventional geothermal resource. Data-driven estimation by a machine-learning approach is a promising way to estimate temperature distributions at depth in geothermal fields. In this study, we developed two methodologies—one based on Bayesian estimation and the other on neural networks—to estimate temperature distributions in geothermal fields. These methodologies can be used to supplement existing temperature logs, by estimating temperature distributions in unexplored regions of the subsurface, based on electrical resistivity data, observed geological/mineralogical boundaries, and microseismic observations. We evaluated the accuracy and characteristics of these methodologies using a numerical model of the Kakkonda geothermal field, Japan, where a temperature above 500 °C was observed below a depth of about 3.7 km. When using geological and geophysical knowledge as prior information for the machine learning methods, the results demonstrate that the approaches can provide subsurface temperature estimates that are consistent with the temperature distribution given by the numerical model. Using a numerical model as a benchmark helps to understand the characteristics of the machine learning approaches and may help to identify ways of improving these methods.

     

Estimation of standing stock of chlorophylla and primary production from remote-sensed ocean color in the Oyashio region, the Western subarctic Pacific, during the spring bloom in 1997

In order to examine the applicability of remotely-sensed ocean color for the estimation of phytoplankton biomass and primary production in the Oyashio region, the western subarctic Pacific, vertical distributions of chlorophylla concentration and primary production were observed in April and May 1997. Spring bloom was observed in both April and May, and the surface concentration of chlorophylla exceeded 40 mg m⁻³. The relationship between the standing stocks of chlorophylla within the layer from the sea surface to one optical depth (0–1/k layer) and the surface chlorophylla concentration is expressed as a Michaelis-Menten equation. The mean ratio of the standing stock of chlorophylla in the euphotic layer to that in the 0–1/k layer was 4.41, this ratio did not significantly differ from 4.61 which was obtained at homogeneous distribution of chlorophylla within the euphotic layer. These facts suggest that the distribution of chlorophylla could be assumed to be homogeneous in the euphotic layer during the spring bloom. Results of primary production measurements by simulatedin situ method were compared with those by an algorithm with two variables; chlorphylla and non-spectral PAR. Daily primary production in the euphotic layer estimated by the algorithm varied in a range of 38–274% of that estimated by incubation, although the primary productions by the algorithm agreed with those by the incubation at a half of stations. Primary production within the euphotic layer calculated using simply the surface data was the same as that estimated using vertical distribution of chlorophylla. These results show that the primary production in the euphotic layer may be estimated from the remote sensed measurements during the spring bloom in the Oyashio region.