Divergence beneath the Brillouin sphere and the phenomenology of prediction error in spherical harmonic series approximations of the gravitational field.

The Brillouin sphere is defined as the smallest sphere, centered at the origin of the geocentric coordinate system, that incorporates all the condensed matter composing the planet. The Brillouin sphere touches the Earth at a single point, and the radial line that begins at the origin and passes through that point is called the singular radial line. For about 60 years there has been a persistent anxiety about whether or not a spherical harmonic (SH) expansion of the external gravitational potential,V, will converge beneath the Brillouin sphere. Recently, it was proven that the probability of such convergence is zero. One of these proofs provided an asymptotic relation, called Costin's formula, for the upper bound,EN, on the absolute value of the prediction error,eN, of a SH series model,VN(θ,λ,r), truncated at some maximum degree,N=nmax. When the SH series is restricted to (or projected onto) a particular radial line, it reduces to a Taylor series (TS) in1/r. Costin's formula isEN≃BN-b(R/r)N, whereRis the radius of the Brillouin sphere. This formula depends on two positive parameters:b, which controls the decay of error amplitude as a function ofNwhenris fixed, and a scale factorB. We show here that Costin's formula derives from a similar asymptotic relation for the upper bound,Anon the absolute value of the TS coefficients,an, for the same radial line. This formula,An≃Kn-k, depends on degree,n, and two positive parameters,kandK, that are analogous tobandB. We use synthetic planets, for which we can compute the potential,V, and also the radial component of gravitational acceleration,gr=∂V/∂r, to hundreds of significant digits, to validate both of these asymptotic formulas. Let superscriptVrefer to asymptotic parameters associated with the coefficients and prediction errors for gravitational potential, and superscriptgto the coefficients and predictions errors associated withgr. For polyhedral planets of uniform density we show thatbV=kV=7/2andbg=kg=5/2almost everywhere. We show that the frequency of oscillation (around zero) of the TS coefficients and the series prediction errors, for a given radial line, is controlled by the geocentric angle,α, between that radial line and the singular radial line. We also derive useful identities connectingKV,BV,Kg, andBg. These identities are expressed in terms of quotients of the various scale factors. The only other quantities involved in these identities areαandR. The phenomenology of 'series divergence' and prediction error (whenr < R) can be described as a function of the truncation degree,N, or the depth,d, beneath the Brillouin sphere. For a fixedr⩽R, asNincreases from very low values, the upper error boundENshrinks until it reaches its minimum (best) value whenNreaches some particular or optimum value,Nopt. WhenN>Nopt, prediction error grows asNcontinues to increase. Eventually, whenN≫Nopt, prediction errors increase exponentially with risingN. If we fix the value ofNand allowR/rto vary, then we find that prediction error in free space beneath the Brillouin sphere increases exponentially with depth,d, beneath the Brillouin sphere. Becausebg=bV-1everywhere, divergence driven prediction error intensifies more rapidly forgrthan forV, both in terms of its dependence onNandd. If we fix bothNandd, and focus on the 'lateral' variations in prediction error, we observe that divergence and prediction error tend to increase (as doesB) as we approach high-amplitude topography.

High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021.

Since April 2002, Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO (FollowOn) satellite gravimetry missions have provided precious data for monitoring mass variations within the hydrosphere, cryosphere, and oceans with unprecedented accuracy and resolution. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatio-temporal changes in climate-induced geophysical phenomena, e.g., terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure (OBP). Here, climate-driven mass anomalies are simulated globally at 1.0° × 1.0° spatial and monthly temporal resolutions from January 1994 to January 2021 using an in-house developed hybrid Deep Learning architecture considering GRACE/-FO mascon and SLR-inferred gravimetry, ECMWF Reanalysis-5 data, and normalized time tag information as training datasets. Internally, we consider mathematical metrics such as RMSE, NSE and comparisons to previous studies, and externally, we compare our simulations to GRACE-independent datasets such as El-Nino and La-Nina indexes, Global Mean Sea Level, Earth Orientation Parameters-derived low-degree spherical harmonic coefficients, and in-situ OBP measurements for validation.

Molecular typing of Leptospira spp. in farmed and wild mammals reveals new host-serovar associations in New Zealand.

AIMS: To apply molecular typing to DNA isolated from historical samples to determine Leptospira spp. infecting farmed and wild mammals in New Zealand. MATERIALS AND METHODS: DNA samples used in this study were extracted from urine, serum or kidney samples (or Leptospira spp. cultures isolated from them) collected between 2007 and 2017 from a range of domestic and wildlife mammalian species as part of different research projects at Massey University. Samples were included in the study if they met one of three criteria: samples that tested positive with a lipL32 PCR for pathogenic Leptospira; samples that tested negative by lipL32 PCR but were recorded as positive to PCR for pathogenic Leptospira in the previous studies; or samples that were PCR-negative in all studies but were from animals with positive agglutination titres against serogroup Tarassovi. DNA samples were typed using PCR that targeted either the glmU or gyrB genetic loci. The resulting amplicons were sequenced and typed relative to reference sequences. RESULTS: We identified several associations between mammalian hosts and Leptospira strains/serovars that had not been previously reported in New Zealand. Leptospira borgpetersenii strain Pacifica was found in farmed red deer (Cervus elaphus) samples, L. borgpetersenii serovars Balcanica and Ballum were found in wild red deer samples, Leptospira interrogans serovar Copenhageni was found in stoats (Mustela erminea) and brushtail possums (Trichosurus vulpecula), and L. borgpetersenii was found in a ferret (Mustela putorius furo). Furthermore, we reconfirmed previously described associations including dairy cattle with L. interrogans serovars Copenhageni and Pomona and L. borgpetersenii serovars Ballum, Hardjo type bovis and strain Pacifica, sheep with L. interrogans serovar Pomona and L. borgpetersenii serovar Hardjo type bovis, brushtail possum with L. borgpetersenii serovar Balcanica, farmed deer with L. borgpetersenii serovar Hardjo type bovis and hedgehogs (Erinaceus europaeus) with L. borgpetersenii serovar Ballum. CONCLUSIONS: This study provides an updated summary of host-Leptospira associations in New Zealand and highlights the importance of molecular typing. Furthermore, strain Pacifica, which was first identified as Tarassovi using serological methods in dairy cattle in 2016, has circulated in animal communities since at least 2007 but remained undetected as serology is unable to distinguish the different genotypes. CLINICAL RELEVANCE: To date, leptospirosis in New Zealand has been diagnosed with serological typing, which is deficient in typing all strains in circulation. Molecular methods are necessary to accurately type strains of Leptospira spp. infecting mammals in New Zealand.

Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model.

Realistic representation of hydrological drought events is increasingly important in world facing decreased freshwater availability. Index-based drought monitoring systems are often adopted to represent the evolution and distribution of hydrological droughts, which mainly rely on hydrological model simulations to compute these indices. Recent studies, however, indicate that model derived water storage estimates might have difficulties in adequately representing reality. Here, a novel Markov Chain Monte Carlo - Data Assimilation (MCMC-DA) approach is implemented to merge global Terrestrial Water Storage (TWS) changes from the Gravity Recovery And Climate Experiment (GRACE) and its Follow On mission (GRACE-FO) with the water storage estimations derived from the W3RA water balance model. The modified MCMC-DA derived summation of deep-rooted soil and groundwater storage estimates is then used to compute 0.5∘ standardized groundwater drought indices globally to show the impact of GRACE/GRACE-FO DA on a global index-based hydrological drought monitoring system. Our numerical assessment covers the period of 2003-2021, and shows that integrating GRACE/GRACE-FO data modifies the seasonality and inter-annual trends of water storage estimations. Considerable increases in the length and severity of extreme droughts are found in basins that exhibited multi-year water storage fluctuations and those affected by climate teleconnections.

Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California.

California's Central Valley, one of the most agriculturally productive regions, is also one of the most stressed aquifers in the world due to anthropogenic groundwater over-extraction primarily for irrigation. Groundwater depletion is further exacerbated by climate-driven droughts. Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry has demonstrated the feasibility of quantifying global groundwater storage changes at uniform monthly sampling, though at a coarse resolution and is thus impractical for effective water resources management. Here, we employ the Random Forest machine learning algorithm to establish empirical relationships between GRACE-derived groundwater storage and in situ groundwater level variations over the Central Valley during 2002-2016 and achieved spatial downscaling of GRACE-observed groundwater storage changes from a few hundred km to 5 km. Validations of our modeled groundwater level with in situ groundwater level indicate excellent Nash-Sutcliffe Efficiency coefficients ranging from 0.94 to 0.97. In addition, the secular components of modeled groundwater show good agreements with those of vertical displacements observed by GPS, and CryoSat-2 radar altimetry measurements and is perfectly consistent with findings from previous studies. Our estimated groundwater loss is about 30 km3 from 2002 to 2016, which also agrees well with previous studies in Central Valley. We find the maximum groundwater storage loss rates of -5.7 ± 1.2 km3 yr-1 and -9.8 ± 1.7 km3 yr-1 occurred during the extended drought periods of January 2007-December 2009, and October 2011-September 2015, respectively while Central Valley also experienced groundwater recharges during prolonged flood episodes. The 5-km resolution Central Valley-wide groundwater storage trends reveal that groundwater depletion occurs mostly in southern San Joaquin Valley collocated with severe land subsidence due to aquifer compaction from excessive groundwater over withdrawal.

Bridging the gap between GRACE and GRACE-FO missions with deep learning aided water storage simulations.

The monthly high-resolution terrestrial water storage anomalies (TWSA) during the 11-months of gap between GRACE (Gravity Recovery And Climate Experiment) and its successor GRACE-FO (-Follow On) missions are missing. The continuity of the GRACE-like TWSA series with commensurate accuracy is of great importance for the improvement of hydrologic models both at global and regional scales. While previous efforts to bridge this gap, though without achieving GRACE-like spatial resolutions and/or accuracy have been performed, high-quality TWSA simulations at global scale are still lacking. Here, we use a suite of deep learning (DL) architectures, convolutional neural networks (CNN), deep convolutional autoencoders (DCAE), and Bayesian convolutional neural networks (BCNN), with training datasets including GRACE/-FO mascon and Swarm gravimetry, ECMWF Reanalysis-5 data, normalized time tag information to reconstruct global land TWSA maps, at a much higher resolution (100 km full wavelength) than that of GRACE/-FO, and effectively bridge the 11-month data gap globally. Contrary to previous studies, we applied no prior de-trending or de-seasoning to avoid biasing/aliasing the simulations induced by interannual or longer climate signals and extreme weather episodes. We show the contribution of Swarm and time inputs which significantly improved the TWSA simulations in particular for correct prediction of the trend component. Our results also show that external validation with independent data when filling large data gaps within spatio-temporal time series of geophysical signals is mandatory to maintain the robustness of the simulation results. The results and comparisons with previous studies and the adopted DL methods demonstrate the superior performance of DCAE. Validations of our DCAE-based TWSA simulations with independent datasets, including in situ groundwater level, Interferometric Synthetic Aperture Radar measured land subsidence rate (e.g. Central Valley), occurrence/timing of severe flash flood (e.g. South Asian Floods) and drought (e.g. Northern Great Plain, North America) events occurred within the gap, reveal excellent agreements.

Relationship between cyanobacterial bloom impacted drinking water sources and hepatocellular carcinoma incidence rates.

Freshwater cyanobacterial blooms have increased in geographic distribution and intensity in recent decades worldwide. Cyanotoxins produced by many of these blooms, such as microcystins, are observed to play a role in tumor promotion and have been associated with increased liver cancer rates at the population level. Exposure occurs primarily via contaminated water (ingestion, inhalation, dermal contact), either from treated drinking water or during recreation in impacted surface waters; additional sources of exposure include consumption of fresh produce grown in cyanotoxin-contaminated environments or through the consumption of seafood caught in bloom-impacted waters. The current ecological study investigates whether populations served by cyanobacterial bloom-impacted surface waters for their drinking water source have higher hepatocellular carcinoma (HCC) incidence rates than those served by non-impacted surface waters and groundwater. Census tract level cancer incidence in the state of Ohio, United States was modeled using a negative binomial generalized linear model, controlling for differences in demographic composition (e.g. age, race, and income) at the census tract level. Presence of cyanobacterial blooms in surface waters was estimated using satellite multi-spectral remote sensing and in situ public water system cyanotoxin monitoring data. Census tracts estimated to be served by bloom-impacted surface waters had 14.2% higher HCC incidence rates than those served by non-bloom-impacted surface waters (incidence rate ratio, IRR: 1.142; 95% CI: 1.037-1.257). Additionally, these bloom-impacted census tracts had a 17.4% higher HCC incidence rate as compared to those estimated to receive drinking water from a groundwater source (IRR: 1.174; 95% CI: 1.101-1.252). No statistical difference was found in HCC incidence rates when comparing areas presumed to be served by non-bloom-impacted surface waters and those presumed to be served by groundwater sources. An important consideration for environmental justice, areas estimated to be served by bloom-impacted surface waters had higher levels of poverty and included a higher percentage of racial and ethnic minority populations than areas served by groundwater. These findings support the need for additional in-depth research into the potential hepatic carcinogenicity and exposures of cyanotoxins in those areas where severe blooms are chronically observed.

Water level changes, subsidence, and sea level rise in the Ganges-Brahmaputra-Meghna delta.

Being one of the most vulnerable regions in the world, the Ganges-Brahmaputra-Meghna delta presents a major challenge for climate change adaptation of nearly 200 million inhabitants. It is often considered as a delta mostly exposed to sea-level rise and exacerbated by land subsidence, even if the local vertical land movement rates remain uncertain. Here, we reconstruct the water-level (WL) changes over 1968 to 2012, using an unprecedented set of 101 water-level gauges across the delta. Over the last 45 y, WL in the delta increased slightly faster (∼3 mm/y), than global mean sea level (∼2 mm/y). However, from 2005 onward, we observe an acceleration in the WL rise in the west of the delta. The interannual WL fluctuations are strongly modulated by El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) variability, with WL lower than average by 30 to 60 cm during co-occurrent El Niño and positive IOD events and higher-than-average WL, by 16 to 35 cm, during La Niña years. Using satellite altimetry and WL reconstructions, we estimate that the maximum expected rates of delta subsidence during 1993 to 2012 range from 1 to 7 mm/y. By 2100, even under a greenhouse gas emission mitigation scenario (Representative Concentration Pathway [RCP] 4.5), the subsidence could double the projected sea-level rise, making it reach 85 to 140 cm across the delta. This study provides a robust regional estimate of contemporary relative WL changes in the delta induced by continental freshwater dynamics, vertical land motion, and sea-level rise, giving a basis for developing climate mitigation strategies.

Understanding the global hydrological droughts of 2003-2016 and their relationships with teleconnections.

Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003-2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydro-meteorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are strongly interrelated. Our numerical results indicate that the 3- to 6-month hydrological droughts occur more frequently than the other timescales. Longer memory of water storage changes (than water fluxes) has found to be the reason of detecting extended hydrological droughts in regions such as the Middle East and Northern Africa. Through CCA, we show that the El Niño Southern Oscillation (ENSO) has major impact on the magnitude and evolution of hydrological droughts in regions such as the northern parts of Asia and most parts of the Australian continent between 2006 and 2011, as well as droughts in the Amazon basin, South Asia, and North Africa between 2010 and 2012. The Indian ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are found to have regional influence on the evolution of hydrological droughts.

Recent high-resolution Antarctic ice velocity maps reveal increased mass loss in Wilkes Land, East Antarctica.

We constructed Antarctic ice velocity maps from Landsat 8 images for the years 2014 and 2015 at a high spatial resolution (100 m). These maps were assembled from 10,690 scenes of displacement vectors inferred from more than 10,000 optical images acquired from December 2013 through March 2016. We estimated the mass discharge of the Antarctic ice sheet in 2008, 2014, and 2015 using the Landsat ice velocity maps, interferometric synthetic aperture radar (InSAR)-derived ice velocity maps (~2008) available from prior studies, and ice thickness data. An increased mass discharge (53 ± 14 Gt yr-1) was found in the East Indian Ocean sector since 2008 due to unexpected widespread glacial acceleration in Wilkes Land, East Antarctica, while the other five oceanic sectors did not exhibit significant changes. However, present-day increased mass loss was found by previous studies predominantly in west Antarctica and the Antarctic Peninsula. The newly discovered increased mass loss in Wilkes Land suggests that the ocean heat flux may already be influencing ice dynamics in the marine-based sector of the East Antarctic ice sheet (EAIS). The marine-based sector could be adversely impacted by ongoing warming in the Southern Ocean, and this process may be conducive to destabilization.

A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme.

Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country's water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003-2013, and their interactions with rainfall in Bangladesh. To improve the model's estimates of water storages, terrestrial water storage (TWS) data obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission are assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble-based sequential technique of the Square Root Analysis (SQRA) filter. We investigate the capability of the data assimilation approach to use a non-regional hydrological model for a regional case study. Based on these estimates, we investigate relationships between the model derived sub-surface water storage changes and remotely sensed precipitations, as well as altimetry-derived river level variations in Bangladesh by applying the empirical mode decomposition (EMD) method. A larger correlation is found between river level heights and rainfalls (78% on average) in comparison to groundwater storage variations and rainfalls (57% on average). The results indicate a significant decline in groundwater storage (∼32% reduction) for Bangladesh between 2003 and 2013, which is equivalent to an average rate of 8.73 ± 2.45mm/year.

Diabetes in older people: position statement of The Hong Kong Geriatrics Society and the Hong Kong Society of Endocrinology, Metabolism and Reproduction.

Following a survey on the clinical practice of geriatricians in the management of older people with diabetes and a study of hypoglycaemia in diabetic patients, a round-table discussion with geriatricians and endocrinologists was held in January 2015. Consensus was reached for six domains specifically related to older diabetic people: (1) the considerations when setting an individualised diabetic management; (2) inclusion of geriatric syndrome screening in assessment; (3) glycaemic and blood pressure targets; (4) pharmacotherapy; (5) restrictive diabetic diet; and (6) management goals for nursing home residents.

Ten-year survey of cyanobacterial blooms in Ohio's waterbodies using satellite remote sensing.

Cyanobacterial blooms are on the rise globally and are capable of adversely impacting human, animal, and ecosystem health. Blooms dominated by cyanobacteria species capable of toxin-production are commonly observed in eutrophic freshwater. The presence of cyanobacterial blooms in selected Ohio lakes, such as Lake Erie and Grand Lake St. Marys, has been well studied, but much less is known about the geographic distribution of these blooms across all of Ohio's waterbodies. We examined the geographic distribution of cyanobacterial blooms in Ohio's waterbodies from 2002 to 2011, using a nested semi-empirical algorithm and remotely sensed data from the Medium Resolution Imaging Spectrometer (MERIS) onboard the European Space Agency's Envisat. We identified: 62 lakes, reservoirs, and ponds; 7 rivers; 6 marshes and wetlands; and 3 quarries with detectable cyanobacteria pigment (phycocyanin) concentrations. Of the 78 waterbodies identified in our study, roughly half (54%; n=42) have any reported in situ microcystins monitoring results from state monitoring programs. Further, 90% of the waterbodies identified reached phycocyanin pigment concentrations representative of levels potentially hazardous to public health. This gap in lakes potentially impacted by cyanobacterial blooms and those that are currently monitored presents an important area of concern for public health, as well as ecosystem health, where unknown human and animal exposures to cyanotoxins may occur in many of Ohio's waterbodies. Our approach may be replicated in other regions around the globe with potential cyanobacterial bloom presence, in order to assess the intensity, geographic distribution, and temporal pattern of blooms in lakes not currently monitored for the presence of cyanobacterial blooms.

Spatiotemporal variability and environmental factors of harmful algal blooms (HABs) over western Lake Erie.

Over the past decades, numerous studies have been carried out in understanding causes of Harmful Algal Blooms (HABs) and their dynamics, yielding great knowledge in this field. Lake Erie, the fourth-largest lake of the five Great Lake, is among those highly vulnerable to the impacts of HABs and has received substantial attention from the public, water management sectors, and academic field. Building upon previous work, this study aims to characterize spatiotemporal variability of Chlorophyll a (Chl-a), which is an important indicator of HABs, and to explore relative importance of environmental factors associated with HABs in the west Lake Erie. Ten years of biweekly Chl-a information over western Lake Erie were derived from MERIS data at the pixel scale. Based on the MERIS-derived information high concentrations of Chl-a were observed in the south near shore area in spring and fall and in the west corner area of western Lake Erie in all three seasons except winter. Wavelet analysis suggested that the 0.5- and 1-year periods were dominant modes for the Chl-a series. The Multivariate Adaptive Regression Splines (MARS) analysis was performed to explore factors associated with the dynamics of Chl-a. The results suggested that overall both phenological (e.g. wind) and ecological (e.g. nutrient levels) factors exhibited significant correlations with the remotely-sensed imagery based observations of Chl-a despite spatial and temporal variations. The important phenological and ecological factors include solar radiation and wind speed in spring, water temperature, solar radiation, and total Kjeldahl nitrogen concentration in summer, wind speed in fall, and water temperature and streamflow in winter. Both consistency and differences of findings of the study with others in the region may suggest strengths and limitations of the remotely sensed imagery-based analysis, offering valuable information for future work.

Time-varying land subsidence detected by radar altimetry: California, Taiwan and north China.

Contemporary applications of radar altimetry include sea-level rise, ocean circulation, marine gravity, and icesheet elevation change. Unlike InSAR and GNSS, which are widely used to map surface deformation, altimetry is neither reliant on highly temporally-correlated ground features nor as limited by the available spatial coverage, and can provide long-term temporal subsidence monitoring capability. Here we use multi-mission radar altimetry with an approximately 23 year data-span to quantify land subsidence in cropland areas. Subsidence rates from TOPEX/POSEIDON, JASON-1, ENVISAT, and JASON-2 during 1992-2015 show time-varying trends with respect to displacement over time in California's San Joaquin Valley and central Taiwan, possibly related to changes in land use, climatic conditions (drought) and regulatory measures affecting groundwater use. Near Hanford, California, subsidence rates reach 18 cm yr(-1) with a cumulative subsidence of 206 cm, which potentially could adversely affect operations of the planned California High-Speed Rail. The maximum subsidence rate in central Taiwan is 8 cm yr(-1). Radar altimetry also reveals time-varying subsidence in the North China Plain consistent with the declines of groundwater storage and existing water infrastructure detected by the Gravity Recovery And Climate Experiment (GRACE) satellites, with rates reaching 20 cm yr(-1) and cumulative subsidence as much as 155 cm.

Moho topography, ranges and folds of Tibet by analysis of global gravity models and GOCE data.

The determination of the crustal structure is essential in geophysics, as it gives insight into the geohistory, tectonic environment, geohazard mitigation, etc. Here we present the latest advance on three-dimensional modeling representing the Tibetan Mohorovicic discontinuity (topography and ranges) and its deformation (fold), revealed by analyzing gravity data from GOCE mission. Our study shows noticeable advances in estimated Tibetan Moho model which is superior to the results using the earlier gravity models prior to GOCE. The higher quality gravity field of GOCE is reflected in the Moho solution: we find that the Moho is deeper than 65 km, which is twice the normal continental crust beneath most of the Qinghai-Tibetan plateau, while the deepest Moho, up to 82 km, is located in western Tibet. The amplitude of the Moho fold is estimated to be ranging from -9 km to 9 km with a standard deviation of ~2 km. The improved GOCE gravity derived Moho signals reveal a clear directionality of the Moho ranges and Moho fold structure, orthogonal to deformation rates observed by GPS. This geophysical feature, clearly more evident than the ones estimated using earlier gravity models, reveals that it is the result of the large compressional tectonic process.

Cyanobacteria blooms and non-alcoholic liver disease: evidence from a county level ecological study in the United States.

BACKGROUND: Harmful cyanobacterial blooms present a global threat to human health. There is evidence suggesting that cyanobacterial toxins can cause liver damage and cancer. However, because there is little epidemiologic research on the effects of these toxins in humans, the excess risk of liver disease remains uncertain. The purpose of this study is to estimate the spatial distribution of cyanobacterial blooms in the United States and to conduct a Bayesian statistical analysis to test the hypothesis that contamination from cyanobacterial blooms is a potential risk factor for non-alcoholic liver disease. METHODS: An ecological study design was employed, in which county-specific gender and age standardized mortality rates (SMR) of non-alcoholic liver disease in the United States were computed between 1999 and 2010. Bloom coverage maps were produced based on estimated phycocyanin levels from MERIS (Medium Resolution Imaging Spectrometer) water color imageries from 08/01/2005 to 09/30/2005. A scan statistical tool was used to identify significant clusters of death from non-alcoholic liver disease. A map of local indicator of spatial association (LISA) clusters and a Bayesian spatial regression model were used to analyze the relationship between cyanobacterial bloom coverage and death from non-alcoholic liver disease. RESULTS: Cyanobacterial blooms were found to be widely spread in the United States, including coastal areas; 62% of the counties (1949 out of 3109) showed signs of cyanobacterial blooms measured with MERIS. Significant clusters of deaths attributable to non-alcoholic liver disease were identified in the coastal areas impacted by cyanobacterial blooms. Bayesian regression analysis showed that bloom coverage was significantly related to the risk of non-alcoholic liver disease death. The risk from non-alcoholic liver disease increased by 0.3% (95% CI, 0.1% to 0.5%) with each 1% increase in bloom coverage in the affected county after adjusting for age, gender, educational level, and race. CONCLUSIONS: At the population level, there is a statistically significant association between cyanobacterial blooms and non-alcoholic liver disease in the contiguous United States. Remote sensing-based water monitoring provides a useful tool for assessing health hazards, but additional studies are needed to establish a specific association between cyanobacterial blooms and liver disease.

Earth surface deformation in the North China Plain detected by joint analysis of GRACE and GPS data.

Mass redistribution of the Earth causes variable loading that deforms the solid Earth. While most recent studies using geodetic techniques focus on regions (such as the Amazon basin and the Nepal Himalayas) with large seasonal deformation amplitudes on the order of 1-4 cm due to hydrologic loading, few such studies have been conducted on the regions where the seasonal deformation amplitude is half as large. Here, we use joint GPS and GRACE data to investigate the vertical deformation due to hydrologic loading in the North China Plain, where significant groundwater depletion has been reported. We found that the GPS- and GRACE-derived secular trends and seasonal signals are in good agreement, with an uplift magnitude of 1-2 mm/year and a correlation of 85.0%-98.5%, respectively. This uplift rate is consistent with groundwater depletion rate estimated from GRACE data and in-situ groundwater measurements from earlier report studies; whereas the seasonal hydrologic variation reflects human behavior of groundwater pumping for agriculture irrigation in spring, leading to less water storage in summer than that in the winter season. However, less than 20% of weighted root-mean-squared (WRMS) reductions were detected for all the selected GPS stations when GRACE-derived seasonal deformations were removed from detrended GPS height time series. This discrepancy is probably because the GRACE-derived seasonal signals are large-scale, while the GPS-derived signals are local point measurements.