ALBERT

Description: The combination of groundwater withdrawal, hydrocarbon extraction, salt-dome movement and faulting have caused widespread subsidence in the Houston-Galveston region (HGR). Subsidence results from primary consolidation consisting of inelastic (nonrecoverable) and elastic (recoverable) compaction caused by subsurface fluid withdrawal and secondary consolidation (creep) over time caused by overburden pressure. Subsidence in the HGR is monitored using borehole extensometers that were installed at 13 locations across Harris and Galveston counties between 1962 and 1980. By 1977, withdrawals from the Chicot and Evangeline aquifers resulted in groundwater-level declines of about 114 and 115 m relative to predevelopment water levels, respectively in parts of Harris County. By 1979, as much as 3 m of land subsidence was estimated to have occurred in localized areas of the HGR. Land subsidence can be hazardous in populated areas because it exacerbates the effects of storm surge and impedes storm-water runoff by decreasing land-surface elevations in areas where water accumulates. To assess aquifer compaction in response to changes in groundwater levels, a bulk land-surface subsidence rate is assumed to be the sum of the primary consolidation rate and the negligibly variable component of overburden pressure referred to as the “pseudo-constant secondary consolidation rate.” From 1931 to 1976, groundwater levels decreased as groundwater withdrawal rates increased from 0.57 to 4.3 million m3 d−1, causing pressure heads in aquitards the Chicot and Evangeline aquifers to continually decline. In response to reductions in groundwater withdrawal rates from 4.3 to 3.0 million m3 d−1 between 1976 and 2001, groundwater levels rebounded, decreasing inelastic compaction rates in some parts of the HGR from as much as about 40 mm yr−1 in the early 1980s to negligible amounts by 2000. Inelastic consolidation from about 1937 to 2000 contributed to land-surface subsidence and its associated effects. Land-surfaces have rebounded in localized areas of the HGR where groundwater levels rebounded significantly. Pseudo-constant secondary consolidation rates were computed at each of the 13 extensometers and ranged from 0.48 to 8.49 mm yr−1 in areas where groundwater levels in the two aquifers were stabilizing. This secondary consolidation subsidence is beyond the control of any groundwater-level management schemes because it is caused by geohistorical overburden pressure on the two aquifers.

Description: Agricultural residues are among the most abundant biomass burned globally, especially in China. However, there is little information on primary emissions and photochemical evolution of agricultural residue burning. In this study, indoor chamber experiments were conducted to investigate primary emissions from open burning of rice, corn and wheat straws and their photochemical aging as well. Emission factors of NOx, NH3, SO2, 67 non-methane hydrocarbons (NMHCs), particulate matter (PM), organic aerosol (OA) and black carbon (BC) under ambient dilution conditions were determined. Olefins accounted for  〉  50 % of the total speciated NMHCs emission (2.47 to 5.04 g kg−1), indicating high ozone formation potential of straw burning emissions. Emission factors of PM (3.73 to 6.36 g kg−1) and primary organic carbon (POC, 2.05 to 4.11 gC kg−1), measured at dilution ratios of 1300 to 4000, were lower than those reported in previous studies at low dilution ratios, probably due to the evaporation of semi-volatile organic compounds under high dilution conditions. After photochemical aging with an OH exposure range of (1.97–4.97)  ×  1010 molecule cm−3 s in the chamber, large amounts of secondary organic aerosol (SOA) were produced with OA mass enhancement ratios (the mass ratio of total OA to primary OA) of 2.4–7.6. The 20 known precursors could only explain 5.0–27.3 % of the observed SOA mass, suggesting that the major precursors of SOA formed from open straw burning remain unidentified. Aerosol mass spectrometry (AMS) signaled that the aged OA contained less hydrocarbons but more oxygen- and nitrogen-containing compounds than primary OA, and carbon oxidation state (OSc) calculated with AMS resolved O ∕ C and H ∕ C ratios increased linearly (p  

Description: The ozonolysis of α-phellandrene, a highly reactive conjugated monoterpene largely emitted by Eucalypt species, is characterised in detail for the first time using a smog chamber at the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. Gas-phase species were monitored by a proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF), with yields from a large number of products obtained, including formaldehyde (5–9 %), acetaldehyde (0.2–8 %), glyoxal (6–23 %), methyl glyoxal (2–9 %), formic acid (22–37 %) and acetic acid (9–22 %). Higher m∕z second-generation oxidation products were also observed, with products tentatively identified according to a constructed degradation mechanism. OH yields from α-phellandrene and its first-generation products were found to be 35 ± 12 and 15 ± 7 %, respectively, indicative of prominent hydroperoxide channels. An average first-generation rate coefficient was determined as 1.0 ± 0.7  ×  10−16 cm3 molecule−1 s−1 at 298 K, showing ozonolysis as a dominant loss process for both α-phellandrene and its first-generation products in the atmosphere. Endocyclic conjugation in α-phellandrene was also found to be conducive to the formation of highly condensible products with a large fraction of the carbon mass partitioning into the aerosol phase, which was monitored with a scanning mobility particle sizer (SMPS) and a high-resolution time-of-flight aerosol mass spectrometer (AMS). Nucleation was observed almost instantaneously upon ozonolysis, indicating the rapid formation of extremely low-volatility compounds. Particle nucleation was found to be suppressed by the addition of either NO2 or a Criegee scavenger, with it being proposed that stabilised Criegee intermediates are important for new particle formation in the system. Aerosol yields ranged from 25 to 174 % depending on mass loadings, with both first- and second-generation products identified as large contributors to the aerosol mass. In short, with a high chemical reactivity and aerosol-forming propensity, α-phellandrene is expected to have an immediate impact on the local environment to which it is emitted, with ozonolysis likely to be an important contributor to the significant blue haze and frequent nocturnal nucleation events observed over Eucalypt forests.

Description: Rapidly evolving computational techniques are making a large gap between scientific aspiration and code implementation in climate modeling. In this work, we design a simple computing library to bridge the gap and decouple the work of ocean modeling from parallel computing. This library provides 12 basic operators that feature user-friendly interfaces, effective programming, and implicit parallelism. Several state-of-the-art computing techniques, including computing graph and just-in-time compiling, are employed to parallelize the seemingly serial code and speed up the ocean models. These operator interfaces are designed using native Fortran programming language to smooth the learning curve. We further implement a highly readable and efficient ocean model that contains only 1860 lines of code but achieves a 91 % parallel efficiency in strong scaling and 99 % parallel efficiency in weak scaling with 4096 Intel CPU cores. This ocean model also exhibits excellent scalability on the heterogeneous Sunway TaihuLight supercomputer. This work presents a promising alternative tool for the development of ocean models.

Description: Agricultural residues are among the most abundant biomass burned globally, especially in China. However, there is rare information on primary emissions and photochemical evolution of agricultural residues burning. In this study, indoor chamber experiments were conducted to investigate primary emissions from open burning of rice, corn and wheat straws and their photochemical aging as well. Emission factors of NOx, NH3, SO2, 67 non-methane hydrocarbons (NMHCs), particulate matter (PM), organic aerosol (OA) and black carbon (BC) under ambient dilution conditions were determined. Olefins accounted for 〉 50 % of the total NMHCs emission (2.47 to 5.04 g kg−1), indicating high ozone formation potential of straw burning emissions. Emission factors of PM (3.73 to 6.36 g kg−1) and primary organic carbon (POC, 2.05 to 4.11 gC kg−1), measured at dilution ratios of 1300 to 4000, were lower than those reported in previous studies at low dilution ratios, probably due to the evaporation of semi-volatile organic compounds under high dilution conditions. After photochemical aging with OH exposure range of (1.97–4.97) × 1010 molecule cm−3 s in the chamber, large amounts of secondary organic aerosol (SOA) were produced with OA mass enhancement ratios (the mass ratio of total OA to primary OA) of 2.4–7.6. The 20 known precursors could only explain 5.0–27.3 % of the observed SOA mass, suggesting that the major precursors of SOA formed from open straw burning remain unidentified. Aerosol mass spectrometry (AMS) signaled that the aged OA contained less hydrocarbons but more oxygen- and nitrogen-containing compounds than primary OA, and carbon oxidation state (OSc) calculated with AMS resolved O / C and H / C ratios increased linearly (p 

Description: The ozonolysis of α-phellandrene, a highly reactive conjugated monoterpene largely emitted by Eucalypt species, is characterised in detail for the first time using a smog chamber at the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. Gas-phase species were monitored by a proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF), with yields from a large number of products obtained, including formaldehyde (5–9 %), acetaldehyde (0.2–8 %). glyoxal (6–23 %), methyl glyoxal (2–9 %), formic acid (22–37 %) and acetic acid (9–22%). Higher m/z second-generation oxidation products were also observed, with products tentatively identified according to a constructed degradation mechanism. OH yields from α-phellandrene and its first-generation products were found to be 35 ± 12 % and 15 ± 7 % respectively, indicative of prominent hydroperoxide channels. An average first-generation rate coefficient was determined as 1.0 ± 0.7 × 10−16 cm3 molecule−1 s−1 at 298 K, showing ozonolysis as a dominant loss process for both α-phellandrene and its first-generation products in the atmosphere. Endocyclic conjugation in α-phellandrene was also found to be conducive to the formation of highly condensible products, with a large fraction of the carbon mass partitioning into the aerosol phase, monitored with a scanning mobility particle sizer (SMPS) and a high-resolution time-of-flight aerosol mass spectrometer (AMS). Nucleation was observed almost instantaneously upon ozonolysis, indicating the rapid formation of extremely low volatility compounds. Particle nucleation was found to be suppressed by the addition of a Criegee scavenger, suggesting that stabilised Criegee intermediates are important for new particle formation in the system. Aerosol yields ranged from 25–174 % dependant on mass loadings, with both first- and second-generation products identified as large contributors to the aerosol mass. Effective densities ranged from 1.29–1.90 g cm−3. The aerosol oxidation state was also found to be dependent on mass loadings, with parametrisation of the bulk mass indicating a large contribution of highly functionalised low- and semi-volatile organic compounds to the aerosol phase. With a high chemical reactivity and aerosol forming propensity α-phellandrene is expected to have an immediate impact on the local environment to which it is emitted, with ozonolysis therefore likely to be an important contributor to the significant blue haze and frequent nocturnal nucleation events observed over Eucalypt forests.

Description: Statistical Data Assimilation (SDA) is the transfer of information from field or laboratory observations to a user selected model of the dynamical system producing those observations. The data is noisy and the model has errors; the information transfer addresses properties of the conditional probability distribution of the states of the model conditioned on the observations. The quantities of interest in SDA are the conditional expected values of functions of the model state, and these require the approximate evaluation of high dimensional integrals. We introduce a conditional probability distribution and use the Laplace method with annealing to identify the maxima of the conditional probability distribution. The annealing method slowly increases the precision term of the model as it enters the Laplace method. In this paper, we extend the idea of precision annealing (PA) to Monte Carlo calculations of conditional expected values using Metropolis-Hastings methods.

Description: Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.

Description: The increasing complexity of climate models combined with rapidly evolving computational techniques introduces a large gap in climate modelling. In this work, we design a simple computing library to decouple the work of ocean modelling from the work of parallel computing. The library provides twelve basic operators that feature user-friendly interfaces, effective programming and automatic parallelization. We further implement a highly readable and efficient ocean model that contains only 1860 lines of code but achieves a 91 % parallel efficiency in strong scaling and 99 % parallel efficiency in weak scaling with 4096 Intel CPU cores. This ocean model also exhibits excellent scalability on the Sunway TaihuLight supercomputer. This work presents a valuable example for the development of the next generation of ocean models.

Description: Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in water science has so far been gradual, but the related fields are now ripe for breakthroughs. This paper proposes that DL-based methods can open up a viable, complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data for scientists to further evaluate. Interrogative studies are invoked to interpret DL models. In addition, we lay out several opinions shared by authors: (1) deep learning may bring forth transformative progress to the field of hydrology due to its ability to assimilate big data and identify commonalities and differences; (2) The community may benefit greatly from a variety of shared datasets and open competitions; (3) Big hydrologic data can be obtained via various ways including data compilation and working with citizen scientists, which offers the co-benefits of education and stakeholder engagement; (4) Water sciences, and hydrology in particular, offer a unique set of challenges that can, in turn, stimulate advances in machine learning; and (5) An urgent need for research is hydrology-customized methods for interpreting knowledge extracted by deep learning.