Are spatial patterns of soil moisture at plot scales generalisable across catchments,climates, and other characteristics? A synthesis of synoptic soil moisture across the Mid-Atlantic

The spatial variation of soil moisture over very small areas (<100 m²) can have nonlinear impacts on cycling and flux rates resulting in bias if it is not considered, but measuring this variation is difficult over extensive temporal and spatial scales. Most studies examining spatial variation of soil moisture were conducted at hillslope (0.01 km²) to multi-catchment spatial scales (1000 km²). They found the greatest variation at mid wetness levels and the smallest variation at wet and dry wetness levels forming a concave down relationship. There is growing evidence that concave down relationships formed between spatial variation of soil moisture and average soil moisture are consistent across spatial scales spanning several orders of magnitude, but more research is needed at very small, plot scales (<100 m²). The goal of this study was to characterise spatial variation in shallow soil moisture at the plot scale by relating the mean of measurements collected in a plot to the standard deviation (SD). We combined data from a previous study with thousands of new soil moisture measurements from 212 plots in eight catchments distributed across the US Mid-Atlantic Region to (1) test for a generalisable mean–SD relationship at plot scales, (2) characterise how landcover, land use, season, and hillslope position contribute to differences in mean–SD relationships, and (3) use these generalised mean–SD relationships to quantify their impacts on catchment scale nitrification and denitrification potential. Our study found that 98% of all measurements formed a generalised mean–SD relationship like those observed at hillslope and catchment spatial scales. The remaining 2% of data comprised a mean–SD relationship with greater spatial variation that originated from two riparian plots reported in a previous study. Incorporating the generalised mean–SD relationship into estimates of nitrification and denitrification potential revealed strong bias that was even greater when incorporating mean–SD observations from the two riparian plots with significantly greater spatial variation.     

Hydrologic processes that govern stormwater infrastructure behaviour

Using water budget data from published literature, we demonstrate how hydrologic processes govern the function of various stormwater infrastructure technologies. Hydrologic observations are displayed on a Water Budget Triangle, a ternary plot tool developed to visualize simplified water budgets, enabling side‐by‐side comparison of green and grey approaches to stormwater management. The tool indicates ranges of hydrologic function for green roofs, constructed wetlands, cisterns, bioretention, and other stormwater control management structures. Water budgets are plotted for several example systems to provide insight on structural and environmental design factors, and seasonal variation in hydrologic processes of stormwater management systems. Previously published water budgets and models are used to suggest appropriate operational standards for several green and grey stormwater control structures and compare between conventional and low‐impact development approaches. We compare models, characterize and quantify water budgets and expected ranges for green and grey infrastructure systems, and demonstrate how the Water Budget Triangle tool may help users to develop a data‐driven approach for understanding design and retrofit of green stormwater infrastructure.     

Enhancement of long period components of recorded and synthetic ground motions using InSAR

Tall buildings and flexible structures require a better characterization of long period ground motion spectra than the one provided by current seismic building codes. Motivated by that, a methodology is proposed and tested to improve recorded and synthetic ground motions which are consistent with the observed co-seismic displacement field obtained from interferometric synthetic aperture radar (InSAR) analysis of image data for the Tocopilla 2007 earthquake (M_w=7.7) in Northern Chile. A methodology is proposed to correct the observed motions such that, after double integration, they are coherent with the local value of the residual displacement. Synthetic records are generated by using a stochastic finite-fault model coupled with a long period pulse to capture the long period fling effect.It is observed that the proposed co-seismic correction yields records with more accurate long-period spectral components as compared with regular correction schemes such as acausal filtering. These signals provide an estimate for the velocity and displacement spectra, which are essential for tall-building design. Furthermore, hints are provided as to the shape of long-period spectra for seismic zones prone to large co-seismic displacements such as the Nazca-South American zone.     

The appearance of Ulva laetevirens (Ulvophyceae,Chlorophyta) in the northeast coast of the United States of America

Introduced species may outcompete or hybridize with native species, resulting in the loss of native biodiversity or even alteration of ecosystem processes. In this study, we reported an alien distromatic Ulva species, which was found in an embayment (Holly Pond) connected with Long Island Sound, USA. The morphological and anatomical observations in combination with molecular data were used for its identification to species. Anatomy of collected specimens showed that the cell shape in rhizoidal and basal regions was round and the marginal teeth along the basal and median region were not found. These characteristics were primarily identical to the diagnostic characteristics of Ulva laetevirens Areschoug (Chlorophyta). The plastid-encoding tufA and nucleusencoding ITS1 were used for its molecular identification. Phylogenetic analysis for the tufA gene placed the specimens from Holly Pond in a well-supported clade along with published sequences of U. laetevirens identified early without any sequence divergence. In ITS tree, the sample also formed well-supported clades with the sequences of U. laetevirens with an estimated sequence divergence among the taxa in these clades as low as 1%. These findings confirmed the morpho-anatomical conclusion. Native to Australia, this species was reported in several countries along the Mediterranean coast after the late of 1990s. This is the first time that U. laetevirens is found in the northeast coast of United States and the second record for Atlantic North America.     

Implications of sampling design and sample size for national carbon accounting systems

Background  

Countries willing to adopt a REDD regime need to establish a national Measurement, Reporting and Verification (MRV) system that provides information on forest carbon stocks and carbon stock changes. Due to the extensive areas covered by forests the information is generally obtained by sample based surveys. Most operational sampling approaches utilize a combination of earth-observation data and in-situ field assessments as data sources.     

Error control of iterative linear solvers for integrated groundwater models

An open problem that arises when using modern iterative linear solvers, such as the preconditioned conjugate gradient method or Generalized Minimum RESidual (GMRES) method, is how to choose the residual tolerance in the linear solver to be consistent with the tolerance on the solution error. This problem is especially acute for integrated groundwater models, which are implicitly coupled to another model, such as surface water models, and resolve both multiple scales of flow and temporal interaction terms, giving rise to linear systems with variable scaling. This article uses the theory of "forward error bound estimation" to explain the correspondence between the residual error in the preconditioned linear system and the solution error. Using examples of linear systems from models developed by the US Geological Survey and the California State Department of Water Resources, we observe that this error bound guides the choice of a practical measure for controlling the error in linear systems. We implemented a preconditioned GMRES algorithm and benchmarked it against the Successive Over-Relaxation (SOR) method, the most widely known iterative solver for nonsymmetric coefficient matrices. With forward error control, GMRES can easily replace the SOR method in legacy groundwater modeling packages, resulting in the overall simulation speedups as large as 7.74×. This research is expected to broadly impact groundwater modelers through the demonstration of a practical and general approach for setting the residual tolerance in line with the solution error tolerance and presentation of GMRES performance benchmarking results.     

A simulation of probabilistic wildfire risk components for the continental United States

This simulation research was conducted in order to develop a large-fire risk assessment system for the contiguous land area of the United States. The modeling system was applied to each of 134 Fire Planning Units (FPUs) to estimate burn probabilities and fire size distributions. To obtain stable estimates of these quantities, fire ignition and growth was simulated for 10,000 to 50,000 “years” of artificial weather. The fire growth simulations, when run repeatedly with different weather and ignition locations, produce burn probabilities and fire behavior distributions at each landscape location (e.g., number of times a “cell” burns at a given intensity divided by the total years). The artificial weather was generated for each land unit using (1) a fire danger rating index known as the Energy Release Component (ERC) which is a proxy for fuel moisture contents, (2) a time-series analysis of ERC to represent daily and seasonal variability, and (3) distributions of wind speed and direction from weather records. Large fire occurrence was stochastically modeled based on historical relationships to ERC. The simulations also required spatial data on fuel structure and topography which were acquired from the LANDFIRE project (). Fire suppression effects were represented by a statistical model that yields a probability of fire containment based on independent predictors of fire growth rates and fuel type. The simulated burn probabilities were comparable to observed patterns across the U.S. over the range of four orders of magnitude, generally falling within a factor of 3 or 4 of historical estimates. Close agreement between simulated and historical fire size distributions suggest that fire sizes are determined by the joint distributions of spatial opportunities for fire growth (dependent on fuels and ignition location) and the temporal opportunities produced by conducive weather sequences. The research demonstrates a practical approach to using fire simulations at very broad scales for purposes of operational planning and perhaps ecological research.     

Analysis of bacterial diversity and metals in produced water, seawater and sediments from an offshore oil and gas production platform

Produced water is one of the largest waste products routinely discharged into the ocean from offshore oil and gas platforms. This study analyzed bacterial communities and metals in the produced water, surrounding seawater, and sediment around the Thebaud platform. The bacterial community within the produced water was different from the seawater (SAB = 13.3), but the discharge had no detectable effect on the bacterial communities in the seawater (SAB > 97). In contrast, genomic analysis of sediments revealed that the bacterial community from 250 m was different (SAB = 70) from other locations further from the discharge, suggesting that the produced water had a detectable effect on the bacterial community in the sediment closest to the discharge. These near-field sediments contained elevated concentrations of manganese and iron that are associated with the produced water effluent. The results suggested that the discharge of produced water has influenced the bacterial community structure of sediments adjacent to the platform.