Long-term hydrology and aquatic biogeochemistry data from H. J. Andrews Experimental Forest,Cascade Mountains,Oregon

The H. J. Andrews Experimental Forest (HJA) encompasses the 6400 ha Lookout Creek watershed in western Oregon, USA. Hydrologic, chemistry and precipitation data have been collected, curated, and archived for up to 70 years. The HJA was established in 1948 to study the effects of harvest of old-growth conifer forest and logging-road construction on water quality, quantity and vegetation succession. Over time, research questions have expanded to include terrestrial and aquatic species, communities and ecosystem dynamics. There are nine small experimental watersheds and 10 gaging stations in the HJA, including both reference and experimentally treated watersheds. Gaged watershed areas range from 8.5 to 6242 ha. All gaging stations record stage height, water conductivity, water temperature and above-stream air temperature. At nine of the gage sites, flow-proportional water samples are collected and composited over 3-week intervals for chemical analysis. Analysis of stream and precipitation chemistry began in 1968. Analytes include dissolved and particulate species of nitrogen and phosphorus, dissolved organic carbon, pH, specific conductance, suspended sediment, alkalinity, and major cations and anions. Supporting climate measurements began in the 1950s in association with the first small watershed experiments. Over time, and following the initiation of the Long Term Ecological Research (LTER) grant in 1980, infrastructure expanded to include a set of benchmark and secondary meteorological stations located in clearings spanning the elevation range within the Lookout Creek watershed, as well as a large number of forest understory temperature stations. Extensive metadata on sensor configurations, changes in methods over time, sensor accuracy and precision, and data quality control flags are associated with the HJA data.     

Unveiling buried aeolian landscapes: reconstructing a late Holocene dune environment using 3D ground-penetrating radar

Across the UK, sandy beaches and dunes protect coastal infrastructure from waves and extreme water levels during large-scale storms, while providing important habitats and recreational opportunities. Understanding their long-term evolution is vital in managing their condition in a changing climate. Recently, ground-penetrating radar (GPR) methods have grown in popularity in geomorphological applications, yielding centimetre-scale resolution images of near-surface stratigraphy and structure, thus allowing landscape evolution to be reconstructed. Additionally, abrupt changes in palaeo-environments can be visualized in three dimensions. Although often complemented by core data, GPR allows interpretations to be extended into areas with minimal ground-truth control. Nonetheless, GPR data interpretation can be non-intuitive and ambiguous, and radargrams may not initially resemble the expected subsurface geometry. Interpretation can be made yet more onerous when handling the large 3D data volumes that are facilitated with modern GPR technology. Here we describe the development of novel semi-automated GPR feature-extraction tools, based on ‘edge detection’ and ‘thresholding’ methods, which detect regions of increased GPR reflectivity which can be applied to aid in the reconstruction of a range Quaternary landscapes. Since reflectivity can be related to lithological and/or pore fluid changes, the 3D architecture of the palaeo-landscape can be reconstructed from the features extracted from a geophysical dataset. We present 500 MHz GPR data collected over a buried Holocene coastal dune system in North Wales, UK, now reclaimed for use as an airfield. Core data from the site, reaching a maximum depth 2 m, suggest rapid vertical changes from sand to silty-organic units, and GPR profiles suggest similar lateral complexity. By applying thresholding methods to GPR depth slices, these lateral complexities are effectively and automatically mapped. Furthermore, automatic extraction of the local reflection power yields a strong correlation with the depth variation of organic content, suggesting it is a cause of reflectivity contrast. GPR-interpolated analyses away from core control thus offer a powerful proxy for parameters derived from invasive core logging. The GPR data collected at Llanbedr airfield highlight a complex dune system to a depth of 2.8 m, probably deposited in several phases over ~700 years, similar to elsewhere in North Wales.     

Volume contents

Volume Contents

Volume contents     

The system of the Milky Way, LMC and SMC

A simple sticky-particle numerical model has been developed in order to check whether extended structures of gas created due to the dynamical evolution of the Galaxy and the Magellanic Clouds system can be explained as remnants of a tidal interaction. Influence of dissipative nature of gaseous medium has been taken into account. The most remarkable features are: the Magellanic Stream, the common HI envelope surrounding both the LMC and SMC and the bridge extended between the Clouds. In contrast to previous works of Murai and Fujimoto (1980), Gardiner et al. (1994) and H and Rohlfs (1994) no presumptions were done on the actual galactocentric velocities of the Magellanic Clouds. The mean values of the LMC and SMC velocity vectors obtained from the Hipparcos proper motion measurements (Kroupa and Bastian, 1997) were used in order to verify whether they allow to reproduce the observed HI distribution. Numerical simulations showed that tidal forces are really significant for the evolution of extended structures such as the Magellanic Stream but this approach becomes unsufficient for the internal regions of galaxies where self-gravity and dissipative properties of the gas cannot be neglected. More precise proper motion measurements are urgently needed. This revised version was published online in August 2006 with corrections to the Cover Date.     

Soil moisture: a residual problem underlying AGCMs

This paper critically reviews and intercompares land surface schemes (LSSs) as used in atmospheric general circulation models (AGCMs) to simulate soil moisture and its response to a warmer climate, and potential evapotranspiration approaches as used in operational soil moisture monitoring and in predicting the response of soil moisture to a warmer climate. AGCM predictions of overall soil moisture change are in broad agreement but disagree sharply in some regions. Intercomparison projects have sought to evaluate the LSSs used by AGCMs for both accuracy and consistency. These studies have found that different LSSs can produce very different simulations even when supplied with identical atmospheric forcing. As well, LSSs that produce similar surface results from present-day or control climates often diverge when forced with climatic change data. Furthermore, no single LSS has been identified that produces an adequate simulation of all of temperature, moisture, evapotranspiration and runoff. AGCM LSSs must resolve the surface energy balance (SEB) in order to compute realistic heat fluxes between with the atmospheric model. LSSs have been used with AGCMs in both on-line (fully coupled) and off-line modes. In off-line climatic change experiments, AGCM predictions of atmospheric temperature and precipitation have been used, along with model downward radiative fluxes at the surface, to drive their own uncoupled LSS. However, there are simple non-energy-balance methods for estimating evapotranspiration that have been traditionally used in agricultural and meteorological applications. These schemes compute a potential evapotranspiration (PE) based on temperature and/or net radiation inputs, with the PE modified based on the availability of soil moisture. Operational PE approaches have also been used with AGCM data in off-line climate change experiments. The advantages of this approach are that it is simpler and requires less information, although (like the off-line SEB approach) it leaves out the simulation of feedbacks between the surface and the atmosphere.Although the SEB approach is essential for LSSs that must be coupled to AGCMs, this does not necessarily make it superior to an off-line operational PE LSS when it comes to quantities such as soil moisture. The quality of current observational data is insufficient to demonstrate that either approach is better than the other. Both approaches should continue to be used and intercompared when predicting the impacts of climatic change on soil moisture.     

Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon

Landslide inventory maps are necessary for assessing landslide hazards and addressing the role slope stability plays in landscape evolution over geologic timescales. However, landslide inventory maps produced with traditional methods — aerial photograph interpretation, topographic map analysis, and field inspection — are often subjective and incomplete. The increasing availability of high-resolution topographic data acquired via airborne Light Detection and Ranging (LiDAR) over broad swaths of terrain invites new, automated landslide mapping procedures. We present two methods of spectral analysis that utilize LiDAR-derived digital elevation models of the Puget Sound lowlands, Washington, and the Tualatin Mountains, Oregon, to quantify and automatically map the topographic signatures of deep-seated landslides. Power spectra produced using the two-dimensional discrete Fourier transform and the two-dimensional continuous wavelet transform identify the characteristic spatial frequencies of deep-seated landslide morphologic features such as hummocky topography, scarps, and displaced blocks of material. Spatial patterns in the amount of spectral power concentrated in these characteristic frequency bands highlight past slope instabilities and allow the delineation of landslide terrain. When calibrated by comparison with detailed, independently compiled landslide inventory maps, our algorithms correctly classify an average of 82% of the terrain in our five study areas. Spectral analysis also allows the creation of dominant wavelength maps, which prove useful in analyzing meter-scale topographic expressions of landslide mechanics, past landslide activity, and landslide-modifying geomorphic processes. These results suggest that our automated landslide mapping methods can create accurate landslide maps and serve as effective, objective, and efficient tools for digital terrain analysis.     

Carbon budget for a subtropical seagrass dominated coastal lagoon: How important are seagrasses to total ecosystem net primary production?

It has been assumed that because seagrasses dominate macrophyte biomass in many estuaries they also dominate primary production. We tested this assumption by developing three carbon budgets to examine the contribution of autotrophic components to the total ecosystem net primary production (TENPP) of Lower Laguna Madre, Texas. The first budget coupled average photosynthetic parameters with average daily irradiance to calculate daily production. The second budget used average photosynthetic parameters and hourly in situ irradiance to estimate productivity. The third budget integrated temperature-adjusted photosynthetic parameters (using Q₁₀=2) and hourly in situ irradiance to estimate productivity. For each budget TENPP was calculated by integrating production from each autotroph based on the producers’ areal distribution within the entire Lower Laguna Madre. All budgets indicated that macroalgae account for 33–42% of TENPP and seagrasses consistently accounted for about 33–38%. The contribution by phytoplankton was consistently about 15–20%, and the contribution from the benthic microalgae varied between 8% and 36% of TENPP, although this may have been underestimated due to our exclusion of the within bed microphytobenthos component. The water column over the seagrass beds was net heterotrophic and consequently was a carbon sink consuming between 5% and 22% of TENPP, TENPP ranged between 5.41×10¹⁰ and 2.53×10¹¹ g C yr⁻¹, depending on which budget was used. The simplest, most idealized budget predicted the highest TENPP, while the more realistic budgets predicted lower values. Annual production rates estimated using the third budget forHalodule urightii andThalassia testudinum compare well with field data. Macroalgae and microalgae contribute 50–60% of TENPP, and seagrass may be more important as three-dimensional habitat (i.e., structure) than as a source of organic carbon to the water column in Lower Laguna Madre.