Balancing trade‐off issues in land use change and the impact on streamflow and salinity management

The south‐west region of the Goulburn–Broken catchment in the south‐eastern Murray–Darling Basin in Australia faces a range of natural resource challenges. A balanced strategy is required to achieve the contrasting objectives of remediation of land salinization and reducing salt export, while maintaining water supply security to satisfy human consumption and support ecosystems. This study linked the Catchment Analysis Tool (CAT), comprising a suite of farming system models, to the catchment‐scale CATNode hydrological model to investigate the effects of land use change and climate variation on catchment streamflow and salt export. The modelling explored and contrasted the impacts of a series of different revegetation and climate scenarios. The results indicated that targeted revegetation to only satisfy biodiversity outcomes within a catchment is unlikely to have much greater impact on streamflow and salt load in comparison with simple random plantings. Additionally, the results also indicated that revegetation to achieve salt export reduction can effectively reduce salt export while having a disproportionately smaller affect on streamflows. Furthermore, streamflow declines can be minimized by targeting revegetation activities without significantly altering salt export. The study also found that climate change scenarios will have an equal if not more significant impact on these issues over the next 70 years. Uncertainty in CATNode streamflow predictions was investigated because of the effect of parameter uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.     

The potential for agricultural land use change to reduce flood risk in a large watershed

Effects of agricultural land management practices on surface runoff are evident at local scales, but evidence for watershed‐scale impacts is limited. In this study, we used the Soil and Water Assessment Tool model to assess changes in downstream flood risks under different land uses for the large, intensely agricultural, Raccoon River watershed in Iowa. We first developed a baseline model for flood risk based on current land use and typical weather patterns and then simulated the effects of varying levels of increased perennials on the landscape under the same weather patterns. Results suggest that land use changes in the Raccoon River could reduce the likelihood of flood events, decreasing both the number of flood events and the frequency of severe floods. The duration of flood events were not substantially affected by land use change in our assessment. The greatest flood risk reduction was associated with converting all cropland to perennial vegetation, but we found that converting half of the land to perennial vegetation or extended rotations (and leaving the remaining area in cropland) could also have major effects on reducing downstream flooding potential. We discuss the potential costs of adopting the land use change in the watershed to illustrate the scale of subsidies required to induce large‐scale conversion to perennially based systems needed for flood risk reduction. Copyright © 2013 John Wiley & Sons, Ltd.     

Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park,Ohio

The purpose of this study was to detect shallow landslides using hillshade maps derived from light detection and ranging (LiDAR)-based digital elevation model (DEM) derivatives. The landslide susceptibility mapping used an artificial neural network (ANN) approach and backpropagation method that was tested in the northern portion of the Cuyahoga Valley National Park (CVNP) located in northeast Ohio. The relationship between landslides and predictor attributes, which describe landform classes using slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index, was extracted from LiDAR-based DEM using geographic information system (GIS). The approach presented in this paper required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42.6 % of known landslides that were associated with 1.56 % of total area were correctly predicted. In contrast, the very low susceptibility class that represented 82.68 % of the total area was associated with 1.20 % of known landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization to help park officials in effective management and planning.     

Opportunistic natural experiments using digital telemetry: a transit disruption case study

ABSTRACT

In the past decade society has entered a technological period characterized by handheld computing that supports input and processing from numerous sensors. Today’s mobile phones offer the ability to integrate input from sensors monitoring various external and internal sources (e.g., accelerometer, magnetometer, microphone, GPS, wireless Internet, and Bluetooth). Furthermore, these raw inputs can be integrated and processed in ways that can offer novel representations of human behaviour. As a result, new opportunities to examine and better understand human spatial behaviour are available; one such application is the constant monitoring of a group of people over an extended period of time. Such a research setting lends itself to natural experiments that emerge as a result of regular and on-going observations. We report here on the observation of a natural experiment that took place in the context of a month-long monitoring study of 28 participants using mobile phone-based ubiquitous sensor monitoring. The implications for public health and transportation planning are discussed.     

Implementing landslide path dependency in landslide susceptibility modelling

Landslide susceptibility modelling—a crucial step towards the assessment of landslide hazard and risk—has hitherto not included the local, transient effects of previous landslides on susceptibility. In this contribution, we implement such transient effects, which we term “landslide path dependency”, for the first time. Two landslide path dependency variables are used to characterise transient effects: a variable reflecting how likely it is that an earlier landslide will have a follow-up landslide and a variable reflecting the decay of transient effects over time. These two landslide path dependency variables are considered in addition to a large set of conditioning attributes conventionally used in landslide susceptibility. Three logistic regression models were trained and tested fitted to landslide occurrence data from a multi-temporal landslide inventory: (1) a model with only conventional variables, (2) a model with conventional plus landslide path dependency variables, and (3) a model with only landslide path dependency variables. We compare the model performances, differences in the number, coefficient and significance of the selected variables, and the differences in the resulting susceptibility maps. Although the landslide path dependency variables are highly significant and have impacts on the importance of other variables, the performance of the models and the susceptibility maps do not substantially differ between conventional and conventional plus path dependent models. The path dependent landslide susceptibility model, with only two explanatory variables, has lower model performance, and differently patterned susceptibility map than the two other models. A simple landslide susceptibility model using only DEM-derived variables and landslide path dependency variables performs better than the path dependent landslide susceptibility model, and almost as well as the model with conventional plus landslide path dependency variables—while avoiding the need for hard-to-measure variables such as land use or lithology. Although the predictive power of landslide path dependency variables is lower than those of the most important conventional variables, our findings provide a clear incentive to further explore landslide path dependency effects and their potential role in landslide susceptibility modelling.     

Rapid U‐Pb Geochronology by Laser Ablation Multi‐Collector ICP‐MS

Detrital zircon (DZ) U‐Pb laser ablation‐inductively coupled plasma‐mass spectrometry (LA‐ICP‐MS) has revolutionised the way geologists approach many Earth science questions. Although recent research has focused on rapid sample throughput, acquisition rates are limited to 100–300 analyses h^?1. We present a method to acquire zircon U‐Pb dates at rates of 120, 300, 600 and 1200 analyses h^?1 (30, 12, 6 and 3 s per analysis) by multi‐collector LA‐ICP‐MS. We demonstrate the efficacy of this method by analysing twelve zircon reference materials with dates from ~ 3465 to ~ 28 Ma. Mean offset from high‐precision dates increases with faster rates from 0.9% to 1.1%; mean random 1s uncertainty increases from 0.6% to 1.3%. We tested this new method on a sandstone sample previously characterised by large‐n DZ geochronology. Quantitative comparison shows increased correspondence among age distributions comprising > 300 dates. This new method holds promise for DZ geochronology because (a) it requires no major changes to hardware, but rather modifications to software; (b) it yields robust age distributions well‐suited for quantitative analysis and maximum depositional age calculations; (c) there is only a minor sacrifice of accuracy and measurement uncertainty; and (d) there is less burden to researchers in terms of time investment and analytical cost.     

Influence of the vertical distribution of cyanobacteria in the water column on the remote sensing signal

An increased intensity of cyanobacterial blooms and their potentially harmful effects have attracted the attention of environmental agencies, water authorities and the general public worldwide. Reliable operational monitoring methods of coastal waters, lakes and ponds are needed. Mapping of the surface extent of cyanobacterial blooms with remote sensing is straightforward, but recognizing waters dominated by cyanobacteria throughout the water column and quantitative mapping of cyanobacterial biomass with remote sensing is more complicated. Unlike most algae, cyanobacteria can regulate their buoyancy and move vertically in the water column. We used the Hydrolight 4.2 radiative transfer model and the specific optical properties of three species of cyanobacteria to study the impact of vertical distribution of cyanobacteria on the remote sensing signal. The results show that the vertical distribution of cyanobacteria in the water column has a significant impact on the remote sensing signal. This result indicates that developing remote sensing methods for quantitative mapping of cyanobacterial biomass is much more complex than quantitative mapping of an algal biomass that is uniformly distributed in the top mixed layer of water column.     

Observations of a Radio-Quiet Solar Preflare

The preflare phase of the flare SOL2011-08-09T03:52 is unique in its long duration, in that it was covered by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) and the Nobeyama Radioheliograph, and because it showed three well-developed soft X-ray (SXR) peaks. No hard X-rays (HXR) are observed in the preflare phase. Here we report that no associated radio emission at 17 GHz was found either, despite the higher sensitivity of the radio instrument. The ratio between the SXR peaks and the upper limit of the radio peaks is higher by more than one order of magnitude than the ratio in regular flares. The result suggests that the ratio between acceleration and heating in the preflare phase was different than in regular flares. Acceleration to relativistic energies, if any, occurred with lower efficiency.     

Mesoscale physical processes and zooplankton transport-retention in the northern Norwegian shelf region

Northern Norwegian shelf regions are highly productive, supporting fisheries rich in commercially important species such as cod, herring and capelin. It has been long recognized that the mesoscale jets, meanders and eddies associated with interactions between the North Atlantic Current, Norwegian Coastal Current and regional bottom topographic features such as troughs, banks and shelfbreaks play important roles in transporting and retaining zooplankton. To investigate zooplankton distributions and their correspondence with the physical fields, three large-scale surveys with mesoscale resolutions on physical and biological fields were conducted in northern Norwegian shelf regions between latitudes 68°15′N and 70°15′N in springs of 2000–2002. Survey results provide insights into the relationships between zooplankton distributions and the physical features such as fronts, the Norwegian Coastal Current and eddies related to topographic features. The physical and biological data are integrated and analyzed focusing on water types, estimation of geostrophic currents from direct current measurements, along-shelf transport of zooplankton, and retention of zooplankton by the mesoscale meander–eddy over a typical bank area on the shelf. The estimated mean transport in the upper 100 m on the shelf in the survey region is approximately 6.4×10³ tonnes wet weight day⁻¹ northward. High zooplankton abundances were found over both Malangsgrunnen and Sveinsgrunnen banks. The specific accumulation rate from northward–southward transport in the upper 100 m over Malangsgrunnen was approximately 0.08 day⁻¹, while variable currents with an offshore gradient of zooplankton abundance over Sveinsgrunnen implies an offshore dispersion of coastal-originated zooplankton cohort.