Spread and status of seven submerged pest plants in New Zealand lakes

The distribution of seven submerged aquatic pest plants is reported. Lake vegetation surveys recorded pest plants in 27.9% of 344 lakes, with two species co‐occurring in 5.8%, and three species in 2.6% of lakes. Egeria densa was most frequent (15.4% of lakes), followed by Ceratophyllum demersum (9.0%), Lagarosiphon major (7.3%), and Utricularia gibba (5.5%). Spread since 2000 has continued for five pest plants, with 34 lakes invaded by U. gibba over 2004–08 alone. Early regional sites in proximity to human population centres were likely plant liberations and numerous potential founder colonies remain in garden ponds. Human activities were important for inter‐lake dispersal, with the exception of bird‐dispersed U. gibba. Significant lake associations between pest plants, and with presence of six exotic fish species, suggest common dispersal pathways and similar introduction risks. Therefore, predictions of future spread should be possible based on sources, dispersal pathways, and identifying key risk factors for lakes.     

Submerged vegetation of Lakes Sumner,Marion, Katrine,Taylor, and Sheppard in Canterbury,New Zealand

The submerged vegetation of Lakes Sumner, Marion, Katrine, Taylor, and Sheppard was surveyed in May 1987. These high‐altitude lakes lie at c. 600 m a.s.l., within the largely unmodified upper Hurunui catchment. Submerged vegetation was diverse and included numerous short shallow‐water species, dense swards of Isoetes alpinus, low covers of taller native vascular plants, and charophyte meadows beyond the depth limit of vascular plants to a maximum of 15 m. A sparse deepwater bryophyte community was observed from 11 to 32 m depth in Lake Sumner. Displacement of native vegetation by dense growths of the adventive oxygen weed Elodea canadensis over mid‐depths of 3–6 m was noted in all lakes, except Lake Marion.     

Mapping river bathymetries: Evaluating topobathymetric LiDAR survey

Advances in topobathymetric LiDARs could enable rapid surveys at sub-meter resolution over entire stream networks. This is the first step to improving our knowledge of riverine systems, both their morphology and role in ecosystems. The Experimental Advanced Airborne Research LiDAR B (EAARL-B) system is one such topobathymetric sensor, capable of mapping both terrestrial and aquatic systems. Whereas the original EAARL was developed to survey littoral areas, the new version, EAARL-B, was also designed for riverine systems but has yet to be tested. Thus, we evaluated the ability of EAARL-B to map bathymetry and floodplain topography at sub-meter resolution in a mid-size gravel-bed river. We coupled the EAARL-B survey with highly accurate field surveys (0.03 m vertical accuracy and approximately 0.6 by 0.6 m resolution) of three morphologically distinct reaches, approximately 200 m long 15 m wide, of the Lemhi River (Idaho, USA). Both point-to-point and raster-to-raster comparisons between ground and EAARL-B surveyed elevations show that differences (ground minus EAARL-B surveyed elevations) over the entire submerged topography are small (root mean square error, RMSE, and median absolute error, M, of 0.11 m), and large differences (RMSE, between 0.15 and 0.38 m and similar M) are mainly present in areas with abrupt elevation changes and covered by dense overhanging vegetation. RMSEs are as low as 0.03 m over paved smooth surfaces, 0.07 m in submerged, gradually varying topography, and as large as 0.24 m along banks with and without dense, tall vegetation. EAARL-B performance is chiefly limited by point density in areas with strong elevation gradients and by LiDAR footprint size (0.2 m) in areas with topographic features of similar size as the LiDAR footprint. © 2018 John Wiley & Sons, Ltd.     

Landslide susceptibility analysis based on citizen reports

Landslide susceptibility estimates are essential for reducing the risk posed by landslides to social and economic well-being. However, estimates of landslide susceptibility depend on reliable landslide inventories whose production requires extensive field or remote sensing efforts. Further, most inventories are not updated through time and thus may not capture the influence of changes in climate and/or land use. Inventories based on citizen reports of landslide occurrence, have the potential to overcome these limitations. Such an inventory can be produced from citizen reports to a 311-phone and online system, a nationwide database that updates real-time and records reported landslides location and timing. Whereas this landslide inventory is promising, it has not used for landslide susceptibility analyses and may be associated with spatial uncertainties and reporting biases. In this study we explore the use of 311-based landslide inventory for landslide susceptibility estimates in Pittsburgh, PA, USA, where landslide risk is among the highest in the nation. We compare the 311-based inventory to field-validated inventories through a multi-pronged approach that combines field validation of 311-reported landslides, probabilistic analysis of the association between landslides and the underlying topographic and geologic factors, and spatial filtering. Our results show that: (a) approximately 70% of the 311-reported landslides are associated with an identifiable landslide in the field; (b) the spatial uncertainty of the 311-reported landslides is 104 ± 25 m; (c) 311-reported landslides differ from other inventories in that they are primarily associated with proximity to roads, however, field-correction of 311-reported landslide locations rectifies this anomaly; (d) a simple spatial filter, scaled by the uncertainty in location as determined from a subset of the 311-data, can increase the consistency between the 311-reported inventory and field-validated inventories. These results suggest that 311-based landslide inventories can improve susceptibility estimates at a relatively low cost and high temporal resolution.     

Effect of transect location,transect spacing and interpolation methods on river bathymetry accuracy

Digital elevation models (DEMs) of river channel bathymetries are developed by interpolating elevations between data collected at discrete points or along transects. The accuracy of interpolated bathymetries depends on measurement error, the density and distribution of point data, and the interpolation method. Whereas point measurement errors can be minimized by selecting the most efficient equipment, the effect of data density and interpolation method on river bathymetry is relatively unknown. Thus, this study focuses on transect‐based collection methods and investigates the effects of transect location, the spacing between transects, and interpolation methods on the accuracy of interpolated bathymetry. This is accomplished by comparing four control bathymetries generated from accurate and high resolution, sub‐meter scale data to bathymetries interpolated from transect data extracted from the control bathymetries using two transect locating methods and four interpolation methods. The transect locating methods are a morphologically‐spaced and an equally‐spaced model. The four interpolation methods are Ordinary Kriging, Delaunay Triangulation, and Simple Linear, which are applied in curvilinear coordinates (Delaunay Triangulation is also applied in Cartesian coordinates), and Natural Neighbor only in Cartesian Coordinates. The bathymetric data were obtained from morphologically simple and complex reaches of a large (average bankfull width = 90 m) and a small (average bankfull width = 17 m) river. The accuracy of the developed DEMs is assessed using statistical analysis of the differences between the control and interpolated bathymetries and hydraulic parameters assessed from bankfull water surface elevations. Results indicate that DEM accuracy is not influenced by the choice of transect location method (with same averaged cross‐section spacing) or a specific interpolation method, but rather by the coordinate system for which the interpolation method is applied and the spacing between transects. They also show negligible differences between the mean depths and surface areas calculated from bathymetries with dense or coarse spacing. Copyright © 2016 John Wiley & Sons, Ltd.     

Evaluating the performance of topobathymetric LiDAR to support multi-dimensional flow modelling in a gravel-bed mountain stream

Stream biophysical processes are commonly studied using multi-dimensional numerical modelling that quantifies flow hydraulics from which parameters such as habitat suitability, stream carrying capacity, and bed mobility are derived. These analyses would benefit from accurate high-resolution stream bathymetries spanning tens of kilometres of channel, especially in small streams or where navigation is difficult. Traditional ground-based survey methods are limited by survey time, dense vegetation and stream access, and are usually only feasible for short reaches. Conversely, airborne topobathymetric LiDAR surveys may overcome these limitations, although limited research is available on how errors in LiDAR-derived digital elevation models (DEMs) might propagate through flow models. This study investigated the performance of LiDAR-derived topobathymetry in support of multi-dimensional flow modelling and ecohydraulics calculations in two gravel-bedded reaches (approximately 200 m long), one morphologically complex and one morphologically simple, and at the segment scale (32 km-long stream segment) along a 15 m-wide river in central Idaho, USA. We compared metre and sub-metre-resolution DEMs generated from RTK-GPS ground and Experimental Advanced Airborne Research LiDAR-B (EAARL-B) surveys and water depths, velocities, shear stresses, habitat suitability, and bed mobility modelled with two-dimensional (2D) hydraulic models supported by LiDAR and ground-surveyed DEMs. Residual statistics, bias (B), and standard deviation (SD) of the residuals between depth and velocity predicted from the model supported by LiDAR and ground-survey topobathymetries were up to −0.04 (B) and 0.09 m (SD) for depth and −0.09 (B) and 0.20 m s⁻¹ (SD) for velocity. The accuracy (B = 0.05 m), precision (SD = 0.09 m), and point density (1 point m⁻²) of the LiDAR topobathymetric survey (regardless of reach complexity) were sufficient to support 2D hydrodynamic modelling and derivative stream habitat and process analyses, because these statistics were comparable to those of model calibration with B = 0 m and SD = 0.04 m for water surface elevation and B = 0.05 m s⁻¹ and SD = 0.22 m s⁻¹ for velocity in our investigation. © 2020 John Wiley & Sons, Ltd.     

On the role of spatial resolution on snow estimates using a process‐based snow model across a range of climatology and elevation

Hydrological processes in mountainous settings depend on snow distribution, whose prediction accuracy is a function of model spatial scale. Although model accuracy is expected to improve with finer spatial resolution, an increase in resolution comes with modelling costs related to increased computational time and greater input data and parameter information. This computational and data collection expense is still a limiting factor for many large watersheds. Thus, this work's main objective is to question which physical processes lead to loss in model accuracy with regard to input spatial resolution under different climatic conditions and elevation ranges. To address this objective, a spatially distributed snow model, iSnobal, was run with inputs distributed at 50‐m—our benchmark for comparison—and 100‐m resolutions and with aggregated (averaged from the fine to the large resolution) inputs from the 50‐m model to 100‐, 250‐, 500‐, and 750‐m resolution for wet, average, and dry years over the Upper Boise River Basin (6,963 km²), which spans four elevation bands: rain dominated, rain–snow transition, and snow dominated below treeline and above treeline. Residuals, defined as differences between values quantified with high resolution (>50 m) models minus the benchmark model (50 m), of simulated snow‐covered area (SCA) and snow water equivalent (SWE) were generally slight in the aggregated scenarios. This was due to transferring the effects of topography on meteorological variables from the 50‐m model to the coarser scales through aggregation. Residuals in SCA and SWE in the distributed 100‐m simulation were greater than those of the aggregated 750 m. Topographic features such as slope and aspect were simplified, and their gradient was reduced due to coarsening the topography from the 50‐ to 100‐m resolution. Therefore, solar radiation was overestimated, and snow drifting was modified and caused substantial SCA and SWE underestimation in the distributed 100‐m model relative to the 50‐m model. Large residuals were observed in the wet year and at the highest elevation band when and where snow mass was large. These results support that model accuracy is substantially reduced with model scales coarser than 50 m.     

Role of temporal resolution of meteorological inputs for process‐based snow modelling

Accurate snow accumulation and melt simulations are crucial for understanding and predicting hydrological dynamics in mountainous settings. As snow models require temporally varying meteorological inputs, time resolution of these inputs is likely to play an important role on the model accuracy. Because meteorological data at a fine temporal resolution (~1 hr) are generally not available in many snow‐dominated settings, it is important to evaluate the role of meteorological inputs temporal resolution on the performance of process‐based snow models. The objective of this work is to assess the loss in model accuracy with temporal resolution of meteorological inputs, for a range of climatic conditions and topographic elevations. To this end, a process‐based snow model was run using 1‐, 3‐, and 6‐hourly inputs for wet, average, and dry years over Boise River Basin (6,963 km²), which spans rain dominated (≤1,400 m), rain–snow transition (>1,400 and ≤1,900 m), snow dominated below tree line (>1,900 and ≤2,400 m), and above tree line (>2,400 m) elevations. The results show that sensitivity of the model accuracy to the inputs time step generally decreases with increasing elevation from rain dominated to snow dominated above tree line. Using longer than hourly inputs causes substantial underestimation of snow cover area (SCA) and snow water equivalent (SWE) in rain‐dominated and rain–snow transition elevations, due to the precipitation phase mischaracterization. In snow‐dominated elevations, the melt rate is underestimated due to errors in estimation of net snow cover energy input. In addition, the errors in SCA and SWE estimates generally decrease toward years with low snow mass, that is, dry years. The results indicate significant increases in errors in estimates of SCA and SWE as the temporal resolution of meteorological inputs becomes coarser than an hour. However, use of 3‐hourly inputs can provide accurate estimates at snow‐dominated elevations. The study underscores the need to record meteorological variables at an hourly time step for accurate process‐based snow modelling.     

Australian state government road funding policies in the 1970s and 1980s

Australian road funding in the 1970s and 1980s has been characterised by conflict between federal and state governments over control of spending. This has influenced both total funds made available and spending priorities between national, arterial and local roads. Having the constitutional responsibility for roads, state governments have refused to discuss priorities with the Commonwealth, have not stated policies, have given the Australian government as little public acknowledgement as possible, have spent money on markedly different priorities, and yet are changing their means of determining priorities to reflect changing community wishes. Interestingly, at neither state nor federal level is there evidence of pursuance of a single, dominant policy objective.