scholarly journals How Sensitive are Spatial Estimates of Wilderness Recreation Values to Information about Hiking Destinations?

2020 ◽  
Vol 35 (1) ◽  
pp. 19-41
Author(s):  
José J. Sánchez ◽  
Kenneth Baerenklau
Keyword(s):  
Wilderness Recreation ◽  
Spatial Estimates

scholarly journals Spatial distribution of water level impact to back-barrier bays

2018 ◽  
Author(s):  
Alfredo L. Aretxabaleta ◽  
Neil K. Ganju ◽  
Zafer Defne ◽  
Richard P. Signell
Keyword(s):  
Water Level ◽  
Sea Level ◽  
Water Levels ◽  
Analytical Models ◽  
Barnegat Bay ◽  
Sea Level Fluctuations ◽  
Flooding Hazard ◽  
Short Period ◽  
Inlet Geometry ◽  
Spatial Estimates

Abstract. Water level in semi-enclosed bays, landward of barrier islands, is mainly driven by offshore sea level fluctuations that are modulated by bay geometry and bathymetry, causing spatial variability in the ensuing response (transfer). Local wind setup can have a secondary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Inlet geometry and bathymetry primarily regulate the magnitude of the transfer between open ocean and bay. Tides and short-period offshore oscillations are more damped in the bays than longer-lasting offshore fluctuations, such as storm surge and sea level rise. We compare observed and modeled water levels at stations in a mid-Atlantic bay (Barnegat Bay) with offshore water level proxies. Observed water levels in Barnegat Bay are compared and combined with model results from the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to evaluate the spatial structure of the water level transfer. Analytical models based on the dimensional characteristics of the bay are used to combine the observed data and the numerical model results in a physically consistent approach. Model water level transfers match observed values at locations inside the Bay in the storm frequency band (transfers ranging from 70–100 %) and tidal frequencies (10–55 %). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The approach provides transfer estimates for locations inside the Bay where observations were not available resulting in a complete spatial characterization. The approach allows for the study of the Bay response to alternative forcing scenarios (landscape changes, future storms, and rising sea level). Detailed spatial estimates of water level transfer can inform decisions on inlet management and contribute to the assessment of current and future flooding hazard in back-barrier bays and along mainland shorelines.

Managing brown bears and wilderness recreation on the Kenai Peninsula, Alaska, USA

1992 ◽  
Vol 16 (2) ◽  
pp. 249-254 ◽  
Author(s):  
Michael J. Jacobs ◽  
Catherine A. Schloeder
Keyword(s):  
Brown Bears ◽  
Kenai Peninsula ◽  
Wilderness Recreation

scholarly journals Geostatistical modeling of “lethal coconut palm crown atrophy”, a new disease in the State of Pará

2021 ◽  
Vol 42 (6supl2) ◽  
pp. 3603-3616
Author(s):  
Adriano da Silva Gama ◽  
◽  
Paulo Roberto Silva Farias ◽  
Keyword(s):  
Distribution Pattern ◽  
Temporal Distribution ◽  
Spherical Model ◽  
Coconut Palm ◽  
Coefficient Of Determination ◽  
Data Variability ◽  
Range Vector ◽  
Spatial Estimates ◽  
Spreading Disease ◽  
Kriging Maps

’Lethal Coconut Palm Crown Atrophy’ (LCCA) is a rapidly spreading disease in Brazil, capable of quickly killing coconut trees and threatening the commercial exploration of this plant. The objective of this work was to characterize the spatial and temporal distribution pattern of LCCA in green dwarf coconut commercial plantation areas, located the municipality of Santa Izabel, mesoregion of Northeastern Pará, Brazil. Surveys were carried out at monthly intervals between January 2014 and December 2018, checking for plants with LCCA-characteristic symptoms. Geostatistics was applied to perform spatial-temporal disease estimates based on semivariogram modeling and preparation of ordinary kriging maps. These spatial estimates are conducted through interpolations that characterize data variability in the area. The spherical model yielded the best fit to the spatial distribution of the disease, as it presented the best coefficient of determination (R²), with the range varying between 14m and 45m. The Spatial Dependence Index (SDI) was moderate in the evaluations carried out between 2014 and 2017 (in the 0.26-0.64 range), but not in 2018, when it was strong (0.23). The values of the clustering intensity of LCCA-symptomatic plants were estimated in non-sampled points. The spherical fit model of the data indicates an aggregated distribution pattern, shown by aggregation patches in the plantation, graded by values of dissemination intensity. The kriging maps allowed the observation that the disease expands between plants in the same line, suggesting the possibility of the presence of a short-range vector.

scholarly journals Validation of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

2019 ◽  
Author(s):  
Edward H. Bair ◽  
Karl Rittger ◽  
Jawairia A. Ahmad ◽  
Doug Chabot
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Median Number ◽  
In Situ Measurements ◽  
Snow Properties ◽  
Wide Range ◽  
Spatial Estimates ◽  
Snow Water ◽  
Critical Layers

Abstract. Ice and snowmelt feed the Indus and Amu Darya rivers, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem until recently when we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). For each station, accumulated precipitation and SWE were derived from snow depth using the SNOWPACK model. High-resolution (500 m) reconstructed SWE estimates from the ParBal model were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provide stability information. Following previous work, the median number of critical layers and percentage of facets across all of the pixels containing the AKAH stations was computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at the peak. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. For stratigraphy, a heavily faceted snowpack is observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

Using bioeconomic models to assess research priorities: a case study on afforestation as a carbon sequestration tool

2006 ◽  
Vol 36 (4) ◽  
pp. 886-900 ◽  
Author(s):  
Daniel W McKenney ◽  
Denys Yemshanov ◽  
Glenn Fox ◽  
Elizabeth Ramlal
Keyword(s):  
Carbon Sequestration ◽  
Hybrid Poplar ◽  
Research Priorities ◽  
Cost Benefit ◽  
Forest Products ◽  
Carbon Price ◽  
Decay Rates ◽  
Biological Variables ◽  
Fuel Substitution ◽  
Spatial Estimates

We have developed a spatial cost–benefit afforestation model that includes the tracking of five carbon pools. In this application we represent three possible afforestation strategies that could be implemented in Canada using plantations of hybrid poplar, hardwoods, and softwoods with average expected growth rates of 12–14, 5–7, and 5–7 m3· ha–1·year–1 respectively. The model provides spatially explicit insights into the cost effectiveness of afforestation as a carbon sequestration tool. Here we develop an elasticity metric and experiment to assess model sensitivity and use the results to make recommendations about research priorities. The most important biological variables across all scenarios include site suitability, which is related to refining the spatial estimates of potential yields, biomass to carbon ratios, and wood density. The most important economic variables include refinement and lowering of establishment costs and agricultural opportunity costs. Parameters that have a low impact on the break-even carbon price, suggesting refinements in knowledge in these areas would be relatively less beneficial, include decay rates for forest products, stand senescence age (the age when stand mortality reaches its maximum), bioenergy and pulpwood prices, and mean residual time for leaf litter. Less importance was also placed on the proportions of forest products in the total harvest and refining a fossil fuel substitution coefficient.

scholarly journals Global hotspots for the occurrence of compound events

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nina N. Ridder ◽  
Andy J. Pitman ◽  
Seth Westra ◽  
Anna Ukkola ◽  
Hong X. Do ◽  
...  
Keyword(s):  
Western Europe ◽  
Climate Models ◽  
Global Scale ◽  
Reanalysis Data ◽  
Economic Losses ◽  
Multiple Hazards ◽  
Weather And Climate ◽  
Spatial Estimates ◽  
Compound Events ◽  
Climate Events

AbstractCompound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.

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