scholarly journals Spatial variability in forest growth – climate relationships in the Olympic Mountains, Washington

2006 ◽  
Vol 36 (1) ◽  
pp. 77-91 ◽  
Author(s):  
Jill M Nakawatase ◽  
David L Peterson
Keyword(s):  
Pacific Northwest ◽  
Spatial Scales ◽  
Climatic Variability ◽  
Management Strategies ◽  
Basal Area ◽  
Forest Growth ◽  
Spatial And Temporal Variability ◽  
Olympic Mountains ◽  
Mean Climate ◽  
High Elevations

For many Pacific Northwest forests, little is known about the spatial and temporal variability in tree growth – climate relationships, yet it is this information that is needed to predict how forests will respond to future climatic change. We studied the effects of climatic variability on forest growth at 74 plots in the western and northeastern Olympic Mountains. Basal area increment time series were developed for each plot, and Pearson's correlation analysis and factor analysis were used to quantify growth–climate relationships. Forest growth in the Olympic Mountains responds to climatic variability as a function of mean climate and elevation. Low summer moisture limits growth across all elevations in the dry northeastern Olympics. Growth at low elevations in the wet western Olympics is associated with phases of the Pacific Decadal Oscillation and with summer temperature. Heavy winter snowpack limits growth at high elevations in the western Olympics. In the warmer greenhouse climate predicted for the Olympic Mountains, productivity at high elevations of the western Olympics will likely increase, whereas productivity at high elevations in the northeastern region and potentially in low elevations of the western region will likely decrease. This information can be used to develop adaptive management strategies to prepare for the effects of future climate on these forests. Because growth–climate relationships on the Olympic Peninsula vary at relatively small spatial scales, those relationships may assist modeling and other efforts to provide more accurate predictions at local to regional scales.

Spatial and temporal variability in forest growth in the Olympic Mountains, Washington: sensitivity to climatic variability

2006 ◽  
Vol 36 (1) ◽  
pp. 92-104 ◽  
Author(s):  
Melisa L Holman ◽  
David L Peterson
Keyword(s):  
Forest Type ◽  
Spatial Scales ◽  
Picea Sitchensis ◽  
Forest Growth ◽  
Spatial And Temporal Variability ◽  
Growth Responses ◽  
Growth Variability ◽  
Olympic Mountains ◽  
Positive Growth ◽  
Growth Correlations

We compared annual basal area increment (BAI) at different spatial scales among all size classes and species at diverse locations in the wet western and dry northeastern Olympic Mountains. Weak growth correlations at small spatial scales (average R = 0.084–0.406) suggest that trees are responding to local growth conditions. However, significant positive growth correlations between geographically adjacent forest types (R = 0.440–0.852) and between watersheds (R = 0.430) indicate that there is a common overarching growth-limiting factor (e.g., climate) that affects tree growth over large areas. The Sitka spruce (Picea sitchensis (Bong.) Carrière) forest type is the most sensitive to environmental change with the highest mean sensitivity (0.345), the highest potential for annual growth change (mean BAI = 0.0047 m2), and the highest growth variability (coefficient of variation = 0.498). In addition, this forest type is most likely to exhibit extreme positive growth responses (4.2% of years have BAI values 2 standard deviations above the mean). Low-elevation coniferous forests are relatively sensitive to changes in growth-limiting factors (in contrast to the traditional view) and may play an important role in storing carbon in a warmer climate.

scholarly journals Optimal design of a climatological network: beyond practical considerations

2013 ◽  
Vol 2 (2) ◽  
pp. 199-212 ◽  
Author(s):  
G. S. Mauger ◽  
K. A. Bumbaco ◽  
G. J. Hakim ◽  
P. W. Mote
Keyword(s):  
Network Design ◽  
Pacific Northwest ◽  
Spatial Scales ◽  
Optimal Placement ◽  
Optimal Network Design ◽  
Optimal Network ◽  
The Us ◽  
Mean Climate ◽  
Expert Input ◽  
Practical Constraints

Abstract. Station locations in existing environmental networks are typically chosen based on practical constraints such as cost and accessibility, while unintentionally overlooking the geographical and statistical properties of the information to be measured. Ideally, such considerations should not take precedence over the intended monitoring goal of the network: the focus of network design should be to adequately sample the quantity to be observed. Here we describe an optimal network design technique, based on ensemble sensitivity, that objectively locates the most valuable stations for a given field. The method is computationally inexpensive and can take practical constraints into account. We describe the method, along with the details of our implementation, and present-example results for the US Pacific Northwest, based on the goal of monitoring regional annual-mean climate. The findings indicate that optimal placement of observing stations can often be highly counterintuitive, thus emphasizing the importance of objective approaches. Although at coarse scales the results are generally consistent, sensitivity tests show important differences, especially at smaller spatial scales. These uncertainties could be reduced with improvements in datasets and improved estimates of the measurement error. We conclude that the method is best suited for identifying general areas within which observations should be focused, and suggest that the approach could serve as a valuable complement to land surveys and expert input in designing new environmental observing networks.

Managing burned landscapes: evaluating future management strategies for resilient forests under a warming climate

2014 ◽  
Vol 23 (7) ◽  
pp. 915 ◽  
Author(s):  
K. L. Shive ◽  
P. Z. Fulé ◽  
C. H. Sieg ◽  
B. A. Strom ◽  
M. E. Hunter
Keyword(s):  
Climate Change ◽  
Ponderosa Pine ◽  
Prescribed Burning ◽  
Fire Severity ◽  
Management Strategies ◽  
Basal Area ◽  
Forest Growth ◽  
Climate Change Effects ◽  
Ponderosa Pine Forests ◽  
And Shifts

Climate change effects on forested ecosystems worldwide include increases in drought-related mortality, changes to disturbance regimes and shifts in species distributions. Such climate-induced changes will alter the outcomes of current management strategies, complicating the selection of appropriate strategies to promote forest resilience. We modelled forest growth in ponderosa pine forests that burned in Arizona’s 2002 Rodeo–Chediski Fire using the Forest Vegetation Simulator Climate Extension, where initial stand structures were defined by pre-fire treatment and fire severity. Under extreme climate change, existing forests persisted for several decades, but shifted towards pinyon–juniper woodlands by 2104. Under milder scenarios, pine persisted with reduced growth. Prescribed burning at 10- and 20-year intervals resulted in basal areas within the historical range of variability (HRV) in low-severity sites that were initially dominated by smaller diameter trees; but in sites initially dominated by larger trees, the range was consistently exceeded. For high-severity sites, prescribed fire was too frequent to reach the HRV’s minimum basal area. Alternatively, for all stands under milder scenarios, uneven-aged management resulted in basal areas within the HRV because of its inherent flexibility to manipulate forest structures. These results emphasise the importance of flexible approaches to management in a changing climate.

scholarly journals Use of passive-microwave data to monitor spatial and temporal variations of snow cover at tree line near Churchill, Manitoba, Canada

2002 ◽  
Vol 34 ◽  
pp. 58-64 ◽  
Author(s):  
Frédérique C. Pivot ◽  
Claude Kergomard ◽  
Claude R. Duguay
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Climatic Variability ◽  
Passive Microwave ◽  
Spatial And Temporal Variability ◽  
Large Area ◽  
Spectral Difference ◽  
Microwave Data

AbstractWe evaluated the contribution of Special Sensor Microwave/Imager (SSM/I) passive-microwave data to the monitoring of spatial and temporal variability of snow cover in the Churchill area, Manitoba, Canada. Because of the coarse spatial resolution of current passive-microwave sensors, the estimation of snow water equivalent using empirical equations with these instruments is largely compromised in complex areas such as Churchill (forest–tundra ecotone). However, with its high frequency of observations and the availability of a long time series (1988–99), passive-microwave data from the SSM/I radiometer remain a very valuable tool for monitoring the temporal evolution of snow cover at various spatial scales. Through winter 1997/98, we first examined the passive-microwave signatures at the local scale and we identified the major stages of the snow period. Principal-component analysis (PCA) applied on spectral-difference (Tb(19H) - Tb(37H))time series (1988–99) enabled us to identify spatio-temporal effects over a large area. PCA also permitted the extraction of indices of relevance for monitoring climatic variability and climate change (annual snow-cover duration, dates of snow-cover appearance and disappearance).

scholarly journals Optimal design of a climatological network: beyond practical considerations

2013 ◽  
Vol 3 (1) ◽  
pp. 193-219
Author(s):  
G. S. Mauger ◽  
K. A. Bumbaco ◽  
G. J. Hakim ◽  
P. W. Mote
Keyword(s):  
Network Design ◽  
Pacific Northwest ◽  
Spatial Scales ◽  
Optimal Placement ◽  
Optimal Network Design ◽  
Optimal Network ◽  
The Us ◽  
Mean Climate ◽  
Expert Input ◽  
Practical Constraints

Abstract. Station locations in existing environmental networks are typically chosen based on practical constraints such as cost and accessibility, while unintentionally overlooking the geographical and statistical properties of the information to be measured. Ideally, such considerations should not take precedence over the intended monitoring goal of the network: the focus of network design should be to adequately sample the quantity to be observed. Here we describe an optimal network design technique, based on ensemble sensitivity, that objectively locates the most valuable stations for a given field. The method is computationally inexpensive and can take practical constraints into account. We describe the method, along with the details of our implementation, and present example results for the US Pacific Northwest, based on the goal of monitoring regional annual-mean climate. The findings indicate that optimal placement of observing stations can often be highly unintuitive, thus emphasizing the importance of objective approaches. Although at coarse scales the results are generally consistent, sensitivity tests show important differences in the regions highlighted for new measurements, especially at smaller spatial scales. These uncertainties could be reduced with improvements in datasets and improved estimates of measurement error. We conclude that the method is best suited for identifying general areas within which observations should be focused, and suggest that the approach could serve as a valuable complement to land surveys and expert input in designing new environmental observing networks.

scholarly journals Deriving Tree Size Distributions of Tropical Forests from Lidar

2021 ◽  
Vol 13 (1) ◽  
pp. 131
Author(s):  
Franziska Taubert ◽  
Rico Fischer ◽  
Nikolai Knapp ◽  
Andreas Huth
Keyword(s):  
Size Distribution ◽  
Spatial Scales ◽  
Basal Area ◽  
Tree Height ◽  
Tree Size ◽  
Diameter Distribution ◽  
Forest Inventories ◽  
Allometric Relations ◽  
Lidar Measurements ◽  
Derived Data

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

Seagrass characterization on the southern Pacific coast of Costa Rica: history, vegetation, and environment

2020 ◽  
Vol 63 (5) ◽  
pp. 429-438
Author(s):  
Jimena Samper-Villarreal ◽  
Jorge Cortés
Keyword(s):  
Costa Rica ◽  
Pacific Coast ◽  
Management Strategies ◽  
Shoot Density ◽  
Nutrient Concentrations ◽  
Seagrass Habitat ◽  
Spatial And Temporal Variability ◽  
Seagrass Species ◽  
Seagrass Biomass ◽  
Herbarium Collections

AbstractSeagrass conservation and management requires scientific understanding of spatial and temporal variability, information that is currently limited for the Eastern Tropical Pacific (ETP). Here, we analysed seagrass presence based on previous reports, herbarium collections and stakeholder knowledge, combined with field characterization in Golfo Dulce, southern Pacific coast of Costa Rica. Seagrasses were found at multiple locations along a narrow border close to shore and in up to 6 m depth within Golfo Dulce, dating back to 1969. Two seagrass species were found, Halophila baillonii and Halodule beaudettei. Seagrass biomass values for Golfo Dulce (12.0 ± 8.5 g DW m−2) were lower and water nutrient concentrations were higher than previously reported in the gulf. Shoot density (1513 ± 767 shoots m−2) was similar to previous reports. Stable isotope values in seagrass were −11.3 ± 1.0‰ δ13C and 1.2 ± 0.9‰ δ15N; while those in sediments were −26.1 ± 1.3 and 2.5 ± 0.9‰. In Golfo Dulce, isotopic values of both seagrass species do not overlap with other known primary producers. Management strategies should aim to minimize known seagrass stressors, protect potential seagrass habitat, and take into account the dynamic life strategies of the two seagrass species found.

scholarly journals Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kuang-Yu Chang ◽  
William J. Riley ◽  
Sara H. Knox ◽  
Robert B. Jackson ◽  
Gavin McNicol ◽  
...  
Keyword(s):  
Temperature Sensitivity ◽  
Spatial Scales ◽  
Biogeochemical Model ◽  
Spatial And Temporal Variability ◽  
Biogeochemical Models ◽  
Model Parameterization ◽  
Machine Learning Model ◽  
Meta Analyses ◽  
The Relationship ◽  
Global Carbon

AbstractWetland methane (CH4) emissions ($${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.

A framework for the Integrated Assessment of SDG trade-offs in the Sundarbans Biosphere Reserve.

2021 ◽  
Author(s):  
Charlotte Marcinko ◽  
Robert Nicholls ◽  
Tim Daw ◽  
Sugata Hazra ◽  
Craig Hutton ◽  
...  
Keyword(s):  
Environmental Change ◽  
Integrated Assessment ◽  
Spatial Scales ◽  
Strong Dependence ◽  
Management Strategies ◽  
Rural Livelihoods ◽  
Biosphere Reserve ◽  
Integrated Analysis ◽  
Trade Offs ◽  
The Sundarbans

<p>The United Nations Sustainable Development Goals (SDGs) and their corresponding targets are significantly interconnected, with many interactions, synergies and trade-offs between individual goals across multiple temporal and spatial scales.  We propose a framework for the Integrated Assessment Modelling (IAM) of a complex deltaic socio-ecological system in order to analyse such SDG interactions. We focus on the Sundarbans Biosphere Reserve (SBR), India within the Ganges-Brahmaputra-Meghna Delta. It is densely populated with 4.4 million people (2011), high levels of poverty and a strong dependence on rural livelihoods. It is only 50 km from the growing megacity of Kolkata (about 15 million people in 2020). The area also includes the Indian portion of the world’s largest mangrove forest – the Sundarbans – hosting the iconic Bengal Tiger. Like all deltaic systems, this area is subject to multiple drivers of environmental change operating across different scales. The IAM framework is designed to investigate current and future trends in socio-environmental change and explore associated policy impacts, considering a broad range of sub-thematic SDG indicators. Integration is achieved through the soft coupling of multiple sub-models, knowledge and data of relevant environmental and socio-economic processes.  The following elements are explicitly considered: (1) agriculture; (2) aquaculture; (3) mangroves; (4) fisheries; and (5) multidimensional poverty. Key questions that can be addressed include the implications of changing monsoon patterns, trade-offs between agriculture and aquaculture, or the future of the Sundarbans mangroves under sea-level rise and different management strategies, including trade-offs with land use to the north.  The novel high-resolution analysis of SDG interactions allowed by the IAM will provide stakeholders and policy makers the opportunity to prioritize and explore the SDG targets that are most relevant to the SBR and provide a foundation for further integrated analysis.</p>

scholarly journals Variability of Cloudiness over Mountain Terrain in the Western United States

2017 ◽  
Vol 18 (5) ◽  
pp. 1227-1245 ◽  
Author(s):  
Edwin Sumargo ◽  
Daniel R. Cayan
Keyword(s):  
United States ◽  
Large Scale ◽  
Spatial Scales ◽  
High Elevation ◽  
Empirical Orthogonal Functions ◽  
Western United States ◽  
Spatial And Temporal Variability ◽  
Regional Patterns ◽  
Cloud Albedo

Abstract This study investigates the spatial and temporal variability of cloudiness across mountain zones in the western United States. Daily average cloud albedo is derived from a 19-yr series (1996–2014) of half-hourly Geostationary Operational Environmental Satellite (GOES) images. During springtime when incident radiation is active in driving snowmelt–runoff processes, the magnitude of daily cloud variations can exceed 50% of long-term averages. Even when aggregated over 3-month periods, cloud albedo varies by ±10% of long-term averages in many locations. Rotated empirical orthogonal functions (REOFs) of daily cloud albedo anomalies over high-elevation regions of the western conterminous United States identify distinct regional patterns, wherein the first five REOFs account for ~67% of the total variance. REOF1 is centered over Northern California and Oregon and is pronounced between November and March. REOF2 is centered over the interior northwest and is accentuated between March and July. Each of the REOF/rotated principal components (RPC) modes associates with anomalous large-scale atmospheric circulation patterns and one or more large-scale teleconnection indices (Arctic Oscillation, Niño-3.4, and Pacific–North American), which helps to explain why anomalous cloudiness patterns take on regional spatial scales and contain substantial variability over seasonal time scales.

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