Predicting Forest Microclimate in Heterogeneous Landscapes (original) (raw)
Related papers
Methods in Ecology and Evolution, 2014
1. The microclimate experienced by organisms is determined by local weather conditions. Yet the environmental data available for predicting the effect of climate on the distribution and abundance of organisms are typically in the form of long-term average monthly climate measured at standardized heights above the ground. 2. Here, we demonstrate how hourly microclimates can be modelled mechanistically over decades at the continental scale with biologically suitable accuracy. 3. We extend the microclimate model of the software package NICHE MAPPER to capture spatial and temporal variation in soil thermal properties and integrate it with gridded soil and weather data for Australia at 0Á05°r esolution. 4. When tested against historical observations of soil temperature, the microclimate model predicted 85% of the variation in hourly soil temperature across 10 years from the surface to 1 m deep with an accuracy of 2-3Á3°C (c. 10% of the temperature range at a given depth) across an extremely climatically diverse range of sites. 5. This capacity to accurately and mechanistically predict hourly local microclimates across continental scales creates new opportunities for understanding how organisms respond to changes in climate.
Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter?
Frontiers of Biogeography
The urgency of predicting future impacts of environmental change on vulnerable populations is advancing the development of spatially explicit habitat models. Continental-scale climate and microclimate layers are now widely available. However, most terrestrial organisms exist within microclimate spaces that are very small, relative to the spatial resolution of those layers. We examined the effects of multi-resolution, multi-extent topographic and climate inputs on the accuracy of hourly soil temperature predictions for a small island, generated at a very high spatial resolution (<1 m 2 ) using the mechanistic microclimate model in NicheMapR. Achieving an accuracy comparable to lower-resolution, continentalscale microclimate layers (within about 2-3°C of observed values) required the use of daily weather data as well as high resolution topographic layers (elevation, slope, aspect, horizon angles), while inclusion of site-specific soil properties did not markedly improve predictions. Our results suggest that large-extent microclimate layers may not provide accurate estimates of microclimate conditions when the spatial extent of a habitat or other area of interest is similar to or smaller than the spatial resolution of the layers themselves. Thus, effort in sourcing model inputs should be focused on obtaining high resolution terrain data, e.g., via LiDAR or photogrammetry, and local weather information rather than in situ sampling of microclimate characteristics.
Agricultural and Forest Meteorology, 2010
Temperature exerts a fundamental control on ecosystem function, species' distributions and ecological processes across a range of spatial scales. At the landscape scale, near-surface air temperature may vary spatially over short distances, particularly in mountainous regions. Both the magnitude and spatial pattern of surface temperature may vary on diurnal, seasonal and inter-annual timescales. Furthermore, temperatures measured at the surface of vegetation, influenced by the energy balance of the surface, can differ considerably from air temperature. In order to explore spatial patterns in temperature across the Moor House sector of the Moor House-Upper Teesdale National Nature Reserve (NNR), Northern Pennines, UK, we derived an empirical linear regression model to predict air temperature at 1 m height as a function of landscape metrics derived from a digital elevation model (DTM), and coupled this to an existing physical land-surface model (JULES) in order to predict and map thermal climate at the vegetation surface across the study area. Spatial patterns in temperature associated with altitudinal lapse rate, katabatic flow and a local fö hn effect were incorporated into the regression model. JULES was driven using spatially distributed air temperatures from the empirical model, along with distributed solar and long-wave radiation flux estimates adjusted for surface slope and aspect, and sky-view in order to model the surface energy balance and predict thermal climate at the vegetation surface (skin temperature). Aggregate properties such as annual degree days above 5 8C (GDD5), number of ''frost days'' when the temperature fell below 0 8C (FD0) and number of ''severe frost days'' when the minimum temperature fell below À5 8C (FDÀ5) were mapped across the reserve for the years 1994-2006. Spatial mapping of surface temperature revealed differences in the 12-year average spatial pattern between GDD5, FD0 and FDÀ5, and differences in the spatial patterns of FD0 and FDÀ5 between different years, depending on the relative strength of lapse rates, temperature inversions and the fö hn effect. The location of ''warm'' and ''cool'' microclimates within the study area varies depending on the dominant atmospheric conditions in a given year and on the thermal property of interest. While GDD5 tended to decrease and FD0 increased with increasing altitude in all years, following the gradients in average temperature, the magnitude of these relationships varied considerably between years. FDÀ5 increased in some years and decreased in others, due to the influence of temperature inversions during extreme cold temperature events. We conclude, that in order to predict the landscapescale response of species and communities to climatic change in upland areas accurately, it will be necessary to take into account changes in the frequency and magnitude of different synoptic atmospheric conditions under future climate scenarios. ß
Forest Ecology and Management, 2010
In the western United States, mechanical thinning and prescribed fire are common forest management practices aimed at reducing potential wildfire severity and restoring historic forest structure, yet their effects on forest microclimate conditions are not well understood. We collected microclimate data between 1998 and 2003 in a mixed-conifer forest in California's Sierra Nevada. Air and soil temperatures, relative humidity, photosynthetically active radiation (PAR), wind speed, soil heat flux, and soil volumetric moisture were measured at the center of 18 four-ha plots. Each plot was assigned one of six combinations of thinning and burning treatments, and each treatment was thus given three replications. We found that spatial variability in microclimate, quantified as standard deviations among monthly values of each microclimatic variable across different locations (n≤18), was significantly high and was influenced primarily by elevation and canopy cover. The combination of thinning and burning treatments increased air temperature from 58.1% to 123.6%. Soil temperatures increased in all thinned plots. Air moisture variables indicated that treatments made air drier, but soil moisture increased in the range 7.9–39.8%, regardless of treatment type. PAR increased in the range 50.4–254.8%, depending on treatment type. Treatments combining thinning and burning increased wind speed by 15.3–194.3%. Although soil heat flux increased dramatically in magnitude in some plots, overall treatment effects on G were not statistically significant. We discussed the significance and implications of the spatial variability of microclimate and the treatment effects to various ecological processes and to forest management.
Spatial models reveal the microclimatic buffering capacity of old-growth forests
Science advances, 2016
Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decr...
Microclima: An r package for modelling meso‐ and microclimate
Methods in Ecology and Evolution, 2018
Climate is of fundamental importance to the ecology and evolution of all organisms. However, studies of climate–organism interactions usually rely on climate variables interpolated from widely spaced measurements or modelled at coarse resolution, whereas the conditions experienced by many organisms vary over scales from millimetres to metres. To help bridge this mismatch in scale, we present models of the mechanistic processes that govern fine‐scale variation in near‐ground air temperature. The models are flexible (enabling application to a wide variety of locations and contexts), can be run using freely available data and are provided as an R package. We apply a mesoclimate model to the Lizard Peninsula in Cornwall to provide hourly estimates of air temperature at resolution of 100 m for the period Jan‐Dec 2010. A microclimate model is then applied to a 1 km2 region of the Lizard Peninsula, Caerthillean Valley (49.969°N, 5.215°W), to provide hourly estimates of near‐ground air temp...
2009
Current predictions of how species will respond to climate change are based on coarse-grained climate surfaces or idealized scenarios of uniform warming. These predictions may erroneously estimate the risk of extinction because they neglect to consider spatially heterogenous warming at the landscape scale or identify refugia where species can persist despite unfavourable regional climate. To address this issue, we investigated the heterogeneity in warming that has occurred in a 10 km × 10 km area from 1972 to 2007. We developed estimates by combining long-term daily observations from a limited number of weather stations with a more spatially comprehensive dataset (40 sites) obtained during 2005-2006. We found that the spatial distribution of warming was greater inland, at lower elevations, away from streams, and at sites exposed to the northwest (NW). These differences corresponded with changes in weather patterns, such as an increasing frequency of hot, dry NW winds. As plant species were biased in the topographic and geographic locations they occupied, these differences meant that some species experienced more warming than others, and are at greater risk from climate change. This species bias could not be detected at coarser scales. The uneven seasonal nature of warming (e.g. more warming in winter, minimums increased more than maximums) means that climate change predictions will vary according to which predictors are selected in species distribution models. Models based on a limited set of predictors will produce erroneous predictions when the correct limiting factor is not selected, and this is difficult to avoid when temperature predictors are correlated because they are produced using elevation-sensitive interpolations. The results reinforce the importance of downscaling coarse-grained (∼50 km) temperature surfaces, and suggest that the accuracy of this process could be improved by considering regional weather patterns (wind speed, direction, humidity) and topographic exposure to key wind directions.
Forest Ecology and Management, 2014
A major aim of sustainable forest management is the maintenance or recolonisation of harvested areas by species that were present pre-disturbance. Forest influence (a type of edge effect that focuses on the effect of mature forests on adjacent disturbed forest) is considered to be an important factor that contributes to the ability of mature forest species to re-colonise disturbed areas. Forest influence occurs in two main ways by: (1) by providing a source of propagules or individuals for recolonisation; and (2) by its influence on the biotic and abiotic conditions of the disturbed forest. This study focuses on forest influence's impact on microclimate conditions of adjacent disturbed areas regenerating after harvesting. In particular, the study investigates whether microclimate within a regenerating forest changes with increasing distance from a mature forest edge, and whether the magnitude of microclimatic change varies over diurnal, seasonal and successional time scales.
2004
Kittel, TGF, NA Rosenbloom, JA Royle, C Daly, WP Gibson, HH Fisher, P Thornton, D Yates, S Aulenbach, C Kaufman, R McKeown, D Bachelet, DS Schimel, and VEMAP2 Participants. 2004. The VEMAP Phase 2 bioclimatic database I: A gridded historical (20th century) climate dataset for modeling ecosystem dynamics across the conterminous United States. Climate Research 27:151-170 -------------- Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous United States as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5º latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables included are: precipitation, minimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed a series of techniques that rely on geostatistical and physical relationships to create the temporally and spatially-complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to interpolate these monthly station data to the grid. We implemented a stochastic weather generator (modified WGEN) to disaggregate the resulting gridded monthly series to dailies. Solar radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100-yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. We found statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion model input datasets are available online at data archive centers. Key words: Climate data, Climate variability and climate change, Ecosystem and vegetation dynamics, Ecological modelling, VEMAP, United States, Geostatistics, Palmer Drought Severity Index"