Geology topics

Steven W. Running

Publications and source records attributed to Steven W. Running.

9 recordsLinked to original sources

USGS2015

Large divergence of satellite and Earth system model estimates of global terrestrial CO 2 fertilization

Atmospheric mass balance analyses suggest that terrestrial carbon (C) storage is increasing, partially abating the atmospheric [CO 2 ] growth rate , although the continued strength of this important ecosystem service remains uncertain . Some evidence suggests that these increases will persist owing to positive responses of vegetation growth (net primary productivity; NPP) to rising atmospheric [CO 2 ] (that is, ‘CO 2 fertilization ’ ) . Here, we present a new satellite-derived global terrestrial NPP data set , which shows a significant increase in NPP from 1982 to 2011. However, comparison against Earth system model (ESM) NPP estimates reveals a significant divergence, with satellite-derived increases (2.8 ± 1.50%) less than half of ESM-derived increases (7.6   ±   1.67%) over the 30-year period. By isolating the CO 2 fertilization effect in each NPP time series and comparing it against a synthesis of available free-air CO 2 enrichment data , we provide evidence that much of the discrepancy may be due to an over-sensitivity of ESMs to atmospheric [CO 2 ], potentially reflecting an under-representation of climatic feedbacks and/or a lack of representation of nutrient constraints . Our understanding of CO 2 fertilization effects on NPP needs rapid improvement to enable more accurate projections of future C cycle–climate feedbacks; we contend that better integration of modelling, satellite and experimental approaches offers a promising way forward.

Nature Climate Change↗

USGS2014

Agricultural conversion without external water and nutrient inputs reduces terrestrial vegetation productivity

Driven by global population and standard of living increases, humanity co-opts a growing share of the planet's natural resources resulting in many well-known environmental trade-offs. In this study, we explored the impact of agriculture on a resource fundamental to life on Earth: terrestrial vegetation growth (net primary production; NPP). We demonstrate that agricultural conversion has reduced terrestrial NPP by ~7.0%. Increases in NPP due to agricultural conversion were observed only in areas receiving external inputs (i.e., irrigation and/or fertilization). NPP reductions were found for ~88% of agricultural lands, with the largest reductions observed in areas formerly occupied by tropical forests and savannas (~71% and ~66% reductions, respectively). Without policies that explicitly consider the impact of agricultural conversion on primary production, future demand-driven increases in agricultural output will likely continue to drive net declines in global terrestrial productivity, with potential detrimental consequences for net ecosystem carbon storage and subsequent climate warming.

Geophysical Research Letters↗

USGS2012

Bioenergy potential of the United States constrained by satellite observations of existing productivity

United States (U.S.) energy policy includes an expectation that bioenergy will be a substantial future energy source. In particular, the Energy Independence and Security Act of 2007 (EISA) aims to increase annual U.S. biofuel (secondary bioenergy) production by more than 3-fold, from 40 to 136 billion liters ethanol, which implies an even larger increase in biomass demand (primary energy), from roughly 2.9 to 7.4 EJ yr –1 . However, our understanding of many of the factors used to establish such energy targets is far from complete, introducing significgant uncertainty into the feasibility of current estimates of bioenergy potential. Here, we utilized satellite-derived net primary productivity (NPP) data—measured for every 1 km 2 of the 7.2 million km 2 of vegetated land in the conterminous U.S.—to estimate primary bioenergy potential (PBP). Our results indicate that PBP of the conterminous U.S. ranges from roughly 5.9 to 22.2 EJ yr –1 , depending on land use. The low end of this range represents the potential when harvesting residues only, while the high end would require an annual biomass harvest over an area more than three times current U.S. agricultural extent. While EISA energy targets are theoretically achievable, we show that meeting these targets utilizing current technology would require either an 80% displacement of current crop harvest or the conversion of 60% of rangeland productivity. Accordingly, realistically constrained estimates of bioenergy potential are critical for effective incorporation of bioenergy into the national energy portfolio.

Environmental Science & Technology↗

USGS2012

Bioenergy potential of the United States constrained by satellite observations of existing productivity

Background/Question/Methods Currently, the United States (U.S.) supplies roughly half the world’s biofuel (secondary bioenergy), with the Energy Independence and Security Act of 2007 (EISA) stipulating an additional three-fold increase in annual production by 2022. Implicit in such energy targets is an associated increase in annual biomass demand (primary bioenergy) from roughly 2.9 to 7.4 exajoules (EJ; 1018 Joules). Yet, many of the factors used to estimate future bioenergy potential are relatively unresolved, bringing into question the practicality of the EISA’s ambitious bioenergy targets. Here, our objective was to constrain estimates of primary bioenergy potential (PBP) for the conterminous U.S. using satellite-derived net primary productivity (NPP) data (measured for every 1 km2 of the 7.2 million km2 of vegetated land in the conterminous U.S) as the most geographically explicit measure of terrestrial growth capacity. Results/Conclusions We show that the annual primary bioenergy potential (PBP) of the conterminous U.S. realistically ranges from approximately 5.9 (± 1.4) to 22.2 (± 4.4) EJ, depending on land use. The low end of this range represents current harvest residuals, an attractive potential energy source since no additional harvest land is required. In contrast, the high end represents an annual harvest over an additional 5.4 million km2 or 75% of vegetated land in the conterminous U.S. While we identify EISA energy targets as achievable, our results indicate that meeting such targets using current technology would require either an 80% displacement of current croplands or the conversion of 60% of total rangelands. Our results differ from previous evaluations in that we use high resolution, satellite-derived NPP as an upper-envelope constraint on bioenergy potential, which removes the need for extrapolation of plot-level observed yields over large spatial areas. Establishing realistically constrained estimates of bioenergy potential seems a critical next step for effectively incorporating bioenergy into future U.S. energy portfolios.

Conference Paper↗

USGS2005

Assessing climate change effects on mountain ecosystems using integrated models: A case study

Mountain systems are characterized by strong environmental gradients, rugged topography and extreme spatial heterogeneity in ecosystem structure and composition. Consequently, most mountainous areas have relatively high rates of endemism and biodiversity, and function as species refugia in many areas of the world. Mountains have long been recognized as critical entities in regional climatic and hydrological dynamics but their importance as terrestrial carbon stores has only been recently underscored (Schimel et al. 2002; this volume). Mountain ecosystems, therefore, are globally important as well as unusually complex. These ecosystems challenge our ability to understand their dynamics and predict their response to climatic variability and global-scale environmental change. To meet this challenge, mountain scientists increasingly are modeling the vast array of relationships that comprise ecosystem dynamics. Dynamic modeling can examine the interactions between land management strategies and climatic change to develop appropriate responses to future human demands on mountain systems. Modeling provides spatially and temporally explicit, quantified results that can be validated in the field, thus providing feedback to our understanding of ecosystem dynamics. Modeling results, particularly maps and other visual tools, also give a concrete dimension to our understanding of the scale and magnitude of potential future changes. Modeling alerts scientists and land managers to apparently counter-intuitive outcomes of ecosystem responses to climate change or management decisions. For instance, in an early modeling exercise for northwest Montana, USA, Running and Nemani (1991) found that streamflow in a warmer future climate decreased by 30% in the Swan Range even when precipitation was increased by 10% in a particular climate change scenario. This unexpected response was due to enhanced forest growth, and increased evapotranspiration, resulting from the earlier snowmelt and extended growing season. There is a rich legacy of models that address climate and weather, hydrology, forest growth (e.g. gap dynamics and succession), forest fires (e.g. fuel loading) and land cover change (cf. Bugmann et al., this volume). Much less common, however, are attempts to fully integrate models from various disciplines to create a robust system that adequately addresses the entire range of ecosystem dynamics. In addition, fine-resolution modeling of entire mountain ranges (i.e. regional ecosystem scale) is not as common as global or continental scale modeling or watershed/catchment scale modeling. However, this is the scale that is germane to policy decisions such as in the western US and Canada, i.e. in those areas that contain most of the mountainous terrain of North America. This paper describes our efforts to implement an integrated regional modeling approach while characterizing potential future responses of a mountain ecosystem to climate change. Our study area was Glacier National Park in northwestern Montana, USA. Glacier Park is a 4082 km” mountain wilderness that straddles the continental divide and contains over 150 summits of up to 3150 m elevation in the Lewis and Livingston mountain ranges.

Book chapter↗

USGS2002

Fuzzy logic merger of spectral and ecological information for improved montane forest mapping.

Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.

Montana↗

USGS1998

Assessing simulated ecosystem processes for climate variability research at Glacier National Park, USA

Glacier National Park served as a test site for ecosystem analyses that involved a suite of integrated models embedded within a geographic information system. The goal of the exercise was to provide managers with maps that could illustrate probable shifts in vegetation, net primary production (NPP), and hydrologic responses associated with two selected climatic scenarios. The climatic scenarios were (a) a recent 12-yr record of weather data, and (b) a reconstituted set that sequentially introduced in repeated 3-yr intervals wetter–cooler, drier–warmer, and typical conditions. To extrapolate the implications of changes in ecosystem processes and resulting growth and distribution of vegetation and snowpack, the model incorporated geographic data. With underlying digital elevation maps, soil depth and texture, extrapolated climate, and current information on vegetation types and satellite-derived estimates of leaf area indices, simulations were extended to envision how the park might look after 120 yr. The predictions of change included underlying processes affecting the availability of water and nitrogen. Considerable field data were acquired to compare with model predictions under current climatic conditions. In general, the integrated landscape models of ecosystem processes had good agreement with measured NPP, snowpack, and streamflow, but the exercise revealed the difficulty and necessity of averaging point measurements across landscapes to achieve comparable results with modeled values. Under the extremely variable climate scenario significant changes in vegetation composition and growth as well as hydrologic responses were predicted across the park. In particular, a general rise in both the upper and lower limits of treeline was predicted. These shifts would probably occur along with a variety of disturbances (fire, insect, and disease outbreaks) as predictions of physiological stress (water, nutrients, light) altered competitive relations and hydrologic responses. The use of integrated landscape models applied in this exercise should provide managers with insights into the underlying processes important in maintaining community structure, and at the same time, locate where changes on the landscape are most likely to occur.

Montana↗

USGS1995

A remote sensing based vegetation classification logic for global land cover analysis

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

Remote Sensing of Environment↗

USGS1994

A vegetation classification logic-based on remote-sensing for use in global biogeochemical models

A simple new classification logic for global vegetation is proposed. The critical features of this classification are that: it is based on simple, observable, unambiguous character- istics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation; the structural characteristics can be determined by remote sensing, so that repeatable and efficient global re-classifications of existing vegetation will be possible; and the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km Normalized Difference Vegetation Index database. Currently recognized global biome classes can easily be derived from this classification by adding climate descriptors and defining mixtures of these fundamental six vegetation classes.

Ambio↗