Radar remote sensing proposed for monitoring freeze‐thaw transitions in boreal regions

Running, Steven W.; Way, J.; McDonald, K. C.; Kimball, J. S.; Frolking, S.; Keyser, Alisa R.; Zimmerman, R.

doi:10.1029/99eo00158

Cited by 44 publications

(25 citation statements)

References 8 publications

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“…Alterations to surface energy and hydrological budgets that accompany changes in terrestrial snow regimes (e.g., Hinzman and Kane, 1992;Groisman et al, 1994;Kuang and Yung, 2000) may induce, 3076 D. G. DYE over seasonal and annual time scales, changes in plant productivity, soil respiration and net ecosystem CO 2 exchange (Oechel et al 1997;Goulden et al 1998;Hobbie and Chapin, 1998;Running et al, 1999;Black et al, 2000). Indeed, the observed trend toward earlier snow-cover disappearance in spring is consistent with the reported trend toward earlier spring uptake of atmospheric CO 2 by Northern Hemisphere terrestrial ecosystems (Keeling et al, 1996) and with satellite observations of earlier green-up of vegetation in northern high-latitude land areas (Zhou et al, 2001).…”

Section: Discussionmentioning

confidence: 99%

“…Although the potential exists for monitoring the terrestrial snow-cover cycle (or surface freeze-thaw cycle) with other satellite remote sensing systems (e.g. passive and active microwave sensors, new-generation optical sensors), such research has been undertaken only recently Running et al 1999;Hall et al, 2000;Zhang and Armstrong, 2001). As an alternative approach to validation, the relationship between the snow-cover cycle and SCA was examined.…”

Section: Validation Issuesmentioning

confidence: 99%

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Variability and trends in the annual snow‐cover cycle in Northern Hemisphere land areas, 1972–2000

Dye

2002

Hydrological Processes

196

170

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Abstract:This study investigated variability and trends in the annual snow-cover cycle in regions covering high-latitude and high-elevation land areas in the Northern Hemisphere. The annual snow-cover cycle was examined with respect to the week of the last-observed snow cover in spring (WLS), the week of the first-observed snow cover in autumn (WFS), and the duration of the snow-free period (DSF). The analysis used a 29-year time-series of weekly, visible-band satellite observations of Northern Hemisphere snow cover from NOAA with corrections applied by D. Robinson of Rutgers University Climate Laboratory. Substantial interannual variability was observed in WLS, WFS and DSF (standard deviations of 0Ð8-1Ð1, 0Ð7-0Ð9 and 1Ð0-1Ð4 weeks, respectively), which is related directly to interannual variability in snow-cover area in the regions and time periods of snow-cover transition. Over the nearly three-decade study period, WLS shifted earlier by 3-5 days/decade as determined by linear regression analysis. The observed shifts in the annual snow-cover cycle underlie a significant trend toward a longer annual snow-free period. The DSF increased by 5-6 days/decade over the study period, primarily as a result of earlier snow cover disappearance in spring. The observed trends are consistent with reported trends in the timing and length of the active growing season as determined from satellite observations of vegetation greenness and the atmospheric CO 2 record.

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Section: Discussionmentioning

confidence: 99%

Section: Validation Issuesmentioning

confidence: 99%

Variability and trends in the annual snow‐cover cycle in Northern Hemisphere land areas, 1972–2000

Dye

2002

Hydrological Processes

196

170

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“…For example, the length of the annual growing season in interior Alaska has increased from Ϸ130 days to Ϸ145 days since the 1950s (31). This lengthening of the growing season is also apparent in the timing of the spring thaw in sockeye nursery lakes in Bristol Bay (unpublished data).…”

Section: Changes In Freshwater and Ocean Environmentsmentioning

confidence: 94%

Biocomplexity and fisheries sustainability

Hilborn

Quinn

Schindler

et al. 2003

Proc. Natl. Acad. Sci. U.S.A.

795

842

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A classic example of a sustainable fishery is that targeting sockeye salmon in Bristol Bay, Alaska, where record catches have occurred during the last 20 years. The stock complex is an amalgamation of several hundred discrete spawning populations. Structured within lake systems, individual populations display diverse life history characteristics and local adaptations to the variation in spawning and rearing habitats. This biocomplexity has enabled the aggregate of populations to sustain its productivity despite major changes in climatic conditions affecting the freshwater and marine environments during the last century. Different geographic and life history components that were minor producers during one climatic regime have dominated during others, emphasizing that the biocomplexity of fish stocks is critical for maintaining their resilience to environmental change.climate change ͉ resilience ͉ Pacific salmon ͉ endangered species ͉ biodiversity A t a time of growing concern about the sustainability of many of the world's fisheries, several stand out as providing long-term sustainable yield. Among the most prominent successes are the fisheries for sockeye salmon in Bristol Bay, Alaska (Fig. 1), that have seen record returns and catches in the last two decades. This success is due in part to several factors including (i) favorable ocean conditions in recent decades, (ii) a single, accountable management agency, and (iii) a well established program of limited entry to the fishery. However, the biocomplexity of the stock structure has also played an critical role in providing stability and sustainability. Here we provide evidence for the effects of biocomplexity on sustainability and emphasize that conserving biocomplexity within fish stocks is important for maintaining their resilience to future environmental change. The Biodiversity Of Bristol Bay SockeyeHoming of Pacific salmon (Oncorhynchus spp.) to their natal sites results in reproductive isolation of populations, allowing natural selection to operate on heritable phenotypic traits, and the result is a wealth of distinct, locally adapted populations (1, 2). Sockeye salmon (Oncorhynchus nerka), for example, display a wide variety of life history types, each associated predictably with certain breeding and rearing habitats (3). The diversity of phenotypes thus reflects the adaptation of populations to the diversity of suitable habitats. Spawning by salmonid fishes generally takes place in lotic habitats, and Bristol Bay sockeye salmon spawn in streams and rivers ranging from 10 cm to several meters deep, and in substrate ranging from small gravel to cobble (4, 5). Some creeks have spring-fed ponds with much finer substrate and deeper, slowly flowing water, and these too are used for spawning. Sockeye also spawn in groundwater-fed beaches at the outwash areas of rivers and along hillsides with substantial groundwater inputs. In these habitats, sockeye may spawn from the shoreline to depths of several meters. Finally, sockeye may also spawn on the rocky beaches o...

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“…However, climate-related processes of the boreal and arctic regions are poorly observed, partly because of the enormous size and remoteness of the regions, the adverse environmental conditions and the sparse surface weather station network. In Siberia, the density of reporting surface weather and hydrologic stations is of the order of 1 per million km 2 or less (Running et al, 1999). This makes satellite observations a key technology for monitoring subarctic snowpacks.…”

Section: Introductionmentioning

confidence: 99%

Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm

Grippa

Mognard

Toan

et al. 2004

Remote Sensing of Environment

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Radar remote sensing proposed for monitoring freeze‐thaw transitions in boreal regions

Cited by 44 publications

References 8 publications

Variability and trends in the annual snow‐cover cycle in Northern Hemisphere land areas, 1972–2000

Variability and trends in the annual snow‐cover cycle in Northern Hemisphere land areas, 1972–2000

Biocomplexity and fisheries sustainability

Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm

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