Search USGS⌕ Search

Geology topics

David C. Douglas

Publications and source records attributed to David C. Douglas.

At least 127 records · Page 7Linked to original sources
USGS2005

Variations in the Arctic's multiyear sea ice cover: A neural network analysis of SMMR-SSM/I data, 1979-2004

A 26-year (1979-2004) observational record of January multiyear sea ice distributions, derived from neural network analysis of SMMR-SSM/I passive microwave satellite data, reveals dense and persistent cover in the central Arctic basin surrounded by expansive regions of highly fluctuating interannual cover. Following a decade of quasi equilibrium, precipitous declines in multiyear ice area commenced in 1989 when the Arctic Oscillation shifted to a pronounced positive phase. Although extensive survival of first-year ice during autumn 1996 fully replenished the area of multiyear ice, a subsequent and accelerated decline returned the depletion to record lows. The most dramatic multiyear sea ice declines occurred in the East Siberian, Chukchi, and Beaufort Seas.

Geophysical Research Letters↗
USGS2005

Polar climate: Arctic sea ice

Recent decreases in snow and sea ice cover in the high northern latitudes are among the most notable indicators of climate change. Northern Hemisphere sea ice extent for the year as a whole was the third lowest on record dating back to 1973, behind 1995 (lowest) and 1990 (second lowest; Hadley Center–NCEP). September sea ice extent, which is at the end of the summer melt season and is typically the month with the lowest sea ice extent of the year, has decreased by about 19% since the late 1970s (Fig. 5.2), with a record minimum observed in 2002 (Serreze et al. 2003). A record low extent also occurred in spring (Chapman 2005, personal communication), and 2004 marked the third consecutive year of anomalously extreme sea ice retreat in the Arctic (Stroeve et al. 2005). Some model simulations indicate that ice-free summers will occur in the Arctic by the year 2070 (ACIA 2004).

Arctic↗
USGS2005

Correlated declines in Pacific arctic snow and sea ice cover

Simulations of future climate suggest that global warming will reduce Arctic snow and ice cover, resulting in decreased surface albedo (reflectivity). Lowering of the surface albedo leads to further warming by increasing solar absorption at the surface. This phenomenon is referred to as “temperature–albedo feedback.” Anticipation of such a feedback is one reason why scientists look to the Arctic for early indications of global warming. Much of the Arctic has warmed significantly. Northern Hemisphere snow cover has decreased, and sea ice has diminished in area and thickness. As reported in the Arctic Climate Impact Assessment in 2004, the trends are considered to be outside the range of natural variability, implicating global warming as an underlying cause. Changing climatic conditions in the high northern latitudes have influenced biogeochemical cycles on a broad scale. Warming has already affected the sea ice, the tundra, the plants, the animals, and the indigenous populations that depend on them. Changing annual cycles of snow and sea ice also affect sources and sinks of important greenhouse gases (such as carbon dioxide and methane), further complicating feedbacks involving the global budgets of these important constituents. For instance, thawing permafrost increases the extent of tundra wetlands and lakes, releasing greater amounts of methane into the atmosphere. Variable sea ice cover may affect the hemispheric carbon budget by altering the ocean–atmosphere exchange of carbon dioxide. There is growing concern that amplification of global warming in the Arctic will have far-reaching effects on lower latitude climate through these feedback mechanisms. Despite the diverse and convincing observational evidence that the Arctic environment is changing, it remains unclear whether these changes are anthropogenically forced or result from natural variations of the climate system. A better understanding of what controls the seasonal distributions of snow and ice is fundamental to the problem.

Arctic Research of the United States↗
USGS2004

Spatial and temporal multiyear sea ice distributions in the Arctic: A neural network analysis of SSM/I data, 1988-2001

Arctic multiyear sea ice concentration maps for January 1988-2001 were generated from SSM/I brightness temperatures (19H, 19V, and 37V) using modified multiple layer perceptron neural networks. Learning data for the neural networks were extracted from ice maps derived from Okean and ERS satellite imagery to capitalize on the stability of active radar multiyear ice signatures. Evaluations of three learning algorithms and several topologies indicated that networks constructed with error back propagation learning and 3-20-1 topology produced the most consistent and physically plausible results. Operational neural networks were developed specifically with January learning data, and then used to estimate daily multiyear ice concentrations from daily-averaged SSM/I brightness temperatures during January. Monthly mean maps were produced for analysis by averaging the respective daily estimates. The 14-year series of January multiyear ice distributions revealed dense and persistent cover in the central Arctic surrounded by expansive regions of highly fluctuating interannual cover. Estimates of total multiyear ice area by the neural network were intermediate to those of other passive microwave algorithms, but annual fluctuations and trends were similar among all algorithms. When compared to Radarsat estimates of multiyear ice concentration in the Beaufort and Chukchi Seas (1997-1999), average discrepancies were small (0.9-2.5%) and spatial coherency was reasonable, indicating the neural network's Okean and ERS learning data facilitated passive microwave inversion that emulated backscatter signatures. During 1988-2001, total January multiyear ice area declined at a significant linear rate of -54.3 x 10 3 km 2 /yr -1 (-1.4%/yr -1 ). The most persistent and extensive decline in multiyear ice concentration (-3.3%/yr -1 ) occurred in the southern Beaufort and Chukchi Seas. In autumn 1996, a large multiyear ice recruitment of over 106 km 2 (mostly in the Siberian Arctic) fully replenished the previous 8-year decline in total area, but it was followed by an accelerated and compensatory decline during the subsequent 4 years. Seventy-five percent of the interannual variation in January multiyear sea ice area was explained by linear regression on two atmospheric parameters: the previous inter's (JFM) Arctic Oscillation index as a proxy to melt duration and the previous year's average sea level pressure gradient across the Fram Strait as a proxy to annual ice export. Consecutive year changes (1994-2001) in January multiyear ice volume were significantly correlated with duration of the intervening melt season (R 2 = 0.73, -80.0 km 3 /d -1 ), emphasizing a large thermodynamic influence on the Arctic's mass sea ice balance during summers with anomalous melt durations.

Journal of Geophysical Research↗
USGS2004

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations. The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.

Remote Sensing of Environment↗
USGS2004

Estimating the time of melt onset and freeze onset over Arctic sea-ice area using active and passive microwave data

Accurate calculation of the time of melt onset, freeze onset, and melt duration over Arctic sea-ice area is crucial for climate and global change studies because it affects accuracy of surface energy balance estimates. This comparative study evaluates several methods used to estimate sea-ice melt and freeze onset dates: (1) the melt onset database derived from SSM/I passive microwave brightness temperatures ( T b s) using Drobot and Anderson's [J. Geophys. Res. 106 (2001) 24033] Advanced Horizontal Range Algorithm (AHRA) and distributed by the National Snow and Ice Data Center (NSIDC); (2) the International Arctic Buoy Program/Polar Exchange at the Sea (IABP/POLES) surface air temperatures (SATs); (3) an elaborated version of the AHRA that uses IABP/POLES to avoid anomalous results (Passive Microwave and Surface Temperature Analysis [PMSTA]); (4) another elaborated version of the AHRA that uses T b variance to avoid anomalous results (Mean Differences and Standard Deviation Analysis [MDSDA]); (5) Smith's [J. Geophys. Res. 103 (1998) 27753] vertically polarized T b algorithm for estimating melt onset in multiyear (MY) ice (SSM/I 19V–37V); and (6) analyses of concurrent backscattering cross section ( σ °) and brightness temperature ( T b ) from OKEAN-01 satellite series. Melt onset and freeze onset maps were created and compared to understand how the estimates vary between different satellite instruments and methods over different Arctic sea-ice regions. Comparisons were made to evaluate relative sensitivities among the methods to slight adjustments of the T b calibration coefficients and algorithm threshold values. Compared to the PMSTA method, the AHRA method tended to estimate significantly earlier melt dates, likely caused by the AHRA's susceptibility to prematurely identify melt onset conditions. In contrast, the IABP/POLES surface air temperature data tended to estimate later melt and earlier freeze in all but perennial ice. The MDSDA method was least sensitive to small adjustments of the SMMR–SSM/I inter-satellite calibration coefficients. Differences among methods varied by latitude. Freeze onset dates among methods were most disparate in southern latitudes, and tended to converge northward. Surface air temperatures (IABP/POLES) indicated freeze onset well before the MDSDA method, especially in southern peripheral seas, while PMSTA freeze estimates were generally intermediate. Surface air temperature data estimated latest melt onset dates in southern latitudes, but earliest melt onset in northern latitudes. The PMSTA estimated earliest melt onset dates in southern regions, and converged with the MDSDA northward. Because sea-ice melt and freeze are dynamical transitional processes, differences among these methods are associated with differing sensitivities to changing stages of environmental and physical development. These studies contribute to the growing body of documentation about the levels of disparity obtained when Arctic seasonal transition parameters are estimated using various types of microwave data and algorithms.

Remote Sensing of Environment↗
USGS2004

Winter ecology of Spectacled Eiders: Environmental characteristics and population change

We described characteristics of the wintering area used by Spectacled Eiders ( Somateria fischeri ) in the Bering Sea, Alaska, and evaluated these characteristics in relation to long-term population trends. Remoteness, limited daylight, and extreme weather conditions precluded direct observations, so we derived the location of the wintering area from satellite telemetry, ice conditions from remotely sensed data, weather conditions from archived data sets, and benthic communities from the literature. Based on analyses of two indices spanning 1957-2002 and 1988-2002, we identified no single environmental parameter that explained the precipitous decline in nesting populations in western Alaska. In general, we found that the number of days with extreme sea ice in winter, extreme winds, and winds in spring explained the greatest variability in annual indices. These analyses support the conclusion that annual population estimates on the breeding grounds can be negatively impacted by extended periods of dense sea-ice concentration and weather during the previous winter. Examination of population indices did not support the hypothesis that changes in benthic community on the wintering grounds have contributed to the decline or inhibited the recovery of the Spectacled Eider breeding population in western Alaska.

Alaska↗
USGS2004

Duration of the Arctic sea ice melt season: Regional and interannual variability, 1979-2001

Melt onset dates, freeze onset dates, and melt season duration were estimated over Arctic sea ice, 1979–2001, using passive microwave satellite imagery and surface air temperature data. Sea ice melt duration for the entire Northern Hemisphere varied from a 104-day minimum in 1983 and 1996 to a 124-day maximum in 1989. Ranges in melt duration were highest in peripheral seas, numbering 32, 42, 44, and 51 days in the Laptev, Barents-Kara, East Siberian, and Chukchi Seas, respectively. In the Arctic Ocean, average melt duration varied from a 75-day minimum in 1987 to a 103-day maximum in 1989. On average, melt onset in annual ice began 10.6 days earlier than perennial ice, and freeze onset in perennial ice commenced 18.4 days earlier than annual ice. Average annual melt dates, freeze dates, and melt durations in annual ice were significantly correlated with seasonal strength of the Arctic Oscillation (AO). Following high-index AO winters (January–March), spring melt tended to be earlier and autumn freeze later, leading to longer melt season durations. The largest increases in melt duration were observed in the eastern Siberian Arctic, coincident with cyclonic low pressure and ice motion anomalies associated with high-index AO phases. Following a positive AO shift in 1989, mean annual melt duration increased 2–3 weeks in the northern East Siberian and Chukchi Seas. Decreasing correlations between consecutive-year maps of melt onset in annual ice during 1979–2001 indicated increasing spatial variability and unpredictability in melt distributions from one year to the next. Despite recent declines in the winter AO index, recent melt distributions did not show evidence of reestablishing spatial patterns similar to those observed during the 1979–88 low-index AO period. Recent freeze distributions have become increasingly similar to those observed during 1979–88, suggesting a recurrent spatial pattern of freeze chronology under low-index AO conditions.

Journal of Climate↗
USGS2004

Diminishing sea ice in the western Arctic Ocean

Since the advent of satellite passive microwave radiometry (1978), variations in sea ice extent and concentration have been carefully monitored from space. An estimated 7.4% decrease in sea ice extent has occurred in the last 25 yr (Johannessen et al. 2004), with recent record minima (e.g., Maslanik et al. 1999; Serreze et al. 2003) accounting for much of the decline. Comparisons between the time series of Arctic sea ice melt dynamics and snowmelt dates at the NOAA–CMDL Barrow Observatory (BRW) reveal intriguing correlations. Melt-onset dates over sea ice (Drobot and Anderson 2001) were cross correlated with the melt-date time series from BRW, and a prominent region of high correlation between snowmelt onset over sea ice and the BRW record of melt dates was approximately aligned with the climatological center of the Beaufort Sea Anticyclone (BSA). The BSA induces anticyclonic ice motion in the region, effectively forcing the Beaufort gyre. A weak gyre caused by a breakdown of the BSA diminishes transport of multiyear ice into this region (Drobot and Maslanik 2003). Similarly, the annual snow cycle at BRW varies with the position and intensity of the BSA (Stone et al. 2002, their Fig. 6). Thus, variations in the BSA appear to have far-reaching effects on the annual accumulation and subsequent melt of snow over a large region of the western Arctic. A dramatic increase in melt season duration (Belchansky et al. 2004) was also observed within the same region of high correlation between onset of melt over the ice pack and snowmelt at BRW (Fig. 5.7). By inference, this suggests linkages between factors that modulate the annual cycle of snow on land and processes that influence melting of snow and ice in the western Arctic Ocean.

Bulletin of the American Meteorological Society↗
USGS2003

Long-term change in eelgrass distribution at Bahía San Quintín, Baja California, Mexico, using satellite imagery

Seagrasses are critically important components of many marine coastal and estuarine ecosystems, but are declining worldwide. Spatial change in distribution of eelgrass, Zostera marina L., was assessed at Bahía San Quintín, Baja California, Mexico, using a map to map comparison of data interpreted from a 1987 Satellite Pour l'Observation de la Terre multispectral satellite image and a 2000 Landsat Enhanced Thematic Mapping image. Eelgrass comprised 49% and 43% of the areal extent of the bay in 1987 and 2000, respectively. Spatial extent of eelgrass was 13% less (-321 ha) in 2000 than in 1987 with most losses occurring in subtidal areas. Over the 13-yr study period, there was a 34% loss of submerged eelgrass (-457 ha) and a 13% (+136 ha) gain of intertidal eelgrass. Within the two types of intertidal eelgrass, the patchy cover class (<85% cover) expanded (+250 ha) and continuous cover class (≥85% cover) declined (-114 ha). Most eelgrass losses were likely the result of sediment loading and turbidity caused by a single flooding event in winter of 1992-1993. Recent large-scale agricultural development of adjacent uplands may have exacerbated the effects of the flood. Oyster farming was not associated with any detectable losses in eelgrass spatial extent, despite the increase in number of oyster racks from 57 to 484 over the study period.

Baja California↗
USGS2003

Variable migratory patterns of different adult rainbow trout life history types in a southwest Alaska watershed

Radiotelemetry was used to document population structure in adult rainbow trout Oncorhynchus mykiss from the Alagnak River, southwest Alaska. Rainbow trout (N = 134) longer than 440 mm were implanted with radio transmitters and tracked for varying periods from July 1997 to April 1999. Fifty-eight radio-tagged fish were tracked for sufficient duration (at least 11 months) to allow description of seasonal migratory patterns. Unique seasonal movements of fish suggested discrete, within-basin population structure. Telemetry data documented the existence of multiple migratory and nonmigratory groups of rainbow trout, indicating unique life history patterns. The observed groups consisted of what we defined as a lake-resident ecotype, a lake-river ecotype, and a riverine ecotype; the riverive ecotype demonstrated both highly migratory and nonmigratory movement behavior. Considerable variation in movement patterns was found within both the lake-river group and the river migratory group. Radio-tagged trout did not migrate between the two Alagnak watershed lakes in either year of the study, suggesting lake fidelity in the population structure. Alagnak River rainbow trout may have evolved the observed seasonal movement patterns to optimize winter thermal refugia and summer food availability of salmon eggs and carcasses.

Alaska↗
USGS2002

Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.<9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap concentrations were no more than 3.1% (S.D.<5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total ice concentrations than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total ice concentrations than the NASA Team algorithm. Total ice concentrations derived from OKEAN-01 and SSM/I satellite imagery were highly correlated during winter, spring, and fall, with mean differences of less than 8.1% (S.D.<15%) for the NASA Team algorithm, and less than 2.8% (S.D.<13.8%) for the Bootstrap algorithm. Respective differences between SSM/I NASA Team and SSM/I Bootstrap total concentrations were less than 5.3% (S.D.<6.9%). Monthly mean differences between SSM/I and OKEAN differed annually by less than 6%, with smaller differences primarily in winter. The NASA Team and Bootstrap algorithms underestimated the total sea ice concentrations relative to the RADARSAT ScanSAR no more than 3.0% (S.D.<9%) and 1.2% (S.D.<7.5%) during cold months, and no more than 12% and 7% during summer, respectively. ScanSAR tended to estimate higher ice concentrations for ice concentrations greater than 50%, when compared to SSM/I during all months. ScanSAR underestimated total sea ice concentration by 2% compared to the OKEAN-01 algorithm during cold months, and gave an overestimation by 2% during spring and summer months. Total NASA Team and Bootstrap sea ice concentration estimates derived from coincident SSM/I and OKEAN-01 data demonstrated mean differences of no more than 5.3% (S.D.<7%), 3.1% (S.D.<5.5%), 2.0% (S.D.<5.5%), and 7.3% (S.D.<10%) for fall, winter, spring, and summer periods, respectively. Large disagreements were observed between the OKEAN and NASA Team results in spring and summer for estimates of the first-year (FY) and multiyear (MY) age classes. The OKEAN-01 algorithm and data tended to estimate, on average, lower concentrations of young or FY ice and higher concentrations of total and MY ice for all months and seasons. Our results contribute to the growing body of documentation about the levels of disparity obtained when seasonal sea ice concentrations are estimated using various types of satellite data and algorithms.

Remote Sensing of Environment↗
USGS2002

Use of satellite telemetry to identify common loon migration routes, staging areas and wintering range

We developed a satellite transmitter attachment technique for adult Common Loons ( Gavia immer ) that would help in identifying important migration routes, staging areas, and the location of wintering grounds of birds that breed in the north central United States. During the autumn and winter of 1998, the migration of six adult loons that were radio marked in northern Wisconsin and Minnesota was monitored. The results of this work offer insight into autumn movement patterns of Common Loons. Timing of autumn staging and migration to wintering grounds appeared to be related to low pressure systems that delivered winter weather to the Upper Midwest. Most of the radiomarked birds staged on the Great Lakes and then followed one of two distinct migration routes to the Gulf of Mexico and Atlantic coasts. Several of the birds used lakes and reservoirs in the southeastern United States during migration. This study provides a basis for more extensive studies of Common Loon migration.

Waterbirds↗
USGS2002

Land cover

Documenting the distribution of land-cover types on the Arctic National Wildlife Refuge coastal plain is the foundation for impact assessment and mitigation of potential oil exploration and development. Vegetation maps facilitate wildlife studies by allowing biologists to quantify the availability of important wildlife habitats, investigate the relationships between animal locations and the distribution or juxtaposition of habitat types, and assess or extrapolate habitat characteristics across regional areas. To meet the needs of refuge managers and biologists, satellite imagery was chosen as the most cost-effective method for mapping the large, remote landscape of the 1002 Area. Objectives of our study were the following: 1) evaluate a vegetation classification scheme for use in mapping. 2) determine optimal methods for producing a satellite-based vegetation map that adequately met the needs of the wildlife research and management objectives; 3) produce a digital vegetation map for the Arctic Refuge coastal plain using Lands at-Thematic Mapper(TM) satellite imagery, existing geobotanical classifications, ground data, and aerial photographs, and 4) perform an accuracy assessment of the map.

Alaska, Northwest Territories, Yukon Territory↗
USGS2002

The porcupine caribou herd

Documentation of the natural range of variation in ecological, life history, and physiological characteristics of caribou ( Rangifer tarandus ) of the Porcupine caribou herd is a necessary base for detecting or predicting any potential effects of industrial development on the performance (e.g., distribution, demography, weight-gain of individuals) of the herd. To demonstrate an effect of development, post-development performance must differ from pre-development performance while accounting for any natural environmental trends. We had 2 working hypotheses for our investigations: 1) performance of the Porcupine caribou herd was associated with environmental patterns and habitat quality, and 2) access to important habitats was a key influence on demography. We sought to document the range of natural variation in habitat conditions, herd size, demography (defined here as survival and reproduction), sources and magnitude of mortality, distribution, habitat use, and weight gain and loss, and to develop an understanding of the interactions among these characteristics of the herd. In addition, we investigated ways that we could use this background information, combined with auxiliary information from the adjacent Central Arctic caribou herd, to predict the direction and magnitude of any potential effects of industrial oil development in the 1002 Area of the Arctic National Wildlife Refuge on Porcupine caribou herd calf survival on the herd's calving grounds during June.

Alaska, Northwest Territories, Yukon Territory↗