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Michael E. Budde

Publications and source records attributed to Michael E. Budde.

4 recordsLinked to original sources

Phenological adjustment in arctic bird species: relative importance of snow melt and ecological factors

Previous studies have documented advancement in clutch initiation dates (CIDs) in response to climate change, most notably for temperate-breeding passerines. Despite accelerated climate change in the Arctic, few studies have examined nest phenology shifts in arctic breeding species. We investigated whether CIDs have advanced for the most abundant breeding shorebird and passerine species at a long-term monitoring site in arctic Alaska. We pooled data from three additional nearby sites to determine the explanatory power of snow melt and ecological variables (predator abundance, green-up) on changes in breeding phenology. As predicted, all species (semipalmated sandpiper, Calidris pusilla , pectoral sandpiper, Calidris melanotos , red-necked phalarope, Phalaropus lobatus , red phalarope, Phalaropus fulicarius , Lapland longspur, Calcarius lapponicus ) exhibited advanced CIDs ranging from 0.40 to 0.80 days/year over 9 years. Timing of snow melt was the most important variable in explaining clutch initiation advancement (“climate/snow hypothesis”) for four of the five species, while green-up was a much less important explanatory factor. We found no evidence that high predator abundances led to earlier laying dates (“predator/re-nest hypothesis”). Our results support previous arctic studies in that climate change in the cryosphere will have a strong impact on nesting phenology although factors explaining changes in nest phenology are not necessarily uniform across the entire Arctic. Our results suggest some arctic-breeding shorebird and passerine species are altering their breeding phenology to initiate nesting earlier enabling them to, at least temporarily, avoid the negative consequences of a trophic mismatch.

Alaska

Enhancing the Simplified Surface Energy Balance (SSEB) Approach for Estimating Landscape ET: Validation with the METRIC model

Evapotranspiration (ET) can be derived from satellite data using surface energy balance principles. METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) is one of the most widely used models available in the literature to estimate ET from satellite imagery. The Simplified Surface Energy Balance (SSEB) model is much easier and less expensive to implement. The main purpose of this research was to present an enhanced version of the Simplified Surface Energy Balance (SSEB) model and to evaluate its performance using the established METRIC model. In this study, SSEB and METRIC ET fractions were compared using 7 Landsat images acquired for south central Idaho during the 2003 growing season. The enhanced SSEB model compared well with the METRIC model output exhibiting an r2 improvement from 0.83 to 0.90 in less complex topography (elevation less than 2000 m) and with an improvement of r2 from 0.27 to 0.38 in more complex (mountain) areas with elevation greater than 2000 m. Independent evaluation showed that both models exhibited higher variation in complex topographic regions, although more with SSEB than with METRIC. The higher ET fraction variation in the complex mountainous regions highlighted the difficulty of capturing the radiation and heat transfer physics on steep slopes having variable aspect with the simple index model, and the need to conduct more research. However, the temporal consistency of the results suggests that the SSEB model can be used on a wide range of elevation (more successfully up 2000 m) to detect anomalies in space and time for water resources management and monitoring such as for drought early warning systems in data scarce regions. SSEB has a potential for operational agro-hydrologic applications to estimate ET with inputs of surface temperature, NDVI, DEM and reference ET.

Agricultural Water Management

Agriculture and food availability -- remote sensing of agriculture for food security monitoring in the developing world

For one-sixth of the world’s population - roughly 1 billion children, women and men - growing, buying or receiving adequate, affordable food to eat is a daily uncertainty. The World Monetary Fund reports that food prices worldwide increased 43 percent in 2007-2008, and unpredictable growing conditions make subsistence farming, on which many depend, a risky business. Scientists with the U.S. Geological Survey (USGS) are part of a network of both private and government institutions that monitor food security in many of the poorest nations in the world.

Earthzine

Integration of MODIS-derived metrics to assess interannual variability in snowpack, lake ice, and NDVI in southwest Alaska

Impacts of global climate change are expected to result in greater variation in the seasonality of snowpack, lake ice, and vegetation dynamics in southwest Alaska. All have wide-reaching physical and biological ecosystem effects in the region. We used Moderate Resolution Imaging Spectroradiometer (MODIS) calibrated radiance, snow cover extent, and vegetation index products for interpreting interannual variation in the duration and extent of snowpack, lake ice, and vegetation dynamics for southwest Alaska. The approach integrates multiple seasonal metrics across large ecological regions. Throughout the observation period (2001-2007), snow cover duration was stable within ecoregions, with variable start and end dates. The start of the lake ice season lagged the snow season by 2 to 3??months. Within a given lake, freeze-up dates varied in timing and duration, while break-up dates were more consistent. Vegetation phenology varied less than snow and ice metrics, with start-of-season dates comparatively consistent across years. The start of growing season and snow melt were related to one another as they are both temperature dependent. Higher than average temperatures during the El Ni??o winter of 2002-2003 were expressed in anomalous ice and snow season patterns. We are developing a consistent, MODIS-based dataset that will be used to monitor temporal trends of each of these seasonal metrics and to map areas of change for the study area.

Remote Sensing of Environment