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M.R. Clark

Publications and source records attributed to M.R. Clark.

6 recordsLinked to original sources
USGS2016

10,000 m under the sea: An overview of the HADES expedition to Kermadec Trench

The hadal zone of the world oceans (6000– 11,000 m) occupies <1% of the marine realm and is found almost exclusively in trenches but represents ~40% of the total ocean depth range. Jamison et al. (2010 & Jamison, 2015) have reviewed the current state of knowledge about the hydrology, physical characteristics, food supply, ecology and biodiversity of life in hadal trenches. This review concluded that, there appears to be a high level of endemism based on the few specimens collected from historical sampling efforts in the 1950s (Danish Galathea and Soviet Vitjaz expeditions), but because trenches are still largely unexplored there is a lot we do not know about the ecological structure and functioning of hadal environments. However, relatively recent advances in technology using remotely operated vehicles (ROV) and landers can help us explore hadal trenches in greater detail.

Conference Paper↗
USGS2011

Spatial interpolation schemes of daily precipitation for hydrologic modeling

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs.

Colorado, Georgia↗
USGS2011

Soil carbon distribution in Alaska in relation to soil-forming factors

The direction and magnitude of soil organic carbon (SOC) changes in response to climate change remain unclear and depend on the spatial distribution of SOC across landscapes. Uncertainties regarding the fate of SOC are greater in high-latitude systems where data are sparse and the soils are affected by sub-zero temperatures. To address these issues in Alaska, a first-order assessment of data gaps and spatial distributions of SOC was conducted from a recently compiled soil carbon database. Temperature and landform type were the dominant controls on SOC distribution for selected ecoregions. Mean SOC pools (to a depth of 1-m) varied by three, seven and ten-fold across ecoregion, landform, and ecosystem types, respectively. Climate interactions with landform type and SOC were greatest in the uplands. For upland SOC there was a six-fold non-linear increase in SOC with latitude (i.e., temperature) where SOC was lowest in the Intermontane Boreal compared to the Arctic Tundra and Coastal Rainforest. Additionally, in upland systems mineral SOC pools decreased as climate became more continental, suggesting that the lower productivity, higher decomposition rates and fire activity, common in continental climates, interacted to reduce mineral SOC. For lowland systems, in contrast, these interactions and their impacts on SOC were muted or absent making SOC in these environments more comparable across latitudes. Thus, the magnitudes of SOC change across temperature gradients were non-uniform and depended on landform type. Additional factors that appeared to be related to SOC distribution within ecoregions included stand age, aspect, and permafrost presence or absence in black spruce stands. Overall, these results indicate the influence of major interactions between temperature-controlled decomposition and topography on SOC in high-latitude systems. However, there remains a need for more SOC data from wetlands and boreal-region permafrost soils, especially at depths > 1 m in order to fully understand the effects of climate on soil carbon in Alaska.

Geoderma↗
USGS2010

Feather lead concentrations and 207Pb/206Pb ratios reveal lead exposure history of California Condors (Gymnogyps californianus)

Lead poisoning is a primary factor impeding the survival and recovery of the critically endangered California Condor ( Gymnogyps californianus ). However, the frequency and magnitude of lead exposure in condors is not well-known in part because most blood lead monitoring occurs biannually, and biannual blood samples capture only ∼10% of a bird’s annual exposure history. We investigated the use of growing feathers from free-flying condors in California to establish a bird’s lead exposure history. We show that lead concentration and stable lead isotopic composition analyses of sequential feather sections and concurrently collected blood samples provided a comprehensive history of lead exposure over the 2−4 month period of feather growth. Feather analyses identified exposure events not evident from blood monitoring efforts, and by fitting an empirically derived timeline to actively growing feathers, we were able to estimate the time frame for specific lead exposure events. Our results demonstrate the utility of using sequentially sampled feathers to reconstruct lead exposure history. Since exposure risk in individuals is one determinant of population health, our findings should increase the understanding of population-level effects from lead poisoning in condors; this information may also be helpful for other avian species potentially impacted by lead poisoning.

California↗
USGS2004

The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields

A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5 days) when there is high accuracy in the forecasts. At longer forecast lead times, the downscaled spatial correlations are close to zero. Similarly, the observed temporal persistence is only partly present at short forecast lead times. A method is presented for reordering the ensemble output in order to recover the space-time variability in precipitation and temperature fields. In this approach, the ensemble members for a given forecast day are ranked and matched with the rank of precipitation and temperature data from days randomly selected from similar dates in the historical record. The ensembles are then reordered to correspond to the original order of the selection of historical data. Using this approach, the observed intersite correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology also has applications for recovering the space-time variability in modeled streamflow.

Journal of Hydrometeorology↗