Environmental Monitoring and Assessment of a Great River Ecosystem: The Upper Missouri River Pilot

Schweiger, E. William; Bolgrien, David W.; Angradi, Ted R.; Kelly, John R.

doi:10.1007/s10661-005-1010-4

Cited by 18 publications

(16 citation statements)

References 31 publications

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“…Satellite remote sensing of land cover has proven very useful for management of environmental and natural resources, sea and coastline studies and observing land use/land cover changes on global and regional scales for use in weather forecasting and climate modelling (e.g. Pielke et al, 1999;Schweiger et al, 2005;Sertel et al, 2007a;Kaya et al, 2008). However, most of the remotely sensed derived land cover datasets are not 100% accurate, even if developed from the most advanced satellite images (Matthews, 1983;Sellers et al, 1996aSellers et al, , 1996bFriedl et al, 2002;Yang, 2004;Ge et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Impacts of land cover data quality on regional climate simulations

Sertel

Robock

Örmeci

2009

Intl Journal of Climatology

129

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ABSTRACT:The land surface influences local, regional and global climate across many time scales. Accurate representation of land surfaces is an important factor for climate modelling studies because land surfaces control the partitioning of available energy and water. Here we introduce new, up-to-date and accurate land cover data for the Marmara Region, Turkey derived from Landsat Enhanced Thematic Mapper (ETM+) images into the Weather Research and Forecasting (WRF) model. We used several image processing techniques to create accurate land cover data from Landsat sensor images obtained between 2001 and 2005. By comparing the new land cover data with the default WRF land cover data, we found that there are two types of error in WRF land cover data that caused misrepresentation of the study region. WRF uses Global Land Cover Characteristics (GLCC) data created from images acquired during 1992 and 1993 and it does not reflect current land cover. And the GLCC includes misclassifications. As a result of these errors, GLCC data do not represent urban areas in the cities of Istanbul, Izmit and Bursa and there are spectral mixing problems between classes, e.g. croplands, urban areas and forests. We used WRF land cover and our new land cover data to conduct numerical simulations. Using meteorological station data within the study area, we found that simulation with the new land cover dataset produces more accurate temperature simulations for the region, thus demonstrating the importance of accurate land cover data.

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Section: Introductionmentioning

confidence: 99%

Impacts of land cover data quality on regional climate simulations

Sertel

Robock

Örmeci

2009

Intl Journal of Climatology

129

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“…The survey design included a GISbased sample frame (using river center lines from the National Hydrography Dataset (NHD) http:// nhd.usgs.gov/index.html) and random site selection using probability survey, applying spatial balance for site dispersal and representativeness (Stevenson, 1997;McDonald et al, 2004;Schweiger et al, 2004). State-scale (Upper Mississippi and Missouri rivers) and reach scale-assessments (Ohio River) were explicitly included (Angradi, 2006;Angradi et al, 2008).…”

Section: Field Samplingmentioning

confidence: 99%

Assessing the condition of the Missouri, Ohio, and Upper Mississippi rivers (USA) using diatom-based indicators

et al. 2012

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Diatom-based indicators were developed to assess environmental conditions in the Missouri, Ohio, and Upper Mississippi rivers. Disturbance gradients, comprising the first two principal components derived from a suite of stressor variables, included a trophic gradient (Trophic) and a gradient reflecting agriculture and other development activities (Ag/Dev). Diatombased indicators were developed by creating models using weighted average calibration and regressionbased transfer functions to relate planktonic and periphytic diatom species assemblages to each disturbance gradient. The most predictive disturbance models combined phytoplankton and periphyton assemblages into a single bioindicator model (observed versus inferred: Trophic r 2 boot ¼ 0:56; Ag/Dev r 2 boot ¼ 0:70). The geographic applicability of bioindicators was assessed by limiting sample geographical range during model calibrations. Geographic scale was limited by creating bioindicators using samples from: (a) each river, and (b) combined Mississippi/Missouri samples excluding Ohio River sites which were chemically unique. Indicator performance decreased with geographically restrictive models, therefore river basinwide models, developed across all three rivers, is recommended. The most effective diatom-based disturbance bioindicators for this great river ecosystem could be applied using phytoplankton, periphyton, or combined assemblages to infer both trophic and agriculture/development disturbances.

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“…Spatial balance was incorporated into site selection to disperse the sites and increase the representativeness of the sample. See McDonald et al (2004), Schweiger et al (2004), and www.epa.gov/nheerl/arm for additional design details. A single sample design was created for all three rivers.…”

Section: Survey Designmentioning

confidence: 99%

A bioassessment approach for mid-continent great rivers: the Upper Mississippi, Missouri, and Ohio (USA)

Angradi

Bolgrien

Jicha

et al. 2008

Environ Monit Assess

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The objectives of the Environmental Monitoring and Assessment Program for Great River Ecosystems (EMAP-GRE) are to (1) develop and demonstrate, in collaboration with states, an assessment program yielding spatially unbiased estimates of the condition of mid-continent great rivers; (2) evaluate environmental indicators for assessing great rivers; and (3) assess the current condition of selected great river resources. The purpose of this paper is to describe EMAP-GRE using examples based on data collected in 2004-2006 with emphasis on an approach to determining reference conditions. EMAP-GRE includes the Upper Mississippi River, the Missouri River, and the Ohio River. Indicators include biotic assemblages (fish, macroinvertebrates, plankton, algae), water chemistry, and aquatic and riparian physical habitat. Reference strata (river reaches for which a single reference expectation is appropriate) were determined by ordination of the fish assemblage and examination of spatial variation in environmental variables. Least disturbed condition of fish assemblages for reference strata was determined by empirical modeling in which we related fish assemblage metrics to a multimetric stressor gradient. We inferred least disturbed condition from the y-intercept, the predicted condition when stress was least. Thresholds for dividing the resource into management-relevant condition classes for biotic indicators were derived using predicted least disturbed condition to set the upper bound on the least disturbed condition class. Also discussed are the outputs of EMAP-GRE, including the assessment document, multimetric indices of condition, and unbiased data supporting state and tribal Clean Water Act reporting, adaptive management, and river restoration.

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Environmental Monitoring and Assessment of a Great River Ecosystem: The Upper Missouri River Pilot

Cited by 18 publications

References 31 publications

Impacts of land cover data quality on regional climate simulations

Impacts of land cover data quality on regional climate simulations

Assessing the condition of the Missouri, Ohio, and Upper Mississippi rivers (USA) using diatom-based indicators

A bioassessment approach for mid-continent great rivers: the Upper Mississippi, Missouri, and Ohio (USA)

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