Landscape-based cumulative effects models for predicting stream response to mountaintop mining in multistressor Appalachian watersheds

Merriam, Eric R.; Petty, J. Todd; Strager, Michael P.; Maxwell, Aaron E.; Ziemkiewicz, Paul

doi:10.1086/681970

Cited by 20 publications

(32 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pressures on freshwater ecosystems comprise hydrologic and hydraulic stress, sediment intake, and substrate stability, among others, which can be represented by process-based models developed in Merriam et al, 2013Merriam et al, , 2015Pond et al, 2017;Waite et al, 2010, 2012Frimpong et al, 2005Van Sickle & Burch Johnson, 2008 Generalized linear models • Straightforward implementation and interpretation…”

Section: Knowledge Gap Analysismentioning

confidence: 99%

“…Stream health models are developed for establishing reference conditions (Feio & Poquet, 2011;Hawkins, Norris, Hogue, & Feminella, 2000), predicting biological variables and indicators in unsampled locations (Merriam, Petty, Strager, Maxwell, & Ziemkiewicz, 2015;Waite et al, 2010), classifying streams by impairment condition Maloney, Weller, Russell, & Hothorn, 2009), and predicting biological variables and indicators given the implementation of conservation practices (Hall et al, 2017;Sowa et al, 2016) and changes in environmental and landscape stressors (Einheuser et al, 2012;Einheuser, Nejadhashemi, Wang, Sowa, & Woznicki, 2013;. Stream health models are developed for establishing reference conditions (Feio & Poquet, 2011;Hawkins, Norris, Hogue, & Feminella, 2000), predicting biological variables and indicators in unsampled locations (Merriam, Petty, Strager, Maxwell, & Ziemkiewicz, 2015;Waite et al, 2010), classifying streams by impairment condition Maloney, Weller, Russell, & Hothorn, 2009), and predicting biological variables and indicators given the implementation of conservation practices (Hall et al, 2017;Sowa et al, 2016) and changes in environmental and landscape stressors (Einheuser et al, 2012;Einheuser, Nejadhashemi, Wang, Sowa, & Woznicki, 2013;.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Hernandez‐Suarez

Nejadhashemi

2018

Ecohydrology

View full text Add to dashboard Cite

During the last three decades, explaining cause-effect relationships between natural and anthropogenic disturbances with measures of stream health have motivated the growing application of statistical, machine learning, and soft computing methods.The aim of this review is to provide insight into the most widely used methods for predicting biological variables based on macroinvertebrate and fish species in riverine ecosystems. Therefore, we describe several methods including multiple linear regression, generalized linear models, generalized additive models, boosted regression trees, random forests, artificial neural networks, fuzzy logic-based, and Bayesian belief networks along with recent applications of these. Moreover, issues regarding variable selection, model interpretability, ensemble modelling, and model evaluation and overfitting are discussed. Recent advances have suggested the need for integrated modelling systems to enhance the predictive ability and improve interpretability.However, trade-offs between model complexity and accuracy demand research efforts in uncertainty quantification/propagation in model ensembles. Additionally, models should be perceived as complementary tools that require further validation with field measurements. Therefore, a consensus regarding monitoring and modelling practices for stream health applications is recommended.

show abstract

Section: Knowledge Gap Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Hernandez‐Suarez

Nejadhashemi

2018

Ecohydrology

View full text Add to dashboard Cite

show abstract

“…3 Valley fills in White Oak Creek consists of one large contemporary valley fill (0.1 km 2 ) and several fills (0.1 km 2 ). 4 Calculated as B + C + D. 5 Underground mining within topographically delineated catchment area. 6 Area of underground mining updip of stream gaging station.…”

Section: Study Areamentioning

confidence: 99%

“…e QF/Q is the ratio of quickflow to total flow based on Hewlett and Hippert (1967). f Hydrograph separation unsuccessful for events 1,4,6,7,11,19 due to the separation slope being greater than the rising limb of the hydrograph. 9 5 t h p e r c e n t i l e 7 5 t h p e r c e n t i l e me d i a n 5 t h p e r c e n t i l e 2 5 t h p e r c e n t i l e o u t l i e r mi n e d c a t c h me n t f o r e s t e d c a t c h me n t Figure 6.…”

Section: Rainfall-runoff Relationshipsmentioning

confidence: 99%

“…MTM is a special form of surface mining adapted to mountain landscapes that removes forests, soils and overlying bedrock to gain access to underground coal seams. This practice leads to likely permanent and wholesale changes to catchment organization, structure, soils and vegetation that have important implications for catchment hydrology, geochemistry and ecosystem health [2][3][4]. The hydrological impacts of this practice are under increasing scrutiny following several devastating floods (e.g., [5]) and well-established patterns of biological impairment downstream of MTM [4,6,7] that implicate disturbance-driven changes in hydrology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Landscape-Scale Disturbance: Insights into the Complexity of Catchment Hydrology in the Mountaintop Removal Mining Region of the Eastern United States

Miller

Zégre

2016

Land

View full text Add to dashboard Cite

Abstract:Few land disturbances impact watersheds at the scale and extent of mountaintop removal mining (MTM). This practice removes forests, soils and bedrock to gain access to underground coal that results in likely permanent and wholesale changes that impact catchment hydrology, geochemistry and ecosystem health. MTM is the dominant driver of land cover changes in the central Appalachian Mountains region of the United States, converting forests to mine lands and burying headwater streams. Despite its dominance on the landscape, determining the hydrological impacts of MTM is complicated by underground coal mines that significantly alter groundwater hydrology. To provide insight into how coal mining impacts headwater catchments, we compared the hydrologic responses of an MTM and forested catchment using event rainfall-runoff analysis, modeling and isotopic approaches. Despite similar rainfall characteristics, hydrology in the two catchments differed in significant ways, but both catchments demonstrated threshold-mediated hydrologic behavior that was attributed to transient storage and the release of runoff from underground mines. Results suggest that underground mines are important controls for runoff generation in both obviously disturbed and seemingly undisturbed catchments and interact in uncertain ways with disturbance from MTM. This paper summarizes our results and demonstrates the complexity of catchment hydrology in the MTM region.

show abstract

Complex contaminant mixtures in multistressor Appalachian riverscapes

Merriam

Petty

Strager

et al. 2015

Enviro Toxic and Chemistry

Self Cite

View full text Add to dashboard Cite

Runoff from watersheds altered by mountaintop mining in the Appalachian region (USA) is known to pollute headwater streams, yet regional-scale assessments of water quality have focused on salinization and selenium. The authors conducted a comprehensive survey of inorganic contaminants found in 170 stream segments distributed across a spectrum of historic and contemporary human land use. Principal component analysis identified 3 important dimensions of variation in water chemistry that were significantly correlated with contemporary surface mining (principal component 1: elevated dominant ions, sulfate, alkalinity, and selenium), coal geology and legacy mines (principal component 2: elevated trace metals), and residential development (principal component 3: elevated sodium and chloride). The combination of these 3 dominant sources of pollutants produced a complex stream-to-stream patchwork of contaminant mixtures. Seventy-five percent of headwater streams (catchments < 5 km(2) ) had water chemistries that could be classified as either reference (49%), development only (18%), or mining only (8%). Only 21% of larger streams (catchments > 5 km(2) ) were classified as having reference chemistries, and chemistries indicative of combined mining and development contaminants accounted for 47% of larger streams (compared with 26% of headwater streams). Extreme degradation of larger streams can be attributed to accumulation of contaminants from multiple human land use activities that include contemporary mountaintop mining, underground mining, abandoned mines, and untreated domestic wastewater. Consequently, water quality improvements in this region will require a multicontaminant remediation approach.

show abstract

Landscape-based cumulative effects models for predicting stream response to mountaintop mining in multistressor Appalachian watersheds

Cited by 20 publications

References 38 publications

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Landscape-Scale Disturbance: Insights into the Complexity of Catchment Hydrology in the Mountaintop Removal Mining Region of the Eastern United States

Complex contaminant mixtures in multistressor Appalachian riverscapes

Contact Info

Product

Resources

About