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How Big of an Effect Do Small Dams Have? Using Geomorphological Footprints to Quantify Spatial Impact of Low-Head Dams and Identify Patterns of Across-Dam Variation.

Fencl JS, Mather ME, Costigan KH, Daniels MD - PLoS ONE (2015)

Bottom Line: Dams are significant disruptions to streams.Both characteristics of individual dams and the context of neighboring dams affected low-head dam impacts within the river network.For these reasons, low-head dams require a different, more integrative, approach for research and management than the individualistic approach that has been applied to larger dams.

View Article: PubMed Central - PubMed

Affiliation: Kansas Cooperative Fish and Wildlife Research Unit, Division of Biology, Kansas State University, Manhattan, Kansas, United States of America.

ABSTRACT
Longitudinal connectivity is a fundamental characteristic of rivers that can be disrupted by natural and anthropogenic processes. Dams are significant disruptions to streams. Over 2,000,000 low-head dams (<7.6 m high) fragment United States rivers. Despite potential adverse impacts of these ubiquitous disturbances, the spatial impacts of low-head dams on geomorphology and ecology are largely untested. Progress for research and conservation is impaired by not knowing the magnitude of low-head dam impacts. Based on the geomorphic literature, we refined a methodology that allowed us to quantify the spatial extent of low-head dam impacts (herein dam footprint), assessed variation in dam footprints across low-head dams within a river network, and identified select aspects of the context of this variation. Wetted width, depth, and substrate size distributions upstream and downstream of six low-head dams within the Upper Neosho River, Kansas, United States of America were measured. Total dam footprints averaged 7.9 km (3.0-15.3 km) or 287 wetted widths (136-437 wetted widths). Estimates included both upstream (mean: 6.7 km or 243 wetted widths) and downstream footprints (mean: 1.2 km or 44 wetted widths). Altogether the six low-head dams impacted 47.3 km (about 17%) of the mainstem in the river network. Despite differences in age, size, location, and primary function, the sizes of geomorphic footprints of individual low-head dams in the Upper Neosho river network were relatively similar. The number of upstream dams and distance to upstream dams, but not dam height, affected the spatial extent of dam footprints. In summary, ubiquitous low-head dams individually and cumulatively altered lotic ecosystems. Both characteristics of individual dams and the context of neighboring dams affected low-head dam impacts within the river network. For these reasons, low-head dams require a different, more integrative, approach for research and management than the individualistic approach that has been applied to larger dams.

No MeSH data available.


Related in: MedlinePlus

Regression for Environmental Correlates of Dam Footprints.Univariate regressions for environmental correlates of (A) downstream, (B) upstream, and (C) total dam footprints expressed in terms of multiples of wetted widths. The relationship shown corresponds to the top model amongst competing univariate regressions testing dam height, distance to nearest upstream dam, number of upstream dams, and height of nearest upstream dam. The corresponding equation and correlation (adjusted R-sq) between the dam footprint and the corresponding explanatory variable are indicated in each panel.
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pone.0141210.g009: Regression for Environmental Correlates of Dam Footprints.Univariate regressions for environmental correlates of (A) downstream, (B) upstream, and (C) total dam footprints expressed in terms of multiples of wetted widths. The relationship shown corresponds to the top model amongst competing univariate regressions testing dam height, distance to nearest upstream dam, number of upstream dams, and height of nearest upstream dam. The corresponding equation and correlation (adjusted R-sq) between the dam footprint and the corresponding explanatory variable are indicated in each panel.

Mentions: Number of upstream dams was inversely related to downstream footprint size and explained 56% of variation in the downstream footprint (Y = -14.18 (SE ± 5.25) X + 79.46 (SE ± 14.05); P = 0.054, adjusted R2 = 0.56; Fig 9A). On the Lower Cottonwood River, Cottonwood Falls and Soden had two and three upstream dams, respectively. On the Upper Neosho River, the most upstream low-head dam, Riverwalk, had one upstream dam, namely Council Grove Reservoir, and the most downstream dam, Emporia, had four upstream dams (Fig 2 and Table 3). The other four variables (dam height, distance to the nearest dam, height of the nearest upstream dam, and drainage area) were not present in top models (ΔAIC > 2).


How Big of an Effect Do Small Dams Have? Using Geomorphological Footprints to Quantify Spatial Impact of Low-Head Dams and Identify Patterns of Across-Dam Variation.

Fencl JS, Mather ME, Costigan KH, Daniels MD - PLoS ONE (2015)

Regression for Environmental Correlates of Dam Footprints.Univariate regressions for environmental correlates of (A) downstream, (B) upstream, and (C) total dam footprints expressed in terms of multiples of wetted widths. The relationship shown corresponds to the top model amongst competing univariate regressions testing dam height, distance to nearest upstream dam, number of upstream dams, and height of nearest upstream dam. The corresponding equation and correlation (adjusted R-sq) between the dam footprint and the corresponding explanatory variable are indicated in each panel.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4634923&req=5

pone.0141210.g009: Regression for Environmental Correlates of Dam Footprints.Univariate regressions for environmental correlates of (A) downstream, (B) upstream, and (C) total dam footprints expressed in terms of multiples of wetted widths. The relationship shown corresponds to the top model amongst competing univariate regressions testing dam height, distance to nearest upstream dam, number of upstream dams, and height of nearest upstream dam. The corresponding equation and correlation (adjusted R-sq) between the dam footprint and the corresponding explanatory variable are indicated in each panel.
Mentions: Number of upstream dams was inversely related to downstream footprint size and explained 56% of variation in the downstream footprint (Y = -14.18 (SE ± 5.25) X + 79.46 (SE ± 14.05); P = 0.054, adjusted R2 = 0.56; Fig 9A). On the Lower Cottonwood River, Cottonwood Falls and Soden had two and three upstream dams, respectively. On the Upper Neosho River, the most upstream low-head dam, Riverwalk, had one upstream dam, namely Council Grove Reservoir, and the most downstream dam, Emporia, had four upstream dams (Fig 2 and Table 3). The other four variables (dam height, distance to the nearest dam, height of the nearest upstream dam, and drainage area) were not present in top models (ΔAIC > 2).

Bottom Line: Dams are significant disruptions to streams.Both characteristics of individual dams and the context of neighboring dams affected low-head dam impacts within the river network.For these reasons, low-head dams require a different, more integrative, approach for research and management than the individualistic approach that has been applied to larger dams.

View Article: PubMed Central - PubMed

Affiliation: Kansas Cooperative Fish and Wildlife Research Unit, Division of Biology, Kansas State University, Manhattan, Kansas, United States of America.

ABSTRACT
Longitudinal connectivity is a fundamental characteristic of rivers that can be disrupted by natural and anthropogenic processes. Dams are significant disruptions to streams. Over 2,000,000 low-head dams (<7.6 m high) fragment United States rivers. Despite potential adverse impacts of these ubiquitous disturbances, the spatial impacts of low-head dams on geomorphology and ecology are largely untested. Progress for research and conservation is impaired by not knowing the magnitude of low-head dam impacts. Based on the geomorphic literature, we refined a methodology that allowed us to quantify the spatial extent of low-head dam impacts (herein dam footprint), assessed variation in dam footprints across low-head dams within a river network, and identified select aspects of the context of this variation. Wetted width, depth, and substrate size distributions upstream and downstream of six low-head dams within the Upper Neosho River, Kansas, United States of America were measured. Total dam footprints averaged 7.9 km (3.0-15.3 km) or 287 wetted widths (136-437 wetted widths). Estimates included both upstream (mean: 6.7 km or 243 wetted widths) and downstream footprints (mean: 1.2 km or 44 wetted widths). Altogether the six low-head dams impacted 47.3 km (about 17%) of the mainstem in the river network. Despite differences in age, size, location, and primary function, the sizes of geomorphic footprints of individual low-head dams in the Upper Neosho river network were relatively similar. The number of upstream dams and distance to upstream dams, but not dam height, affected the spatial extent of dam footprints. In summary, ubiquitous low-head dams individually and cumulatively altered lotic ecosystems. Both characteristics of individual dams and the context of neighboring dams affected low-head dam impacts within the river network. For these reasons, low-head dams require a different, more integrative, approach for research and management than the individualistic approach that has been applied to larger dams.

No MeSH data available.


Related in: MedlinePlus