Estimating Locations of Perennial Streams in Idaho Using a Generalized Least-Squares Regression Model of 7-Day, 2-Year Low Flows

Wood, Molly S.; Rea, Alan; Skinner, Kenneth D.; Hortness, Jon E.

doi:10.3133/sir20095015

Cited by 7 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the draft standards for the NHD high‐resolution data, an intermittent stream “contains water for only part of the year, but more than just after rainstorms and at snowmelt,” and a perennial stream “contains water throughout the year, except for infrequent periods of severe drought” (Wood et al ., ). The ephemeral category is defined as “contains water only in direct response to precipitation.…”

Section: Methodsmentioning

confidence: 97%

GIS‐Based Stream Classification in a Mountain Watershed for Jurisdictional Evaluation

Caruso

2014

J American Water Resour Assoc

View full text Add to dashboard Cite

This study analyzed stream characteristics in a mountain watershed in southwestern Colorado and developed a three‐level hierarchical classification scheme using national datasets to demonstrate jurisdictional evaluation as “waters of the United States (U.S.)” under U.S. Clean Water Act Section 404 at the watershed scale. The National Hydrography Dataset and USGS StreamStats were used with field observations to classify streams in the 53 km2 Cement Creek Watershed based on flow duration (Level 1), stream order (Level 2), and other biophysical metrics (Level 3). Kruskal‐Wallis tests and discriminant analysis showed significant differences among Level 2 classes. Level 3 classification used cluster analysis for stream length, distance to the downstream traditional navigable water (TNW), and the ratio of mean annual flow from the source stream to the TNW. Results showed all perennial and intermittent streams are jurisdictional relatively permanent waters (RPWs), which include over a third of all streams, 64% are intermittent or ephemeral, and almost half are ephemeral first order. All ephemeral reaches are non‐RPWs requiring significant nexus evaluation to determine jurisdiction. These ephemeral first‐order streams can contribute 5% of the annual flow to the TNW at the confluence, while the Cement Creek main stem contributes 21% of the TNW flow. The study demonstrated that the classification provides key biophysical and regulatory information to aid jurisdictional evaluations in mountain watersheds.

show abstract

Section: Methodsmentioning

confidence: 97%

GIS‐Based Stream Classification in a Mountain Watershed for Jurisdictional Evaluation

Caruso

2014

J American Water Resour Assoc

View full text Add to dashboard Cite

show abstract

“…However, we provide examples of indicators that could fall within each category, and we discuss the mechanisms governing the relationship between indicators and hydrology, as well as factors that mediate those relationships. [33,34] Channel slope Control Reach Decadal [35] Entrenchment ratio Control Reach Multi-annual [36] Catchment area Control Catchment - [37,38] Potential evapotranspiration Control Catchment Annual [39] Precipitation Control Catchment Daily, annual, decadal [35,40] Precipitation-vegetation feedback Control Catchment Annual [41] Percent sand and gravel deposits Control Catchment - [37] Percent grassland Control Catchment - [39] Percent forest Control Catchment - [35] The environmental filter concept [42,43] portrays environmental conditions as selective filters on the distribution and abundance of species. Without the combination of traits (i.e., measurable properties of organisms that reflect behavior, life history, physio-morphological adaptation, resource use, and mobility) allowing persistence under certain environmental conditions, species are eliminated.…”

Section: Scientific Basis Of Sdam Indicatorsmentioning

confidence: 99%

“…Species probabilities were used as weights for a wetland prevalence index developed for predicting the inundation duration class (aquatic, transitional, and upland). Least-square regression equations using catchment-scale indicators have been used to differentiate flow duration classes in Idaho, where the annual minimum flow is a surrogate for flow duration [38]. Related to SDAMs, logistic models with catchment-scale indicators have also been used to predict the probability of stream reaches being wet or dry at different parts of the year [189].…”

Section: Assemble Interim Sdammentioning

confidence: 99%

Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development

Fritz

Nadeau

Kelso

et al. 2020

Water

View full text Add to dashboard Cite

Streamflow duration is used to differentiate reaches into discrete classes (e.g., perennial, intermittent, and ephemeral) for water resource management. Because the depiction of the extent and flow duration of streams via existing maps, remote sensing, and gauging is constrained, field-based tools are needed for use by practitioners and to validate hydrography and modeling advances. Streamflow Duration Assessment Methods (SDAMs) are rapid, reach-scale indices or models that use physical and biological indicators to predict flow duration class. We review the scientific basis for indicators and present conceptual and operational frameworks for SDAM development. Indicators can be responses to or controls of flow duration. Aquatic and terrestrial responses can be integrated into SDAMs, reflecting concurrent increases and decreases along the flow duration gradient. The conceptual framework for data-driven SDAM development shows interrelationships among the key components: study reaches, hydrologic data, and indicators. We present a generalized operational framework for SDAM development that integrates the data-driven components through five process steps: preparation, data collection, data analysis, evaluation, and implementation. We highlight priorities for the advancement of SDAMs, including expansion of gauging of nonperennial reaches, use of citizen science data, adjusting for stressor gradients, and statistical and monitoring advances to improve indicator effectiveness.

show abstract

“…More intermittent segments are included in detailed maps based on field surveys (e.g., Brooks and Colburn, 2011), but large-scale field surveys are prohibitively laborious. As alternatives to surveys and remote sensing, empirical models have been used to predict the locations of intermittent river segments based on catchment characteristics (Bent and Steeves, 2006;Heine et al, 2004;Wood et al, 2009). These models perform moderately well in the areas for which they are defined and provide information about intermittenceenvironment associations over large, environmentally heterogeneous areas.…”

Section: Introductionmentioning

confidence: 99%

Regionalization of patterns of flow intermittence from gauging station records

Snelder¹,

Datry²,

Lamouroux³

et al. 2013

Preprint

View full text Add to dashboard Cite

Understanding large-scale patterns in flow intermittence is important for effective water resource management. We used daily flow records from 628 gauging stations on rivers with minimally modified flows distributed throughout France to predict regional patterns of flow intermittence. For each station we calculated two annual times-series describing flow intermittence; the frequency of zero-flow periods (consecutive days of zero-flow) in each year of record (FREQ; yr<sup>−1</sup>), and the total number of zero-flow days in each year of record (DUR; days). These time series were used to calculate two indices for each station, the mean annual frequency of zero-flow periods (mFREQ; yr<sup>−1</sup>), and the mean duration of zero-flow periods (mDUR; days). Approximately 20% of stations had recorded at least one zero-flow period. Dissimilarities between pairs of gauges calculated from the annual times-series (FREQ and DUR) and geographic distances were weakly correlated, indicating that there was little spatial synchronization of zero-flow. A flow-regime classification for the gauging stations discriminated intermittent and perennial stations, and an intermittence classification grouped intermittent stations into three classes based on the values of mFREQ and mDUR. We used Random Forest (RF) models to relate the flow-regime and intermittence classifications to several environmental characteristics of the gauging station catchments. The RF model of the flow-regime classification had a cross-validated Cohen's kappa of 0.47, indicating fair performance and the intermittence classification had poor performance (cross-validated Cohen's kappa of 0.35). Both classification models identified significant environment-intermittence associations, in particular with regional-scale climate patterns and also catchment area, shape and slope. However, we suggest that the fair-to-poor performance of the classification models is because intermittence is also controlled by processes operating at scales smaller than catchments, such as groundwater-table fluctuations and seepage through permeable channels. We suggest that high spatial heterogeneity in these small-scale processes partly explains the low spatial synchronization of zero-flows. While 20% of gauges were classified as intermittent, the flow-regime model predicted 39% of all river segments to be intermittent, indicating that the gauging station network under-represents intermittent river segments in France. Predictions of regional patterns in flow intermittence provide useful information for applications including environmental flow-setting, estimating assimilative capacity for contaminants, designing bio-monitoring programs and making preliminary estimates of the effects of climate change on flow intermittence

show abstract

Estimating Locations of Perennial Streams in Idaho Using a Generalized Least-Squares Regression Model of 7-Day, 2-Year Low Flows

Cited by 7 publications

References 10 publications

GIS‐Based Stream Classification in a Mountain Watershed for Jurisdictional Evaluation

GIS‐Based Stream Classification in a Mountain Watershed for Jurisdictional Evaluation

Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development

Regionalization of patterns of flow intermittence from gauging station records

Contact Info

Product

Resources

About