Using SWAT to determine reference nutrient conditions for small and large streams

Makarewicz, Joseph C.; Lewis, Theodore W.; Rea, Evan; Winslow, Mellissa J.; Pettenski, Dale

doi:10.1016/j.jglr.2014.12.022

Cited by 12 publications

(6 citation statements)

References 41 publications

(79 reference statements)

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“…The Genesee River flows north from Gold, Pennsylvania (elevation: 693 m) to enter Lake Ontario in Rochester, New York (elevation: 74.5 m). The river has a total length of 247 km and a 6408 km 2 drainage area with land cover along the river corridor dominated by agriculture (52%) and forest (40%), with smaller amounts of developed land (5%), including a mixture of residential, commercial, and industrial uses, wetlands and water (2%), and other non-developed lands (1%) [21]. The New York Natural Heritage Program describes the Genesee River (Figure 1) as being in poor ecological health with high ecosystem stress [22].…”

Section: Site Descriptionmentioning

confidence: 99%

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Quackenbush

Stehman

2021

Remote Sensing

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Riparian vegetation delineation includes both the process of delineating the riparian zone and classifying vegetation within that zone. We developed a holistic framework to assess riparian vegetation delineation that includes evaluating channel boundary delineation accuracy using a combination of pixel- and object-based metrics. We also identified how stream order, riparian zone width, riparian land use, and image shadow influenced the accuracy of delineation and classification. We tested the framework by evaluating vegetation vs. non-vegetation riparian zone maps produced by applying random forest classification to aerial photographs with a 1 m pixel size. We assessed accuracy of the riparian vegetation classification and channel boundary delineation for two rivers in the northeastern United States. Overall accuracy for the channel boundary delineation was generally above 80% for both sites, while object-based accuracy revealed that 50% of delineated channel was less than 5 m away from the reference channel. Stream order affected channel boundary delineation accuracy while land use and image shadows influenced riparian vegetation classification accuracy; riparian zone width had little impact on observed accuracy. The holistic approach to quantification of accuracy that considers both channel boundary delineation and vegetation classification developed in this study provides an important tool to inform riparian management.

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Section: Site Descriptionmentioning

confidence: 99%

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Quackenbush

Stehman

2021

Remote Sensing

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“…Land cover in the area is dominated by agriculture (52%) and forest (40%), with smaller amounts of developed land (5%), including a mixture of residential, commercial, and industrial uses, wetlands and water (2%), and other nondeveloped lands (1%) (Makarewicz et al. 2015). We used U.S. Geological Survey stream gage #04231600 in Rochester, New York to bound the northern end of our study area due to the lack of water level data for the Genesee River northward beyond this point.…”

Section: Methodsmentioning

confidence: 99%

“…The southern end was located at 41.939°N, 77.813°W near Ulysses, Pennsylvania following Makarewicz et al. (2015).…”

Section: Methodsmentioning

confidence: 99%

Using Google Earth Engine to Assess Temporal and Spatial Changes in River Geomorphology and Riparian Vegetation

Quackenbush

Stehman

2021

J American Water Resour Assoc

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We developed a new approach using a cloud‐based remote sensing and geospatial analysis platform, Google Earth Engine, to quantify temporal changes in river channel location and adjacent riparian vegetation extent and fraction. Our new method uses publicly available 1 m aerial images and eliminates manual processing need by incorporating an automatic image classification algorithm. Classification of riparian vegetation is enhanced by increasing the temporal resolution to monthly and by mapping vegetation at high spatial resolution. We illustrate the application of our method by characterizing temporal and spatial trends in riparian vegetation and river channel position for the mainstem of the Genesee River, New York, from 2006 to 2015. Annual change in riparian vegetation extent along the Genesee River ranged from a loss of 17% to a gain of 13%, and 64 km (28%) of the river demonstrated channel migration across consecutive aerial images. Our method also successfully distinguished differences between sections of the river above and below a dam. The enhanced capacity to map vegetation monthly allowed us to identify that seasonal active vegetation fractions along the Genesee River peaked at 75% in the summer and remained lower than 25% in winter. Our method allows stakeholders and managers to process remotely sensed imagery and investigate trends in river channel and riparian vegetation dynamics over time, while reducing the costs of data processing and storage.

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“…More than one method has been widely used to analyze and predict water resources, especially in lake level forecasting. The Soil and Water Assessment Tool (SWAT) can simulate water, sediment, and nutrient yield in a watershed by using input data from GIS and applying different agricultural practices, climate change, and land use (Bosch et al 2011;Makarewicz et al 2014;Schiefer et al 2013). Nowadays, the use of artificial intelligence methods as an approach to solving complex nonlinear prob-lems and lake level forecasting has increased (Myronidis et al 2012;Shafaei and Kisi 2016;Shaghaghi et al 2017;Shiri et al 2016;Wang et al 2009;Zaji et al 2018;Zeynoddin et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Spectral–time analysis of cycle fluctuations in lake water levels in Belarus and Poland

Volchak

Kirvel

Choiński

et al. 2020

Limnological Review

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On the basis of the mean volume of annual water levels of 25 lakes (9 Belarusian and 16 Polish) over a period of 55 years (1956-2010) spectral time analysis of water fluctuations has been executed. The choice of the lakes was based on two factors, i.e. the continuous period of observation and insignificant anthropogenic influence. The complex analysis of water level fluctuation cycles has shown that for Belarus the cycles observed most often were 3, 5 and 10-year cycles. Polish lakes also have three cycles, but in the majority of them they amount to 5 and 10-years. It can be assumed that this is impacted by the continental climate growing to the east. Most probably it is one of the key factors defining the diversity of water fluctuations in all the analyzed lakes.

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Using SWAT to determine reference nutrient conditions for small and large streams

Cited by 12 publications

References 41 publications

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Using Google Earth Engine to Assess Temporal and Spatial Changes in River Geomorphology and Riparian Vegetation

Spectral–time analysis of cycle fluctuations in lake water levels in Belarus and Poland

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