River Classification as a Geographic Tool in the Age of Big Data and Global Change

Praskievicz, Sarah

doi:10.1111/gere.12251

Cited by 10 publications

(2 citation statements)

References 59 publications

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“…Moreover, classification can also be used for regionalizing other river‐specific findings, such as modelling the climate‐change impacts for a particular watershed. The availability of large spatial datasets, computational power, and machine‐learning algorithms makes these kinds of unsupervised classification applications more feasible and impactful than ever before (Praskievicz, ).…”

Section: Discussionmentioning

confidence: 99%

Unsupervised hydrologic classification of rivers: Watershed controls on natural and anthropogenic flow regimes, Alabama, USA

Praskievicz

Luo

2019

Hydrological Processes

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Watershed hydrology has often focused on modelling studies of individual watersheds, which consider each river system as unique. Classification is an alternative approach that instead focuses on the similarities among different watersheds.Although both supervised and unsupervised hydrologic classifications have been developed, few previous studies have used classification to assess the degree of anthropogenic modification of hydrologic regime. Here, we conducted an unsupervised hydrologic classification of 189 U.S. Geological Survey gages, including 41 minimally impacted gages from the Hydro-Climatic Data Network (HCDN), in the five major interstate river basins in the U.S. state of Alabama. For the natural classification, the most significant predictor variables for cluster membership were related to compressive strength of bedrock, bedrock depth, hydraulic conductivity, elevation, temperature, and soil texture, and several land-cover variables were also significant in the anthropogenic classification. We then developed two random-forest models: one based on all 189 gages using both natural and anthropogenic variables from the Stream-Catchment (StreamCat) dataset and one based on the 41 HCDN gages using natural StreamCat variables only. We used the random-forest models to predict natural and anthropogenic normative hydrologic class for over 158,000 National Hydrography Dataset Plus catchments in the study area. Catchments that changed their class between the natural and anthropogenic classifications can be identified as those that have a large amount of anthropogenic influences on their hydrologic regime, including many catchments on the coast, in the north-western Coastal Plain, in the Interior Low Plateaus, and in the Piedmont. Using unsupervised hydrologic classifications is a promising approach for uncovering the physical processes that affect hydrologic regime. There are also potential applications in river management, including predicting the hydrologic behaviour of ungaged watersheds, identifying relatively unimpaired rivers to serve as conservation and restoration targets, and regionalization of environmental instream flow standards and climate-change impacts.

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Section: Discussionmentioning

confidence: 99%

Unsupervised hydrologic classification of rivers: Watershed controls on natural and anthropogenic flow regimes, Alabama, USA

Praskievicz

Luo

2019

Hydrological Processes

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“…This classification can play an important role in the monitoring, analysis, and projection of environmental changes. (Detenbeck et al, 2000;Olden et al, 2012;Praskievicz, 2018).…”

Section: Introductionmentioning

confidence: 99%

Identification of Hydrologic Landscapes in Brazilian basins and its application as indicator of water yield and storage

Mortene¹,

Machado²

2020

Preprint

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This study aimed to use the Hydrologic Landscapes as environmental classification method in the Piracicaba, Capivari and Jundiaí river basins, validate and use it as an indicator of water yield and storage areas. The method comprises in the same Information Plan (IP) factors that interact with the hydrological cycle in its terrestrial phase. The state of São Paulo, where most of the area is located, constantly suffer from water scarcity. Climate, soil permeability, aquifer permeability, and relief data were used as evaluation units with the aim of identifying favorable areas of water yield/storage. Each pixel received a value of HLR - Hydrologic Landscape Region, which summarizes hydro-geomorphologic characteristics of the site. To evaluate the method efficiency, the annual water yield (R/P) of each sub-basin was calculated using flow data from its main rivers. This data is necessary to identify the factor "m" (Fuh’s equation), a parameter associated with characteristics of the river basin, such as slope and water infiltration into the soil. The values of water yield and the "m" factor corroborate with previous studies, proving that parameters chosen as evaluation units are effective to identify favorable areas for water yield and storage. The classification in Hydrologic Landscapes has proved to be an effective tool in the identification of these areas, which is essential for the optimization of limited financial resources applied in water resources management projects. The results indicate that basins can be considered as water yield areas and, at the same time, have high storage capacity, since the coefficients R / P and factor "m" had a positive correlation.

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“Reading a River” Through Google Scholar Hyperlinks: Comparing Four Major International River Systems

Praskievicz

2019

Handbook of the Changing World Language Map

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River Classification as a Geographic Tool in the Age of Big Data and Global Change

Cited by 10 publications

References 59 publications

Unsupervised hydrologic classification of rivers: Watershed controls on natural and anthropogenic flow regimes, Alabama, USA

Unsupervised hydrologic classification of rivers: Watershed controls on natural and anthropogenic flow regimes, Alabama, USA

Identification of Hydrologic Landscapes in Brazilian basins and its application as indicator of water yield and storage

“Reading a River” Through Google Scholar Hyperlinks: Comparing Four Major International River Systems

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