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New Brunswick hydrometric network analysis and rationalization
The availability of hydrometric data, as well as its spatial distribution, is important for water resources management. An overly dense network or an under developed network can cause inaccurate hydrological regional estimates. The objective of this study is to propose a methodology for rationalizing a network, specifically the New Brunswick Hydrometric Network. A hierarchical clustering analysis allowed dividing the province into two regions (North and South), based on latitude and high flow timing. These groups were subsequently split separately into three homogeneous subgroups, based on the generalized extreme value (GEV) distribution shape parameter of each station for annual maximum flow series. An entropy method was then applied to compute the amount of information shared between stations, ranking each station’s importance. A station with a lot of shared information is redundant (less important), whereas one with little shared information is unique (very important). The entropy method appears to be a useful decisional tool in a network rationalization.
New Brunswick hydrometric network analysis and rationalization
The availability of hydrometric data, as well as its spatial distribution, is important for water resources management. An overly dense network or an under developed network can cause inaccurate hydrological regional estimates. The objective of this study is to propose a methodology for rationalizing a network, specifically the New Brunswick Hydrometric Network. A hierarchical clustering analysis allowed dividing the province into two regions (North and South), based on latitude and high flow timing. These groups were subsequently split separately into three homogeneous subgroups, based on the generalized extreme value (GEV) distribution shape parameter of each station for annual maximum flow series. An entropy method was then applied to compute the amount of information shared between stations, ranking each station’s importance. A station with a lot of shared information is redundant (less important), whereas one with little shared information is unique (very important). The entropy method appears to be a useful decisional tool in a network rationalization.
New Brunswick hydrometric network analysis and rationalization
2017
Article (Journal)
English
New Brunswick hydrometric network analysis and rationalization
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