scholarly journals A comparison of methods to predict historical daily streamflow time series in the southeastern United States

2015 ◽  
Author(s):  
William H. Farmer ◽  
Stacey A. Archfield ◽  
Thomas M. Over ◽  
Lauren E. Hay ◽  
Jacob H. LaFontaine ◽  
...  
Keyword(s):  
United States ◽  
Time Series ◽  
Southeastern United States ◽  
Comparison Of Methods ◽  
Daily Streamflow ◽  
Daily Streamflow Time Series

scholarly journals A web-based software tool to estimate unregulated daily streamflow at ungauged rivers

2012 ◽  
Vol 5 (3) ◽  
pp. 2503-2526 ◽  
Author(s):  
S. A. Archfield ◽  
P. A. Steeves ◽  
J. D. Guthrie ◽  
K. G. Ries III
Keyword(s):  
Water Management ◽  
United States ◽  
Time Series ◽  
General Framework ◽  
Software Tool ◽  
Management Decisions ◽  
Ecological Functions ◽  
Web Based ◽  
Daily Streamflow ◽  
Daily Streamflow Time Series

Abstract. Streamflow information is critical for solving any number of hydrologic problems. Often times, streamflow information is needed at locations which are ungauged and, therefore, have no observations on which to base water management decisions. Furthermore, there has been increasing need for daily streamflow time series to manage rivers for both human and ecological functions. To facilitate negotiation between human and ecological demands for water, this paper presents the first publically-available, map-based, regional software tool to interactively estimate daily streamflow time series at any user-selected ungauged river location. The map interface allows users to locate and click on a river location, which then returns estimates of daily streamflow for the location selected. For the demonstration region in the northeast United States, daily streamflow was shown to be reliably estimated by the software tool, with efficiency values computed from observed and estimated streamflows ranging from 0.69 to 0.92. The software tool provides a general framework that can be applied to other regions for which daily streamflow estimates are needed.

scholarly journals Towards a publicly available, map-based regional software tool to estimate unregulated daily streamflow at ungauged rivers

2013 ◽  
Vol 6 (1) ◽  
pp. 101-115 ◽  
Author(s):  
S. A. Archfield ◽  
P. A. Steeves ◽  
J. D. Guthrie ◽  
K. G. Ries III
Keyword(s):  
Water Management ◽  
United States ◽  
Time Series ◽  
General Framework ◽  
Software Tool ◽  
Management Decisions ◽  
Ecological Functions ◽  
Daily Streamflow ◽  
Water Withdrawals ◽  
Daily Streamflow Time Series

Abstract. Streamflow information is critical for addressing any number of hydrologic problems. Often, streamflow information is needed at locations that are ungauged and, therefore, have no observations on which to base water management decisions. Furthermore, there has been increasing need for daily streamflow time series to manage rivers for both human and ecological functions. To facilitate negotiation between human and ecological demands for water, this paper presents the first publicly available, map-based, regional software tool to estimate historical, unregulated, daily streamflow time series (streamflow not affected by human alteration such as dams or water withdrawals) at any user-selected ungauged river location. The map interface allows users to locate and click on a river location, which then links to a spreadsheet-based program that computes estimates of daily streamflow for the river location selected. For a demonstration region in the northeast United States, daily streamflow was, in general, shown to be reliably estimated by the software tool. Estimating the highest and lowest streamflows that occurred in the demonstration region over the period from 1960 through 2004 also was accomplished but with more difficulty and limitations. The software tool provides a general framework that can be applied to other regions for which daily streamflow estimates are needed.

scholarly journals Ordinary kriging as a tool to estimate historical daily streamflow records

2016 ◽  
Vol 20 (7) ◽  
pp. 2721-2735 ◽  
Author(s):  
William H. Farmer
Keyword(s):  
Time Series ◽  
Ordinary Kriging ◽  
Order Approximation ◽  
Superior Performance ◽  
Daily Streamflow ◽  
History Of ◽  
First Order Approximation ◽  
And Behavior ◽  
Kriging Approach ◽  
Daily Streamflow Time Series

Abstract. Efficient and responsible management of water resources relies on accurate streamflow records. However, many watersheds are ungaged, limiting the ability to assess and understand local hydrology. Several tools have been developed to alleviate this data scarcity, but few provide continuous daily streamflow records at individual streamgages within an entire region. Building on the history of hydrologic mapping, ordinary kriging was extended to predict daily streamflow time series on a regional basis. Pooling parameters to estimate a single, time-invariant characterization of spatial semivariance structure is shown to produce accurate reproduction of streamflow. This approach is contrasted with a time-varying series of variograms, representing the temporal evolution and behavior of the spatial semivariance structure. Furthermore, the ordinary kriging approach is shown to produce more accurate time series than more common, single-index hydrologic transfers. A comparison between topological kriging and ordinary kriging is less definitive, showing the ordinary kriging approach to be significantly inferior in terms of Nash–Sutcliffe model efficiencies while maintaining significantly superior performance measured by root mean squared errors. Given the similarity of performance and the computational efficiency of ordinary kriging, it is concluded that ordinary kriging is useful for first-order approximation of daily streamflow time series in ungaged watersheds.

scholarly journals Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings

2019 ◽  
Vol 569 ◽  
pp. 573-586 ◽  
Author(s):  
Moctar Dembélé ◽  
Fabio Oriani ◽  
Jacob Tumbulto ◽  
Grégoire Mariéthoz ◽  
Bettina Schaefli
Keyword(s):  
Time Series ◽  
Gap Filling ◽  
Daily Streamflow ◽  
Direct Sampling ◽  
Daily Streamflow Time Series
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