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

2016 ◽  
Author(s):  
W. H. Farmer
Keyword(s):  
Time Series ◽  
Ordinary Kriging ◽  
Order Approximation ◽  
Covariance Structure ◽  
Superior Performance ◽  
Spatial Covariance ◽  
Daily Streamflow ◽  
First Order Approximation ◽  
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 covariance 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 covariance 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 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

scholarly journals A Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time. Part I: Development and Applications to Paleoclimate Reconstruction Problems

2010 ◽  
Vol 23 (10) ◽  
pp. 2759-2781 ◽  
Author(s):  
Martin P. Tingley ◽  
Peter Huybers
Keyword(s):  
Time Series ◽  
Covariance Matrix ◽  
Expectation Maximization Algorithm ◽  
Covariance Structure ◽  
Model Parameters ◽  
Data Types ◽  
Spatial Covariance ◽  
Bayesian Algorithm ◽  
Climate Anomalies ◽  
Instrumental Values

Abstract Reconstructing the spatial pattern of a climate field through time from a dataset of overlapping instrumental and climate proxy time series is a nontrivial statistical problem. The need to transform the proxy observations into estimates of the climate field, and the fact that the observed time series are not uniformly distributed in space, further complicate the analysis. Current leading approaches to this problem are based on estimating the full covariance matrix between the proxy time series and instrumental time series over a “calibration” interval and then using this covariance matrix in the context of a linear regression to predict the missing instrumental values from the proxy observations for years prior to instrumental coverage. A fundamentally different approach to this problem is formulated by specifying parametric forms for the spatial covariance and temporal evolution of the climate field, as well as “observation equations” describing the relationship between the data types and the corresponding true values of the climate field. A hierarchical Bayesian model is used to assimilate both proxy and instrumental datasets and to estimate the probability distribution of all model parameters and the climate field through time on a regular spatial grid. The output from this approach includes an estimate of the full covariance structure of the climate field and model parameters as well as diagnostics that estimate the utility of the different proxy time series. This methodology is demonstrated using an instrumental surface temperature dataset after corrupting a number of the time series to mimic proxy observations. The results are compared to those achieved using the regularized expectation–maximization algorithm, and in these experiments the Bayesian algorithm produces reconstructions with greater skill. The assumptions underlying these two methodologies and the results of applying each to simple surrogate datasets are explored in greater detail in Part II.

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.

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