scholarly journals Selection of multiple donor gauges via Graphical Lasso for estimation of daily streamflow time series

2021 ◽  
Author(s):  
German A. Villalba ◽  
Xu Liang ◽  
Yao Liang
Keyword(s):  
Time Series ◽  
Daily Streamflow ◽  
Graphical Lasso ◽  
Daily Streamflow Time Series ◽  
Selection Of

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

Time series analysis and adaptive filtering in hydrology

1984 ◽  
Vol 16 (01) ◽  
pp. 17-18
Author(s):  
J. W. Delleur
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
River Flow ◽  
Time Series Models ◽  
Arma Models ◽  
Multivariate Models ◽  
Varying Coefficients ◽  
Daily Streamflow ◽  
Hydrologic Time Series ◽  
Selection Of

Most time series models in hydrology are used for river flow forecasting, for generation of synthetic data sequences or for the study of physical characteristics underlying the hydrological processes. The models are formulated as linear stochastic difference equations. Three phases are considered for the selection of a model based on a satisfactory representation of a given empirical time series: identification, estimation and validation. Several criteria have been proposed for the selection of the order of ARMA models. The Akaike information criterion (Ale) is popular among hydrologists, but the posterior probability criterion has the advantage of optimality and asymptotic consistency. There are numerous applications of AR or ARMA models to annual streamflow series which are stationary. Seasonal, monthly, weekly or daily streamflow series are cyclically stationary and generally exhibit periodicities in the mean and variance and possibly in the autocorrelation structure. Removal of the periodicity has been accomplished by fitting harmonic series or by subtracting the seasonal mean and dividing by the seasonal standard deviation, and a time series model is then fitted to the residual series. Alternatively, ARMA models with time-varying coefficients are also used. The multiplicative ARlMA model of Box and Jenkins is less frequent in hydrology because of the difficulty in the identification of the parameter structure. Multivariate models are used when river flows at different sites are considered. Parameter estimation in multivariate time series models can become cumbersome because of the dimensionality of the problem. Often the covariance matrix of the noise term is not known in advance and limited information estimates are used. Multivariate models have been used for annual and monthly series. Disaggregation models have been used to subdivide a yearly series into monthly or weekly series or to disaggregate a main river flow into tributary flows while maintaining certain space and time cross-correlations. The aggregation of monthly into yearly time series has been shown to improve the parameter estimation of the yearly series. Hydrologic time series occasionally exhibit changes in level due to natural or man-made causes such as forest fires, volcanic eruption, climatological change, urbanization etc. These situations can be treated making use of intervention analysis.

Time series analysis and adaptive filtering in hydrology

1984 ◽  
Vol 16 (1) ◽  
pp. 17-18
Author(s):  
J. W. Delleur
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
River Flow ◽  
Time Series Models ◽  
Arma Models ◽  
Multivariate Models ◽  
Varying Coefficients ◽  
Daily Streamflow ◽  
Hydrologic Time Series ◽  
Selection Of

Most time series models in hydrology are used for river flow forecasting, for generation of synthetic data sequences or for the study of physical characteristics underlying the hydrological processes. The models are formulated as linear stochastic difference equations. Three phases are considered for the selection of a model based on a satisfactory representation of a given empirical time series: identification, estimation and validation. Several criteria have been proposed for the selection of the order of ARMA models. The Akaike information criterion (Ale) is popular among hydrologists, but the posterior probability criterion has the advantage of optimality and asymptotic consistency. There are numerous applications of AR or ARMA models to annual streamflow series which are stationary. Seasonal, monthly, weekly or daily streamflow series are cyclically stationary and generally exhibit periodicities in the mean and variance and possibly in the autocorrelation structure. Removal of the periodicity has been accomplished by fitting harmonic series or by subtracting the seasonal mean and dividing by the seasonal standard deviation, and a time series model is then fitted to the residual series. Alternatively, ARMA models with time-varying coefficients are also used. The multiplicative ARlMA model of Box and Jenkins is less frequent in hydrology because of the difficulty in the identification of the parameter structure. Multivariate models are used when river flows at different sites are considered. Parameter estimation in multivariate time series models can become cumbersome because of the dimensionality of the problem. Often the covariance matrix of the noise term is not known in advance and limited information estimates are used. Multivariate models have been used for annual and monthly series. Disaggregation models have been used to subdivide a yearly series into monthly or weekly series or to disaggregate a main river flow into tributary flows while maintaining certain space and time cross-correlations. The aggregation of monthly into yearly time series has been shown to improve the parameter estimation of the yearly series. Hydrologic time series occasionally exhibit changes in level due to natural or man-made causes such as forest fires, volcanic eruption, climatological change, urbanization etc. These situations can be treated making use of intervention analysis.

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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