Articles | Volume 20, issue 7
https://doi.org/10.5194/hess-20-2721-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-2721-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
12 Jul 2016
Research article |
| 12 Jul 2016
Ordinary kriging as a tool to estimate historical daily streamflow records
U.S. Geological Survey, Box 25046, Denver Federal Center, MS 410,
Denver, CO 80225, USA
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Cited
26 citations as recorded by crossref.
- Combining streamflow observations and hydrologic simulations for the retrospective estimation of daily streamflow for ungauged rivers in southern Quebec (Canada) S. Lachance-Cloutier et al. 10.1016/j.jhydrol.2017.05.011
- An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction Z. Yaseen et al. 10.1016/j.jhydrol.2018.11.069
- Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network A. Ahmadi et al. 10.1080/02626667.2019.1610565
- Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review Y. Guo et al. 10.1002/wat2.1487
- Calibration of a hydrologic model in data-scarce Alaska using satellite and other gridded products K. Schneider & T. Hogue 10.1016/j.ejrh.2021.100979
- Spatial variations of runoff generation at watershed scale M. Vafakhah et al. 10.1007/s13762-018-1784-x
- A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions S. Persiano et al. 10.1080/02626667.2021.1879389
- Newly explored machine learning model for river flow time series forecasting at Mary River, Australia F. Cui et al. 10.1007/s10661-020-08724-1
- Spatiotemporal Prediction and Mapping of Heavy Metals at Regional Scale Using Regression Methods and Landsat 7 A. Mouazen et al. 10.3390/rs13224615
- A Comparative Study of Statistical Methods for Daily Streamflow Estimation at Ungauged Basins in Turkey M. Yilmaz & B. Onoz 10.3390/w12020459
- Hydro-power production and fish habitat suitability: Assessing impact and effectiveness of ecological flows at regional scale S. Ceola et al. 10.1016/j.advwatres.2018.04.002
- Calibration of the US Geological Survey National Hydrologic Model in Ungauged Basins Using Statistical At-Site Streamflow Simulations W. Farmer et al. 10.1061/(ASCE)HE.1943-5584.0001854
- New gridded rainfall dataset over the Korean peninsula: Gap infilling, reconstruction, and validation G. Noh & K. Ahn 10.1002/joc.7252
- Selection of Multiple Donor Gauges via Graphical Lasso for Estimation of Daily Streamflow Time Series G. Villalba et al. 10.1029/2020WR028936
- Reducing the Uncertainty of Radiata Pine Site Index Maps Using an Spatial Ensemble of Machine Learning Models G. Gavilán-Acuña et al. 10.3390/f12010077
- A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations A. Pugliese et al. 10.5194/hess-22-4633-2018
- Geostatistical modeling and traditional approaches for streamflow regionalization in a Brazilian Southeast watershed R. Ferreira et al. 10.1016/j.jsames.2021.103355
- Proposal of methodology for spatial analysis applied to human development index in water basins J. Sales et al. 10.1007/s10708-018-9894-z
- Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework T. Roksvåg et al. 10.5194/hess-24-4109-2020
- Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves W. Farmer et al. 10.5194/hess-22-5741-2018
- Geospatial tools effectively estimate nonexceedance probabilities of daily streamflow at ungauged and intermittently gauged locations in Ohio W. Farmer & G. Koltun 10.1016/j.ejrh.2017.08.006
- Prediction of streamflow regimes over large geographical areas: interpolated flow–duration curves for the Danube region A. Castellarin et al. 10.1080/02626667.2018.1445855
- Comparing Trends in Modeled and Observed Streamflows at Minimally Altered Basins in the United States G. Hodgkins et al. 10.3390/w12061728
- Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
- Synthetic design hydrographs for ungauged catchments: a comparison of regionalization methods M. Brunner et al. 10.1007/s00477-018-1523-3
- Transferring measured discharge time series: Large-scale comparison of Top-kriging to geomorphology-based inverse modeling A. de Lavenne et al. 10.1002/2016WR018716
25 citations as recorded by crossref.
- Combining streamflow observations and hydrologic simulations for the retrospective estimation of daily streamflow for ungauged rivers in southern Quebec (Canada) S. Lachance-Cloutier et al. 10.1016/j.jhydrol.2017.05.011
- An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction Z. Yaseen et al. 10.1016/j.jhydrol.2018.11.069
- Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network A. Ahmadi et al. 10.1080/02626667.2019.1610565
- Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review Y. Guo et al. 10.1002/wat2.1487
- Calibration of a hydrologic model in data-scarce Alaska using satellite and other gridded products K. Schneider & T. Hogue 10.1016/j.ejrh.2021.100979
- Spatial variations of runoff generation at watershed scale M. Vafakhah et al. 10.1007/s13762-018-1784-x
- A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions S. Persiano et al. 10.1080/02626667.2021.1879389
- Newly explored machine learning model for river flow time series forecasting at Mary River, Australia F. Cui et al. 10.1007/s10661-020-08724-1
- Spatiotemporal Prediction and Mapping of Heavy Metals at Regional Scale Using Regression Methods and Landsat 7 A. Mouazen et al. 10.3390/rs13224615
- A Comparative Study of Statistical Methods for Daily Streamflow Estimation at Ungauged Basins in Turkey M. Yilmaz & B. Onoz 10.3390/w12020459
- Hydro-power production and fish habitat suitability: Assessing impact and effectiveness of ecological flows at regional scale S. Ceola et al. 10.1016/j.advwatres.2018.04.002
- Calibration of the US Geological Survey National Hydrologic Model in Ungauged Basins Using Statistical At-Site Streamflow Simulations W. Farmer et al. 10.1061/(ASCE)HE.1943-5584.0001854
- New gridded rainfall dataset over the Korean peninsula: Gap infilling, reconstruction, and validation G. Noh & K. Ahn 10.1002/joc.7252
- Selection of Multiple Donor Gauges via Graphical Lasso for Estimation of Daily Streamflow Time Series G. Villalba et al. 10.1029/2020WR028936
- Reducing the Uncertainty of Radiata Pine Site Index Maps Using an Spatial Ensemble of Machine Learning Models G. Gavilán-Acuña et al. 10.3390/f12010077
- A geostatistical data-assimilation technique for enhancing macro-scale rainfall–runoff simulations A. Pugliese et al. 10.5194/hess-22-4633-2018
- Geostatistical modeling and traditional approaches for streamflow regionalization in a Brazilian Southeast watershed R. Ferreira et al. 10.1016/j.jsames.2021.103355
- Proposal of methodology for spatial analysis applied to human development index in water basins J. Sales et al. 10.1007/s10708-018-9894-z
- Estimation of annual runoff by exploiting long-term spatial patterns and short records within a geostatistical framework T. Roksvåg et al. 10.5194/hess-24-4109-2020
- Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves W. Farmer et al. 10.5194/hess-22-5741-2018
- Geospatial tools effectively estimate nonexceedance probabilities of daily streamflow at ungauged and intermittently gauged locations in Ohio W. Farmer & G. Koltun 10.1016/j.ejrh.2017.08.006
- Prediction of streamflow regimes over large geographical areas: interpolated flow–duration curves for the Danube region A. Castellarin et al. 10.1080/02626667.2018.1445855
- Comparing Trends in Modeled and Observed Streamflows at Minimally Altered Basins in the United States G. Hodgkins et al. 10.3390/w12061728
- Improving predictions of hydrological low-flow indices in ungaged basins using machine learning S. Worland et al. 10.1016/j.envsoft.2017.12.021
- Synthetic design hydrographs for ungauged catchments: a comparison of regionalization methods M. Brunner et al. 10.1007/s00477-018-1523-3
Latest update: 22 Mar 2023
Short summary
The potential of geostatistical tools, leveraging the spatial structure and dependency of correlated time series, for the prediction of daily streamflow time series at unmonitored locations is explored. Simple geostatistical tools improve on traditional estimates of daily streamflow. The temporal evolution of spatial structure, including seasonal fluctuations, is also explored. The proposed method is contrasted with more advanced geostatistical methods and shown to be comparable.
The potential of geostatistical tools, leveraging the spatial structure and dependency of...
