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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- Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA T. Barnhart et al. 10.1002/rra.4028
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41 citations as recorded by crossref.
- Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes P. Bosompemaa et al. 10.1016/j.envsoft.2024.106234
- The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology A. de Lavenne et al. 10.1016/j.envsoft.2022.105562
- Interpolace vybraných průtoků v nepozorovaných povodích IV. řádu v povodí Otavy L. Bureš et al. 10.46555/VTEI.2024.05.002
- 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
- The legacy of STAHY: milestones, achievements, challenges, and open problems in statistical hydrology E. Volpi et al. 10.1080/02626667.2024.2385686
- 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
- Geostatistical analysis and hydrofacies simulation for estimating the spatial variability of hydraulic conductivity in the Jianghan Plain, central China P. Xue et al. 10.1007/s10040-022-02495-4
- Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA T. Barnhart et al. 10.1002/rra.4028
- 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
- Ground settlement prediction for highway subgrades with sparse data using regression Kriging L. Huang et al. 10.1038/s41598-024-75811-5
- A Comparative Study of Statistical Methods for Daily Streamflow Estimation at Ungauged Basins in Turkey M. Yilmaz & B. Onoz 10.3390/w12020459
- Development of a kriging-based regional flood frequency analysis technique for South-East Australia S. Ali & A. Rahman 10.1007/s11069-022-05488-4
- Space-time variations of hydrological drought severities and trends in the semi-arid Euphrates Basin, Turkey O. Katipoğlu & R. Acar 10.1007/s00477-022-02246-7
- 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
- Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation Q. Chen et al. 10.1016/j.jhydrol.2022.127970
- 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
- A first continuous and distributed satellite-based mapping of river discharge over the Amazon V. Pellet et al. 10.1016/j.jhydrol.2022.128481
- Integration of Smart City Technologies with Advanced Predictive Analytics for Geotechnical Investigations Y. Cong & S. Inazumi 10.3390/smartcities7030046
- New double decomposition deep learning methods for river water level forecasting A. Ahmed et al. 10.1016/j.scitotenv.2022.154722
- 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
- New Double Decomposition Deep Learning Methods for Stream-Flow Water Level Forecasting Using Remote Sensing Modis Satellite Variables, Climate Indices and Observations A. Ahmed et al. 10.2139/ssrn.4002418
- 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
- Streamflow data availability in Europe: a detailed dataset of interpolated flow-duration curves S. Persiano et al. 10.5194/essd-14-4435-2022
- Using Universal Kriging for Spatiotemporal Data of Soil Pollution with Metals in Al Karama Industrial Area in Mosul City M. Ali & G. Dhahir 10.32441/kjps.07.02.p9
- 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: 04 Nov 2024
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...
