Articles | Volume 21, issue 5
Hydrol. Earth Syst. Sci., 21, 2509–2530, 2017
https://doi.org/10.5194/hess-21-2509-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 2509–2530, 2017
https://doi.org/10.5194/hess-21-2509-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
16 May 2017
Research article
| 16 May 2017
Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction
Roland Baatz et al.
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Cited
21 citations as recorded by crossref.
- The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling P. Schattan et al. 10.1016/j.rse.2019.111603
- Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model A. Patil et al. 10.3389/frwa.2021.729592
- A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture L. Scheiffele et al. 10.1002/vzj2.20083
- The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis H. Zhao et al. 10.3390/rs13163068
- Extension of cosmic-ray neutron probe measurement depth for improving field scale root-zone soil moisture estimation by coupling with representative in-situ sensors H. Nguyen et al. 10.1016/j.jhydrol.2019.02.018
- On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties G. Brunetti et al. 10.2136/vzj2018.06.0123
- Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling K. Dimitrova-Petrova et al. 10.1016/j.jhydrol.2020.124878
- The TERENO-Rur Hydrological Observatory: A Multiscale Multi-Compartment Research Platform for the Advancement of Hydrological Science H. Bogena et al. 10.2136/vzj2018.03.0055
- Combining static and portable Cosmic ray neutron sensor data to assess catchment scale heterogeneity in soil water storage and their integrated role in catchment runoff response K. Dimitrova-Petrova et al. 10.1016/j.jhydrol.2021.126659
- Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data E. Pinnington et al. 10.5194/hess-25-1617-2021
- Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors D. McJannet et al. 10.5194/hess-21-6049-2017
- Uncertainty, sensitivity and improvements in soil moisture estimation with cosmic-ray neutron sensing G. Baroni et al. 10.1016/j.jhydrol.2018.07.053
- Accuracy and precision of the cosmic‐ray neutron sensor for soil moisture estimation at humid environments J. Iwema et al. 10.1002/hyp.14419
- Vom Punkt zur Fläche in der Messung des Wasseräquivalents der Schneedecke – Mehrwert von Cosmic-Ray Neutron Sensoren in der regionalen Schneemodellierung P. Schattan et al. 10.1007/s00506-018-0500-x
- Cosmic Ray Neutron Sensing for Simultaneous Soil Water Content and Biomass Quantification in Drought Conditions J. Jakobi et al. 10.1029/2018WR022692
- Status and Perspectives on the Cosmic-Ray Neutron Method for Soil Moisture Estimation and Other Environmental Science Applications M. Andreasen et al. 10.2136/vzj2017.04.0086
- Practical Data Products From Cosmic-Ray Neutron Sensing for Hydrological Applications T. Franz et al. 10.3389/frwa.2020.00009
- Intercomparison of cosmic-ray neutron sensors and water balance monitoring in an urban environment M. Schrön et al. 10.5194/gi-7-83-2018
- Toward Better Understanding of Terrestrial Processes through Long-Term Hydrological Observatories H. Bogena et al. 10.2136/vzj2018.10.0194
- Dynamic groundwater recharge simulations based on cosmic‐ray neutron sensing in a tropical wet experimental basin L. Barbosa et al. 10.1002/vzj2.20145
- Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing P. Schattan et al. 10.1002/2016WR020234
20 citations as recorded by crossref.
- The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling P. Schattan et al. 10.1016/j.rse.2019.111603
- Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model A. Patil et al. 10.3389/frwa.2021.729592
- A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture L. Scheiffele et al. 10.1002/vzj2.20083
- The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis H. Zhao et al. 10.3390/rs13163068
- Extension of cosmic-ray neutron probe measurement depth for improving field scale root-zone soil moisture estimation by coupling with representative in-situ sensors H. Nguyen et al. 10.1016/j.jhydrol.2019.02.018
- On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties G. Brunetti et al. 10.2136/vzj2018.06.0123
- Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling K. Dimitrova-Petrova et al. 10.1016/j.jhydrol.2020.124878
- The TERENO-Rur Hydrological Observatory: A Multiscale Multi-Compartment Research Platform for the Advancement of Hydrological Science H. Bogena et al. 10.2136/vzj2018.03.0055
- Combining static and portable Cosmic ray neutron sensor data to assess catchment scale heterogeneity in soil water storage and their integrated role in catchment runoff response K. Dimitrova-Petrova et al. 10.1016/j.jhydrol.2021.126659
- Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data E. Pinnington et al. 10.5194/hess-25-1617-2021
- Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors D. McJannet et al. 10.5194/hess-21-6049-2017
- Uncertainty, sensitivity and improvements in soil moisture estimation with cosmic-ray neutron sensing G. Baroni et al. 10.1016/j.jhydrol.2018.07.053
- Accuracy and precision of the cosmic‐ray neutron sensor for soil moisture estimation at humid environments J. Iwema et al. 10.1002/hyp.14419
- Vom Punkt zur Fläche in der Messung des Wasseräquivalents der Schneedecke – Mehrwert von Cosmic-Ray Neutron Sensoren in der regionalen Schneemodellierung P. Schattan et al. 10.1007/s00506-018-0500-x
- Cosmic Ray Neutron Sensing for Simultaneous Soil Water Content and Biomass Quantification in Drought Conditions J. Jakobi et al. 10.1029/2018WR022692
- Status and Perspectives on the Cosmic-Ray Neutron Method for Soil Moisture Estimation and Other Environmental Science Applications M. Andreasen et al. 10.2136/vzj2017.04.0086
- Practical Data Products From Cosmic-Ray Neutron Sensing for Hydrological Applications T. Franz et al. 10.3389/frwa.2020.00009
- Intercomparison of cosmic-ray neutron sensors and water balance monitoring in an urban environment M. Schrön et al. 10.5194/gi-7-83-2018
- Toward Better Understanding of Terrestrial Processes through Long-Term Hydrological Observatories H. Bogena et al. 10.2136/vzj2018.10.0194
- Dynamic groundwater recharge simulations based on cosmic‐ray neutron sensing in a tropical wet experimental basin L. Barbosa et al. 10.1002/vzj2.20145
1 citations as recorded by crossref.
Latest update: 08 Dec 2022
Short summary
Soil moisture is a major variable that affects regional climate, weather and hydrologic processes on the Earth's surface. In this study, real-world data of a network of cosmic-ray sensors were assimilated into a regional land surface model to improve model states and soil hydraulic parameters. The results show the potential of these networks for improving model states and parameters. It is suggested to widen the number of observed variables and to increase the number of estimated parameters.
Soil moisture is a major variable that affects regional climate, weather and hydrologic...
