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000840429 0247_ $$2doi$$a10.5194/hess-21-5009-2017
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000840429 0247_ $$2ISSN$$a1607-7938
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000840429 1001_ $$00000-0002-0220-0677$$aSchrön, Martin$$b0$$eCorresponding author
000840429 245__ $$aImproving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity
000840429 260__ $$aKatlenburg-Lindau$$bEGU$$c2017
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000840429 520__ $$aIn the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.
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000840429 7001_ $$00000-0001-6098-3094$$aKöhli, Markus$$b1
000840429 7001_ $$0P:(DE-HGF)0$$aScheiffele, Lena$$b2
000840429 7001_ $$0P:(DE-HGF)0$$aIwema, Joost$$b3
000840429 7001_ $$0P:(DE-Juel1)129440$$aBogena, Heye$$b4
000840429 7001_ $$0P:(DE-HGF)0$$aLv, Ling$$b5
000840429 7001_ $$0P:(DE-HGF)0$$aMartini, Edoardo$$b6
000840429 7001_ $$00000-0003-2873-7162$$aBaroni, Gabriele$$b7
000840429 7001_ $$00000-0002-4914-692X$$aRosolem, Rafael$$b8
000840429 7001_ $$0P:(DE-HGF)0$$aWeimar, Jannis$$b9
000840429 7001_ $$00000-0002-1132-2342$$aMai, Juliane$$b10
000840429 7001_ $$00000-0002-5966-1829$$aCuntz, Matthias$$b11
000840429 7001_ $$0P:(DE-HGF)0$$aRebmann, Corinna$$b12
000840429 7001_ $$00000-0003-1667-0060$$aOswald, Sascha E.$$b13
000840429 7001_ $$0P:(DE-HGF)0$$aDietrich, Peter$$b14
000840429 7001_ $$0P:(DE-HGF)0$$aSchmidt, Ulrich$$b15
000840429 7001_ $$0P:(DE-HGF)0$$aZacharias, Steffen$$b16
000840429 773__ $$0PERI:(DE-600)2100610-6$$a10.5194/hess-21-5009-2017$$gVol. 21, no. 10, p. 5009 - 5030$$n10$$p5009 - 5030$$tHydrology and earth system sciences$$v21$$x1607-7938$$y2017
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