dataesr/scientific-paragraphs-categorization huggingface dataset:
(2010)  suggested the following theoretical relation: θ (N, N 0 ) = 0. 0808 N/N 0 -0. 372 -0. 115, (1) where N 0 is a site-specific calibration parameter. It is determined once for each dataset by comparing the CRNS product, θ (N , N 0 ), with the actual soil moisture condition in the field. However, neutrons are sensitive to all kinds of hydrogen in the footprint; hence, the variable θ denotes the water equivalent of soil moisture, θ sm , and additional hydrogen pools, θ offset. The latter comprises for example lattice water, θ lw  (Dong et al. , 2014) , as well as the water equivalent from soil organic carbon, θ org  (Hawdon et al. , 2014) , biomass, θ bio , and other dynamic contributors, θ other : θ = θ sm + θ offset , where θ offset = θ lw + θ org + θ bio + θ other. (2) Quantities related to absolute water equivalent are either given in units of gravimetric percent (θ g in % ≡ 100 g g -1 ) or volumetric percent (θ v in % ≡ 100 cm 3 cm -3 ).
If no indices "v" or "g" are indicated and units are not mentioned in the context, this work uses default units of volumetric percent. For calibration and validation purposes, the water equivalent in the footprint volume is typically determined independently by an average of point measurements, for example from gravimetric samples or data from soil moisture monitoring networks. However, those locations can contribute differently to the apparent average of soil moisture as seen by the neutron detector, for example, depending on their distance r from the CRNS probe and their depth d below the soil surface. Depending on their individual contributions, different weights can be assigned to each data point in the calculation of a so-called weighted average. Among the variety of weighting concepts in the literature, we have selected two of the main and most frequently used strategies from recognized publications, which are based on distinct physical assumptions.
Horizontal weighting in the footprint area In this work we make use of three horizontal weighting functions to average soil moisture measurements at distances r from the CRNS probe. Firstly, the equal average (weights equal 1), which was usually applied for validation with soil moisture networks and remote sensing products. It was also used for calibration datasets if locations were arranged according to the COSMOS standard sampling scheme  (25, 75, 200 m) , such that the samples automatically represent areas of equal contribution to the neutron signal. These calculations were based on a simple exponential sensitivity function  (Zreda et al. , 2008)  and presented by  Franz et al. (2012a)  and  Zreda et al. (2012). Secondly, the conventional weighting approach uses an (almost) exponential sensitivity function based on Monte Carlo simulations from  Zreda et al.
Uncertainty due to partial coverage In addition to the considerations about the representative domain, the arrangement of the soil samples can play a crucial role for the CRNS evaluation performance. If the locations of the soil samples (or in situ monitored soil profiles) do not cover the CRNS footprint representatively, the corresponding data would not be able to explain parts of the neutron signal. Many sites exhibit highly irregular configurations where the soil monitoring network covers only parts of the CRNS footprint. The corresponding uncertainty in the CRNS evaluation can be estimated as follows. Let S be the domain of the representative volume of the sample locations (e. g. , the areal extent of the soil moisture monitoring network), and let be the spatial domain of the CRNS footprint as defined in the previous section.
Performance measures To evaluate the performance of time series and calibration data, we apply prominent measures used in environmental and hydrological research. The robustness of this approach is evaluated by applying different performance measures, which is a common strategy to falsify new methodological approaches (see, e. g. ,  Glaser et al. , 2016). Popular efficiency measures are the Nash-Sutcliffe efficiency (NSE)  (Nash and Sutcliffe, 1970)  and the more modern Kling-Gupta efficiency (KGE)  (Gupta et al. , 2009) , while the root mean square error (RMSE) and the Pearson correlation coefficient (ρ) are well-established standard approaches. NSE = 1 - (A -B) 2 (B -B ) 2 , KGE = 1 -(ρ(A, B) -1) 2 + σ A σ B -1 2 + A B -1 2 1 2 , RMSE = (A -B) 2 1 2 , ρ = (A -A )(B -B ) σ A σ B , where A = θ (N, N 0 ) denotes the water equivalent measured by the CRNS (N 0 needs to be calibrated), B denotes the actual field soil water equivalent, θ, measured by independent instruments, and x = 1 n n 1 x denotes the average (expected value) of a set of data points x.
In the ideal case of optimal agreement between the variables A and B, the measures would reach NSE = 1, KGE = 1, RMSE = 0, and ρ = 1. NSE normalizes the mean squared error by the observed variance, where the mean observed variable B is used as a baseline. Following this approach, site-specific variations could translate to biased estimation of model skills among different sites. On the other hand, the KGE measure is a revised version of NSE that allows one to analyze the relative importance of the linear correlation ρ, variability σ , and bias • of simulated and observed variables  (Gupta et al. , 2009). RMSE is simply a measure of the differences between two time series, but is prone to biased datasets and outliers. The correlation ρ is an accepted approach in experimental geophysics to identify similar or unknown effects (e.
g. ,  Fu et al. , 2015)  in two time series. However, if many factors could explain a single observation, only using the correlation measure may lead to false recognition of coincidental effects. The KGE is the most appropriate performance measure for time series data as it combines three distinct measures to optimally account for absolute errors and anomalies (compare also  Heidbüchel et al. , 2016). In the following analysis, we have thus optimized the KGE value between the CRNS and the independent soil moisture data to find a single calibration parameter N 0 per site. (e. g. , SoilNet; see also  Bogena et al. , 2010). These datasets are usually applied to validate the performance of CRNS sensors; however, the present study takes advantage of the continuous recordings in order to calibrate the CRNS probe.
Complex land use in the T. W. Daniel Experimental Forest (US) The T. W. Daniel Experimental Forest lies in the mountaintops in the Wasatch Mountains (IMW), which is one of four components of the Intermountain West of the United States and a transition zone of different climate regimes in both the seasonal and inter-annual timescales. The landscape of the TWDEF site is a patchwork of four domain vegetation communities common to the IMW. Forest communities include aspen and conifer, predominantly Engelmann Spruce, and subalpine fir. Non-forest communities include grasses, forbs, and sagebrush. For each dominant vegetation type, three plots and three subplots within each plot were randomly chosen. Time series data were evaluated from TDT sensors at 10, 25, and 50 cm and interpolated up to the surface using hydrophysical simulations  (Lv et al.
Results and discussion The equal, conventional, and revised weighting approaches have been tested at six distinct research sites. In Sect. 4. 1 we have analyzed the calibration datasets at the Sheepdrove Organic Farm and at the Braunschweig site, in order to test the explanatory power of the theoretical relation, N (θ) (Eq. 1). Section 4. 2 discusses the uncertainty related to a time series dataset in the Schäfertal catchment, where the footprint is only partly covered by monitored profiles. Section 4. 3 analyzes the potential of CRNS and time series datasets in Grosses Bruch and Wüstebach to identify additional hydrological processes. At the TWDEF site in Sect. 4. 4, we use monitoring profiles in distinct parts of the footprint, which are individually weighted based on their areal contribution to the neutron signal.
Improvement of the calibration performance In the farmlands of Great Britain, managed fields are often divided by strips of hedges or unmanaged grassland. While unmanaged patches appear to be ideal positions for environmental-monitoring equipment, the presented example shows that CRNS measurements can be biased from the intended information about the field site. Three calibration datasets were collected at various wetness conditions within 9 months. The sampling design was based on the COSMOS standard sampling scheme at 25, 75, and 200 m, plus an additional location at 1 m near the CRNS probe. Figure  5  demonstrates how the equal (red) and conventional (orange) weightings of the three calibration datasets deviate significantly from the unique theoretical relation N (θ )  (Desilets et al. , 2010).
Furthermore, the experiment clearly shows the importance of a proper positioning of the CRNS probe. If a sensor is dedicated to measuring soil moisture in a certain field, it should be ideally placed in that field. CRNS stations at the field border can be biased by different local characteristics, such as land use or soil properties. Insights from the British grassland have also been confirmed with calibration datasets from an agricultural site www. hydrol-earth-syst-sci. net/21/5009/2017/ Hydrol. Earth Syst. Sci. , 21, 5009-5030, 2017  ). The conventional weighting approach is not able to provide a unique theoretical line through the 3 calibration days. Furthermore, for a given neutron observation the difference between estimated moisture (lines) and actual soil moisture (ellipses) indicates unrealistic biomass dynamics throughout the study period (see explanations in the text).
The revised approach converges the datasets to confirm the accepted neutron theory almost in a single calibration curve within uncertainties (size of ellipses). near Braunschweig. During the agricultural season in 2014,  Scheiffele (2015)  used the COSMOS standard sampling scheme for three calibration campaigns in May (very small crop, mediocre soil moisture), July (maximum water content in biomass, dry soil), and October (biomass residues after harvest, mediocre soil moisture). The general behavior of the soil moisture dynamics could be reproduced well (Fig. 6 ), independent of the campaign used for calibration (i. e. , determination of N 0 ). In all three cases, the neutron counts reflect that soil has dried considerably from May to July, to levels below 10 %, followed by a period of high precipitation and irrigation that led to increased soil wetness in Octo- ber.
However, the performance of the sensor to reflect exact soil moisture states depends on the calibration dataset. Using the conventional averaging approach, the corresponding calibration curves in Fig. 6  (orange lines) indicate a non-unique relationship between neutrons and soil moisture throughout the study period; i. e. , hydrogen pools other than soil moisture may have changed, where biomass is the most likely candidate. For example, following the calibration curve from May (solid orange line), the neutron counts detected in July and October would correspond to lower soil water content than actually measured in the field (ellipses); i. e. , these neutron observations were higher than expected. This mismatch could be misinterpreted as a reduced amount of biomass in July and October, because decreasing biomass water equivalent usually corresponds to increasing neutron counts  (Franz et al.
der dry conditions (where θ v (N ) ≤ 4 %). Consequently, the partial coverage of the CRNS footprint by the irregularly distributed SoilNet hampers the proper evaluation of the CRNS data, and especially of the weighting strategies. Nevertheless, the Schäfertal data show that the revised weighting approach is robust enough to improve the overall CRNS performance (Fig. 7 ), even though the sensor is situated in complex terrain where the SoilNet sampling locations are not representative the CRNS footprint. As the revised approach shows the best accuracy in all four statistical measures, the RMSE v is still higher than the measurement error of the daily mean (≈ 2 %). This indicates that deviations can be attributed (1) to the insufficient coverage of the Soil-Net, and (2) to different processes in different parts of the footprint (speculative examples are vegetation growth, forest water interception, snow accumulation, evapotranspiration, plowing, etc.
Identification of additional hydrological processes The Grosses Bruch pasture site is a good example of how an inappropriate averaging approach could hinder sufficient interpretation of time series data. Figure  8  shows the soil moisture signal predicted from a stationary CRNS probe and the weighted signal of a soil moisture monitoring network (SoilNet) with sensors installed at depths from 0. 05 up to 0. 6 m. Following the precipitation events in the second half of October, the shallow groundwater and loamy texture allowed large water ponds to reside permanently in the outer regions of the SoilNet (light blue indication on the map). As distant areas contribute much less to the CRNS signal than closer ones, the revised weighting approach has significantly reduced the influence of the saturated point data on the apparent CRNS average.
Without the revised method, the CRNS product would have overestimated the absolute volumetric field saturation by more than 5 %. Additionally, beginning in the middle of September, many cows had been present at this site, which are assumed to have led to large variations of the neutron signal and thus to a non-meaningful expression of correlation-related measures. In the Wüstebach forest site, weighted averaging of the soil moisture monitoring network is performed based on the data presented in  Bogena et al. (2013). The analysis shows three interesting effects on the resulting soil moisture signal in Fig. 9. Firstly, the signal processed with the revised weighting approach (blue) is wetter than the conventionally weighted signal (orange). This effect is reasonable due to the higher soil water contents of the groundwater-influenced riparian zone, where the CRNS is located, compared to the terrestrial soils at the hillslopes.
Secondly, the CRNS signal which was calibrated to the revised weighted soil moisture (light blue) outperforms the signal that was calibrated on the conventionally weighted soil moisture (light orange). This performance gain is robust in terms of the four measures. In order to avoid incorrect conclusions from overcalibration of the data during rain events (periods of high interception water), we repeated the same analysis for dry periods only. In this case the revised approach again led to the highest performance (not shown) and confirmed the robustness of this approach. Thirdly, differences between CRNS and SoilNet appear to be significantly more prominent for the revised approach (blue) in periods following huge precipitation events (May, July, and October). Those periods can probably be attributed to expected canopy water storage, interception stor- age, groundwater rise, and nearby accumulation of ponds.
Ponded water in local hollows, trenches, and the litter layer are not visible in the soil profiles of the monitoring network, which are typically installed in solid and elevated ground. In contrast, their effect can be visible in stronger oscillations and shifts of the CRNS signal. The analysis demonstrates that the revised weighting of calibration data is essential to identify residual hydrological effects which otherwise can get lost by overfitting. By comparing CRNS data and point measurements, residual information could be used to identify additional processes like biomass dynamics or rainfall interception  (Baroni and Oswald, 2015). The methods presented here can support efforts to identify those residuals to a much higher precision. In order to properly quantify the excess water storages in future studies, we would recommend calibrating the CRNS signal only in periods when the site had not been exposed to rain events for a few consecutive days.
Conclusions In this paper a general procedure for horizontal and vertical weighting of point measurements has been presented in order to calibrate and validate the CRNS soil moisture product. The method is based on revised spatial sensitivity functions (or weighting functions) from neutron physics simulations  (Köhli et al. , 2015). Notably, the revised functions have been further advanced in the present work with an updated version of the URANOS neutron transport code, by adding dependency on air pressure and vegetation height, and by extending the analysis to distances below 0. 5 m. The performance of the conventional weighting functions has been compared with the revised functions using datasets from a variety of distinct sites located in Germany, the UK, and the US. The improvements of the CRNS performances for each site are summarized in Table  2 , including a note that highlights specific features of the analyses.
The study has led to the following conclusions. 1. The revised averaging of observed point data improved the performance measures of the CRNS product for all investigated sites when compared with the equal and conventional approaches. The method is thus applicable to arbitrarily distributed sampling locations without prior knowledge of soil and land-use features. 2. The results show that unrealistic deviations of multiple calibration datasets from the theoretical line can be removed by applying the revised weighting functions. Thus they support the original hypothesis by  Desilets et al. (2010)  of a single calibration campaign to capture the local soil moisture dynamics. The approach can thus substantially reduce the calibration efforts for CRNS probes, in contrast to recent findings from  Iwema et al.
4. 4). 5. Data points in the first 0 to 10 m radius and 0 to 20 cm depth around the sensor are most important for calibration and validation purposes. It is thus recommended to reduce the uncertainty of those measurements, e. g. , by increasing the number of samples in that area. 6. As previous studies have shown, the CRNS soil moisture signal could be calibrated to match the simple, equal average of the areal soil moisture in the footprint. However, important hydrological features could be missed by doing so and data interpretation might become misleading. When CRNS is combined with independent soil moisture measurements, the revised weighting approach has the potential to reveal hydrological features that were otherwise lost in the signal by overcalibration. The approach improved the accuracy by which the CRNS probe was able to sense total changes in water storages other than soil moisture, e.
g. , from water in the biomass or litter layer, interception water, groundwater rise, as well as ponding in remote or local areas. The revised weighting functions presented here are provided in the Supplement in R, MATLAB, and Excel (see Appendix C). Furthermore, an approximated weighting function W * r (Appendix B) has been suggested to simplify quick analysis of the horizontal contributions independently of the local wetness conditions. However, the latter approach should be taken with care, for its adequate performance has not been sufficiently confirmed in this work. Within this study many datasets have been reanalyzed to test the revised weighting approach. Due to its overall success, it is recommended to also revisit other studies, especially where the conventional approaches have not led to the expected results (e.
Figure6. Recalibration of the CRNS sensor in an agricultural maize field (Braunschweig, Germany). Sizes of the circles indicate the corresponding uncertainty range of the measurement, while every such measurement corresponds to a calibration curve θ(N , N 0 ). The conventional weighting approach is not able to provide a unique theoretical line through the 3 calibration days. Furthermore, for a given neutron observation the difference between estimated moisture (lines) and actual soil moisture (ellipses) indicates unrealistic biomass dynamics throughout the study period (see explanations in the text). The revised approach converges the datasets to confirm the accepted neutron theory almost in a single calibration curve within uncertainties (size of ellipses).
Acknowledgements. Martin Schrön  acknowledges kind support by the  Helmholtz Impulse and Networking Fund  through the  Helmholtz Interdisciplinary School for Environmental Research (HIGRADE). Joost Iwema  is funded by the  Queen's School of Engineering, University of Bristol ,  EPSRC , grant code  EP/L504919/1. Rafael Rosolem ,  Joost Iwema , and  Sheepdrove Organic Farm  activities are funded by the  Natural Environment Research Council  ( A MUlti-scale Soil moisture-Evapotranspiration Dynamics study -AMUSED ; grant number  NE/M003086/1 ). Gabriele Baroni  had financial support from the  Deutsche Forschungsgemeinschaft (DFG)  under  CI 26/13-1  in the framework of research unit FOR 2131 " Data Assimilation for Improved Characterization of Fluxes across Compartmental Interfaces ". Corinna Rebmann  and  Matthias Cuntz  acknowledge the support of this study by the  Integrated Carbon Observation System (ICOS)  infrastructure through the  Federal Ministry of Research and Education (BMBF).
We acknowledge the NMDB database (http://www. nmdb. eu),founded under the European Union's  FP7 programme  (contract no. 213007 ) to provide data for incoming radiation, especially from monitors  Jungfraujoch  ( Physikalisches Institut, University of Bern ) and  Kiel (Institute for Experimental and Applied Physics, University of Kiel). The research was funded and supported by the  Terrestrial Environmental Observatories (TERENO) , which is a joint collaboration program involving several Helmholtz Research Centers in Germany. The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association. Edited by:  Bob Su Reviewed  by:  Yijian Zeng  and three anonymous referees.
Data availability. Data from the Sheepdrove Organic Farm are available upon request. Data from Grosses Bruch will soon be available in the TERENO Data Discovery Portal (http://teodoor. icg. kfa-juelich. de/ibg3searchportal2/index. jsp). Data from Braunschweig, Schäfertal, Wüstebach, and T. W. D. E. Forest are available from the given references (see Table1). www. hydrol-earth-syst-sci. net/21/5009/2017/ Hydrol. Earth Syst. Sci. , 21, 5009-5030, 2017.
5 (7, 8) before manual finishing. SSPACE version 2 (9) and Opera version 2 (10) were used to combine the contigs helped by GapFiller version 1. 10 (11). This yielded a draft genome consisting of 14 scaffolds composed of 123 contigs, for a total of 5,854,893 bp and a GϩC content of 67. 64%. Noncoding genes and miscellaneous features were predicted using RNAmmer (12), ARAGORN (13), Rfam (  14 ), PFAM (15), and Infernal (  16 ). Coding DNA sequences (CDSs) were predicted using Prodigal (17), and functional annotation was achieved using BLASTp against the GenBank database (18) and the Clusters of Orthologous Groups (COG) database  (19, 20). The genome was shown to encode 54 predicted RNAs, including one each of the 5S rRNA, 16S rRNA, and 23S rRNA genes and 51 tRNAs. A total of 3,923 genes (74.
Description of measurements used for comparison Ground-based measurements were collected from three sites of the UV network of the National Science Foundation (NSF) of the USA and two sites of the Finnish Meteorological Institute (FMI). Table  1  reports the geographical coordinates of the stations, time period of data and their source, type of instruments, spectral interval and step of measurements. The Barrow site is located approximately 6 km northeast of the Barrow city in Alaska, on the coast of the Chukchi Sea, part of the Arctic Ocean, usually covered by ice between November and July. The snow cover of the surroundings of the station extends from October until June. According to  Bernhard et al. (2008) , the effective UV albedo of the surface reaches its maximum of approximately 0. 8 during March-April.