dataesr/scientific-paragraphs-categorization huggingface dataset:
These bulletins are complemented by economic situation notes intended for farmers, but also for the economic operators on the various markets. These notes exist for milk, cereals, and processed fruits and vegetables, i. e. , produce that can be stored and for which it is useful to know the state of the market and stockpiles. To produce this information, the FNAB takes stock of crop-year results from data collective from biological farmers and GRAB and OFEO representatives. In the case of vegetables, two crop-year assessments are made, one at harvest time (summer) and one at the end of the crop year (winter). These reviews also give some elements about the coming crop year (volumes, prices, and grades) in order to be able to look ahead and adjust market release dates. One FNAB employee in charge of these activities attests to the usefulness of the landmarks that these assessments provide, saying that the information exchange has value not just as a result, but also as a discussion process: "The operators need this information.
First, sea spectral models proposed by Elfouhaily et al. , Hwang et al. , Romeiser et al. , Apel et al. , Fung et al. , and Pierson et al. , are compared with each other from different points of view, e. g. , the omnidirectional parts, the angular spreading functions, the autocorrelation functions, and the slope variances. We find that the spectra given by Elfouhaily and Hwang could reflect realistic wind sea waves more accurately. Then, the scattering coefficients are simulated in fully monostatic and bistatic configurations. Regarding the monostatic scattering, the results simulated using EM scattering models are compared with those obtained from the measured UAVSAR data in the L band and the empirical model CMOD5 in the C band. Comparisons are made for various incident angles, wind speeds, and wind directions.
Scattering from the Sea Surface in the Monostatic Configuration Since most of the current microwave scatterometers measure in the backscattered direction (monostatic case), this makes the problem of EM scattering in the monostatic configuration become a practically meaningful problem. In this context, we first deal with the monostatic configuration. In the following, on the basis of the theories in Section 2, we present some numerical simulations by using SSA-1 and different sea spectra in the fully-monostatic configuration. The scattering coefficients evaluated with SSA-1 and different spectral models are compared with the UAVSAR data in the L band and the empirical model CMOD5 in the C band, respectively. Figure  8  shows the UAVSAR images. The dark areas are the oil spills induced by the accident.
Therefore, the difference of the wind direction between these two SAR images is about 180 •. In our simulations, the wind speed is set as 5 m/s, and the wind direction is set as the upwind direction. In Figure  9 , one can note that the difference between the NRCSs acquired from the two SAR images is quite small. Both for VV and HH polarizations, the scattering coefficients evaluated with SSA-1 are larger than the measured SAR data. Obviously, none of the curves coincide well with UAVSAR data. Relatively speaking, the curve of the E-spectrum is slightly better than other curves (the difference between Elfouhaily curve and SAR data curves is less than 3 dB for VV polarization, and 4 dB for HH polarization). For the F-spectrum, there exist oscillations for the incident angle larger than 45 • which are caused by the integration of the oscillation function in Equation  (8).
Remote Sens. 2018, 10, x FOR PEER REVIEW 10 of 20 HH polarizations, the scattering coefficients evaluated with SSA-1 are larger than the measured SAR data. Obviously, none of the curves coincide well with UAVSAR data. Relatively speaking, the curve of the E-spectrum is slightly better than other curves (the difference between Elfouhaily curve and SAR data curves is less than 3 dB for VV polarization, and 4 dB for HH polarization). For the Fspectrum, there exist oscillations for the incident angle larger than 45° which are caused by the integration of the oscillation function in Equation (  8 ).
Evaluation with CMOD5 As most measured data are acquired under particular wind speed and wind direction, it is impossible to evaluate the accuracy of simulated results in all wind conditions. In the following, in order to evaluate the numerical simulations under different conditions, the simulated results are compared with the empirical model CMOD5. Simulations are made as functions of the incident angle, wind speed, wind direction, polarization state, and at a frequency equal to 5. 3 GHz.
In the backscattering case, the results obtained with SSA-1 are compared with UAVSAR data in the L band and the empirical model CMOD5 in the C band. Compared with measured SAR data in the L band, both for VV and HH polarizations, the scattering coefficients evaluated using SSA-1 model are larger than the experimental data. However, the results obtained using E-spectrum are slightly better than the ones obtained using other spectra. Compared with CMOD5 in the C band, we find it difficult to accurately simulate the NRCS merely based on one spectral model in all wind speeds or wind directions. It is advised that different spectral models should be employed in different configurations to obtain accurate predictions. The following conclusions can be drawn from our analysis: (1) For incident angle variation: The R-spectrum matches well with CMOD5 in the upwind direction.
In the backscattering case, the results obtained with SSA-1 are compared with UAVSAR data in the L band and the empirical model CMOD5 in the C band. Compared with measured SAR data in the L band, both for VV and HH polarizations, the scattering coefficients evaluated using SSA-1 model are larger than the experimental data. However, the results obtained using E-spectrum are slightly better than the ones obtained using other spectra. Compared with CMOD5 in the C band, we find it difficult to accurately simulate the NRCS merely based on one spectral model in all wind speeds or wind directions. It is advised that different spectral models should be employed in different configurations to obtain accurate predictions. The following conclusions can be drawn from our analysis: For incident angle variation: The R-spectrum matches well with CMOD5 in the upwind direction.
Remote Sens. 2018, 10, x FOR PEER REVIEW 9 of 20 using SSA-1 and different sea spectra in the fully-monostatic configuration. The scattering coefficients evaluated with SSA-1 and different spectral models are compared with the UAVSAR data in the L band and the empirical model CMOD5 in the C band, respectively. 4. 1. 1. Evaluation with UAVSAR Data in the L Band The SAR images are collected by UAVSAR (uninhabited aerial vehicle synthetic aperture radar) during the DWH (Deepwater Horizon) oil spill accident in the Gulf of Mexico in 2010. UAVSAR is a fully-polarimetric L-band SAR. The center frequency of the incident wave is 1. 2575 GHz. Two multilook (3 and 12 looks in the range and azimuth directions, respectively) UAVSAR images with a 5 m slant range resolution and 7. 2 m azimuth resolution are employed in this part.
Study area Figure  1 (a) shows a map of the Arctic circumpolar tundra region (ACTR), which we defined using GLDAS and vegetation data obtained from the 1° GLDAS2/Noah Dominant Vegetation Type datasets (https://ldas. gsfc. nasa. gov/gldas/GLDASvegetation. php) using the NOAHv3. 3 vegetation dataset from GLDAS2. In the GLDAS, tundra regions are divided into two distinct types: mixed tundra and wooded tundra. In this study, we only present data for wooded tundra in order to avoid uncertainties associated with identifying mixed tundra along the narrow coastal areas of the Arctic Ocean, where it is difficult to distinguish between the ocean and land due to the coarse spatial resolution of GRACE data. In addition, we exclud from our analysis the areas in the ACTR that are within 300 km of glaciers and ice sheets (the masked-out areas in Figure  1 (a) ) in order to remove the effects of glacial and ice sheet melt on the TWS anomalies.
The first catchment is the Lena River Basin in eastern Siberia of the Russian Federation, which has an area of approximately 2,430×10 3 km 2 that is comprised largely of continuous permafrost (71%). The second is the Yukon River basin in Alaska, which has an area of 850×10 3 km 2 ; it is covered by both continuous permafrost (23. 2%) and discontinuous permafrost (74. 7%). The third catchment is the Mackenzie River basin in northern Canada, which covers an area of 1,790×10 3 km 2 , 20. 0% of which is covered by continuous permafrost and 39. 2% is covered by discontinuous permafrost. we did not apply any interpolation to cover the periods of data loss. In our analysis, we use the ensemble mean of the three GRACE datasets (i. e. , a simple arithmetic mean of the CSR, JPL and GFZ datasets) as recommended by Sakumura et al.
[16]  and the official GRACE Science Data System. In doing so, we are able to reduce random errors and enhance common signatures included within each GRACE dataset. The GRACE data used in this study express the Earth's gravity field in the form of spherical harmonic coefficients (Stokes coefficients). These Stokes coefficients are estimated up to a degree and order of 60 or 96 depending on the analysis center and observation period. Here, we truncated the Stokes coefficients at a degree and order of 60 (corresponding to a spatial resolution of ~300 km), since the higher-frequency coefficients are especially contaminated by short wavelength noise from temporal aliasing errors  [17]. In addition, we used filtering techniques to further reduce sources of short wavelength noise. We applied a Gaussian filter with a radius of 240 km.
Reanalysis Data The GLDAS1 and GLDAS2 products  [23]  are used to calculate TWS anomalies and assess TWS estimates from GRACE data. In this study, to examine the uncertainty in the GLDAS1 products, we use a multi-model ensemble of GLDAS1 data consisting of four different land surface models: 1) the Noah land surface model (LSM)  [24] , 2) the Community Land Model (CLM)  [25] , 3) the variable infiltration capacity (VIC) model  [26] , and 4) the MOSAIC LSM  [27]. Each product has a monthly time step and a spatial resolution of 1. 0° × 1. 0°. The TWS derived from the multi-model GLDAS1 ensemble is defined as TWSGLDAS1. Its ensemble spread (standard deviation) provides a measure of the uncertainty in the land surface models of the GLDAS1 products. GLDAS2, which currently uses only the Noah LSM and employs the Princeton meteorological forcing dataset  [28]  as the only source of forcing data, was corrected using observation-based datasets of precipitation, air temperature, and radiation.
Inundation Extent Area (IEA) Variations of surface inundation extent areas (IEAs) among the regions are analyzed using data from the Global Inundation Extent from Multi-Satellites (GIEMS) database developed by  [31, 32]  as a 25-km-resolution inundated area map with a monthly temporal resolution. This dataset has been widely used and evaluated over the northern Arctic regions as in Papa et al. [33]  and Frappart et al. [34]. To assess how variations in the surface water (mostly within inundation areas) among the regions were related to spatial changes in the TWS, we jointly compared the spatial changes in the TWS identified using variations in the GRACE data with the mean IEA for both July and August of each year (referred to as JA IEA). This represents the largest inundation area in these regions throughout the year.
g. , lakes, rivers and wetlands; mm month -1 ). Hereafter, we refer to the TWS values determined using GRACE data as TWSGRACE. The GLDAS-based TWS determination includes only Δ SWE, Δ CW, and Δ SM from equation  (1). Because Δ CW is a minor factor, the TWS determined from the GLDAS data depends primarily on Δ SWE and Δ SM. Hereafter, we refer to the TWS values determined from the GLDAS1 and GLDAS2 products as TWSGLDAS1 and TWSGLDAS2, respectively, and the P and E values determined from GLDAS1 and GLDAS2 are accordingly referred to as PGLDAS1, PGLDAS2, EGLDAS1, and EGLDAS2.
TWS changes across the arctic circumpolar tundra region We analyze the temporal variations of TWS (i. e. , from GRACE, GLDAS1, and GLDAS2) from 2002 to 2016 along with other variables (i. e. , the temperature, precipitation, evapotranspiration, and IEA) for the entire ACTR. First, we examined the consistency among the TWS estimates from the GRACE, GLDAS1, and GLDAS2 datasets based on the coefficients of determination (R 2 ) and differences in the linear trends. Second, we analyzed the possible mechanisms for the changes in the mean annual TWS of the ACTR by comparing the TWS estimates against other hydrological and climate components. Note that the mean annual TWS is simply the average of available monthly TWS data over all possible years, because some monthly TWS data were missing from the overall dataset as shown in section 2.
TWS in the Arctic circumpolar tundra In this section, we present the updated TWS data series for the ACTR from 2002 to 2016 and assess the regional TWS variability. these strong correlations among the three TWS datasets, we calculated a TWS change of more than 80% at the ACTR-scale using the GLDAS products, thus allowing us to determine the primary factor controlling the changes in TWSGRACE. The similarities among the three datasets imply that, like TWSGLDAS1 and TWSGLDAS2, TWSGRACE also largely depends on the SWE and SM parameters. Even so, the negative trend in TWSGLDAS1 is nearly three times larger than the trends in TWSGRACE and TWSGLDAS2, which were very similar to each other. Overall, the inter-annual variation in TWSGRACE can be best explained by the GLDAS2 dataset. The mean annual TWS is plotted in Figure  2 (b) for all years except 2002, when insufficient data were available (less than nine months).
In these smoothed datasets, TWSGRACE was again more strongly correlated with TWSGLDAS2 (r = 0. 67, p = 0. 009) than with TWSGLDAS1 (r = 0. 50, p = 0. 069). TWSGRACE, TWSGLDAS1, and TWSGLDAS2 decreased by -1. 2 mm year -1 , -4. 5 mm year -1 , and -1. 3 mm year -1 , respectively. Thus, the long-term trends observed in the mean annual TWSGRACE were most consistent with those observed in TWSGLDAS2. Overall, these TSW data show a decline throughout the ACTR between 2002 and 2016, thereby providing evidence that hydrological changes are not only limited in the upper reaches of the Lena River basin as reported in  [10]  but also expanding throughout the whole ACTR. 3. 1. 2 Precipitation, Evapotranspiration, and air temperature from GLDAS and IEA. Next, we present the monthly air temperature (Ta), precipitation (P), evapotranspiration (E), and IEA derived from the GLDAS1 and GLDAS2 datasets and assess their effects on the mean annual TWSGRACE (Figure  3 ).
The 2-m air temperatures in the ACTR from the GLDAS1 data are very close to those calculated from the GLDAS2 data (Figure  3 (a), r = 1. 0, p < 0. 0001). However, GLDAS2 produced consistently higher P values than GLDAS1 (GLDAS1 -GLDAS2 = -11. 4 mm, r = 0. 77, p < 0. 0001), while the inter-annual variability in E produced from GLDAS1 was larger than that observed from GLDAS2 (Figure  3 (c), r = 0. 96, p < 0. 0001) due to higher (lower) values for E in summer (winter). However, these differences cancel out when we calculate the all-term averaged bias and are within the range of uncertainty associated with the various LSMs, as indicated by vertical red bars in Figure  3(c). The mean JA IEA shows a decrease between 2002 and 2006; overall, however, the mean JA IEA could not explain the decrease in TWSGRACE.
Regional trends in TWS, P, and E Here, we consider the regional trends in the mean annual TWS, and P and E from both GLDAS1 and GLDAS2. significant (p < 0. 05). Higher regression correlation coefficients are seen in the North American continent. In terms of regional changes in the TWS, a significant large negative trend in the TWS can be mainly seen in the region along the Gulf of Alaska and the Northwestern Territory in Canada. Meanwhile, the negative trends in the TWS in the Eurasian continent were weaker than those in the North American continent. To further understand these regional trends across the ACTR, we examined the distributions of P and E using the mean annual datasets from GLDAS1 and GLDAS2 (Figures spatial variations in PGLDAS1 and PGLDAS2, and in EGLDAS1 and EGLDAS2 can be explained by the differences in the forcing data and in the land surface models in GLDAS1 and GLDAS2.
Confirmation of the IEA as a proxy for evapotranspiration In the previous sections, we proposed that E was a principal driving factor in the spatiotemporal variations in TWSGRACE by using GLDAS1 and GLDAS2 products. Here, we discuss additional evidence using IEA data as a proxy for evapotranspiration, since evapotranspiration has a direct effect on the extent of surface water (e. g. , the IEA). To confirm the impacts of evapotranspiration on TWSGRACE, we analyzed the similar spatiotemporal variabilities of TWSGRACE Overall, the spatiotemporal variations in TWSGRACE can be linked to changes in the IEA via changes in evapotranspiration. Thus, the results we obtained in this study can be supported by independent IEA data sets. Since evapotranspiration is probably responsible for the observed IEA variations (no precipitation changes are observed), and since the IEA contributes to the TWS variations and trends, our findings support the role of changes in E on the TWS trends.
This confirms E as a driving factor for the decline in the TWS in the Yukon River catchment. Here, we consider only GLDAS2-based E data due to the greater consistency between the GLDAS2 and GRACE products. permafrost. Therefore, the high rLAG in the autumn within the Lena River Basin indicates that the autumn TWS can serve as a climatic signature for permafrost-dominated river runoff in any given year, as was noted previously by Suzuki et al. [10]. The influence on the autumn TWS from the preceding year is discernible, despite the inclusion of the snow water equivalent over the land. However, in basins such as the Mackenzie River, which are not dominated by permafrost, the TWS from the preceding year had no effect on the river runoff of the following year.
Hydrological changes in the tundra throughout the ACTR Through our results, the GRACE-based TWS record shows a clear decrease in the TWS throughout the ACTR and that the JJA air temperature increased. The spatiotemporal variations in the TWS throughout the ACTR were related to the IEA, especially when the IEA increased. The weaker correlation between E and the IEA (i. e. , when the IEA decreased) can be explained by subsurface processes, because all of the strong negative trends in the IEA can be observed in non-continuous permafrost zones that degraded rapidly. The previous GRACE-based TWS trends in eastern Siberia  [21]  and in the three largest Siberian river basins (i. e. , the Lena, Yenisey, and Ob River basins)  [37]  all showed positive trends using shorter data periods (e. g. , 5 or 8 years) than the present study.
In addition, Huang et al. [9]  showed that the aridity of most of the Artic region, including eastern Siberia and the North American continent, will increase in this century using data from the CMIP5. Our results correspond to those of Huang et al. [9]. Furthermore, using the CMIP5 dataset, Cook et al. [38]  demonstrated that the expansion of dry areas can be attributed to globally widespread increases in the potential evapotranspiration while the spatial heterogeneous response of the precipitation can be used to define the drying and wetting patterns over the global land surface. Their conclusions confirm our results, i. e. , that the TWS in the ACTR is driven by increasing evapotranspiration. Thus, we propose that the TWS in the ACTR is driven by the IEA, which is correspondingly driven by increasing summer air temperatures and evapotranspiration.
Inconsistencies between GLDAS1 and GLDAS2 Our analyses demonstrated a greater consistency between TWSGRACE and the GLDAS2 product than the GLDAS1 data throughout the ACTR and in the largest pan-Arctic river basins. However, there are also significant differences between the GLDAS products. For example, the values of P in GLDAS2 exceed those in GLDAS1, although the air temperatures are consistent.
Acknowledgments: Portions of this study were supported by a  Grant-in-Aid for Scientific Research (C)  (No. 16K00581 ) and a  Grant-in-Aid for Challenging Exploratory Research  (no. 25550022 ). In addition, this study was Preprints (www. preprints. org)| NOT PEER-REVIEWED | Posted: 8 December 2017 doi: 10. 20944/preprints201712. 0049. v1partly carried out by the  Joint Research Program  of the  Institute for Space-Earth Environmental Research (ISEE) ,  Nagoya University.
Conclusions We present a record of the GRACE-derived TWS in the Arctic circumpolar tundra region (ACTR) and in the three largest pan-Arctic river basins from April 2002 to December 2016. Using the multi-model ensemble of reanalyzed data from GLDAS1 and the newly developed reanalyzed data from GLDAS2, including other key parameters such as Ta, P, E or remote-sensing based IEA, we find the following conclusions: 1. The negative trend in the TWS throughout the ACTR was primarily driven by evapotranspiration, which is accordingly driven by summer air temperatures as confirmed using the IEA. This negative trend could be expected to continue in the future due to further warming conditions during the summer season at high latitudes, leading to substantial reductions in the TWS throughout the Arctic.
4. The GLDAS2-based products corresponded better with the spatiotemporal variability in TWSGRACE than the GLDAS1-based products. GLDAS2 is likely more suitable for analyzing hydrological changes in Arctic regions than GLDAS1. Given the availability of river runoff data, our analysis of the relationship between river runoff and the TWS anomalies was limited to the three largest pan-Arctic river basins. To improve our understanding of the Arctic freshwater cycle, additional GRACE data regarding river runoff is required. In particular, the TWSGRACE data in the Yukon River clearly indicated a negative trend in our analysis, and the data were not affected by river runoff. Although this corroborates the negative TWS trends as strongly related to evapotranspiration throughout the ACTR and in the Yukon River Basin, the mechanism for this is still unknown and should represent the focus of future research.
3. 1. 1 11 Figure 2(a) illustrates the temporal variations in the monthly TWS estimates averaged across the ACTR from GRACE, GLDAS1, and GLDAS2 data. From 2002 to 2016, the GRACE, GLDAS1, and GLDAS2 TWS datasets exhibit negative trends of -1. 8 mm year -1 , -1. 6 mm year -1 , and -5. 1 mm year -1 , respectively. TWSGRACE and TWSGLDAS2 show the best agreement in the linear trends (r = 0. 92, p < 0. 0001), despite the larger seasonal amplitude in TWSGLDAS2 due to a larger winter TWS. These rates of decline exceed the uncertainty of -1. 0 mm year -1 associated with TWSGRACE and thus confirm the ability of the GRACE-based TWS data to reflect temporal trends in the ACTR.
The same equation is used for the carbon emissions. This being so, in order to differentiate the primary energy demand (and carbon emissions), which considers the different stages in building life cycle, the terms "embodied primary energy" and "embodied carbon emissions" will be applied. It is important to highlight that the methodology for LCA does not impose a specific calculation software or database. It only requires the use of recognized and reliable sources for the quantification of the chosen indicators. Therefore, the LCA was carried out through the use of spreadsheets or existing LCA tools, depending on national contexts, such as the Swiss Eco-Bat  [29]  or by adapting existing tools, e. g. , the ASCOT tool  [30, 31]  developed for Denmark. In the context of the methodology used in this study, both LCA and Life Cycle Costs (LCC) were carried out for a study period of 60 years, for which all contributions of materials and energy consumptions are calculated.