dataesr/scientific-paragraphs-categorization huggingface dataset:
Microwave vegetation optical depth Previous implementations of LDAS-Monde have directly assimilated LAI products from optical observations. In order to test how best to improve initial conditions of the model, vegetation optical depth is used and transformed into an LAI proxy. This study applies the same re-scaling methodology as  Kumar et al. (2020)  to the VODCA VOD dataset, adding LDAS-Monde's capabilities of directly updating the RZSM control variables and the potential joint assimilation with SSM. VOD itself is the measure of attenuation of microwave radiation passing through a vegetation canopy  (Jackson and Schmugge, 1991). The attenuation, which is a function of microwave frequency, can also be directly linked to vegetation water content  (Jackson et al. , 1982; Wigneron et al. , 1993; Owe et al.
, 1982; Meesters et al. , 2005; Owe et al. , 2008; van der Schalie et al. , 2017). The dataset is compiled from the AMSR-E, AMSR2, SSM/I, TMI, and WindSat sensors and separates the syntheses into C-, X-, and Ku-band VOD retrievals. Ku-band VOD from VODCA did not encompass the entire period of interest in this study, stopping in 2017. While both the C-and X-band VOD may suitably represent vegetation over a wider array of land cover, X-band VOD was ultimately chosen to be assimilated in this study as  Kumar et al. (2020)  found large improvements in ET estimations from X-band VOD assimilation relative to C-band VOD. Because the TMI sensor aboard the Tropical Rainfall Measuring Mission (TRMM) satellite is in a 35 • inclination orbit and thus does not encompass the entirety of the CONUS domain and because, in the X-band of VODCA, TMI is the only sensor between 1998 and late 2002, the year 2003 was chosen as a starting point in order to have full-domain observations.
Each sensor source in VODCA is first processed by removing locations known to be influenced by radio frequency interference (RFI); removing observations where land surface temperature (LST) is below freezing (as due to the changing dielectric permittivity of water and ice, the VOD cannot be accurately retrieved in frozen conditions); and removing negative values of VOD, which are data artifacts and not physically possible. Daytime retrievals were found to have higher errors than their nighttime counterparts, and thus only nighttime retrievals are used in VODCA. The sensor datasets are then individually matched based on the VOD band by using an improved cumulative distribution function matching scheme to correct for systematic differences between the sensors (details of the processing can be found in  Moesinger et al.
, 2020). Finally, where multiple sensor observations are available, the bands are then merged via the arithmetic mean. This VODCA dataset is available globally at a 0. 25 • × 0. 25 • spatial resolution. Due to the different number of sensors depending on each VOD band (and geographic location) and the timing of the satellite overpasses, the merged product provides observations for at least 40 % of all days with at least one sensor and upwards of 70 % with two or more. Figure  2  shows the time series response of CGLS LAI (solid green line), VODCA X-band VOD (VODX) (dashed red line), and VODCA C-band VOD (VODC) (dotted blue line) near Lincoln, Nebraska, from 2003 to 2018. This pixel is composed primarily of C 3 and C 4 crops. LAI observations have a far more predictable and seasonal pattern.
X-band VOD is also a stronger signal compared to C-band VOD. The peaks are relatively close in timing in this case but can also be offset due to the difference in peak vegetation water content. While this figure demonstrates that there is a correlation between LAI and VOD, it also shows that one cannot be substituted for the other. As was done in  Kumar et al. (2020) , VOD observations are seasonally linearly re-scaled to match observed LAI over the same period, in this case from the CGLS LAI dataset. Before linear re-scaling, the LAI and VOD observations are first scaled and matched to the same 0. 25 • ×0. 25 • grid. A linear monthly re-scaling was then performed using a 3-month moving-window period to best match the two datasets over seasonal timescales. Over an entire year, this re-scaling is represented by 12 monthly equations, each taking into account the climatologies of the months preceding and succeeding it, and it is applied on a per-pixel basis.
Each monthly equation is the same from one year to another. Each equation results from a first-order linear regression. In addition to this cumulative distribution function matching, a 30 d rolling average is applied after the re-scaling to smooth the resulting LAI proxy and allow for better performance of the assimilated data. VOD is sensitive to short-term changes in vegetation water content such as rainwater interception  (Saleh et al. , 2006). This day-to-day variability does not reflect changes in LAI. For the sake of clarity regarding the re-scaling methodology, the Python code segment responsible for re-scaling of VOD values is given in the Supplement to this article. Re-scaling is required because the ISBA LSM cannot simulate VOD directly, and thus we cannot assimilate VOD data directly into the model.
, , b, 2011)). The land surface is treated as a combination of soil and vegetation in the model, with each having unique temperatures, fluxes, and coupling with the atmosphere. Thermal infrared bands from the Geostationary Operational Environmental Satellite (GOES) sensors estimate land surface temperature (LST) and provide the driving force for ALEXI over the United States, with Meteosat Second Generation (MSG) providing data over Europe and Africa. Global products use the Geoland2 land cover database  (Lacaze et al. , 2010)  to estimate LST. Regional vegetation cover is estimated from MODIS-derived LAI products. Aerodynamic and atmospheric boundary layer conditions are derived from North American Regional Reanalysis (NARR)  (Mesinger et al. , 2006) , the Weather Research and Forecasting (WRF) model  (Skamarock et al.
, 2005; Baldocchi, 2008; Lasslop et al. , 2010). This network of in situ measurements is then taken and combined with MODIS imagery for quality control and feature selection, put through several machine learning approaches, and finally combined with seasonal gridded satellite and meteorological observations to generate global carbon and energy flux products, FLUXCOM  (Tramontana et al. , 2016; Jung et al. , 2018). This study uses the global GPP product from FLUXCOM to evaluate the performance of vegetation parameters inde-pendent from the LAI assimilated by LDAS-Monde. When this study was performed, the global FLUXCOM GPP data at a 0. 5 • × 0. 5 • resolution were available from 1980 to 2013. It must be noted that there has been a word of caution that interannual variability patterns of FLUXCOM data may not be completely realistic  (Jung et al.
Sensor calibration is also performed annually. A thermistor is also placed alongside the soil moisture sensor at all plots and depths. An average at each depth is calculated from the three plots every 5 min, and output data are typically publicly available within an hour of the reading. Figure  3  shows the locations of the USCRN in situ observations. Four sensor depths are selected and are matched to ISBA soil layers, 5 cm (WG3), 20 cm (WG_20), 50 cm (WG6), and 100 cm (WG8). This comparison uses the ISBA soil layers to directly compare against the point measurements of USCRN, https://doi. org/10. 5194/bg-19-2557-2022 Biogeosciences, 19, 2557-2581, 2022 but it is important to keep in mind that WG3, WG_20, WG6, and WG8 are layers of soil. WG3 is from 5 to 10 cm; WG_20 is a weighted average of WG4 and WG5 (as performed in  Mucia et al.
Experimental setup and assessment The experiments performed and reported in this study occur over the contiguous United States (CONUS) from 2003 to 2018 at a 0. 25 • × 0. 25 • spatial resolution. This domain, as shown in Fig. 3 , is defined by 20 to 55 • N and 130 to 60 • W. The year 2003 was chosen as the start date as this is approximately when the TRMM mission (containing the TMI sensor used in VODCA) ceases to be the only functioning VOD dataset included in VODCA. Because of the limited geographic extent of this mission (only up to 35 • N), the analysis would be skewed. Table  2  provides the experiment names used throughout to reference the assimilation setups, and briefly describes what data are assimilated for each one. Besides the open loop (OL), SEKF LAI, SEKF VODX, SEKF SSM, SEKF LAI SSM, and SEKF VODX SSM, the SEKF VODX10 experiment was run at the same time, and this experiment uses VODX observations from VODCA as before, but it has filtered those observations to coincide only with where and when LAI observations from CGLS exist.
This is used to test whether the changes produced between SEKF LAI and SEKF VODX are truly from the more frequent assimilation or from the quantifiable differences between matched VODX and LAI. If the SEKF VODX10 results closely resemble SEKF LAI results but SEKF VODX results are very different, this indicates the frequency of assimilated observations is the primary cause of those differences. The primary statistical score used in this study is Pearson's correlation coefficient (r). The correlation is chosen as it is a simple yet effective measure of proximity to reference datasets. The average correlation as well as distribution of correlations can allow the quick assessment of improvement or degradation and is consistent with previous studies of LSMs and LSVs. Root mean square deviation (RMSD) was also calculated for the comparisons to reference observations, which gave the same results as with correlation and is thus not shown.
When analyzing the statistical scores of USCRN, several conditions are applied. First, frozen soil conditions are avoided by only calculating scores based on observations when temperature measurements are above 4 • C. As ISBA separately calculates frozen and liquid soil moisture, when conditions are close to freezing, there can be significant errors. Second, only stations with more than 100 observations (at the respective depths) are calculated for a sufficient number of data points. Finally, p values are calculated alongside the correlations, and stations without p values of significance, as defined by p > 0. 05, are screened out. Bootstrapping is also used to calculate confidence intervals and thus determine statistical significance between different experiments. Essentially, bootstrapping is the repeated removal of random points in the dataset and recalculation of the desired score or variable.
This transition from VODX to matched VODX shows that the transformation is viable and strongly improves the correlation. This matched VODX product is able to be assimilated as an LAI proxy into LDAS-Monde. This seasonal linear re-scaling does come with some drawbacks however, including the merging of observed errors from the LAI and VOD as well as errors produced by the re-scaling, but this simple transformation does provide the opportunity to study more frequent assimilation of observations in place of LAI using a readily available dataset.
Assessment with satellite-derived observations This section analyzes the impact of using and assimilating matched VODX as an LAI proxy in the LDAS-Monde system. For this analysis, the primary variables of interest are LAI, GPP, ET, and SSM. Out of the four, GPP, ET, and SSM are the truly independent datasets to compare to when investigating the assimilation of LAI or VODX. Most of the main conclusions are then drawn from the analysis of these variables. Comparisons to LAI observations are still presented, but as the LAI was used in the assimilation itself or to re-scale the VOD, it acts more as a benchmark for the assimilation. The average monthly correlations are presented in Fig. 7  for the OL, SEKF LAI, SEKF VODX, and SEKF VODX10. First looking at LAI, Fig. 7a , the whole CONUS domain sees added value during the months of May through September when assimilating matched VODX in place of LAI.
As seen in panel (a), there is no discernible difference between the single and joint assimilation for LAI. But as panels (b) and (c) show, jointly assimilating vegetation and SSM produces slightly improved monthly correlations over the whole CONUS domain for GPP and ET respectively. These improvements are primarily seen in the months of June through August, and while the changes are small, they indicate consistent improvements over the single assimilation of LAI or matched VODX. Regarding the variable of SSM, the joint assimilation strongly improves the monthly correlations from LAI to LAI SSM and from VODX to VODX SSM. To reiterate, because the SSM observations assimilated are the same used for comparison in panel (d), it is merely an indication that the data assimilation is truly shifting model soil moisture values closer to those of observations.
Discussion 4. 1 Analysis of re-scaled X-band VOD as an LAI proxy In the comparison of VOD and LAI before seasonal linear rescaling, it is immediately apparent that vegetation type plays a large role in their relationship. These values seen and described in the results seem to indicate that heavily forested regions have only weak correlations between VOD and LAI observations. In this research, the improvements to GPP from the assimilation of X-band VOD can be explained by a better sensitivity of X-band VOD to the leaf biomass. While direct assimilation of VOD may be possible in some data assimilation systems (such as L-band VOD in CCDAS, as performed by  Scholze et al. , 2019) , this is not possible in LDAS-Monde, as the NIT version of ISBA simulates neither wood biomass nor specific leaf area (SLA), both necessary for simulating VOD.
Additionally, the objective of VOD data assimilation in CCDAS is to constrain certain model parameters, while the objective of assimilating re-scaled X-band VOD in LDAS-Monde is to sequentially assimilate observations in order to constrain the day-to-day trajectory of the ISBA state variables, without changing model parameter values. Although ISBA is an uncalibrated model, it performs as well as other state-of-the-art models in inter-comparison experiments (e. g. , Fig. B2  in  Friedlingstein et al. , 2020) , even without assimilation. Moreover, studies have shown that VOD may be sensitive to rainwater interception by leaves (e. g. ,  Saleh et al. , 2006). The ISBA model is able to simulate interception, but there is no simple way to simulate the physical interception effect on VOD. It is for this reason that a statistical re-scaling of VOD towards an LAI proxy was pursued.
A new version of the ISBA model includes a multiple energy budget together with a representation of litter in forests  (Napoly et al. , 2017)  that will be generalized to low vegetation in the next version of SURFEX. Using this new capability could improve the benefit of assimilating SMOS (Soil Moisture And Ocean Salinity), SMAP (Soil Moisture Active Passive) or ASCAT (Advanced Scatterometer) SSM retrievals or level-1 observations. Another step in the direction of improving initial conditions consists of directly assimilating level-1 data, such as ASCAT radar backscatter or microwave brightness temperature from passive instruments into LDAS-Monde. This would allow the use of all the information contained in the signal, including soil moisture and vegetation, and at the same time reduce observations errors in the data assimilation  (Shamambo et al.
The developments presented in this study stemmed from SURFEX version 8. 1. LDAS-Monde technical documentation and contact points are freely available at https://opensource. umr-cnrm. fr/attachments/2961/OpenLdasMonde_V2. 0. tar. gz (last access: 5 May 2022; CNRM, 2019). //doi. org/10. 5285/0683e320d8634a37aa1d9ef62dd41a0d, Dorigo et al. , 2020; https://data. ceda. ac. uk/neodc/esacci/soil_ moisture/data/daily_files/COMBINED/v04. 7, last access: 5 May 2022), CGLS LAI from Copernicus (https://land. copernicus. vgt. vito. be/PDF/portal/Application. html#Browse;Root=512260; Collection=1000083;Time=NORMAL,NORMAL,-1,,,-1,,, https://doi. org/10. 5194/bg-19-2557-2022 Biogeosciences, 19, 2557-2581, 2022 last access: 5 May 2022; Copernicus, 2020), VODCA from Zenodo (https://doi. org/10. 5281/zenodo.
2575599, Moesinger et al. , 2019), USCRN soil moisture observations from NOAA (https://www. ncei. noaa. gov/access/crn/qcdatasets. html, last access: 5 May 2022; NCEI NOAA, 2022), and FLUXCOM GPP from https://www. fluxcom. org/ (last access: 5 May 2022; FluxCom, 2022).
2575599, Moesinger et al. , 2019), USCRN soil moisture observations from NOAA (https://www. ncei. noaa. gov/access/crn/qcdatasets. html, last access: 5 May 2022; NCEI NOAA, 2022), and FLUXCOM GPP from https://www. fluxcom. org/ (last access: 5 May 2022; FluxCom, 2022). Data availability. ERA5 data are available from Hers- bach et al. (2018) (https://doi. org/10. 24381/cds. adbb2d47), CCI SSM from the ESA Climate Office (https:.
SHOWCASE will deliver new harmonised datasets describing levels of biodiversity indicators and environmental and economic variables, generating knowledge on large-scale patterns that are difficult to assess in individual projects. It will also present a critical assessment of the types and combinations of incentives that most effectively trigger farmers to manage biodiversity on their farms. The results produced by SHOWCASE align with the Convention on Biological Diversity's Strategic Plan for 2011-2020 and a number of Sustainable Development Goals (SDGs), particularly SDGs 15 (Life on land -Sustainably manage forests, combat desertification, halt and reverse land degradation, halt biodiversity loss) and SDG 12 (Responsible consumption and production).
SHOWCASE will participate in the Pilot on Open Research Data in Horizon 2020, in line with the Commission's Open Access to research data policy for facilitating access, re-use and preservation of its research data. Research data underlying scientific publications will be deposited in an institutional repository of the relevant project partner. Besides research data and peer-reviewed publications, Open Access will be provided to other types of scientific output of the project such as monographs, books, conference proceedings and reports. To coordinate data management within the project, SHOWCASE will develop a guiding Data Management Plan (DMP). Finally, the vision of SHOWCASE is that communication should be bi-directional, i. e. not only delivering new results to the public, but also receiving their knowledge.
As a consequence, the French Ministry in charge of Environment launched studies to develop damage models for different sectors, such as: residential sector, public infrastructures, agricultural sector, and commercial and industrial sector. In this article, we focus on our contribution to produce damage functions for the agricultural sector through the development of the model floodam. agri. However, the methodology for all sectors share the same principles: no sufficient data from past events were available to build damage models on a statistical analysis, so process-based modelling approaches have been adopted and they were fed with expert knowledge. This development context has led to particular requirements. The Ministry needed ready-to-use French National Damage Function but the damage functions should be applicable and explainable to the various stakeholders who would use it at the watershed scale to evaluate their projects.
Overview of French National Damage Functions The database used to locate agricultural assets in France is the Graphical Plot Register (GPR). It lists the agricultural parcels according to a defined typology. In France, there is no database for the census of agricultural buildings. The damage functions produced with floodam. agri have been built to be compatible with this. They indicate the estimated expected value of damage in euros by hectare, depending on the water depth, submersion duration, season of occurrence of the flood, and flow speed. Using floodam. agri, damage functions were produced for 15 of the 28 sorts of crop of the GPR typology. These 15 sorts accounted for 89% of agricultural areas located in flood-prone areas in metropolitan France in 2010, according to the GPR database.
Damage to buildings and their contents (inputs, equipment and stocks) has not yet been taken into account because, for the application of the damage functions, it is currently not possible to locate agricultural buildings in the existing database. floodam. agri also does not consider induced damage at the farm scale i. e damage induced on farm activity due to direct damage on farm equipment for example as evaluated in  Brémond and Grelot (2012)  or indirect damage at the scale of the area affected by a flood as for example damage propagation on cooperatives as evaluated in Nortes Martínez (2019a) and described in Nortes Martínez (2019b). Indeed, in an operational way, it remains very difficult to obtain information concerning the links between farm buildings and parcels of the same farm or the links between farms and cooperatives.
agri and if yes, if the estimation is explicit or implicit and how to estimate the consequences. These processes have been identified based on litterature and during the individual interviews. The parameters used to characterise the floods are : (i) the height, (ii) the duration of submersion, (iii) the velocity, and (iv) the season. The ranges of values considered for each parameter are indicated in table  5. We described the time of occurrence of the flood in terms of schedule of physiological stages instead of time of the year to maintain the adaptability of our model to different contexts. It was defined in collaboration with the experts consulted. As an example, the table 6 presents the physiological stages selected at the French scale for winter wheat. As for the flood height, for each crop, crop height data were also collected (trunk height, fruit height, maximum height).
Updatability : the origin and the vintage of the data specified In this section, the methodological framework (table  1 ) is used to describe the updatability of floodam. agri. U1 : Are all the data used in the model and their sources made explicit? To produce Flood Damage Functions, floodam. agri requires : i/ an estimate of usual yields,ii/ an estimate of selling prices, iii/ an estimate of intermediate consumptions, iv/ physiological stages and crop management sequence. The table 9 lists all the data and their source used in floodam. agri. There is no homogeneous database that provides information on all the technical and economic data of the crops. We had to collect this information from different databases depending on the crop and sometimes complete this information based on expert opinion.
It is therefore all the more important to be rigorous about making the data used explicit. U2 : Are the vintages of the data used in the model specified? The vintage used and the frequency of update are specified in the table 10. Since the databases used are heterogeneous, the vintages of the databases are also heterogeneous. The table 10 shows the update frequency of the databases used. Updating the data that is published annually is easy. On the other hand, to update data from documents whose publication frequency is not predetermined requires checking for each data if a new edition has been produced. If not, a validation with experts should be renewed. To sum up, tables 9 and 10 shows that the updtability of data is not homogeneous. Three modalities can be distinguished : input data come from a single database which tracked over time (eg yields) input date come from different databases with different update frequencies (eg selling prices and intermediate consumptions) input date come from expert knowledge (eg physiological stages).