dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and conclusions This study presents an evaluation of stratospheric ozone and water vapor changes from the pre-industrial to the end of the 21 st century in simulations performed by CMIP6 models under a range of future SSP scenarios. In total, for the historical period 1850-2014 ozone data was available from 14 models, while water vapor data was available from 10, and a subset of these models had also performed simulations under several SSP scenarios. For zonal mean stratospheric ozone mixing ratios there is good agreement between the CMIP6 MMM and observations from the SWOOSH combined dataset, with biases within ±10%, while for TCO there is good agreement between the CMIP6 MMM and the NIWA-BS dataset from 40°S-90°N, with biases within ±20 DU (<±10%). Largest percentage zonal mean ozone mixing ratios biases occur in the tropical upper stratosphere, while for TCO the largest biases occur between 90°S-40°S.
expansion indices for the spherical harmonics are adapted to the data to avoid over-fitting of the regression model. Once fitted, the regression model was then used to estimate TCO values in regions where data were missing. Such regression model derived fields were spliced into the unfilled fields to ensure a smooth transition from the measurements into the regression model https://doi. org/10. 5194/acp-2019-1202 Preprint. Discussion started: 17 February 2020 c Author(s) 2020. CC BY 4. 0 License. derived values. Monthly mean fields were calculated from the filled daily fields to provide the validation datasets used for the CMIP6 models evaluated here. The Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset is a merged record of stratospheric ozone and water vapor measurements collected by a subset of limb sounding and solar occultation satellites spanning 1984 to the present (seeDavis et al.
TCO climatologies (latitude vs month) for all 14 discussed CMIP6 models are shown in Figure3. Overall the observed climatology patterns and annual cycle amplitudes, compared here against the NIWA-BS dataset, are well represented in the CMIP6 MMM and the individual models: lower values and smallest amplitude in the tropics that increase to the poles, with the highest TCO values around 60°S between August and November, and in the NH polar regions between January and May, and the smallest TCO values in the SH polar regions during the ozone hole period. Some models slightly underestimate TCO https://doi. org/10. 5194/acp-2019-1202 Preprint. Discussion started: 17 February 2020 c Author(s) 2020. CC BY 4. 0 License.
https://doi. org/10. 5194/acp-2019-1202 Preprint. Discussion started: 17 February 2020 c Author(s) 2020. CC BY 4. 0 License. calls into question whether TCO values are an accurate measure of stratospheric ozone recovery, or if other metrics can more accurately reflect the profile changes expected for stratospheric ozone recovery without being influenced by tropospheric changes. Stratospheric water vapor mixing ratios are poorly represented in many of the CMIP6 models investigated in this study. For the climatological 2000-2014 period, the models are dry biased, particularly in the upper stratosphere. This results from several of the models studied here not including any representation of water vapor formed from the oxidation of CH4 in the stratosphere. The seasonal cycle and water vapor mixing ratios for individual CMIP6 models at 70 hPa in the tropics shows poor agreement with the SWOOSH dataset, and further highlights the difficulties climate models have had over several generations of model intercomparison projects in the tropical tropopause region.
agencies who support CMIP6 and ESGF. JK and PTG thank NERC for financial support through NCAS. BH was supported by the European Union's Horizon 2020 Framework Programme for Research and Innovation "Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO)" project under Grant https://doi. org/10. 5194/acp-2019-1202 Preprint. Discussion started: 17 February 2020 c Author(s) 2020. CC BY 4. 0 License. Agreement No. 641816. GZ and OM were supported by the NZ Government's Strategic Science Investment Fund (SSIF) through the NIWA programme CACV. We thank the GFDL model development team, who led the development of GFDL-CM4 and GFDL-ESM4, as well as the numerous scientists and technical staff at GFDL who contributed to the development of these models and conducted the CMIP6 simulations used here.
With SCADA systems, the managers are able to monitor the water levels and velocities from distance. They are also able to control the water flow via hydraulic structures which are fully automated. In this occasion, the operators are enabled to track the faults and take the necessary maintenance actions  [3]. As the benefits of this field is that, all the models can be adjusted and integrated in a computer as a (SCADA) system by employing a programming language such as MATLAB  [2]  which makes it possible to form an instructional software for controlling the irrigation equipment  [16]. This method is economic and includes reliable data to be transferred, in case of necessity, to the other sections and is flexible and totally easy to be implemented. It obviously improves the efficiency of the water distribution system and the supply of water in the required domains.
However, discovering the full potential of DT will require more research to improve the traditional data collection and the whole procedure and it is also necessary to apply the communication interface between real and physical twins  [9]. There are some researches done with the operation of a DT in water distribution systems (WDS). [14]  introduced a primary form of Cyber-Physical System (CPS) comprised of an Epanet hydraulic model with the mathematical Matlab software in order to construct an intelligent cyber WDS that could implement certain valves and allocate distributing the water equitably. The work is famous to be a basic example of an intelligent decision support system. Later on in 2015,  [22]  worked in finding a definition of a CPS managing the quality of water. Based on this job, the CPS needs many components such as sensors and communication network, computing technologies for models, managing the set of data and its analysis and the predictive controllers.
Digital Twin framework The framework for the DT of a hydrographical networks is based on a WDS structure on which a hydrological model can be associated (see Figure  1 ). The hydrological model aims at predicting the runoff according to the rain by considering several predictive horizons. This information is very useful to reduce uncertainties on inputs and for that of predictive control algorithms. In case that no hydrological model is available, the hydraulic model and collected data can be used to estimate the unknown input/output. It is ,however, necessary to have accurate models of the hydraulic devices and accurate measures (discharges, or levels). Inverse modelling approaches are used to estimate the unknown input/output of the past events. The DT is then connected to a software such as Matlab or to a computer code and programs that implement the expert rules or the predictive and adaptive control strategies.
g. QGIS  6  is very interesting in case of the topography of the canals; so that it is directly accessible. There is an exhaustive literature on hydraulic devices while they are characterized by the nonlinear dynamics. Some of the software solutions could integrate models of hydraulic devices (weir, gate, etc. ), however, for hydraulic devices presenting more complex dynamics, Machine Learning techniques are available to identify black-box or grey-box models based on the measurement. These techniques, e. g. Support Vector Machine (SVM), are suitable for nonlinear and hybrid dynamics. Although, the techniques require a rich and exhaustive database. Finally, when it comes to the calibration of the hydraulic models, the friction coefficient is used in general. Here again, available data are required.
DT of the canal of Calais The objective of this section is to illustrate the steps of the DT by considering the canal of Calais; located in the north of France. The particularity and the management objectives of the canal of Calais are firstly described. This canal is modelled with the software SIC 2 by considering average physical characteristics. Then, the method to obtain a dynamical model of the hydraulic devices is presented. Finally, the unknown inputs of the canal of Calais are estimated based on an identification technique.
Then, the level can oscillate around the NNL, limiting the use of pumps. However when the uncontrolled input flows are too important, the pumps have to be operated with the aim of avoiding or at least limiting the flood. After modelling the Calais canal in SIC 2 , as a first study, an average profile of the Calais canal is considered. The length, width and depth of the canal are L = 26. 72km, W = 20m and D = 2. 2m, respectively. The average flow is Q 0 = 1m 3 /s and the Manning's roughness coefficient is n r = 0. 035m 1/3 /s. The software SIC 2 is easily linked with Matlab  [8]. Some simulations are performed by using step at the upstream of the canal. The obtained delays and attenuation of the hydrographs are close to those observed on the real system. These tests aim at validating the performance of the selected software.
These functions are identified according to the available data by considering the mathematical function of relation 1. The parameters a 1 , b 1 and c 1 have to be determined according to the available data for each type of tide. The nonlinear regression nlinfit of Matlab is used to determine the value of the parameters which are given in Table  2  and the plot of the functions are shown in Figure  4. This Figure shows a good estimation of the discharge functions. Considering the second gate G 2 , when O G 1 = 25dm and G 2 is open, the average discharges are equal to 15. 25m 3 /s during spring tide, 13. 9m 3 /s during average tide and 12. 55m 3 /s during dead tide. f (O G 1 ) = a 1 (1 -exp(-b 1. O G 1 c 1 )) (1).
Estimation of the unknown inputs The proposed approach for estimating the unknown inputs is based on the simulated model and real data. This first approach aims at being simple and easy to use. It consists determining the difference of volume in the canal according to relation 2. ∆ V (k) = ∆ Z (k). S Calais (2) with S Calais the area of the canal such as S Calais = L. W and ∆ Z (k) = Z Calais (k) -ẐCalais (k), where Z Calais (k) is the level measured in Calais and ẐCalais (k) is the estimated level from the software SIC 2. The difference of volume ∆ V (k) is averaged on a time window ∆ T leading to µ ∆ V |∆ T. The difference of discharges between two periods of time is given by relation 3. These values correspond to the unknown inputs on the Calais canal. ∆ Q = µ ∆ T +1 ∆ V -µ ∆ T ∆ V ∆ T (3).
Conclusion In this paper, a framework of a digital twin is proposed to reproduce the dynamics of the Calais canal and to implement the advanced control on the hydraulic devices of the canal. The canal is simulated by the software SIC 2. The strategy behind the new management of the canal followed the goals of navigation and prohibition/limitation of flood events. For achieving these two objectives, the water level and the outlet gates of the canal should have been controlled. Thus, the analysis have done on both the outlet gates dynamics and the unknown inputs. Afterwards, the simulated level of the water and the discharge at the outlet gates have been inspected. Although, there were some differences in the simulated figures due to the unknown discharges, it is still possible that after recognizing them, by doing a play-back of the scenarios, there would be an enhance in the management strategies.
Climate change is already affecting agro-ecosystems and threatening food security by reducing crop productivity and increasing harvest uncertainty. Mobilizing crop diversity could be an efficient way to mitigate its impact. We test this hypothesis in pearl millet, a nutritious staple cereal cultivated in arid and low-fertility soils in sub-Saharan Africa. We analyze the genomic diversity of 173 landraces collected in West Africa together with an extensive climate dataset composed of metrics of agronomic importance. Mapping the pearl millet genomic vulnerability at the 2050 horizon based on the current genomic-climate relationships, we identify the northern edge of the current areas of cultivation of both early and late flowering varieties as being the most vulnerable to climate change. We predict that the most vulnerable areas will benefit from using landraces that already grow in equivalent climate conditions today.
Genomic diversity reflects the geographical origin of varieties. We first built a genomic dataset comprising 173 landraces originating from 10 Sahelian countries (Fig. 1 ). Using 100 plants per landrace in a pool-sequencing design, we estimated allele frequencies for each landrace at 138,948 polymorphic singlenucleotide polymorphisms (SNPs, Supplementary Data 1 and 2). Principal component analyses (PCAs) of SNP allele frequencies with complete data on landraces clearly separated according to their geographical origin (Fig. 1c, d ) and landraces were found to cluster according to the country. Modeling SNP and climate with a GF approach. We used a gradient forest (GF) approach to model variation in allelic frequencies along environmental gradients  20, 21  using both genomic and climate observations at the 173 locations sampled.
This approach enables identification of the cut-off in allele frequencies associated with major changes in environmental conditions together with the identification of major climatic drivers of the genomic composition. GF models were built using the 16,632 SNPs with an allele frequency higher than 10%. The climate dataset consisted in 157 metrics of agronomical importance, i. e. , onset of the monsoon and other metrics related to precipitation, temperature, and solar radiation (Supplementary Table  1 ). These metrics were calculated for a period of from 30 to 180 days after monsoon onset, corresponding to the pearl millet growing period, as farmers traditionally sow their fields after the first significant rainfall. These metrics were obtained for 17 climate models (Supplementary Table  2 ).
The climate models were developed by different independent climate modeling groups around the world, associated in the CMIP5 international working group (Coupled Model Intercomparison Project  28  ). The 17 models are a subset of the climate models considered by experts of the Intergovernmental panel on Climate Change. GF models were built separately for each of the 17 climate models based on the climate that prevailed on the date the landraces were collected in the field. The most important climate predictors associated with the genomic data are the minimum intensity of solar radiation at the beginning of the growing season, monsoon onset, and precipitation intensity at the beginning of the growing season (Supplementary Table  3 ). We found an average of 88% of the SNPs that can be predicted by the environmental variables (from 14,544 to 14,757 SNPs with a R 2 > 0) depending on the climate model considered (Supplementary Table  3 ).
Methods Plant material. A total of 173 geo-referenced landraces originating from 10 West African and Central African countries were sampled. The landraces were collected by the Institut de la Recherche pour le Développement (IRD) on dedicated missions conducted between 1974 and 1989 (Supplementary Data 1). One hundred seedlings of each of the 173 landraces were grown in IRD greenhouse facilities in Montpellier (France) from seed stocks maintained as part of the IRD collection. This corresponds to a total of 17,300 seedlings grown for DNA extraction. The same quantity of leaf material was collected from each of the 100 seedlings of each landrace for DNA extraction. Pool-sequencing, bioinformatic analysis. Bait design. A total of 152,619 biotinylated 80 bp baits were designed and synthesized by Mycroarray (Ann Arbor, Michigan, USA, reference: 160606-32).
Read filtering and mapping step. Adapter sequences and extremities with low quality scores (-q 20) were removed from row reads using Cutadapt (v1. 10 51 ). Reads were filtered based on their length (L > 35 bases) and on their quality mean values (Q > 30) using a freely available Perl script (https://github. com/SouthGreenPlatform/ arcad-hts/blob/master/scripts/arcad_hts_2_Filter_Fastq_On_Mean_Quality. pl). The separately trimmed forward and reverse reads were then re-synchronized into pairs with an in-house Perl script. The filtered paired-end reads were mapped to the pearl millet reference genome  14  using BWA (Burrows-Wheeler Aligner, v0. 7. 2  52  ), with the MEM algorithm and standard parameters. Unmapped, low mapping quality (MAPQ < 20) and improperly paired reads were filtered out with SAMtools (v1.
1  53  ). The exome mean coverage was estimated for each accession with QualiMap (v2. 2  54  ) using the gene annotation from ref. 14. Local realignment was performed using IndelRealigner and variant calling was performed using UnifiedGenotyper in the Genome Analysis Toolkit (GATK v3. 7  55  ). SNP calling. Raw SNPs were filtered out using the GATK VariantFiltration tool with the following criteria: bi-allelic SNP, depth coverage >10 and <250 per accession, <3 SNPs in a window of 5 bp, frequency of the alternate allele to be called as an SNP (AF > 0. 003 corresponding to a minimum count of five reads with the alternative allele throughout the dataset). For each accession, SNPs with a total read count of <20 reads were set to NA. Finally, only the SNPs with complete data were considered in the final SNP set.
PCA analysis. PCA analysis was performed on allelic frequencies using the prcomp() R function to summarize variations in population structure. Climate data. Climate datasets. The data in the observed climate dataset were extracted from the EWEMBI climate dataset  56. The EWEMBI dataset covers the entire globe at 0. 5°horizontal and daily temporal resolution from 1979 to 2013 and combines several reference datasets: ERA-Interim reanalysis data  57  , WATCH forcing data methodology applied to ERA-Interim reanalysis data  58  , eartH2Observe forcing data (E2OBS  59  ), and NASA/GEWEX Surface Radiation Budget data (SRB  60  ). The SRB data were used to bias-correct E2OBS shortwave and longwave radiation  61. Variables included in the EWEMBI dataset are Near Surface Relative Humidity, Near Surface Specific Humidity, Precipitation, Snowfall Flux, Surface Air Pressure, Surface Downwelling Longwave Radiation, Surface Downwelling Shortwave Radiation, Near Surface Wind Speed, Near Surface Air Temperature, Daily Maximum Near Surface Air Temperature, Daily Minimum Near Surface Air Temperature, Eastward Near Surface Wind, and Northward Near Surface Wind.
A sample of this dataset was extracted for a period corresponding to the sample collecting missions among the available data in the dataset (1979-1989, mean value over the 10-year period) at a pixel resolution of 0. 5°× 0. 5°(~50 × 50 km). In the present study, the pearl millet cultivation area is delimited by the convex hull of our landrace coordinates. This area extends over 3. 1 million km 2 , including 1041 pixels. Climate data for the sampled locations were inferred from the pixel in which the landrace coordinates are located. Future climate projections consisted in bias-corrected daily climate data extracted from a subset of 17 climate models among the CMIP5 dataset  28  (Supplementary Table  4 ). Climate data from the 17 climate models were corrected using the Cumulative Distribution Function transfer method to reduce errors in the present-day simulations of the CMIP5 models compared to observed data  28, 58, 62, 63.
The monsoon onset was assessed from rainfall data in each pixel in the cultivation area and for each model. This metric has been found to be highly correlated with the sowing date of pearl millet in the West African cultivation area  64. All the other 156 climate metrics were mean, minimum, or maximum values of a climate parameter or number of events calculated within a period of 30, 60, 90, 120, 150, or 180 days after monsoon onset (6 × 26 metrics). GF predictions of genomic vulnerability. We used GF (R package, gradientForest  21, 33  ) to model the importance of changes in allele frequency along the environmental gradients. GF is a machine-learning approach derived from the random forest algorithm. It is based on regression trees linking allele frequencies with environmental data observed at the locations of the 173 landraces.
As in ref. 20  , genomic vulnerability (also referred to as genomic offset in ref. 21 ) was calculated as the Euclidean distance between the genomic composition under the past and future projected climates. This analysis enabled assessment of the genomic vulnerability of pearl millet landraces currently cultivated in West Africa at the 2050 and 2100 horizons. Genomic vulnerability was obtained using both the dataset of total climate metrics and the subset of uncorrelated climate metrics following  20  with a maximum Pearson's correlation threshold of 0. 7. GF model assisted migration scenarios. We wanted to find out which currently cultivated landraces would be best adapted to future climate conditions projected in the most vulnerable areas in order to infer migration. This analysis relied on the GF models previously built for each of the 17 climate models.
Each climate model was analyzed separately and then combined. For a given climate model, we identified vulnerable areas using a spatial approach that clustered the 10% most vulnerable pixels (see details in Supplementary Fig. 9 ). Clusters of vulnerable pixels were designed using DBscan (Density-Based Spatial Clustering of Applications with Noise; dbscan R package  65  ), which groups pixels that are closely packed together. Clustering was based on the geographic distance between vulnerable pixels measured with the distm function in the geosphere R package. Only clusters with at least four vulnerable pixels and separated by <1200 km were retained for further analysis. This distance corresponds to the value that best optimizes the spatial distribution of the vulnerable areas within the 3. 1 million km 2 region considered here.
Measuring flowering time. Flowering time was measured in six trials composed of two completely randomized blocks in 2016 and four trials in 2017. All the trials were performed at the International Crops Research Institute for the Semi-Arid Tropics field station in Sadoré, Niger (Lat. 13. 2375, Long. 2. 2797). Ten plants were phenotyped for each landrace. Sowing dates ranged from 15 June to 6 July, depending on the trial. The trials were conducted under rainfed conditions with supplementary sprinkler irrigation if necessary. GWAS analysis for flowering time. Analyses associating SNP allele frequencies with flowering time were performed using a subset of 27,409 SNPs with a minor allele frequency >5%. We adjusted both a simple linear model that did not account for population structure and a LFMM (with the lfmm R package  31, 32  ) designed for the correction of unobserved confounding factors such as population structure.
We estimated the number of latent factors from the screeplot generated from a PCA of the genomic data with the prcomp R function. After model selection based on Q-Q plots (Supplementary Fig. 4 ), we considered association results obtained from the LFMM analyses with five latent factors. We used a false discovery rate (FDR) of 5% to select associated SNPs (with the qvalue R package, v 2. 18. 0). Intersecting the position of the SNPs with the position of the pearl millet genes  24  using the bedtools program (v2. 27. 1), we identified annotated candidate genes involved in variations in flowering time. The GF model linking change in allele frequencies with climate provides a correlation (R 2 ) for each SNP by assessing their relative importance in the model based on the cross-validation procedure detailed in ref.