dataesr/scientific-paragraphs-categorization huggingface dataset:
Reservoir storage Reservoir storage fills with water behind dams through flows above and below the ground and stores water for a residence time. It loses water through discharge to other storage compartments, evaporation, groundwater recharge, and water abstraction for human water use. A total of 10 models (CLM4. 5, CLM5. 0, DBH, JULES-W1, Mac-PDM. 20, mHM, MPI-HM, ORCHIDEE, VIC, and WAYS) do not include reservoir storage for ISIMIP2b (Tables 9, 10, S35; Fig. 2 ). Six models compute outflow from reservoirs (Table S37), while evaporation from reservoirs is computed by four models (Table  S38 ). In general, most of the models use the Global Reservoir and Dam database (GRanD:  Lehner et al. , 2011)  but with a different number of active managed reservoirs used for reservoir operation during simulations.
Domestic, livestock, and industry sectors Five models (CWatM, H08, MATSIRO, PCR-GLOBWB, and WaterGAP2) simulate water abstraction, water consumption, and return flow for the domestic sector (household; see Tables  S59-S64 ). Three models (MATSIRO, PCR-GLOBWB, and CWatM) combine manufacturing and electricity sectors into one sector, the industry sector. CWatM only calculates total abstraction from groundwater or surface water. MATSIRO and LPJmL used input data for water demand of the domestic and industry sectors, offered by the ISIMIP2b framework. These input datasets provide water consumption, but not return flow from these sectors. Consumption water can return to the atmosphere as evapotranspiration. LPJmL used input data for domestic and industrial water consumption data, provided by the ISIMIP2b framework, and assumed that only the consumed water amount is withdrawn.
The CLM team improved the irrigation scheme  (Thiery et al. , 2017 (Thiery et al. , , 2020)) , the extraction of groundwater  (Felfelani et al. , 2020) , the representation of land cover and land management  (Meier et al. , 2018; Hirsch et al. , 2017 Hirsch et al. , , 2018)) , and the implementation of reservoirs  (Hauser et al. , 2019). Numerous developments can be followed on the model's GitHub page (https://github. com/ESCOMP/CTSM, last access: 2 November 2020). CWatM developed a groundwater scheme with linkages to MODFLOW for 5 arcmin and 30 arcsec spatial resolution. The CWatM modelling group plans to develop a reservoir storage including different operation schemes (e. g. energy, irrigation) to increase the temporal resolution (at 1 h) and to also apply a global calibration for ungauged catchments, such as using the Budyko framework  (Greve et al.
, 2020) , applying both the degree-day method and energy balance method to estimate snow accumulation and melt, and applying several methods to estimate evaporation based on changing CO 2 concentration. DBH plans to include human water uses (industrial and domestic sectors), either by developing a new module or using the simulations from other models (e. g. WFaS dataset), to calibrate the model in the new ISIMIP3 simulation round and to improve the input-output module to read and write netcdf files. The H08 modelling team used an approximate Bayesian computation technique to calibrate four parameters that are transferred to other regions containing no observations, which are mainly based on Köppen-Geiger regions. The modelling group also increased the spatial resolution to 5 min and improved the representation of crops used for biofuel in the model.
The JULES-W1 modelling group plans to make a technical update that will enable the river routing module to estimate discharge. The LPJmL group developed an improved energy balance module and soil hydrological scheme that can estimate permafrost dynamics  (Schaphoff et al. , 2013)  and made the model source code freely available on GitHub (https://github. com/PIK-LPJmL/LPJmL, last access: 2 November 2020;  Schaphoff et al. , 2018) , hoping to engage a broader scientific community in LPJmL model development and applications. The Mac-PDM. 20 modelling group plans to develop a water use module. The MATSIRO modelling group has implemented a land use change process, terrestrial biogeochemical processes, and an additional crop growth process into MATSIRO to develop a new modelling framework. As key interactions are taken into account and all processes are coupled, important boundary conditions for hydrological simulations can be dynamically simulated internally.
, 2018). The VIC modelling group developed different irrigation practices  (Shah et al. , 2019a and b)  and included a reservoir  (Dang et al. , 2020a and b)  and a groundwater scheme in the model structure. The WaterGAP2 modelling group plans to update the GRanD dataset used by the model, to include water temperature calculations, to couple the new developed groundwater model  (Reinecke et al. , 2019) , and to update the nonirrigation water use datasets. The WAYS modelling group plans to develop a new human water use module to consider agricultural, industrial, and domestic water use in the water cycle.
Recommendations for future multi-model intercomparison projects and extended assessments We assert that this study was realized through a multi-model intercomparison project (ISIMIP) and is based on communication and collaboration. Ideally, through a unified perspective and effective collaborations toward physically realistic hydrologic models  (Clark et al. , 2015a (Clark et al. , , 2017)) , communities will fill in existing knowledge gaps  (Wagener, 2020) , improve the quality of the input data and the processes in the models, and implement the missing processes in the models. In addition to these statements, we propose focusing the effective collaborations on effective "wish lists", including specific research questions, goals to answer these questions, methods to achieve the goals, datasets to be used, tasks to be done, and, at the end of the project, a retrospective analysis on what has been done and what could be improved.
Certainly, simulating the terrestrial water cycle on the global scale involves many challenges, as we presented in this study. Other challenges have also been synthesized by reviewing articles published by the climate, global hydrological, and vegetation communities and have been classified according to the 23 unsolved problems in hydrology (UPH) identified by  Blöschl et al. (2019)  (Table  S97 ). In summary, these challenges can generally be overcome through innovative and creative collaboration among communities and investment in technical infrastructure. In the end,  Arheimer et al. (2020)  showed that the catchment models can be applied at a global scale because of the new global datasets, increased computational capacity, new methods to estimate parameters, and collaboration. Ultimately, specific features of GWMs such as dam operation, human water abstractions, routing approaches, and calibration might become a part of Earth system models  (Wood et al.
evapotranspiration; CO 2 : CO 2 fertilization effect; DGVM: dynamic global vegetation model; DVPNV: dynamic vegetation composition on potential natural vegetation areas; f (LAI): function of leaf area index; f (LAI, SAI): function of leaf area index (LAI) and stem area index (SAI); f (veg): function of vegetation type; HWSD: Harmonized World Soil Database (FAO/IIASA/ISRIC/ISSCAS/JRC (2012): Harmonized World Soil Database (version 1. 2), FAO, Rome, Italy and IIASA, Laxenburg, Austria, available at: http://www. fao. org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/, last access: 10 June 2021); L: light; PFTs: plant functional types; RCZ: runoff properties varies with climate zones; SLD: soil layers depth from top to bottom; TSD: total soil layer depth; Snowacc: snow accumulation; S: space; W : water; ×: not included in the model.
(1969) / R sat / f (soil) RCZ √ / based on 30 min flow drainage direction map (DDM30) / 0. 5 m s -1 / × / Rfd al. (2000) / R sat / f (soil) √ / continuity equation derived from linear reser-voir model / 1 m s -1 / × / linear storage buffer; Rfd 13 TOPMODEL Beven and Kirkby (1979) / R ho , R sat / f (soil) √ / linear reservoir, TRIP / 0. 5 m s -1 / × / TRIP √ / √ / 728 global reservoirs and 6134 "local reservoirs" / following H08, ret-rospective: Pokhrel et al. (2012) × × √ : included in the model; ×: not included in the model for ISIMIP2b simulations; Dgws: dynamic groundwater scheme; GRanD: Global Reservoir and Dam database, according to Lehner et al. (2011); gw: groundwater; Rsu: surface runoff; Rsat: Rsu modelled as saturation excess overland flow; R ho : Rsu modelled as infiltration excess or Hortonian overland flow; f (gw): sub-surface flow or interflow modelled as a function of groundwater; f (soil): sub-surface flow or interflow modelled as a function of soil moisture (soil); Rfd: the model routes runoff along flow direction; RTM: River Transport Model; TRIP: Total Runoff Integrating Pathways.
Acknowledgements. The present study was possible through the international ISIMIP framework. Camelia-Eliza Telteu  and  Hannes Müller  Schmied are supported by the  German Federal Ministry of Education and Research  (grant no. 01LS1711F ). Guoyong Leng  is supported by the  National Key Research and Development Program of China  (no. 2020YFA0608502 ) and the  National Natural Science Foundation of China  (no. 42077420 ). Yadu Pokhrel received support from the  National Science Foundation (CAREER Award ; grant no. 1752729 ). Junguo Liu have been supported by the  National Natural Science Foundation of China  (grant no. 41625001 ), the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  (grant no. XDA20060402 ), and  Highlevel Special Funding of the Southern University of Science and Technology  (grant no.
We are grateful for valuable comments and suggestions received from  Wouter Knoben  and  Laura Devitt  that helped to streamline and improve the consistency of the paper. Financial support. This research and open-access publication were funded by the  German Federal Ministry of Education and Research  (grant no. 01LS1711F ). Review statement. This paper was edited by  Jeffrey Neal  and reviewed by  Laura Devitt  and  Wouter Knoben.
1 to 4 m variable, derived separately from re- mote sensing data Number of soil layers 6 1, f (FC) 11 2 3 1 1 / the complete root zone (Potential) evapotranspiration scheme Hargreaves-Samani Penman-Monteith reference Evapotranspiration Penman-Monteith (Monteith, 1965) based on the correc- tion term developed by Chris Milly (1992) Hamon Penman-Monteith Priestley-Taylor with varying alpha values for arid and humid areas Penman-Monteith Model Interception Vegetation scheme scheme partition / photosynthesis scheme mHM f (veg) 3 major vegetation classes: (forest, impervious, pervious) for parameter regionalization + long-term dynamics based on LAI-based on GIMMS MPI-HM × prescribed, Land Surface Pa- rameter dataset 2 / × ORCHIDEE f (LAI) tile approach with 17 vegetation types (PFTs); CO 2 PCR-GLOBWB f (veg) natural vegetation (short and tall vegetation) and agriculture (rainfed, rice irrigated, and non- rice irrigated) prescribed an- nually by HYDE dataset and MIRCA, GLOBCOVER / × VIC f (veg) any number of vegetation types with fixed characteristics can be represented (PFTs) / × WaterGAP2 f (LAI) LAI development model based on T and P / × WAYS f (LAI) 14 static vegetation types (PFTs) with fixed characteris- tics / × AET: actual evapotranspiration; CO 2 : CO 2 fertilization effect; f (LAI): function of leaf area index; f (veg): function of vegetation type; f (FC): function of field capacity; P : precipitation; PFTs: plant functional types; SG: subgrid; SLD: soil layers depth from top to bottom; TSD: total soil layer depth; Snowacc: snow accumulation; T : subgrid temperature (daily average) (0 • C); ×: not included in the model.
Bold font indicates LSMs, and italic font indicates GHMs. Notes: 1 Data source: http://www. isimip. org (last access: 2 November 2020). 2 Zhao et al. (2017). 3 Thober et al. (2019); 4 and 5 WaterGAP2, Döll et al. (2012). Lehner et al. (2011)odel; ×: not included in the model for ISIMIP2b simulations; GRanD: Global Reservoir and Dam database according toLehner et al. (2011); gw: groundwater; Rsu: https://doi. org/10. 5194/gmd-14-3843-2021Geosci. Model Dev. , 14, 3843-3878, 2021.
, 2017b). Improving the physical representation of source terms is thus a critical topic. One of the purposes of the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project  (Hirtl et al. , 2019)  was to develop and assess the integration of observations for air flight applications. Measurements from satellites and ground-based stations were considered for assimilation into dispersion models. Besides, some previous work has shown the benefits of assimilation of aerosol optical depth (AOD;  Sič et al. , 2016)  and of lidar data  (El Amraoui et al. , 2020)  for the 3D representation of aerosols. So it is worth investigating whether the assimilation of AOD and of lidar profiles may benefit ash modelling, particularly when the ash cloud moves far from the volcano source.
The present article assesses the relative performance of different source terms and of the assimilation of satellite and ground-based data for the representation of 3D concentrations of ash during a phase of the eruption of Eyjafjallajökull in 2010. The experiments are performed with the MOCAGE model and its assimilation scheme. MOCAGE is the model developed and used by the Toulouse VAAC. The plan of the article is as follows. Section 2 presents the MOCAGE model and the different observation datasets that are used in the case study. In Sect. 3, the different source terms are presented and their performance is compared. In Sect. 4, the assimilation of ground-based lidar data is presented and assessed compared to in situ measurements of ash concentrations. The conclusion in Sect. 5 includes some perspectives of this work.
, 2021c). Besides the SEVIRI brightness temperatures in the thermal infrared, VACOS uses auxiliary data, including the satellite viewing zenith angle, the skin temperature from a numerical weather prediction model and clear-sky brightness temperatures derived from SEVIRI images. VACOS has a fairly good volcanic ash detection probability  (Piontek et al. , 2021a)  for ash layers with column loads between 0. 2 and 1 g m -2 (between 1 and 10 g m -2 ) of approximately 93 % (99 %) and also allows for the quantification of the ash load of the plume with a mean absolute percentage error of ca. 40 % (26 %). The capacity of VACOS data to detect ash and to estimate ash load during this eruption phase has also been assessed by  Plu et al. (2021). The overall conclusion is that VACOS can be reliably applied to detect volcanic ash concentrations larger than 0.
2 g m -2 and that the ash load estimates are in good agreement with estimates in the literature. A comparison with other satellite products shows similar peak values of ash load (3 g m -2 against 2 g m -2 ;  Prata and Prata, 2012). Comparable ash loads have also been found at locations where lidar-based measurements have been made, e. g. around 1 g m -2 east of England on 17 May  (Francis et al. , 2012). So VACOS data may be used as a reasonable reference dataset for assessing and comparing the performance of different model outputs. There is however underestimation of the high ash load values (above 10 g m -2 ) that can be retrieved. For such ash load, the typical volcanic ash spectral signature in the thermal infrared might vanish if the ash plume becomes opaque  (Watkin, 2003). Besides, for simplicity, VACOS also neglects the impact of SO 2 and ice-coated ash  (Piontek et al.
EARLINET lidar profiles The European Aerosol Research Lidar Network (EAR-LINET) was established in 2000, and it is now one of the components of ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure). In 2010, EARLINET investigated the spatio-temporal distribution of the Eyjafjallajökullemitted ash plume over the European continent thanks to the almost continuous observations performed at its 27 lidar stations distributed over Europe  (Pappalardo et al. , 2013). A database devoted to reporting the geometrical and optical properties together with identification of the aerosol type for each of the aerosol layers observed during the whole related period is available at https://www. earlinet. org/ (last access: 8 December 2021). Between 13 and 20 May, a significant ash layer was detected over the Cabauw and Hamburg lidar stations.
Impact of the assimilation of MODIS AOD In order to evaluate the benefit of the assimilation of MODIS AOD for ash representation in MOCAGE, two additional simulations have been carried out using the two source terms. MODIS AOD data from Aqua and Terra have been assimilated, using the configuration described in Sect. 2. 3. 2. Assimilation is performed using the MOCAGE 3D-FGAT scheme at an hourly step with a 3 h window, continuously from 10 May. Cumulative daily maps of the assimilated hourly values at 0. 2 • are shown in Fig. 4. In the areas where ash is present (between Iceland and the British Isles), there are many assimilated AOD grid points. On 14 and 16 May, some high AOD values belong to the plume and are presumably affected by volcanic ash. Figure  5  illustrates the effect of assimilation of MODIS AOD by comparison to the simulations without assimilation (Fig.
Some pre-processing of the raw lidar profiles is needed due to the fact that the vertical resolution of EAR-LINET backscatter profiles is much finer (100 m) than the MOCAGE vertical resolution. In order to avoid inconsistencies in the assimilation process, the lidar profiles are regridded at a resolution similar to that of MOCAGE. Two different datasets are prepared for assimilation: -EARLINET mean. The assimilated value is the mean value of lidar backscatter coefficients between two MOCAGE levels. -EARLINET max. The assimilated value is the maximum value of lidar backscatter coefficients between two MOCAGE levels. Such profiles ("mean" and "max") have been processed in order to assess the sensitivity of the assimilation to the pre-processing of lidar data. The high values of backscatter coefficients that can be seen in the lowest levels are kept for assimilation.
Conclusions This study investigated the benefit for the 3D representation of volcanic ash of a resolved source term and of the assimilation of different observation datasets, using the MOCAGE model. The main findings are as follows: -The use of a resolved source term instead of a parameterised source term induces a more realistic representation of the horizontal dispersion of the ash plume. -A positive impact of the assimilation of MODIS AOD on the horizontal dispersion of the plume has been shown, but this effect is rather low and local compared to source term improvement. -The continuous assimilation of lidar profiles from two ground-based stations improves the vertical distribution of ash and helps to simulate ash concentrations closer to those values obtained from in situ observations.
Acknowledgements. The authors thank  Arnau Folch  for providing the FPLUME model for implementation in MOCAGE. Financial support. This work has been conducted within the framework of the  EUNADICS-AV  project, which has received funding from the  European Union 's  Horizon 2020 research programme Societal Challenge -Smart, Green and Integrated Transport  under grant agreement no. 723986. Review statement. This paper was edited by  Marcus Hirtl  and reviewed by  Andreas Stohl  and one anonymous referee.
2016). Sequencing library quality was assessed using a Bioanalyzer high-sensitivity chip, and the 40 libraries were multiplexed and sequenced on a single lane of Illumina HiSeq 4000 at the University of Chicago Genomics Facility, yielding an average of 6. 4 million 50-bp single-end reads per sample. Raw sequencing reads were mapped to an M. galloprovincialis reference transcriptome  (Moreira et al. 2015)  using Bowtie 2  (Langmead and Salzberg 2012)  and yielding an overall alignment rate of 75. 6%. A custom Perl script written by Misha Matz (available at https:// github. com/z0on/tag-based_RNAseq) was used to count the number of reads mapping to each putative gene in the reference for all individuals.
Identifying Population-Specific Patterns of Gene Expression and Plasticity Differences in gene expression were used to (1) assess patterns of population-specific responses to each pH treatment, (2) explore the extent to which observed population differentiation could be related to habitat pH, and (3) determine whether pH plasticity differed between the populations. The R package DESeq2 and associated protocols  (Love et al. 2014)  were used to normalize and filter transcript counts and compute log 2 fold change (LFC) in expressions for each gene using a generalized linear model and a series of defined contrasts. For each contrast, genes were filtered such that the count of transcripts mapping to it were 110 across all samples, with 125 reads mapping in at least two individuals. We quantified population differentiation by computing LFCs between populations in the benign and stressful pH conditions, using the coastal population expression values as the reference (n gene p 12,809 and 12,733 for comparisons in the benign and stressful pH treatments, respectively).
Next, we quantified differences in expression plasticity by computing LFCs for each population between treatments (n gene p 12,927 and 12,674 for quantification of the coastal and lagoon population response, respectively). Differentially expressed genes were identified as those exhibiting a Benjamini-Hochberg false discovery rate-corrected P ! :1  (Love et al. 2014) , and patterns of expression of these candidates were visualized using heat maps produced with the pheatmap package in R  (Kolde 2012). The putative function of differentially expressed genes was explored with annotation data generated in the reference transcriptome provided in  Moreira et al. (2015). To visualize and quantify the molecular phenotypic differentiation of the populations in each treatment, we conducted principal component analysis (PCA) on the regularized log-transformed transcript counts data using the plotPCA function in DESeq2.
Figure3: Results from principal component analysis assessing transcriptome-wide differentiation in gene expression profiles between individuals native to the coastal (circles) and lagoon (diamonds) habitats in benign (n genes p 12,809; A) and stressful (n genes p 12,733; B) pH treatments. Data underlying figure 3 have been deposited in the Dryad Digital Repository (https://doi. org/10. 5061/dryad. tqjq2bvxc; Bitter et al. 2020).
Figure4: Overlaid distributions of log 2 fold change of pH-responsive genes between populations (dark gray; mean value: solid line; n genes p 29) and all other genes differentially expressed between populations (light gray; mean value: dashed line; n genes p 231) in the benign pH treatment (F 1, 2,581 p 13:62, P p :001). Data underlying figure 4 have been deposited in the Dryad Digital Repository (https://doi. org /10. 5061/dryad. tqjq2bvxc; Bitter et al. 2020).
Figure5: A, Overlaid distributions of transcriptome-wide changes in gene expression in the coastal (dark gray; mean value: solid line) and lagoon (light gray; mean value: dotted line) populations (D p 0:059, P ! :001) in response to stressful pH conditions. B, Differences in the amount of upregulated (F 1, 5,866 p 2:05, P p :13), downregulated (F 1, 5,056 p 4:12, P p :043), and absolute (F 1, 22,714 p 25:4, P p :001) gene expression change in response to the stressful pH conditions between the coastal (circles) and lagoon (diamonds) populations. Data underlying figure 5 have been deposited in the Dryad Digital Repository (https://doi. org/10. 5061/dryad. tqjq2bvxc; Bitter et al. 2020).