dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979-2012 using a reanalysis-based meteorological forcing dataset (0. 5 • resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates.
Yet, the availability of this information is lacking in many regions of the world  (Pozzi et al. , 2013). To capture the uncertainty that stems from the simplifications and assumptions in our models, a global reanalysis dataset of water resources should contain a large number of global hydrological and land surface models that assimilate the most important satellite-based products  (Sood and Smakhtin, 2015). This should be available as a reference for local studies and for the support of policy and decision making for transboundary watersheds and for global applications such as flood risk analysis  (Trigg et al. , 2016)  for which the global land surface and hydrological models are the key components. Only a limited number of global reanalysis datasets that can support water resources analysis is available.
, 2011). During the EMBRACE programme the  WATCH Forcing Data (1958 -2001)  methodology was applied to the more recent ERA-Interim reanalysis  (Dee et al. , 2011)  to create the WFDEI (WATCH Forcing Data methodology applied to ERA-Interim reanalysis;  Weedon et al. , 2014). We use the WFDEI dataset to force a set of 10 global models, both land surface models (LSMs) and global hydrological models (GHMs). By using a sizeable set of models we take steps to mitigate some of the errors and uncertainties that are introduced in individual models by the simplified representation of spatially heterogeneous real world processes like water and energy balances, river routing and seasonal varying vegetation cover  (Beven and Binley, 1992; Vrugt et al. , 2005; Gosling et al. , 2010). As a general principle, this is superior to the results of any individual model and as good as or better than the best model at each point and time  (Dirmeyer et al.
, 2006; Harding et al. , 2011). However, this does not necessarily mean that this is the case for the set of models that we use or that some models do not perform considerably better in specific locations, climatic conditions or for specific variables (e. g. runoff) than others. The multi-model ensemble presented here inherits a number of models from the WATCH project supplemented by additional models, a new forcing dataset (WFDEI), a WFDEIderived reference potential evapotranspiration dataset and a new modelling protocol. Furthermore, we introduce the data repository where the results are stored in an open format including all data needed for other groups to perform a similar exercise. In the end this can contribute to a better understanding of the characteristics of the increasing number of global models  (Bierkens, 2015).
The repository comes with (downscaling) tools and river basin management models such as WaterWorld  (Mulligan, 2012)  to increase usage of the data at the basin scale outside of the research community. In this paper we present the first version of the dataset, which is based on the current state of the art of the contributing modelling systems and will provide a benchmark to evaluate improvements made to the models and forcing data in the coming years. The main goal of this paper is to provide a multi-decadal dataset of water balance components from an ensemble of models that is open and of use for further research and applications. Secondly, we investigate whether the ensemble mean in this dataset is superior to the individual models given the diverse set of models, and if so, for which variables.
First, we describe the methods and models we have used. Secondly, we investigate the characteristics of the resulting dataset using the multi-model signal-to-noise ratio (SNR) to investigate multi-model agreement and the tools from The International Land Model Benchmarking Project (ILAMB,  Luo et al. , 2012; Mu et al. , 2016)  to compare the model output to reference datasets for evapotranspiration, total water storage, soil moisture, snow water equivalent and snow cover. Thirdly, the terrestrial water budget is used to compare the results with previous efforts. Finally we present conclusions and an outlook for further versions of this dataset.
Methodology and modelling protocol Each of the models used produced results for the period 1979-2012 based on the provided meteorological forcing. In total 10 models were used, both large-scale hydrological models and land surface models with extended hydrological schemes (see the list below and Table  1 ), all running offline (i. e. not connected to an atmospheric model) while driven by the same reanalysis-based forcing dataset. Given the different nature of the models a single spin-up procedure was not feasible. The spin-up procedure was chosen for each model individually to match each model's requirements with the goal to best represent the climatic conditions over the simulation period. HTESSEL-CaMa represents the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land  (Balsamo et al.
LISFLOOD  (Van Der Knijff et al. , 2010)  is a spatially distributed, grid-based rainfall-runoff and channel routing model that has been designed primarily for the simulation of the water cycle in large river basins. The model is made up of a two-layer soil water balance sub-model, sub-models for the simulation of groundwater and subsurface flow, a sub-model for the routing of surface runoff to the nearest river channel, and a submodel for the routing of channel flow. The model was initialized by running the full 1979-2012 period before starting the main run. ORCHIDEE is a land surface scheme resulting from the coupling of the SECHIBA land surface scheme and the carbon and vegetation model STOMATE. It consists of a hydrological module  (Krinner et al. , 2005)  and a routing  (Ngo-Duc et al.
SWBM (Simple Water Balance Model ) is a global model that derives soil moisture, evapotranspiration (ET) and runoff from meteorological information alone, i. e. does not use any information on soil or vegetation characteristics  (Orth and Seneviratne, 2013). The model parameters have been determined by calibrating the model against multiple reference datasets in Europe. These spatially uniform parameters were applied globally to derive the eartH2Observe simulations. The spinup was done by running the first year 5 times. The resulting soil moisture and snow fields were then used to start the actual simulation. W3RA (worldwide water resources assessment) is based on the landscape hydrology component model of the AWRA system (AWRA-L version 1. 0;  Van Dijk, 2010; van Dijk et al. , 2014). AWRA-L can be considered a hybrid between a simplified grid-based land surface model and a nonspatial (or so-called "lumped") catchment model applied to individual grid cells (model code available at http://www.
, 2016b). For each grid cell, we used calibrated parameters from the 10 most similar catchments and averaged the ensemble of model outputs. The model was initialized using the first 10 years of the forcing data before starting the main run. HBV-SIMREG, SWBM, LISFLOOD and WaterGAP3 all have been calibrated in previous studies based on observed runoff data, although these calibration efforts were done with different forcing datasets (see the respective model papers listed above). The other models rely on a priori parameter estimation alone. The data used to force the models were from the WFDEI dataset  (Weedon et al. , 2011 (Weedon et al. , , 2014 ) that comprised the period between and including 1979 and 2012 and contains both 3-hourly time intervals and daily time intervals. WFDEI is based on the ECMWF ERA-Interim reanalysis  (Dee et al.
, 2011)  with a spatial resolution of 0. 5 • , and is corrected with the CRU dataset  (Harris et al. , 2014b ) using a sequential elevation correction of surface meteorological variables plus monthly bias correction from gridded observations. Compared to the original WFDEI dataset we applied several data formatting changes to facilitate its usage, storage and dissemination. In order to avoid land-sea mask problems with different models all oceans were filled with data from the original ERA-Interim for all variables apart from precipitation for which the ERA-Interim/Land dataset was used  (Balsamo et al. , 2015). Furthermore, the files were reformatted to netCDF4 and some metadata attributes have been modified to comply with the Climate and Forecast (CF) conventions. Table  2  provides an overview of the WFDEI variables used in this study.
A list of the most important output variables is presented in Table  3. If a model does not represent a process, the associated variables are not available in the dataset. The water and energy fluxes follow the mathematical convention, i. e. positive onto the surface and negative away from the surface (see details in Table  3 ). For example, runoff has a negative signal. Although most models can only supply a subset of the requested variables listed in Table  3  we have defined this rather large number of variables so that specific fluxes/stores from models that can supply those can be used for analysis later on. Similar to other global forcing datasets  (Li and Ma, 2010; Rust et al. , 2014; Sheffield et al. , 2006; Rienecker et al. , 2011) , the WFDEI forcing contains a number of problems.
For WFDEI we identified five issues. (i) The rainfall in Gabon in Africa seems to be unrealistically high. A likely reason for this could be a unit error in the reported precipitation. (ii) There are some concerns about the energy forcing terms of WFDEI (SWdown, LWdown) over the Amazon region (an underestimation of SWdown and overestimation of LWdown, coming from the ERA-Interim data). (iii) In a number of time stamps there is some incoming radiation noise at night time (about 0. 05 W m -2 ), also inherited from the original ERA-Interim data, resulting from the processing of the fluxes archived by the atmospheric model in ERA-Interim. (iv) Large positive values of SWdown > 5 W m -2 at night time in some islands and coastal points introduced during the WFDEI data processing. (v) A total of nine grid points out of 67 209 were found with a substantial conversion of liquid rainfall (in ERA-Interim) into snowfall (in WFDEI).
For some models, this can lead to a continuous accumulation of snow over those points. Although some of these problems could have been addressed it was decided to keep the original WFDEI dataset to guarantee consistency with other studies in the literature. The simulations were performed from 1 January 1979 to 31 December 2012 in a continuous run. With respect to static fields (e. g. soil physical parameters, land cover type) each modelling group used their own datasets, as this is considered to be part of the modelling system, and exchanging these fields between models is not straightforward. Two simple quality control tests were applied to the data: (i) generic metadata and quality control (including mass balance checks) and (ii) comparison of minimum, maximum and mean fields. The first test is automatic (script available at: https://github.
com/earth2observe/project-tools), while the second relies on the inspection of aggregated statistics. To ensure uniform input and use of the data the project's servers have been configured to host the forcing data and also provide an interchange platform for the project using a THREDDS data server  (Domenico et al. , 2006). This server is also used to distribute the data to the rest of the world and includes an interactive portal (http://wci. eartH2Observe. eu). The flow of data for the current model runs is depicted in Fig. 1. Direct access to the THREDDS server is available at http://wci. earth2observe. eu/thredds/catalog. html while a mirror of the data is hosted at http://al-tc002. xtr. deltares. nl: 8080/thredds/catalog. html. All model outputs passed the metadata consistency checks. The simulations from WaterGAP3, PCR-GLOBWB and JULES have grid points with residuals above the defined threshold (5.
SNRs were calculated for three model variables: evapotranspiration, runoff and root zone soil moisture plus an ensemble of global precipitation datasets. See Appendix A for a detailed description of the SNR calculations. We performed the calculations with monthly mean anomalies to focus on the model agreement in terms of intraseasonal to inter-annual variability. Since all models were driven by the same atmospherical conditions, low values of SNR can be directly associated with differences in the representation of processes such as energy partitioning and runoff generation -i. e. ensemble uncertainty. However, since one single forcing was used, the ensemble is missing an important source of uncertainty: the driving data. Precipitation is likely the main source of uncertainty; it is very important for the terrestrial water balance while at the same time it is difficult to observe both locally and remotely.
To put our results into perspective, we also computed the SNR of an ensemble of precipitation datasets including three atmospheric The SNRs were computed for the period January 1980 to December 2012 by removing the mean annual cycle in each grid point from each ensemble member such that y and y i are both equal to zero; see Appendix A. Due to the differences in the representation of soil moisture storage among the models, the ensemble mean is dominated by those models with a higher water holding capacity and hence larger absolute soil moisture variability. Therefore, the root zone soil moisture was first transformed to percentiles before performing the calculations for each model  (Wang et al. , 2011). The SNR varies between 0 and +∞ with values below 1 indicating that the inter-model variability is larger than the ensemble mean variability, i.
e. low inter-model agreement, and values above 1 indicating a high inter-model agreement. The multi-model consistencies in terms of inter-annual variability evaluated by the SNR are shown in Fig. 2ac  for the different variables, and aggregated by climate types in Fig. 3a-c. The results highlight regions with a low inter-model agreement over (i) snow-dominated regions (runoff, evapotranspiration and root zone soil moisture) and (ii) tropical rainforest and monsoon regions (evapotranspira-tion), whereas the temperate areas show a high inter-model agreement. Comparing these results with the precipitation datasets' agreement (Figs. 2d  and 3d ), which were not included in the driving data, the large uncertainty in the tropical areas is not reflected in the runoff or root zone soil moisture. On the other hand, there is little disagreement in the precipitation datasets in cold regions, which could be caused by the fact that in these regions we rely mostly on reanalysis data sources, while the multi-model ensemble contains a large spread.
Verification with external datasets We use the ILAMB system (The International Land Model Benchmarking Project;  Luo et al. , 2012; Mu et al. , 2016)  to compare the model results against benchmark data mostly derived from satellite remote sensing, of evapotranspiration (ET), terrestrial water storage anomaly (TWSA), soil moisture anomaly (SMA), snow water equivalent (SWE) and snow cover fraction (SC). For evapotranspiration both the GLEAM-V2a and V3b datasets  (Miralles et al. , 2011; Martens et al. , 2017)  and the MODIS evapotranspiration estimates  (Mu et al. , 2011)  were used while for soil moisture anomaly we used the combined ac-tive+passive microwave ESA CCI soil moisture dataset  (Dorigo et al. , 2015 (Dorigo et al. , , 2012)). The snow cover dataset was obtained from the Interactive Multisensor snow and ice mapping System (IMS;  Ramsay, 1998; Helfrich et al.
, 2007)  while for SWE we used GLOBSNOW-2  (Takala et al. , 2011; Pulliainen, 2006). The TWSA dataset was obtained from GRACE data (JPL;  Landerer and Swenson, 2012 ). The complete benchmark results are available at the dataset storage entry page at https://github. com/ earth2observe/water-resource-reanalysis-v1 while a summary is given in Appendix B, Table  B1  to B6. A project report presenting in-depth verification is provided in the Supplement. ILAMB provides a scoring system to relate modelled results to reference datasets. In the ILAMB system multiple performance metrics are calculated, and additionally these metrics are converted to scores ranging between 0 and 1 to facilitate comparison and averaging. In this study three performance metrics are calculated for each of the five model variables evaluated (ET, TWSA, SMA, SWE, SC): total bias, root mean square error (RMSE) and phase difference (difference in months between peak values); furthermore a total of five 0-1 scores are calculated, for global bias, RMSE, seasonal cycle, spatial distribution and inter-annual variability, plus a 0-1 overall score that summarizes them.
The metrics and scoring are explained in detail in the ILAMB documentation (http: //earth2observe. github. io/water-resource-reanalysis-v1/ assets/pdf/ILAMB_metrics_document. pdf). For the case of anomaly variables (TWSA and SMA), the bias and spatial distribution scores are calculated over the standard deviations of the variables, and not directly over the anomalies. The current study is not an exhaustive test of the performance of the different models, instead we focus on the multi-model ensemble, commenting on specific models only when this is thought to be important to the ensemble as a whole. Apart from the global level, the ILAMB results are also available for a number of predefined regions (biomes; see Fig. 4 ). Although there are a number of uncertainties associated with TWSA as estimated by GRACE measurements  (Long et al.
, 2014; Riegger et al. , 2012) , resulting from the uncertainty of the GRACE data itself and the leakage corrections, the results provide an independent means of evaluating our model results. Both the peak month phase difference and the ensemble mean bias are negative for most models compared to GRACE (see Fig. 5 ) which could indicate that these models lack a store such as rivers or lakes  (Kim et al. , 2009)  or that the size of the included stores is inadequate. Table  4  lists the models and the variables used to calculate TWSA in this study. Results for the WaterGAP3 model should be interpreted with caution because it lacks total moisture which is a large store in many models. RMSE is largest in highprecipitation regions and lowest in dry areas (Fig. 5 , bottom panel). Overall, total scores for TWSA are close between the models, while the highest values are recorded for the ensemble mean, suggesting that the lowest-scoring models do not have a large detrimental effect on the ensemble mean.
The definition of the variables can be found in Table  3. SWE CanopInt SurfStor TotMoist GroundMoist HTESSEL-CaMa x x x x - JULES x x - x - LISFLOOD x - - x x ORCHIDEE x - x x - PCR-GLOBWB x x x x x SURFEX-TRIP x x x x - SWBM x - - x - W3RA x - - x x WaterGAP3 x x x - - HBV-SIMREG x - - x x et al. , 2012 )). The method used to estimate net radiation may also play a large role. Although the results show that the ensemble mean provides best overall performance, the spread in ET is large (between 1. 66 and 1. 33 mm day -1 ). All models provided SWE, while only six models provided SC. Total performance against the reference dataset was highest for ORCHIDEE, WaterGAP3 and LISFLOOD although the bias is fairly large for all models (see Fig. 6 ). The phase difference seems to be influenced most by rather poor scores in the Himalaya.
The total ensemble mean score for SWE is 0. 67, which is lower than the highest model score of 0. 74, suggesting that in this case the model mean should be used with care. However, GlobSnow has been shown to miss early season snow and SWE levels can drop too rapidly in spring  (Takala et al. , 2011). As most models show a later spring melt than GlobSnow it remains unclear if this is a model deficiency. A number of models show an unrealistic build-up of snow over time in Europe (HBV-SIMREG and PCR-GLOBWB), boreal North America (HT-ESSEL, HBV-SIMREG, PCR-GLOBWB), central Asia (HT-ESSEL, HBV-SIMREG) and Southeast Asia (HTESSEL, JULES, LISFLOOD). This may be caused by the fact that the models have been driven by a different dataset (with different temperature and radiation characteristics) than what they have been developed with.
Snow cover fraction (SC) estimated by the models compares well to the IMS results. Here SURFEX-TRIP performs best but performance in general is much closer compared to the SWE results and the ensemble mean seems to provide a good estimate. Getting the phase correct in the Himalayan region seems to be the most challenging parameter for the models (see Fig. 7 ). Although current satellite-derived surface soil moisture products that cover a long period have a number of limitations  (Su et al. , 2016; Loew et al. , 2013) , and are also not completely model-independent, they have been shown to capture intra-and inter-annual soil moisture variability, and this variability is not dependent on an external atmospheric forcing dataset. In addition, comparison with land surface models and in situ data showed good correlation  (Albergel et al.
They also show that for example ORCHIDEE performs well in cold regions but tends to underestimate runoff in the other parts of the globe. This seems to be confirmed by the SC and SWE results for ORCHIDEE, and by its ET results that indicate that ORCHIDEE overestimates ET in high-ET regions. Combining the results of  Beck et al. (2016a)  and the current results also shows that the models that have been calibrated on discharge do not necessarily give the best results for the variables we have used here. This is especially true for the HBV-SIMREG model which provides best results for discharge but has overall lowest scores when comparing it to the other datasets.
Data availability All data are made available via the eartH2Observe server which can be accessed via the WCI portal (http://wci. earth2observe. eu; see Fig. 1 ) which offers plotting and collaboration features, or direct via a THREDDS server allowing access via OPeNDAP, WCS WMS and direct HTTP download (ftp is also supported). The main servers are hosted at PLMA-Ltd (Plymouth, United Kingdom) and a mirror server is hosted at Deltares (Delft, the Netherlands). Data are stored on the server in netCDF-cf compliant files. All data generated for this paper are freely available via the OCD Open Database Licence (http: //opendatacommons. org/licenses/odbl/summary/). The DOI for the data is https://doi. org/10. 5281/zenodo. 57760.
Conclusions and outlook For most of the variables we have found that the ensemble of land-surface and hydrological models gives satisfactory results and the agreement between the models is good for large parts of the terrestrial earth. The multi-model agreement in terms of monthly anomalies using the SNR provided an insight into the main regions/variables where the dataset shows a reduced multi-model agreement: (i) snow-dominated regions (in all three variables -evapotranspiration, runoff and root zone soil moisture) and (ii) tropical rainforest and monsoon regions (only for evapotranspiration). Furthermore, the SNR of an ensemble of precipitation datasets was calculated, indicating a large uncertainty of precipitation in the tropics, which is not reflected in the ensemble runoff from the models.