dataesr/scientific-paragraphs-categorization huggingface dataset:
Both variables are available over the entire study domain on a 0. 25 • regular grid and for the time period from 2003 until 2017 with a daily resolution. The third evaluation variable is snow water equivalent (SWE) which is available in the ERA5-Land dataset (Muñoz Sabater, 2019, variable sd therein; see Fig. 2d ). ERA5-Land combines a large number of historical observations into global estimates of surface variables using advanced modeling and data assimilation systems. ERA5-Land is available for the entire study period and domain on a daily temporal scale and a 0. 1 • regular grid. The data are given in meters of water equivalent and have been converted to converted to kilograms per square meter (i. e. , millimeter) to allow for comparison with model outputs. The ERA5-Land dataset was chosen over the available ground truth SWE observations like the CanSWE dataset (Canadian historical Snow Water Equivalent dataset;  Vionnet et al.
, 2021)  because (a) the ERA5-Land dataset is available over the entire modeling domain, while CanSWE is only available for the Canadian portion, (b) the ERA5-Land dataset is gridded, allowing for similar comparison approaches as used for AET and SSM, and (c) the ERA5-Land dataset is available on a daily scale, while the frequency of data in CanSWE varies from biweekly to monthly, which would have limited the number of data available for model evaluation. We, however, provide a comparison of the ERA5-Land and CanSWE observations by comparing the grid cells containing at least one snow observation station available in CanSWE and derive the KGE of those two time series over the days on which both datasets provide estimates. The results show that 83 % of the locations show an at least medium agreement between the ERA5-Land SWE estimates and the SWE observations provided through the CanSWE dataset (KGE larger than or equal 0.
48). A detailed description and display of this comparative analysis can be found in Sect. S1 in the Supplement. These three auxiliary evaluation datasets were downloaded by the project leads and merged into single files from annual files, such that the team only had to deal with one file per variable. The data were subsequently cropped to the modeling domain and then distributed to the project teams in standard NetCDF files with a common structure, e. g. , spatial variables are labeled in the same way, to enable an easier handling in processing. Besides these preprocessing steps, the gridded datasets were additionally aggregated to the basin scale, resulting in 212 files -one for each basin -to allow for not only a grid-cell-wise comparison but also a basin-level comparison. The reader should refer to Sects.
We therefore agreed to report only the standardized soil moisture simulations, which are defined as follows: SSM = SSM -µ SSM σ SSM , (5) where µ SSM is the mean, and σ SSM denotes the standard deviation of a surface soil moisture time series. The mean and variance of those standardized time series SSM hence collapse to 0 and 1, respectively, and the KGE components β and α are noninformative. The Pearson correlation r, however, is informative. Note that the Pearson correlation for standardized and non-standardized datasets is the same (r(SSM ) = r(SSM)). Several levels of model performance are introduced in this study to allow for a categorization of results and models. The thresholds defined in the following for the individual components have been chosen based on expert opinions and reflect the subjective level of quality expected from the models.
Basin-wise and grid-wise model comparisons for individual simulated variables We decided to use two approaches to compare the model outputs with streamflow observations and the reference datasets for AET, SSM, and SWE in order to account for the wide range of spatial discretization approaches used by the participating models. In total, six models were set up in a lumped mode (basin-wise output), two models operated on a subbasin discretization, and five models are set up in a gridded mode where the model grids do not necessarily match the grid of the AET, SSM, or SWE reference datasets. Please refer to Table  1  for the spatial resolution of the individual models. In the calibration and validation phases of the project (Sect. 2. 5), the simulated time series of streamflow are compared in the traditional way with the observed time series of streamflow at gauging stations.
We used the Kling-Gupta efficiency metric (Eq. 1) to compare the simulated daily streamflow estimates with the corresponding observations. In the evaluation phase (Sect. 2. 6) the additional variables were compared to their simulated counterparts. The additional variables chosen for evaluation are gridded and on a daily timescale. Model evaluation for additional variables is compared with the following two strategies. The first comparison is performed basin-wise, meaning that reference datasets and simulations are both aggregated to basin level, e. g. , gridded evapotranspiration AET(x, y, t) is aggregated to area-weighted mean evapotranspiration AET(t) of all grid cells that contribute to a basin domain. This results in 212 observed time series per variable and 212 simulated time series per model and variable.
The corresponding basinwise observed and simulated time series are then compared using KGE (Eq. 1) for evapotranspiration and snow water equivalent and the Pearson correlation coefficient (Eq. 4) for surface soil moisture. The second comparison strategy is to compare all models in a grid-cell-wise manner, thereby appropriately enabling the evaluation of how well distributed models can simulate spatial patterns within basins. In this approach, all model outputs are regridded to the grid of the reference datasets (i. e. , 0. 25 • regular grid for GLEAM's evapotranspiration and surface soil moisture and 0. 1 • regular grid for ERA5-Land's snow water equivalent). Regridding is here meant as the areally weighted model outputs contributing to each grid cell available in the reference datasets; no higherorder interpolation was applied.
Basin-wise model performance regarding auxiliary variables In this section, the performance of models regarding additional variables at the basin scale (comparable to streamflow) will be analyzed for 12 models. The 13th model, i. e. , machine-learning-based LSTM-lumped, is not (yet) able to provide estimates for those variables and will not be considered for the remaining analyses. The basin-wise comparison of additional variables implies that the reference datasets and the subbasin-based and gridded model outputs were aggregated to basin level. Once the outputs and reference datasets are aggregated in space, the resulting daily time series are compared. The 212 calibration and validation locations are compared without distinguishing them (as done for streamflow) since the additional variables were never calibrated, and their performances are therefore of validation quality.
Basin-wise comparison of actual evapotranspiration Regarding actual evapotranspiration (AET), the locally calibrated models exhibit, on average, a lower performance than the regionally calibrated, physically based models (Fig. 4a ). The only exception is the HYMOD2-lumped model. During its development, the focus was on improving the realism of simulated AET, which was achieved by introducing a model structure in HYMOD2-lumped that produced simulated AET results closely approximating GLEAM AET  (Roy et al. , 2017). In other words, the HYMOD2-lumped model was implicitly preinformed by this dataset, which would explain the superior performance seen here. Note that all evaporation-related (and other) parameters were recalibrated using only streamflow observations in this study and were not reused from the development stage presented by  Roy et al.
72), MESH-CLASS-Raven (0. 76), MESH-SVS-Raven (0. 76), and GEM-Hydro-Watroute (0. 76). It is notable that the four models that perform best are all physically based models that explicitly simulate soil moisture as a state variable. WATFLOOD-Raven seems to be an outlier with its poor median KGE r performance of 0. 50. Especially stations located in the Lake Erie watershed show low correlation values compared to the reference dataset, which can be explained by used in this study (141 calibration locations and 71 validation locations). The performance is evaluated for observations available between 2001 and 2017 (the overall period used for streamflow calibration and validation). The Kling-Gupta efficiency (KGE) is used to evaluate the simulations of actual evapotranspiration and snow water equivalent, while the Pearson correlation is used for the (standardized) surface soil moisture.
the fact that WATFLOOD does not explicitly simulate SSM, and therefore, the rescaled Upper Zone Storage (UZS) was used as a proxy for SSM. Specific parameter settings were chosen to mimic the behavior in urban areas (i. e. , low value of infiltration coefficient and low value of upper zone retention limiting UZS) and restricting fluctuations in the UZS as this would be expected for SSM, but these parameter settings are likely not enough to fully capture SSM dynamics in urban areas. There are -similar to AET -no significant performance differences between regions. No basin can be identified as an outlier with significantly lower or higher performance, which indicates that all models are in good agreement with the GLEAM-v3. 5 soil moisture reference dataset, and no inconsistency can be diagnosed.
The locations with poor performance (red color across most models) are located in the same geographical area, i. e. , the land area south of Lake Huron, north of Lake Erie, and west of Lake Ontario which is -besides being the most densely populated area of the Great Lakes watershed -heavily impacted by lake effects in precipitation and complex precipitation phase  (Mekis et al. , 2020) , which affects snow accumulation and melt. Another reason for the poor performances of the models in this region might be the quality of the forcing datasets which are known to have problems in lake areas. It is not surprising that most models exhibit lower performances in these regions compared to elsewhere. It is, however, notable that some models, e. g. , the locally calibrated Blended-Raven model and the regionally calibrated WATFLOOD-Raven model perform exceptionally well at locations where other models deteriorate in performance, even though it does not use special snow processes but the basic formulation of the temperature index model  (Anderson, 1973).
Indeed, when looking at the true KGE for actual SSM values, only MESH-SVS-Raven and GEM-Hydro-Watroute showed reasonable performances (not shown here), partly because SVS represents the actual SSM values over thin superficial soil layers. However, a physically based model (WATFLOOD-Raven) shows an outlier with a poor performance across all locations for SSM. For snow water equivalent, there are as many locally calibrated/conceptual models performing well as there are physically based models, i. e. , Blended-lumped and Blended-Raven vs. MESH-SVS-Raven and GEM-Hydro-Watroute. The analysis of basin-aggregated estimates might, however, favor models that are set up and run on this (basin) scale. The distributed/gridded models might be indeed superior when the subgrid variability is analyzed. Hence, the next section will focus on analyzing the models on the native, gridded resolution of the reference dataset rather than aggregating models and reference dataset to basin scale.
Grid-cell-wise model performance regarding auxiliary variables In this section, the results of the model comparisons for the three additional variables are presented. In contrast to the previous section, the variables are compared at the grid cell scale rather than per basin. This means that all model outputs were regridded to the grid of the corresponding reference dataset. The performance metrics are then compared at each grid cell, leading to maps of performances presented in Fig. 5  for actual evapotranspiration, Fig. 6  for surface soil moisture, and Fig. 7  for snow water equivalent. The results will be presented for only 12 models as the machine-learningbased LSTM-lumped model did not simulate these additional variables. The results will be presented and discussed in the following three sections (Sects.
Grid-cell-wise comparison of actual evapotranspiration The results of the grid-cell-wise comparisons regarding actual evapotranspiration (Fig. 5 ) are consistent with the basinwise comparisons discussed before (see Sect. 3. 2. 1 and Fig. 4a ). The best models (visually and by numbers) are the locally calibrated HYMOD2-lumped and the regionally calibrated SWAT-Raven. Other models with a good performance are MESH-SVS-Raven and GEM-Hydro-Watroute, while the weakest models are the two locally calibrated LBRM-CC-lumped and GR4J-lumped models. Note that the median performance per model is not exactly the same as the performance value for the basin-wise comparison (Sect. 3. 2; Fig. 4 ). This is due to the fact that the basin-wise performances are derived as being the median over 212 basins, while in the grid-cell-wise comparisons they are derived over the 1213 grid-cells that appear in the reference dataset and are available in all 12 model outputs.
Grid-cell-wise comparison of surface soil moisture The results of the comparison of grid-cell-wise model outputs with the gridded reference dataset of surface soil moisture (Fig. 6 ) are consistent with the results previously discussed for the basin-wise comparisons (see Sect. 3. 2. 2 and Fig. 4b ) but allow for a more detailed analysis of the regions of poor performance, which might help to point to potential model improvements. Note that the median performances per model are not exactly the same values as the ones reported for the basin-wise comparison, since those are derived as the median over 212 basins, while here they are derived over the 1213 grid cells that appear in the reference dataset and are available in all 12 model outputs. Also note that the following discussion of results will list the individual models in reverse order compared to their appearance in the panels of Fig.
Grid-cell-wise comparison of snow water equivalent The performance of the grid-cell-wise model outputs compared to a gridded reference dataset of snow water equivalent (Fig. 7 ) are consistent overall with the results previously discussed for the basin-wise comparisons (see Sect. 3. 2. 3 and Fig. 4c ). The results, however, highlight the large variability in performances in space and across models. Note that the median performances per model are not exactly the same values as the ones for the basin-wise comparisons. The latter are derived as being the median over 212 basins, while here they are derived over the 6174 grid cells that appear in the reference dataset and are available in all 12 model outputs. In contrast to the two previous variables analyzed, the difference between the basin-wise and the grid-cell-wise comparisons are significant for some models.
This result emphasizes the importance of decisions made to evaluate model performances. Basin-wise comparisons seem to be easier to communicate and derive -especially for a wide range of models with different underlying model discretizations. But comparisons that use the native resolution of reference datasets (here gridded) might be more appropriate. We showcase here that even models that are not set up in gridded mode, i. e. , lumped and subbasin-based models can be converted to a grid scale to match the resolution of the reference datasets. The results also show that lumped models not necessarily deteriorate when compared on a grid cell scale. The results indeed suggest quite the contrary since, for example, models like LBRM-CC-lumped and GR4J-lumped increase their median KGE score because they predicated the snow water equivalent accurately in these large basins.
58) shows a different pattern of weak regions. The low-performing regions are now mostly located in the Ottawa River basin and east of Lake Superior. The weaker-performing regions seem to be mostly Only grid cells that are available in the reference dataset and all 12 models are displayed (1213 grid cells in total) and used to derive the Pearson correlation coefficients (KGE r ) based on all available summer month observations (June to October) between 2001 and 2017. The median KGE r across space is added as a label at the top of each panel. The color bar has labels added to categorize the performance into medium, good, and excellent. The machine-learning-based LSTM model is not displayed, as this is the only model not simulating surface soil moisture explicitly. Note that only VIC-Raven, SWAT-Raven, MESH-CLASS-Raven, MESH-SVS-Raven, and GEM-Hydro simulate (surface) soil moisture explicitly, while the remaining models used proxy variables for soil moisture.
Summary grid-cell-wise comparisons of auxiliary variables The grid-cell-wise comparisons lead, in general, to similar conclusions to the basin-wise evaluation with more detailed insights regarding spatial performance distributions. The main conclusion -as seen for the snow water equivalent variable -is that one needs to be aware of the underlying assumptions when results are compared on a basin scale, i. e. , when outputs are spatially aggregated before the comparison. This leads to situations where (a) large basins will have the same weight when compared to smaller basins and (b) large variability in the performances within a basin can cancel each other out when aggregated in space before the performance is evaluated. The impact of this depends on the resolution and spatial variability of the reference dataset; it seems that actual evapotranspiration and surface soil moisture are more robust, i.
It cannot be confirmed that physically based models are always better performing than more conceptual models. It might be beneficial to use locally calibrated, simplified models to augment physically based models. The variability in model performance (meaning the degree of patchwork patterns) looks reasonable even for lumped models; no large discontinuities can be observed. This is most likely be caused by the large set and good spatial distribution of locations (basins) used for training. A smaller set or a more clustered selection of training basins would have led to larger regions of weak performance, as can be seen of the Ottawa River basin where this indeed impacted the overall performance. We encourage followup studies to look into more local comparisons of the model outputs derived here and provided as open datasets.
Conclusions We present a carefully designed framework to compare models of different types (lumped, gridded, and subbasin-based, conceptual and physically based, locally, regionally, and globally calibrated) consistently. The framework includes using the same data to set up and force the models, using (mostly) the same routing, and the same methods of model performance estimation regarding streamflow and additional variables such as actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The strong focus on standardizing datasets and methods leads to observed performance differences that are, to a large extent, specific to the model or specific to subjective model building decisions (e. g. , calibration details) rather than performance differences between models caused by input data of different quality or inconsistencies in the postprocessing simulation outputs.
The majority of modeling teams learned about a poor assumption they made, which was a data processing error or a bug in their model code, as a direct result of them interacting with other study co-authors not on their modeling team. First of all, this is evidence of the value of model intercomparison studies for collectively improving hydrologic model quality. Second of all, this suggests that, whenever possible, model intercomparisons should be iterative so as to enable teams to make changes to their model/approach. This is of course a challenge given that most study co-authors received no funding explicitly supporting their participation in the model intercomparison. Overall, this study generates a valuable model intercomparison dataset (fully documented input and model output datasets).
This allows new models to be added to the intercomparison by future researchers. Just as important is that this rich dataset is available for followup studies focused on learning why we saw the differences we did (e. g. , given dominant LSTM-lumped performance for streamflow, which LSTM model build decisions were most important?). Our analysis and conclusions below are necessarily focused on reporting observed performance similarities and differences among the models. The metrics for evaluating the model performance for each of the variables are presented together with a multi-objectivebased Pareto method to integrate all performance metrics proposing an objective approach to evaluate model across all four variables, leading to insights for specific models and groups of models. Furthermore, not only the calibration performance of the models regarding streamflow but also the performances in three different validation experiments (temporal, spatial, and spatiotemporal validation) are produced.
All datasets have been cropped to the study domain (black line). The actual evapotranspiration and surface soil moisture (panels b and d) are not available over the lake area (missing grid cells). Panels (b-d) show the mean annual estimates for the auxiliary variables. The average soil moisture (panel c) is based on summer time steps only (June to October), while for the snow water equivalent annual mean only values larger than 1 mm of daily snow water equivalent were considered. In both cases, this is what was considered to evaluate model performance regarding these variables.
Acknowledgements. This study contains modified  Copernicus Climate Change Service Information for ERA5-Land  for the period 2000 to 2017 (last access: 5 October 2021). The work was made possible by the facilities of the  Shared Hierarchical Academic Re-search Computing Network  (SHARCNET; https://www. sharcnet. ca, last access:  6 July 2022 ) and  Compute/Calcul Canada. Frederik Kratzert has been partially supported by a  Google Faculty Research Award. The Federal State of Upper Austria supports the  ELLIS Unit Linz , the  LIT AI Lab , and the  Institute  for  Machine Learning  where  Martin Gauch ,  Daniel Klotz , and Frederik Kratzert are/were employed during this project. Martin Gauch  has been supported by the  LIT DeepFlood project. This is NOAA Great Lakes Environmental publication no.
Extreme drought events in Amazon forests are expected to become more frequent and more intense with climate change, threatening ecosystem function and carbon balance. Yet large uncertainties exist on the resilience of this ecosystem to drought. A better quantification of tree hydraulics and mortality processes is needed to anticipate future drought effects on Amazon forests. Most state-of-theart dynamic global vegetation models are relatively poor in their mechanistic description of these complex processes. Here, we implement a mechanistic plant hydraulic module within the ORCHIDEE-CAN-NHA r7236 land surface model to simulate the percentage loss of conductance (PLC) and changes in water storage among organs via a representation of the water potentials and vertical water flows along the continuum from soil to roots, stems and leaves.