dataesr/scientific-paragraphs-categorization huggingface dataset:
Global Hydrological Simulations from the ISIMIP Database The present study is conducted in the framework of a research project through a partnership with NA water industries and the Ouranos Consortium (https://www. ouranos. ca/en/ accessed on 1 November 2021). In that project, the ISIMIP2a database is only explored. Among the 13 gHMs participating in ISIMIP2a, 4 gHMs, namely, DBH  [39] , H08  [40] , LPJml [41], and PCR-GLOBWB  [42] , are used to ensure a comparison with the rHM simulations (presented in Section 2. 3) over the 1971-2010 period. Each gHM is driven by four global daily gridded meteorological datasets for a total of 16 gHM/driver combinations per catchment. The four global daily gridded meteorological datasets used within the ISIMIP2a database are briefly presented in Table  1.
All datasets are used to drive the gHMs. As WFDEI. GPCC starts in 1979, WFDEI combines WATCH (before 1979) and WFDEI. GPCC (after 1979) in ISIMIP2a. The four gHMs are selected as they are the only ones available that contain the varsoc socio-economic scenario for the four global meteorological forcing datasets. The varsoc scenario includes changes in climate, population, gross domestic product (GDP), land use, technological progress, and other variables to reflect as best as possible the state of the world in the historical period  [47, 48]. This socio-economic scenario should be more representative than naturalized runs or those with a fixed present-day socio-economic scenario. All gHMs are run at the daily time step with a regular grid spatial aggregation, and they cover the globe at a spatial resolution of 0.
5 • (~50 km). A short description of each gHM, including model settings, specifications, and main hydrological processes, is provided in Table  2. For a full technical description of the ISIMIP2a protocol and simulation data from the water (global) sector, see  [47, 48]. For each of the 198 study catchments, gHM-simulated discharge is obtained by selecting the value of the grid point with the maximum discharge (variable 'dis' in ISIMIP2a) within the catchment boundaries for each day. This allows comparisons of the gauged discharge values at the catchment outlet.
Regional Hydrological Simulations Based on the HYSETS Database Two rHMs driven by the Princeton PGMFD v2 daily gridded meteorological dataset are used as a reference to compare the performance of the ISIMIP2a gHMs to site-specific rHMs, a necessary step towards meeting the objective of this study. It is thus expected that the rHM performance in simulating discharges will be better than that of the gHMs. The selected rHMs are GR4J (modèle du Génie Rural à 4 paramètres Journaliers-a daily rural engineering model with four parameters;  [53] ) and HMETS (Hydrological Model of École de technologie supérieure;  [54] ), two lumped models operating at the daily time step. They have been widely used for various water-related purposes  [55] [56] [57]. A short description of the main characteristics of the rHMs is provided in Table  3.
The first step is to calibrate the two rHMs for the 198 catchments with the Princeton PGMFD v2 dataset (https://esg. pik-potsdam. de/search/isimip/ accessed on 1 November 2021) and the HYSETS hydrometric data. Climate data is averaged at the catchment scale using an unweighted average of all grid points within each catchment boundary  [38]. The calibration period is made over the first  20 years (1971-1990) , whereas the validation is done based on the remaining 20 years (1991-2010). The parameters of the rHMs, which include the snowmelt module parameters, are calibrated with observed daily discharge data. Automatic calibrations are performed with different combinations of model parameters, and the optimal combination of parameter values is selected based on the objective function. The Shuffled Complex Evolution optimization algorithm, developed at the University of Arizona (SCE-UA), is used to obtain optimal parameter values for the rHMs  [61].
A weekly time step is considered to minimize the impact of timing issues with gHMs, whose internal routing schemes are often too coarse to generate well-timed flow events. These metrics are also used to assess the quality of the high-flow and low-flow simulations by the gHMs and the rHMs, as they are common evaluation criteria for mean seasonal dynamics. Performance of both gHMs and rHMs are judged "satisfactory" for the weekly discharge simulations at the catchment-scale if NSE > 0. 50 and relative bias ≤ ±25% (partly based on  [64] ). The NSE and relative bias values in evaluation are important since poor performance should limit the ability of the gHM-climate-dataset combinations to produce reliable trends for climate change impact studies.
gHM Performance in Simulating Discharge at the Catchment Scale gHM performance in simulating discharge is measured by comparing it to that of the two rHMs taken individually for the 198 catchments over the 1971-2010 period, except for the gHM-WATCH combination, where the evaluation period is 1971-2001 due to WATCH data availability (see Table  1 ). Figure  4  shows the average NSE values of the discharge for the 16 gHM-climate-dataset combinations and the two rHMs. The same approach is utilized on the PBIAS metric, which is often used in operational reservoir management. Figure  5  shows the PBIAS values for the gHM-climate-dataset combinations and the two rHMs. The box-and-whisker plots are based on the 198 catchments. From Figure  4 , the gHMs perform poorly in simulating the discharge at the catchment scale, with negative median NSE values; the 25th-75th quantile boxes are practically below zero level in all cases.
DBH displays the poorest performance whatever the global meteorological dataset used to drive the gHM. The gHM-Princeton combination generates lower variability in the simulated discharge values, as depicted by the box plot amplitude. Statistically, the gHM-WATCH combination performs the worst, with considerable uncertainty in the results. To test if it could be attributed to the different evaluation periods between WATCH and the other datasets, we computed the NSE values for all gHM-climate-dataset combinations for the 1971-2001 period. The results showed no significant improvement of gHM performance over the common period (not presented here). As expected, both rHM-Princeton combinations outperform all the gHM-climate-dataset combinations, with satisfactory median NSE values (NSE > 0. 5).
For precipitation, the mean interannual cycles differed significantly between the four datasets at the catchment scale, with the major discrepancies observed for the less rainy months (autumn and winter months). The highest differences in precipitation among the four global meteorological datasets were seen for the high-elevation Mexican catchment; the Princeton dataset showed an different overall pattern of mean daily precipitation over that catchment, with a lower precipitation amount throughout the year (Figure  6c ). Fewer inter-dataset differences were observed in the mean interannual cycles of maximum and minimum temperatures. The four global meteorological forcings are, overall, in good agreement over the catchments, except for the high-elevation Mexican catchment, where the strongest variability in temperature is observed; again, for the Rio Grande River Basin, the Princeton dataset generates a different pattern compared to the other three datasets with colder temperatures Figures  7c  and 8c ).
The ability of each gHM-climate-dataset combination to simulate catchment-scale discharge was then assessed by visual hydrograph comparison (Figure  9  and Figures S1, S3 and S5), through the analysis of the Taylor diagrams, which provide a summary of the relative skill of the gHMs (Figure  10  and Figures S2, S4 and S6), and of statistic criteria for the high-and low-flow periods (Tables  5  and 6 ). For the Baleine River Basin, the comparison of the gHM-climate-dataset combinations with the observations and the rHMs shows significant differences in the reproduction of the mean interannual cycles of the simulated discharge (Figures  9  and 10 ). Most of the gHM-climate-dataset combinations display spatial correlation coefficients above 0. 6, except for the LPJml, with high RMSVD values (Figure  10 ).
The high flow period is overestimated in all the DBH-dataset combinations (Figure  9  and Table  5 ). In the H08 simulations, an overestimation of the peak flow is present regardless of the dataset used to drive the gHM. The peak flow is both overestimated and delayed in all the LPjml simulations; however, there are more satisfactory bias values for the high flow period (Table  5 ). PCR-GLOBWB and H08 provide more realistic discharge patterns and variability than DBH and LPJml (Figure  10 ). In particular, the PCR-GLOBWB-Princeton and PCR-GLOBWB-WATCH combinations almost capture both the magnitude and timing of the peak flow, which translate into acceptable relative bias values of high flows (Table  5 ); both the PCR-GLOBWB-Princeton and PCR-GLOBWB-WATCH combinations are the closest to the rHM simulations (Figure  9 ).
Most of the gHM-climate-dataset combinations give satisfactory bias values for the low flows (Table  5 ). For the Liard River Basin, all the DBH and H08 simulations depict an overly vigorous mean interannual cycle of discharge with overestimated high flows (Figure  S1  and Table  5 ). Most of the gHM-climate-dataset combinations display correlation coefficients above 0. 6 but with high standard deviation and RMSD values (Figure  S2 ). The magnitude of the spring peak flow is consistently overestimated in the LPJml simulations, with a similar time offset whatever the dataset used as forcing. Such finding is observed for both river basins submitted to a subarctic climate (Table  4 ). The misrepresentation of the spring peak flow is linked to the misrepresentation of the snowmelt peak, caused either by a cold bias in temperature or poor representation of snow processes by the gHM.
Since all global meteorological datasets give similar patterns of air temperature and the other gHMs do not provide delayed spring peak flow, we attribute that to the snowpack state processing into LPJml, which relies on a degree-day approach with a precipitation factor (Table  2 ). All the PCR-GLOBWB-dataset combinations give smooth mean interannual cycles of the simulated discharge, with strong relative bias values of high and low flows (Table  5 ). As for the Rio Grande River Basin, the mean interannual cycle of discharge is captured by any gHM-climate-dataset combination (Figure  S3 ). LPJml displays the poorest model skill for this catchment. H08 and PCR-GLOBWB display the highest correlation coefficient values and the lowest standard deviation and RMSD values for this catchment (Figure  S4 ).
Both the high and low flows tend to be underestimated by the gHMs (except for PCR-GLOBWB), with significant bias values (Table  5 ). Given the similar high flow underestimation by DBH, H08, and LPJmL, when driven by the four global meteorological datasets (Figure  S3 ), but depicting marked discrepancies in seasonal climate patterns over that high-elevation catchment (Figures  7 8 9 ), the systematic underestimation of the highflow period seems to be more related to the internal pathways of the gHMs for depicting hydrological processes, such as PET (Table  2 ), rather than the quality of the global meteorological datasets. The errors in simulating weekly low flows, particularly perceptible on the DBH, H08, and LPJmL simulations, are linked to the challenges faced by the gHMs in accurately representing groundwater and baseflow processes.
The rHMs reproduced the overall seasonal cycle of discharge for this catchment, with correlation coefficients above 0. 6 (Figure  S4 ); however, the low flows are overestimated, and the magnitude of the peak flow is consistently underestimated (Figure  S3  and Table  5 ). Regarding the Susquehanna River Basin, the gHM-climate-dataset combinations perform poorly in reproducing seasonal discharge (Figure  S5 ) and systematically provide a time offset of the spring peak flow. This is confirmed with the analysis of the Taylor diagram with strong RMSD values (Figure  S6 ). H08, DBH, and PCR-GLOBWB tend to better capture the mean interannual cycles of discharge, with a good simulation of low flows with minimal bias values, especially for the H08-dataset and PCR-GLOBWB-dataset combinations (Table  5 ).
Potential Factors Controlling gHM Performance To explain the gHMs' poor ability to simulate discharge at the catchment scale, two levels of issue that can impact model efficiency are explored. For the four catchments, we questioned the impact of the geomorphological features (catchment size, altitude, geographical location) as well as the global meteorological forcing. We investigated the influence of the geomorphological features on the PBIAS and NSE values of high flows and low flows for each gHM and for the two rHMs by considering all meteorological datasets combined (Tables  5  and 6 ). The results show that the poorest gHM performance at simulating seasonal flows are obtained for the Liard River Basin in the northwest territories (Canada), followed by the Rio Grande River Basin in Oaxaca (Mexico), which are the highest elevation catchments and the largest (S = 325,000 km 2 ) and the smallest (S = 11,982 km 2 ) sized catchments, respectively, among the four sites.
All gHMs lead to a significant overestimation of seasonal flows for the Liard River Basin and a significant underestimation of seasonal flows for the Rio Grande River Basin, with negative NSE values in both cases. For the Baleine and Susquehanna River Basins, the two northeastern NA catchments with a mean altitude below 500 m and a drainage area between 30,000 and 70,000 km 2 , the overall gHM performance at simulating seasonal flows is slightly improved, especially for the high flows in terms of PBIAS only. Even though the geomorphological features of the catchments contribute to the gHM performance, this improvement remains minor. The same finding can be transposed to the two rHMs since the values of statistical criteria do not significantly increase with any geomorphological features. We then investigated the influence of the global meteorological forcing on the PBIAS and NSE values of high flows and low flows by comparing each global dataset for all gHMs combined and the two rHMs (Tables  5  and 6 ).
Results show that the gHM-dataset combinations lead to significant bias in the high-flow simulations, with some sparse exceptions such as the gHM-Princeton combination for the Baleine River Basin as well as the gHM-WATCH and gHM-WFDEI combinations for the Susquehanna River Basin. As for the low flows, all gHM-dataset combinations tend to provide more reliable simulations over the Baleine and Rio Grande River Basins, while they fail at producing acceptable results in terms of relative bias over the Liard and Susquehanna River Basins. There is, thus, no global driving dataset that consistently outperforms others locally for seasonal flow simulations when they are used as inputs to the gHMs. However, both rHMs, forced by the Princeton dataset, led to improved simulations of seasonal flows, with more acceptable values of PBIAS and NSE.
On the Use of gHMs and rHMs Driven by Global Meteorological Datasets When looking at the NA region, we found some variability between the global driving datasets for the 198 catchments combined (Figures  4  and 5 ). The use of Princeton in the gHM-climate-dataset combinations leads to more consistent discharge simulations with reduced error. However, when looking at the scale of the specific site, the gHMs used in this study do not appear to be sensitive to the choice of the global meteorological datasets since the datasets lead to similar levels of efficiency in terms of discharge simulations (Tables  5  and 6 ). At the local scale, a higher variability between the gHMs is observed, as compared to the inter-dataset variability (Figures  9  and 10 ). The driving forcing datasets seem to have more influence regionally than at the catchment scale, where, locally, even though the global driving datasets display some discrepancies in seasonal climate patterns, especially for precipitation (Figures  6 7 8 ), the differences would be more linked to the way the gHMs represent the main hydrological processes and their resolution.
However, the moderate performances of the rHM-Princeton combinations for the four catchments, especially for the high-elevation catchment, point to limitations in global data quality, which seriously constrain any efforts at hydrological modeling for the NA region, as is the case for the Southern Africa region  [65]. Disentangling the factors explaining the poor quality of the global meteorological datasets is not easy as more than one characteristic differs between each dataset (e. g. , bias correction, reanalysis; see Table  1 ). Many studies have reported the importance of incorporating observational or pseudo-observational (satellite remote sensing data, radar data) inputs into global meteorological products to better depict spatiotemporal climate variability in complex areas  [66, 67].
One study  [68]  compared seven global datasets in five Köppen climate zones and underlined that there are some global datasets that perform well under a specific climate but that no single one performs best for all climate types. Another study  [69]  showed that the WFDEI dataset only provides better discharge simulations than reanalysis in the subtropical and humid continental climate regions. Using global meteorological datasets for hydrological modeling, such as in ISIMIP2a, relies on pragmatic reasons (e. g. , spatiotemporal resolution, period of available datasets). Global dataset intercomparisons for choosing the most reliable products are often set aside due to the time constraints in such large projects. However, conducting an evaluation of global meteorological dataset quality before using them as forcings to the gHMs is required, especially when the rHMs are also driven by the global datasets, leading to a low level of efficiency in terms of discharge simulations at the catchment scale.
gHMs' Performance between Catchments We find a high variability between gHMs in their ability to reproduce the mean interannual cycles of weekly discharge for the four catchments. This confirms findings from previous catchment-scale studies that emphasize that large spreads from the gHMs are not primarily due to errors in the driving data or local geomorphological aspects but to errors in the gHM structure  [32, 70]. The gHM models include quite simple linear reservoir approaches to routing flows to the outlet. In addition, the DEM resolution used in the gHM models cannot precisely reflect the flow path in complex regions, such as the mountain catchments. Misrepresentations of physical processes can explain the discrepancies between the gHM-simulated and observed (or rHM-simulated) discharges, which are more obvious at the catchment scale than over a continental region such as the NA region.
When aggregating across catchments, no gHM stands out in the reproduction of discharge for the set of specific sites (Figure  9 , Figure  10  and Figures S1-S6 ). This is partly explained by both the generalized parameters and the relatively coarse resolution of the ISIMIP2a gHMs, which prevent them from performing accurately in different locations under different climates. In addition, when driven by different global meteorological datasets, the performance of a given gHM (for instance, PCR-GLOBWB in Figure  9  and Figures S1, S3 and S5) is relatively similar. However, for a given driving dataset (for instance, WFDEI in Figure  9  and Figures S1, S3 and S5), the differences in the gHM structure and parametrization lead to highly contrasted reproductions of mean flow seasonal dynamics between the gHMs.
In hydrological modeling, an increase in model efficiency with the increasing size of catchments is often reported  [77]. Ref. [78]  showed that the drainage area of the catchment is one of the five most significant explanatory variables affecting the discharge simulations. It is expected that for large catchments with a smooth hydrological behavior, it will be easier for the models to reproduce the discharge. This finding cannot be transposed to both the gHMs and rHMs in the present study (Tables  5  and 6 ). Moreover, the meteorological input data for large catchments are known with less uncertainty than for small catchments, which should tend towards a better gHM performance for larger catchments. Again, this is not illustrated in this work. Multi-gHM intercomparison studies carried out over the last few years have revealed large differences among the gHMs  [4, 72].
Both rHMs driven by the Princeton dataset give better results than the gHM-Princeton combinations, and HMETS-Princeton combination provides more satisfactory discharge simulations than the GR4J-Princeton combination. The first finding can be explained by the existence of calibrated parameters in rHMs, allowing compensation of the errors in global datasets at the regional and local scale compared to the gHMs; the second finding is probably related to the number of model parameters since HMETS has a larger set of parameters compared to GR4J (23 calibrated parameters versus 4 calibrated parameters; Table  2 ), leading to a higher degree of freedom and better model adaptability to different regions. Our findings are similar to those of [13], who compared simulations of several rHMs and gHMs for the Lena River Basin in Russia.
Conclusions This study presents a catchment-scale comparative evaluation of the performance of four gHMs driven by four global meteorological datasets against two rHMs driven by one global meteorological dataset using two statistical criteria, Taylor diagrams, and visual hydrograph comparisons. We provide aggregated catchment-scale results, facilitating the comparison of model performance spatially for 198 large-sized catchments over the NA region. These findings are critical, as they provide the basis for the climate change impact studies using the gHMs in the next phase of ISIMIP2a. We find a tendency for most gHM-climate-dataset combinations to overestimate discharges with negative NSE scores and large positive PBIAS and RMSD values. The errors and median values spread for the gHM-simulated discharges are not in good agreement with the observations or the rHM simulations.
Looking at the four selected large-sized catchments, no gHM-climate-dataset combination can simulate the seasonal weekly discharge cycles. The magnitude and timing of the peak flows are poorly captured by most applied gHMs in the four catchments. Our evaluation provides recommendations to the global-scale hydrological modeling community in pursuing efforts to improve the gHMs' ability to reproduce the global characteristics of discharge seasonality. The results obtained by considering the four forcing datasets slightly vary the conclusions drawn at the catchment scale; however, more influence seems to be associated with the gHM resolution and structure. Both the rHMs, driven by the global dataset, provide better simulation results for the 198 NA catchments in terms of the criteria used in this study.
This study evaluates gHM performance regarding river discharge for the NA region. We naturally recognize that a similar evaluation conducted in other regions will be welcome to confirm our findings. Further assessments, including other water balance components such as snowmelt and potential evapotranspiration, could provide more insights into the performance of the gHMs, particularly for the representation of seasonal dynamics at the catchment scale. In addition, extensive cross-validation of the rHMs driven by the ISIMIP2a global meteorological datasets, as well as the use of regionalized rHM parameters, might be considered in future work for providing further insights into rHM accuracy compared to that of the gHMs for large-sized catchments. Our results show that there are still some challenges with accurate catchment-scale simulations of discharges by the gHMs.