• The precipitation increase can offset the impact of warming on mountain snow hydrology. • The offsetting role of precipitation is effective at the high elevations and high latitudes. • The projected precipitation elasticity of annual runoff increases as latitude decreases. • The projected precipitation elasticity of peak snowpack increases as latitude increases. • Elasticities indicated that runoff changes are primarily attributed to precipitation change. Whether or not the impact of warming on mountain snow and runoff can be offset by precipitation increases has not been well examined, but it is crucially important for future downstream water supply. Using the physically based Cold Regions Hydrological Modelling Platform (CRHM), elasticity (percent change in runoff divided by change in a climate forcing) and the sensitivity of snow regimes to perturbations were investigated in three well-instrumented mountain research basins spanning the northern North American Cordillera. Hourly meteorological observations were perturbed using air temperature and precipitation changes and were then used to force hydrological models for each basin. In all three basins, lower temperature sensitivities of annual runoff volume ( ≤ 6% °C −1 ) and higher sensitivities of peak snowpack (−17% °C −1 ) showed that annual runoff was far less sensitive to temperature than the snow regime. Higher and lower precipitation elasticities of annual runoff (1.5 – 2.1) and peak snowpack (0.7 – 1.1) indicated that the runoff change is primarily attributed to precipitation change and, secondarily, to warming. A low discrepancy between observed and simulated precipitation elasticities showed that the model results are reliable, and one can conduct sensitivity analysis. The air temperature elasticities, however, must be interpreted with care as the projected warmings range beyond the observed temperatures and, hence, it is not possible to test their reliability. Simulations using multiple elevations showed that the timing of peak snowpack was most sensitive to temperature. For the range of warming expected from North American climate model simulations, the impacts of warming on annual runoff, but not on peak snowpack, can be offset by the size of precipitation increases projected for the near-future period 2041–2070. To offset the impact of 2 °C warming on annual runoff, precipitation would need to increase by less than 5% in all three basins. To offset the impact of 2 °C warming on peak snowpack, however, precipitation would need to increase by 12% in Wolf Creek in Yukon Territory, 18% in Marmot Creek in the Canadian Rockies, and an amount greater than the maximum projected at Reynolds Mountain in Idaho. The role of increased precipitation as a compensator for the impact of warming on snowpack is more effective at the highest elevations and higher latitudes. Increased precipitation leads to resilient and strongly coupled snow and runoff regimes, contrasting sharply with the sensitive and weakly coupled regimes at low elevations and in temperate climate zones.

The goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (a) to provide tools that quantify the sampling uncertainty in popular performance metrics; (b) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (c) to prescribe the further research that is, needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large-sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models.

The goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (a) to provide tools that quantify the sampling uncertainty in popular performance metrics; (b) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (c) to prescribe the further research that is, needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large-sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models.

Abstract. Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, “equally plausible” ensemble members are used to approximate the probability distribution, hence the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1∘ spatial resolution (approx. 10 km grids) from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at https://doi.org/10.20383/101.0275 (Tang et al., 2020a).

Abstract. Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, “equally plausible” ensemble members are used to approximate the probability distribution, hence the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1∘ spatial resolution (approx. 10 km grids) from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at https://doi.org/10.20383/101.0275 (Tang et al., 2020a).

Abstract. East of the Continental Divide in the cold interior of Western Canada, the Mackenzie and Nelson River basins have some of the world's most extreme and variable climates, and the warming climate is changing the landscape, vegetation, cryosphere, and hydrology. Available data consist of streamflow records from a large number (395) of natural (unmanaged) gauged basins, where flow may be perennial or temporary, collected either year-round or during only the warm season, for a different series of years between 1910 and 2012. An annual warm-season time window where observations were available across all stations was used to classify (1) streamflow regime and (2) seasonal trend patterns. Streamflow trends were compared to changes in satellite Normalized Difference Indices. Clustering using dynamic time warping, which overcomes differences in streamflow timing due to latitude or elevation, identified 12 regime types. Streamflow regime types exhibit a strong connection to location; there is a strong distinction between mountains and plains and associated with ecozones. Clustering of seasonal trends resulted in six trend patterns that also follow a distinct spatial organization. The trend patterns include one with decreasing streamflow, four with different patterns of increasing streamflow, and one without structure. The spatial patterns of trends in mean, minimum, and maximum of Normalized Difference Indices of water and snow (NDWI and NDSI) were similar to each other but different from Normalized Difference Index of vegetation (NDVI) trends. Regime types, trend patterns, and satellite indices trends each showed spatially coherent patterns separating the Canadian Rockies and other mountain ranges in the west from the poorly defined drainage basins in the east and north. Three specific areas of change were identified: (i) in the mountains and cold taiga-covered subarctic, streamflow and greenness were increasing while wetness and snowcover were decreasing, (ii) in the forested Boreal Plains, particularly in the mountainous west, streamflows and greenness were decreasing but wetness and snowcover were not changing, and (iii) in the semi-arid to sub-humid agricultural Prairies, three patterns of increasing streamflow and an increase in the wetness index were observed. The largest changes in streamflow occurred in the eastern Canadian Prairies.

Abstract. East of the Continental Divide in the cold interior of Western Canada, the Mackenzie and Nelson River basins have some of the world's most extreme and variable climates, and the warming climate is changing the landscape, vegetation, cryosphere, and hydrology. Available data consist of streamflow records from a large number (395) of natural (unmanaged) gauged basins, where flow may be perennial or temporary, collected either year-round or during only the warm season, for a different series of years between 1910 and 2012. An annual warm-season time window where observations were available across all stations was used to classify (1) streamflow regime and (2) seasonal trend patterns. Streamflow trends were compared to changes in satellite Normalized Difference Indices. Clustering using dynamic time warping, which overcomes differences in streamflow timing due to latitude or elevation, identified 12 regime types. Streamflow regime types exhibit a strong connection to location; there is a strong distinction between mountains and plains and associated with ecozones. Clustering of seasonal trends resulted in six trend patterns that also follow a distinct spatial organization. The trend patterns include one with decreasing streamflow, four with different patterns of increasing streamflow, and one without structure. The spatial patterns of trends in mean, minimum, and maximum of Normalized Difference Indices of water and snow (NDWI and NDSI) were similar to each other but different from Normalized Difference Index of vegetation (NDVI) trends. Regime types, trend patterns, and satellite indices trends each showed spatially coherent patterns separating the Canadian Rockies and other mountain ranges in the west from the poorly defined drainage basins in the east and north. Three specific areas of change were identified: (i) in the mountains and cold taiga-covered subarctic, streamflow and greenness were increasing while wetness and snowcover were decreasing, (ii) in the forested Boreal Plains, particularly in the mountainous west, streamflows and greenness were decreasing but wetness and snowcover were not changing, and (iii) in the semi-arid to sub-humid agricultural Prairies, three patterns of increasing streamflow and an increase in the wetness index were observed. The largest changes in streamflow occurred in the eastern Canadian Prairies.

Snowmelt contributions to streamflow in mid-latitude mountain basins typically dominate other runoff sources on annual and seasonal timescales. Future increases in temperature and changes in precipitation will affect both snow accumulation and seasonal runoff timing and magnitude, but the underlying and fundamental roles of mountain basin geometry and hypsometry on snowmelt sensitivity have received little attention. To investigate the role of basin geometry in snowmelt sensitivity, a linear snow accumulation model and the Cold Regions Hydrological Modeling (CRHM) platform driven are used to estimate how hypsometry affects basin-wide snow volumes and snowmelt runoff. Area-elevation distributions for fifty basins in western Canada were extracted, normalized according to their elevation statistics, and classified into three clusters that represent top-heavy, middle, and bottom-heavy basins. Prescribed changes in air temperature alter both the snow accumulation gradient and the total snowmelt energy, leading to snowpack volume reductions (10–40%), earlier melt onsets (1–4 weeks) and end of melt season (3 weeks), increases in early spring melt rates and reductions in seasonal areal melt rates (up to 50%). Basin hypsometry controls the magnitude of the basin response. The most sensitive basins are bottom-heavy, and have a greater proportion of their area at low elevations. The least sensitive basins are top-heavy, and have a greater proportion of their area at high elevations. Basins with similar proportional areas at high and low elevations fall in between the others in terms of sensitivity and other metrics. This work provides context for anticipating the impacts of ongoing hydrological change due to climate change, and provides guidance for both monitoring networks and distributed modeling efforts.

Snowmelt contributions to streamflow in mid-latitude mountain basins typically dominate other runoff sources on annual and seasonal timescales. Future increases in temperature and changes in precipitation will affect both snow accumulation and seasonal runoff timing and magnitude, but the underlying and fundamental roles of mountain basin geometry and hypsometry on snowmelt sensitivity have received little attention. To investigate the role of basin geometry in snowmelt sensitivity, a linear snow accumulation model and the Cold Regions Hydrological Modeling (CRHM) platform driven are used to estimate how hypsometry affects basin-wide snow volumes and snowmelt runoff. Area-elevation distributions for fifty basins in western Canada were extracted, normalized according to their elevation statistics, and classified into three clusters that represent top-heavy, middle, and bottom-heavy basins. Prescribed changes in air temperature alter both the snow accumulation gradient and the total snowmelt energy, leading to snowpack volume reductions (10–40%), earlier melt onsets (1–4 weeks) and end of melt season (3 weeks), increases in early spring melt rates and reductions in seasonal areal melt rates (up to 50%). Basin hypsometry controls the magnitude of the basin response. The most sensitive basins are bottom-heavy, and have a greater proportion of their area at low elevations. The least sensitive basins are top-heavy, and have a greater proportion of their area at high elevations. Basins with similar proportional areas at high and low elevations fall in between the others in terms of sensitivity and other metrics. This work provides context for anticipating the impacts of ongoing hydrological change due to climate change, and provides guidance for both monitoring networks and distributed modeling efforts.

Abstract. Probabilistic methods are very useful to estimate the spatial variability in meteorological conditions (e.g., spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, equally plausible ensemble members are used to approximate the probability distribution, hence uncertainty, of a spatially distributed meteorological variable conditioned on the available information. The ensemble can be used to evaluate the impact of the uncertainties in a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1° spatial resolution from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using the Bayesian Model Averaging. Optimal Interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated based on OI estimates and spatiotemporally correlated random fields. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvement for precipitation occurring in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We also discuss the limitations of the current framework and highlight that persistent efforts are needed to further develop probabilistic methods and ensemble datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The whole dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at https://doi.org/10.20383/101.0275 (Tang et al., 2020a).

Abstract. Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially complete station observations for North America, this study seeks to develop an SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (Tmin⁡), and maximum temperature (Tmax⁡) data for 27 276 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year-long records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming that 30 % of observations from stations were missing – this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean value corrections were applied to the final estimates. The median Kling–Gupta efficiency (KGE′) values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, Tmin⁡, and Tmax⁡, respectively. The SCDNA is closer to station observations than the four benchmark gridded products and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modeling. The dataset is available at https://doi.org/10.5281/zenodo.3735533 (Tang et al., 2020).

Abstract Changes in Canadian Prairie streamflow, particularly trends over time, have not been well studied but are particularly relevant for food and water security in this vast agricultural region. Streamflow records for this region are often unsuitable for conventional trend analysis; streams are often intermittent and have only a few days per year with flow, and stations operate only during the warm season, because of a lack of flow during the very cold Prairie winter. This study takes an alternative approach; streamflow data for the period from March to October for individual years between 1910 and 2015 from 169 hydrometric stations from the Prairie and adjacent areas in Canada were converted to annual cumulative runoff series. These 5895 individual station-years were then clustered based upon their shape, using dynamic time warping. Three clusters of cumulative annual runoff were found; the first and most common type has infrequent days with flow and low total annual runoff [0–50 mm], the second has more days with flow and slightly greater runoff [48–175 mm], and the least common third type has the fewest days without flow, includes perennial streams, and has much greater annual runoff [>173 mm]. For each hydrometric station a time series of annual cluster memberships was created. Trends in the fractions of cluster types were determined using logistic regression, with spatial groupings of these time series over five-year periods. Trends in the fractions of types within an ecoregion indicate spatially consistent and organized changes in the pattern of runoff over the region. In the western Canadian Prairies, particularly in the Mixed Grassland and Cypress Upland ecoregions, drying is occurring, as indicated by the increased frequency of the dry type. In the northern and eastern Canadian Prairies, conditions are shifting to greater runoffs, particularly in the Aspen Parkland, where the wet types are increasing in frequency.

Abstract. Hydrological processes are widely understood to be sensitive to changes in climate, but the effects of changes in vegetation and soils have seldom been considered. The response of mountain hydrology to future climate and vegetation/soil changes is modelled in three snowmelt dominated mountain basins in the North American Cordillera. A Cold Regions Hydrological Model developed for each basin was driven with perturbed observed meteorological time series. Monthly perturbations were developed from differences in eleven regional climate model outputs between the present and future scenarios. Future climate change in these basins results in decreased modelled peak snow water equivalent (SWE) but increased evapotranspiration in all basins. All three watersheds became more rainfall-dominated. In Wolf Creek in the Yukon Territory, an insignificant increasing effect of vegetation change on peak SWE was found to be important enough to offset the significant climate change effect on alpine snow. In Marmot Creek in the Canadian Rockies, a combined effect of soil and climate changes on increasing annual runoff becomes significant while their individual effects are not statistically significant. In the relatively warmer Reynolds Mountain East catchment in Idaho, USA, only vegetation change decreases annual runoff volume and changes in soil, climate, or combination of them do not affect runoff. At high elevations in Wolf and Marmot Creeks, modelled vegetation/soil changes moderated the impact of climate change on peak SWE, the timing of peak SWE, evapotranspiration, and annual runoff volume. At medium elevations, these changes intensified the impact of climate change, decreasing peak SWE, and sublimation. The modelled hydrological impacts of changes in climate, vegetation, and soil in mountain environments are similar in magnitude but not consistently in the direction in all biomes; in some combinations, this results in enhanced impacts at lower elevations and latitudes and offsetting effects at higher elevations and latitudes.

Abstract. Hydrological processes are widely understood to be sensitive to changes in climate, but the effects of concomitant changes in vegetation and soils have seldom been considered in snow-dominated mountain basins. The response of mountain hydrology to vegetation/soil changes in the present and a future climate was modeled in three snowmelt-dominated mountain basins in the North American Cordillera. The models developed for each basin using the Cold Regions Hydrological Modeling platform employed current and expected changes to vegetation and soil parameters and were driven with recent and perturbed high-altitude meteorological observations. Monthly perturbations were calculated using the differences in outputs between the present- and a future-climate scenario from 11 regional climate models. In the three basins, future climate change alone decreased the modeled peak snow water equivalent (SWE) by 11 %–47 % and increased the modeled evapotranspiration by 14 %–20 %. However, including future changes in vegetation and soil for each basin changed or reversed these climate change outcomes. In Wolf Creek in the Yukon Territory, Canada, a statistically insignificant increase in SWE due to vegetation increase in the alpine zone was found to offset the statistically significant decrease in SWE due to climate change. In Marmot Creek in the Canadian Rockies, the increase in annual runoff due to the combined effect of soil and climate change was statistically significant, whereas their individual effects were not. In the relatively warmer Reynolds Mountain in Idaho, USA, vegetation change alone decreased the annual runoff volume by 8 %, but changes in soil, climate, or both did not affect runoff. At high elevations in Wolf and Marmot creeks, the model results indicated that vegetation/soil changes moderated the impact of climate change on peak SWE, the timing of peak SWE, evapotranspiration, and the annual runoff volume. However, at medium elevations, these changes intensified the impact of climate change, further decreasing peak SWE and sublimation. The hydrological impacts of changes in climate, vegetation, and soil in mountain environments were similar in magnitude but not consistent in direction for all biomes; in some combinations, this resulted in enhanced impacts at lower elevations and latitudes and moderated impacts at higher elevations and latitudes.

Abstract How mountain hydrology at different elevations will respond to climate change is a challenging question of great importance to assessing changing water resources. Here, three North American Cordilleran snow-dominated basins—Wolf Creek, Yukon; Marmot Creek, Alberta; and Reynolds Mountain East, Idaho—each with good meteorological and hydrological records, were modeled using the physically based, spatially distributed Cold Regions Hydrological Model. Model performance was verified using field observations and found adequate for diagnostic analysis. To diagnose the effects of future climate, the monthly temperature and precipitation changes projected for the future by 11 regional climate models for the mid-twenty-first century were added to the observed meteorological time series. The modeled future was warmer and wetter, increasing the rainfall fraction of precipitation and shifting all three basins toward rainfall–runoff hydrology. This shift was largest at lower elevations and in the relatively warmer Reynolds Mountain East. In the warmer future, there was decreased blowing snow transport, snow interception and sublimation, peak snow accumulation, and melt rates, and increased evapotranspiration and the duration of the snow-free season. Annual runoff in these basins did not change despite precipitation increases, warming, and an increased prominence of rainfall over snowfall. Reduced snow sublimation offset reduced snowfall amounts, and increased evapotranspiration offset increased rainfall amounts. The hydrological uncertainty due to variation among climate models was greater than the predicted hydrological changes. While the results of this study can be used to assess the vulnerability and resiliency of water resources that are dependent on mountain snow, stakeholders and water managers must make decisions under considerable uncertainty, which this paper illustrates.

AbstractIn cold conditions, early winter precipitation occurs as snowfall and contributes to the accumulating seasonal snowpack. In a warming climate, precipitation may occur as rainfall in mountai...

The operational history of one of Canada’s longest operating hydrometric stations is reviewed in detail, including flood estimates that precede formal hydrometric monitoring. The assessment inspects the early and operational history, the published streamflow record and the stage-discharge measurements collected since 1909. Methods used to estimate pre-operational high flows and the operational history are reviewed to establish potential issues with changes in technology, location and measurement sections. The streamflow record is screened for discontinuities and change. The stage-discharge measurements used to establish the rating curve for open-water and ice-covered periods are assessed and used to establish the degree of support for the published data over the period of record. In the period 1882 to 1909, occasional high-stage estimates were used to estimate peak discharge, but with considerable uncertainty due to lack of stream velocity measurements and bed profiles. For the period 1909–1914 it is diff...