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Item type:Publication, Access status: without files , The 2021 heatwave results in simultaneous but different hydrological responses over Canada west of 100°W(Elsevier, 2024-02-08); ; ; The 2021 Western North America heatwave resulted in record high air temperatures over a large area of Canada west of 100°. The heatwave persisted from mid-June into July, depending upon the threshold used to define the heatwave. The heatwave was a weather event that was short lived but had a widespread transient impact on hydrology in this single year. These impacts were more evident in nival systems because of the prominent role of snowmelt. In normal years, the timing of hydrological processes, particularly snowmelt, is a function of latitude and elevation; in 2021 the heatwave resulted in simultaneous high rates of snowmelt across rivers where a snowpack existed at the time of the event, and strong diurnal melt signal was a diagnostic. In 2021, rivers throughout Canada west of 100°W responded strongly to the heatwave, but the response depended on three factors: location relative to the heat dome, the hydrologic regime, and the amount of snow present at the time. The melt signal was strongest in those basins that had not reached their annual peak often having discharges in the highest 5 % of historical observations for that day of the year. Discharges rapidly declined in basins already in recession indicating the rapid depletion of the snowpack. In basins with little or no remaining snow, streamflow often declined during the same period often to levels in the lowest 5 % of historical observations for that day of the year.Publicationtype: Journal Article25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The abuse of popular performance metrics in hydrologic modeling(Wiley, 2021-09-01); ; 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.Publicationtype: Journal Article4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Emdna: an ensemble meteorological dataset for north america(Copernicus Publications, 2021-07-13); ; ; ; 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 0.1g spatial resolution (approx. 10g€¯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 10.20383/101.0275 (Tang et al., 2020a).Publicationtype: Journal Article2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018Station-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).Publicationtype: Review Article9