In this study, we evaluate uncertainties propagated through different climate data sets in seasonal and annual hydrological simulations over 10 subarctic watersheds of northern Manitoba, Canada, using the variable infiltration capacity (VIC) model. Further, we perform a comprehensive sensitivity and uncertainty analysis of the VIC model using a robust and state-of-the-art approach. The VIC model simulations utilize the recently developed variogram analysis of response surfaces (VARS) technique that requires in this application more than 6,000 model simulations for a 30-year (1981-2010) study period. The method seeks parameter sensitivity, identifies influential parameters, and showcases streamflow sensitivity to parameter uncertainty at seasonal and annual timescales. Results suggest that the Ensemble VIC simulations match observed streamflow closest, whereas global reanalysis products yield high flows (0.5-3.0 mm day −1 ) against observations and an overestimation (10-60%) in seasonal and annual water balance terms. VIC parameters exhibit seasonal importance in VARS, and the choice of input data and performance metrics substantially affect sensitivity analysis. Uncertainty propagation due to input forcing selection in each water balance term (i.e., total runoff, soil moisture, and evapotranspiration) is examined separately to show both time and space dimensionality in available forcing data at seasonal and annual timescales. Reliable input forcing, the most influential model parameters, and the uncertainty envelope in streamflow prediction are presented for the VIC model. These results, along with some specific recommendations, are expected to assist the broader VIC modelling community and other users of VARS and land surface schemes, to enhance their modelling applications. K E Y W O R D S hydrological modelling, lower Nelson River basin, sensitivity analysis, uncertainty assessment, VARS, VIC model, VIC parameters, water balance 1 | INTRODUCTION Numerical modelling of a river basin remains essential for both climate and ecological studies as it provides vital information about the hydrological cycle and water availability for societies and ecosystems. Although recent developments and advances have been achieved in hydrological modelling and computational power, addressing efficiently the uncertainties in hydrological simulation remains a critical
This study evaluates the 1981-2010 spatiotemporal differences in six available climate datasets (daily total precipitation and mean air temperature) over the Lower Nelson River Basin (LNRB) in ten of its sub-watersheds at seasonal and annual time scales. We find that the Australian National University spline interpolation (ANUSPLIN), and inverse distance weighted (IDW) interpolated observations from 14 Environment and Climate Change Canada (ECCC) meteorological stations show dry biases, whereas reanalysis products tend to overestimate precipitation across most of the basin. All datasets exhibit prominent disagreement in precipitation trends whereby the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and European Union Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI) show exceptional wetting trends, while the IDW and ANUSPLIN data manifest drying trends. Mean air temperature trends generally agree across most of the datasets; however, the North American Regional Reanalysis (NARR) and IDW show stronger warming relative to other datasets. Overall, analyses of the different climate datasets and their ensemble reveal that the choice of input dataset plays a crucial role in the accurate estimation of historical climatic conditions, particularly when assessing trends, for the LNRB. Using the ensemble has the distinct advantage of preserving the unique strengths of all datasets and affords the opportunity to estimate the uncertainty for hydrologic modelling and climate change impact studies. RÉSUMÉ [Traduit par la rédaction] Cette étude évalue les différences, dans le temps et dans l'espace, de six séries de données climatologiques enregistrées de 1981 à 2010 (précipitations quotidiennes totales et température moyenne de l'air) sur le bassin inférieur du fleuve Nelson dans dix de ses sous-bassins versants, et ce, à des échelles saisonnière et annuelle. Nous constatons que l'interpolation par la fonction spline de l'Australian National University (ANUSPLIN) et les observations interpolées pondérées inversement à la distance (IDW) issues de 14 stations météorologiques d'Environnement et Changement climatique Canada présentent des biais secs, tandis que les produits de réanalyse ont tendance à surestimer les précipitations sur la presque totalité du bassin. Toutes les séries de données présentent des désaccords marqués quant aux tendances des précipitations. Les données provisoires du Centre européen pour les prévisions météorologiques à moyen terme (ERA-Interim du CEPMMT) et les données de forçage du projet Water and Global Change (WATCH-Forcing-Data-ERA-Interim) de l'Union européenne montrent des tendances humides exceptionnelles, tandis que les données issues de l'IDW et de l'ANUSPLIN révèlent des tendances sèches. Les tendances des températures moyennes de l'air concordent généralement d'une série de données à l'autre. Toutefois, la réanalyse régionale nord-américaine (NARR) et l'IDW montrent un réchauffement plus marqué par rapport aux autres séries de données...
It is common in the literature to not consider all sources of uncertainty simultaneously: input, structural, parameter, and observed calibration data uncertainty, particularly in data-sparse environments due to data limitations and the complexities that arise from data limitations when propagating uncertainty downstream in a modelling chain. This paper presents results for the propagation of multiple sources of uncertainty towards the estimation of streamflow uncertainty in a data-sparse environment. Uncertainty sources are separated to ensure low likelihood uncertainty distribution tails are not rejected to examine the interaction of sources of uncertainty. Three daily resolution hydrologic models (HYPE, WATFLOOD, and HEC-HMS), forced with three precipitation ensemble realizations, generated from five gridded climate datasets, for the 1981–2010 period were used to examine the effects of cumulative propagation of uncertainty in the Lower Nelson River Basin as part of the BaySys project. Selected behavioral models produced an average range of Kling-Gupta Efficiency scores of 0.79–0.68. Two alternative methods for behavioral model selection were also considered that ingest streamflow uncertainty. Structural and parameter uncertainty was found to be insufficient, individually, by producing some uncertainty envelopes narrower than observed streamflow uncertainty. Combined structural and parameter uncertainty, propagated to simulated streamflow, often enveloped nearly 100% of observed streamflow values, however, high and low flow years were generally a source for lower reliabilities in simulated results. Including all sources of uncertainty generated simulated uncertainty bounds that enveloped most of the observed flow uncertainty bounds including improvement for high and low flow years across all gauges although the uncertainty bounds generated were of low likelihood. Overall, accounting for each source of uncertainty added value to the simulated uncertainty bounds when compared to hydrometric uncertainty; the inclusion of hydrometric uncertainty was key for identifying the improvements to simulated ensembles.
The spatial and temporal performance of an ensemble of five gridded climate datasets (precipitation) (North American Regional Reanalysis, European Centre for Medium-Range Weather Forecasts interim reanalysis, European Union Water and Global Change (WATCH) Watch Forcing data ERA-Interim, Global Forcing Data-Hydro, and The Australian National University spline interpolation) was evaluated towards quantifying gridded precipitation data ensemble uncertainty for hydrologic model input. Performance was evaluated over the Nelson–Churchill Watershed via comparison to two ground-based climate station datasets for year-to-year and season-to-season periods (1981–2010) at three spatial discretizations: distributed, sub-basin aggregation, and full watershed aggregation. All gridded datasets showed spatial performance variations, most notably in year-to-year total precipitation bias. Absolute minimum and maximum realizations were generated and assumed to represent total possible uncertainty bounds of the ensemble. Analyses showed that high magnitude precipitation events were often outside the uncertainty envelope; some increase in spatial aggregation, however, enveloped more observations. Results suggest that hydrologic models can reduce input uncertainty with some spatial aggregation, but begin to lose information as aggregation increases. Uncertainty bounds also revealed periods of elevated uncertainty. Assessing input ensemble bounds can be used to include high and low uncertainty periods in hydrologic model calibration and validation.
This study investigates the impacts of climate change on the hydrology and soil thermal regime of ten sub-arctic watersheds (northern Manitoba, Canada) using the Variable Infiltration Capacity (VIC) model. We utilize statistically downscaled and bias-corrected forcing datasets based on 17 general circulation model (GCM) - representative concentration pathways (RCP) scenarios from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to run the VIC model for three 30-year periods: a historical baseline (1981–2010), and future projections (2021–2050: 2030s and 2041–2070: 2050s), under representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP5 Multi-Model Ensemble (MME) mean-based VIC simulations indicate a 15–20% increase and 10% decrease in the projected annual precipitation and snowfall, respectively over the southern portion of the basin and >20% rainfall increase over the higher latitudes of the domain by the 2050s. Snow accumulation is projected to decline across all sub-basins, particularly in the lower latitudes. Projected uncertainties in major water balance components (i.e., evapotranspiration, surface runoff, and streamflow) are more substantial in the wetland and lake-dominated Grass and Gunisao watersheds than their eight counterparts. Future warming increases soil temperatures >2.5°C by the 2050s, resulting in 40–50% more baseflow. Further analyses of soil temperature trends at three different depths show the most pronounced warming in the top soil layer (1.6°C 30-year-1 in the 2050s), whereas baseflow increases substantially by 19.7% and 46.3% during the 2030s and 2050s, respectively. These results provide crucial information on the potential future impacts of warming soil temperatures on the hydrology of sub-arctic watersheds in north-central Canada and similar hydro-climatic regimes.
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