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Item type:Publication, Access status: without files , A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections(2024-12); ; ; In cold regions, climate change, including warming and changes in snowmelt dynamics, have profound impacts on streamflow patterns, often leading to flooding events. Understanding the projected changes in hydrometeorological factors contributing to streamflow, such as snowmelt runoff and rain-on-snowmelt, is crucial for effective adaptation and mitigation strategies. It is also essential to consider uncertainty in streamflow projections and incorporate this uncertainty into flood predictions. This study presents a blueprint for calculating probabilistic future streamflow and flow duration curves in a mountainous cold region. The methodology integrated forcings from an atmospheric model (WRF) with a hydrological model (MESH) at fine spatial (4 km) and temporal (3-hourly) resolutions. To account for uncertainty, an ensemble of 15 CanRCM4 regional climate simulations with varying boundary conditions for the RCP 8.5 scenario was utilized. A novel method was developed to perturb the CanRCM4 simulations’ precipitation using a WRF Pseudo Global Warming run to incorporate uncertainty. This comprehensive approach revealed significant uncertainty in streamflow driven by internal variability. WRF-driven simulations without uncertainty showed a decrease in future streamflow extremes, while the perturbed simulations demonstrated the potential for substantially higher values under climate change. Moreover, the study derived probabilistic flow duration curves from the streamflow projections, aiding in estimating flood frequency for floodplain mapping when driving local hydraulic models. The methodology developed in this work can be extended to other river basins in Canada and elsewhere where gridded downscaled climate model outputs are available.Publicationtype: Journal Article11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The perils of regridding: examples using a global precipitation dataset(American Meteorological Society (AMS), 2021-11-01); ; ; Gridded precipitation datasets are used in many applications such as the analysis of climate variability/ change and hydrological modeling. Regridding precipitation datasets is common for model coupling (e.g., coupling atmospheric and hydrological models) or comparing different models and datasets. However, regridding can considerably alter precipitation statistics. In this global analysis, the effects of regridding a precipitation dataset are emphasized using three regridding methods (first-order conservative, bilinear, and distance-weighted averaging). The differences between the original and regridded dataset are substantial and greatest at high quantiles. Differences of 46 and 0.13 mm are noted in high (0.95) and low (0.05) quantiles, respectively. The impacts of regridding vary spatially for land and oceanic regions; there are substantial differences at high quantiles in tropical land regions, and at low quantiles in polar regions. These impacts are approximately the same for different regridding methods. The differences increase with the size of the grid at higher quantiles and vice versa for low quantiles. As the grid resolution increases, the difference between original and regridded data declines, yet the shift size dominates for high quantiles for which the differences are higher. While regridding is often necessary to use gridded precipitation datasets, it should be used with great caution for fine resolutions (e.g., daily and sub-daily), because it can severely alter the statistical properties of precipitation, specifically at high and low quantiles.Publicationtype: Journal Article7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Quantifying the effects of Prairie depressional storage complexes on drainage basin connectivityRunoff in many locations within the Canadian Prairies is dominated by intermittent fill-and-spill between depressions. As a result, many basins have varying fractions of their areas connected to their outlets, due to changing depressional storage. The objective of this research is to determine the causes of the relationships between water storage and the connected fraction of depression-dominated Prairie basins. It is hypothesized that the shapes of the relationship curves are influenced by both the spatial and frequency distributions of depressional storage. Three sets of numerical experiments are presented to test the hypothesis. The first set of experiments demonstrates that where the number of depressions is small, their size and spatial distributions are important in controlling the relationship between the volume of depressional storage and the connected fraction of a basin. As the number of depressions is increased, the areal fractions of the largest depressions decrease, which reduces the importance of the spatial distribution of depressions. The second set of experiments demonstrates that the curve enveloping the connected fraction of a basin can be derived from the frequency distribution of depression areas, and scaling relationships between the area, volume and catchment area of the depressions, when the area of the largest depression is no greater than approximately 5% of the total. The third set of experiments demonstrates that the presence of a single large depression can strongly influence the relationship between the depressional storage and the connected fraction of a basin, depending on the relative size of the large depression, and its location within the basin. A single depression containing 30% of the total depressional area located near the outlet was shown to cause a basin to be nearly endorheic. A similar depression near the top of a basin was demonstrated not to fill and was therefore unable to contribute flows. The implications of the findings for developing hydrological models of large Prairie drainage basins are discussed.Publicationtype: Journal Article2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Assessment of extremes in global precipitation products: how reliable are they?(American Meteorological Society (AMS), 2020-12-01); ; ; ; Global gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how precipitation varies across time and space, specifically in regions where ground-based observations are scarce. While the application of global precipitation products has become widespread, there is limited knowledge on how well these products represent the magnitude and frequency of extreme precipitation—the key features in triggering flood hazards. Here, five global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR, and WFDEI) are compared to each other and to surface observations. The spatial variability of relatively high precipitation events (tail heaviness) and the resulting discrepancy among datasets in the predicted precipitation return levels were evaluated for the time period 1979–2017. The analysis shows that 1) these products do not provide a consistent representation of the behavior of extremes as quantified by the tail heaviness, 2) there is strong spatial variability in the tail index, 3) the spatial patterns of the tail heaviness generally match the Köppen–Geiger climate classification, and 4) the predicted return levels for 100 and 1000 years differ significantly among the gridded products. More generally, our findings reveal shortcomings of global precipitation products in representing extremes and highlight that there is no single global product that performs best for all regions and climates.Publicationtype: Journal Article3