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Abstract Retrospective estimation of daily streamflow for all rivers within a territory is of practical interest for sustainable and optimal water management. This implies, however, the availability of methods for providing accurate estimations of flow for ungauged rivers. This study compares the potential of statistical interpolation (SI)—a simple data assimilation technique that combines observations and simulations from hydrological modelling—with four other approaches: nearest neighbour, direct use of outputs from hydrological modelling, ordinary and topological kriging. Through subsampling cross-validation analyses based on the modified Kling-Gupta efficiency indicator, we show that SI compares favourably with these other approaches. While the performance of other methods depends on the configuration of the ungauged site in regards to the neighbouring reference sites, SI is less affected by these configurations. SI outperforms the other approaches particularly where the ungauged site is relatively distant from observation sites. In these cases, SI performance depends on the performance of the background model that relies on simulations of hydrological processes forced by precipitation and temperature observations. Our findings offer the potential for heightened performance estimates through an improvement of hydrological modelling and the use of more complex assimilation techniques for exploiting the model.
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The potential impacts of floods are of significant concern to our modern society raising the need to identify and quantify all the uncertainties that can impact their simulations. Climate simulations at finer spatial resolutions are expected to bring more confidence in these hydrological simulations. However, the impact of the increasing spatial resolutions of climate simulations on floods simulations has to be evaluated. To address this issue, this paper assesses the sensitivity of summer–fall flood simulations to the Canadian Regional Climate Model (CRCM) grid resolution. Three climate simulations issued from the fifth version of the CRCM (CRCM5) driven by the ERA-Interim reanalysis at 0.44°, 0.22° and 0.11° resolutions are analysed at a daily time step for the 1981–2010 period. Raw CRCM5 precipitation and temperature outputs are used as inputs in the simple lumped conceptual hydrological model MOHYSE to simulate streamflows over 50 Quebec (Canada) basins. Summer–fall flooding is analysed by estimating four flood indicators: the 2-year, 5-year, 10-year and 20-year return periods from the CRCM5-driven streamflows. The results show systematic impacts of spatial resolution on CRCM5 outputs and seasonal flood simulations. Floods simulated with coarser climate datasets present smaller peak discharges than those simulated with the finer climate outputs. Smaller catchments show larger sensitivity to spatial resolution as more detail can be obtained from the finer grids. Overall, this work contributes to understanding the sensitivity of streamflow modelling to the climate model’s resolution, highlighting yet another uncertainty source to consider in hydrological climate change impact studies.
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This work explores the performances of the hydrologic model Hydrotel, applied to 36 catchments located in the Province of Quebec, Canada. A local calibration (each catchment taken individually) scheme and a global calibration (a single parameter set sought for all catchments) scheme are compared in a differential split-sample test perspective. Such a methodology is useful to gain insights on a model’s skills under different climatic conditions, in view of its use for climate change impact studies. The model was calibrated using both schemes on five non-continuous dry and cold years and then evaluated on five dissimilar humid and warm years. Results indicate that, as expected, local calibration leads to better performances than the global one. However, global calibration achieves satisfactory simulations while producing a better temporal robustness (i.e., model transposability to periods with different climatic conditions). Global calibration, in opposition to local calibration, thus imposes spatial consis...
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AbstractA snow model forced by temperature and precipitation is used to simulate the spatial distribution of snow water equivalent (SWE) over a 600,000 km2 portion of the province of Quebec, Canada. We propose to improve model simulations by assimilating SWE data from sporadic manual snow surveys with a particle filter. A temporally and spatially correlated perturbation of the meteorological forcing is used to generate the set of particles. The magnitude of the perturbations is fixed objectively. First, the particle filter and direct insertion were both applied on 88 sites for which measured SWE consist of more or less five values per year over a period of 17 years. The temporal correlation of perturbations enables to improve the accuracy and the ensemble dispersion of the particle filter, while the spatial correlation lead to a spatial coherence in the particle weights. The spatial estimates of SWE obtained with the particle filter are compared with those obtained through optimal interpolation of the sno...
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Abstract Study Region: In Canada, dams which represent a high risk to human loss of life, along with important environmental and financial losses in case of failure, have to accommodate the Probable Maximum Flood (PMF). Five Canadian basins with different physiographic characteristics and geographic locations, and where the PMF is a relevant metric have been selected: Nelson, Mattagami, Kenogami, Saguenay and Manic-5. Study Focus: One of the main drivers of the PMF is the Probable Maximum Precipitation (PMP). Traditionally, the computation of the PMP relies on moisture maximization of high efficiency observed storms without consideration for climate change. The current study attempts to develop a novel approach based on traditional methods to take into account the non-stationarity of the climate using an ensemble of 14 regional climate model (RCM) simulations. PMPs, the 100-year snowpack and resulting PMF changes were computed between the 1971-2000 and 2041-2070 periods. New Hydrological Insights for the Region: The study reveals an overall increase in future spring PMP with the exception of the most northern basin Nelson. It showed a projected increase of the 100-year snowpack for the two northernmost basins, Nelson (8%) and Manic-5 (3%), and a decrease for the three more southern basins, Mattagami (-1%), Saguenay (-5%) and Kenogami (-9%). The future spring PMF is projected to increase with median values between -1.5% and 20%.