Fang X.,University of Saskatchewan |
Pomeroy J.W.,University of Saskatchewan |
Ellis C.R.,University of Saskatchewan |
Ellis C.R.,Silvatech Consulting Ltd. |
And 5 more authors.
Hydrology and Earth System Sciences | Year: 2013
One of the purposes of the Cold Regions Hydrological Modelling platform (CRHM) is to diagnose inadequacies in the understanding of the hydrological cycle and its simulation. A physically based hydrological model including a full suite of snow and cold regions hydrology processes as well as warm season, hillslope and groundwater hydrology was developed in CRHM for application in the Marmot Creek Research Basin (∼ 9.4 km2), located in the Front Ranges of the Canadian Rocky Mountains. Parameters were selected from digital elevation model, forest, soil, and geological maps, and from the results of many cold regions hydrology studies in the region and elsewhere. Non-calibrated simulations were conducted for six hydrological years during the period 2005-2011 and were compared with detailed field observations of several hydrological cycle components. The results showed good model performance for snow accumulation and snowmelt compared to the field observations for four seasons during the period 2007-2011, with a small bias and normalised root mean square difference (NRMSD) ranging from 40 to 42% for the subalpine conifer forests and from 31 to 67% for the alpine tundra and treeline larch forest environments. Overestimation or underestimation of the peak SWE ranged from 1.6 to 29%. Simulations matched well with the observed unfrozen moisture fluctuation in the top soil layer at a lodgepole pine site during the period 2006-2011, with a NRMSD ranging from 17 to 39%, but with consistent overestimation of 7 to 34%. Evaluations of seasonal streamflow during the period 2006-2011 revealed that the model generally predicted well compared to observations at the basin scale, with a NRMSD of 60% and small model bias (1%), while at the sub-basin scale NRMSDs were larger, ranging from 72 to 76%, though overestimation or underestimation for the cumulative seasonal discharge was within 29%. Timing of discharge was better predicted at the Marmot Creek basin outlet, having a Nash-Sutcliffe efficiency (NSE) of 0.58 compared to the outlets of the sub-basins where NSE ranged from 0.2 to 0.28. The Pearson product-moment correlation coefficient of 0.15 and 0.17 for comparisons between the simulated groundwater storage and observed groundwater level fluctuation at two wells indicate weak but positive correlations. The model results are encouraging for uncalibrated prediction and indicate research priorities to improve simulations of snow accumulation at treeline, groundwater dynamics, and small-scale runoff generation processes in this environment. The study shows that improved hydrological cycle model prediction can be derived from improved hydrological understanding and therefore is a model that can be applied for prediction in ungauged basins. © 2013 Author(s).
Sadeghian A.,Global Institute for Water Security |
Sadeghian A.,University of Saskatchewan |
de Boer D.,Global Institute for Water Security |
de Boer D.,University of Saskatchewan |
And 6 more authors.
Journal of Great Lakes Research | Year: 2015
Many water quality processes in lakes and reservoirs are temperature dependent. Water temperature affects stratification and mixing, dissolved oxygen solubility, biological and physiological processes and aquatic species tolerances. Anticipated increases in air temperature due to climate change may also influence in-lake processes. In order to make valid predictions regarding the impact of climate change on the thermal structure of Lake Diefenbaker and determine the potential effects of management strategies, a hydrodynamic model was utilized for the period 2011-2012. Lake Diefenbaker is a long (181. km) and narrow (average width of 2.2. km) reservoir in Saskatchewan, Canada formed along the South Saskatchewan River (SSR) by the construction of the Gardiner and Qu'Appelle River dams in the 1960s. The model was calibrated using Monte-Carlo and combined global and local optimization techniques, which provided insights into parameter sensitivity and identifiability of relevance to monitoring needs. A new ice algorithm was also used, incorporating the effect of snow on the ice surface. In particular, this study provides a novel quantitative analysis of the contribution of different heat sources and sinks to the lake's heat budget, providing invaluable information for reservoir management practices in response to climate change and uncertainty. Of particular interest is the high input of heat from the lake's main inflow, the South Saskatchewan River, which has a high impact on the heat budget. © 2015 International Association for Great Lakes Research.