Simulating cold-region hydrology in an intensively drained agricultural watershed in Manitoba, Canada, using the Cold Regions Hydrological Model


Cordeiro, M.R.C., Wilson, H.F., Vanrobaeys, J., Pomeroy, J.W., Fang, X. (2017). Simulating cold-region hydrology in an intensively drained agricultural watershed in Manitoba, Canada, using the Cold Regions Hydrological Model. Hydrology and Earth System Sciences, [online] 21(7), 3483-3506.

Plain language summary

Most water runoff from agricultural land on the Prairies and the transport of associated nutrients occur during snowmelt. Most hydrological models have been designed for rainfall driven runoff and to represent the hydrology of warmer regions. We evaluated a physically based model (parameters can be measured in the field and have a physical meaning) in a low slope, clay soil, Prairie watershed with extensive alteration of the stream network through ditching. Simulations realistically represented flow, snow water equivalency, and soil moisture. Snow sublimation, snow transport, and infiltration were shown to be very influential in the generation of runoff. Models that do not account for these processes are unlikely to realistically represent the hydrology of the Prairies. Potential to improve model representations was identified for dry and flooded conditions because how water moves through stream channels changes under these conditions, but most models of flow routing assume travel time is not impacted by back flooding or changing channel lengths.


Etrophication and flooding are perennial problems in agricultural watersheds of the northern Great Plains. A high proportion of annual runoff and nutrient transport occurs with snowmelt in this region. Extensive surface drainage modification, frozen soils, and frequent backwater or ice-damming impacts on flow measurement represent unique challenges to accurately modelling watershed-scale hydrological processes. A physically based, non-calibrated model created using the Cold Regions Hydrological Modelling platform (CRHM) was parameterized to simulate hydrological processes within a low slope, clay soil, and intensively surface drained agricultural watershed. These characteristics are common to most tributaries of the Red River of the north. Analysis of the observed water level records for the study watershed (La Salle River) indicates that ice cover and backwater issues at time of peak flow may impact the accuracy of both modelled and measured streamflows, highlighting the value of evaluating a non-calibrated model in this environment. Simulations best matched the streamflow record in years when peak and annual discharges were equal to or above the medians of 6.7gm3gsg'1 and 1.25g × 107gm3, respectively, with an average Nash-Sutcliffe efficiency (NSE) of 0.76. Simulation of low-flow years (below the medians) was more challenging (average NSEg < g0), with simulated discharge overestimated by 90g% on average. This result indicates the need for improved understanding of hydrological response in the watershed under drier conditions. Simulation during dry years was improved when infiltration was allowed prior to soil thaw, indicating the potential importance of preferential flow. Representation of in-channel dynamics and travel time under the flooded or ice-jam conditions should also receive attention in further model development efforts. Despite the complexities of the study watershed, simulations of flow for average to high-flow years and other components of the water balance were robust (snow water equivalency (SWE) and soil moisture). A sensitivity analysis of the flow routing model suggests a need for improved understanding of watershed functions under both dry and flooded conditions due to dynamic routing conditions, but overall CRHM is appropriate for simulation of hydrological processes in agricultural watersheds of the Red River. Falsifications of snow sublimation, snow transport, and infiltration to frozen soil processes in the validated base model indicate that these processes were very influential in stream discharge generation.