Simulating cold-region hydrology in an intensively drained agricultural watershed in Manitoba, Canada, using the Cold Regions Hydrological Model
Bibliographic record
Abstract
Abstract. 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 6.7 m3 s−1 and 1.25 × 107 m3, respectively, with an average Nash–Sutcliffe efficiency (NSE) of 0.76. Simulation of low-flow years (below the medians) was more challenging (average NSE < 0), with simulated discharge overestimated by 90 % 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".