This site requires Cookies enabled in your browser for login.
WaterNet Home
WaterNet
for
pour le
Canada
Menu
WaterNet
Home
GWFO
Home
Master
List
Data
Centre
Collections
X
Defaults
Select All
Websites
X
Global Water Futures Observatories (GWFO) Global Water Futures (GWF) Global Institute for Water Security (GIWS) International Network of Alpine Research Catchment Hydrology
Legacy Research Programs
X
Changing Cold Regions Network (CCRN) Drought Research Initiative (DRI) International Network of Alpine Research Catchment Hydrology (Legacy Site) Improving Processes & Parameterization for Prediction in Cold Regions Hydrology (IP3) The Mackenzie Global Energy and Water Cycle Experiment (GEWEX) Study (MAGS)
Legacy sites
Map
Utilities
X
Account Settings Metadata Editor Record List Alias List Editor
Data Centre
Data Type Editor
. . .
X
Clear
Select All
Advanced Search
Related items loading ...
Fetching Chart ...
Publication Additional Information Download
Publication Type
Journal Article
Authorship
Costa, D., Pomeroy, J., Baulch, H., Elliott, J., & Wheater, H.
Title
Using an inverse modelling approach with equifinality control to investigate the dominant controls on snowmelt nutrient export
Year
2019
Publication Outlet
Hydrological Processes, 33(23), 2958-2977
DOI
https://doi.org/10.1002/hyp.13463
Citation
Costa, D., Pomeroy, J., Baulch, H., Elliott, J., & Wheater, H. (2019). Using an inverse modelling approach with equifinality control to investigate the dominant controls on snowmelt nutrient export. Hydrological Processes, 33(23), 2958-2977. https://doi.org/10.1002/hyp.13463 .
Abstract
There is great interest in modelling the export of nitrogen (N) and phosphorus (P) from agricultural fields because of ongoing challenges of eutrophication. However, the use of existing hydrochemistry models can be problematic in cold regions because models frequently employ incomplete or conceptually incorrect representations of the dominant cold regions hydrological processes and are overparameterized, often with insufficient data for validation. Here, a process-based N model, WINTRA, which is coupled to a physically based cold regions hydrological model, was expanded to simulate P and account for overwinter soil nutrient biochemical cycling. An inverse modelling approach, using this model with consideration of parameter equifinality, was applied to an intensively monitored agricultural basin in Manitoba, Canada, to help identify the main climate, soil, and anthropogenic controls on nutrient export. Consistent with observations, the model results suggest that snow water equivalent, melt rate, snow cover depletion rate, and contributing area for run-off generation determine the opportunity time and surface area for run-off–soil interaction. These physical controls have not been addressed in existing models. Results also show that the time lag between the start of snowmelt and the arrival of peak nutrient concentration in run-off increased with decreasing antecedent soil moisture content, highlighting potential implications of frozen soils on run-off processes and hydrochemistry. The simulations showed TDP concentration peaks generally arriving earlier than NO3 but also decreasing faster afterwards, which suggests a significant contribution of plant residue Total dissolved Phosphorus (TDP) to early snowmelt run-off. Antecedent fall tillage and fertilizer application increased TDP concentrations in spring snowmelt run-off but did not consistently affect NO3 run-off. In this case, the antecedent soil moisture content seemed to have had a dominant effect on overwinter soil N biogeochemical processes such as mineralization, which are often ignored in models. This work demonstrates both the need for better representation of cold regions processes in hydrochemical models and the model improvements that are possible if these are included.
Program Affiliations
GWF: Global Water Futures
Project Affiliations
GWF-Groundwater, Climate Change and Water Security in the Canadian Prairies
Publication Stage
Published
Download Links
https://doi.org/10.1002/hyp.13463
© 2026 - WaterNet Version 2026-06-07
Global Water Futures Observatories
Powered by
G W F Net
T-2022-12-05-d1ol6tLbMV0iNrbRGmCmCMw Publication 1.0