Journal article
Crops as sensors: Using crop yield data to increase the robustness of hydrologic and biogeochemical models
Journal of hydrology (Amsterdam), Vol.592, 125599
2021
DOI: 10.1016/j.jhydrol.2020.125599
Abstract
•Crop data and flow partitioning constraints enhance the robustness of hydrologic models.•Crop yield calibration did not significantly impact hydrologic predictions while reduced models’ parameter uncertainty.•Flow partitioning constraints reduced prediction uncertainty and improved prediction of nitrate loads due to right flow pathways.•Getting the right answers for the right reasons improves model transferability.
Hydrologic models are most commonly calibrated only for streamflow at one or multiple stations within the watershed. However, it has been argued that in distributed hydrologic models with large numbers of parameters, it is possible to get good streamflow statistics with a completely incorrect representation of the internal watershed processes. Consequently, these models fail when they are used for other purposes, for example, biogeochemical prediction. Specifically, in watersheds dominated by agricultural land use, there is information on crop yield that is rarely used for constraining hydrologic fluxes. However, crops can potentially act as distributed sensors in the landscape and help in constraining evapotranspiration fluxes and improving model consistency. Our main goal in this paper is to address this important process understanding and show how using the information on crop yield helps to identify the proper model structure, thereby improving the robustness of hydrologic models.Using a 32,660 km2 agricultural watershed in Iowa as a case study, we used stepwise model refinement to show how the consideration of additional data sources can increase model consistency. We first developed a hydrologic model using the Soil and Water Assessment Tool that provided excellent monthly streamflow statistics at eight stations within the watershed. However, comparing crop yield measurements with modeled results revealed a strong underestimation in model estimates, and it was apparent that the model was getting the “right” streamflow by lowering the evapotranspiration flux and allowing the crops to die. Adding crop yield as an additional calibration target allowed us to identify the models’ structural deficiency and alter the potential evapotranspiration estimation method, thereby considerably improved model predictions of crop yield, while not altering streamflow predictions considerably. The modified model was able to (i) better capture variations in nitrate loads at the catchment outlet with no calibration and (ii) reduce parameter uncertainty, model prediction uncertainty, and equifinality. These findings highlight that using additional data sources to improve the hydrological consistency of distributed models increases their robustness and predictive ability.
Details
- Title: Subtitle
- Crops as sensors: Using crop yield data to increase the robustness of hydrologic and biogeochemical models
- Creators
- Idhayachandhiran Ilampooranan - Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaJerald L Schnoor - Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA 52242, USANandita B Basu - Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Resource Type
- Journal article
- Publication Details
- Journal of hydrology (Amsterdam), Vol.592, 125599
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.jhydrol.2020.125599
- ISSN
- 0022-1694
- eISSN
- 1879-2707
- Grant note
- DOI: 10.13039/501100004490, name: University of Waterloo; DOI: 10.13039/501100000038, name: Natural Sciences and Engineering Research Council of Canada; DOI: 10.13039/100000001, name: National Science Foundation
- Language
- English
- Date published
- 2021
- Academic Unit
- Occupational and Environmental Health; Civil and Environmental Engineering
- Record Identifier
- 9983997969602771
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