Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions
Abstract
:1. Introduction
2. Methods to Determine CSAs
2.1. Simple Methods
2.1.1. Phosphorus Index (PI)
2.1.2. Topographic Index (TI)
2.1.3. Other Miscellaneous Methods
2.2. Detailed HWQ Models for CSAs
3. Current State and Challenges of Determining CSAs
3.1. Level of Model Complexity and Scaling Issues
3.2. Representation of Phosphorus Transformation and Transport Processes
3.3. Landscape Connectivity to Stream System Accounted for
3.4. Modeling Practices
3.5. Canadian Hydrological Conditions Conceptualization in Models
3.5.1. Runoff Generation Mechanism
3.5.2. Landscape Depressions
3.5.3. Cold-Climate Conditions
3.5.4. Tile Drains
4. Future Research Directions
- A better database is needed to improve the model prediction capability. More importantly, the TI method will benefit from better resolution data, such as Light Detection and Ranging (LiDAR) data, which could also be helpful to incorporate the saturation excess runoff generation into HWQ models.
- Most BMPs are implemented at the field scale. Therefore, it is important to improve field or sub-field scale modeling capability of the existing methods. This is particularly important while using semi-distributed HWQ models such as SWAT. This can be achieved by developing an ArcGIS extension that can identify an HRU location within a sub-basin. This will also facilitate evaluation of field and sub-field level output.
- There also is a need for improvements in the predictions of dissolved P in surface runoff from soil, which is now typically based on simple extraction coefficients relating a measure of soil P (usually routine soil tests) to dissolved P concentrations in runoff. Most existing methods use a constant value for all soil types. It is, therefore, necessary to conduct research to identify the variations in extraction coefficients with soil properties such as soil texture.
- With significant variations at field and watershed scale estimation of P loss, it is important to further investigate if different algorithms for sediment and P loss should be used.
- Canadian hydrology exhibits a distinct seasonal pattern. It is, therefore, necessary to quantify and include the impact of seasonal hydrology of P generation and transport processes in HWQ modeling approaches.
- With better understanding of physical, chemical, and biological processes affecting water quality, future research should also focus on improving the representation of processes, such as P stratification and legacy sources in HWQ models. Both vertical stratifications of P in no-till soils and legacy sources of P need to be incorporated as they affect the short-term benefits of remedial practices applied to CSAs.
- Another research area that needs attention is nutrient processes in the wetland modules. For example, the ‘Wetland’ and ‘Pond’ modules of the SWAT model do not consider nutrient transformation. However, several field studies have indicated that transformation of nutrients indeed occurs within the ponds, wetlands, or potholes.
- Development of a ‘Toolbox’ to identify CSAs also needs serious attention and could be a step forward. In fact, Sharpley et al. [28] envisioned the need and value of such a ‘Toolbox’. Especially given the fact that it is very difficult to answer all pertaining questions related to identifying CSAs of NPS pollution using a single model/method. The envisioned ‘Toolbox’ can host a range of different models/methods, ranging from a simple method such as topographic index to a complex HWQ model. This will provide the end users with a variety of options to try to use different methods/models to identify CSAs. Users may opt to use the simple methods for screening larger watersheds while detailed HWQ models may be used to provide absolute values of P loss and long-term effects of future management scenarios on P loss. The ‘Toolbox’ can be hosted in a dedicated platform or can be made available as a web-based modeling framework such as the Hydrology and Water Quality System (HAWQS) using SWAT framework [202]. The ‘Toolbox’ essentially offers features such as an automated workflow of input data preparing and processing for an area of user’s choice and output data repository to store model and scenario runs. We believe that provision of such a ‘Toolbox’ renders the redundancies associated with pre-processing of large volume of input data (e.g., spatial and meteorological) which is often time-intensive and error-prone. Recent advances in computing facilities and web technologies also support the idea of making the envisioned ‘Toolbox’ available to the wider public.
- With advances in computational resources and techniques, future research on CSAs should focus on the sources of uncertainty, uncertainty quantification methods, and incorporation of all possible uncertainties in HWQ modeling approaches.
- With advances in instrumentation technology, future research should also focus on field research to identify CSAs. Special attention is needed on the quantification on the seasonal variability of CSAs. Data collected from such experiments will be useful to evaluate the CSA prediction capability of HWQ models.
- It has been shown that the majority of NPS pollution would be exported in the late winter and early spring months [31,171,203] and, therefore, constitute hot-moments of NPS pollution export. Sampling campaigns should be effective enough to capture the variability in such important time/seasons. Identification of the proper locations for water quality monitoring is equally important too. Thus, a cost-effective and efficient water quality monitoring framework, as suggested by Alilou et al. [204] is desired. Moreover, detailed HWQ model ideally needs continuous water quality data for proper calibration (and/or validation). However, such data are rarely available because of logistic issues. Often, sporadic water quality measurements are available which hinders proper model calibration (and/or validation) and model results may deem to be unreliable. This is a general problem in NPS pollution modeling. One possible solution is to carryout dedicated sampling campaigns to cover wide range of streamflow/field conditions (e.g., dry, wet, 24 h, etc.) which helps to verify a model’s ability in respective conditions [205]. However, this requires a team of dedicated crew waiting for such an event to occur, which may not always be available [206]. Another possibility is to install automatic samplers to measure some explanatory variables (e.g., specific conductance, turbidity) and using an established relationship (e.g., regression model), other water quality variables (e.g., total suspended solids, total phosphorus) are predicted [207]. However, uncertainties associate with such estimations render their use in calibrating (and/or validating) detailed HWQ models. The use of remote sensing technologies in validating HWQ model based CSAs can also be an alternative as shown by Shrestha et al. (in press). In the study, they used oblique aerial images to qualitatively validate CSA of phosphorus in a watershed. Such a technique may be useful for a large watershed, which needs water quality monitoring at multiple locations.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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---|---|---|
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Niraula et al., [117] | SWAT, USA; Saugahatchee Creek Watershed & Magnolia River Watershed | CSAs identified at HRU level |
Niraula et al., [45] | SWAT and GWLF, Alabama, USA; Saugahatchee Creek Watershed | Study area of 570 km2, CSAs identified at sub-basin level |
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Rousseau et al., [121] | GIBSI, Quebec, Canada, Beaurivage River Basin | Watershed scale (718 km2) |
Shang et al., [122] | SWAT, China; Lake Erhai Basin | CSAs are identified at sub-basin level |
Shrestha et al., [123] | AGNPS, Canada, Holtby watershed | Watershed (3.74 km2), CSAs identified at cell level |
Tripathi et al., [124] | SWAT, Bihar, India; Nagwan Watershed | Watershed (92.46 km2), CSAs are identified at sub-watershed level |
Wang and Lin, [125] | AnnAGNPS, China, Dage subwatershed of Chaohe River | Watershed (1876 km2) |
Winchell et al., [50] | SWAT, USA-CAN border; Lake Champlain Missisquoi Bay Watershed | Watershed (310 km2), CSAs identified at HRU level, considers saturation excess using topographic index |
White et al., [126] | SWAT, Oklahoma, USA; 6 different watersheds | Watershed (23,000–174,000 km2), CSAs are identified at HRU level |
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Rudra, R.P.; Mekonnen, B.A.; Shukla, R.; Shrestha, N.K.; Goel, P.K.; Daggupati, P.; Biswas, A. Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions. Agriculture 2020, 10, 468. https://doi.org/10.3390/agriculture10100468
Rudra RP, Mekonnen BA, Shukla R, Shrestha NK, Goel PK, Daggupati P, Biswas A. Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions. Agriculture. 2020; 10(10):468. https://doi.org/10.3390/agriculture10100468
Chicago/Turabian StyleRudra, Ramesh P., Balew A. Mekonnen, Rituraj Shukla, Narayan Kumar Shrestha, Pradeep K. Goel, Prasad Daggupati, and Asim Biswas. 2020. "Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions" Agriculture 10, no. 10: 468. https://doi.org/10.3390/agriculture10100468
APA StyleRudra, R. P., Mekonnen, B. A., Shukla, R., Shrestha, N. K., Goel, P. K., Daggupati, P., & Biswas, A. (2020). Currents Status, Challenges, and Future Directions in Identifying Critical Source Areas for Non-Point Source Pollution in Canadian Conditions. Agriculture, 10(10), 468. https://doi.org/10.3390/agriculture10100468