Anuja Thapa


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Incorporating social dimensions in hydrological and water quality modeling to evaluate the effectiveness of agricultural beneficial management practices in a Prairie River Basin
Lori Bradford, Anuja Thapa, A. P. Duffy, Elmira Hassanzadeh, Graham Strickert, Bram Noble, Karl–Erich Lindenschmidt
Environmental Science and Pollution Research, Volume 27, Issue 13

There is growing interest to develop processes for creating user-informed watershed scale models of hydrology and water quality and to assist in decision-making for balanced policies for managing watersheds. Watershed models can be enhanced with the incorporation of social dimensions of watershed management as brought forward by participants such as the perspectives, values, and norms of people that depend on the land, water, and ecosystems for sustenance, economies, and overall wellbeing. In this work, we explore the value of combining both qualitative and quantitative methods and social science data to enhance salience and legitimacy of watershed models so that end-users are more engaged. We discuss pilot testing and engagement workshops for building and testing a systems dynamics model of the Qu'Appelle Valley to gather insights from local farmers and understand their perceptions of Beneficial Management Practices (BMPs). Mixed-method workshops with agricultural producers in the Qu'Appelle Watershed gathered feedback on the developing model and the incorporation of social determinants affecting decision-making. Analysis of focus groups and factor analysis of Q-sorts were used to identify the desired components of the model, and whether it supported farmers' understanding of the potential effects of BMPs on water quality. We explored farmers' engagement with models testing BMPs and the potential of incorporating their decision processes within the model itself. Finally, we discuss the reception of the process and the practicality of the approach in providing legitimate and credible decision support tools for a community of farmers.

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“Garbage in, Garbage Out” Does Not Hold True for Indigenous Community Flood Extent Modeling in the Prairie Pothole Region
Anuja Thapa, Lori Bradford, Graham Strickert, Xiaolei Yu, Anthony Johnston, Kelsey Watson-Daniels
Water, Volume 11, Issue 12

Extensive land use changes and uncertainties arising from climate change in recent years have contributed to increased flood magnitudes in the Canadian Prairies and threatened the vulnerabilities of many small and indigenous communities. There is, thus, a need to create modernized flood risk management tools to support small and rural communities’ preparations for future extreme events. In this study, we developed spatial flood information for an indigenous community in Central Saskatchewan using LiDAR based DEM and a spatial modeling tool, the wetland DEM ponding model (WDPM). A crucial element of flood mapping in this study was community engagement in data collection, scenario description for WDPM, and flood map validation. Community feedback was also used to evaluate the utility of the modelled flood outputs. The results showed the accuracy of WDPM outputs could be improved not only with the quality of DEM but also with additional community-held information on contributing areas (watershed information). Based on community feedback, this accessible, spatially-focused modeling approach can provide relevant information for community spatial planning and developing risk management strategies. Our study found community engagement to be valuable in flood modeling and mapping by: providing necessary data, validating input data through lived experiences, and providing alternate scenarios to be used in future work. This research demonstrates the suitability and utility of LiDAR and WDPM complemented by community participation for improving flood mapping in the Prairie Pothole Region (PPR). The approach used in the study also serves as an important guide for applying transdisciplinary tools and methods for establishing good practice in research and helping build resilient communities in the Prairies.