Abstract:The contributing areas of streams in the Prairie regions of Canada and the northern U.S. are dominated by complexes of wetlands which store and release water. Prior research has suggested the existence of hysteresis between the total volume of water stored in prairie wetlands within a drainage basin and the basin's contributing area. To simulate the relationship between storage and contributing area in a way that accounts for hysteresis, two wetland hydrology models with vastly different levels of complexity were devised. The fully distributed Wetland Digital Elevation Model (DEM) Ponding Model (WDPM) applies simple fluxes of runoff and evaporation to a DEM of a prairie wetland complex. The parameterized Pothole Cascade Model (PCM) applies simulated fluxes of water to collections of conceptual models of wetlands and is less demanding in computations and data. Prior research showed that both models produced hysteretic relationships between water storage and contributing area, but the PCM produced smaller estimates of contributing area than did the WDPM, likely due to its spatial simplification. Using sequential remote sensing observations of wetland area after snowmelt, this study shows that the frequency distribution of the open water areas of prairie wetlands is similar to that produced by the WDPM when the wetlands are close to being completely filled. The remotely sensed observations show evidence of hysteresis in the open water area frequency distributions, as predicted by the fully distributed WDPM. To enable the parameterized PCM to produce the same type of hysteretic relationships as the WDPM, scaling relationships between the maximum area of a wetland and the area of upland draining into it were included. The parameterized PCM is suitable for application with prairie snow redistribution, snowmelt, infiltration, runoff and evapotranspiration routines as part of semi-distributed hydrological modelling of prairie wetland basins such as that implemented in the Cold Regions Hydrological Model.

Wetland conservation and restoration contribute to improved watershed functions through providing both water quantity benefits in terms of flood attenuation and water quality benefits such as retention of sediment and nutrients. However, it is important to quantify these environmental benefits for informed decision making. This study uses a ''hydrologic equivalent wetland'' concept in the Soil and Water Assessment Tool to examine the effects of various wetland restoration scenarios on stream flow and sediment at a watershed scale. The modeling system was applied to the 25,139 ha Broughton's Creek watershed in western Manitoba in Canada. As a representative prairie watershed, the Broughton's Creek watershed experienced historic wetland losses from 2,998 ha in 1968 to 2,379 ha in 2005. Modeling results showed that if wetlands in the Broughton's Creek watershed can be restored to the 1968 level, the peak discharge and average sediment loading can be reduced by 23.4 and 16.9%, respectively at the watershed outlet. Based on wetland and stream drainage areas estimated by the model and empirical nutrient export coefficients, the corresponding water quality benefits in terms of reductions in total phosphorus and nitrogen loadings were estimated at 23.4%. The modeling results are helpful for designing effective watershed restoration strategies in the Broughton's Creek watershed. The developed methodology can be also applied to other study areas for examining the environmental effects of wetland restoration scenarios.

In the northern Great Plains, most runoff transport of N, and P to surface waters has historically occurred with snowmelt. In recent years, significant rainfall runoff events have become more frequent and intense in the region. Here, we examine the influence of landscape characteristics on hydrology and nutrient export in nine tributary watersheds of the Assiniboine River in Manitoba, Canada, during snowmelt runoff and with an early summer extreme rainfall runoff event (ERRE). All watersheds included in the study have land use that is primarily agricultural, but with differing proportions of land remaining as wetlands, grassland, and that has been artificially drained. Those watersheds with greater capacity for storage of water in surface depressions (noneffective contributing areas) exhibited lower rates of runoff and nutrient export with snowmelt. During the ERRE, higher export of total P (TP), but not total N, was observed from those watersheds with larger amounts of contributing area that had been added through artificial surface drainage, and this was associated primarily with higher TP concentrations. Increasing or restoring the storage of water on the landscape is likely to reduce nutrient export; however, the importance of antecedent conditions was evident during the ERRE, when small surface depressions were at or near capacity from snowmelt. Total P concentrations observed during the summer ERRE were as high as those observed with snowmelt, and N/P ratios were significantly lower. If the frequency of summer ERREs increases with climate change, this is likely to result in negative water quality outcomes.

The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.

Mapping and monitoring surface water features is important for sustainably managing this critical natural resource that is in decline due to numerous natural and anthropogenic pressures. Satellite Synthetic Aperture Radar is a popular and inexpensive solution for such exercises over large scales through the application of thresholds to distinguish water from non-water. Despite improvements to threshold methods, threshold selection is traditionally manual, which introduces subjectivity and inconsistency over large scales. This study presents a novel method for objectively determining and applying a threshold to determine water masks from Synthetic Aperture Radar (SAR) imagery on a scene-by-scene basis. The method was applied to Radarsat-2 and simulated Radarsat Constellation Mission scenes, and validated against two independent validation sources with high accuracy (Kappa ranging from 0.85 to 0.93). Expectedly, greatest misclassification occurs near shorelines, which are often ecologically important zones. Comparisons between Radarsat-2 and Radarsat Constellation Mission thresholds and outputs suggest that the latter is a capable successor for surface water applications. This work represents a foundational step toward objectivity and consistency in large-scale water mapping and monitoring.