The wetlands in our study are in the Albertan extent of the Prairie Pothole Region (PPR) (Fig. 1). Wetlands in this region are mainly depressions filled with ponded water, each formed in the last glacial period (Wright, 1972). Spring snow melt is the largest contributor to ponded water amounts, either from direct precipitation into the wetland or as runoff over frozen ground from upland areas (Hayashi et al., 1998). Potholes can differ in the length of time they contain ponded water, which can range from a few weeks after snowmelt to the entire year (Stewart and Kantrud, 1971).

The provincial merged wetland inventory (Alberta Merged Wetland Inventory, published by Alberta Environment and Parks) and the Canadian national wetland inventory (Canadian Wetlands Inventory, published by Environment and Climate Change Canada) do not assign permanence classes or provide measurements (e.g., water volume, depth) that could be used to classify the wetlands in our study region by permanence class. We acquired permanence class data from two smaller wetland inventories (Government of Alberta, 2014) that delineate the location, boundary and permanence class of PPR wetlands based on Stewart and Kantrud's classification (Stewart and Kantrud, 1971) (Table 1). The two wetland inventories differ in their accuracy (Evans et al., 2017) and include wetlands from the Grassland, Parkland and the southern edge of the Boreal Forest natural regions of Alberta. The Grassland Natural Region comprises mixed-grass prairie, and the Parkland Natural Region comprises deciduous trees and grasses. Both are semi-arid regions with potential evapotranspiration rates that are greater than annual precipitation (Downing and Pettapiece, 2006). The Parkland Natural Region, however, experiences more precipitation than the Grassland Natural Region (Downing and Pettapiece, 2006). While most of the Boreal Forest Natural Region is dominated by coniferous trees and annual precipitation amounts typically exceed evapotranspiration rates (Downing and Pettapiece, 2006), the southern margin of the Boreal Forest Natural Region in Alberta contains pothole wetlands and more semi-arid to subhumid climate conditions (Brown et al., 2010; Devito et al., 2005). Our study of the Boreal Forest Natural Region considers only on this southern margin sometimes called the boreal transition zone.

For our analysis, we selected a subset of wetlands from the Alberta Merged Wetland Inventory within each natural region (Fig. 1). To ensure wetland conditions were indicative of the natural regions within which they resided, we excluded those within 500 m of a natural region boundary. Then, we randomly selected 12 000 wetlands in the southern Boreal Forest and Parkland natural regions (3000 per permanence class) and 16 000 in the Grassland Natural Region (4000 per permanence class). To ensure spatial independence among sampled wetlands and their relationship to land cover as well as coincide with previous analysis of open-water wetlands (Ridge et al., 2017), topography (Branton et al., 2020) and land cover (Evans et al., 2017), we did not select wetlands that were within 1000 m of another selected wetland.

The distribution of wetland sizes was strongly right-skewed across the three natural regions of interest (Appendix A). Wetlands were typically small, with Boreal Forest Natural Region wetlands possessing the largest median size (2.26 ha), followed by Parkland Natural Region wetlands (1.54 ha) and Grassland Natural Region wetlands (0.58 ha), though size varies with permanence class (Appendix A). In the Grassland Natural Region, the largest wetlands tended to be permanently ponded, whereas the largest wetlands in the Boreal Forest and Parkland natural regions tended to be seasonally ponded (Appendix A). The combination of wetland size and our digital elevation model (DEM) resolution of 25 m suggests that our median wetland sizes would occupy 36, 25 and 9 cells for the Boreal Forest, Parkland and Grassland natural regions, respectively, defining our ability to capture variability among wetland sizes and shape.

To select variables representative of climate, land cover/land use and topography that would be useful in testing the relative contribution of these three factors in predicting prairie pothole wetland permanence class, we conducted a literature review using the Web of Science. We limited the search to papers published between 1950 and 2018 with the following key words: (1) Prairie Pothole Region – PPR, Northern Great Plains, Alberta, Saskatchewan, Manitoba and Dakota; (2) weather – climate, temperature and precipitation; (3) disturbance – land use, agriculture, disturbance, oil and gas, grazing, and roads; and (4) pond permanence – watershed, hydroperiod, permanence class, catchment and wetland. We used “OR” operators between key words under the same class and “AND” operators between each key word class. To characterize topography, we selected variables that are commonly used to describe topographic variations, based on a previous review (Branton and Robinson, 2019). Details and results from this review are reported in Table 2.

We aimed to quantify the relative contribution of annual data on climate variables, land cover/land use, and topography for different wetland permanence classes and determine the ability of these drivers to predict wetland permanence class. Achieving these two outcomes involved four steps: reducing the number of variables to an orthogonal and parsimonious set for application, visualizing if wetlands could be partitioned based on their permanence class, parametrizing and calibrating a predictive model, and then predicting permanence class and assessing model fit. These analyses were performed in R (R Core Team, 2019), and while they quantify a relationship among our independent variables with wetland permanence, they do not infer causation.

Before predicting wetland permanence class based on land use and land cover, topography, and annual data on climate variables, we first determined which metrics were collinear within their metric class. Based on a maximum allowable correlation Pearson correlation of 0.9, we reduced our 30 metrics to 19 that reflected climate (7), land cover/land use (4) and topography (8) (Table 2). Next, we incorporated these 19 variables into a PCA to explore partitioning of permanence classes in accordance with the annual data on climate, land cover, and topography variables and to facilitate comparison among the three natural regions. Wetlands in the Grassland Natural Region appeared to be better aligned with all three domains than the wetlands in the southern Boreal Forest and Parkland natural regions (Fig. 2).

We built an extreme gradient boosting model for each natural region (southern Boreal Forest, Parkland and Grassland) in our study area. Our models had moderate to high error rates for both the training (43 %–50 %) and test datasets (48 %–61 %; Appendix D), which indicates a balance between bias and overfitting. Clearly, annual data on climate, land use/cover and topography alone are not sufficient to perfectly predict wetland permanence class. We conclude that while our models are useful in ranking the relative importance of climate, land cover/land use and topography variables in predicting wetland permanence class, they are not a comprehensive overview of the factors determining permanence class of a given wetland (see Sect. 4.5). Notably, we focus on the context of each wetland (surrounding topography, land cover/land use and climate) rather than wetland-specific properties that would influence permanence class (e.g., basin morphology).

In the Parkland and Grassland natural regions, annual data on climate explained the greatest amount of variance in wetland permanence class, based on relative gain values (Fig. 3a–c). As anticipated, our results suggest that climate conditions vary systematically among the natural regions (Fig. 4a–d). Among the climate variables included in our analyses, spring temperature (Boreal Forest Natural Region: 6.87 ∘C [0.425 SD]; Parkland Natural Region: 6.85 ∘C [0.206 SD]; Grassland Natural Region: 8.14 ∘C [0.892 SD]) explained the highest magnitude of variance in predicting permanence class in the Grassland Natural Region (Figs. 3a, 2c) but was less important in the southern Boreal Forest and Parkland natural regions where values are less extreme (Fig. 4a). Winter snowpack (Boreal Forest Natural Region: 92.15 cm [20 SD]; Parkland Natural Region: 67.65 cm [14.99 SD]; Grassland Natural Region: 42.14 cm [15.06 SD]) explained the highest magnitude of variance in predicting permanence class in the southern Boreal Forest and Parkland natural regions, and these amounts were distinctly lower in the warmer Grassland Natural Region (Fig. 4c).

Land cover/land use was the second most important category of drivers of wetland permanence class, following annual data on climate in the Grassland Natural Region (Fig. 3f), but not in the southern Boreal Forest or Parkland Natural Region (Fig. 3). Yet, unlike climate, land cover/land use did not vary systematically among the three natural regions (Fig. 4e–h). Wetlands surrounded by cropland had shorter hydroperiods in the southern Boreal Forest and Parkland natural regions (Fig. 5d–e), but wetlands surrounded by natural vegetation had shorter hydroperiods in the Grassland Natural Region (Fig. 5f).

Topography was the most important category of drivers of wetland permanence class in the southern Boreal Forest and second most important in the Parkland Natural Region, and the order of importance for the terrain metrics was nearly the same in both natural regions (Fig. 3g–i). Though topography metrics were the least important category in the Grassland Natural Region (Fig. 3i), apart from deviation from mean elevation (Fig. 4i), variables associated with topography did not systematically vary among natural regions (Fig. 4j–l).

Our findings suggest that wetland permanence class in the Prairie Pothole Region of Alberta correlates with climate, topography and, to a lesser extent, to surrounding land cover/land use. Generally, across the three natural regions, wetlands with shorter hydroperiods (e.g., temporary and seasonal) were typically situated in landscapes with higher spring snowpack amounts (Fig. 3a–c) and spring temperatures (e.g., Fig. 5a). Longer hydroperiod wetlands were typically situated in landscapes with more summer precipitation and lower spring temperatures (e.g., Fig. 5c), occupying relatively low topographic positions with low terrain convexity (e.g., Fig. 5g, h), and, in the Grassland Natural Region, were sometimes surrounded by less natural cover (Fig. 5f), though in the southern Boreal Forest Natural Region they were more common where cropland was less than 25 % cover (Fig. 5d) and less than 75 % in the Parkland Natural Region (Fig. 5e). Interestingly, the relative importance of variables in predicting the occurrence of both shorter- and longer-hydroperiod wetlands was shared, and this agreement was strongest between the southern Boreal Forest and Grassland natural regions (Appendices F and H).

The sensitivity of wetland hydroperiods to annual climate data is corroborated in existing literature, which emphasizes that the semi-arid climate drives the region's sensitivity to climate change (Fay et al., 2016; Johnson et al., 2004; Schneider, 2013). In the southern Boreal Forest and Grassland natural regions, regions with warmer spring temperatures are likely to experience an earlier onset of spring snowmelt, higher water deficits (Schneider, 2013; Zhang et al., 2011) and lower pond permanence classes for wetlands, whereas cooler peak spring temperatures favour greater pond permanence in these natural regions. In the southern Boreal Forest and Parkland natural regions, winter snowpack depth was the most important climate variable, and this we attribute to temporarily and seasonally ponded wetlands requiring a minimum threshold of winter snowpack amount to persist, whereas permanently ponded wetlands also benefit from precipitation in other seasons and so can exist at lower winter snowpack amounts (Fig. 5b). Because climate forecasts suggest that warmer springs and changes in precipitation timing are likely (Zhang et al., 2011), our finding that climate was the most important domain of variables in predicting permanence class supports previous studies that suggest PPR wetlands are sensitive climate change (Johnson et al., 2010; Paimazumder et al., 2013; Schneider, 2013; Viglizzo et al., 2015; Zhang et al., 2011).

Despite recognition that topography is a useful proxy in wetland mapping (Branton and Robinson, 2019; Los Huertos and Smith, 2013) and that topography must influence surface-runoff-generating processes that are essential to wetland function (Hayashi et al., 2016; Mushet et al., 2018), the relative importance of topography in hydrological processes is somewhat debated (Devito et al., 2005). Simulations predicting the influence of climate change on the size and isolation of prairie pothole wetlands have focused on climate and land cover/land use (Anteau et al., 2016; Chasmer et al., 2012; Conly et al., 2001; Johnson and Poiani, 2016; McCauley et al., 2015; Steen et al., 2016; Voldseth et al., 2007). Consequently, (1) there is a lack of research quantifying topographic characteristics of wetlands and the landscapes within which they occur; (2) links between topography, vegetation and wetland condition have not been rigorously studied; and (3) policy and guidelines on wetland mitigation and compensation prescribe width-to-length ratios and slopes that are characteristic of permanently ponded wetlands (Environmental Partnerships and Education Branch Alberta, 2007), which are less abundant in all three natural regions (Table 1). Despite remaining numerically more abundant, small and more temporarily ponded wetlands are being preferentially lost in Alberta's Prairie Pothole Region (Serran et al., 2017). If we had a better understanding of how topographic structure determines wetland hydrology/function, we could revise policy and regulations governing wetland management to ensure we better match natural landscapes in their frequency and distribution of wetland permanence classes.

Existing literature identified land cover/land use as the second greatest driver of wetland conditions following climate (Anteau et al., 2016). In the Grassland Natural Region, the terrain is relatively flat compared to the southern Boreal Forest and Parkland natural regions (Alberta Tourism Parks and Recreation, 2015). Consequently, after the important role of annual data on climate in the more arid Grassland Natural Region (Government et al., 2014), land cover/land use might be a stronger driver of permanence class than topography. Importantly, the percent cover of natural vegetation is typically low in the Grassland Natural Region, where most land has been converted to cropland or pastureland (Alberta Tourism Parks and Recreation, 2015). Combined with the process of consolidation drainage, which shunts water from scattered low hydroperiod wetlands, concentrating it in larger more permanently ponded wetlands downstream (McCauley et al., 2015), this leads to Grassland Natural Region landscapes with more natural cover being more likely to contain temporary and seasonal wetlands. Thus, wetlands surrounded by natural vegetation may have shorter hydroperiods because cropland resists infiltration and natural vegetation intercepts snow-sourced surface runoff (Anteau, 2012; van der Kamp et al., 2003; Voldseth et al., 2007), which can account for up 27 % of ponded water amounts (van der Kamp et al., 2003).

Because some landscapes in the PPR are flatter than others (Schneider, 2013), and land use activities can modify the terrain (Anteau, 2012; Wiltermuth and Anteau, 2016; Anteau et al., 2016), our findings do highlight the importance of considering land use in forecasting the impacts of climate change on PPR wetlands. The Boreal Forest and Parkland Natural Region wetlands have stronger overlaps in topography metrics and annual data on climate, and, as a result, differences in land use within these regions may be integral in determining future shifts in the frequency distribution of permanence classes. Forecasts for the province of Alberta suggest there will be expansions in the agricultural industry within the next decade (Government of Alberta, 2015), and this suggests that climate impacts on Albertan PPR wetlands will be compounded by land use activities.

Semi-permanent and permanently ponded wetlands typically occur in regional or spatial neighbourhood topographic lows (as opposed to simply local depressions, e.g., perched wetlands), likely because they (1) can hold larger volumes of ponded water (i.e., larger pond size/volume, Novikmec et al., 2016) and (2) receive higher volumes of water inputs from the surrounding landscape (e.g., surface runoff, groundwater, Euliss et al., 2004, 2014; LaBaugh et al., 1998; Toth, 1963). We are unable to partition the natural hydrogeological effects of topographic position on wetland permanence class from the effects of human alteration of the surrounding landscape, yet the importance of topographic position to wetland permanence class is likely reinforced by consolidation drainage when wetlands situated higher in the landscape are drained and the water is redirected to wetlands positioned lower in the landscape (McCauley et al., 2015; Wiltermuth and Anteau, 2016). Because of consolidation drainage, we may observe increases in the hydroperiod of wetlands in topographic lows of the wetlandscape (e.g., sites with low topographic position index values). In the arid but heavily farmed Grassland Natural Region, consolidation drainage can eliminate temporary and seasonally ponded wetlands from areas with limited remaining natural cover (Serran et al., 2017). This aligns with our model results: although the probability of observing a permanent or semi-permanent class wetland was greatest at the lower end of the range of crop cover in our landscapes, the threshold of crop cover above which wetlands were most probably seasonal or temporary in class was higher in the Grassland Natural Region, lower in the Parkland Natural Region and lowest in the Boreal Forest Natural Region. Thus, we recommend that future research investigate the role of topographic position on permanence class, in the absence of human disturbance to control for the influence of consolidation drainage.

Our model misclassification error rates were relatively high (Appendix D), indicating imperfect matching between model-predicted permanence class and inventory-reported permanence class for our study wetlands. One key source of uncertainty in our analysis is that the accuracy of the inventory in assigning wetlands a given permanence class is not validated, and in interpreting our model error we must assume that the permanence classes we derived from the inventories are correct, though we know the two inventories differ in their mapping accuracy (Evans et al., 2017). Yet, we hypothesize that our inability to account for soil characteristics (Schneider, 2013) and bathymetry (Huertos and Smith, 2013) likely contributes to misclassification by our models (Appendix D). Schneider (2013) stated that within natural regions, both elevation (which we did account for) and soil characteristics can vary across the landscape. As such, wetlands situated similarly in the landscape may not have the same soil characteristics, and soil characteristics are understood to influence wetland hydrology by dictating the proportion of incident precipitation that is converted to surface run of Hayashi et al. (2016). Though Schneider (2013) also mentioned an influence of disturbance history on ecosystems, prior work in our study region reported no temporal lag in wetland environmental conditions and surrounding land cover (Kraft et al., 2019).

The lack of extensive data on basin morphology identifies a gap that would enrich the presented research by enabling direct classification of wetland permanence from raw bathymetric data. Such data would likely reduce the misclassification error rates of our ensemble models, which rely only on annual data on climate, land use and topography in predicting wetland permanence. Furthermore, these data would provide added value to those conducting research on above- and below-ground hydrologic connectivity and contributing areas (e.g., Chen et al., 2020), as well as those evaluating the impacts of climate change on wetland permanence and subsequently flora and fauna health and resilience (e.g., LaBaugh et al., 2018). As new technologies for mapping wetland bathymetry become more widely available (e.g., bathymetric lidar; Paine et al., 2015; Wang and Philpot, 2007), an opportunity will exist to better understand the link between wetland pattern and process.

Potentially some proportion of model error can be attributed to the use of a single year of climate and land use data as well as our relatively coarse (25 m) digital elevation model. However, it is likely that the contributions of these factors are minimal given that (1) the climate data used (year 2014) is representative of average conditions, coincides with fieldwork, and yielded the strongest among the variables interrogated, and therefore improving the quality of its contribution will not change the qualitative outcome of the presented analysis; (2) previous research found that physiochemical conditions in a wetland are quite congruent with surrounding land cover of the same year with only minor differences when catchments were defined with 10 m versus 25 m resolution DEMs (Kraft et al., 2019); and (3) there was no detectible difference in wetland catchment size when they were derived from DEMs of low (10 m) versus high (3 m) resolution (McCauley and Anteau, 2014).

Lastly, wetland permanence classes are ordinal, and consequently not all misclassifications are equal. From an ecohydrological perspective, a discrepancy between model-predicted and inventory-reported permanence class can be minor (e.g., temporary vs. seasonal) or major (e.g., temporary vs. permanent), and this is not accounted for in the overall misclassification error rate. When we investigate the class-based misclassification error rates, it is apparent that all models were most successful in classifying wetlands at the extreme ends of the permanence class spectrum, and misclassification error rates were higher for seasonal and semi-permanent wetlands (Appendix D). Interestingly, the Grassland Natural Region model tended to misclassify seasonal wetlands as temporary and semi-permanent wetlands as permanent (i.e., misclassified into adjoining classes), whereas the Parkland Natural Region model tended to misclassify seasonal and semi-permanent wetlands more evenly across the three other permanence classes. Overall, these extreme gradient boosting models are valuable for comparing the relative importance of the climate, topographic and landscape domains of predictor variables, despite misclassification error rates.