Figure 10. Monthly precipitation from station observations, precipitation biases from the default (DEF) and wetland scheme (WS) simulations, and the precipitation difference between WS and DEF simulations in the summer (May-August) for three-year simulations.
4 Discussion
LSMs and coupled ESMs, reasonable representations of wetland spatial extents and dynamic water storage are challenging in light of data scarcity, coarse model resolution, and insufficient understanding of the physical processes (Ringeval et al., 2012). However, because wetland extents play a key role in land-atmosphere interactions and carbon feedback to the climate system, researchers have long been interested in estimating wetland extents in hydrology-climate simulations from global to regional scales. For example, the WETCHIMP project gathered 10 participating GCMs for simulating global wetland extents and their CH4 emissions (Wania et al., 2013; Melton et al., 2013). Many of these GCMs used prescribed wetland maps from global surveys or remote sensing products, such as the Global Lake and Wetland Database (Lehner and D\(\ddot{o}\)ll, 2004) and GIEMS (Prigent et al., 2007), or used the TOPMODEL-based \(F_{\text{sat}}\) to simulate a subgrid “saturated” fraction to represent wetlands extents.
Although the TOPMODEL method can simulate some spatial heterogeneity and temporal dynamics of wetland extent, it generally underestimates both the maximum value and the seasonal variability. As we showed in Section 2.1, the TOPMODEL-based method in Noah-MP simulates a much lower\(F_{\text{sat}}\) value than the highly dynamic GIEMS product. Here we provide two possible reasons for the discrepancy between TOPMODEL\(F_{\text{sat}}\) and surface water dynamics from satellites. (1) The first underlying assumption of the TOPMODEL method requires “steady state” precipitation and soil moisture heterogeneity, which is more likely in wet, relatively shallow soils on moderate slopes (Beven and Kirkby, 1979; Kirkby et al., 2021). However, this is not the case in the Prairie Pothole Region, where the climate is usually semi-arid and the large-scale topography is flat with small-scale variation. (2) Another possible reason for this discrepancy is that the TOPMODEL method calculates a critical topographic index value when the local water table is at the surface; this value is used to determine the\(F_{\text{sat}}\) fraction through the integration of its probability distribution function. However, in the PPR, frozen soils in wintertime prevent interaction between the soil moisture and groundwater (Ireson et al., 2013). Therefore, in the TOPMODEL method, the exponential function will simulate less seasonal variation in the surface water dynamics. A large portion of global wetlands and peatlands are located in high latitude regions where winter soil freezing is very common.
In our modification of the \(F_{\text{sat}}\) formulation, we used the first layer of soil saturation to indicate the sub-grid spatial extent of the saturated portion – the extent of wetlands. This method empirically assumes the grid cell mean soil moisture saturation can be translated into a spatial fraction for surface saturation and shows a highly variable \(F_{\text{sat}}\) value compared to the default TOPMODEL method, in terms of the maximum and minimum extent, and seasonality (Section 3.1). Moreover, we also incorporate a spatially varied maximum \(F_{\text{satmx}}\) map from the GIEMS product to replace the default global mean value (0.38) in Noah-MP and WRF. Both these modifications improve the spatial heterogeneity and the temporal dynamics of wetland extents in the PPR.
Additionally, we incorporated a dynamic surface water storage scheme to simulate the hydrological processes in wetlands. Although this scheme is simple, we aim to capture three important processes – the filling of wetlands by snowmelt and rainfall, the evaporation of wetland water into the atmosphere, and the excess water spilling to surface runoff. These three processes are the key components in the wetland water and energy cycle during the warm season open-water period. Our results showed increase of ET with a decrease of surface runoff and an increase of latent heat with decreases of sensible heat. This finding aligns with our expectations, as well as with previous VIC model wetland and lake simulations in the U.S. Midwest region (Mishra and Cherkauer et al., 2010).
Moreover, our scheme provides greater potential to explore wetlands’ feedback to the atmosphere in coupled WRF-NoahMP-Wetland simulation. In the default simulation, which already includes the MMF groundwater scheme (Barlage et al., 2015, 2021), warm biases still exist at about 1~3 degrees in the U.S. Great Plains. Without the groundwater scheme, the summertime warm biases could be as high as 4~6 degrees. By adding the wetland scheme on top of the MMF groundwater scheme, the warm biases in the U.S. can be further reduced by 0.5~1.5 degrees, but it also introduces 1-degree cool biases in Southern Manitoba, where wetland extents are large. While the temperature cooling effect is evident, wetland feedback to precipitation is less clear and is more ambiguous. A previous study using WRF with a prescribed soil moisture threshold to indicate wetlands in the Great Plains at coarser resolution (12-km) also showed a temperature cooling effect, but the precipitation effect was negligible (Capehart et al., 2012).
One of the highlights of this study is the wetland cooling effect to the atmospheric temperature. Previous studies have documented this effect in detail, but they have been specific to different wetland characteristics and dominant vegetation types (Pitman, 1991; Bonan, 1995). In our study, we used general open-water storage to characterize wetland interactions with the atmosphere, omitting these variations in specific wetland types but gaining more generic conclusions in a much larger region. The wetland cooling effect on temperature, especially during extreme heatwave events, echoes a previous study in the Central U.S. where we found land surface characteristics could effectively reduce the frequency, intensity, and duration of extreme heatwaves (Zhang et al., 2018). However, more pronounced cooling occur in non-heatwave years (2005 and 2007) than in 2006, because the cooling effect also depends on water availability, hence, cannot be too dry.
In recent years, the tradeoffs between agriculture and wetland conservation has been a serious topic of discussion among the public, universities, and government agencies. It has been shown that the agricultural land expansion at the cost of wetland drainage increases the risk of emerging flooding in springtime (Dumanski et al., 2015; Pattison-Williams et al., 2018). Wetland drainage also results in increased nutrient export (Badiou et al., 2018; Wilson et al., 2019) and carbon release to the atmosphere (Badiou et al., 2011). This study suggests that the loss of wetlands for croplands also reduces resilience to drought and high temperature, which may cause crop failures due to water and heat stress (Hatifield, 2016).
However, the loss of wetlands to agricultural, industrial and residential land is not confined to the PPR but are common problems worldwide and require humans’ attention (The Rasmar Convention 2007; Nature Geoscience, 2021). These land use modifications not only threaten the local environment but also contribute to the global carbon balance and eventually cause problems for human beings. Understanding the effects of development is challenging. It is hoped that these threats to the future can inspire future studies on wetlands for their hydrological, climatic, ecological, environmental functions and that solutions can be found for humans to interact with nature peacefully and sustainably.