Plain Language Summary
A large number of wetlands exist in the Prairie Pothole Region (PPR) across U.S. and Canada. These wetlands are important to our environment as they can provide flood control and cool the temperature, but they are poorly represented in previous land surface model studies. In this study, we updated a dynamic wetland module in the Noah-MP land surface model to reasonably estimate wetland extent and seasonal variation in the PPR. This wetland module shows significant impacts to the surface energy and water balance and, hence, regional temperature. The results show that wetland features would effectively cool the air temperature 1~3 in summer, especially for regions with high wetland coverage. The implication of this study is very useful for wetland conservation agencies and climate scientists, as this cooling effect could potentially mitigate heat stress under climate change.
1 Introduction
Wetlands are important and unique ecosystems that play vital roles in Earth’s ecosystem balance and biodiversity. Although wetlands occupy a small portion of the global land surface (~6%), they store about one third of terrestrial carbon (Lehner and D\(\ddot{o}\)ll, 2004; Mitra et al., 2005; Mitsch and Gosselink, 2007). Moreover, due to their unique productivity, wetlands support a wide variety of plants, birds, and amphibians, and are areas of high biodiversity (The Ramsar Convention, 2007). Wetlands are natural reservoirs to prevent flooding, especially in high latitude and mountainous regions (Hayashi et al., 2016; Pattison-Williams et al., 2018). After springtime snowmelt or heavy rainfall, surface runoff can be stored in wetlands, effectively reducing the peak flow and delaying the peak time of flooding, hence, mitigating flooding impacts. From a climate regulation perspective, the presence of surface water and the moisture of wetland soils can effectively store surface energy and favor energy partitioning to latent heat flux over sensible heat. Specifically, greater partitioning of latent heat flux over sensible heat flux in wetland water bodies decreases summer temperature (Bonan, 1995) and reduces daily air temperature variability (Hostetler et al. 1993). This land-atmosphere interaction is analogous to the soil moisture-temperature feedback (Seneviratnes et al., 2010), inducing a cooling effect to surrounding environments.
The North American Prairie Pothole Region (PPR contains millions of small wetlands, known as “potholes”, due to its unique geology, hydrology, and climate conditions. The retreat of continental ice sheets over 11, 000 years ago left glacial deposition upon the landscape, forming millions of depressions. These depressions are isolated from large river networks and are poorly hydraulically connected. The cold winters allow snow to accumulate over cold seasons, and springtime runoff and seasonal rainfall are major water inputs to these wetlands. Over the warm season, evaporation exceeds precipitation, drying surface water and exposing the underlying soils. The persistence and storage of wetland ponds depend on receiving seasonal rainfall and connection with shallow groundwater. Under extremely wet conditions, strong rainfall or sudden snowmelt increases the water level of wetlands, exceeding the maximum capacity. Several filled wetlands will spill water to other surrounding wetlands, a “fill-and-spill” process, and form a largely connected wetland complex (van der Kamp and Hayashi, 2009; Mekonnen et al., 2014; Vanderhoof et al., 2018). These complex interactions between climate, wetland, and groundwater make it challenging to simulate in traditional hydrological models and land surface models (LSMs).
Given their importance to global and regional environments, the need to represent wetland physics in earth system models (ESMs) and LSMs has emerged in recent decades. In the Community Land Model (Oleson et al., 2008) and Noah-MP LSM (Niu et al., 2011; Yang et al., 2011), a relationship has been established between grid cell saturated fraction and the depth of groundwater, based on the TOPMODEL hydrological model (Beven and Kirkby, 1979) and its application in LSMs (Famiglietti and Wood, 1991, 1994a). This method assumes the sub-grid representation of grid cell saturation is based on a redistribution of water table depth, given the variation of slope and contributing areas in the grid cell. A sub-grid saturated fraction \(F_{\text{sat}}\) is defined for the local water table at the surface and can be used for runoff generation as saturationed excess runoff. While this may be sufficient estimation over a large grid resolution in many GCM models (~50-100 km), it is not sufficiently detailed for high-resolution regional simulation (~5-10 km). Despite its limitations, TOPMODEL- based\(F_{\text{sat}}\) is widely used in many LSMs and ESMs, particularly in representing global wetland extents. The discrepancies in projecting wetland extents have significant implications for modeled CH4 emissions, as summarized in a wetland CH4 inter-comparison modeling project (WETCHIMP, Wania et al., 2013, Melton et al., 2013).
On the other hand, many models have incorporated surface water storage schemes to represent the dynamics of lakes and wetlands to investigate their impacts on the energy and water cycles. For example, Pitman (1991) incorporated a sub-grid scheme for water surfaces and their contribution to latent and sensible heat as the weighted average over the fraction of water, vegetated and bare ground surface in a coarse resolution (~2°) GCM. The Variable Infiltration Capacity model (VIC, Liang et al., 1994) has developed a dynamic lake and wetland scheme to study the impacts of surface water heterogeneity on energy and water balance (Bowling and Lettenmaier, 2010). Results show that incorporating wetlands increases the annual ET by 5% and decreases runoff by ~ 12% in the U.S. Midwest region. Latent heat fluxes also increase, with corresponding decreases in sensible heat fluxes . Despite robust results in surface energy and water balance, this research is not coupled with regional climate models, therefore omitting the feedback from wetlands to temperature and precipitation.
The purpose of this study is to quantify the impacts of wetlands on the surface energy and water balances, as well as their feedback to regional climate in a high-resolution convection-permitting regional climate model (CPRCM, Prein et al., 2015). For this purpose, we have established three steps: (1) Develop a physical process-based parameterization of sub-grid wetland extent and a dynamic wetland storage scheme; (2) Explore the impacts of inclusion of this wetland parameterization on the surface energy and water balance in offline regional land-surface hydrology simulations using Noah-MP; (3) Investigate the interactions between the wetland hydrological cycle and its feedback to regional climate using a coupled Weather Research & Forecasting (WRF, Skamarock et al., 2019) and Noah-MP model system. In particular, we want to investigate the potential cooling effect of surface wetlands in mitigating summertime heat stress, especially during the widespread high-intensity heatwave of 2006 in Southern Canada and the U.S.
2 Materials and Methods
2.1 Global Inundation Extent from Multiple Satellites (GIEMS-2)
The 1993-2007 Global Inundation Extent from Multiple Satellites (GIEMS-2) is a unique dataset that provides estimates of surface water extent and dynamics, based on a collection of satellite observations (https://lerma.obspm.fr/spip.php?article91&lang=en). The satellite data are used to calculate monthly-mean inundated fractions of equal-area grid cells (0.25°x0.25° at the equator), taking into account the contribution of vegetation (Prigent et al., 2001, 2007, 2012; Papa et al., 2010). Such estimates use both passive and active microwave measurements, along with visible and near-infrared reflectance to capitalize on their complementary strengths, to extract maximum information about inundation characteristics, and to minimize problems related to one instrument only. The technique is globally applicable without any tuning for particular environments. The GIEMS data have been widely used to evaluate surface wetland extents in multiple GCM intercomparison studies for simulating wetland extents (Wania et al., 2012; Melton et al., 2012).
2.2 Convection-permitting regional climate simulation
Convection-permitting models (CPMs) are atmospheric models whose grid spacing is fine enough (usually < 5-km) to permit convection and resolve mesoscale orography (Rasmussen et al., 2011; Prein et al., 2015; Liu et al., 2017). Long-term high-resolution climate downscaling using CPMs provides important added value to improve precipitation forecasts, which is critical to surface wetland hydrology, as well as for resolving fine-scale land surface heterogeneity (Kenden et al., 2017).
The WRF convection-permitting regional climate simulation over the Contiguous U.S. (CONUS WRF, Liu et al., 2017) provides the opportunity for long-term (13-year), high-resolution (4-km) land surface modeling (Zhang et al., 2020). The CONUS WRF consists of simulations for the current climate and for future climate using the Pseudo Global Warming (PGW) method (Sch\(\ddot{a}\)r et al., 1996, Rasmussen et al. 2011). The current climate simulation is a retrospective run from 2000-10-01 to 2013-10-01, forced by ERA-Interim (Dee et al., 2011) as boundary and initial conditions. For the future simulations, a delta climate perturbation, derived from the 19-model ensemble in the CMIP5 project under RCP8.5 scenario at the end of the 21st century, is added upon the ERA-Interim forcing. The future simulation represents an equivalent 13-year period at the end of the 21st century. The CONUS WRF forcing has been used in multiple climate, hydrology, and land surface studies (Zhang et al., 2020; Fang et al., 2021). In this study, we use CONUS WRF forcing in the PPR for offline land-surface model regional simulations to study the impacts of incorporating a surface wetland scheme on the regional energy and water balance in the PPR.
2.3 Application of TOPMODEL in LSMs
TOPMODEL (TOPography based hydrological MODEL) is a rainfall-runoff model that uses topography data to reflect dynamic process response in downslope hydrology, especially in runoff generation on variable contributing areas (Beven and Kirkby, 1979; Beven et al., 2020). Its basic assumption is that the runoff generation response to steady state rainfall is proportional to the spatial variation of moisture content in a drainage basin and can be characterized by its topographic variation, characterized by digital topography analysis. In the model, a topographic index is defined, \(\Lambda=ln(\frac{a}{\text{tanβ}})\), where \(a\) is the area draining through a point from upslope andtanβ is the local slope angle. High index values are likely to saturate first, hence, they indicate potential subsurface or surface contributing areas (Beven, 1997).
The simplicity of the model comes from the assumption that all the points of the same value of the index respond similarly in the catchment. Therefore, it is not necessary to calculate all the points in a catchment, but rather to integrate the hydrologic response of each interval of index values in a representative distribution function. At steady state, a critical threshold value for the local topographic index (\(\Lambda_{\text{cri}}\)) can be obtained when local water table depth is at the surface, compared to the grid cell mean water table depth. Hence, a subgrid fraction \(F_{\text{sat}}\) can be defined by integrating the topographic index interval from this critical value to the maximum, following its probability distribution function:
\(F_{\text{sat}}=\int_{\Lambda_{\text{cri}}}^{\infty}{pdf(\Lambda)d\Lambda\text{\ \ }}\)(1)
This probability distribution function was assumed to be a three-parameter gamma distribution by Sivapalan et al. (1987).
This \(F_{\text{sat}}\) fraction is an important parameter in partitioning surface water using the saturation runoff mechanism, i.e., the \(F_{\text{sat}}\) portion of the surface water from rainfall and snowmelt becomes surface runoff and the remaining (1-\(F_{\text{sat}}\)) becomes infiltration. The sub-grid \(F_{\text{sat}}\) is also critical in controlling surface energy balance and land-atmosphere interactions (Famiglietti and Wood, 1994a&b). In Famiglietti and Wood (1994a&b), a Soil-Vegetation-Atmosphere Transfer Scheme (SVATS) is applied at local-, catchment- and macro-scales to demonstrate the sub-grid soil moisture heterogeneity in controlling both evapotranspiration and runoff. The total evapotranspiration over the sub-grid topographic index in a grid cell is the integration of the potential evaporation from saturated portion to drier land surface outside the transitional region, where evapotranspiration is restricted by active vegetation and soil moisture (Famiglietti and Wood 1994a). This framework for incorporating TOPMODEL into LSMs (TOPLATS) was utilized in the NASA GISS land surface model (Stieglitz et al., 1997) and the NASA Catchment Land Surface Model (CLSM, Koster et al., 2000; Bechtold et al., 2018) among others.
Due to its computational simplicity, the \(F_{\text{sat}}\) fraction is also very popular to represent surface wetland extent in large-scale global models (Gedney and Cox, 2003; Ringeval er al., 2011). The temporal and spatial variation of \(F_{\text{sat}}\) is based on groundwater dynamics interacting with soil moisture, simulating the expansion and shrinkage of surface wetlands. Although the meaning of saturation is not necessarily the same as inundation of wetland soils, this fractional area to some degree reflects the wetness conditions in a given grid cell, as well as its function partitioning surface water in “saturation excess” runoff generation. Thus, it has been widely applied in various LSMs and multiple modeling studies simulating wetland extents (WETCHIMP, Wania et al., 2013; Melton et al., 2013).
In the Noah-MP LSM, the energy balance is calculated separately for two sub-grid semitiles: a fractional vegetated area (\(F_{\text{veg}}\)) and a fraction bare ground area (1-\(F_{\text{veg}}\)). In this semitile scheme, shortwave radiation transfer is computed over the entire grid, while longwave radiation, sensible and latent heat flux, and ground heat flux are computed separately over these two tiles. As such, these two tiles in a Noah-MP grid neglect the large extent and seasonal variability of open-water wetlands. The total latent (LH) and sensible heat (SH) of these two semitiles are aggregated in a weighted function:
\(LH=F_{\text{veg}}\left(\text{LE}_{\text{gv}}+\text{LE}_{v}\right)+(1-F_{\text{veg}})\text{LE}_{\text{gb}}\)(2)
\(SH=F_{\text{veg}}\left(\text{SH}_{\text{gv}}+\text{SH}_{v}\right)+(1-F_{\text{veg}})\text{SH}_{\text{gb}}\)(3)
Where the subscript v represents the vegetation canopy, gvis ground under canopy and gb is the bare ground flux.
Additionally, the TOPMODEL-based runoff generation model is utilized for surface water partitioning: \(F_{\text{sat}}\) portion of the surface available water (\(Q_{\text{insurf}}\)) from snowmelt or precipitation becomes surface runoff (\(R_{\text{srf}}\)) and (1-\(F_{\text{sat}}\)) portion becomes infiltration (\(Q_{\text{infil}}\)). In Niu and Yang (2005), the probability distribution function of \(F_{\text{sat}}\) in equation (1) is replaced by an exponential function of the water table depth (\(Z_{\nabla}\), equation (6)) and has been utilized in both CLM (Oleson et al., 2008) and Noah-MP LSM (Niu et al., 2011; Yang et al., 2011). \(F_{\text{satmx}}\) is the maximum saturated fraction in a grid cell derived from digital elevation model (DEM).
\(R_{\text{srf}}=Q_{\text{insurf}}*F_{\text{sat}}\) (4)
\(Q_{\text{infil}}=Q_{\text{insurf}}*\left(1-F_{\text{sat}}\right)\)(5)
\(F_{\text{sat}}=F_{\text{satmx}}*exp(-0.5*f*(Z_{\nabla}-2))\) (6)
However, the above water balance setting does not reflect dynamic water movement in prairie wetlands. These wetland depressions actively receive surface water from snowmelt and rainfall, but there is no surface water storage process in Noah-MP, so that the simulated surface runoff component will leave the model grid. Additionally, this setting further neglects evaporation from the wetland surface to the atmosphere and discharge to surrounding wetlands in the fill-and-spill process. Therefore, a dynamic surface wetland storage scheme, incorporating both sub-grid energy and water balance, is needed to represent the complex hydrological processes in the prairie wetland landscape and their potential feedback to the atmosphere.
2.4 Modifying \(F_{\text{sat}}\) fraction to represent wetlands
The original TOPMODEL-based \(F_{\text{sat}}\), based on an exponential function of water table depth, does not reasonably reflect the magnitude and seasonal variation of wetland extent in the Prairies. Figures 1 and 2 show the spatial distribution and temporal evolution of the inundation fraction from GIEMS and Noah-MP simulated \(F_{\text{sat}}\) fraction in the PPR region from 2000 to 2014. It is clear that the modeled\(F_{\text{sat}}\) has underestimated the maximum extent while overestimating the minimum extent. This is because of two reasons: (1) the parameter \(F_{\text{satmx}}\) is a fixed value (0.38) for the global mean; and, (2) the seasonally frozen soil and glacial till with low hydraulic conductivity prevent direct groundwater connection with surface water, hence the water table dynamic is not a good indicator of surface water extent in the PPR. Detailed reasons for this discrepancy are provided in the discussion section.