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Mary M. Forrester and Reed M. Maxwell

Abstract

Credible soil moisture redistribution schemes are essential to meteorological models, as lower boundary moisture influences the balance of surface turbulent fluxes and atmospheric boundary layer (ABL) development. While land surface models (LSMs) have vastly improved in their hydrologic representation, several commonly held assumptions, such as free-draining lower boundary, one-dimensional moisture flux, and lack of groundwater representation, can bias the terrestrial water balance. This study explores the impact of LSM hydrology representation on ABL development in the Weather Research and Forecasting (WRF) meteorological model. The results of summertime WRF simulations with Noah LSM, characterized by 2-m-thick soil and one-dimensional flow, are shown for a domain in the Colorado Rocky Mountain headwaters region. A reference WRF simulation is compared to 1) the same model with soil moisture initialized by the hydrologic model ParFlow; 2) a deep, free-draining simulation; and 3) WRF coupled to ParFlow, a three-dimensional, integrated groundwater-surface water model. Results show that both lateral transport of groundwater and the rate of drainage from the lower soil layer can weaken or reverse the coupling strength between evaporative fraction and ABL over a 5-month summer period. The resulting shifts in low-level moist convection in river valleys and thermally driven airflows yield strengthened anabatic upslope winds and perturbations to regional precipitation.

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John L. Williams III and Reed M. Maxwell

Abstract

Feedbacks between the land surface and the atmosphere, manifested as mass and energy fluxes, are strongly correlated with soil moisture, making soil moisture an important factor in land–atmosphere interactions. It is shown that a reduction of the uncertainty in subsurface properties such as hydraulic conductivity (K) propagates into the atmosphere, resulting in a reduction in uncertainty in land–atmosphere feedbacks that yields more accurate atmospheric predictions. Using the fully coupled groundwater-to-atmosphere model ParFlow-WRF, which couples the hydrologic model ParFlow with the Weather Research and Forecasting (WRF) atmospheric model, responses in land–atmosphere feedbacks and wind patterns due to subsurface heterogeneity are simulated. Ensembles are generated by varying the spatial location of subsurface properties while maintaining the global statistics and correlation structure. This approach is common to the hydrologic sciences but uncommon in atmospheric simulations where ensemble forecasts are commonly generated with perturbed initial conditions or multiple model parameterizations. It is clearly shown that different realizations of K produce variation in soil moisture, latent heat flux, and wind for both point and domain-averaged quantities. Using a single random field to represent a control case, varying amounts of K data are sampled and subsurface data are incorporated into conditional Monte Carlo ensembles to show that the difference between the ensemble mean prediction and the control saturation, latent heat flux, and wind speed are reduced significantly via conditioning of K. By reducing uncertainty associated with land–atmosphere feedback mechanisms, uncertainty is also reduced in both spatially distributed and domain-averaged wind speed magnitudes, thus improving the ability to make more accurate forecasts, which is important for many applications such as wind energy.

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Reed M. Maxwell and Norman L. Miller

Abstract

Traditional land surface models (LSMs) used for numerical weather simulation, climate projection, and as inputs to water management decision support systems, do not treat the LSM lower boundary in a fully process-based fashion. LSMs have evolved from a leaky-bucket approximation to more sophisticated land surface water and energy budget models that typically have a specified bottom layer flux to depict the lowest model layer exchange with deeper aquifers. The LSM lower boundary is often assumed zero flux or the soil moisture content is set to a constant value; an approach that while mass conservative, ignores processes that can alter surface fluxes, runoff, and water quantity and quality. Conversely, groundwater models (GWMs) for saturated and unsaturated water flow, while addressing important features such as subsurface heterogeneity and three-dimensional flow, often have overly simplified upper boundary conditions that ignore soil heating, runoff, snow, and root-zone uptake. In the present study, a state-of-the-art LSM (Common Land Model) and a variably saturated GWM (ParFlow) have been coupled as a single-column model.

A set of simulations based on synthetic data and data from the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS), version 2(d), 18-yr dataset from Valdai, Russia, demonstrate the temporal dynamics of this coupled modeling system. The soil moisture and water table depth simulated by the coupled model agree well with the Valdai observations. Differences in prediction between the coupled and uncoupled models demonstrate the effect of a dynamic water table on simulated watershed flow. Comparison of the coupled model predictions with observations indicates certain cold processes such as frozen soil and freeze/thaw processes have an important impact on predicted water table depth. Comparisons of soil moisture, latent heat, sensible heat, temperature, runoff, and predicted groundwater depth between the uncoupled and coupled models demonstrate the need for improved groundwater representation in land surface schemes.

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James M. Gilbert, Reed M. Maxwell, and David J. Gochis

Abstract

The boundary layer, land surface, and subsurface are important coevolving components of hydrologic systems. While previous studies have examined the connections between soil moisture, groundwater, and the atmosphere, the atmospheric response to regional water-table drawdown has received less attention. To address this question, a coupled hydrologic–atmospheric model [Parallel Flow hydrologic model (ParFlow) and WRF] was used to simulate the San Joaquin River watershed of central California. This study focuses specifically on the planetary boundary layer (PBL) in simulations with two imposed water-table configurations: a high water table mimicking natural conditions and a lowered water table reflecting historic groundwater extraction in California’s Central Valley, although effect of irrigation was not simulated. An ensemble of simulations including three boundary layer schemes and six initial conditions was performed for both water-table conditions to assess conceptual and initial condition uncertainty. Results show that increased regional water-table depth is associated with a significant increase in peak PBL height for both initial condition and boundary layer scheme conditions, although the choice of scheme interacts to affect the magnitude of peak PBL height change. Analysis of simulated land surface fluxes shows the change in PBL height can be attributed to decreasing midday evaporative fraction under lowered water-table conditions. Furthermore, the sensitivity of PBL height to changes in water-table depth appears to depend on local water-table variation within 10 m of the land surface and the regional average water-table depth. Finally, soil moisture changes associated with lowered water tables are linked to changes in PBL circulation as indicated by vertical winds and turbulence kinetic energy.

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Jennifer L. Jefferson, Reed M. Maxwell, and Paul G. Constantine

Abstract

Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.

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Mauro Sulis, John L. Williams, Prabhakar Shrestha, Malte Diederich, Clemens Simmer, Stefan J. Kollet, and Reed M. Maxwell

Abstract

This study compares two modeling platforms, ParFlow.WRF (PF.WRF) and the Terrestrial Systems Modeling Platform (TerrSysMP), with a common 3D integrated surface–groundwater model to examine the variability in simulated soil–vegetation–atmosphere interactions. Idealized and hindcast simulations over the North Rhine–Westphalia region in western Germany for clear-sky conditions and strong convective precipitation using both modeling platforms are presented. Idealized simulations highlight the strong variability introduced by the difference in land surface parameterizations (e.g., ground evaporation and canopy transpiration) and atmospheric boundary layer (ABL) schemes on the simulated land–atmosphere interactions. Results of the idealized simulations also suggest a different range of sensitivity in the two models of land surface and atmospheric parameterizations to water-table depth fluctuations. For hindcast simulations, both modeling platforms simulate net radiation and cumulative precipitation close to observed station data, while larger differences emerge between spatial patterns of soil moisture and convective rainfall due to the difference in the physical parameterization of the land surface and atmospheric component. This produces a different feedback by the hydrological model in the two platforms in terms of discharge over different catchments in the study area. Finally, an analysis of land surface and ABL heat and moisture budgets using the mixing diagram approach reveals different sensitivities of diurnal atmospheric processes to the groundwater parameterizations in both modeling platforms.

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Reed M. Maxwell, Julie K. Lundquist, Jeffrey D. Mirocha, Steven G. Smith, Carol S. Woodward, and Andrew F. B. Tompson

Abstract

Complete models of the hydrologic cycle have gained recent attention as research has shown interdependence between the coupled land and energy balance of the subsurface, land surface, and lower atmosphere. PF.WRF is a new model that is a combination of the Weather Research and Forecasting (WRF) atmospheric model and a parallel hydrology model (ParFlow) that fully integrates three-dimensional, variably saturated subsurface flow with overland flow. These models are coupled in an explicit, operator-splitting manner via the Noah land surface model (LSM). Here, the coupled model formulation and equations are presented and a balance of water between the subsurface, land surface, and atmosphere is verified. The improvement in important physical processes afforded by the coupled model using a number of semi-idealized simulations over the Little Washita watershed in the southern Great Plains is demonstrated. These simulations are initialized with a set of offline spinups to achieve a balanced state of initial conditions. To quantify the significance of subsurface physics, compared with other physical processes calculated in WRF, these simulations are carried out with two different surface spinups and three different microphysics parameterizations in WRF. These simulations illustrate enhancements to coupled model physics for two applications: water resources and wind-energy forecasting. For the water resources example, it is demonstrated how PF.WRF simulates explicit rainfall and water storage within the basin and runoff. Then the hydrographs predicted by different microphysics schemes within WRF are compared. Because soil moisture is expected to impact boundary layer winds, the applicability of the model to wind-energy applications is demonstrated by using PF.WRF and WRF simulations to provide estimates of wind and wind shear that are useful indicators of wind-power output.

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