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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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a b s t r a c tHillslope scale runoff is generated as a result of interacting factors that include water influx rate, surface and subsurface properties, and antecedent saturation. Heterogeneity of these factors affects the existence and characteristics of runoff. This heterogeneity becomes an increasingly relevant consideration as hydrologic models are extended and employed to capture greater detail in runoff generating processes. We investigate the impact of one type of heterogeneity -subsurface permeability -on runoff using the integrated hydrologic model ParFlow. Specifically, we examine the sensitivity of runoff to variation in three-dimensional subsurface permeability fields for scenarios dominated by either Hortonian or Dunnian runoff mechanisms. Ten thousand statistically consistent subsurface permeability fields are parameterized using a truncated Karhunen-Loéve (KL) series and used as inputs to 48-h simulations of integrated surface-subsurface flow in an idealized 'tilted-v' domain. Coefficients of the spatial modes of the KL permeability fields provide the parameter space for analysis using the active subspace method. The analysis shows that for Dunnian-dominated runoff conditions the cumulative runoff volume is sensitive primarily to the first spatial mode, corresponding to permeability values in the center of the threedimensional model domain. In the Hortonian case, runoff volume is sensitive to multiple smaller-scale spatial modes and the locus of that sensitivity is in the near-surface zone upslope from the domain outlet. Variation in runoff volume resulting from random heterogeneity configurations can be expressed as an approximately univariate function of the active variable, a weighted combination of spatial parameterization coefficients computed through the active subspace method. However, this relationship between the active variable and runoff volume is more well-defined for Dunnian runoff than for the Hortonian scenario.

Integrated hydrologic models coupled to land surface models link water and energy movement among the subsurface, land surface, and atmosphere. These connections are especially important when estimating a complex, nonlinear process like evaporation. A comprehensive sensitivity study of an evaporation parameterization was conducted using the integrated ParFlow-Common Land Model (PF-CLM). Estimates of ground evaporation using three forms of the same equation, two simplified closed-form solutions and one fully-coupled PF-CLM equation, are systematically compared. The parameterizations vary in complexity, coupling strength, and nonlinearity. Forcing data from three climate regions (alpine, plains, and tropical) are used to compare estimates of bare ground evaporation across all three formulations, thus exploring the process and coupling sensitivity in a novel way. The overall behavior of ground evaporation is consistent throughout the year for all parameterizations, but magnitudes vary with respect to parameterization complexity during energylimited and water-limited times of the year. A relationship between ground evaporation and ground temperature is shown to exist across all climates and aggregate by pressure, wind speed, and air temperature in the plains climate. Furthermore, results show how increasing complexity through the addition of land surface conditions, atmospheric conditions, and atmospheric stability uniquely compound to influence the relationship between bare ground evaporation and subsurface pressure. Identification of sensitive interactions and unique relationships is necessary to further understand and predict hydrologic processes like evaporation.