Resistance Parameterization Research Articles

Development of Rice Paddy Model Based on Noah LSM: Consistent Parameterization of Subcanopy Resistance from the Ponded Water to Dense Rice Canopy

Development of Rice Paddy Model Based on Noah LSM: Consistent Parameterization of Subcanopy Resistance from the Ponded Water to Dense Rice Canopy

Laboratory observations for examining estimates of soil dry surface layer thickness with parsimonious models

ABSTRACT Soil dry surface layer (DSL) thickness is often considered a key parameter for land surface resistance to gas exchange. Commonly used, simple models for DSL thickness are typically empirical in nature and based on limited observational evidence. Laboratory experiments were performed to test soil condition and boundary effects on DSL formation. DSL thickness was analyzed in soil columns with varying texture, initial water content, and potential evaporation rate. DSLs formed to greater depth in fine-textured compared to coarse-textured soils, when beginning from similar initial water content. Based on experiments, we compared a simple but physically-based mass balance DSL model to an empirical DSL model from the literature. The mass balance model performed better than the empirical relative-wetness-based model, and is similar in structure to the current DSL parameterization in the Community Land Model. Results suggest soil resistance parameterizations can be improved by employing simple, but texture-dependent, physically-based DSL formulations.

Spatial patterns and recent temporal trends in global transpiration modelled using eco-evolutionary optimality

Transpiration from vegetation accounts for about two thirds of land evapotranspiration (ET), and exerts important effects on of global water, energy, and carbon cycles. Resistance-based ET partitioning models using remote sensing data are one of the main methods to estimate global land transpiration, overcoming the limitation by the sparse distribution and short observation periods of site-level measurements. However, the uncertainties of estimated transpiration for these models mainly come from the resistance parameterization based on specific empirical parameters across different plant functional types (PFT). A model based on eco-evolutionary optimization (P model) has recently been proposed to simulate stomatal conductance without the need of calibrated parameters. Here, we calculated global long-term (1982–2018) monthly transpiration with the Penman-Monteith (PM) equation using canopy conductance estimated by the P model (PM-P) and Ball-Berry-Leuning model (PM-BBL). Using the observations of SAPFLUXNET and FLUXNET sites as reference, the performance of PM-P was comparable with that of PM-BBL and Global Land Evaporation Amsterdam model (GLEAM). Multi-year mean and trends in growing season transpiration estimated by GLEAM and the PM-P model revealed a similar spatial distribution globally. Both GLEAM and the PM-P model showed a widespread increasing trend of growing season transpiration over 72.06%∼80.38% of global land, especially for some main greening hotspots with >3.0 mm/year. The good performance of the P model indicated that it could avoid the uncertainties emerging from the resistance parameterization with too many empirical parameters and had the potential to accurately estimate global transpiration.

Assessment and intercomparison of ozone dry deposition schemes over two ecosystems based on Noah-MP in China

Dry deposition of ozone (O3) is a major sink of O3 in near-surface air. The uncertainties in any existing dry deposition schemes are large. In this study, we incorporate four dry deposition schemes into the Noah with multi-parameterization options model (Noah-MP) to assess the importance of choice of different dry deposition schemes on simulated dry deposition velocity of O3 (Vd(O3)) over forest and agricultural ecosystems. Our simulated Vd(O3) values from different schemes were compared with observational data. All schemes performed similarly for agricultural ecosystems (correlation coefficient of approximately 0.4). For the forest ecosystem, Ball–Berry type schemes performed better than Jarvis-type schemes, with correlation coefficients for Vd(O3) between simulations and observations being approximately 0.55 and 0.38, respectively. To further improve the efficacy of the simulation of Vd(O3), we modified two canopy stomatal resistance mechanisms. For the Jarvis stomatal resistance mechanism, the minimum stomatal resistance was modified, with a mean bias of daytime reduction of by 39.3% for the forest ecosystem and 60.9% for the agricultural ecosystem. For Ball–Berry stomatal resistance mechanism, the stomatal resistance mechanism and nitrogen-limiting scheme for photosynthesis were modified. However, the results showed no significant improvement in the Vd(O3) simulation. The results demonstrate the importance of canopy resistance parameterization over agricultural and forest ecosystems, thus making a strong case for further evaluation and model improvement of O3 dry deposition in different ecosystems.

Porosity Models for Large-Scale Urban Flood Modelling: A Review

In the context of large-scale urban flood modeling, porosity shallow-water models enable a considerable speed-up in computations while preserving information on subgrid topography. Over the last two decades, major improvements have been brought to these models, but a single generally accepted model formulation has not yet been reached. Instead, existing models vary in many respects. Some studies define porosity parameters at the scale of the computational cells or cell interfaces, while others treat the urban area as a continuum and introduce statistically defined porosity parameters. The porosity parameters are considered either isotropic or anisotropic and depth-independent or depth-dependent. The underlying flow models are based either on the full shallow-water equations or approximations thereof, with various flow resistance parameterizations. Here, we provide a review of the spectrum of porosity models developed so far for large-scale urban flood modeling.

Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau.

This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0–10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.

Comparison of Soil Water and Heat Transfer Modeling Over the Tibetan Plateau Using Two Community Land Surface Model (CLM) Versions

AbstractSoil water and heat transfer is one of the most important parts of water and energy partition between atmosphere and land surface, and it is more complicated over the cold regions. In this study, the observed soil moisture and temperature are selected from four sites over the Tibetan Plateau (TP) to evaluate the performances two versions of Community Land Model (CLM), that is, CLM4.5 and CLM5.0. In addition, soil temperature observations from 67 sites and soil moisture observations from Maqu and Naqu monitoring network over the TP were used to evaluate the performances of regional simulations. The results indicated that the simulated soil temperature generally coincided with that of the observed, while CLM5.0 outputs are closer to the observed soil temperature in the arid and semiarid regions compared to CLM4.5. Generally, CLM5.0 tended to overestimate soil moisture at most sites at four soil depths (5, 10, 20, and 40 cm) but got some improvements at Maqu site. The overestimation of soil moisture was mainly caused by the introduction of a dry surface layer‐based (DSL) soil evaporation resistance parameterization in CLM5.0, which improves the soil evaporation simulation over the TP, especially in the semiarid region. Moreover, we tried to distinguish the factors that affect the soil water and heat transfer in the models. The results showed that soil property data play a main role in soil water and heat transfer modeling.

A numerical modelling of flows in an open channel with emergent vegetation

ABSTRACT Simulations of shallow-water flows in an open channel with vegetation are considered. The simple formulation of bulk resistance makes numerical realization of vegetation straightforward. Stem-scale eddies due to individual plants are not modelled. While the vegetation resistance parameterization has been frequently used, few studies exist that comprehensively evaluate the modelling skills of such a highly simplified approach in capturing the large-scale dynamics which are often of practical interest. This study provides such an assessment by conducting a range of comparisons between the simulations and data. While the simple model is remarkably effective, capturing the large-scale features that are comparable with the data (e.g. the two-layer structure of the shear layer resulting from the Kelvin–Helmholtz instability at the canopy–water interface, the steady wake behind a vegetation patch and formation of patch-scale von Kármán vortices), we emphasize the discrepancies in some aspects. These findings inform large-scale modelling and the needs for improvement.

Uncertainties Caused by Resistances in Evapotranspiration Estimation Using High-Density Eddy Covariance Measurements

AbstractQuantifying the uncertainties caused by resistance parameterizations is fundamental for understanding, improving, and developing terrestrial evapotranspiration (ET) models. Using high-density eddy covariance (EC) tower observations in a heterogeneous oasis in northwest China, this study evaluates the impacts of resistances on the estimation of latent heat flux (LE), the energy equivalent of ET, by comparing resistance parameterizations with different complexities under one- and two-source Penman–Monteith (PM) equations. The results showed that the mean absolute percent error (MAPE) for the LE estimates from the one- and two-source PM equations varied from 32% to 53%, and the uncertainties were caused mainly by the resistance parameterizations. Calibrating the parameters required in the resistance estimations could improve the performance of the PM equations; specifically, the MAPEs for the one-source PM equations were approximately 16%, whereas they were 38% for the two-source PM equations, emphasizing that multiple resistances result in increased uncertainties. The following conclusions were reached: 1) the empirical and biophysical parameters required in resistance estimations were responsible for the uncertainty; 2) increasingly complex resistance parameterizations resulted in greater uncertainties in LE estimates; and 3) models without resistance parameterizations exhibited reduced uncertainties in LE estimates.

An observation-driven optimization method for continuous estimation of soil moisture over large heterogeneous areas

ABSTRACTIn most remote sensing-based soil moisture (SM) retrieval methods, in-situ SM measurements are commonly used for validation purposes. Few studies have investigated whether such measurements can be used for calibration. In this paper, an observation-driven optimization method was proposed to estimate SM from remote sensing observations. Specifically, the optimization method was developed within the surface temperature-vegetation index (TVX) framework for the definition of objective function and constraints. In-situ SM measurements were used to optimize the theoretical boundaries of the TVX feature space. We demonstrated the applicability of the new method with Moderate Resolution Imaging Spectroradiometer (MODIS) products over the Southern Great Plains (SGP) of the United States of America. Results indicate that the accuracy produced using only one site for calibration has reached a level comparable with those produced by traditional methods. Moreover, the method has not only bypassed the complex parameterization of aerodynamic and surface resistance but also achieved continuous monitoring of SM. That is just the capacity that the traditional TVX method does not possess. Therefore, although our optimization method requires the ancillary of in-situ observations, its simplicity proves that it is a useful tool for a quick and continuous monitoring of SM over large heterogeneous areas.

Az emberi test kültéri hőterhelésének a humán állapothatározóktól való függése

A new clothing resistance model for estimating outdoor thermal load of a walking individual has been proposed by our research team. The model is used as a tool to analyse the relationship between clothing resistance (estimated by this new model) and body structural parameters in different weather conditions. More than 3000 children’s and adults’ body structural – BMI, relative fat mass (fatBMI) and relative muscle mass (muscleBMI) data, as well as simulated data of weather conditions having influence on thermal perception were used in the analysis.
 The relationship between BMI, fatBMI, muscleBMI and clothing resistance showed very strong relations both in simulated winter and summer conditions, in both sexes in almost every age-group. The bigger the BMI, fatBMI, muscleBMI, the smaller clothing resistance (smaller heating demand) was estimated in winter weather conditions, while the bigger clothing resistance (smaller cooling demand) was estimated in summer weather conditions. Females’ clothing resistance was higher in winter weather conditions, while was smaller in summer conditions than the clothing resistance of their male age-peers having the same relative body mass, fat mass or muscle mass. BMI showed the strongest relations with clothing resistance both in summer and winter thermal stresses. The preliminary results of the project confirmed that age, gender, BMI and presumably relative fat and muscle mass should be built in the procedure of parameterization of clothing resistance.

The Improvement of Turbulent Heat Flux Parameterization for Use in the Tropical Regions Using Low Wind Speed Excess Resistance Parameter

AbstractReliable simulation of turbulent heat fluxes needed for modeling land‐atmosphere interactions remains a challenge over the humid tropical region. This may be connected with the inadequate parameterization of the roughness lengths for momentum (z0m) and heat (z0h) transfer usually expressed in terms of excess resistance factor (κB−1). This paper assesses the performance of existing κB−1 schemes developed for high wind speed conditions over the humid tropical region. Thereafter, a more appropriate κB−1 suitable for low wind speed condition is developed for use in the aerodynamic resistance parameterization. Based on observed surface heat fluxes and profile measurements of wind speed and temperature from Nigeria Micrometeorological Experimental site, new κB−1 parameterization was derived through the application of the Monin‐Obukhov similarity theory and Brutsaert theoretical model for heat transfer. The derived , where Re* is the Reynolds number. Turbulent flux parameterization with this new formula provides better estimates of heat fluxes with reference to results from existing κB−1 schemes. The R2 increased by about 85%, while mean bias error and root‐mean‐square error in the parameterized QH based on the derived κB−1 reduced by about 63% and 66.7%, respectively. Similarly, the R2 increased by about 38%, while mean bias error and root‐mean‐square error in the parameterized QE based on the derived κB−1 reduced by about 47.8% and 52.6%, respectively. The derived κB−1 gave better estimates of QH than QE during the daytime. The derived κB−1 scheme corrects a well‐documented, large overestimation of turbulent heat fluxes, and it is therefore recommended for use in regions where low wind speed is prevalent.

Aerodynamic Resistance Parameterization for Heterogeneous Surfaces Using a Covariance Function Approach in Spectral Space

AbstractSimulating the influence of heterogeneous surfaces on atmospheric flow using mesoscale models (MSM) remains a challenging task, as the resolution of these models usually prohibits resolving important scales of surface heterogeneity. However, surface heterogeneity impacts fluxes of momentum, heat, or moisture, which act as lower boundary conditions for MSM. Even though several approaches for representing subgrid-scale heterogeneities in MSM exist, many of these approaches rely on Monin–Obukhov similarity theory, preventing those models from resolving all scales of surface heterogeneity. To improve upon these residual heterogeneity scales, a novel heterogeneity parameterization is derived by linking the heterogeneous covariance function in spectral space to an associated homogeneous one. This covariance function approach is subsequently used to derive a parameterization of the aerodynamic resistance to heat transfer of the surface layer. Here, the effect of surface heterogeneity enters as a factor applied to the stability correction functions of the bulk similarity approach. To perform a first comparison of the covariance function approach against the conventional bulk similarity and tile approaches, large-eddy simulations (LESs) of distinct surface heterogeneities are conducted. The aerodynamic resistances from these three parameterizations are subsequently tested against the LES reference by resolving the surface heterogeneities with six different test-MSM grids of varying cell dimension. The results of these comparisons show that the covariance function approach proposed here yields the smallest deviations from the LES reference. In addition, the smallest deviation of the covariance function approach to the reference is observed for the LES with the largest surface heterogeneity, which illustrates the advantage of this novel parameterization.

Parameterization of Urban Sensible Heat Flux from Remotely Sensed Surface Temperature: Effects of Surface Structure

Sensible heat exchange has important consequences for urban meteorology and related applications. Directional radiometric surface temperatures of urban canopies observed by remote sensing platforms have the potential to inform estimations of urban sensible heat flux. An imaging radiometer viewing the surface from nadir cannot capture the complete urban surface temperature, which is defined as the mean surface temperature over all urban facets in three dimensions, which includes building wall surface temperatures and requires an estimation of urban sensible heat flux. In this study, a numerical microclimate model, Temperatures of Urban Facets in 3-D (TUF-3D), was used to model sensible heat flux as well as radiometric and complete surface temperatures. Model data were applied to parameterize an effective resistance for the calculation of urban sensible heat flux from the radiometric (nadir view) surface temperature. The results showed that sensible heat flux was overestimated during daytime when the radiometric surface temperature was used without the effective resistance that accounts for the impact of wall surface temperature on heat flux. Parameterization of this additional resistance enabled reasonably accurate estimates of urban sensible heat flux from the radiometric surface temperature.

Improved Spatiotemporal Representativeness and Bias Reduction of Satellite‐Based Evapotranspiration Retrievals via Use of In Situ Meteorology and Constrained Canopy Surface Resistance

AbstractEvapotranspiration (ET) is a key component of the atmospheric and terrestrial water and energy budgets. Satellite‐based vegetation index approaches have used remotely sensed vegetation and reanalysis meteorological properties with surface energy balance models to estimate global ET (MOD16 ET). We reconstructed satellite retrievals using in situ meteorology (Argonne‐ET) and evaluated them using a dense network of surface turbulent heat flux measurements. Argonne‐ET resolves spatial heterogeneity of ET across the U.S. Southern Great Plains that is not well characterized by MOD16; MOD16 ET exhibits spatial autocorrelation (Moran's I significantly >0 in May–Aug.), whereas in situ and Argonne‐ET exhibit characteristics of a random spatial process (Moran's I not significantly different from 0). The skill in resolving ET temporal variability is not significantly different between MOD16 and Argonne‐ET (correlation coefficient = 0.75 and 0.72, respectively). However, the root‐mean‐square errors were significantly lower for Argonne‐ET (36 W/m2) than MOD16 (43 W/m2), and MOD16 exhibits substantial bias in annual ET relative to in situ measurements (−38%). This is attributed to overestimation in the dry canopy surface resistance (rs) parameterization. Using rs constrained to the range of typical measured values, Argonne‐ET substantially reduces the bias in the annual ET (+1%). The improved ET estimates are critical for regional water budget analyses. The methodology presented herein also demonstrates the ability to retrieve high temporally resolved (30 min; cf. 8‐day MOD16) ET that can be used for development of processed‐based diagnostics of model biases and to elucidate avenues to improve ET model parameterizations.

Evaluation and Intercomparison of Five North American Dry Deposition Algorithms at a Mixed Forest Site.

To quantify differences between dry deposition algorithms commonly used in North America, five models were selected to calculate dry deposition velocity (Vd) for O3 and SO2 over a temperate mixed forest in southern Ontario, Canada, where a 5-year flux database had previously been developed. The models performed better in summer than in winter with correlation coefficients for hourly Vd between models and measurements being approximately 0.6 and 0.3, respectively. Differences in mean Vd values between models were on the order of a factor of 2 in both summer and winter. All models produced lower Vd values than the measurements of O3 in summer and SO2 in summer and winter, although the measured Vd may be biased. There was not a consistent tendency in the models to overpredict or underpredict for O3 in winter. Several models produced magnitudes of the diel variation of Vd (O3) comparable to the measurements, while all models produced slightly smaller diel variations than the measurements of Vd (SO2) in summer. A few models produced larger diel variations than the measurements of Vd for O3 and SO2 in winter. Model differences were mainly due to different surface resistance parameterizations for stomatal and nonstomatal uptake pathways, while differences in aerodynamic and quasi-laminar resistances played only a minor role. It is recommended to use ensemble modeling results for ecosystem impact assessment studies, which provides mean values of all the used models and thus can avoid too much overestimations or underestimations.

Parameterization of canopy resistance for modeling the energy partitioning of a paddy rice field

Models for predicting hourly canopy resistance (r c) and latent heat flux (LET) based on the Penman–Monteith (PM) and bulk transfer methods are presented. The micrometeorological data and LET were observed during paddy rice-growing seasons in 2010 in Japan. One approach to model r c was using an aerodynamic resistance (r a) and climatic resistance (r *), while another one was based on a relationship with solar radiation (SR). Nonlinear relationships between r c and r *, and between r c and SR were found for different growing stages of the rice crop. The constructed r c models were integrated to the PM and bulk transfer methods and compared with measured LET using a Bowen ratio–energy balance method. The root mean square errors (RMSEs) were 155.2 and 170.5 W m−2 for the bulk transfer method with r c estimated using r * and with a function of SR, respectively, while the RMSEs were 87.4 and 85.7 W m−2 for the PM method with r c estimated using r * and SR, respectively. The r c integrated PM equation provided better performance than the bulk transfer equation. The results also revealed that neglecting the effect of r a on r c did not yield a significant difference in predicting LET.

Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model

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.

New turbulent resistance parameterization for soil evaporation based on a pore‐scale model: Impact on surface fluxes in CABLE

AbstractThe Community Atmosphere Biosphere Land Exchange (CABLE) land surface model overestimates evapotranspiration (E) at numerous flux tower sites during boreal spring. The overestimation of E is not eliminated when the nonlinear dependence of soil evaporation on soil moisture or a simple litter layer is introduced into the model. New resistance terms, previously developed from a pore‐scale model of soil evaporation, are incorporated into the treatment of under canopy water vapor transfer in CABLE. The new resistance terms reduce the large positive bias in spring time E at multiple flux tower sites and also improve the simulation of daily sensible heat flux. The reduction in the spring E bias allows the soil to retain water into the summer, improving the seasonality of E. The simulation of daily E is largely insensitive to the details of the implementation of the pore model resistance scheme. The more physically based treatment of soil evaporation presented here eliminates the need for empirical functions that reduce evaporation as a function of soil moisture that are included in many land surface models.

Evaporation estimates using weather station data and boundary layer theory

AbstractGlobal estimates of evapotranspiration remain a challenge. In this study, we show that the daily course of air temperature and specific humidity available at routine weather stations can be used to estimate evapotranspiration and the evaporative fraction, the ratio of latent heat flux to available energy at the surface. Indeed, the diurnal increase in air temperature reflects the magnitude of the sensible heat flux and the increase of specific humidity after sunrise reflects the amplitude of evapotranspiration. The method is physically constrained and based on the budget of heat and moisture in the boundary layer. Unlike land surface‐based estimates, the proposed boundary layer estimate does not rely on ad hoc surface resistance parameterizations (e.g., Penman‐Monteith). The proposed methodology can be applied to data collected at weather stations to estimate evapotranspiration and evaporative fraction under cloudy conditions and in the pre–remote sensing era.