Denis Mutiibwa

On the dynamics of stomatal resistance: relationships between stomatal behavior and micrometeorological variables and performance of Jarvis-type parameterization.

Two Jarvis-type models, the original Jarvis model (J-model) and the Green and McNaughton model (GM-model), were evaluated for estimating leaf stomatal resistance (rs) for a non-stressed maize canopy. The parameters of the models were optimized, and their performances were compared against measured rs. A new modified Jarvis-type model (NMJ-model) was developed by including a new term, rs_min exp(-LAI), where rs_min = minimum stomatal resistance (s m-1) and LAI = green leaf area index, to account for the influence of canopy development stage on rs, especially during partial canopy. Through an extensive field campaign, hourly rs, photosynthetic photon flux density (PPFD), air and leaf temperatures (Ta and TL), porometer cup temperature (Tc), air relative humidity above the canopy (RHa) and on the leaf surface (RHL), wind speed (u3) and direction at 3 m, vapor pressure deficit (VPD), LAI, and incoming shortwave radiation (Rs) were measured for a subsurface drip-irrigated non-stressed maize canopy. The relationship between rs and Ta, TL, Tc, RHa, VPD, Rs, u3, and wind direction are presented. On a seasonal average basis, the J-model and the NMJ-model had similar performance in estimating rs (r2 = 0.74, RMSD = 48.8 s m-1 for the J-model; and r2 = 0.74, RMSD = 50.1 s m-1 for the NMJ-model; RMSD = root mean square difference between modeled and measured rs). The inclusion of the variation in the LAI and rs_min term (rs_min exp(-LAI)) during the growing season in the NMJ-model improved the rs estimates, especially in the higher rs range (rs > 250 s m-1), as compared with the J-model. When the period for only partial canopy cover is considered, when LAI ranged from 1.20 to approximately 2.5, the addition of the LAI term in the NMJ-model resulted in 8% improvement in r2 and 10% improvement in RMSD relative to the J-model (r2 = 0.64, RMSD = 35.5 s m-1 for the NMJ-model; and r2 = 0.59, RMSD = 39.0 s m-1 for the J-model) when estimating rs. The estimated rs using TL rather than Ta correlated very well with the measured values, but with a slight decrease in performance for both the J-model and the NMJ-model. Overall, the performance of the J-model and the NMJ-model decreased by 17% when TL rather than Ta was used to estimate rs as compared with the measured rs. While the results demonstrated the superior performance of the NMJ-model over the J-model, especially during the partial canopy conditions, its performance needs further investigation and validation in other climatic settings.

On the Scaling up Soybean Leaf Level Stomatal Resistance to Canopy Resistance for One-Step Estimation of Actual Evapotranspiration

Canopy resistance (rc), which represents the composite diffusive resistance to water vapor transfer from vegetation surfaces to the atmosphere, plays an important role in describing the water vapor and energy fluxes and CO2 exchange mechanisms and is an essential component of the complex ecophysiological and turbulent transport and evapotranspiration models. While one-step (direct) application of combination-based energy balance models (i.e., Penman-Monteith, PM) requires rc to solve for actual evapotranspiration (ETa), a remaining challenge in practical application of PM-type models is the scaling up of leaf-level stomatal resistance (rs) to rc to represent an integrated resistance from the plant community to quantify field-scale evaporative losses. We validated an integrated approach to scale up rs to the canopy. Through an extensive field campaign, we measured diurnal rs for a subsurface drip-irrigated soybean [Glycine max (L.) Merr.] canopy and integrated several microclimatic and in-canopy radiation transfer parameters to scale up rs to rc. Using microclimatic and plant factors such as leaf area index for sunlit and shaded leaves, plant height, solar zenith angle, direct and diffuse radiation, and light extinction coefficient, we scaled up soybean rs as a primary function of measured photosynthetic photon flux density (PPFD). We assumed that PPFD is the primary and independent driver of rc; hence, the scaling approach relied heavily on measured PPFD-rs response curves. We present experimental verifications of scaled up rc by evaluating the performance of the scaled up rc values in estimating ETa. In addition, we solved the PM model on an hourly time step using the scaled up rc values and compared the PM-estimated ETa with the Bowen ratio energy balance system (BREBS)-measured ETa. The relationship between rs and PPFD was asymptotic, and rs showed strong dependence to PPFD, as PPFD alone explained 67% to 88% of the variability in rs. Beyond a certain amount of PPFD (400 to 500 µmol m-2 s-1), rs became less responsive to PPFD. At smaller PPFD (0 to about 150 µmol m-2 s-1) and greater rs (>70 to 80 s m-1) range, rs was very sensitive to PPFD. The rc_min, rc_avg, and rc_max ranged from 42 to 104 s m-1, 69 to 183 s m-1, and 95 to 261 s m-1, respectively, throughout the season. The seasonal average rc_min, rc_avg, and rc_max were 54, 92, and 129 s m-1, respectively. Canopy resistances were higher in early growing season during partial canopy closure, lower during mid-season, and high again in late season due to leaf aging and senescence. The ETa estimates from the PM model using scaled up rc values correlated very well with the BREBS-measured ETa. The average root mean square difference (RMSD) between the BREBS-measured and PM-estimated ETa was 0.08 mm h-1 (r2 = 0.91; n = 827), and estimates were within 3% of the measured ETa on an hourly basis. On a daily time step, RMSD was 0.64 mm d-1 (r2 = 0.86; n = 83), and the estimates were within 4% of the measured data. The approach successfully synthesized the whole-canopy resistance for use in PM-type combination-energy balance equations by scaling up from rs using a straightforward model of in-canopy radiation transfer.

Recent spatiotemporal patterns in temperature extremes across conterminous United States

With a warming climate, understanding the physical dynamics of hot and cold extreme events has taken on increased importance for public health, infrastructure, ecosystems, food security, and other domains. Here we use a high-resolution spatial and temporal seamless gridded land surface forcing data set to provide an assessment of recent spatiotemporal patterns in temperature extremes over the conterminous United States (CONUS). We asked the following: (1) How are temperature extremes changing across the different regions of CONUS? (2) How do changes in extremes vary on seasonal, annual, and decadal scales? (3) How do changes in extremes relate to changes in mean conditions? And (4) do extremes relate to major modes of ocean-atmosphere variability? We derive a subset of the CLIMDEX extreme indices from the North American Land Data Assimilation phase 2 forcing data set. While there were warming trends in all indices, daytime temperature extremes warmed more than nighttime. Spring warming was the strongest and most extensive across CONUS, and summer experienced the strongest and most extensive decrease in cold extremes. Increase in winter warm extremes appeared weakening relative to the rapid 1950–1990 increase found in previous studies. The Northeast and Midwest experienced the most warming, while the Northwest and North Great Plains saw the least. We found changes in average temperatures were more associated with changes in cold extremes than warm extremes. Since 2006 there have been 5 years when more than 5% of the U.S. experienced at least 90 warm days, something not observed in the previous 25 years. The unusually warm first decade of 21st century could have been associated with the warm conditions of near El Nino–Southern Oscillation-neutral phase of the decade, and possibly amplified by anthropogenic forcing. The widespread, lengthy, and severe extreme hot events documented here during the past three decades underscore the need to implement thoughtful adaptation plans in the very near future, to the growing evidence of increasing warm extremes across United States.

Net radiation dynamics: performance of 20 daily net radiation models as related to model structure and intricacy in two climates.

We compared daily net radiation (Rn) estimates from 19 methods with the ASCE-EWRI Rn estimates in two climates: Clay Center, Nebraska (sub-humid) and Davis, California (semi-arid) for the calendar year. The performances of all 20 methods, including the ASCE-EWRI Rn method, were then evaluated against Rn data measured over a non-stressed maize canopy during two growing seasons in 2005 and 2006 at Clay Center. Methods differ in terms of inputs, structure, and equation intricacy. Most methods differ in estimating the cloudiness factor, emissivity (e), and calculating net longwave radiation (Rnl). All methods use albedo (a) of 0.23 for a reference grass/alfalfa surface. When comparing the performance of all 20 Rn methods with measured Rn, we hypothesized that the a values for grass/alfalfa and non-stressed maize canopy were similar enough to only cause minor differences in Rn and grass- and alfalfa-reference evapotranspiration (ETo and ETr) estimates. The measured seasonal average a for the maize canopy was 0.19 in both years. Using a = 0.19 instead of a = 0.23 resulted in 6% overestimation of Rn. Using a = 0.19 instead of a = 0.23 for ETo and ETr estimations, the 6% difference in Rn translated to only 4% and 3% differences in ETo and ETr, respectively, supporting the validity of our hypothesis. Most methods had good correlations with the ASCE-EWRI Rn (r2 > 0.95). The root mean square difference (RMSD) was less than 2 MJ m-2 d-1 between 12 methods and the ASCE-EWRI Rn at Clay Center and between 14 methods and the ASCE-EWRI Rn at Davis. The performance of some methods showed variations between the two climates. In general, r2 values were higher for the semi-arid climate than for the sub-humid climate. Methods that use dynamic e as a function of mean air temperature performed better in both climates than those that calculate e using actual vapor pressure. The ASCE-EWRI-estimated Rn values had one of the best agreements with the measured Rn (r2 = 0.93, RMSD = 1.44 MJ m-2 d-1), and estimates were within 7% of the measured Rn. The Rn estimates from six methods, including the ASCE-EWRI, were not significantly different from measured Rn. Most methods underestimated measured Rn by 6% to 23%. Some of the differences between measured and estimated Rn were attributed to the poor estimation of Rnl. We conducted sensitivity analyses to evaluate the effect of Rnl on Rn, ETo, and ETr. The Rnl effect on Rn was linear and strong, but its effect on ETo and ETr was subsidiary. Results suggest that the Rn data measured over green vegetation (e.g., irrigated maize canopy) can be an alternative Rn data source for ET estimations when measured Rn data over the reference surface are not available. In the absence of measured Rn, another alternative would be using one of the Rn models that we analyzed when all the input variables are not available to solve the ASCE-EWRI Rn equation. Our results can be used to provide practical information on which method to select based on data availability for reliable estimates of daily Rn in climates similar to Clay Center and Davis.

Land Surface Temperature and Surface Air Temperature in Complex Terrain

Land surface temperature (LST) is a fundamental physical property relevant to many ecological, hydrological, and atmospheric processes. There is a strong relationship between LST and near surface air temperature (Tair), although the two temperatures have different physical meaning and responses to atmospheric conditions. In complex terrain, these differences are amplified; yet it is in these environments that remotely sensed LST may be most valuable in prediction and characterization of spatial-temporal patterns of Tair due to typical paucity of meteorological stations in mountainous regions. This study presents an analysis on the suitability and limitations of using LST as a proxy or an input variable for predicting Tair in complex mountainous topography. Explicitly, we investigated the influence of key environmental, topographic, and instrumental factors on the relation between LST and measured Tair in two mountainous ecoregions of Nevada. The relation between LST and Tair was found to be strongest during late summer and fall, and weakest during winter and early spring. Increasing terrain roughness was found to diminish the relation between between LST and Tair. There was a strong agreement between nighttime Tair lapse rates and LST lapse rates. Given the inadequacy of several gridded Tair products in capturing minimum temperature cold-air pooling and inversions, using LST as an input variable in the interpolation process would enhance capture of temperature inversions in grid-ded data over complex terrain. Crucially, the relationship between LST and Tair did not differ significantly across the two distinct mountainous ecoregions.

Evaluating the Impact of Daily Net Radiation Models on Grass and Alfalfa-Reference Evapotranspiration Using the Penman-Monteith Equation in a Subhumid and Semiarid Climate

Net radiation Rn is the main driving force of evapotranspiration ET and is a key input variable to the Penman-type combination and energy balance equations. However, Rn is not commonly measured. This paper analyzes the impact of 19 net radiation models that differ in model structure and intricacy on estimated grass and alfalfa-reference ET ETo and ETr, respectively and investi- gates how climate, season and cloud cover influence the impact of the Rn models on ETo and ETr. Datasets from two locations Clay Center, Nebraska, subhumid; and Davis, California, a Mediterranean-type semiarid climate were used. Rn values computed from the 19 models were used in the standardized ASCE-EWRI Penman-Monteith equation to estimate ETo and ETr on a daily time step. The influence of seasons on the estimation of Rn and on estimated ETo and ETr was investigated in winter November-March and summer May-September months. To analyze the influence of clouds on the impact of Rn models, relative shortwave radiation Rrs was used as a means to express the cloudiness of the days as: 0 Rrs0.35 for completely cloudy days; 0.35 Rrs0.70 for partially cloudy days; and 0.70 Rrs1.0 for clear sky days. The performances of Rn models showed variations at the same location and between the locations for the same model based on methods used to calculate various model parameters. The most significant impact of Rn on estimated ETo and ETr was related to the methods used to calculate atmospheric emissivity rather than methods used to calculate clear sky solar radiation Rso or cloud adjustment factor f. Rn models that used average air temperature to compute and an estimated f resulted in good performances at both locations. Empirical models that assumed f=1.0 showed poor to average performances at both locations. While model performances varied based on methods used to calculate Rso, f, and , there were significant seasonal variations in performances of models that calculated as a function of actual vapor pressure of the air ea. The seasonal variations in performances of these models were greater under subhumid climate at Clay Center than in semiarid climate at Davis, Calif. The models that calculated as a function of ea performed better under completely cloudy days than on other days, more so at Clay Center. Methods used to calculate have a significant impact on the Rn model performance, especially in unstable climatic conditions such as at Clay Center where there are frequent and rapid changes in climatic variables in a given day and throughout the year. The results of this study can be used as a reference tool to provide practical information on which method to select based on the data availability for reliable estimates of daily Rn relative to the ASCE-EWRI Rn method in subhumid and semiarid climates similar to Clay Center, Neb. and Davis, Calif.

Evaluation of Valiantzas’ Simplified Forms of the FAO-56 Penman-Monteith Reference Evapotranspiration Model in a Humid Climate

AbstractThe unavailability of some meteorological variables, especially solar radiation and wind speed, is the main constraint for reference evapotranspiration (ETo) estimation using the standard U...

Transferability of Jarvis-Type Models Developed and Re-Parameterized for Maize to Estimate Stomatal Resistance of Soybean: Analyses on Model Calibration, Validation, Performance, Sensitivity, and Elasticity

Abstract. In a previous study by the same authors, a new modified Jarvis model (NMJ-model) was developed, calibrated, and validated to estimate stomatal resistance (r s ) for maize canopy on an hourly time step. The NMJ-model’s unique subfunctions, different from the original Jarvis model (J-model), include a photosynthetic photon flux density (PPFD)-r s response subfunction developed from field measurements and a new physical term, A exp(1/LAI) , where A is the minimum stomatal resistance and LAI is the green leaf area index, to account for the influence of canopy development on r s , especially during partial canopy stage in the early season and in late-season stage during leaf aging and senescence. This study evaluated the transferability of the J-model and NMJ-models that were re-parameterized and calibrated for maize canopy to estimate soybean r s . Due to the differences in physiological and photosynthetic pathway differences between the two crops, the r s response to the same environmental variables, i.e., PPFD, vapor pressure deficit (VPD), and air temperature (T a ), were substantially different. Thus, this study demonstrated the inherent limitation in applying the Jarvis-type models that were calibrated for maize to soybean without re-calibration. Maize-calibrated models performed poorly in estimating soybean r s , with the coefficient of determination (r 2 ) ranging from 0.30 to 0.38 and the root mean square difference (RMSD) between the estimated and measured r s ranging from 94.4 to 166 s m -1 . The J-model and NMJ-model were re-calibrated by parameter optimization method for soybean. The J-model calibrated well; however, the validation had poor performance results. The NMJ-model had a good calibration, resulting in a good r 2 (0.71) and a small RMSD (13.7 s m -1 ). The NMJ-model validation produced superior results to the J-model, explaining more than 80% of the variation in the measured r s (RMSD = 38.4 s m -1 ). These results show the robustness and practical accuracy of the NMJ-model in estimating r s over different canopies if well calibrated for a specific crop. In terms of sensitivity and elasticity analyses, among all parameters, r s estimates were most sensitive to uncertainties introduced in parameter a 1 of the PPFD subfunction due to its exponential impact on r s in the NMJ-model. Therefore, for accurate estimates of r s , uncertainties in parameter a 1 should not exceed the range of -2% and 2% so that the error in estimated r s is kept between -3.5% and 3.6%. The study observed that the relative change in r s due to uncertainties in parameters a 2 and a 3 of the VPD subfunction was a linear function and less sensitive than the PPFD subfunction. The sensitivity of r s to uncertainties in temperature subfunction parameters (a 4 and a 5 ) was higher than that of VPD subfunction parameters, but less than that of PPFD subfunction parameters. The uncertainty in parameters a 4 and a 5 should range within -10% and 10%, and the calibration of these parameters should be determined with greater precision as compared with the VPD subfunction parameters. The study confirmed that the addition of the r s_min and the A exp(1/LAI) terms, which were not accounted for in the original J-model, improved the model accuracy for estimating soybean r s .