Daily Rn Research Articles

Patterns and Drivers of Surface Energy Flux in the Alpine Meadow Ecosystem in the Qilian Mountains, Northwest China.

Alpine meadows are vital ecosystems on the Qinghai-Tibet Plateau, significantly contributing to water conservation and climate regulation. This study examines the energy flux patterns and their driving factors in the alpine meadows of the Qilian Mountains, focusing on how the meteorological variables of net radiation (Rn), air temperature, vapor pressure deficit (VPD), wind speed (U), and soil water content (SWC) influence sensible heat flux (H) and latent heat flux (LE). Using the Bowen ratio energy balance method, we monitored energy changes during the growing and non-growing seasons from 2022 to 2023. The annual average daily Rn was 85.29 W m-2, with H, LE, and G accounting for 0.56, 0.71, and -0.32 of Rn, respectively. Results show that Rn is the main driver of both H and LE, highlighting its crucial role in turbulent flux variations. Additionally, a negative correlation was found between air temperature and H, suggesting that high temperatures may suppress H. A significant positive correlation was observed between soil moisture and LE, further indicating that moist soil conditions enhance LE. In conclusion, this study demonstrates the impact of climate change on energy distribution in alpine meadows and calls for further research on the ecosystem's dynamic responses to changing climate conditions.

Balanço de Radiação sobre água de Baixa Turbidez Purificada Artificialmente para Irrigação de Fumo Cultivado sob Sombreamento. II. Análise do Albedo da água e Modelagem da Radiação Líquida

Abstract The albedo of a water surface and the energy available for evaporation are strongly correlated and studying such processes is of paramount importance for water security at farm and regional levels. In this paper, water albedo (αw) and net all-wave radiation (Rn) were analyzed after being measured above the surface of an artificially cleaned and low-turbidity water used for tobacco irrigation. It was observed that αw decreased as the sun elevation (θ) increased, especially for clear and near clear skies. The results showed that αw can be reasonably predicted with a power law model either in terms of θ or Sg (incoming solar radiation) across different cloud cover conditions. From this study, a mean daily albedo of 0.05 is recommended. Three approaches were considered for estimation of daily Rn. In the first, a linear regression model strongly fitted Rn data in terms of Sg solely. The second option based on the definition of Rn was [0.95Sg - Lnet(56)], where Lnet(56) is net longwave (LW) radiation as used in the FAO56 model for reference evapotranspiration estimation, and the third was [0.95Sg - Lnet(MLR)], where MLR stands for multiple linear regression. The disadvantages of approaches (1) and (3), based on regressions, is that they are constrained to the type of water stored in the farm and the climatic conditions of the region. The performance of approach (2), where Lnet(56) is a widely used model, was comparable to the others can potentially be improved with a site-specific calibration. All three approaches for estimating daily Rn proposed in this study can possibily be extended to clear water that did not go through any filtration process.

The spatiotemporal variation of land surface heat fluxes in Tibetan Plateau during 2001–2022

The surface energy budget is important for understanding of energy and water cycle processes in the Tibetan Plateau (TP). In this study, the daily sensible (SH) and latent (LE) heat fluxes at the horizontal resolution of 1° are first estimated using the maximum entropy production (MEP) model (hereinafter SHMEP and LEMEP) in the entire TP during 2001–2022. The MEP model is built on physical and statistical principles to simulate surface heat fluxes. The surface net radiation, soil moisture (SM), and land surface temperature (LST) are the main driving variables for MEP model. To select the relatively accurate MEP input data, the merged surface net radiation (Rn–merged) under all-sky conditions are generated from CERES, ISCCP-FH, and ERA5 using the Bayesian Model Averaging scheme. Besides, the TP SM and LST from various data sources are evaluated using the in-situ observations at site scale. Based on the daily Rn–merged, ERA5 SM, CERES LST, and the MEP model, the daily SH and LE are estimated in the entire TP. The results show the daily SHMEP and LEMEP perform well at the validation sites, with the regional mean correlation coefficient (R) above 0.7, root-mean-square error (RMSE) of <19 W m−2, absolute value of bias and mean absolute error (MAE) below 11 W m−2. The monthly SHMEP has the regional mean R of 0.96 and RMSE of 6.30 W m−2 at all the measurement stations. For LEMEP, the regional mean R and RMSE values are 0.93 and 10.01 W m−2, respectively. The MEP simulation results are superior to the SH and LE in ERA5, ERA-Interim, MERRA-2, and JRA-55 reanalysis datasets and previous studies, especially for LE. Based on this new dataset, the spatial and temporal varying characteristics of SH and LE in the TP are analyzed. The annual mean SHMEP value are large in the western TP, the Qaidam Basin in the northern TP, and the Himalaya ranges, and small in the southeastern TP. The annual mean LEMEP has the maximum value in southeastern TP, and minimum value in western TP and the Qaidam Basin. The annual mean SHMEP and LEMEP over the entire TP are 34.79 W m−2 and 20.16 W m−2, with the significant declining trends of −0.17 W m−2 year−1 and − 0.052 W m−2 year−1 during the study period, respectively. The spatial distributions of the MEP surface heat fluxes and their trends are mainly influenced by the model inputs of Rn and SM in the TP.

Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing

The study of energy, water, and carbon exchanges between ecosystems and the atmosphere is important in understanding the role of vegetation in regional microclimates. However, they are still relatively scarce when it comes to Caatinga vegetation. This study aims to identify differences in the dynamics of critical environmental variables such as net radiation (Rn), evapotranspiration (ET), and carbon fluxes (gross primary production, GPP) in contrasting recovered Caatinga (dense Caatinga, DC) and degraded Caatinga (sparse Caatinga, SC) in the state of Paraíba, northeastern Brazil. Estimates were performed using the Surface Energy Balance Algorithm for Land (SEBAL), and comparisons between estimated and measured data were conducted based on the coefficient of determination (R2). The fluxes were measured using the Eddy Covariance (EC) method for comparison with the same variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the Terra satellite. The estimates showed higher Rn values for the DC, indicating that this area should have greater energy availability for physical, biological, and chemical processes. The R2 between daily Rn estimates and observations was 0.93. The ET estimated using the SEBAL showed higher differences in relation to the observed values; however, it presented better spatial discrimination of the surface features. The MOD16A2 algorithm, however, presented ET values closer to the observed data and agreed with the seasonality of the Enhanced Vegetation Index (EVI). The DC generally showed higher ET values than the SC, while the MODIS data (GPP MOD17A2H) presented a temporal behavior closer to the observations. The difference between the two areas was more evident in the rainy season. The R2 values between GPP and GPP MOD17A2H were 0.76 and 0.65 for DC and SC, respectively. In addition, the R2 values for GPP Observed and GPP modeled were lower, i.e., 0.28 and 0.12 for the DC and SC, respectively. The capture of CO2 is more evident for the DC considering the whole year, with the SC showing a notable increase in CO2 absorption only in the rainy season. The GPP estimated from the MOD17A2H showed a predominant underestimation but evidenced the effects of land use and land cover changes over the two areas for all seasons.

The Hi-GLASS all-wave daily net radiation product: Algorithm and product validation

The surface net radiation (Rn) represents the balance of the radiative budget on the land surface and drives many physical and biological processes. An accurate and long-term product for global daily coverage of Rn at a high spatial resolution is needed for a variety of applications at regional and local scales. This study proposes two algorithms, called the downward shortwave radiation (DSR)-based algorithm and the top-of-atmosphere (TOA)-based algorithm, to estimate Rn by using Landsat data. The DSR-based algorithm consists of three conditional models, and was developed based on the analysis of the relationship between Rn and shortwave radiation as well as ancillary information from ground measurements and various datasets. The TOA-based algorithm was developed by linking Rn to TOA observations from Landsat sensors and ancillary information. The two algorithms were developed by using the random forest method. The results of their validation against ground measurements showed that the DSR-based algorithm outperformed the TOA-based algorithm in terms of accuracy, with a determination coefficient (R2) of 0.93, root-mean-squared error (RMSE) of 17.58 Wm−2, and bias of −4.27 Wm−2. It was stable under various conditions. We then applied the DSR-based algorithm to generate a product of the global daily Rn, called the High-resolution (Hi)- Global LAnd Surface Satellite (GLASS), from 2013 to 2018 at a spatial resolution of 30 m under a clear sky based on remotely sensed products, including the DSR from GLASS, the normalized difference vegetation index (NDVI) obtained from Landsat, surface broadband albedo from Hi-GLASS, and meteorological factors based on reanalysis data from MERRA2. Following its validation using in-situ observations from 2013 to 2018, the overall accuracy of the daily Rn acquired by Hi-GLASS under clear sky was found to be satisfactory, with a value of R2 of 0.90 and an RMSE of 25.03 Wm−2. Moreover, compared with the daily Rn obtained from the GLASS product at a spatial resolution of 5 km, that obtained by Hi-GLASS can better characterize the surface by providing more details and capturing the variations in the measurements, especially large and small values. However, due to limitations of the available datasets and the algorithm, the data on Rn for most regions lacked information on cloudy skies and areas at high latitudes. This information thus cannot be provided by Hi-GLASS yet. Moreover, the influence of the topography on values of Rn has not been thoroughly considered. Nonetheless, values of Rn under clear sky obtained from Hi-GLASS offer promise for use in a wide range of areas, and efforts are underway to improve this product.

Successful Outpatient Treatment and Monitoring Following Administration of Various Anti-CD19 Chimeric Antigen Receptor Therapies in B-Cell Lymphomas

Background: Chimeric Antigen Receptor (CAR) T-cell therapy is available as a salvage option for patients with relapsed/refractory B-cell lymphomas (DLBCL, FL, and MCL). Historically, most CAR T-cell therapy has been administered in the inpatient setting due to the potential need to closely monitor or treat cytokine release syndrome (CRS) and neurologic toxicity (NT). However, the introduction of rigorous outpatient monitoring programs incorporating nurse-driven education, patient/caregiver-driven after-hours reporting, and prophylactic corticosteroids may enable outpatient administration and monitoring of CAR T-cell in a large proportion of patients. Outpatient monitoring can improve quality of life for the patient as well as reduce healthcare costs. Objective: Describe clinical outcomes in a cohort of patients who received a variety of commercial CAR T-cell therapies and were subsequently monitored in the outpatient setting. Study Design: This is a retrospective analysis of 20 patients treated with outpatient CAR T-cell therapy between February 2021 and March 2022 with all patients having at least 100 days of post-infusion follow up. Additional data points included progression free survival, overall survival, HCT-CI score, prior lines of treatment, ECOG scores (before and after treatment), and incidence of cytokine release syndrome and neurological toxicity. Responses to treatment were determined based on radiographic tumor evaluation by metabolic response with PET-CT. Patients were monitored with daily RN triage visits and in the absence of complications, were seen twice weekly by an advanced practice provider (APP) or MD. Patients did not use remote monitoring devices and both patients and caregivers were encouraged to self-report any symptoms of CRS or NT to a 24 hour on-call APP/MD. Results: Of the 20 patients in the cohort, 2, 3, 14, and 1 patients received tisagenlecleucel, axicabtagene ciloleucel, lisocabtagene maraleucel, and brexucabtagene autoleucel, respectively. The median age was 69.5 (range 46-81). 60% of patients were male. 65% (13/20) of patients were treated with prophylactic dexamethasone. 55% of patients had an IPI score of 3-4. 25% (5/20) of patents had an ECOG score of 2 or greater. Median HCT-CI score was 1 and ≥2 in 9/20 (45%) patients (range 0-5). Overall, 10/20 (50%) patients were admitted in the first month of therapy (all initially for management of CRS), and no patients were admitted within the first 72 hours after infusion. The incidence of CRS was 11/20 (55%) with grade 3+ CRS of 5%. The incidence of NT was 9/20 (45%), with grade 3+ NT of 20%. Progression free survival in the cohort was 65% at 6 months and 60% at 1 year, and overall survival was 85% and 75%, respectively. 2 (10%) patients died prior to day 30 of treatment, one due to MRSA bacteremia (which developed >1 week into hospital admission), one due to progressive disease. No patients were admitted directly to the ICU. Conclusions: In a single-center cohort of patients who received CAR T-cell therapy and were subsequently monitored in the outpatient setting without remote monitoring devices, only half of the patients were admitted in the 1st month of therapy and no patients were admitted within the first 72 hours after infusion. 2 patients died in the first month of treatment for reasons unlikely to be related to outpatient monitoring. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Radiation estimation and crop growth trajectory reconstruction by novel algorithms improve MOD16 evapotranspiration predictability for global multi-site paddy rice ecosystems

Reliable estimations in evapotranspiration (ET) of paddy rice ecosystems by satellite products are critical because of their important roles in regional hydrological processes and climate change. However, the NASA MODIS ET products (MOD16A2) and its derivatives do not have good correlations with all global paddy rice ET observations. In this research, MOD16 model sensitivity analyses and parameter optimization strategies were conducted in order to solve the problem. Results suggested that underestimation of daily net radiation (Rn) in overcast conditions and less satisfactory reconstruction of field-scale leaf area index (LAI) growth trajectory from the start date of field flooding and transplanting (FFTD) to the end of growing seasons by MODIS coarse vegetation index were identified as two major causes. A Light and Temperature-Driven Growth model and a Phenology-based LAI temporal Smoothing method fusion algorithm (LTDG_PhenoS) and an improved Rn estimation method were introducted and evaluated in paddy rice fields in South Korea, Japan, China, Philippines, India, Spain, Italy, and the USA from 2002 to 2019. The LTDG_PhenoS algorithm considers Landsat and MODIS EVI observations and meteorological data as input variables and 30-m LAI daily time series as outcomes. Introducing the global cloudy index algorithm resulted in improved estimations of daily Rn under all-sky conditions, with a significant decrease of root mean square error (RMSE) from 1.87 to 1.11 MJ m−2 day−1. The LTDG_PhenoS algorithm well reconstructed crop LAI growth dynamics from the FFTD to the end of rice growing seasons, with a substantial decline of RMSE from 1.49 to 0.27 m2/m−2. The FFTD estimations by the LTDG_PhenoS algorithm had an R2 of 0.97 and a small RMSE of less than 12-days. Daily ET rates estimated by novel algorithms had a substantial decline in RMSE from 2.88 to 0.90 mm day−1.

A global long-term ocean surface daily/0.05° net radiation product from 1983–2020

The all-wave net radiation (Rn) on the ocean surface characterizes the available radiative energy balance and is important to understand the Earth’s climate system. Considering the shortcomings of available ocean surface Rn datasets (e.g., coarse spatial resolutions, discrepancy in accuracy, inconsistency, and short duration), a new long-term global daily Rn product at a spatial resolution of 0.05° from 1983 to 2020, as part of the Global High Resolution Ocean Surface Energy (GHOSE) products suite, was generated in this study by fusing several existing datasets including satellite and reanalysis products based on the comprehensive in situ measurements from 68 globally distributed moored buoy sites. Evaluation against in-situ measurements shows the root mean square difference, mean bias error and correlation coefficient squared of 23.56 Wm−2, 0.88 Wm−2 and 0.878. The global average ocean surface Rn over 1983–2020 is estimated to be 119.71 ± 2.78 Wm−2 with a significant increasing rate of 0.16 Wm−2 per year. GHOSE Rn product can be valuable for oceanic and climatic studies.

Estimation of the All-Wave All-Sky Land Surface Daily Net Radiation at Mid-Low Latitudes from MODIS Data Based on ERA5 Constraints

The surface all-wave net radiation (Rn) plays an important role in the energy and water cycles, and most studies of Rn estimations have been conducted using satellite data. As one of the most commonly used satellite data sets, Moderate Resolution Imaging Spectroradiometer (MODIS) data have not been widely used for radiation calculations at mid-low latitudes because of its very low revisit frequency. To improve the daily Rn estimation at mid-low latitudes with MODIS data, four models, including three models built with random forest (RF) and different temporal expansion models and one model built with the look-up-table (LUT) method, are used based on comprehensive in situ radiation measurements collected from 340 globally distributed sites, MODIS top-of-atmosphere (TOA) data, and the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data from 2000 to 2017. After validation against the in situ measurements, it was found that the RF model based on the constraint of the daily Rn from ERA5 (an RF-based model with ERA5) performed the best among the four proposed models, with an overall validated root-mean-square error (RMSE) of 21.83 Wm−2, R2 of 0.89, and a bias of 0.2 Wm−2. It also had the best accuracy compared to four existing products (Global LAnd Surface Satellite Data (GLASS), Clouds and the Earth’s Radiant Energy System Edition 4A (CERES4A), ERA5, and FLUXCOM_RS) across various land cover types and different elevation zones. Further analyses illustrated the effectiveness of the model by introducing the daily Rn from ERA5 into a “black box” RF-based model for Rn estimation at the daily scale, which is used as a physical constraint when the available satellite observations are too limited to provide sufficient information (i.e., when the overpass time is less than twice per day) or the sky is overcast. Overall, the newly-proposed RF-based model with ERA5 in this study shows satisfactory performance and has strong potential to be used for long-term accurate daily Rn global mapping at finer spatial resolutions (e.g., 1 km) at mid-low latitudes.

How does film mulching modify available energy, evapotranspiration, and crop coefficient during the seed–maize growing season in northwest China?

Plastic film mulching has been widely applied in farmland around the world, which could complicate the mass and energy exchange between soil surface and atmosphere. Although previous studies suggested that film mulching reduced available energy (Rn − G) and crop evapotranspiration (ET) in some crop growth stages, it was unclear how film mulching would influence energy partition, crop ET and crop coefficient (Kc) during the entire growing season. A two-year field experiment in a mulched (FM) and a non-mulched (NM) seed–maize field under drip irrigation was conducted in Shiyang River Basin of northwest China to quantify the effects of FM on energy balance components, ET and Kc, by measuring the net radiation (Rn), the soil heat flux (G), the evaporation (E) on ground surface, the transpiration (T), and the soil water content along the soil profile. In particular, ET was obtained by a water balance method and a three-temperature (3T) model combined with remote sensing. Results showed that FM significantly decreased daily Rn and daily G before jointing stage (i.e., the initial stage), which caused 19.0% reduction of the daily Rn − G. The effects of FM on Rn, G and Rn − G diminished after jointing stage (i.e., the crop development, middle and late stages). The reduction of Rn − G under FM treatment during the initial stage was accompanied with the decrease of ET during the initial stage, consequently 6.2% less total ET in the entire seed–maize growing season was recorded compared with NM treatment. FM had a significant effect on the partition of ET components, and E and T were 0.52 and 1.10 times of those under NM treatment, respectively. FM significantly reduced Kc during the initial stage (Kc-ini) by 55.3%, while in the middle (Kc-mid) and late (Kc-end) stages, it decreased by 5.7% and 9.7%, respectively, compared with NM treatment. The Kc varied with the leaf area index (LAI) following the parabolic relationship for both treatments. A structural equation model (SEM) showed that among the soil and crop factors, FM and soil temperature (ST) were the main factors affecting Rn − G, ET and Kc during the initial stage; while during the crop development, middle and late stages, ST and LAI were the main factors affecting Rn − G and ET, and Kc was mainly affected by LAI. SEM further confirmed that the FM impact was more significant during the initial stage than during the crop development, middle and late stages.

Estimated net radiation in an Amazon–Cerrado transition forest by Landsat 5 TM

The Amazon–Cerrado transition forest is an extensive region with unique characteristics of radiation exchanges. The measurements of the net radiation (Rn) in this ecosystem are limited to the local scale, and their spatial distribution can be carried out by remote sensing techniques, of which accuracy needs to be evaluated. Thus, the objective of this study was to analyze the accuracy of the model of surface Rn derived from measured solar radiation and estimates of normalized difference vegetation index (NDVI), surface albedo (α), and land surface temperature (LST) estimated by images of Landsat 5 TM in an Amazon–Cerrado transition forest. The Rn, NDVI, α, and LST were estimated by Landsat 5 TM images and related to micrometeorological measurements in a tower of the study area. There was seasonality of micrometeorological variables with higher values of incident solar radiation, air temperature, and vapor pressure deficit during the dry season. However, there was no seasonality of Rn. NDVI decreased and α increased during the dry season, while LST was nearly constant. The Rn had negative correlation with α and positive with NDVI. Both instantaneous and daily Rn estimated with Landsat 5 TM images showed high correlation and low error values when compared with Rn measured in the study area.

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia.

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001–December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.

Determination of instantaneous and daily net radiation from TM – Landsat 5 data in a subtropical watershed

Remote sensing makes it possible to identify the changes that occur on the surface of the Earth as a result of natural and/or man-made phenomena. Such changes impact on the net radiation at surface which in turn controls the Earth's climate. The present study aims to determine the impact of land use changes on net radiation at surface in a tropical watershed in Brazil, based on satellite images. The instantaneous net radiation (Rn,ins) (at the time of the satellite overpass) and the daily net radiation (Rn,24h) were both estimated by TM – Landsat 5 images and complementary weather data. The net radiation (Rn) estimated from remote sensing data was compared to the measurements taken from two micrometeorological towers located in the study area. Most Rn,ins values were found to be between 457.4Wm−2 and 760.0Wm−2 during the months with more intense solar radiation (February, March, and November), especially in the areas with more vegetation cover (sugarcane and eucalyptus plantations and areas with woody savanna vegetation, locally called Cerradão). The months with the highest thermal and radiative contrast (June and November) were selected to show the spatial distribution of the daily (Instantaneous) Rn, which ranged from 28.0 (420)Wm−2 to 98.0 (520)Wm−2 in June and from 83.0 (450)Wm−2 to 264.0 (800)Wm−2 in November 9. The model used to calculate Rn,24h provided values close to those taken at surface, even on days with higher cloud cover after the satellite overpass. The Mean Absolute Error (MAE), Mean Relative Error (MRE), and Root Mean Square Error (RMSE) associated with the Rn,24h computations in the sugar cane plantation were 8.3Wm−2, 8.4%, and 10.4Wm−2, respectively, confirming the applicability and accuracy of the results. The Rn patterns registered on the woody savanna throughout the year differ very much from those found in cropped areas, particularly in sugar cane plots. This may cause an impact on the watershed climate.

Determinação do saldo radiativo na Ilha do Bananal, TO, com imagens orbitais

O sensoriamento remoto possibilita identificar alterações que ocorrem na superfície terrestre, resultantes de fenômenos naturais e/ou antrópicos, muitas das quais podem ser diagnosticadas a partir do monitoramento das trocas radiativas à superfície. Neste sentido, o presente estudo objetiva determinar o saldo da radiação à superfície apenas com o emprego de imagens TM-Landsat 5, na Ilha do Bananal, TO. Foram selecionadas imagens de 2005 sem presença de nuvens e gerados mapas do saldo de radiação instantâneo e diário utilizando-se o algoritmo SEBAL. Os dados obtidos foram validados com medições realizadas no interior da área estudada. O saldo de radiação instantâneo médio situou-se entre 488 e 535 W m-2; enquanto a variação espacial do saldo de radiação diário - Rn24h médio, foi de 132,7 a 145,3 W m-2. As áreas antropizadas apresentaram redução no saldo radiativo. O Erro Médio Relativo, Médio Absoluto e a Raiz do Erro Quadrático Médio, associados ao Rn24h apresentaram valores de 1,95%, 2,78 W m-2 e 3,36 W m-2; respectivamente, evidenciando a importância da técnica empregada.

Relationship between net radiation and broadband solar radiation in the Tibetan Plateau

The characteristics of net radiation (R n) (0.3–10 µm) in Lhasa and Haibei in the Tibetan Plateau were analyzed based on long-term in-situ measurements of surface radiation data. The monthly average of daily R n reached a minimum during the winter period followed by an increase until May and then a decline until January. This variation is consistent with solar activity. The annual mean daily total R n values were 0.92 MJ m−2 d−1 and 0.66 MJ m−2 d−1 in Lhasa and Haibei, respectively. A relationship between R n and broadband solar radiation (R s) was demonstrated by a good linear correlation at the two sites. R n can be an accurate estimate from R s. The estimated R n values were similar to the observed values, and the relative deviations between the estimates and measurements of R n were 2.8% and 3.8% in Lhasa and Haibei, respectively. The application of the R n estimating model to other locations showed that it could provide acceptable estimated R n values from the R s data. Furthermore, we analyzed the influence of clouds on R n by different clear index (K s), defined as the ratio of R s to the extraterrestrial solar irradiance on a horizontal surface. The results indicate that more accurate results are associated with increased cloudy conditions. The influence of the albedo was also considered, but its inclusion in the model resulted in only a slight improvement. Because surface albedo is not usually measured, an expression based solely on global solar radiation could be of more extensive use.

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.

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.

Predicting Daily Net Radiation Using Minimum Climatological Data

Net radiation (\IR\dn\N) is a key variable for computing reference evapotranspiration and is a driving force in many other physical and biological processes. The procedures outlined in the Food and Agriculture Organization Irrigation and Drainage Paper No. 56 [FAO56 (reported by Allen et al. in 1998)] for predicting daily \IR\dn\N have been widely used. However, when the paucity of detailed climatological data in the United States and around the world is considered, it appears that there is a need for methods that can predict daily \IR\dn\N with fewer input and computation. The objective of this study was to develop two alternative equations to reduce the input and computation intensity of the FAO56-\IR\dn\N procedures to predict daily Rn and evaluate the performance of these equations in the humid regions of the southeast and two arid regions in the United States. Two equations were developed. The first equation [measured-Rs-based (\IR\Ds-M\N)] requires measured maximum and minimum air temperatures (\IT\dmax\N and \IT\Dmin\N), measured solar radiation (\IR\ds\N), and inverse relative distance from Earth to sun (\Id\dr\N). The second equation [predicted-\IR\ds\N-based (\IR\Ds-P\N)] requires \IT\Dmax\N, \IT\Dmin\N, mean relative humidity (RH\Dmean\N), and predicted \IR\ds\N. The performance of both equations was evaluated in different locations including humid and arid, and coastal and inland regions (Gainesville, Fla.; Miami, Fla.; Tampa, Fla.; Tifton, Ga.; Watkinsville, Ga.; Mobile, Ala.; Logan, Utah; and Bushland, Tex.) in the United States. The daily Rn values predicted by the \IR\Ds-M\N equation were in close agreement with those obtained from the FAO56-\IR\dn\N in all locations and for all years evaluated. In general, the standard error of daily Rn predictions (SEP) were relatively small, ranging from 0.35 to 0.73 MJ m\U-2\N d\U-1\N with coastal regions having lower SEP values. The coefficients of determination were high, ranging from 0.96 for Gainesville to 0.99 for Miami and Tampa. Similar results, with approximately 30% lower SEP values, were obtained when daily predictions were averaged over a three-day period. Comparisons of \IR\Ds-M\N equation and FAO56-Rn predictions with the measured \IR\dn\N values showed that the \IR\Ds-M\N equations’ predictions were as good or better than the FAO56-\IR\dn\N in most cases. The performance of the Rs-P equation was quite good when compared with the measured \IR\dn\N in Gainesville, Watkinsville, Logan, and Bushland locations and provided similar or better daily \IR\dn\N predictions than the FAO56-\IR\dn\N procedures. The \IR\Ds-P\N equation was able to explain at least 79% of the variability in \IR\dn\N predictions using only Tmax, Tmin, and RH data for all locations. It was concluded that both proposed equations are simple, reliable, and practical to predict daily \IR\dn\N. The significant advantage of the Rs-P equation is that it can be used to predict daily \IR\dn\N with a reasonable precision when measured \IR\ds\N is not available. This is a significant improvement and contribution for engineers, agronomists, climatologists, and others when working with National Weather Service climatological datasets that only record \IT\Dmax\N and \IT\Dmin\N on a regular basis.

Positional variation in the soil energy balance beneath a row-crop canopy

When crops are grown in a row configuration, differential shading of the soil, coupled with other soil-canopy micrometeorological interactions, may result in large positional variations in the soil energy balance. Experiments were conducted near Manhattan, KS, to examine positional variations in the soil energy balance beneath a soybean ( Glycine max) canopy during periods of partial cover. Continuous measurements of net radiation ( Rn) and soil heat flux ( G) were obtained at four equally spaced positions between one pair of plant rows. Net radiation was calculated from radiation, temperature, and emissivity measurements of the soil, canopy, and sky. Soil heat flux was determined at each location from detailed heat flux and subsurface temperature measurements. When the soil surface was dry, sensible heat flux ( H) was estimated as a residual of the heat balance by assuming that latent heat flux was negligible. Measurements of H and soil-air temperature gradients were used to estimate aerodynamic transport coefficients at each position. Results indicate that the magnitude and pattern of the soil heat balances are strongly dependent on location beneath the canopy. The patterns of G, Rn, and surface temperature at each measurement position were associated with the patterns of shortwave soil irradiance, which were functions of sun-canopy geometry. Soil irradiance between the plant rows sometimes exceeded global irradiance because of reflected radiation from the canopy. Daily Rn between the rows was five to ten times greater than that measured directly beneath the canopy, depending on environmental conditions. Large positional variations in G were also documented. Temperature differences between the sunlit and shaded portions of the surface exceeded 25°C when the surface was dry. Air temperatures, measured at 5 cm above each position, were also dependent on position with respect to the plant rows. When the surface was dry, the majority of sensible heat flux occurred from the soil directly between the plant rows, exceeding 400 W m −2 under certain conditions. Estimates of local aerodynamic transfer coefficients for the soil surface ranged from 1 to 50 mm s −1, but were highly variable and not correlated with above-canopy windspeeds or position beneath the canopy. Results suggest that positional variation in aerodynamic transport from the soil cannot be discerned with the methods used in this study.

Energy budget measurements over three cover types in eastern Arizona

Three years of continuous records of net radiation Rn were taken over a pine forest, a cienega, and a clear‐cut opening. Seasonal patterns of the portion of Rn going into evapotranspiration ET were determined for these cover types by energy budget measurements in the Bowen ratio equation. By using the average daily Rn over two summer periods (May to June and July to September) and the mean ratios of ET to Rn, ET for the three cover types for the growing season was estimated at 19.4 in. for the forest, 15.3 in. for the clear‐cut opening and 13.2 in. for the cienega. Weighting these amounts by the extent of the type resulted in a watershed average ET of 18.4 in. for the growing season.