Net Solar Radiation Research Articles

Relationships of ozone formation sensitivity with precursors emissions, meteorology and land use types, in Guangdong-Hong Kong-Macao Greater Bay Area, China

Due to the influences of precursors emissions, meteorology, geography and other factors, ozone formation sensitivity (OFS) is generally spatially and temporally heterogeneous. This study characterized detailed spatial and temporal variations of OFS in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2012 to 2016 based on OMI satellite data, and analyzed the relationships of OFS with precursors emissions, meteorology and land use types (LUTs). From 2012 to 2016, the OFS tended to be NOx-limited in GBA, with the value of FNR (HCHO/NO2) increasing from 2.04 to 2.22. According to the total annual emission statistics of precursors, NOx emissions decreased by 33.1% and VOCs emissions increased by 35.2% from 2012 to 2016, directly resulting in OFS tending to be NOx-limited. The Grey Relation Analysis results show that total column water (TCW), surface net solar radiation (SSR), air temperature at 2 m (T2) and surface pressure (SP) are the top four meteorological factors with the greatest influences on OFS. There are significant positive correlations between FNR and T2, SSR, TCW, and significant negative correlations between FNR and SP. In GBA, the OFS tends to be NOx-limited regime in wet season (higher T2, SSR, TCW and lower SP) and VOCs-limited regime in dry season (lower T2, SSR, TCW and higher SP). The FNR displays obvious gradient variations on different LUTs, with the highest in “Rural areas”, second in “Suburban areas” and lowest in “Urban areas”.

Long‐Term Trends of High Aerosol Pollution Events and Their Climatic Impacts in North America Using Multiple Satellite Retrievals and Modern‐Era Retrospective Analysis for Research and Applications version 2

AbstractMean magnitudes and temporal trends in aerosol optical depth (AOD) from satellite observations and an aerosol reanalysis exhibit a negative‐positive east‐northwest dipole across the contiguous United States with large magnitude negative trends over the eastern United States while small magnitude positive trends over the northwestern states. Based on analyses of Modern‐Era Retrospective Analysis for Research and Applications version 2, the AOD reduction over the eastern United States appears to be largely attributable to reductions in aerosol sulfate, while there have been marked increases in aerosol‐organic and elemental carbon over almost all of the contiguous United States and particularly the northwestern states. Long‐term trends of high aerosol pollution events (HAPEs; days with AOD over the long‐term local 90th daily AOD percentile) during 2000–2017 also indicate that over the eastern United States, both the frequency and spatial scale of summer HAPEs exhibit significant negative trends, while those of moderate aerosol events (days with AOD between the local 30th and 70th AOD percentiles) exhibit weak upward trends. Opposing trends, of smaller magnitude, are derived in the northwestern United States and southwestern Canada. Net and shortwave solar radiation show positive trends at the surface and top of atmosphere under clear‐sky conditions over the eastern United States consistent with the reduction in AOD. However, skin temperatures do not indicate significant trends during all days or HAPEs, indicating that the aerosol‐induced radiation trends are not sufficient to manifest trends in surface temperature. Precipitation during HAPEs appears to have increased during 2000–2017 over the eastern and central United States, indicating that the reduction in aerosol may already be enhancing the precipitation through aerosol‐cloud interactions.

Exploring the influence of environmental factors in partitioning evapotranspiration along an elevation gradient on Mount Gongga, eastern edge of the Qinghai-Tibet Platea, China

Evapotranspiration (ET) is a crucial part of the global hydrological cycle, and quantifying ET components is significant for understanding the global water cycle and energy balance. However, there is no consensus on the value of ET components, especially in topographic abrupt change zone, such as eastern margin of the Qinghai-Tibet Platea, where values of ET changes along the altitudinal gradients. Our aim is to explore the influencing factors in partitioning evapotranspiration and how ET components change with increasing elevations. A novel approach was proposed to estimate ET components by adding net solar radiation (Rn) instead of the vapor pressure deficit (VPD) into the underlying water use efficiency (uWUE) model based on one-year continuous measurements of flux data along the elevation gradient on Mount Gongga. Correlation analysis shows that the uWUE model’s performance can be improved significantly by considering Rn instead of VPD, with correlation coefficients increasing by 35%–64%. The ratios of transpiration (T) to ET (T/ET) were 0.47, 0.48, 0.50 and 0.35 for the deciduous broadleaf forest (BF), mixed coniferous and deciduous broadleaf forest (MF), evergreen needle forest (ENF) and shrub land (SL), respectively. Leaf area index (LAI) and air temperature (Ta) were the two main controlling factors in determining T/ET during the growing season and at an annual scale, while Rn and Ta played more important roles during the dormant season. This study highlights the importance of incorporating Rn in partitioning evapotranspiration by using the water use efficiency (WUE) method in a humid mountainous region, which can improve the estimation of T/ET on a global scale.

Impacts of Mist Spray on Rice Field Micrometeorology and Rice Yield under Heat Stress Condition

Heat stress is one of the common agrometeorological hazards in rice production in the middle and lower reaches of the Yangtze River in China. To study the mechanism of mist spray in ameliorating heat stress injury, a field experiment was conducted at Nanjing (China) with an early and a late hybrid rice varieties (Oryza sativa L.). The mist spray treatments were conducted at the flowering period, which were at August 6-10 for early rice variety and September 1-5 for late one. Four treatments at different irrigation times (T1: 08:00; T2: 12:00; T3: 14:00; CK: no mist spray; mist spray amount of 1 L·m−2) were included. The temperature and humidity at the different heights of the rice canopy and the net solar radiation above the canopy were measured. The leaf senescence, chlorophyll content, photosynthetic rate and the yields of the rice were determined. The results showed that mist spray rapidly reduced the temperature and increased the relative humidity in the canopy. The cooling effect was most significant at the top of the canopy and decreased downward from the top of canopy. The duration of the temperature decrease caused by the mist spray was 2 h. Mist spray could lead to an increase in latent heat flux (LE) and a decrease in sensible heat flux (H) in the rice field. The mist spray treatments delayed the senescence of the rice leaves, increased the activity levels of the superoxide dismutase, peroxidase, catalase, and soluble protein, reduced the malondialdehyde content, increased leaf chlorophyll content, photosynthetic rate and yield. The T2 treatment showed the most significant effect against heat stress, with the yield of the two varieties increased 13.7 and 13.6% respectively. Compared with mist spray at 08:00 or 14:00, spraying at 12:00 had the strongest resistance to heat stress in rice field.

Assessing the impact of climate changes on the potential yields of maize and paddy rice in Northeast China by 2050

Northeast China is the main crop production region in China, and future climate change will directly impact crop potential yields, so exploring crop potential yields under future climate scenarios in Northeast China is extremely critical for ensuring future food security. Here, this study projected the climate changes using 12 general circulation models (GCMs) under two moderate Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and 6.0) from 2015 to 2050. Then, based on the Global Agro-ecological Zones (GAEZ) model, we explored the effect of climate change on the potential yields of maize and paddy rice in Northeast China during 2015–2050. The annual relative humidity increased almost throughout the Northeast China under two RCPs. The annual precipitation increased more than 400 mm in some west, east, and south areas under RCP 4.5, but decreased slightly in some areas under RCP 6.0. The annual wind speed increased over 2 m/s in the west region. The annual net solar radiation changes varied significantly with latitude, but the changes of annual maximum temperature and minimum temperature were closely related to the terrain. Under RCP 4.5, the average maize potential yield increased by 34.31% under the influence of climate changes from 2015 to 2050. The average rice potential yield increased by 16.82% from 2015 to 2050. Under RCP 6.0, the average maize and rice potential yields increased by 25.65% and 6.34% respectively. The changes of maize potential yields were positively correlated with the changes of precipitation, wind speed, and net solar radiation (the correlation coefficients were > 0.2), and negatively correlated with the changes of relative humidity, minimum and maximum temperature under two RCPs. The changes of rice potential yields were positively correlated with the changes of precipitation (correlation coefficient = 0.15) under RCP 4.5. Under RCP 6.0, it had a slight positive correlation with net solar radiation, relative humidity, and wind speed.

Environmental factors associated with the abundance of forest wiregrass (Tetrarrhena juncea), a flammable understorey grass in productive forests

When flammable plant species become dominant they can influence the flammability of the entire vegetation community. Therefore, it is important to understand the environmental factors affecting the abundance of such species. These factors can include disturbances such as fire, which can promote the dominance of flammable grasses causing a positive feedback of flammability (grass–fire cycle). We examined the potential factors influencing the abundance of a flammable grass found in the understoreys of forests in south-east Australia, the forest wiregrass (Tetrarrhena juncea R.Br.). When wiregrass is abundant, its structural characteristics can increase the risk of wildfire ignition and causes fire to burn more intensely. We measured the cover of wiregrass in 126 sites in mountain ash forests in Victoria, Australia. Generalised additive models were developed to predict cover using climatic and site factors. The best models were selected using an information theoretic approach. The statistically significant factors associated with wiregrass cover were annual precipitation, canopy cover, disturbance type, net solar radiation, precipitation seasonality and time since disturbance. Canopy cover and net solar radiation were the top contributors in explaining wiregrass cover variability. Wiregrass cover was predicted to be high in recently disturbed areas where canopy cover was sparse, light levels high and precipitation low. Our findings suggest that in areas with wiregrass, disturbances such as fire that reduce canopy cover can promote wiregrass dominance, which may, in turn, increase forest flammability.

Design of smart wetting of building materials as evaporative cooling measure for improving the urban climate during heat waves

An urban microclimate model is used to design a smart wetting protocol for multilayer street pavements in order to maximize the evaporative cooling effect as a mitigation measure for thermal discomfort during heat waves. The microclimate model covers a computational fluid dynamics (CFD) model for solving the turbulent air, heat and moisture flow in the air domain of a street canyon. The CFD model is coupled to a model for heat and moisture transport in porous urban materials, to a radiative exchange model, determining the net solar and longwave radiation on each urban surface and to a wind driven rain model able to determine the wetting flux on each surface during a rain event. We first evaluate the evaporative cooling potential for different pavement systems during normal summer conditions after a long rain event during night in order to select an optimal pavement system. Then, we design a smart wetting protocol answering the questions ‘when’, ‘how much’ and ‘how long’ a pavement should be artificially wetted for having a maximum cooling effect. We found that a daily amount of 5mm wetting over 10 minutes in the morning, preferentially between 8:00 and 10:00 am, guarantees a maximal evaporative cooling for one day and night during a heat wave.

Spatio-Temporal Assessment of Climate in Response to Solar Radiation Changes over Nigeria Using Satellite Data

Spatiotemporal assessment of climate elements in response to solar radiation changes is vital for understanding the interaction between solar energy budget and climate over Nigeria. In this work, the spatio-temporal assessment of climate changes in response to solar radiation budget was done using regression and correlation analysis on satellite remote sensing and gridded observation data. The satellite data sets include; the Top Net Solar radiation data, obtained from European Medium Range Weather Forecast Reanalysis version 5 data set (ERA5) and Extended Reconstructed Sea Surface (ERSST) data set. The gridded observation climate data sets were obtained from Climate Research Unit (CRU) of University of East Anglia. The 250 x 250 m Digital Elevation data sets were obtained from Shuttle Radar Topographic Mission (SRTM). Results showed the Top net solar radiation (J/m²), precipitation and temperature indicated trends (R-square values) of 8643.9 (0.08), -0.287 (0.06) and 0.019 (0.26) per year respectively. The correlation between Top net radiation and temperature shows, 7, 2 and 91% pixels to be negatively, zero and positively correlated while the correlation between Top net radiation and precipitation shows, 71, 8 and 21% pixels respectively to be negatively, zero and positively correlated. Results shows that there was no direct relationship between Elnino Southern Oscillation (ENSO) but arguably, temperature showed indirect relationship with Top net solar radiation. Also, residual analysis was applied to delineate areas that have no direct relationship between radiation and climate parameters.

Spatial-temporal changes in NPP and its relationship with climate factors based on sensitivity analysis in the Shiyang River Basin

As a typical inland river basin in China, the Shiyang River Basin is characterized by its special mountain-basin structure. The ecological health in the basin is related to the sustainable development of the economy and society. At present, there are few studies on net primary productivity (NPP) in the Shiyang River Basin, and the existing analysis of the relationship between NPP and climatic factors is lacking. The upper mountainous area and the middle and lower oasis areas in the Shiyang River Basin were selected as the study area. The NPP of the study area was estimated using the Thornthwaite Memorial Model. The spatial and temporal characteristics of NPP were analyzed by Sen’s slope method. Based on the sensitivity analysis, the correlation of the main climate factors to NPP was estimated. According to the aforementioned work, the variation trend of the future NPP was predicted. The results showed that NPP in the study area increased from 1981 to 2015 with the increase in temperature and precipitation. The spatial heterogeneity of the change trend of NPP was not significant, but the spatial heterogeneity of the rangeability was strong. The NPP was highly sensitive to precipitation, relative humidity and net solar radiation. By integrating the changes in climatic elements, the temperature, precipitation and relative humidity contributed the main parts of the change in NPP. The NPP is predicted to increase by 4.9–8.1% by 2050 according to the amplitude of climate change over the past 35 yrs.

Uncertainty in Net Surface Heat Flux due to Differences in Commonly Used Albedo Products

AbstractThe ocean surface albedo is responsible for the distribution of solar (shortwave) radiant energy between the atmosphere and ocean and therefore is a key parameter in Earth’s surface energy budget. In situ ocean observations typically do not measure upward reflected solar radiation, which is necessary to compute net solar radiation into the ocean. Instead, the upward component is computed from the measured downward component using an albedo estimate. At two NOAA Ocean Climate Station buoy sites in the North Pacific, the International Satellite Cloud Climatology Project (ISCCP) monthly climatological albedo has been used, while for the NOAA Global Tropical Buoy Array a constant albedo is used. This constant albedo is also used in the Coupled Ocean–Atmosphere Response Experiment (COARE) bulk flux algorithm. This study considers the impacts of using the more recently available NASA Cloud and the Earth’s Radiant Energy System (CERES) albedo product for these ocean surface heat flux products. Differences between albedo estimates in global satellite products like these imply uncertainty in the net surface solar radiation heat flux estimates that locally exceed the target uncertainty of 1.0 W m−2 for the global mean, set by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO). Albedo has large spatiotemporal variability on hourly, monthly, and interannual time scales. Biases in high-resolution SWnet (the difference between surface downwelling and upwelling shortwave radiation) can arise if the albedo diurnal cycle is unresolved. As a result, for periods when satellite albedo data are not available it is recommended that an hourly climatology be used when computing high-resolution net surface shortwave radiation.

Variability in meteorological parameters and their impact on evapotranspiration in a humid zone of Pakistan

AbstractPakistan lies in the region over the globe where effects of climate change due to global warming are more likely to occur. These climatic changes are reflected in significant gradual fluctuations in measurements of meteorological parameters. Agricultural activity in any region depends upon the role of components of hydrological cycle over the region. An important component of hydrological cycle is evapotranspiration (ET), which is closely linked with changes in meteorological parameters. This study investigates impact of probable variation in four important meteorological parameters i.e. temperature (T), net solar radiation (Rn), vapour pressure (ea) and wind speed (U) on ET to estimate possible changes in reference ET in a humid zone of Pakistan by employing observed climatological records for 30 years (1981–2010). Analyses are done on the basis of possible ±20% changes in climatological mean values of the observed meteorological parameters. The analyses indicate that 20% rise in observed values of T, Rn and U increases value of total (annual) ET demand in the zone up to 11.93%, 16.37% and 2.83% respectively, and 20% rise in the observed values of vapour pressure decreases value of total (annual) ET till −2.5%. Moreover, the analyses also showed that ET is more sensitive to T and Rn in monsoon, vapour pressure in winter, and to wind speed in summer. Hence, this study might be useful in planning and formulating future strategies to meet expected future water requirements due to global warming in humid zone of Pakistan and its surrounding region.

Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands

Wetlands are extraordinary ecosystems and important climate regulators that also contribute to reduce natural disaster risk. Unfortunately, wetlands are declining much faster than forests. The safeguarding of the wetlands also needs knowledge of the dynamics that control the water balance of these environments. Therefore, an accurate estimation of evapotranspiration in wetlands is an essential task.When adequate experimental data are available, some algorithms deriving from Artificial Intelligence research represent a promising alternative to the most common estimation techniques.In this study, starting from daily measurements of climatic variables such as net solar radiation, depth to water, wind speed, mean relative humidity, maximum temperature, minimum temperature, and mean temperature, using the Random Forest, Additive Regression of Decision Stump, Multilayer Perceptron and k-Nearest Neighbors algorithms, 24 estimation models, different in input variables, have been developed and compared. The data have been provided by USGS. They have been obtained from a measuring site in wetlands of Indian River County, Florida using the eddy-covariance technique.The accuracy of these models based on AI algorithms remains good even if the number of input variables is reduced from 7 to 3. Net solar radiation, mean temperature and mean relative humidity or wind speed measurements allow obtaining a sufficiently accurate estimation model. Random Forest and k-Nearest Neighbors provide slightly better performance than Additive Regression of Decision Stump and Multilayer Perceptron. The analyzed models show in most cases the lowest accuracy in the range 2–4 mm/day, while the highest accuracy is obtained in the ranges 0–2 mm/day and 6–8 mm/day, with the exception of the models based on the Additive Regression, which show similar levels of accuracy in the different considered sub-intervals.

Evaluation of the Solar Radiation in a Seismic Zone

The solar radiation affects the life on Earth and it is important in meteorology, climatology, solar energy, agriculture, hydrology and seismology, too. The Sun warms the earth which reflects a part of the energy. An effect of tectonic stress increases the ground temperature that is radiated into the atmosphere. We study the possibility to use the variation of reflected energy in correlation with seismicity, radon concentration, CO2 emission, ULF radio waves, telluric currents, air ionization, temperature in borehole and acoustic noise. In this case, the solar radiation is a precursor factor. This research is important to study the effects of climate change. Analyzing the solar energy budget we find information about the atmosphere: aerosols, ozone, sulfur dioxide, carbon dioxide, nitrogen dioxide and pollution. Our multidisciplinary network (AeroSolSys) monitors Vrancea, a Romanian area characterized by deep earthquakes. For this study, we use two stations, Plostina and Vrancioaia, where we have a net radiometer and a pyranometer. The first sensor includes two pyranometers (up and low) and two pyrgeometers (up and low), a Pt-100 and a Thermistor. The spectral range covers both the Solar Radiation, 0.3 to 3 micrometers, and the Far Infrared Radiation, 4.5 to 42 micrometers. In this case, we measure and determine the temperature of sensor necessary for pyrgeometers, pyranometer short-wave radiation up and low EyU - EyL, pyrgeometers long-wave radiation up and low EgU - EgL, Albedo, net solar radiation NSR, net far infrared radiation NFIR, net total radiation NTR, sky temperature SkyT and, land surface temperature LST. In Vrancioaia, we have a pyranometer with range 0.3 – 2.8 micrometers and maxim 1600 W/m2. Two video cameras monitor the sky and the measurements are correlated with meteorological equipment installed in each location. We use information about solar activity from NOAA satellites and Copernicus CAMS. The humidity of ground is important in energy budget and it is estimated from meteorological data and telluric currents. The interval of analysis is 3 hours, between 2016.01.01 – 2018.05.31. It is difficult to notice the effect of tectonic stress since the daily and seasonal variations of solar radiation are high but global warming is obvious. Solar radiation monitoring helps us to understand other phenomena like radon emission or air ionization.

Characteristics of heat fluxes in subsurface shallow depth soil layer as a renewable thermal source for ground coupled heat pumps

The soil heat fluxes (G) in subsurface shallow depth soil layer (SSDSL) are characterized by dynamics of diurnal, monthly, seasonal and annual behaviour. The soil heat very often is underestimated as a part of heat balance of the pedosphere, because its values are relatively small in comparison with sensible turbulent heat and latent heat fluxes. These fluxes concern mainly with net solar radiation and natural thermal properties of soils and plant cover. The soil heat in SSDSL is jointed with commonly widely available and relatively easy acquiring energy resources. This paper aims to present the thermal regime of heat fluxes in SSDSL and its climatic conditions in central Europe, taking into consideration the agriculture periphery characteristics of Wroclaw (Poland). The measurement results gathered across ten years (August 2007–July 2017) and the conducted thermal analysis aim to evaluate the utilisation of SSDSL as a heat source for ground coupled heat pumps (GCHP). The measurements of G (positive soil heat fluxes G > 0 and negative G < 0) were done between the active surface (for both of bare soil and grassy area) and their lower layers on an 8 cm depth. The soil temperature data between 5 cm and 10 cm below the ground and the temperatures of the active soil surfaces are also represented and discussed. The ten-year annual average sums for G > 0 reached 331,8 MJ∙m−2 (92,2 kWh∙m−2) for the bare soil and 179,0 MJ∙m−2 (49,7 kWh∙m−2) for the grassy area. The similar ten-year values for G < 0 reached 330.3 MJ∙m−2 (91.8 kWh/m2) for the bare soil and 170.6 MJ/m2 (47.4 kWh/m2) for the grassy area. The heat fluxes of the 5 cm depth soil were higher than for the 8 cm and 10 cm soil. The obtained results fortify the importance and engineering role of the soil heat fluxes in SSDSL. The current results of the thermal analysis and detailed clarifications of the soil positive and negative heat fluxes confirms the energy potential of SSDSL as an environmental friendly, efficient heat source for operation of GCHP through the year.

Intercomparison of remote-sensing based evapotranspiration algorithms over amazonian forests

Evapotranspiration (ET) is considered a key variable in the understanding of the Amazonian tropical forests and their response to climate change. Remote-Sensing (RS) based evapotranspiration models are presented as a feasible means in order to provide accurate spatially-distributed ET estimates over this region. In this work, the performance of four commonly used ET RS models was evaluated over Amazonia using Moderate Resolution Imaging Spectroradiometer (MODIS) data. RS models included i) Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), ii) Penman-Monteith MODIS operative parametrization (PM-Mu), iii) Surface Energy Balance System (SEBS), and iv) Satellite Application Facility on Land Surface Analysis (LSASAF). These models were forced using two ancillary meteorological data sources: i) in-situ data extracted from Large-Scale Biosphere-Atmosphere Experiment (LBA) stations (scenario I), and ii) three reanalysis datasets (scenario II), including Modern-Era Retrospective analysis for Research and Application (MERRA-2), European Centre for Medium-range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-Interim), and Global Land Assimilation System (GLDAS-2). Performance of algorithms under the two scenarios was validated using in-situ eddy-covariance measurements. For scenario I, PT-JPL provided the best agreement with in-situ ET observations (RMSE = 0.55 mm/day, R = 0.88). Neglecting water canopy evaporation resulted in an underestimation of ET measurements for LSASAF. SEBS performance was similar to that of PT-JPL, nevertheless SEBS estimates were limited by the continuous cloud cover of the region. A physically-based ET gap-filling method was used in order to alleviate this issue. PM-Mu tended to overestimate in-situ ET observations. For scenario II, quality assessment of reanalysis input data demonstrated that MERRA-2, ERA-Interim and GLDAS-2 contain biases that impact model performance. In particular, biases in radiation inputs were found the main responsible of the observed biases in ET estimates. For the region, MERRA-2 tends to overestimate daily net radiation and incoming solar radiation. ERA-Interim tends to underestimate both variables, and GLDAS tends to overestimate daily radiation while underestimating incoming solar radiation. Discrepancies amongst these reanalysis inputs generally explain the observed discrepancies in model spatial and temporal patterns.

Evapotranspiration evaluation models based on machine learning algorithms—A comparative study

The constant need to increase agricultural production, together with the more and more frequent drought events in many areas of the world, requires a more careful assessment of irrigation needs and, therefore, a more accurate estimation of actual evapotranspiration. In recent years, several water management issues have been addressed by means of models derived from Artificial Intelligence research. When using machine learning based models, the main challenging aspects are represented by the choice of the best possible algorithm, the choice of adequately representative variables and the availability of appropriate data sets.Machine learning algorithms may be a powerful tool for the prediction of actual evapotranspiration, when a time series of few years is available. Starting from the measurements of a sufficient number of climatic parameters it is possible to obtain forecasting models characterized by very high accuracy and precision. Three different evapotranspiration models have been compared in this study. The models differ in the input variables. Four variants of each model were applied, varying the machine learning algorithm: M5P Regression Tree, Bagging, Random Forest and Support Vector Regression. The data refers to an experimental site in Central Florida, characterized by humid subtropical climate.The best outcomes have been provided by Model 1, whose input variables were net solar radiation, sensible-heat flux, moisture content of the soil, wind speed, mean relative humidity and mean temperature. Model 3, built from data only of mean temperature, mean relative humidity and net solar radiation, has provided still satisfactory results. Model 2, which adds the wind speed to the input variables of Model 3, has provided results that are absolutely comparable to those of Model 3 itself.

Assessment of actual evapotranspiration variability over global land derived from seven reanalysis datasets

With the motivation to identify whether global warming would lead to an increase in the rate of actual evapotranspiration (AE) over land, an assessment is made of AE variability from an ensemble of seven reanalyses (NCEP‐NCAR, NCEP‐DOE, MERRA, MERRA V2, ERA‐Interim, JRA‐55, and CFSR). By subdividing global land into nine climatic regions using the aridity index (AI), we examine the variability and long‐term trend of regional AE and various related factors. Results indicate that AE shows a significant increasing trend during 1979–2015 in the humid, wet, and arctic regions. Overall, three main parameters, namely, precipitation, surface net solar radiation, and wet‐day frequency, strongly influence the temporal variations of AE over most regions. The possible causes of trends in AE are examined in view of the linear trends of the related meteorological variables. Accordingly, increasing potential evapotranspiration and increasing vapour pressure are the main contributors to increasing AE in humid regions. Increasing temperature is the main contributor to increasing AE in wet regions. Both increasing energy supply and water supply on AE largely explain the significant increasing trend of AE in arctic regions. In view of data availability, a correlation analysis was conducted between AE in individual reanalyses and the results from the Mezentsev–Choudhury–Yang equation and the ensemble of the seven reanalyses. On average, NCEP‐NCAR is the worst among the seven reanalysis datasets in revealing temporal characteristics of AE.

Response of sap flow rate of apple trees to environmental factors in Loess Platea of Western Shanxi Province, China.

To clarify the effects of environmental factors on transpiration process of apple trees in rain-fed ecosystem, the dynamics of sap flow in apple trees from Loess Plateau area in western Shanxi Province of China were monitored using the thermal diffusion technique. Meanwhile, environmental factors including meteorological elements and soil moisture content were measured. The results showed that both net solar radiation (Rn) and atmospheric vapor deficit (VPD) were closely correlated with sap flow rate (Js), as the main ones among all the measured environmental factors. At both hourly and daily scales, the cumulative variances of the three principal components of the environmental factor were above 86%. The first principal component contained VPD and Rn, and explained the variance of over 52% (at hour scale) and 63% (at daily scale), which could be classified as the evaporation demand index (EDI) and as the key comprehensive environmental varia-bles affecting tree sap flow. The second principal component mainly included soil moisture content (SWC) and other factors, which were referred to the soil water and heat supply index. The third principal component mainly included wind speed and other factors, and could be classified into the hydrothermal dissipation index. At the scale of hourly or daily, the response of Js to EDI showed a significant exponential growth relationship. At the hourly time scale, Js of apple trees could be accurately modelled based on the first principal component EDI (R2=0.72). At the daily scale, Js of apple trees could be better modelled based on potential evapotranspiration (ET0) (R2=0.88). Our results were of great significance for clarifying the responses of water transport in apple tree to environmental factors, estimating water consumption of apple tree based on meteorological factors, as well as directing orchard water management.

Estimation of evapotranspiration of a salt marsh in southern South America with coupled Penman-Monteith and surface resistance models

One of the most recommended method to estimate evapotranspiration (ET) of vegetated surfaces with different soil moisture conditions is the Penman-Monteith equation (PM). Canopy and soil conditions are parameterized through the surface resistance or conductance, while the contribution of the canopy to ET is measured by the canopy resistance. The study of natural ecosystems has gained interest because of its importance in water and carbon cycles. However, unlike monocultures, natural environments are composed of a mixture of species that make the estimation of ET with PM troublesome. This feature makes them suitable for ET estimation considering the contribution of both, the canopy and the soil represented by the surface resistance (rs), or the contribution of the canopy, represented by the canopy resistance (rc). This work aims to model the surface and canopy resistances using conventional meteorological, biological and pedological variables observed at a salt marsh used for livestock production in Buenos Aires province, Argentina. Twelve models (M1 to M12) based on the net solar radiation (Rn), air temperature (Ta), air relative humidity (RH), surface wind velocity (U), dew point departure (Dp), aerodynamic resistance (ra), leaf area index (LAI) and volumetric soil water content (ϑs) were obtained using two different regression methodologies. Surface resistances during daytime were calculated inverting the PM equation with ET fluxes measured with the eddy covariance method. PM-derived rs varied between 20 and 1000 s m−1, with a median of 137 s m−1. From 1620 observations, 468 were used for model calibration while 1152 for model validation. M5 and M11 with Rn, RH, ra, LAI predictor variables were the best models with 80.8 s m−1 root mean square error, 0.51 determination coefficient, 0.69 and 0.65 index of agreement, respectively. The modelled resistances allowed the estimation of latent heat fluxes with a root mean quadratic error varying from 60.7 to 69.5 W m-2. These results show the possibility to achieve rs from a minimum set of variables easily measured in the field which in turn, allows to estimate the ET of salt marsh ecosystems with scarce meteorological information.

Impact of snow-darkening by deposition of light-absorbing aerosols on snow cover in the Himalaya-Tibetan-Plateau and influence on the Asian Summer monsoon: A possible mechanism for the Blanford Hypothesis.

The impact of snow darkening by deposition of light absorbing aerosols (LAAs) on snow cover over the Himalaya-Tibetan-Plateau (HTP) and influence on the Asian summer monsoon are investigated using the NASA Goddard Earth Observing System Model Version 5 (GEOS-5). We find that during April-May-June, deposition of LAAs on snow leads to a reduction in surface albedo, initiating a sequence of feedback processes, starting with increased net surface solar radiation, rapid snowmelt in HTP and warming of the surface and upper troposphere, followed by enhanced low-level southwesterlies and increased dust loading over the Himalayas-Indo-Gangetic Plain. The warming is amplified by increased dust aerosol heating, and subsequently amplified by latent heating from enhanced precipitation over the Himalaya foothills and northern India, via the Elevated Heat Pump (EHP) effect during June-July-August. The reduced snow cover in the HTP anchors the enhanced heating over the Tibetan Plateau and its southern slopes, in conjunction with an enhancement of the Tibetan Anticyclone, and the development of an anomalous Rossby wavetrain over East Asia, leading to weakening of the subtropical westerly jet, and northward displacement and intensification of the Mei-Yu rainbelt. Our results suggest that atmosphere-land heating induced by LAAs, particularly desert dust play a fundamental role in physical processes underpinning the snow-monsoon relationship proposed by Blanford more than a century ago.