Actual Vapor Pressure Research Articles

Reduced actual vapor pressure exerts a significant influence on maize yield through vapor pressure deficit amid climate warming.

Understanding the impact of climate warming on crop yield and its associated mechanisms is paramount for ensuring food security. Here, we conduct a thorough analysis of the impact of vapor pressure deficit (VPD) on maize yield, leveraging a rich dataset comprising temporal and spatial observations spanning 40 years across 31 maize-growing locations in Northeast and North China. Our investigation extends to the influencing meteorological factors that drive changes in VPD during the maize growing phase. Regression analysis reveals a linear negative relationship between VPD and maize yield, demonstrating diverse spatiotemporal characteristics. Spatially, maize yield exhibits higher sensitivity to VPD in Northeast China (NEC), despite the higher VPD levels in North China Plain (NCP). The opposite patterns reveal that high VPD not invariably lead to detrimental yield impacts. Temporal analysis sheds light on an upward trend in VPD, with values of 0.05 and 0.02 kPa/10yr, accompanied by significant abrupt changes around 1996 in NEC and 2006 in NCP, respectively. These temporal shifts contribute to the heightened sensitivity of maize yield in both regions. Importantly, we emphasize the need to pay closer attention to the substantial the impact of actual vapor pressure on abrupt VPD changes during the maize growing phase, particularly in the context of ongoing climate warming.

Uniformly elevated future heat stress in China driven by spatially heterogeneous water vapor changes

The wet bulb temperature (Tw) has gained considerable attention as a crucial indicator of heat-related health risks. Here we report south-to-north spatially heterogeneous trends of Tw in China over 1979-2018. We find that actual water vapor pressure (Ea) changes play a dominant role in determining the different trend of Tw in southern and northern China, which is attributed to the faster warming of high-latitude regions of East Asia as a response to climate change. This warming effect regulates large-scale atmospheric features and leads to extended impacts of the South Asia high (SAH) and the western Pacific subtropical high (WPSH) over southern China and to suppressed moisture transport. Attribution analysis using climate model simulations confirms these findings. We further find that the entire eastern China, that accommodates 94% of the country’s population, is likely to experience widespread and uniform elevated thermal stress the end of this century. Our findings highlight the necessity for development of adaptation measures in eastern China to avoid adverse impacts of heat stress, suggesting similar implications for other regions as well.

Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network

Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis.

Association between meteorological variations and the superspreading potential of SARS-CoV-2 infections

BackgroundWhile many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people. MethodsContact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures. ResultsA total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: −0.239 [95 % CI: −0.431 to −0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt. ConclusionsThis study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.

Beyond droplet deposition: Virus transport and deposition modeling in respiratory tract influenced by ambient environment and exhalation activities

Quantifying virus deposition in the respiratory tract during exposure to various infected environments is crucial for developing effective indoor environmental interventions against airborne infectious diseases. This study presents a novel approach that combines a physical model for virus-laden droplet evaporation, size-independent viral load concentrations, and computational fluid dynamics simulations inside the respiratory tracts. We quantify the impact of exhalation activities and actual vapor pressure on virus deposition fraction (DFv) and droplet deposition fraction (DFd) in the respiratory tract by tracking virus-laden droplets from exhalation to inhalation to deposition. Our findings reveal that virus deposition fraction significantly differs from the droplet deposition fraction in the respiratory tract, as the minimum mean absolute percentage error (MAPE) is 0.65. Moreover, the exhalation activities and indoor conditions play a vital role in virus deposition. Considering exhalation activity is necessary for assessing the potential risk of infection in the lung lobes. The deposition fraction and copies of the virus are nearly 2-fold and over 6000-fold higher during patient speaking compared to breathing, respectively. Actual vapor pressure is also vital for the interception of the upper respiratory tract. The DFv decreases from 66.86% to 46.96% as actual vapor pressure changes from low to high. Additionally, our study quantifies the difference in virus deposition fraction between size-dependent and size-independent viral load concentrations, showing the necessity of considering size-dependent viral load concentration. Our findings improve the predictability of infection risks and can refer to the development of effective non-pharmaceutical interventions in indoor environments in the post-pandemic.

Effect of climate change on reference evapotranspiration at the subnational scale: case study of Egypt

PurposeThis study’s fundamental objective is to assess climate change impact on reference evapotranspiration (ETo) patterns in Egypt under the latest shared socioeconomic pathways (SSPs) of climate change scenarios. Additionally, the study considered the change in the future solar radiation and actual vapor pressure and predicted them from historical data, as these factors significantly impact changes in the ETo.Design/methodology/approachThe study utilizes data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze reference ETo. Six models are used, and an ArcGIS tool is created to calculate the monthly average ETo for historical and future periods. The tool considers changes in actual vapor pressure and solar radiation, which are the primary factors influencing ETo.FindingsThe research reveals that monthly reference ETo in Egypt follows a distinct pattern, with the highest values concentrated in the southern region during summer and the lowest values in the northern part during winter. This disparity is primarily driven by mean air temperature, which is significantly higher in the southern areas. Looking ahead to the near future (2020–2040), the data shows that Aswan, in the south, continues to have the highest annual ETo, while Kafr ash Shaykh, in the north, maintains the lowest. This pattern remains consistent in the subsequent period (2040–2060). Additionally, the study identifies variations in ETo , with the most significant variability occurring in Shamal Sina under the SSP585 scenario and the least variability in Aswan under the SSP370 scenario for the 2020–2040 time frame.Originality/valueThis study’s originality lies in its focused analysis of climate change effects on ETo, incorporating crucial factors like actual vapor pressure and solar radiation. Its significance becomes evident as it projects ETo patterns into the near and distant future, providing indispensable insights for long-term planning and tailored adaptation strategies. As a result, this research serves as a valuable resource for policymakers and researchers in need of in-depth, region-specific climate change impact assessments.

AI-based intelligent virtual image meteorological services

Abstract Although modern meteorological service and prediction systems have achieved good applications in numerical models, these models are often influenced by multiple random factors and cannot adapt well to the meteorological service and prediction needs of complex climate regions. Therefore, this article aims to designing an AI intelligent meteorological service platform to simulate what happens when people use it. Based on commonly used AI technologies in meteorological research, the temporal data algorithm mentioned in this article is used for prediction. This article selected the actual daily average water vapor pressure and daily average relative humidity data of three meteorological stations over the past 10 days for analysis. The maximum and minimum values of the actual daily average water vapor pressure in the 10 days of San Jose are 30.2 and 28.1 Pa, respectively, the maximum and minimum values of the actual daily average vapor pressure over 10 days of Cupertino are 30.4 and 28.4 Pa, respectively, and the maximum and minimum values of the actual daily average vapor pressure over 10 days of Santa Clara are 30.5 and 28.4 Pa, respectively, which can prove the effectiveness of AI-based intelligent virtual imaging meteorological services.

Performance of the Copernicus European Regional Reanalysis (CERRA) dataset as proxy of ground-based agrometeorological data

The continuous advances in numerical modeling of the atmosphere, computing power and data assimilation techniques entail frequent updates of numerical weather prediction (NWP) models that show improved forecast skill. This circumstance leads to the recurrent delivery of revised reanalysis databases that provide weather estimates for several decades back in time by combining the latest NWP models with observations. Since climate studies and agriculture water management applications require the availability of accurate and reliable weather data, assessing the performance of reanalysis products contributes to informed choices of potential weather proxy of ground-based agrometeorological data. CERRA (Copernicus European Regional ReAnalysis) dataset is the latest regional reanalysis product released by the European Centre for Medium-Range Weather Forecasts (ECMWF), in August 2022. CERRA is forced by the global ERA5 reanalysis, and it provides weather data with resolution of 5.5 km for the pan-European territory from 1984. For the first time in literature, this study explores the performance of CERRA data at 38 ground-based weather stations located in Sicily, an Italian region with Mediterranean climate, during the irrigation seasons 2003–2022. The objective of the study lies in evaluating CERRA performance with respect to air temperature, actual vapor pressure, wind speed and solar radiation that are input variables for assessing reference evapotranspiration, ETO, which is a key variable for quantifying irrigation volumes needed for water resources studies. The accuracy of ETO estimates depends on those input variables through the equation provided by the Food and Agriculture Organization of the United Nations (FAO), i.e., the FAO Penman-Monteith equation. Here, it is also evaluated the performance of ETO estimates by using CERRA weather inputs in the FAO Penman-Monteith equation. The results show that the performances of CERRA weather data are excellent, especially for air temperature, and this determines that CERRA ETO estimates present high accuracy and reliability with a mean PBIAS and NRMSE equal to 5.6% and 13%, respectively, over the region. Those outcomes lead to the conclusion that CERRA dataset represents a valid alternative to ground-based agrometeorological measurements and their spatial interpolation for water resource regional studies.

Minimizing Area-Specific Resistance of Electrochemical Hydrogen Compressor under Various Operating Conditions Using Unsteady 3D Single-Channel Model.

Extensive research has been conducted over the past few decades on carbon-free hydrogen energy. Hydrogen, being an abundant energy source, requires high-pressure compression for storage and transportation due to its low volumetric density. Mechanical and electrochemical compression are two common methods used to compress hydrogen under high pressure. Mechanical compressors can potentially cause contamination due to the lubricating oil when compressing hydrogen, whereas electrochemical hydrogen compressors (EHCs) can produce high-purity, high-pressure hydrogen without any moving parts. A study was conducted using a 3D single-channel EHC model focusing on the water content and area-specific resistance of the membrane under various temperature, relative humidity, and gas diffusion layer (GDL) porosity conditions. Numerical analysis demonstrated that the higher the operating temperature, the higher the water content in the membrane. This is because the saturation vapor pressure increases with higher temperatures. When dry hydrogen is supplied to a sufficiently humidified membrane, the actual water vapor pressure decreases, leading to an increase in the membrane's area-specific resistance. Furthermore, with a low GDL porosity, the viscous resistance increases, hindering the smooth supply of humidified hydrogen to the membrane. Through a transient analysis of an EHC, favorable operating conditions for rapidly hydrating membranes were identified.

Bowen ratio and evapotranspiration dynamics of oil palm

Abstract. Asyura MF, June T, Salmayenti R. 2023. Bowen ratio and evapotranspiration dynamics of oil palm. Intl J Trop Drylands 7: 37-45. To optimize its biophysical processes, plant needs water and this requirement can be quantified as Evapotranspiration (ET). There are various methods for estimating ET from a vegetated surface, and Bowen ratio is one method commonly used for agricultural purposes. We use Bowen ratio method to estimate ET from oil palm (Elaeis guineensis Jacq.) plantation in PT. Perkebunan Nusantara VI, Batang Hari, Jambi Province, Indonesia for the period 2014-2015 using observed micrometeorology data and looked at the strength of micrometeorological components in influencing the daily and diurnal patterns, which include net radiation (Rnetto), heat fluxes (latent LE and sensible H), actual vapor pressure (ea), soil moisture (SM), wind speed (WS), air temperature (Ta), ground heat fluxes (G), and LE-H. We found that the daily pattern of Bowen ratio was strongly influenced by LE-H, LE, H, SM, shown by a correlation coefficient of -0.85, -0.75, 0.84, -0.58, respectively. The diurnal pattern of Bowen ratio was affected by ea with a correlation coefficient of -0.52, while daily ET was strongly influenced by Rnetto, LE-H, H, Bowen ratio with a correlation coefficient of 0.85, 0.95, -0.63, -0.75 respectively. WS, Ta, Rnetto, G, ea, RH and LE-H affected diurnal ET with a correlation coefficient of 0.72, 0.72, 0.96, 0.89, 0.51, -0.64 and 0.9, respectively. Within this period of 2014-2015 there were 7 months where rainfall was greater than ET (wet period December 2014 to April 2015, November 2015) and 5 months where rainfall was lower than ET (dry period May 2015 to October 2015). During this long dry period of 2015, caused by strong El Nino events, oil palms became vulnerable to drought.

КОЛИЧЕСТВЕННАЯ ОЦЕНКА ПРОСТРАНСТВЕННО-ВРЕМЕННОЙ ИЗМЕНЧИВОСТИ ЭВАПОТРАНСПИРАЦИИ И ОПРЕДЕЛЯЮЩИХ ЕЕ КЛИМАТИЧЕСКИХ ФАКТОРОВ В БАНГЛАДЕШ В 1981-2018 ГГ

Reference evapotranspiration is a vital research item for hydrologic pattern systems in drought-flood risks of a country like Bangladesh. The present research examines spatiotemporal fluctuations in reference evapotranspiration and influencing factors of these fluctuations by applying the Mann-Kendall test with the Theil-Sen’s estimator, Inverse Distance Weighted interpolation method, differential equation and detrending method depending on diurnal meteorological data sets attained from 25 weather observatories between 1981 and 2018. By current research, the FAO 56 Penman-Monteith (PM) model is executed for determining seasonal and annual reference evapotranspiration (ETpmf), and differential equation and detrending method is implemented for assessing correlation, sensitivities, and benefaction of four climatological variables to ETpmf. The MK trend analysis has been performed jointly with the Theil-Sen’s estimator to discover movement of ETpmf and meteorological variables. ETpmf displayed a conspicuous descendant tendency ( p < 0.01) at post-monsoon (-0.87 mm/year), winter (-1.13 mm/year), and annual time scale (-3.31 mm/year) in whole Bangladesh, but at the same period, a non-significant descending tendency of ETpmf was observed in pre-monsoon (-1.47 mm/year) and monsoon (-0.07 mm/year) season in the study area. These changes of ETpmf could be explained by a notable rising tendency in average temperature (TA) and actual vapor pressure (VP) and noteworthy declining tendencies in wind speed (WS) and sunshine duration (SD). ETpmf had been maximum sensitive to TA, a bit less, to VP, SD, and WS in the annual and seasonal periods. TA, WS, and SD produced a positive effect, whereas the VP negatively affected seasonal and annual ETpmf. Despite the negative effect on ETpmf, VP was the second most sensitive to the annual and all seasonal ETpmf after TA. Crosswise the entire study area, WS had the greatest influence to ETpmf, after that TA and SD. At the same time, the positive contribution of VP to ETpmf could not counterbalance the negative contributions of WS, TA, and SD. While the consequence of VP on variations in ETpmf is minor, it has to be considered particularly in the cold period. The counter association among inclining TA and declining ETpmf, which is known as the “evaporation paradox,” happenned in Bangladesh. These research outcomes will be beneficial to develop forthcoming water scheduling and regulation of feasible water resources to lessen climate change effects on agronomic water prerequisites and regulate human induced actions.

Zmiany wilgotności powietrza w Łodzi w latach 1966–2020 w świetle wybranych wskaźników

The aim of the study is to present changes in air humidity in central Poland in the years 1966–2000 in Łódź as an example. The values of air temperature, relative humidity and atmospheric pressure from four observation terms, 00, 06, 12 and 18 UTC, were used. On this basis, the saturated vapour pressure, the current vapour pressure, and the saturation deficit were calculated. Then, the variability of these three indicators and relative humidity was examined. The variability of monthly and seasonal average values of humidity indices in four observation periods was presented, the trends in seasonal variability of humidity indices were calculated and the distribution functions of their distributions were compared in the midday period in three 15-year periods: 1966–1980, 1986–2000 and 2006–2020. It has been shown that the pressure of saturated water vapour is the highest in summer, the lowest in winter, and slightly higher in spring than in autumn at all times, except for the night. It increased significantly in the studied period as a result of the increase in air temperature. A comparison of the distributions in three 15-year periods shows a significant increase in the probability of occurrence of high values of saturation vapour pressure, even above 30hPa. The water vapour pressure in the air is highest in summer and lowest in winter, but in spring it is lower than in autumn. All trend coefficients are positive, but only less than half are statistically significant. A comparison of the distributions over three 15-year periods show a slight increase in the probability of higher values of the actual vapour pressure. The saturation deficit, as the difference between the previous two indicators, increases significantly. Its value in spring is significantly higher than in autumn. The trend is positive, especially in spring and summer, and the comparison of distributions shows that in the last 15 years the probability of high values of saturation deficit increased significantly. The course of relative humidity is the opposite of saturation deficit. In autumn, the relative humidity is definitely higher than in spring. The trend is down. To sum up, warming brings an increase in the capacity of the atmosphere for water vapour, a slight increase in the amount of water vapour in the air, but also a significant increase in saturation deficit and a decrease in relative humidity, which is particularly strong in spring in the first half of the growing season.

Calibration and evaluation of Hargreaves-Samani equation for estimating reference evapotranspiration: A case study in the Amu Darya River Basin, Central Asia

Study regionAmu Darya River Basin (ADRB), Central Asia. Study focusReference-evapotranspiration (ETo) is the most prominent variable for agriculture water-resource management. The Penman-Monteith (PM) equation has been accepted as universal-standard for estimating ETo but it requires extensive climatic-variables. The PM alternative Hargreaves-Samani (HS) equation inadequately estimates ETo with an average error of RMSE 1.84 mm/day, NSE 0.27, and MAE 0.61 mm/day, respectively, in the ADRB. New hydrological insight for the regionThe present study proposes the calibration of original HS-equation based on actual vapor pressure and linear regression to estimate daily ETo at 114 meteorological stations in the ADRB. The locally-calibrated HS-equation coefficient (C) was found variated-widely across the different climatic-conditions and months, ranging from 0.0025 to 0.0186, due to hyper-aridity conditions in the study area. Compared with proposed methods by other studies, the modified-HS equation and empirical-coefficient using newly-proposed variable leads to a significant reduction of the average root means square error and mean absolute error by about 55 % and 52 %, respectively, demonstrates their feasibility and accuracy in the ADRB, Central Asia. In-addition, the modified-HS-equation based on climatic zones were found to be more accurate and comprehensive than geographical localizations. The modified-HS equation and empirical-coefficient using newly-proposed variable and approach is suitable to estimate ETo in a data scarce region (e.g., ADRB), when only temperatures, solar radiation and vapor pressure data are available.

Analysis and Experimental Study on Water Vapor Partial Pressure in the Membrane Distillation Process.

In membrane distillation, the vapor pressure difference is the driving force of mass transfer. The vapor pressure is generally assumed by the saturation pressure and calculated by the Antoine equation. However, in the actual operation process, the feed solutions usually flow in a non-equilibrium state, which does not meet the theoretical and measurement conditions of the vapor-liquid equilibrium (VLE) state. This study tested the actual water vapor pressure of the pure water, lithium bromide (LiBr) solution, lithium chloride (LiCl) solution, and calcium chloride (CaCl2) solution under different flow conditions. The results showed that the actual water vapor pressure was lower than the saturation pressure overall, and the difference increased with temperature but decreased with the mass concentration. Therefore, in vacuum membrane distillation (VMD), air gap membrane distillation (AGMD), and sweeping gas membrane distillation (SGMD), the membrane flux calculated by water vapor saturation pressure was higher than the actual membrane flux, and the relative difference decreased and was less than 10% after 60 °C. In direct contact membrane distillation (DCMD), the water vapor pressure difference on both membrane sides was almost the same by using the saturation vapor pressure or the tested data since the pressure errors were partially offset in parallel flow or counter-flow modes.

Globally assessing the hysteresis between sub-diurnal actual evaporation and vapor pressure deficit at the ecosystem scale: Patterns and mechanisms

• Magnitude of the sub-diurnal AET - VPD hysteresis varies with biomes and climates. • Variation of the hysteresis is mostly attributable to evaporative demand limitation. • Soil moisture supply limitation amplifies the hysteresis during the dry season. • We provide some novel features to diagnose limiting mechanisms of the hysteresis. Hysteresis between sub-diurnal actual evaporation ( AET ) (or one of its components, transpiration) and vapor pressure deficit ( VPD ) at the species or individual ecosystem level has been extensively studied, but the global variation and seasonal variability of this hysteresis across biomes and climates is yet to be fully explored and the limiting mechanisms remain unclear. We hypothesize that the sub-diurnal AET - VPD hysteresis results from the interplay between evaporative demand and soil moisture supply limitations. To test our hypothesis, we quantify the sub-diurnal AET - VPD hysteresis across a broad range of biomes and climates based on the observations from the 89 FLUXNET sites (703 site-years) across the globe. We find that the magnitude of hysteresis varies with biomes and climates and is mostly attributable to evaporative demand limitation in all ten sampled biomes. In seasonally dry locations, however, low soil moisture availability amplifies the hysteresis during the dry season. Sensitivity analysis using a hydraulic model suggests that most ecosystems exhibiting seasonal drought display a more isohydric behavior during the dry season, while shift toward a more anisohydric response during the wet season. Our findings have important implications for understanding sub-diurnal dynamics between vegetation and its surrounding environment, reducing uncertainties in AET simulation at fine spatial and temporal scales, and improving understanding of the ecosystem response to hydrologic stress.

Estimation of Daily Average Global Solar Radiation with Nonlinear Regression Models Developed Using Some Meteorological and Geographical Parameters

In this study, it is aimed to develop empirical models that can be used in estimation of daily average solar radiation (RS) based on some meteorological and geographical parameters. Seven estimation models were developed by nonlinear regression analysis method using various combinations of air temperature (T), relative humidity (RH), extraterrestrial radiation (Ra), saturated (es) and actual vapour pressure (ea) parameters. The models were created using the long-term average daily meteorological data of Kahramanmaraş province (1938 – 2020). The models were tested both these long-term average data and daily meteorological data measured at Kahramanmaraş Sütçü İmam University (KSU) in 2019 and 2020. Long-term average daily actual RS data varied between 4.99 – 32.56 MJ m-2 day-1. The estimated solar radiation values (("RS" ) ̂) with the highest correlation (r = 0.99) with actual RS data were obtained with the RS_7 model, in which the parameters es, ea, T, RH and Ra were used together. The ("RS" ) ̂ values obtained using this model varied between 6.45 to 33.99 MJ m-2 day-1. For the RS_7, which showed the best performance among the seven models, mean absolute percentage error (MAPE) and root mean square error (RMSE) were determined as 4.17% and 0.69 MJ m-2 day-1, respectively. The daily RS values measured in KSU varied between 7.75 – 33.48 MJ m-2 day-1 and 10.51 – 30.23 MJ m-2 day-1 for 2019 and 2020. The ("RS" ) ̂ values closest to the measured RS values were estimated with the RS_7 model. The estimated ("RS" ) ̂ values by this model varied between 11.74 – 33.93 MJ m-2 day-1 and 13.93 – 31.57 MJ m-2 day-1 for 2019 and 2020, respectively. MAPE values were determined as 11.33% and 7.54%, respectively. It is concluded that this model can be used to estimates daily average solar radiation and will be an excellent alternative since it is compatible with the Kahramanmaraş conditions.

Comparison of machine learning and dynamic models for predicting actual vapour pressure when psychrometric data are unavailable

• Machine learning models for predicting e a without psychrometric data were developed. • Machine learning models were compared with recently proposed dynamic empirical model. • The machine learning models using T mean and T min as inputs were superior to those using only T mean or T min . • The XGBoost model using T mean and T min offered the best accuracy in various climate zones. • We recommend global XGBoost model when there are no historical data except for hyper-arid regions. Information of actual vapour pressure ( e a ) is frequently required in many disciplines. However, psychrometric data required to calculate e a are often not readily available. Hence, it is of great importance to develop models to estimate e a when psychrometric data are unavailable. Here, five machine learning models were developed for estimating e a , viz. extreme gradient boosting (XGBoost), extreme learning machine (ELM), kernel-based nonlinear extension of Arps decline (KNEA), multiple adaptive regression splines (MARS), and support vector machine (SVM) models. Their performance was also compared to a dynamic model proposed recently, which estimates e a by adjusting dew point temperature from minimum temperature ( T min ) with dynamic correction factor. Three input combinations using only temperature data (i.e. T min and mean temperature ( T mean )) were considered in the machine learning models. The meteorological data collected from 1,188 stations across six climate zones were used to develop and assess the models. The overall results revealed that the dynamic and machine learning models offered satisfactory e a estimates spanning from hyper arid to humid climates. However, the accuracy of the dynamic model was lower than all machine learning algorithms using either only T min or combinations of T mean and T min in all climate zones. The machine learning models using T mean and T min were superior to those using only T mean or T min . There were comparable performances among the ELM, KNEA, MARS, and SVM models with various input variables; however, the XGBoost model incorporating T mean and T min produced the best accuracy. The computational demand was least for the ELM model, followed by the XGBoost model. Considering the accuracy and computational demand, the XGBoost model is recommended for predicting daily and monthly e a from hyper arid to humid climates when historical data are prior known. When there are no historical data, we recommend using the global XGBoost model incorporating T mean , T min , and aridity index for estimating daily and monthly e a from arid to humid regions, and using the dynamic model in hyper-arid regions.

Interaction between wind speed and net radiation controls reference evapotranspiration variance in the inland river basin of Northwest China

AbstractExploring the variance in reference evapotranspiration (ET0) and its dominant influencing factors is important for climate change, hydrological cycles and water management. Temperature (T), wind speed (U2), net radiation (Rn) and actual vapour pressure (ea) are the major climatic input variables in Penman–Monteith equation. Previous studies have successfully applied the total differential method to determine the relative contributions of changes in these variables to variation in ET0 on different timescales. However, the interaction of climatic variables has not been incorporated into this method. Taking the inland river basin of Northwest China as the study area, we extended the total differential method to decompose the ET0 variance into temporal variance and covariance of T, U2, Rn and ea on intra‐annual and annual scales during 1960–2017. The results indicated that the variance in ET0 on the intra‐annual scale was larger than that on the annual scale. Among the four single climatic variables, U2 variance made larger contributions to intra‐annual and annual ET0 variance with relative contributions of 6.8% and 1.1%, respectively. However, the interaction of climatic variables played a dominant role in the variation in ET0. Specifically, on intra‐annual scale, coupled U2 and Rn primarily controlled the ET0 variance (76.1%), followed by coupled Rn and ea (9.6%); but their positive effects were weakened by the negative effects of coupled T and ea (−2.3%). On annual scale, coupled U2 and Rn still governed the variance in ET0 (97.3%), but the effects were also weakened by other groups of interaction effects. Furthermore, ET0 was highly related to NDVI on an intra‐annual scale (R2 = 0.68, p = 0.00), indicating the strong effect of seasonal vegetation dynamics on ET0 variance. This study provides new insights to quantitatively assess the interaction effects of environmental factors on key hydrometeorological variables.

The impact of climate change on evaporation from the water surface in Ukraine


 
 
 
 Based on the monitoring data, the features of long-term changes of evaporation from the water surface are determined. Data from relatively small evaporators and evaporation basins located in different regions of Ukraine were processed. It was found that during the first part of the observation period, which began in the 1950s, evaporation had the tendency to decrease, while in the second part it increased significantly. To determine the factors of these changes, the existing calculated dependences were analyzed. In most of them, evaporation is determined by three arguments: the partial pressure of saturated water vapour, which corresponds to the water temperature, the actual water vapour pressure, and wind speed. It was determined that the main factor of the modern increase in evaporation is the increase of water temperature, which is accompanied by a significant increase in the partial pressure of saturated water vapour. In particular, the mean water temperature in the Dnipro Reservoirs in May– September during 1977–2020 increased at an average rate of 0.65–0.70 °C per decade, and the air temperature at 0.75 °C per decade. It is important that the relationship between water temperature and the partial pressure of saturated water vapour, which corresponds to it, is nonlinear. Wind speed does not significantly affect evaporation. In addition, in recent decades there has been a tendency to its decrease. An empirical dependence of evaporation on air temperature is proposed. Its nonlinear form indicates a significant increase in evaporation due to the temperature increase. Currently (1991–2020), evaporation from the water surface near Kyiv during the ice-free period is approximately 650 mm, in the south of Ukraine it reaches 1000 mm. The increase in evaporation results in additional water losses and a reduction in available water resources.
 
 
 

Spatiotemporal Distribution for Precipitable Water Vapor and Precipitation Over Iraq during the Period (1979-2019)

Water vapor plays an important role in transporting sensible heat and latent heat and is important in cloud fashioning, and precipitation fashioning, it’s one of the more important greenhouse gases so it plays an essential role in climate change. To strengthen understanding of climate change, it is necessary to analyze the spatial and temporal PWV and P distribution and variations and association between them. This research aims to calculate Precipitable Water Vapor and Precipitation Conversion Efficiency and spatial and temporal PWV and P distribution and association between them for Iraq for the period (1979-2019). In this research temperature and dew point temperature and precipitation has been downloaded from ECMWF to calculate saturation vapor pressure and actual vapor pressure and mixing ratio and that’s necessary to calculate Precipi table Water Vapor and Precipitation Conversion Efficiency. spatial distribution and temporal variation of monthly and total annual and a seasonal average of PWV and P have been studied for twelve stations covering north, east, west, central and southern (Sulaymaniah, Zakho, Kirkuk, Mosul, Erbil, Khanaqine Baghdad, Rutba, Karbala, Smawa, Nasiryia, Amarah). The results showed that larger Precipitable water vapor does not necessarily correspond to larger PCE, as well as the Precipitable water vapor increases in the station that characterizes high temperature and location near a water source so its increase in south station and decrease in the northeast stations . While precipitation increase in stations that located in mountains region and characterize by low temperature so its increase in Sulaymaniyah, Erbil and Zakho stations. And there is a negative relation between P and PWV.