Evaluation of surface temperature and pressure derived from MERRA-2 and ERA5 reanalysis datasets and their applications in hourly GNSS precipitable water vapor retrieval over China

Temperature and pressure play key roles in Global Navigation Satellite System(GNSS) precipitable water vapor(PWV) retrieval. The National Aeronautics and Space Administration(NASA) and European Center for Medium-Range Weather Forecasts(ECMWF) have released their latest reanalysis product: the modern-era retrospective analysis for research and applications, version 2(MERRA-2) and the fifthgeneration ECMWF reanalysis(ERA5), respectively. Based on the reanalysis data, we evaluate and analyze the accuracy of the surface temperature and pressure products in China using the the measured temperature and pressure data from 609 ground meteorological stations in 2017 as reference values.Then the accuracy of the two datasets and their performances in estimating GNSS PWV are analyzed. The PWV derived from the pressure and temperature products of ERA5 and MERRA-2 has high accuracy. The annual average biases of pressure and temperature for ERA5 are-0.07 hPa and 0.45 K, with the root mean square error(RMSE) of 0.95 hPa and 2.04 K, respectively. The annual average biases of pressure and temperature for MERRA-2 are-0.01 hPa and 0.38 K, with the RMSE of 1.08 h Pa and 2.66 K, respectively.The accuracy of ERA5 is slightly higher than that of MERRA-2. The two reanalysis data show negative biases in most regions of China, with the highest to lowest accuracy in the following order: the south,north, northwest, and Tibet Plateau. Comparing the GNSS PWV calculated using MERRA-2(GNSS MERRA-2 PWV) and ERA5(GNSS ERA5 PWV) with the radiosonde-derived PWV from 48 co-located GNSS stations and the measured PWV of the co-location radiosonde stations, it is found that the accuracy of GNSS ERA5 PWV is better than that of GNSS MERRA-2 PWV. These results show the different applicability of surface temperature and pressure products from MERRA-2 and ERA5 data, indicating that both have important applications in meteorological research and GNSS water vapor monitoring in China.

Decadal trends in precipitable water vapor over the Indus River Basin using ERA5 reanalysis data

Precipitable Water Vapor(PWV)constitutes a pivotal parameter within the domains of atmospheric science,and remote sensing due to its profound influence on Earth’s climate dynamics and weather patterns.It exerts a significant impact on atmospheric stability absorption and emission of radiation,thus engendering alterations in the Earth’s radiative equilibrium.As such,precise quantification of PWV holds the potential to enhance weather prognostication and fortify preparedness against severe meteorological phenomena.This study aimed to elucidate the spatial and temporal changes in seasonal and annual PWV across the Indus River Basin and its sub-basins using ERA5 reanalysis datasets.The present study used ERA5 PWV(entire atmospheric column),air temperature at 2 m(t2m)and 500 hPa(T_500hPa),evapotranspiration,and total cloud cover data from 1960 to 2021.Theil Sen slope estimator and Mann-Kendall test were used for trend analysis.Correlation and multiple regression methods were used to understand the association of PWV with other factors.The findings have unveiled the highest increase in mean PWV during the monsoon(0.40 mm/decade),followed by premonsoon(0.37 mm/decade),post-monsoon(0.27 mm/decade),and winter(0.19 mm/decade)throughout the study period.Additionally,the mean PWV exhibited the most pronounced positive trend in the sub-basin Lower Indus(LI),followed by Panjnad(P),Kabul(K),and Upper Indus(UI)across all seasons,except winter.Annual PWV has also risen in the Indus basin and its sub-basins over the last six decades.PWV exhibits a consistent upward trend up to an elevation of 3500 m within the basin which is most pronounced during the monsoon season,followed by the pre-monsoon.The escalating PWV within the basin is reasonably ascribed to increasing air temperatures,augmented evapotranspiration,and heightened cloud cover.These findings hold potential utility for pertinent authorities engaged in water resource management and planning.

Rising trends of global precipitable water vapor and its correlation with flood frequency

Using 4 global reanalysis data sets, significant upward trends of precipitable water vapor(PWV) were found in the 3 time periods of 1958-2020, 1979-2020, and 2000-2020. During 1958-2020, the global PWV trends obtained using the ERA5 and JRA55 data sets are 0.19 ± 0.01 mm per decade(1.15 ± 0.31%)and 0.23 ± 0.01 mm per decade(1.45 ± 0.32%), respectively. The PWV trends obtained using the ERA5,JRA55, NCEP-NCAR, and NCEP-DOE data sets are 0.22 ± 0.01 mm per decade(1.18 ± 0.54%),0.21 ± 0.00 mm per decade(1.76 ± 0.56%), 0.27 ± 0.01 mm per decade(2.20 ± 0.70%) and 0.28 ± 0.01 mm per decade(2.19 ± 0.70%) for the period 1979-2020. During 2000-2020, the PWV trends obtained using ERA5, JRA55, NCEP-DOE, and NCEP-NCAR data sets are 0.40 ± 0.25 mm per decade(2.66 ± 1.51%),0.37 ± 0.24 mm per decade(2.19 ± 1.54%), 0.40 ± 0.26 mm per decade(1.96 ± 1.53%) and 0.36 ± 0.25 mm per decade(2.47 ± 1.72%), respectively. Rising PWV has a positive impact on changes in precipitation,increasing the probability of extreme precipitation and then changing the frequency of flood disasters.Therefore, exploring the relationship between PWV(derived from ERA5 and JRA55) change and flood disaster frequency from 1958 to 2020 revealed a significant positive correlation between them, with correlation coefficients of 0.68 and 0.79, respectively, which explains the effect of climate change on the increase in flood disaster frequency to a certain extent. The study can provide a reference for assessing the evolution of flood disasters and predicting their frequency trends.

Seasonal Difference of the Spatio-Temporal Variation of Precipitable Water Vapor in China

This study analyzes the spatial and temporal distribution characteristics of seasonal precipitable water vapor (PWV) in China between 1979 and 2008. To achieve this, the observed temperature dew point difference and atmospheric pressure at various altitudes of 102 radiosonde stations were utilized. The analysis involved calculating and examining the PWV variations across the different seasons in the study period. The results are illustrated as follows: 1) The annual mean and seasonal mean PWV over China is characterized by decreasing from southeast to northwest. The PWV has obvious seasonal features. It is the least in winter, which is mainly affected by latitude and altitude, and the most in summer, which is mainly affected by the monsoon. It is the medium in spring and autumn, with more in autumn than in spring. 2) The spatial distribution pattern of four seasonal PWV is approximately opposite to its variation coefficient distribution pattern, that is, the monsoon (non-monsoon) areas with more (less) PWV have a smaller (larger) variation amplitude. 3) The distribution pattern of four seasonal PWV shows a consistent distribution pattern in the whole region and the winter characteristics are the most significant. The abnormal variation of PWV shows consistent interdecadal oscillation, and it exhibits an obvious phase transition around 2002 when the PWV has an increasing shift in winter, spring, and summer, while it is more complicated in autumn.

Use of Total Precipitable Water Classification of A Priori Error and Quality Control in Atmospheric Temperature and Water Vapor Sounding Retrieval

This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared （IR） sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder （AIRS） radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water （TPW） are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control （QC） tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications.

Meteorological applications of precipitable water vapor measurements retrieved by the national GNSS network of China

In this study, the Global Navigation Satellite System （GNSS） network of China is discussed, which can be used to monitor atmospheric precipitable water vapor （PWV）. By the end of 2013, the network had 952 GNSS sites, including 260 belonging to the Crustal Movement Observation Network of China （CMONOC） and 692 belonging to the China Meteorological Administration GNSS network （CMAGN）. Additionally, GNSS observation collecting and data processing procedures are presented and PWV data quality control methods are investigated. PWV levels as determined by GNSS and radiosonde are compared. The results show that GNSS estimates are generally in good agreement with measurements of radio- sondes and water vapor radiometers （WVR）. The PWV retrieved by the national GNSS network is used in weather forecasting, assimilation of data into numerical weather prediction models, the validation of PWV estimates by radiosonde, and plum rain monitoring. The network is also used to monitor the total ionospheric electron content.

Moisture Analysis of a Squall Line Case Based on Precipitable Water Vapor Data from a Ground-Based GPS Network in the Yangtze River Delta

A squall line swept eastward across the area of the Yangtze River Delta and produced gusty winds and heavy rain from the afternoon to the evening of 24 August 2002. In this papers the roles of moisture in the genesis and development of the squall line were studied. Based on the precipitable water vapor （PWV） data from a ground-based GPS network over the Yangtze River Delta in China, plus data from a Pennsylvania State University/National Atmospheric Center （PSU/NCAR） mesoscale model （MM5） simulation, initialized by three-dimensional variational （3D-VAR） assimilation of the PWV data, some interesting features are revealed. During the 12 hours prior to the squall line arriving in the Shanghai area, a significant increase in PWV indicates a favorable moist environment for a squall line to develop. The vertical profile of the moisture illustrates that it mainly increased in the middle levels of the troposphere, and not at the surface. Temporal variation in PWV is a better precursor for squall line development than other surface meteorological parameters. The characteristics of the horizontal distribution of PWV not only indicated a favorable moist environment, but also evolved a cyclonic wind field for a squall line genesis and development. The ＂＋2 mm＂ contours of the three-hourly PWV variation can be used successfully to predict the location of the squall line two hours later.

Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso-βScale Heavy Precipitation Event During the 2002 Mei-Yu Season

Recent advances in Global Positioning System （GPS） remote sensing technology allow for a direct estimation of the precipitable water vapor （PWV） from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational （4DVAR） data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems （MCS） along the mei-yu front in China. The experiments with GPS-PWV assimilation cluster and also eliminated the erroneous rainfall successfully simulated the evolution of the observed MCS systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.

Evaluation of ocean tide loading effects on GPS-estimated precipitable water vapour in Turkey

Global Positioning System （GPS） has been widely used to estimate the total zenith tropo- spheric delay （ZTD） and precipitable water vapour （PWV） for weather prediction and at- mospheric research as a continuous and all-weather technique. However, estimations of ZTD and PWV are subject to effects of geophysical models with large uncertainties, particularly imprecise ocean tide models of inland seas in Turkey. In this paper, GPS data from Jan. 1, 2010 to Dec. 31, 2011 are processed using GAMIT/GLOBK at four co-located GPS stations （ISTN, ERZR, SAMN, and IZMI） with Radiosonde from the Turkish Met-Office together with several nearby IGS stations. Four widely used ocean tide models are adopted to evaluate their effects on GPS-estimated PWV, such as IERS recommended FES2004, NAO99b, CSR4.0 and GOT00. Five different strategies are taken without ocean tide model and with four ocean tide models, respectively, which are used to evaluate ocean tide models effects on GPS-estimated PWV through comparing with co-located Radiosonde. Results showed that ocean tide models have greatly affected the estimation of the pre- cipitable water vapour at stations near coasts. The ocean tide model FES2004 gave the best results when compared to Radiosonde with ＋2.12 mm in PWV at stations near coastline. While other ocean tides models agree each other at millimeter level in PWV. However, at inland GPS stations, ocean tide models have less effect on GPS-estimated PWV.

Diurnal variation of precipitable water vapor over Central and South America

Annual and seasonal diurnal precipitable water vapor(PWV)variations over Central and South America are analyzed for the period 2007-2013.PWV values were obtained from Global Navigation Satellite Systems(GNSS)observations of sixty-nine GNSS tracking stations.Histograms by climate categories show that PWV values for temperate,polar and cold dry climate have a positive skewed distribution and for tropical climates(except for monsoon subtype)show a negative skewed distribution.The diurnal PWV and surface temperatures(T)anomaly datasets are analyzed by using principal components analysis(PCA).The first two modes represent more than 90%of the PWV variability.The first PCA mode of PWV variability shows a maximum amplitude value in the late afternoon few hours later than the respective values for surface temperature(T),therefore the temperature and the surface conditions(to yield evaporation)could be the main agents producing this variability;PWV variability in inland stations are mainly represented by this mode.The second mode of PWV variability shows a maximum amplitude at midnight,a possible explanation of this behavior is the effect of the sea/valley breeze.The coastal and valley stations are affected by this mode in most cases.Finally,the"undefined"stations,surrounded by several water bodies,are mainly affected by the second mode with negative eigenvectors.In the seasonal analysis,both the undefined and valley stations constitute the main cases that show a sea or valley breeze only during some seasons,while the rest of the year they present a behavior according to their temperature and the surface conditions.As a result,the PCA proves to be a useful numerical tool to represent the main sub-daily PWV variabilities.

Empirical model for mean temperature and assessment of precipitable water vapor derived from GPS

The estimation of Precipitable Water Vapor （PWV） derived from Global Positioning System （GPS） data at the IGS site WUHN is assessed by comparing with PWV obtained from radiosonde data （No.57494） in Wuhan. The applicability of Saastamoinen （SAAS）, Hopfield and Black models used for estimating Zenith Hydrostatic Delay （ZHD） and Zenith Wet Delay （ZWD） and different models is verified in the estimation of GPS-derived PWV for the applied area. The experimental results demonstrated that ： 1 ） the precision of PWV estimated from Black model used for calculating ZHD （ ZHDs ） is lower than that of SAAS （ ZHDsAAs ） model and Hopfield model （ZHDn） with the RMS of 4. 16 ram; 2） the RMS of PWV estimated from SAAS model used for calculating ZWD （SAAS） is 3.78 ram; 3 ） the well-known Bevis model gives similar accuracy compared with the site-specific models for Tm in terms of surface temperature （ Ts ） and surface pressure （Ps）, which can reach the accuracy inside 1 mm in the GPS-derived PWV estimates.

Change Features of GPS/MET Precipitable Water Vapor in Different Precipitation in Lianyungang

Based on the GPS/MET water vapor monitoring data and conventional meteorological data at Lianyungang station from April to July, 2015, the relationship between precipitable water vapor and real precipitation was studied. According to different precipitation, change trends of precipitable water vapor in convective precipitation and steady precipitation were analyzed. Results showed that necessary condition of precipitation generation was high precipitable water vapor value in the air. Precipitable water vapor change presented wave-shape and phased characters. In convection precipitation, precipitable water vapor changed frequently and had larger change amplitude, while its change was slow in steady precipi- tation. The appearing time of the maximum values of rainfall intensity and precipitable water vapor was not necessarily consistent, but it was known that severe rainfall usually began at the high-value stage of precipitable water vapor, and high-value stage of precipitable water vapor often corresponded to higher precipitation probability. In addition, precipitable water vapor showed different characteristics in the above two different precipitation, and these results could provide a reference for precipitation forecast.

Retrieval of Oceanic Total Precipitable Water Vapor and Cloud Liquid Water from Fengyun-3D Microwave Sounding Instruments

Fengyun-3 D(FY-3 D) satellite is the latest polar-orbiting meteorological satellite launched by China and carries 10 instruments onboard. Its microwave temperature sounder(MWTS) and microwave humidity sounder(MWHS) can acquire a total of 28 channels of brightness temperatures, providing rich information for profiling atmospheric temperature and moisture. However, due to a lack of two important frequencies at 23.8 and 31.4 GHz, it is difficult to retrieve the total precipitable water vapor(TPW) and cloud liquid water path(CLW) from FY-3 D microwave sounder data as commonly done for other microwave sounding instruments. Using the channel similarity between Suomi National Polar-orbiting Partnership(NPP) advanced technology microwave sounder(ATMS) and FY-3 D microwave sounding instruments, a machine learning(ML) technique is used to generate the two missing low-frequency channels of MWTS and MWHS. Then, a new dataset named as combined microwave sounder(CMWS) is obtained,which has the same channel setting as ATMS but the spatial resolution is consistent with MWTS. A statistical inversion method is adopted to retrieve TPW and CLW over oceans from the FY-3 D CMWS. The intercomparison between different satellites shows that the inversion products of FY-3 D CMWS and Suomi NPP ATMS have good consistency in magnitude and distribution. The correlation coefficients of retrieved TPW and CLW between CMWS and ATMS can reach 0.95 and 0.85, respectively.

PRECIPITABLE WATER MEASUREMENTS WITH SUN-PHOTOMETER

In this paper a method is described of retrieving precipitable water from sun-photometer measurements.The quantitative relationship between water vapor transmission and precipitable water is established by means of LOWTRAN 7 model.Calibration of the water vapor absorption channel is made through a modified Langley method.The good agreement between the sun- photometer and radiosonde water vapor retrieval indicates that this method is feasible. The sun-photometer is operated at Hefei to monitor the precipitable water within one year period.Characteristics of both diurnal evolution and within-one-year variation of the precipitable water and their relation with synoptic system as well as surface dew-point temperature are presented and analyzed.Errors in the retrieved precipitable water from the sun-photometer measurements are also calculated and discussed.

Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin,China

Precipitable Water Vapor(PWV),as an important indicator of atmospheric water vapor,can be derived from Global Navigation Satellite System(GNSS)observations with the advantages of high precision and all-weather capacity.GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in mete-orology,particularly for severe weather conditions,for water vapor is not well sampled in the current meteorological observing systems.In this study,an empirical atmospheric weighted mean temperature(Tm)model for Guilin is estab-lished using the radiosonde data from 2012 to 2017.Then,the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017.The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square(RMS)of−0.51 and 2.12 K,respectively,compared with other widely used models.Moreover,the GNSS PWV estimates are validated with the data at Guilin radiosonde station.Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of−0.9 and 3.53 mm,respectively.Finally,an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed.It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements,and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall.It also reveals the moisture variation in several regions of Guilin during a heavy rainfall,which is significant for the moni-toring of rainfalls and weather forecast.

RETRIEVING OVER-OCEAN PRECIPITABLE WATER FROM THE SSM/I MEASUREMENTS WITH SEVERAL REGRESSION ALGORITHMS

A collocated SSM/I and radiosonde measurement data set provided by the NASDA(Japan) was used to retrieve the total precipitable water(PW)over oceans.The retrieval results obtained with several regression algorithms were compared against the radiosonde measurements.It is shown that:(a)the routinely operational algorithm of Alishouse et al.(1990)yields significant underestimation in high PW regime and overestimation in low PW regime;(b)a cubic correction by Colton and Poe(1994)is not sufficient and globally improves slightly the retrieval results;and (c)the regression algorithm with the form of brightness temperature(T_b)function In (280-T_b) gives a little largely scattered retrievals in whole PW range but without considerable over-and underestimates in low and high PW regimes.To improve the estimation of the oceanic precipitable water from the SSM/I measurements,a composite algorithm with different forms of T_b function in low.medium and high PW regimes is proposed and tested.

Seasonal Variations in the Vertical Structure of Water Vapor Optical Depth in the Lower Troposphere over a Tropical Station

Spatio-temporal variations of water vapor optical depth in the lower troposphere (450-3850 m) over Pune (18°32′N, 73°51′E, 559 m Above Mean Sea Level), India have been studied over a period of five years. The mean vertical structure showed that the moisture content is greatest at the lowest level and decreases with increasing altitude, except in the south-west monsoon season (June to September) where an increase upto 950 m has been found. Optical depths are maximum in the monsoon season. The increase from pre-monsoon (March-May) to monsoon season in moisture content on an average is by about 58% in the above altitude range. The temporal variations in surface Relative Humidity and optical depth at 450 m show positive correlation. The amplitude of seasonal oscillation is the largest at 1465 m altitude. The time-height cross-sections of water vapor optical depths in the lower troposphere showed a contrast between years of good and bad monsoon.

Water Vapor Retrievals from Near-infrared Channels of the Advanced Medium Resolution Spectral Imager Instrument onboard the Fengyun-3D Satellite

Water vapor plays a key role in weather, climate and environmental research on local and global scales. Knowledge about atmospheric water vapor and its spatiotemporal variability is essential for climate and weather research. Because of the advantage of a unique temporal and spatial resolution, satellite observations provide global or regional water vapor distributions. The advanced Medium Resolution Spectral Imager (MERSI) instrument-that is, MERSI-II-onboard the Fengyun-3D (FY-3D) meteorological satellite, has been one of the major satellite sensors routinely providing precipitable water vapor (PWV) products to the community using near-infrared (NIR) measurements since June 2018. In this paper, the major updates related to the production of the NIR PWV products of MERSI-II are discussed for the first time. In addition, the water vapor retrieval algorithm based on the MERSI-II NIR channels is introduced and derivations are made over clear land areas, clouds, and sun-glint areas over the ocean. Finally, the status and samples of the MERSI-II PWV products are presented. The accuracy of MERSI-II PWV products is validated using ground-based GPS measurements. The results show that the accuracies of the water vapor products based on the updated MERSI-II instrument are significantly improved compared with those of MERSI, because MERSI-II provides a better channel setting and new calibration method. The root- mean-square error and relative bias of MERSI-II PWV products are typically 1.8-5.5 mm and −3.0% to −14.3%, respectively, and thus comparable with those of other global remote sensing products of the same type.

The Preliminary Study on the Converting Measures between the Four Waters in Wet and Low-lying Farmland

Most of China's wetland areas are located in the Sanjiang Plain.This area has 207×10 4 hm 2 of wet and low lying farmland,of which 59% is cropped.During the 1970s and 1980s,the Chinese government organized intensive scientific research into potential changes to existing natural resources conditions for these farmlands.The aim was to change the water resources regime to one that was beneficial to crop production.Arterial drainage,field drainage and appropriate sub soil treatments were required.The relation between plant products industry and the Four Waters distribution,also the main measures of the Four Waters converting in wet and low lying farmland were discussed in the paper.

Identifying water vapor sources of precipitation in forest and grassland in the north slope of the Tianshan Mountains,Central Asia

Identifying water vapor sources in the natural vegetation of the Tianshan Mountains is of significant importance for obtaining greater knowledge about the water cycle,forecasting water resource changes,and dealing with the adverse effects of climate change.In this study,we identified water vapor sources of precipitation and evaluated their effects on precipitation stable isotopes in the north slope of the Tianshan Mountains,China.By utilizing the temporal and spatial distributions of precipitation stable isotopes in the forest and grassland regions,Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)model,and isotope mass balance model,we obtained the following results.(1)The Eurasia,Black Sea,and Caspian Sea are the major sources of water vapor.(2)The contribution of surface evaporation to precipitation in forests is lower than that in the grasslands(except in spring),while the contribution of plant transpiration to precipitation in forests(5.35%)is higher than that in grasslands(3.79%)in summer.(3)The underlying surface and temperature are the main factors that affect the contribution of recycled water vapor to precipitation;meanwhile,the effects of water vapor sources of precipitation on precipitation stable isotopes are counteracted by other environmental factors.Overall,this work will prove beneficial in quantifying the effect of climate change on local water cycles.