Water Vapor Monitoring Research Articles

Carbon and Water Balances in a Watermelon Crop Mulched with Biodegradable Films in Mediterranean Conditions at Extended Growth Season Scale

The carbon source/sink nature and the water balance of a drip-irrigated and mulched watermelon cultivated under a semi-arid climate were investigated. Biodegradable films, plants and some fruits were left on the soil as green manure. The study spanned from watermelon planting to the subsequent crop (June–November 2023). The eddy covariance technique was employed to monitor water vapor (H2O) and carbon dioxide (CO2) fluxes, which were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method.This method utilizesthe Monin–Obukhov similarity theory to separate stomatal (photosynthesis and transpiration) from non-stomatal (respiration and evaporation) processes. The results indicate that mulching films contribute to carbon sequestration in the soil (+19.3 g C m−2). However, the mulched watermelon crop presented in this study functions as a net carbon source, with a net biome exchange, representing the net rate of C accumulation in or loss from ecosystems, equal to +230 g C m−2. This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 g C m−2). These findings recommend further research to enhance the sustainability of this crop in terms of both water and carbon balances.

Analysis of Different Height Correction Models for Tropospheric Delay Grid Products over the Yunnan Mountains

Accurate tropospheric delays are of great importance for both Global Navigation Satellite System (GNSS)-based positioning and precipitable water vapor monitoring. The gridded tropospheric delay products, including zenith hydrostatic delays (ZHD) and zenith wet delays (ZWD), are the most ideal method for accessing accurate tropospheric delays. The vertical adjustment method is critical for implementing the gridded tropospheric products. In this work, we consider the different models used for grid products and assess their performance over Yunnan mountains with complex topography. We summarize the main results as follows: (1) The products can provide accurate ZHD with mean biases of −2.6 mm and mean Standard Deviation (STD) of 1.5 mm while the ZWD results from grid products show a performance with biases of −0.4 mm and STD of 1.3 cm over the Yunnan area. (2) The Tv-based model shows a better performance than the T0-based model and IGPZWD in rugged areas with large height differences. The grid products can provide hourly ZHD with biases of 3 mm and wet delay with mean biases of within 2 cm and mean STD of below 3 cm in the Yunnan mountains, which exhibit a large height difference of around 1.5 km. (3) The radiosondes results confirm that the Tv-based model has an obvious advantage in calculating ZHD height corrections for differences within 2 km while the T0-model suffers from a loss in accuracy in the case of large height differences. If the site is located more than 1 km below the reference height, the IGPZWD model can provide a better ZWD with a mean bias of 1.5 cm and a mean STD of 1.7 cm. With vertical reduction models, the grid products can provide accurate ZHD and ZWD in real time, even if in complex area.

A GRNN-Based Model for ERA5 PWV Adjustment with GNSS Observations Considering Seasonal and Geographic Variations

Precipitation water vapor (PWV) is an important parameter in numerical weather forecasting and climate research. However, existing PWV adjustment models lack comprehensive consideration of seasonal and geographic factors. This study utilized the General Regression Neural Network (GRNN) algorithm and Global Navigation Satellite System (GNSS) PWV in China to construct and evaluate European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) PWV adjustment models for various seasons and subregions based on meteorological parameters (GMPW model) and non-meteorological parameters (GFPW model). A linear model (GLPW model) was established for model accuracy comparison. The results show that: (1) taking GNSS PWV as a reference, the Bias and root mean square error (RMSE) of the GLPW, GFPW, and GMPW models are about 0/1 mm, which better weakens the systematic error of ERA5 PWV. The overall Bias of the GLPW, GFPW, and GMPW models in the Northwest (NWC), North China (NC), Tibetan Plateau (TP), and South China (SC) subregions is approximately 0 mm after adjustment. The adjusted overall RMSE of the GLPW, GFPW, and GMPW models of the four subregions are 0.81/0.71/0.62 mm, 1.15/0.95/0.77 mm, 1.66/1.26/1.05 mm, and 2.11/1.35/0.96 mm, respectively. (2) The accuracy of the three models is tested using GNSS PWV, which is not involved in the modeling. The adjusted overall RMSE of the GLPW, GFPW, and GMPW models in the four subregions are 0.89/0.85/0.83 mm, 1.61/1.58/1.27 mm, 2.11/1.75/1.68 mm and 3.65/2.48/1.79 mm, respectively. As a result, the GFPW and GMPW models have better accuracy in adjusting ERA5 PWV than the linear model GLPW. Therefore, the GFPW and GMPW models can effectively contribute to water vapor monitoring and the integration of multiple PWV datasets.

Hermetic, Hybrid Multilayer, Sub‐5µm‐Thick Encapsulations Prepared with Vapor‐Phase Infiltration of Metal Oxides in Conformal Polymers for Flexible Bioelectronics

AbstractReliable long‐term function is crucial for flexible and soft bioelectronics. Mechanical defects and permeation of water molecules across the various device layers are the prime drivers of failure, and particularly in applications requiring continuous contact with biofluids (i.e. for implantable applications). To address demanding needs of miniaturization, mechanical compliance and water impermeability, ultra‐thin high‐barrier encapsulations are a promising way to improve device reliability. In this work, the encapsulation properties of vapour phase infiltration (VPI) of inorganic Al2O3 layers deposited by atomic layer deposition (ALD), as bilayers with TiO2, onto polyimide and parylene C organic substrates are investigated. The layers are grown in a single reactor and the infiltration is performed in a single process, with a resulting nanometric infiltration depth. Mechanical integrity and hermeticity are characterized through tensile fragmentation tests and accelerated aging tests. Flexible Magnesium sensors monitor water vapor permeability in situ. A remarkable improvement in the crack onset strain (∽2.56 times), interfacial shear strength (∽2.1 times), water vapour transmission rate (∽5.2 times) and lifetime performance (∽7.1 times) is achieved, compared to the non‐infiltrated ALD coatings. Pluri‐infiltrated multilayers are proposed as alternatives to conventional coatings. This work is envisioned to accelerate research progress on hermetic packaging of flexible bioelectronics.

Innovative aerosol hygroscopic growth study from Mie–Raman–fluorescence lidar and microwave radiometer synergy

Abstract. This study focuses on the characterization of aerosol hygroscopicity using remote sensing techniques. We employ a Mie–Raman–fluorescence lidar (Lille Lidar for Atmospheric Study, LILAS), developed at the ATOLL platform, Laboratoire d'Optique Atmosphérique, Lille, France, in combination with the RPG-HATPRO-G5 microwave radiometer to enable continuous aerosol and water vapor monitoring. We identify hygroscopic growth cases when an aerosol layer exhibits an increase in both aerosol backscattering coefficient and relative humidity. By examining the fluorescence backscattering coefficient, which remains unaffected by the presence of water vapor, the potential temperature, and the absolute humidity, we verify the homogeneity of the aerosol layer. Consequently, the change in the backscattering coefficient is solely attributed to water uptake. The Hänel theory is employed to describe the evolution of the backscattering coefficient with relative humidity and introduces a hygroscopic coefficient, γ, which depends on the aerosol type. The particularity of this method revolves around the use of the fluorescence which is employed to take into account and correct the aerosol concentration variations in the layer. Case studies conducted on 29 July and 9 March 2021 examine, respectively, an urban and a smoke aerosol layer. For the urban case, γ is estimated as 0.47 ± 0.03 at 532 nm; as for the smoke case, the estimation of γ is 0.5 ± 0.3. These values align with those reported in the literature for urban and smoke particles. Our findings highlight the efficiency of the Mie–Raman–fluorescence lidar and microwave radiometer synergy in characterizing aerosol hygroscopicity. The results contribute to advance our understanding of atmospheric processes, aerosol–cloud interactions, and climate modeling.

Improving MODIS-IR precipitable water vapor based on the FIDWFT model

The monitoring of precipitable water vapor (PWV) is of significant importance for meteorological research and rainstorm prediction. The moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used remote sensing PWV instruments aboard the Terra and Aqua satellites. The main objective of this study is to develop a fused inverse distance weighting and the Fourier transform model (FIDWFT) to calibrate MODIS Infrared (IR) PWV. In this research, one year of ground observation data from 17 GPS stations were utilized to evaluate the accuracy of MODIS IR PWV in Hong Kong. Initially, the PWV obtained by one radiosonde was used to verify the GPS PWV. Comparison between GPS and radiosonde showed good agreement with an R-squared of 0.97. After the estimation of GPS PWV in the study area was evaluated, MODIS IR products in the study area were extracted for evaluation. The comparison between MODIS and GPS showed that the R-squared is 0.81 and the mean bias (MB) is −1.60 mm. Considering the spatial interpolation and calibration problems of MODIS products, new model used an inverse distance weighting method to make MODIS IR PWV and GPS PWV completely coincide in space, and fifteen MODIS pixel points were selected for interpolation. Then the MODIS IR PWV was calibrated by GPS PWV using Fourier transform model. Results showed that compared with the MODIS IR PWV before calibration, the R-squared increases from 0.81 to 0.94, the root mean square error decreases from 8.72 mm to 4.98 mm, and the MB decreases from −1.60 mm to −0.83 mm. Therefore, the method is feasible to estimate MODIS IR water vapor, and it achieves better results.

A Refined Atmospheric Weighted Average Temperature Model Considering Multiple Factors in the Qinghai–Tibet Plateau Region

The Qinghai–Tibet Plateau region has significant altitude fluctuations and complex climate changes. However, the current global weighted average temperature (Tm) model does not fully consider the impact of meteorological and elevation factors on it, resulting in existing models being unable to accurately predict the Tm in the region. Therefore, this study constructed a weighted average temperature refinement model (XTm) related to surface temperature, water vapor pressure, geopotential height, annual variation, and semi-annual variation based on measured data from 13 radiosonde stations in the Qinghai–Tibet Plateau region from 2008 to 2017. Using the Tm calculated via the numerical integration method of radiosonde observations in the Qinghai–Tibet Plateau region from 2018 to 2019 as a reference value, the quality of the XTm model was tested and compared with the Bevis model and GPT2w (global pressure and temperature 2 wet) model. The results show that for 13 modeling stations, the bias and root-mean-square (RMS) values of the XTm model were −0.02 K and 2.83 K, respectively; compared with the Bevis, GPT2-1, and GPT2w-5 models, the quality of XTm was increased by 47%, 38%, and 47%, respectively. For the four non-modeling stations, the average bias and RMS values of the XTm model were 0.58 K and 2.78 K, respectively; compared with the other three Tm models, the RMS values and the mean bias were both minimal. In addition, the XTm model was also used to calculate the global navigation satellite system (GNSS) precipitable water vapor (PWV), and its average values for the theoretical RMSPWV and RMSPWV/PWV generated by water vapor calculation were 0.11 mm and 1.03%, respectively. Therefore, in the Qinghai–Tibet Plateau region, the XTm model could predict more accurate Tm values, which, in turn, is important for water vapor monitoring.

Water Vapor Condensation in Nanoparticle Films: Physicochemical Analysis and Application to Rapid Vapor Sensing

Nanomaterial-based humidity sensors hold great promise for water vapor detection because of their high sensitivity and fast response/recovery. However, the condensation of water in nanomaterial films remains unclear from a physicochemical perspective. Herein, the condensation of water vapor in silica nanoparticle films was physicochemically analyzed to bridge the abovementioned gap. The morphology of surface-adsorbed water molecules was characterized using infrared absorption spectroscopy and soft X-ray absorption spectroscopy, and the effect of RH on the amount of adsorbed water was observed using a quartz crystal microbalance. The adsorbed water was found to exist in liquid- and ice-like states, which contributed to high and low conductivity, respectively. The large change in film impedance above 80% RH was ascribed to the condensation of water between the nanoparticles. Moreover, RH alteration resulted in a colorimetric change in the film’s interference fringe. The obtained insights were used to construct a portable device with response and recovery times suitable for the real-time monitoring of water vapor. Thus, this study clarifies the structure of water adsorbed on nanomaterial surfaces and, hence, the action mechanism of the corresponding nanoparticle-based sensors, inspiring further research on the application of various nanomaterials to vapor sensing.

Direct monitoring of water vapor from the free water level of the Vavřinecký pond and its influence on the hydrological balance

Direct monitoring of water vapor from the free water level of the Vavřinecký pond and its influence on the hydrological balance

TDLAS-based water vapor monitoring in narrow channels of polymer electrolyte fuel cells using a single-ended fiber-optic sensor.

The dehydration of electrolyte membranes in polymer electrolyte fuel cells (PEFCs) operating under low-humidity conditions is a critical issue for achieving their high efficiency and high power density. To reduce the membrane dryout, it's necessary to investigate and control the water transport within working fuel cells. This study developed a single-ended fiber-optic sensor based on tunable diode laser absorption spectroscopy (TDLAS) and applied it to the real-time monitoring of the water vapor concentration in the narrow flow channel of a PEFC. The newly proposed wavelength modulation spectroscopy (WMS) technique enabled to quantify the mole fraction of water in the channel over the wide concentration range with high accuracy. The in-situ TDLAS measurement in the PEFC during a low-humidity and load-change operation revealed that the dynamic change of cell voltage is strongly correlated to the dry-wet transition in the anode channel.

A water vapor sensor with 3D pillared metal–organic framework and 2D breathing mode

Two 3D pillared metal–organic frameworks (MOFs) [Cd2(adc)2(bpp)3·4H2O]n (1) and [Cd2(adc)2(bpp)3]n (1b) with a 2D breathing mode of reversible dehydration–rehydration have been designed and synthesized. The dynamic behavior between 1 and 1b was detected by in situ single-crystal XRD analysis, which demonstrated that this 3D pillared MOF contracted and expanded in two dimensions accompanied by dehydration–rehydration. Additionally, 1b exhibited faster water vapor response compared to commercial silica gel desiccant at room temperature (21 °C) and humidity (RH% 50), which was detected by in situ IR spectrum. Therefore, the results suggests that this kind of flexible 3D pillared metal–organic frameworks of 1 and 1b would be a reliable sensor for monitoring water vapor. (H2adc = 4,4′-azodibenzoic acid, bpp = 1,3-Di(4-pyridyl)propane).

Evaluation of Hydrogen Ions Flowing through Protonic Ceramic Fuel Cell Using Ytterbium-Doped Barium Zirconate as Electrolyte

Protonic ceramic fuel cells (PCFCs) are clean power generation devices that do not emit carbon dioxide, and generate electricity when protons (hydrogen ions) pass from the fuel electrode through the electrolyte to form water vapor at the air electrode. As a fuel cell, PCFC is expected to be a highly efficient fuel cell because it can operate at medium to low temperatures of around 600°C and no fuel dilution occurs. However, proton-conducting ceramics can conduct not only protons but also a small amount of holes, which can cause electron leakage and lower the power generation efficiency. Therefore, it is very important to understand the ionic and hole conductivity in a fuel cell.To evaluate the electronic leakage of PCFC, the amount of proton current flowed in PCFC can be measured by an apparatus developed based on the electromotive force of an electrochemical cell using 10 mol% In-doped CaZrO3 (hydrogen sensor) and Y stabilized zirconia (oxygen sensor). Water vapor pressure on cathode was change when hydrogen ion passes thorough PCFC. The current of hydrogen ion can be determined by monitoring water vapor pressure. We measured the current of hydrogen flowing through protonic ceramic fuel cell using ytterbium-doped barium zirconate as electrolyte. Hydrogen ion and hole were found to leak at OCV at 700°C. Then electronic leakage was estimated form the current of hydrogen ion and external current. The electronic leakage was found to be suppressed with increasing electrolyte film thickness. On the other hand, no leakage of hydrogen and holes was observed at 500°C.

Accuracy Analysis of Real-Time Precise Point Positioning—Estimated Precipitable Water Vapor under Different Meteorological Conditions: A Case Study in Hong Kong

Precipitable water vapor (PWV) monitoring with real-time precise point positioning (PPP) is required for the improved early detection of increasingly common extreme weather occurrences. This study takes Hong Kong as the research object. The aim is to explore the accuracy of real-time global navigation satellite system (GNSS) PPP in estimating PWV at low latitudes and under different weather conditions. In this paper, real-time PPP is realized by using observation data from continuously operating reference stations (CORS) in Hong Kong and real-time products from the Centre National d’Etudes Spatiales (CNES). The Tm model calculated using numerical weather prediction (NWP) data converts the zenith tropospheric delay (ZTD) of real-time PPP inversion into PWV and evaluates its accuracy using postprocessing products. The experimental results show that compared with GPS, multi-GNSS can reduce the convergence time of PPP by 29.20% during rainfall periods and by 12.06% during nonrainfall periods. The improvement in positioning accuracy is not obvious, and the positioning accuracy of the two is equivalent. Real-time PPP ZTD experiments show that there are lower average values for bias, standard deviation (STDEV), and root mean square (RMS) during nonrainfall periods than during rainfall periods. Real-time PPP PWV experiments show that there are also lower bias, STDEV, and RMS values during nonrainfall periods than during rainfall periods. The comparative study between rainfall and nonrainfall periods is of great significance for the real-time monitoring and forecasting of water vapor changes.

Realize intelligent anti-counterfeiting and efficient near-infrared emission of indium-based perovskite through crystal phase and energy band engineering

The crystal phase and energy band adjustment of halide perovskites have become the focus of research. Sb3+ doped lead-free indium-based perovskites A2InX5·H2O:Sb3+ (A=Cs+, Rb+; X = Cl-, Br-) are synthesized at room temperature. Through changing the crystal phase structure, an intelligent anti-counterfeiting material is used to monitor water vapor and high temperature. Moreover, the Rb2InBr5·H2O:Sb3+ achieves broadband near-infrared (NIR) emission with a high photoluminescence quantum efficiency (PLQY) of 54.3%. The NIR light-emitting diode shows that the device can effectively achieve NIR imaging. Theoretical calculations further clarified that the regular changes in luminescence originate from energy band modulation. This work provides a broader sight for the synthesis, luminescence mechanism and application of indium-based perovskite.

An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data

As one of the most important greenhouse gases, water vapor plays a vital role in various weather and climate processes. In recent years, a near-infrared ratio technique based on satellite images has become a research hotspot in the field of precipitable water vapor (PWV) monitoring. This study proposes a Level 2A PWV data retrieval method based on Sentinel-2 images (S2-L2A), which considers land-cover types and is more suitable for local areas. The radiative transfer model MODTRAN 5 is used to simulate the atmospheric radiative transfer process and obtain lookup tables (LUTs) for PWV retrieval. The spatial distribution of S2-L2A PWV is validated using Global Positioning System (GPS), Terra-MODIS PWV product (MOD05), and Level 2A product provided by ESA (ESA-L2A), while the time series results are evaluated using MOD05. Results show that the PWV retrieved by S2-L2A is both highly correlated and has low bias with the three PWV products, and is closer to the reference data than the MOD05 and ESA-L2A PWV. The relative PWV value in the morning is: bare soil > vegetation-covered area > construction land; as the elevation increases, the PWV value decreases. This study also analyzes the error distribution of the PWV data retrieved by S2-L2A, and finds that inversion error increases with AOT value, but decreases with elevation and normalized difference vegetation index (NDVI). Compared with the three water vapor products, the PWV data retrieved by the proposed method has high accuracy and can provide large-scale and high-spatial-resolution PWV data for many research fields, such as agriculture and meteorology.

Highly sensitive methane detection using a mid-infrared interband cascade laser and an anti-resonant hollow-core fiber.

For over a decade hollow-core fibers have been used in optical gas sensors in the role of gas cells. However, very few examples of actual real-life applications of those sensors have been demonstrated so far. In this paper, we present a highly-sensitive hollow-core fiber based methane sensor. Mid-infrared distributed feedback interband cascade laser operating near 3.27 µm is used to detect gas inside anti-resonant hollow-core fiber. R(3) line near 3057.71 cm-1 located in ν3 band of methane is targeted. Compact, lens-free optical setup with an all-silica negative curvature hollow-core fiber as the gas cell is demonstrated. Using wavelength modulation spectroscopy and 7.5-m-long fiber the detection limit as low as 1.54 ppbv (at 20 s) is obtained. The demonstrated system is applied for a week-long continuous monitoring of ambient methane and water vapor in atmospheric air at ground level. Diurnal cycles in methane concentrations are observed, what proves the sensor's usability in environmental monitoring.

Simulation Analysis of LEO Constellation Augmented GNSS (LeGNSS) Zenith Troposphere Delay and Gradients Estimation

Compared with current GNSS, which consists of medium- and high-altitude orbit satellites, Low Earth Orbit (LEO) satellites move faster and can greatly improve the observing geometry. In this contribution, LEO constellation augmented GNSS (LeGNSS) troposphere estimation is investigated, and the impacts of relevant factors are analyzed in detail. When the temporal resolutions of zenith troposphere delay (ZTD) and horizontal gradients are 1 h and 2 h, while standard deviations (STDs) of phase and pseudorange observations at the zenith direction are 0.005 m and 0.5 m, the accuracies of ZTD, north gradient, and east gradient augmented by LEO constellation improve by 15.7%, 29.6%, and 16.4%, respectively, compared with GNSS solution. The results of troposphere estimation under obstructed environment and using low-cost dual frequency receivers also become more robust after adding LEO observations. Most importantly, analysis results during periods with rapid varying troposphere parameters suggest that the contribution of LEO constellation becomes even bigger with increasing temporal resolutions of ZTD and horizontal gradients, indicating that LeGNSS can be used to extract tropospheric parameters with improved accuracies at high temporal resolution. Thus, the capability in rapidly capturing severe weather events can be improved by LeGNSS observations, and the performance can become even better with increasing number of LEO satellites. All these results suggest that LeGNSS might be an important tool in improving the performance of troposphere estimation, and would promote GNSS application in water vapor monitoring.

A Novel Global Grid Model for Atmospheric Weighted Mean Temperature in Real-Time GNSS Precipitable Water Vapor Sounding

The atmospheric weighted mean temperature (Tm) is an important parameter in calculating the precipitable water vapor from Global Navigation Satellite System (GNSS) signals. As both GNSS positioning and GNSS precipitable water vapor detection require high-precision spatial and temporal resolutions for calculating Tm, high-precision modeling of Tm has gained widespread attention in recent years. The previous models for calculating Tm have the limitation of too many model parameters or single grid data. Therefore, this study presents a global high-precision Tm model (GGTm-H model) developed from the latest MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version-2) atmospheric reanalysis data provided by United States National Aeronautics and Space Administration (NASA). The accuracy of GGTm-H model was verified by combining the MERRA-2 surface Tm data and 319 radiosonde data. The results highlighted that: (1) When the MERRA-2 Tm data was used as a reference value, the mean annual RMSE of the GGTm-H model was observed to be 2.72K. When compared with the Bevis model, GPT2w-5 and GPT2w-1 models, GGTm-H model showed an improvement of 1.5K, 0.33K, and 0.21K, respectively. (2) When the radiosonde data was used as a reference value, the mean bias and RMSE of the GGTm-H model were <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-0.41$</tex-math></inline-formula> K and 3.82K, respectively. Compared with the other models, the GGTm-H model had the lowest mean annual bias and RMSE. The developed model does not consider any meteorological parameters while calculating Tm. Therefore, it has important applications in the real-time and high-precision monitoring of precipitable water vapor from GNSS signal.

海上可降水量観測のための船舶搭載GNSS解析設定の最適化に関する研究

海上可降水量観測のための船舶搭載GNSS解析設定の最適化に関する研究

Water-Vapour Monitoring from Ground-Based GNSS Observations in Northwestern Argentina

The Central Andes in northwestern Argentina are characterized by steep topographic and climatic gradients. The humid foreland areas at 1 km asl elevation rapidly rise to over 5 km in the eastern Cordillera, and they form an orographic rainfall barrier on the eastern windward side. This topographic setting combined with seasonal moisture transport through the South American monsoon system leads to intense rainstorms with cascading effects such as landsliding and flooding. In order to better quantify the dynamics of water vapour transport, we use high-temporal-resolution global navigation satellite system (GNSS) remote sensing techniques. We are particularly interested in better understanding the dynamics of high-magnitude storms with high water vapour amounts that have destructive effects on human infrastructure. We used an existing GNSS station network with 12 years of time series data, and we installed two new ground stations along the climatic gradient and collected GNSS time series data for three years. For several stations we calculated the GNSS signal delay gradient to determine water vapour transport direction. Our statistical analysis combines in situ rainfall measurements and ERA5 reanalysis data to reveal the water vapour transport mechanism for the study area. The results show a strong relationship between altitude and the water vapour content, as well as between the transportation pathways and the topography.