The improvement of the ability of climate model to simulate the wind speed of 10 meters near the surface in the Arctic region plays an important role in predicting the future climate change in this region.Thirty-two CMIP6 （Coupled Model Intercomparison Project Phase 6） models were selected to provide the simulation results of daily wind speed data near the surface of the Arctic region during the historical test （1979 -2014）， and their abilities to simulate the average wind speed near the surface of the Arctic region at 10 meters and the occurrence probability of strong winds of magnitude 6 were evaluated.Based on this， the models with excellent simulation performance （6 models）， medium simulation performance （12 models） and poor simulation performance （14 models） are selected.The temporal and spatial variation characteristics of average 10 m wind speed near the surface and the probability of 6-level gale in the Arctic region under different emission scenarios in the future are predicted by using the ensemble average of excellent simulation models.The results show that： （1） The observation shows that the Greenland Sea， the Norwegian Sea， the Barents Sea and the Chukchi Sea gain higher 10m average wind speed and probability of 6-level gale in the Arctic region， and they are both smaller in summer and autumn during the year in the above sea areas.（2） In historical experiments， the CMIP6 model can well simulate the spatial distribution characteristics of the average wind speed of 10 m near the surface and the probability of 6-level gale in the Arctic region， and the simulation result of the Atlantic sector of the Arctic Ocean is the best.The 10 m average wind speed near the surface simulated by the models are generally 10%~20% higher than the observation in the Arctic region， and the probability of strong winds of magnitude 6 are generally 2%~4% higher than the observation.The simulation deviation in spring and summer are generally smaller than that in autumn and winter.（3） The variation of 10 m average wind speed near the surface and the probability of 6-level gale in the Arctic region in the 21st century simulated by the excellent group models show significant regional differences.Compared with the historical period， 10 meters average wind speed generally increase in the central area and the Pacific sector of the Arctic Ocean， but generally decrease in the Atlantic sector and the coastal areas of the Arctic Ocean.The probability of strong winds of magnitude 6 increase in the Pacific sector of the Arctic Ocean， but decrease significantly in the Atlantic sector.In terms of temporal variation， the wind speed increase rapidly in the high emission scenario， and the increase speed is fastest in autumn， followed by winter and summer， and slowest in spring.The probability density distribution of wind speed in the Arctic region in the future has not changed significantly compared with the historical period， and there is little difference under different emission scenarios.

This study， through comprehensive observations of the radiation field in the Everest National Nature Reserve （hereinafter referred to as "Everest"）， reveals the spatiotemporal variation patterns and potential impacts on the ecological environment and climate.By establishing a meteorological gradient observation network covering diverse ecological environments， including alpine shrubs， alpine wetlands， and alpine desert grasslands， and utilizing advanced radiation observation equipment combined with data quality control and automated processing， the study collected multi-year， continuous， high-precision measurements of four-component radiation fluxes.The results show the following： （1） The net radiation flux at Everest and alpine shrub sites exhibits an annual increase of 0.7 W·m^-2 on an interannual scale； （2） In terms of multi-year monthly average net radiation flux， the values at Everest and alpine shrub sites increase from 15 W·m^-2 in January， peak at 110 W·m^-2 in August， and then decrease to 14 W·m^-2 by December.The multi-year monthly average downward shortwave radiation flux rises from 210 W·m^-2 in January to a maximum of 375 W·m^-2 in June， followed by a significant drop to 230 W·m^-2 in July， remains relatively stable from July to October， then sharply declines from October， reaching a minimum of 200 W·m^-2 in December； （3） The multi-year summer daily average net radiation flux at Everest and alpine shrub sites rises from 0 W·m^-2 at 07:00 （Beijing Time， the same as after）， peaks at 530 W·m^-2 at 13:00， and declines to -110 W·m^-2 at 20:00.The multi-year summer daily average downward shortwave radiation flux increases from 0 W·m^-2 at 07:00， peaks at 860 W·m^-2 at 13:00， and drops back to 0 W·m^-2 at 21:00.In winter， the daily average net radiation flux rises from -120 W·m^-2 at 08:00， reaches a maximum of 370 W·m^-2 at 14:00， then decreases， reaching -120 W·m^-2 again at 20:00.The daily average downward shortwave radiation flux in winter rises from 0 W·m^-2 at 08:00， peaks at 840 W·m^-2 at 14:00， and falls to 0 W·m^-2 by 20:00； （4） There are significant differences in radiation flux among the stations， with the contrast being particularly prominent between the alpine wetland station and the other two stations.The annual average net radiation， annual average downward shortwave radiation， multi-year monthly average net radiation， multi-year monthly average downward shortwave radiation， and the daily average net and downward shortwave radiation in both summer and winter are all higher at the alpine wetland station than at the Everest and alpine shrub stations.The results of this study provide new insights into understanding climate change in high-altitude regions and offer essential data support for developing remote sensing monitoring technology， improving global climate models， and formulating environmental protection strategies in plateau areas.

On August 13-14， 2022， an extreme rainstorm event occurred in Yuzhong region of Gansu Province， northeast of the Qinghai-Xizang Plateau.Accumulated daily precipitation reached 130.6 mm and the maximum hourly precipitation was 36.6 mm， breaking the heaviest daily precipitation records of the region and causing serious social impact and economic losses.Based on the data of surface minute observation and high altitude observation， Lanzhou Doppler radar and ECMWF Reanalysis v5（ERA5）， by analyzing the observation characteristics， environmental conditions， topographic effects and instability mechanism of the two heavy precipitation stages in this extreme rainstorm， the results show that： （1） The rainstorm was caused by the convergence of weak cold air brought by the shortwave trough in the westerlies and warm and humid air outside the subtropical high in the Longzhong area.The 700 hPa shear line provided the dynamic lifting conditions， and the surface cold front provided the triggering conditions.（2） The radar reflectance factor in the rainstorm process was characterized by persistent strong echoes accompanied by "backward propagation"， low-level jets and obvious convergence.In the second stage， the echo top height behind the cold front was similar to that in the first stage， but the scope was larger and the structure was more compact， and the convective cloud development was more vigorous.（3） The water vapor conditions of the rainstorm were abundant.In the first stage， there was significant convective instability due to strong convergence and upward movement at the lower level and high convective effective potential energy.In the second stage， the upward movement was weakened， the convective effective potential energy was 0， and the dynamic and convective instability conditions were weak.（4） The release of unstable energy triggered by cold front baroclinic frontogenesis was the main triggering mechanism of precipitation in the first stage.After the transit of the cold front， the precipitation in the second stage was formed by the combination of terrain， frontogenic secondary circulation and instability.Since the heavy precipitation after the summer cold front was not common in the northeast part of the Qinghai-Xizang Plateau， forecasters tended to ignore such kind of rainstorms.Therefore， we need to strengthen monitoring and early warning of such rainstorm events.

This study evaluates the simulation capabilities of 16 models with varying resolutions from the High-Resolution Model Intercomparison Project （HighResMIP） in reproducing warm-season （May to September） precipitation over the eastern slope of the Qinghai-Xizang Plateau， using the CN05.1 dataset as observational reference.Through comparative analysis of outputs from multiple models against observational data， this study elucidates model strengths and limitations in capturing spatiotemporal variability， precipitation intensity， and terrain-related mechanisms.Results indicate that high-resolution climate models demonstrate reasonable accuracy in simulating annual and warm-season precipitation spatial patterns， though notable inter-model discrepancies persist.Three models （CMCC-CM2-HR4， CMCC-CM2-VHR4， FGOAL-f3-H） successfully replicate the observed increasing trend in annual precipitation， while others exhibit stable or decreasing trends.Persistent model deficiencies emerge in simulating precipitation frequency and intensity： all models systematically underestimate light precipitation events （<1 mm∙d^-1） while overestimating heavy precipitation frequency （>4 mm∙d^-1）.Medium-to-low-resolution models show a systematic phase lag of approximately 30 days in diurnal precipitation cycles compared to observations.In contrast， the high-resolution models performed better in simulating precipitation frequency than the medium low resolution group.Based on comprehensive evaluation of temporal distribution， frequency characteristics， and model skill scores， the ECWMF model demonstrates superior performance， whereas the FGOAL-f3-H model exhibits significant negative biases.

Research on the characteristics of carbon and water fluxes and water use efficiency （WUE） in the alpine meadow ecosystem of the Qinghai-Xizang Plateau is of crucial significance for accurately assessing the carbon balance， water cycle， and carbon-water coupling of the alpine grassland ecosystem under the background of climate change.In this study， based on the observation data made using eddy correlation in the alpine meadow on the eastern Qinghai-Xizang Plateau from 2012 to 2017， from the Maqu observation point of the Zoige Wetland Ecosystem Research Station of the Northwest Institute of Eco-Environment and Resources， Chinese Academy of Sciences， we analyzed the changes in carbon and water fluxes and WUE during the growing season.By combining multiple stepwise regression and structural equation modeling， we explored the main driving factors of carbon and water fluxes and WUE during the growing season.The results indicate that： （1） The average annual net ecosystem CO₂ exchange （NEE）， ecosystem respiration （Re）， and total gross primary productivity （GPP） of the Maqu alpine meadow ecosystem over 6 years were -109.7， 798.6， and 908.3 gC·m^-2·a^-1， respectively， showing an overall carbon sink； the annual average evapotranspiration （ET） is 446.5 kg·m^-2·a^-1 the 6-year average water use efficiency （WUE） was 2.0 gC·kg^-1.（2） The daily variations of NEE and GPP during the growing season showed a distinct unimodal pattern， peaking around 14:00 （Beijing Time， the same as after）， while Re showed a relatively flat diurnal pattern， lower slightly at night than during the day； the daily variation of ET exhibited a unimodal pattern， with peak monthly and monthly accumulative values in July； WUE displayed an asymmetric "U" curve with the minimum value at 13:00 -14:00， showing significant daily and monthly variations in July and August.（3） During the growing season， both multiple stepwise regression and structural equation models confirmed the dominant role of temperature in controlling carbon fluxes and radiation in controlling ET.Temperature and radiation were identified as the main influencing factors for WUE during the growing season.

The seasonal vegetation greening in the Three River Source Region （TRSR） has a profound impact on the regional ecological environment and water resource security.In this study， the moisture drivers of seasonal vegetation greening in the TRSR and their responses to climate change were investigated using multi-source data from 2003 to 2021， through the application of trend analysis， correlation analysis and partial information decomposition （PID） analysis.The results showed that： （1） From 2003 to 2021， the linear trend of the Leaf Area Index （LAI） generally increased in spring， summer and autumn in TRSR， although the environmental conditions varied significantly between seasons.Linear trends in precipitation， soil moisture （SM） and snow cover （SC） all showed an increasing trend in spring and autumn， with insignificant changes in temperature.In summer， linear trends of temperature were slightly increased， but precipitation and SM slightly decreased， as well as insignificant changes in SC.（2） In terms of the effects of moisture driving factors on LAI： correlation analyses results showed that LAI was significantly positively correlated with SM in spring and summer， but not in autumn.The correlation between LAI and SC was weak in all seasons.By introducing the PID analysis method， the nonlinear and synergistic effects of SM and SC on LAI were effectively revealed.The independent information contribution of SC to LAI changes was higher in spring and autumn， making it the main moisture driver in these seasons， while SM contributed more in summer.At the same time， the synergistic effects of SM and SC played an important role in the changes of LAI in all seasons， with the synergistic information contribution exceeding 30% in all seasons.（3） Response of moisture drivers to climate change： correlation analyses results showed that SM was significantly positively correlated with precipitation in all seasons and significantly negatively correlated with temperature in spring； SC was significantly positively correlated with precipitation in all seasons and significantly negatively correlated with temperature in both spring and autumn.PID analyses also indicated that precipitation was the main meteorological factor influencing changes in SM and SC across the three seasons， with a higher independent contribution than temperature.However， the synergistic effects of temperature and precipitation on SM and SC in all seasons should not be overlooked.

Drought represents a significant contributing factor to global climate-related disasters.It not only endangers the stability of global ecosystems and biodiversity but also has far-reaching implications for socio-economic development.As global climate change intensifies， so too does the frequency and intensity of droughts.Drought events in ecologically fragile regions not only threaten the availability of water resources but also increase the risk of food insecurity， ecological degradation and social conflict.Nevertheless， despite the growing body of research in this area， there remain significant gaps in our understanding of the spatial and temporal characteristics of drought occurrences over the past four decades， as well as its evolutionary trends under different climate scenarios in the future.This study employs the Standardized Precipitation Evapotranspiration Index （SPEI） and CMIP6 climate change scenarios to analyze the spatial and temporal characteristics of global droughts over the past four decades and to predict the evolution of global droughts under different climate scenarios （SSP1-2.6、 SSP2-4.5、 SSP5-8.5） over the next 80 years.The findings of the study indicate that： （1） During the period between 1980 and 2022， there were notable variations in the spatial and temporal characteristics of global drought across different regions.Globally， approximately 57% of the land area does not exhibit a significant drought trend.However， about 33% of the land shows a persistent aridification trend， particularly in some already arid regions， where the intensity of drought has increased.Conversely， only 10% of the area is becoming wetter， indicating that the regions of the globe that are becoming drier are significantly larger than those that are becoming wetter.This suggests that the aridification process is spreading globally； （2） Over the past four decades， the globe has experienced an arid trend with no significant seasonal differences.However， the arid regions in winter are expanding， accounting for 33.2% of the global land area； （3） Different vegetation cover types exhibit varying responses to drought.Sparsely vegetated areas are more susceptible to drought， while densely vegetated areas tend to be wetter.Furthermore， arid climate zones situated within diverse climatic contexts are confronted with more pronounced drought-related challenges.The largest proportion of severe drought is observed in extreme arid zones， which account for up to 67% of the global land area， this indicates a higher frequency of drought events in drylands； （4） The probability of drought events is predicted to increase significantly in Africa， South America， southeastern Asia， and southern North America， particularly in tropical or warm climatic zones， extreme arid zones， and evergreen broadleaf climate zones.Furthermore， droughts are expected to become more frequent and severe， especially in tropical or very warm climate zones， arid zones， and broadleaf evergreen forest regions.The SSP5-8.5 scenario is projected to have the highest probability and intensity of drought events over the next 80 years， and will be challenged by more frequent and severe droughts.The findings of this study underscore the pervasive and severe nature of the global aridification trend， particularly in the context of climate change， where the frequency and intensity of droughts are projected to increase significantly.This trend not only enhances our comprehension of the risk of drought， but also furnishes an essential point of reference for policymakers， water managers， and the general public.In order to mitigate the potential intensification of droughts in the future， it is imperative that all sectors of society implement more proactive and efficacious measures to promote adaptation and mitigate the challenges posed by droughts.The rational management of water resources， improvements in agricultural irrigation techniques， enhanced ecosystem resilience and the strengthening of monitoring and early warning systems for climate change and droughts will ensure global ecological security and facilitate sustainable socio-economic development.

In recent years， rapid urbanization and global warming have led to frequent and severe rainstorm and flood disasters in the Sichuan-Chongqing region.This change will not only have a serious impact on the ecological environment and socio-economic development of the area， but also significantly increase the pressure on urban infrastructure and threaten the safety of people's lives and property.Therefore， it is particularly important to scientifically and accurately analyze the disaster risk of rainstorm and flood in Sichuan-Chongqing region in the past and future.This paper utilized daily precipitation data from 50 selected meteorological stations in the Sichuan-Chongqing region， precipitation data from 5 CMIP6 models， gridded population and economic data under Shared Socioeconomic Pathways （SSPs）， as well as DEM and land use remote sensing data.Firstly， using Taylor diagrams， quantitative indices （S）， and standardized anomaly sequences， the study evaluated the simulation performance of 5 individual CMIP6 models， an equal-weighted aggregation of 5 models （EWA-5）， and unequally-weighted aggregations of 5 models （UEWA-5） for five selected extreme precipitation indices.Then， by building a comprehensive risk assessment model of rainstorm and flood disaster based on disaster risk and vulnerability of disaster bearing body， the study conducted risk assessments， future projections， and comparative analyses of rainstorm and flood disasters during baseline （1995-2014） and future near-term （2025-2044） and long-term （2045-2064） periods under three different climate change scenarios （SSP1-2.6， SSP2-4.5， SSP5-8.5）.Results indicated： （1） The EC-Earth3 model performed best in simulating the five extreme precipitation indices， with correlation coefficients between simulated and observed values of 0.78 for R95p， 0.90 for RX1day， and 0.77 for RX5day.Overall， the simulation performance of UEWA-5 exceeded that of EWA-5.（2） During the baseline period， central Sichuan exhibited high values for the five extreme precipitation indices， followed by eastern Sichuan and Chongqing， while western Sichuan showed lower values.The year 1998 recorded peak values for all five indices， with a maximum single-day precipitation of 86 mm for RX1day and an intensity （SDII） value of 11.3 mm·d^-1.（3） In future periods， the five extreme precipitation indices display a spatial distribution characterized by higher values in central regions and lower values around the periphery.Higher levels of social vulnerability and radiative forcing correlate with larger values of extreme precipitation indices.Comparing the two future periods， values of the indices are larger in the long term， notably with R95p averaging 846.8 mm， an increase of 169.2 mm compared to the near term.（4） During historical periods， areas with higher comprehensive risk of rainstorm and flood disasters were concentrated in central Sichuan and downtown Chongqing.In the two future periods， the high and moderately high-risk areas in central Sichuan are expected to expand， while the moderate-risk areas will shrink.The range of low-risk areas in the western Sichuan Plateau will also decrease， and the risk levels in southern Sichuan and eastern Sichuan-Chongqing border areas will respectively decrease to moderate-low and low-risk zones.Comparing the two future periods， the range of moderately high and moderate-risk areas in central Sichuan is expected to expand， while southwestern Chongqing will transition to a moderate-risk area in the long term.Other regions will generally maintain their original risk levels.Changes in disaster risk levels in the Sichuan-Chongqing region are less pronounced with increasing social vulnerability and radiative forcing， especially in the western Sichuan Plateau and northeastern Sichuan， where changes in disaster risk levels are minimal.The study results can provide important references for reducing disaster risks， enhancing emergency response capabilities， and making scientifically informed decisions for disaster prevention in the Sichuan-Chongqing region.

Using the hourly precipitation data of 80 national stations in Heilongjiang Province， the NCEP/ NCAR and the EC-ERA5 reanalysis data， the diurnal variation characteristics of heavy precipitation （hourly precipitation≥5 mm） by Northeast Cold Vortex （NECV） during the warm season （from May to September） from 1981 to 2022 were analyzed.Multiple typical cases at four times a day were selected for synthesis， which was used to eliminate the influence of intensity change caused by the generation and extinction process of a single case system， and analyze the reasons for the diurnal variation characteristics of heavy precipitation.The results showed that： （1） the heavy rainfall in the warm season was concentrated in June to August， and the heavy rainfall occurred mostly in July， and the maximum precipitation was the heaviest in June and August.The high frequency region of heavy precipitation was located in the southeast quadrant of NECV， the lower frequency could be found in the northeast quadrant.（2） The central position and intensity of NECV corresponding to the large-scale heavy precipitation had obvious diurnal variation characteristics： NECV was stronger at night， and its location was north and west.During the day， NECV was weak， and its position was south to east.The large range of heavy rainfall coupled with the high and low air jet stream appeared： the strong rainfall area was located in the upper jet stream core right back or left front side， the lower jet stream front and left front side.The distribution of the high and low air jet showed significant diurnal variation： the strongest in the afternoon and the best dynamic conditions； the upper level jet stream is weakest at night， and the corresponding upper level divergence conditions are weakest.The southerly air vapor transport was the main at night； During the day， the water vapor transport of the southwest air stream was significantly enhanced， and in the afternoon， the water vapor transport of the southwest air stream was mainly.（3） The wide range of heavy precipitation was corresponded to strong cyclonic activities， and there was little difference in the central positions of cyclones among different times.Both the cyclone intensity and the surface dew point temperature had significant diurnal variation characteristics.The high frequency area of heavy precipitation generally appeared in the large pressure gradient area of the cyclone center and its north or east side， corresponding to the larger dew point temperature.（4） The high frequency of heavy precipitation was related to the distribution of local topography.The mesoscale vertical circulation at night played a more prominent role in the magnitude and high frequency distribution of heavy precipitation on the east side of NECV.The cold air at night was more active， the spatial gradient of nighttime precipitation was larger， and the heavy precipitation was more localized and affected by terrain more significantly.The effect of topography on increased precipitation in daytime was not obvious.The amount of water on the leeward slope was generally greater than that on the windward slope.

Utilizing the Anomaly Numerical-correction with Observations （ANO） based on historical observation data and anomaly integration， in conjunction with the ERA5 reanalysis data， a rectification test was conducted on the forecasting products of the Southwest Center WRF-ADAS Real-time Modeling System （SWMS in short）.This study evaluated the efficiency of the ANO method in enhancing short- to medium-term weather forecasts for meteorological quantities during a catastrophic regional heavy rainfall event over the complex topography from June 20 to 25， 2019.Results revealed that the SWMS model exhibited commendable predictability in the middle and upper troposphere， although its accuracy gradually reduced in the lower layers.After post-processing correction using the ANO method， all the predicted variables showed obvious improvements.The average Anomaly Correlation Coefficient （ACC） for the 500 hPa and 700 hPa geopotential height fields within the 72-hour integration increased by a range of 0.1 to 0.2， reaching approximately 0.8， while the 850 hPa geopotential height ACC showed a maximum enhancement of 0.6.Concurrently， the Root Mean Square Error （RMSE） for the corrected 700 hPa and 850 hPa geopotential heights exhibited significant reductions with an average decrease of 24% and 66%， respectively.The correction outcomes for temperature， specific humidity， and horizontal wind also displayed positive effects， which reveal the efficient correcting performance of the ANO method based on historical observation data in rectifying short- to medium-term numerical forecasts of the SWMS model over complex topography.

Global Climate Models （GCMs） are the primary tools currently used to predict future climate change； however， their coarse spatial resolution limits their ability to assess localized impacts of climate change.To address this issue， statistical downscaling methods based on linear regression equations have been developed to enhance the spatial resolution of GCMs and have continuous improvement and innovation.Meanwhile， machine learning techniques have demonstrated superior performance in various predictive modeling problems， making them potential new tools for statistical downscaling.Therefore， this study applied machine learning model-Light Gradient Boosting Machine （LightGBM ） to construct statistical downscaling model for each site， combined with information flow method to select forecasting factors， and compared with linear regression method （stepwise multiple linear regression method based on Empirical Orthogonal Function） to explore the application ability of LightGBM in the field of statistical downscaling.The two methods were applied to downscale the meteorological element of the Yellow River basin， an important climate change sensitive area in China， establishing statistical downscaling models for 90 stations within the basin to generate temperature and precipitation data for the summer months （June， July， August） from 1965 to 2014.The performance of both methods is evaluated through an analysis of the correlation coefficients， root mean square errors （RMSE）， and spatial distributions between downscaled values and observed values.The results show that both downscaling methods can correct the temperature error of the reanalysis data （ERA5） in the northern part of the basin.LightGBM shows superior inter-site correlation， but 60， 64， 52 sites show higher RMSE than regression method in June， July， and August， respectively.For precipitation downscaling， neither of the two downscaling datasets nor ERA5 could accurately represent the spatial distribution of observed values， but the downscaling value obtained by LightGBM had a higher inter-site correlation coefficient than the regression method， and only 16， 7， 14 sites showed higher RMSE than the regression method in June， July and August.Considering the potential of machine learning methods for modeling nonlinear problems， it is still necessary to further improve the algorithm and improve the quality of downscaling data sets in the future.The advantages and disadvantages of machine learning in downscaling work provided a technical reference and support for using statistical downscaling methods to generate high-resolution temperature and precipitation data in the future.

Scientific and reasonable standards for issuing rainstorm warning signals are critical for preventing rainstorm disasters.Based on hourly precipitation data at ground stations from 45 representative counties in Sichuan Province during April-October in the period of 2019 -2021， combined with historical meteorological disaster risk census data， this study conducted a retrospective analysis of rainstorm warning classifications to compare the spatiotemporal distribution and applicability of warning signals before and after the revision of Sichuan's rainstorm warning standards.Results demonstrate that the revised criteria significantly increased the issuance frequency of red and orange warning signals while reducing yellow warnings， reflecting an overall enhancement in rainstorm alert intensity.The incorporation of a 1-hour rainfall intensity threshold in the updated standards notably amplified the proportion of red warnings in Ganzi， Aba， and Liangshan Prefectures （collectively known as the Three Prefectures region）， substantially improving early warning capabilities in plateau areas.Historical disaster analysis revealed superior performance of the revised criteria， with higher hit rates for extreme rainstorm events， enhanced warning intensity， and improved timeliness.Notably， Wenchuan County exhibited reduced over-alerting due to the "downgrade adjustment" mechanism in warning classification.Overall， the revised standard for rainstorm warnings in Sichuan effectively incorporates the high hazard potential of short-duration heavy rainfall and better distinguishes risk levels between mountainous and non-mountainous areas.It significantly improves the precision， consistency， and relevance of early warnings， aligning with the evolving demands of targeted meteorological services and disaster risk reduction.

In recent years， the urbanization process of Nanning has accelerated significantly， but at the same time， heavy precipitation has also shown a trend of frequent occurrence and increased extremity.To this end， this study used the Weather Research and Forecasting system coupled with Urban Canopy Model （WRF-UCM）， the ERA5 reanalysis data， and sensitivity test to simulate the heavy precipitation process of a low vortex type in Nanning in May 2022.Results show that： （1） After updating the land use information and coupling the urban canopy model， the simulation results of 6-hour cumulative rainfall are consistent with the observed results in the spatiotemporal distribution， and more accurately reflect the evolution of precipitation intensity in the main urban area； （2） Under the background of hilly basin topography， the circumfluence effect and the intensification of the heat island effect， both resulting from changes in surface characteristics driven by Nanning's urban land expansion， are the main factors contributing to precipitation changes， which causes the occurrence time of heavy precipitation to increase by 1~2 hours and the precipitation increase by 10%~30% from the main urban area to the downwind area 20 km， and the precipitation in the southern suburbs and upwind area decreases by about 15%； （3） In the development stage of precipitation process， the decrease of latent heat flux and convective effective potential energy （CAPE） caused by urban land expansion will inhibit the development of deep convective precipitation， but the urban heat island effect caused by the increase of sensible heat and surface soil heat flux in urban areas， as well as the enhancement of urban surface friction and circumfluence on the convergence and uplift of near-surface airflow， promote the increase of cloud and rainfall content and the new development of convective clouds in the front of the low vortex， resulting in the early occurrence of heavy precipitation in urban areas and downwind areas.In the later stage of the precipitation process， the enhanced airflow caused by the proximity of the center of the low vortex makes the effect of circumfluence more obvious， and the re-evaporation of surface water vapor caused by the enhancement of precipitation and the increase of latent heat of condensation at 925~700 hPa have a positive feedback effect on the enhancement of the low vortex， which jointly drives the development of warm cloud precipitation process and improves the precipitation efficiency， and the development of local circulation in the city promotes the mutual transformation of ice particles and rainwater in convective clouds， resulting in the maintenance of convective cloud intensity.As a result， the duration of heavy precipitation is prolonged， resulting in an increase in accumulated rainfall.

Using observation data from 945 ground-based Global Navigation Satellite System （GNSS） stations in the summer of 2023 in China， the double-difference （DD） network algorithm and Precise Point Positioning （PPP） algorithm were used to invert the Precipitable Water Vapor （PWV） of the atmosphere.The accuracy and stability characteristics of the two methods for inverting PWV in different climate regions of China were studied and analyzed using PWV from radiosonde stations at the same location and ERA5 reanalysis data as reference values.The results show that compared with the PPP solution， the DD solution has a stronger correlation with the PWV of radiosonde and ERA5 data， a more concentrated bias frequency distribution， a higher probability of peak area， and a smaller range of bias.Compared with the Radio-PWV， the average biases for the DD-PWV and PPP-PWV are -0.1 mm and 1.1 mm， respectively， and the corresponding root mean square error （RMSE） are 2.4 mm and 3.1 mm， respectively.While compared with the ERA5-PWV， the average Bias of the DD-PWV and PPP-PWV are -0.2 mm and 0.1 mm， respectively， and the corresponding RMSE are 2.7 mm and 3.2 mm， respectively.The average RMSE of the DD solution is less than 3 mm， indicating that the PWV inverted by the DD network method has higher accuracy and stability.The accuracy of GNSS detection of water vapor is generally better in the western non monsoon region than in the eastern monsoon region.The RMSE of the DD solution is more concentrated around the median in various climate regions， while the PPP solution shows different levels of accuracy at different GNSS stations.The PPP solution has a greater degree of dispersion in accuracy and strong instability in temperate and subtropical monsoon climate regions with sufficient water vapor and low detection accuracy.

The QPE_TP algorithm of the reflectivity factor shows significant improvements over traditional precipitation estimation methods.To fully leverage the advantages of polarization radar in quantitative precipitation estimation （QPE）， a tiled regional quantitative precipitation estimation method using a dual-polarization parameter network with S and X-band radars has been applied in this paper.By assessing various precipitation estimation methods based on polarization parameters and traditional methods through five weather events， the results show that the QPE_TP_Z_K_DP comprehensive estimation method exhibits clear advantages over others， especially in strong precipitation areas， where its QPE closely match the actual rainfall distribution， resulting in the smallest error metrics.Therefore， the QPE_TP_Z_K_DP method is an effective approach for enhancing radar QPE compared with other methods.

Evapotranspiration is an important part of the energy balance of terrestrial ecosystems and a key link in the atmospheric water cycle.However， due to the limitation of practical conditions， there is a lack of evaluation of the performance and applicability of long-time remote sensing evapotranspiration products of different underlying surface types at the basin scale， resulting in many uncertainties in the estimation of water demand of the basin ecosystem， the evaluation of water resources， and the management and use of water resources.Therefore， based on the long-term time-series flux monitoring data of the ground positioning stations of different ecosystems in the Heihe River Basin， this paper uses the random forest algorithm to construct a multi-site， long-time series， high-precision ground actual evapotranspiration dataset （15 stations， 113 station years， daily scale）， and selects six commonly used remote sensing evapotranspiration products （SSEBop， GLEAM， MOD16， PML_V2， GLASS and ETMonitor） and extracted the annual values of evapotranspiration products of raster pixels in each ecosystem type site， and evaluated the accuracy and applicability of each remote sensing evapotranspiration product in the Heihe River Basin by R²， RMSE， MAE， Bias and other indicators.The results showed that： （1） SSEBop products had the highest overall accuracy in the Heihe River Basin （R²=0.63， RMSE=251.99 mm·a^-1）， followed by ETMonitor products （R²=0.26， RMSE=275.47 mm·a^-1）， GLEAM products （R² was not significant）， GLASS products with the smallest Bias were -22.57 mm， and GLEAM products were the largest （-317.49 mm）.（2） The performance of the six remote sensing evapotranspiration products in mountain forest system and cropland system was relatively good， and the performance in desert forest system and desert system was the worst， while the actual evapotranspiration of wetland system was generally underestimated.Among them， the SSEBop product was underestimated in all ecological types except the cropland system， while the GLASS product performed well in the desert forest system but seriously overestimated the actual evapotranspiration of the desert system.（3） At the station scale， the ET of wetland ecosystem was the largest， about 1210 mm， and that of desert ecosystem was the smallest， about 180 mm.By evaluating the accuracy and applicability of different remote sensing evapotranspiration products in the Heihe River Basin， this study provides a scientific basis for the selection of evapotranspiration models in the basin under complex terrain， climate and ecosystem conditions in arid areas， and also provides a reference for the scientific management and use of water resources and ecological protection in the basin.

Information on wind profiles within a wind farm is crucial for predicting the output power of wind turbines.As the size of wind turbines continues to increase， commonly used wind towers are unable to obtain complete wind profile information within the wind farm due to their limited observation height.This study proposes an optimized processing procedure for quality control and wind speed inversion of high-quality wind profiles within the boundary layer， based on the Doppler lidar Velocity Azimuth Display （VAD） scanning method， by comparing data quality control and processing methods of varying strictness.The complete vertical wind profile from the near ground to the height affected by the wind turbines is obtained using this method.Compared with the traditional method of only using the Carrier-to-Noise Ratio to control the quality of raw radial wind speed data， the data quality control and processing method proposed in this study can significantly improve the accuracy of wind speed inversion.The correlation coefficients between the inverted wind speed and that observed increase from 0.826 （10 m） and 0.926 （70 m） to 0.932 and 0.958， respectively， while the biases decrease from 0.500 m∙s^-1 （10 m） and 0.063 m∙s^-1 （70 m） to 0.464 m∙s^-1 and 0.034 m∙s^-1.Furthermore， by using the optimized inversion method proposed in this study， it is possible to obtain a complete boundary layer wind profile from the blind zone detected by the Doppler Beam Swinging （DBS） scanning method to the height affected by wind turbine blades.This can be used for research on wind resource assessment， wind power prediction， and the development of parameterization schemes for wind farms.

The level of nitrogen oxides （NO _x ） emission is a key indicator for assessing anthropogenic atmospheric pollutant emissions.Understanding the long-term trends of NO _x emissions and identifying emission hotspots are of significant importance for air pollution control.This study utilized OMI and TROPOMI data to analyze the long-term trends and spatial distribution characteristics of tropospheric NO₂ column density across various regions of Sichuan and Chongqing from 2010 to 2021.Additionally， ground-based NO₂ concentration data from environmental monitoring stations and NO _x emission data from the MEIC inventory were integrated to evaluate the long-term trends of NO _x emissions observed by satellite.Ground-based NO₂ concentration measurements and MEIC NO _x emission inventory data are incorporated to assess the accuracy of satellite observations.The results show that： （1） The spatial distribution of NO₂ column density in the Sichuan-Chongqing region reveals two hotspot areas， including Chengdu and the central urban area of Chongqing.Compared to OMI satellite， TROPOMI， with its higher signal-to-noise ratio and resolution， captures more detailed spatial distribution features； （2） tropospheric NO₂ column density exhibits a declining trend， with rates of -0.115×10¹⁵， -0.096×10¹⁵ and -0.053×10¹⁵ molec·（cm²·a）^-1 in the Chengdu Plain， Chongqing， and the Sichuan-Chongqing region， respectively； （3） NO₂ column density increased with rising NO _x emissions from industrial and transportation sources during 2010 -2012.For the period of 2012 -2015， NO₂ column density decreased by 19.91% across the Sichuan-Chongqing region， which is mainly attributed to substantial emission reductions in power and industrial sectors.From 2015 to 2017， NO₂ concentrations increased by 10.76%， followed by a gradual decline； （4） The trends in satellite-observed NO₂ column density closely align with ground-level ambient NO₂ measurements； （5） NO₂ column density exhibits distinct seasonal variations： in the Sichuan Basin， winter NO₂ concentrations are significantly higher than in other seasons， followed by spring and autumn， with summer concentrations being the lowest.In contrast， in the western Sichuan Plateau， NO₂ concentrations are higher in summer than in winter due to natural sources.

The impact of land-use type change on regional climate through alterations in surface-atmosphere interactions， understanding the effects of land use change on regional climate is essential for informing future land use planning and policy decisions， especially in arid and semi-arid regions where the ecological environment is highly vulnerable and sensitive.Situated on the eastern edge of the Taklamakan Desert， the Cherchen River Basin serves as the only barrier protecting the surrounding county from being overtaken by the advancing desert.Over the past 20 years， human activities have led to an increase in local vegetation and expanded water bodies.However， the effects of these changes on the regional climate remain unclear.Using the WRF （Weather Research and Forecasting） model， MODIS MCD12Q1 6.1（MCD12Q1） global land use type dataset from 2001 and 2021 were employed to analyze how land use changes impacted the regional climate in July 2021 in the Cherchen River Basin.The results indicate that the increase in water bodies and green spaces in the Cherchen River Basin has led to rise in surface evapotranspiration of 2.95 mm.Additionally， the specific humidity at 2 meters above the surface increased by 2×10⁻⁴ kg·kg^-1.This rise in water vapor enhanced precipitation in the southern region of the basin.However， in the northern part， precipitation remained relatively unchanged due to the influence of anticyclonic airflow.Moreover， the additional precipitation resulting from land greening and increased water bodies was insufficient to compensate for the water lost through evapotranspiration.In essence， while local hydrological conditions improved with the expansion of green areas and water bodies， this did not fully offset the losses incurred through evaporation processes.The temperature in the recovery area of Lake Taitema was significantly reduced， the average maximum and minimum temperatures decreased by 1.8 °C and 1.3 °C.In areas outside Lake Taitema， the increase in surface vegetation influences temperature by regulating evapotranspiration， surface albedo， and atmospheric longwave radiation.This effect results in higher maximum temperatures and lower minimum temperatures in the northern part of the basin， while both maximum and minimum temperatures decline in the southern part.Overall， the increase in surface net radiation， driven by a reduction in surface albedo， was the predominant factor， contributing to an average increase of 7.35 W·m^-2 and a corresponding rise of 0.21 °C in average surface temperature.The increased water bodies and green spaces in the Cherchen River Basin have facilitated the transport of water vapor to the Taklamakan Desert （TD） area.This influx has heightened the water vapor content below 5 km in the TD， leading to temperature increases in that region through the regulation of radiative processes.The increase in atmospheric infrared radiation was the primary factor， resulting in a regional average temperature rise of 0.3 °C.Meanwhile， precipitation levels remained largely unchanged due to the prevailing anticyclonic airflow.

Based on field observation data from the A'rou and Daman stations in the Heihe River Basin between 2016 and 2022， this study analyzes the technical performance of domestically developed near-infrared scintillometers （LAS） and dual-band scintillometers （OMS） from three aspects： raw data quality， performance （including measurement accuracy， stability， and environmental adaptability）， and the rationality of long-term observed changes in water and heat flux.The goal is to support the enhancement of the equipment's performance and facilitate the industrialization of domestically developed devices.The results show that： （1） In the inertial subrange， the slope of the power spectrum of raw high-frequency data from the developed LAS and microwave scintillometer （MWS） is consistent with the theoretical value， indicating good raw data quality； （2） Compared to similar foreign devices， the root mean square error （RMSE） of sensible heat flux observed by the developed LAS at the A'rou and Daman stations is 8.81 W·m^-2 and 12.61 W·m^-2， respectively， with average relative errors of 12.5% and 12.51%.Using the German OMS and the American EC systems as references， the RMSE of latent heat flux observed by the developed OMS is 16.24 W·m^-2 and 24.82 W·m^-2， with average relative errors of 13.21% and 12.07%， indicating good measurement accuracy of the developed scintillometers.Additionally， the analysis of multi-year observation results shows that the developed scintillometers exhibit good stability and can operate normally under extreme environmental conditions such as high altitude， high temperature， high humidity， low temperature， and low humidity， demonstrating excellent environmental adaptability； （3） Under relatively homogeneous surface conditions， the sensible and latent heat fluxes obtained by the developed scintillometers show good consistency with those measured by eddy covariance systems， and the seasonal variation patterns of the observed fluxes are generally consistent.These results indicate that the developed scintillometers have strong performance and are capable of conducting long-term surface water and heat flux observations under diverse field conditions.