Quantifying Contribution of Recycled Moisture to Precipitation in Temperate Glacier Region,Southeastern Tibetan Plateau,China

Recycled moisture is an important indicator of the renewal capacity of regional water resources.Due to the existence of Yulong Snow Mountain,Lijiang in Yunnan Province,southeast of the Qinghai-Tibet Plateau,China,is the closest ocean glacier area to the equator in Eurasia.Daily precipitation samples were collected from 2017 to 2018 in Lijiang to quantify the effect of sub-cloud evaporation and recycled moisture on precipitation combined with the d-excess model during monsoon and non-monsoon periods.The results indicated that the d-excess values of precipitation fluctuated between–35.6‰and 16.0‰,with an arithmetic mean of 3.5‰.The local meteoric water line(LMWL)wasδD=7.91δ^(18)O+2.50,with a slope slightly lower than the global meteoric water line(GMWL).Subcloud evaporation was higher during the non-monsoon season than during the monsoon season.It tended to peak in March and was primarily influenced by the relative humidity.The source of the water vapour affected the proportion of recycled moisture.According to the results of the Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)model,the main sources of water vapour in Lijiang area during the monsoon period were the southwest and southeast monsoons.During the non-monsoon period,water vapour was transported by a southwesterly flow.The recycled moisture in Lijiang area between March and October 2017 was 10.62%.Large variations were observed between the monsoon and non-monsoon seasons,with values of 5.48%and 25.65%,respectively.These differences were primarily attributed to variations in the advection of water vapour.The recycled moisture has played a supplementary role in the precipitation of Lijiang area.

Integrated water vapor during active and break spells of monsoon and its relationship with temperature,precipitation and precipitation efficiency over a tropical site

Global Positioning System(GPS)measurements of integrated water vapor(IWV)for two years(2014 and 2015)are presented in this paper.Variation of IWV during active and break spells of Indian summer monsoon has been studied for a tropical station Hyderabad(17.4°N,78.46°E).The data is validated with ECMWF Re-Analysis(ERA)91 level data.Relationships of IWV with other atmospheric variables like surface temperature,rain,and precipitation efficiency have been established through cross-correlation studies.A positive correlation coefficient is observed between IWV and surface temperature over two years.But the coefficient becomes negative when only summer monsoon months(June,July,August,and September)are considered.Rainfall during these months cools down the surface and could be the reason for this change in the correlation coefficient.Correlation studies between IWV-precipitation,IWVprecipitation efficiency(P.E),and precipitation-P.E show that coefficients are-0.05,-0.10 and 0.983 with 95%confidence level respectively,which proves that the efficacy of rain does not depend only on the level of water vapor.A proper dynamic mechanism is necessary to convert water vapor into the rain.The diurnal variations of IWV during active and break spells have been analyzed.The amplitudes of diurnal oscillation and its harmonics of individual spell do not show clear trends but the mean amplitudes of the break spells are approximately double than those of the active spells.The amplitudes of diurnal,semidiurnal and ter-diurnal components during break spells are 1.08 kg/m^(2),0.52 kg/m;and 0.34 kg/m;respectively.The corresponding amplitudes during active spells are 0.68 kg/m^(2),0.41 kg/m;and 0.23 kg/m;.

Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China

Extreme precipitation events bring considerable risks to the natural ecosystem and human life.Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical for the flood prevention and water resources planning and management.In this study,daily precipitation data(1957–2019)were collected from 24 meteorological stations in the Weihe River Basin(WRB),Northwest China and its surrounding areas.We first analyzed the spatial-temporal change of precipitation extremes in the WRB based on space-time cube(STC),and then predicted precipitation extremes using long short-term memory(LSTM)network,auto-regressive integrated moving average(ARIMA),and hybrid ensemble empirical mode decomposition(EEMD)-LSTM-ARIMA models.The precipitation extremes increased as the spatial variation from northwest to southeast of the WRB.There were two clusters for each extreme precipitation index,which were distributed in the northwestern and southeastern or northern and southern of the WRB.The precipitation extremes in the WRB present a strong clustering pattern.Spatially,the pattern of only high-high cluster and only low-low cluster were primarily located in lower reaches and upper reaches of the WRB,respectively.Hot spots(25.00%–50.00%)were more than cold spots(4.17%–25.00%)in the WRB.Cold spots were mainly concentrated in the northwestern part,while hot spots were mostly located in the eastern and southern parts.For different extreme precipitation indices,the performances of the different models were different.The accuracy ranking was EEMD-LSTM-ARIMA>LSTM>ARIMA in predicting simple daily intensity index(SDII)and consecutive wet days(CWD),while the accuracy ranking was LSTM>EEMD-LSTM-ARIMA>ARIMA in predicting very wet days(R95 P).The hybrid EEMD-LSTM-ARIMA model proposed was generally superior to single models in the prediction of precipitation extremes.

Hurricane Florence Makes Landfall in the Southeast USA: Sensitive Dependence on Initial Conditions, Parameterizations, and Integrated Enstrophy

The overall purpose of this paper is to post-evaluate the predictability of Hurricane Florence using the Advanced Research Weather Research Forecast (WRF) (ARW) version of a mesoscale model. This was performed over the period from 0000 UTC 13 September 2018 through 0000 UTC 18 September 2018. The WRF ARW core resolution used here was the 27-km grid spacing chosen to in order to balance finer resolution against in house processing time and storage. The large-scale analysis showed that a change in the Northern Hemisphere flow regime, especially the flow in the western part of the Northern Hemisphere may have contributed partly to the reduced forward speed of the tropical cyclone. In order to measure the predictability of a system, we will use different convective and boundary layer schemes initialized from the same conditions. The results demonstrated that the sign of the local IRE tendency was similar to that of the Northern Hemisphere Integrated Enstrophy. The results also showed that when the boundary layer, convective, and cloud microphysical schemes of the model were varied, the areal coverage of heavy precipitation of Florence was under-forecast by approximately 10% or more, and the heaviest amounts were under-forecast by an average of about 20%.

Population amount risk assessment of extreme precipitation-induced landslides based on integrated machine learning model and scenario simulation

In this study,the future landslide population amount risk(LPAR)is assessed based on integrated machine learning models(MLMs)and scenario simulation techniques in Shuicheng County,China.Firstly,multiple MLMs were selected and hyperparameters were optimized,and the generated 11 models were crossintegrated to select the best model to calculate landslide susceptibility;by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide hazard.Using the town as the basic unit,the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways(SSPs)scenarios in each town were assessed,and then combined with the hazard to estimate the LPAR in 2050.The results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility assessment.The distribution of hazard classes is similar to susceptibility,and with an increase in precipitation,the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard classes.The high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability,whereas the northern towns of Baohua and Qinglin are at the lowest risk class.The LPAR increased with the intensity of extreme precipitation.The LPAR differs significantly among the SSPs scenarios,with the lowest in the“fossil-fueled development(SSP5)”scenario and the highest in the“regional rivalry(SSP3)”scenario.In summary,the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive capability.The results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landslide risks from a developmental perspective.

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.

基于多模式预报的四川盆地强降水订正方法

四川盆地地形地貌复杂,强降水预报难度大,对模式降水预报产品进行订正,是提升强降水预报质量的重要手段。本文选取2018—2019年发生在四川盆地的35次强降水过程,对ECMWF、CMA_MESO和SWC_WARMS三种模式的24 h强降水预报采用常规评分和空间平移两个方法进行检验,并利用最优评分、多模式集成和位移订正三种方法进行订正试验。结果表明:最优评分订正方法可以有效改善各模式降水预报的强度,而多模式集成订正法可以改进降水落区和极值预报,在此基础上计算位移偏差,根据最优的位移偏差值对降水预报进行位移订正,可以进一步改进强降水落区预报效果。然后利用2020—2021年强降水过程进行订正效果验证,结果显示:经订正后的降水极值预报更接近实况,各量级降水预报评分明显优于单一模式,暴雨和大暴雨预报的TS评分提高率较最优单模式分别可达24.3%和42.8%,订正后空报率基本维持,漏报率显著下降,订正效果良好。

基于多种动力-统计方法的中国夏季降水集成预测研究

中国夏季降水预报对中国社会和经济发展具有重要意义,但由于气候系统的不确定性,中国夏季降水预报在当前气象研究中仍然是一个具有挑战性的问题.本文在分析7种动力-统计预报方法对我国夏季降水预报效果的基础上,提出了动态加权集成预报方法.通过比较动态加权集成预报方法与模式系统误差订正、单个动力-统计方法和等权重集成预报的空间距平相关系数(ACC)和预报评分(PS)值,发现动态加权集成预报较系统误差订正、单个动力-统计方法和等权重集成均有更好的预报效果.主要表现为:(1)动态加权集成预报方法2011—2020年夏季降水预报的ACC均值为0.05~0.10,较系统误差订正(-0.08)提高了0.13~0.18,较等权重集成提高了0.02~0.04,且优于绝大多数单个动力-统计预报方法的结果.(2)动态加权集成预报PS均值达到69.3~70.7,明显优于等权重集成预报的67.8~68.5和多数单方案动力-统计预报结果.(3)2021年独立样本检验中,动态加权集成预报中国夏季降水ACC值最高为0.21,明显优于等权重集成预报的最高值0.14,且PS评分值均高于70分.因此,动态加权集成预报可以在单个动力-统计方案预报的基础上,进一步提高中国夏季降水预报的准确率,改进预报技巧稳定性,具有较好的潜在应用价值.

基于前馈神经网络的多模式集成降水预报研究

为提高山东定量降水预报准确率,采用深度前馈神经网络(Deep Forword Neural Networks,DFNN)和降水分级最优TS(Threat Score)权重集成方法对多模式集成降水预报进行研究。对2019年4—9月欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasting,ECMWF)全球数值预报系统、中国气象局上海数值预报模式系统(China Meteorological Administration Shanghai 9 km,CMA-SH9)和中国气象局中尺度天气数值预报系统(China Meteorological Administration Meso⁃scale,CMA-MESO)逐24 h累积降水量预报进行有监督训练,得到4组DFNN(ES、EM、SM、ESM)深度学习模型,并利用多模式降水分级最优TS权重集成方法建立Mul-OTS(Multi-mode Optimal Threat Score)集成模型。用2020年4—9月各模式逐24 h累积降水量进行降尺度格点预报,对5种集成方案对比检验、个例分析应用。结果表明:不同起报时间、不同预报时效,5组集成方案均降低了平均相对误差,ESM方案最好,Mul-OTS方案最差;4组DFNN方案均提高了晴雨准确率,ESM方案最好,Mul-OTS方案低于模式预报;4组DFNN方案均提高了各降水等级TS、ETS评分,对弱降水的提高幅度大于强降水,Mul-OTS方案对小量级降水等级订正是负技巧,对大量级降水等级的订正效果较好,但仍不如ESM方案;个例分析发现降水强度和落区预报ESM方案均优于其他集成方案。因此业务上采用最优的ESM方案建立了定量降水格点预报系统,为智能网格预报提供重要支撑。

一种区域降水预测的时空集成模型

提出了一种融合多种深度学习方法的时空预测集成模型LSTM-ARIMA-SARIMA-BP-CNN(简称LASBC模型),其中ARIMA模型捕捉降水时间序列的近邻性,SARIMA模型捕捉周期性,BP神经网络揭示降水分布的时空相关性,CNN挖掘气象、地理因素对降水的影响,最后通过LSTM网络对4个模型的预测结果进行融合,提高预测精度.以中国西部地区12个城市1985年1月至2021年12月的月降水量数据为主,应用LASBC模型对12个城市的月降水量进行预测,结果显示:LASBC模型的预测精度显著提高;基于预测月降水量,给出了6个主要城市未来10年气候偏干旱或湿润的月份及降水量;未来10年,我国西北干旱区年均降水基本保持稳定,高寒冻土区略有增加,西南湿润区增长趋势明显.

基于Meta分析的土壤呼吸对降水改变的非对称响应

气候变暖已经引起全球降水格局改变。土壤呼吸作为陆地生态系统向大气释放CO_(2)最大的碳库,对降水变化的响应将进一步影响碳循环,从而对全球气候变化产生反馈。尽管以往已有大量关于土壤呼吸与降水变化关系的相关研究,但存在较大争议。因此,亟待进一步深入探究土壤呼吸对降水改变的响应。基于此,研究Meta分析方法,整合了来自Web of Science英文数据库和中国知网文献数据库(CNKI)的284篇已发表的论文和367组数据,进而分析全球中低纬度地区土壤呼吸对降水改变的响应。研究结果表明,土壤呼吸对降水改变的响应呈现出非对称特征,降水量增加能够提高16.7%的土壤呼吸,而降水量减少则会抑制17.88%的土壤呼吸。研究还发现,不同生态系统和气候区域的土壤呼吸对降水改变的响应存在较大差别。其中,降水量增加能够提高草地生态系统22%的土壤呼吸,比森林生态系统土壤呼吸高出12%;而降水量减少则会削弱草地生态系统28%的土壤呼吸,这要比森林生态系统土壤呼吸还高16%。与湿润地区相比,降水量的增加对干旱地区土壤呼吸的促进作用更加明显。而降水量的减少对干旱地区和湿润地区土壤呼吸的影响均无显著差异。此外,本研究也证实了土壤呼吸对不同降水强度和年限的响应也存在差异。在不同降水强度上,无论增加降水还是减少降水,重度增减雨的土壤呼吸均改变最大,即:重度增减雨(>75%)>中度增减雨(25%—75%)>轻度增减雨(<25%);在不同降水年限上,长期增雨对土壤呼吸的促进作用尤为突出,但长期减雨对土壤呼吸影响无显著差异。研究结果可为未来气候情景下陆地生态系统土壤呼吸变化的准确预测以及模型模拟和改进提供重要的科学依据和理论基础。

反应沉淀一体式生物反应器在水质净化厂应急处理中的应用

为解决滨河水质净化厂提标扩建实施阶段服务片区内污水处理缺口,设计采用反应沉淀一体式生物反应器,将该工艺模块安装在水质净化厂的原有池体内,进行原位改造。在调试运行过程中,发现该工艺具有占地省、投资较低、改造时间较短等优点,同时在设计和运行中需着重考虑选用合适的气水比、停留时间和污泥负荷,以最大限度发挥该工艺的优势。

GPM反演降水数据IMERG在浙江省的适用性研究

【目的】为了研究全球降水测量计划(GPM)综合多卫星检索降水产品(IMERG)在浙江省的适用性。【方法】以浙江省为研究区域,基于浙江省自动观测站降水数据,利用相关系数、均方根误差、相对偏差、分类指数统计法分别从年尺度、季尺度、月尺度、日尺度以及小时尺度分析GPM在浙江省的适用性。【结果】(1)春、秋、冬季的GPM和自动站对应效果远好于夏季,月尺度、日尺度、小时尺度下GPM和自动站降水数据对应效果较好,但整体上存在高值区略低估、低值区略高估现象;(2)年尺度与季尺度下GPM与自动站降水平均值的变化趋势一致,但GPM降水平均值较高,其中秋季表现最好;(3)年、月、日3个尺度下,尺度越精细化,相关系数越高、相对偏差越小,均方根误差越小;小时、日、月3个尺度下,降水阈值越低,产品尺度越粗糙,估测能力越高;降水阈值越低,产品尺度越精细,误报降水的比例越低;降水阈值越低,综合探测能力越高;(4)小时尺度下,在0.5~1 mm·h^(-1)区间,GPM虽然对降水事件略有高估,但拟合效果较好。【结论】综合来看,GPM日尺度降水数据产品精度在浙江省有较大应用潜力。

旋风布袋一体式除尘器在钢水精炼系统中的应用

在VD、VOD、RH等真空精炼系统使用干式机械泵系统抽真空时,钢液产生的大量粉尘会随着工艺气体进入真空管道,为保护真空泵,需在机械真空泵前设置除尘器对工艺气体进行除尘。普遍采用在袋式除尘器前加旋风除尘器,造成占地面积大、成本高、运行费用高。文章介绍的除尘器采用旋风布袋一体式的结构形式,分别将这两部分通过两舱的形式安装在一个除尘器里,占地小、成本低。同时通过优化的结构设计,一体式除尘器具有除尘效率高、压损小等特点,维护检修方便快捷,保证了干式机械真空泵长期安全运行。

基于CLDAS的贺兰山区5—9月降水时空分布特征及其与地形的关系分析

利用2008—2016年5—9月中国气象局陆面数据同化系统(CLDAS)格点融合分析降水资料以及降水观测资料,在对CLDAS格点降水融合资料进行验证的基础上,对贺兰山区降水时空分布特征以及与地形的关系进行了分析。结果表明:贺兰山区降水呈“东多西少、南多北少”的分布特征,贺兰山主峰偏西0.1°存在一个超过240 mm的降水高值中心,日降水量极值西侧高于东侧。8月降水量和短时强降水次数最多,11:00—18:00降水次数最多,午后到前半夜短时强降水次数最多。贺兰山区降水以小雨为主,其次是中雨,中雨和小雨雨量占区域总雨量的比例高达85%。贺兰山区降水量随海拔高度的增加而增加,西坡降水随高度的增加率为5.1 mm/hm,东坡降水随高度的增加率为2.1 mm/hm,西坡明显高于东坡。中雨日数与地形高度的相关性较好,其它级别降雨日数与地形相关性不强。

人工增雨飞机机载设备集成系统的改装

为观测播撒催化剂后云中发生的水物质演变情况,需要在应用于人工影响天气作业的飞机中安装先进的探测系统。该系统可优化探测资源、强化集成操作、支撑天地协同,是基于飞机平台的人影工程,应用于人工增雨、防雹减灾、抗御干旱、森林防火等任务,提高了人工影响天气的作业能力、管理水平和服务效益,为经济社会发展提供了有力保障。因此,本文详细介绍了安装于我省人影作业飞机Y12型B3836设备集成系统的组成、功能、操作。

辽宁中部农作物生长季干旱风险度研究

掌握辽宁中部农作物生长季降水量变化与干旱风险度,为辽宁中部农业产业结构调整、规避干旱风险和农业生产管理提供参考。利用沈阳区域气象中心1961—2020年的降水和气温数据,采用气候诊断分析方法,选用积分湿润指数及其干湿指数研究降水量对农作物生长季的满足程度、干旱变化特征及干旱风险程度。结果表明:1961—2020年辽宁中部农作物生长季降水量呈减少趋势,线性减少40.5 mm;降水满足度和干湿指数呈下降趋势,分别线性下降14.5%和0.13;干旱风险度呈显著上升趋势,最大干旱风险度上升17.3%,2000—2020年平均上升11.0个百分点。播种苗期中旱+大旱约3 a一次;旺长期中旱+大旱概率约20 a一次;成熟期中旱+大旱约4 a一次。研究结果为掌握降水变化状态,干旱变化特征,认知干旱的严重程度,规避干旱风险,为“三农”及粮食生产安全以及农业结构调整和防旱减灾提供参考。

焦粉基Fe_(3)O_(4)/C诱导铜盐还原耦合化学沉淀法脱除废水硫氰酸盐

煤焦化过程中产生大量酚-氰-硫氰酸盐等组分复杂的工业废水,对水体生态环境具有潜在危害,研究高效的含硫氰酸盐废水处理技术具有积极的经济、环保意义。以焦粉固废为碳源,采用等体积浸渍,结合限热碳还原法制备磁性Fe_(3)O_(4)/C材料,采用XRD和N 2吸附-脱附技术进行物质结构表征,并应用于诱导铜盐化学沉淀法脱除模拟焦化废水中硫氰酸盐。XRD结果表明,负载的Fe_(3)O_(4)平均晶粒尺寸为10.8 nm,Fe_(3)O_(4)/C材料比表面积6.8 m^(2)·g^(-1)。模拟焦化硫氰酸盐废水(浓度为2.5 g·L^(-1))处理实验结果显示,在铜盐浓度为0.03 mol·L^(-1)时,采用铁质量分数20%Fe_(3)O_(4)/C催化剂(投加量1.25 g),废水初始pH=5.6,45℃下反应2 h,硫氰酸盐脱除率可达99.84%,催化剂稳定性良好;动力学模拟显示反应遵循准二级动力学模型,主要吸附过程受化学作用控制。该研究以焦粉固废为载体,制备了Fe_(3)O_(4)/C材料,通过铁铜物种协同作用,利用化学沉淀法实现了模拟废水硫氰酸盐的高效脱除,为工业化焦化废水处理提供理论支持。

极端降雨对晋西黄土区农地流域泥沙连通性的影响

[目的]研究极端降雨事件对小流域坡沟系统泥沙连通性的影响,进而为流域综合治理与灾后重建提供理论依据。[方法]以地处黄土残塬区的晋西吉县蔡家川农地小流域为研究区,采用无人机与遥感影像对2021年10月山西极端降雨事件前后坡沟系统进行航测与实地调查,分析极端降雨前后的景观格局变化,量化坡沟系统泥沙连通性,识别滑坡点位空间分布与地形特征,评价流域内典型工程措施对极端降雨的响应。[结果]农地流域此次降雨历时84 h,累计降雨量160.4 mm,降雨强度1.9 mm/h,降雨频率0.16%,为百年一遇的极端暴雨。暴雨后流域斑块数量增加,景观形状指数增大,蔓延度指数减小,香农多样性指数减小。暴雨前流域内泥沙连通性分布不均,暴雨后泥沙连通性增大。暴雨后滑坡位点泥沙连通性减小,滑坡多发生在0°~10°和40°~50°的条件下,所占比例分别为29.11%和17.74%。[结论]极端暴雨事件诱发的滑坡影响了泥沙连通性,根据泥沙连通性空间变化可识别水土流失位点,能够用于评估典型水土保持工程措施对极端降雨的响应,研究结果可为流域综合治理及极端降雨事件发生后开展生态恢复提供支撑。

Refined Spatialization of 10-Day Precipitation in China Based on GPM IMERG Data and Terrain Decomposition Using the BEMD Algorithm

Continuous high spatial-resolution 10-day precipitation data are essential for crop growth services and phenological research.In this study,we first use the bidimensional empirical mode decomposition(BEMD)algorithm to decompose the digital elevation model(DEM)data and obtain high-frequency(OR3),intermediate-frequency(OR5),and low-frequency(OR8)margin terrains.Then,we propose a refined precipitation spatialization model,which uses ground-based meteorological observation data,integrated multi-satellite retrievals for global precipitation measurement(GPM IMERG)satellite precipitation products,DEM data,terrain decomposition data,prevailing precipitation direction(PPD)data,and other multisource data,to construct China's high-resolution 10-day precipitation data from2001 to 2018.The decomposition results show mountainous terrain from fine to coarse scales;and the influences of altitude,slope,and aspect on precipitation are better represented in the model after topography is decomposed.Moreover,terrain decomposition data can be added to the model simulation to improve the quality of the simulation product;the simulation quality of the model in summer is better than that in spring and autumn,and is relatively poor in winter;and OR5 and OR8 can be improved in the simulation,with better OR5 and OR8 dynamically selected.In addition,preprocessing the data before precipitation spatialization is particularly important.For example,adding 0.01to the 0 value of precipitation,multiplying the small value of precipitation less than 1 by 10,and performing the normal distributions transform(e.g.,Yeo–Johnson)on the data can improve the simulation quality.