Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Hydrological modeling plays a crucial role in efficiently managing water resources and understanding the hydrologic behavior of watersheds. This study aims to simulate daily streamflow in the Godavari River Basin in Maharashtra using the Soil and Water Assessment Tool (SWAT). SWAT is a process-based hydrological model used to predict water balance components, sediment levels, and nutrient contamination. In this research, we used integrated remote sensing and GIS data, including Digital Elevation Models (DEM), land use and land cover (LULC) maps, soil maps, and observed precipitation and temperature data, as input for developing the SWAT model to assess surface runoff in this large river basin. The Godavari River Basin under study was divided into 25 sub-basins, comprising 151 hydrological response units categorized by unique land cover, soil, and slope characteristics using the SWAT model. The model was calibrated and validated against observed runoff data for two time periods: 2003-2006 and 2007-2010 respectively. Model performance was assessed using the Nash-Sutcliffe efficiency (NSE) and the coefficient of determination (R2). The results show the effectiveness of the SWAT2012 model, with R2 value of 0.84 during calibration and 0.86 during validation. NSE values also ranged from 0.84 during calibration to 0.85 during validation. These findings enhance our understanding of surface runoff dynamics in the Godavari River Basin under study and highlight the suit-ability of the SWAT model for this region.

Why lowland riparian trees are difficult to use for streamflow reconstruction

Existing streamflow reconstructions based on tree-ring analysis mostly rely on species from upland,mainly montane areas,while lowland species(generally plain)areas are rarely used.This limits the understanding of streamflow change history in the lowlands,which is an important basis for water resource management.This study focused on Populus euphratica stands located along the main stream,eastern and western tributaries in the lower reaches of the Heihe River basin(HRb),in arid northwestern China.We investigated how streamflow regulation interferes with ripar-ian trees in lowlands when they used for streamflow recon-struction.Tree-ring width chronologies were developed and analyzed in conjunction with meteorological and hydrologic observation data.The results show streamflow regulation leads in sharp fluctuations in the streamflow allocation between the eastern tributaries and western tributaries.This resulted in instability of the correlation between streamflow at the two tributaries and at the Zhengyixia hydrologic station,with corresponding fluctuations in radial growth of poplar trees on the banks of the two tributaries and at the station.Streamflow regulation altered the natural patterns of seasonal streamflow below the station,changing the time window of poplar response.This study provides useful insight into tree-ring width based streamflow reconstruction in the lowlands.

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater resources.Many machine learning(ML)approaches have been enhanced to improve streamflow prediction.Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches.Current researchers have also emphasised using hybrid models to improve forecast accuracy.Accordingly,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance metrics.This study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML techniques.It’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML techniques.This study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,respectively.Finally,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

罗玉沟流域水沙变化趋势及影响因素

[目的]探讨小流域水沙变化趋势及影响因素,为黄土高原丘陵沟壑区水土流失治理和水土保持措施布设提供参考依据。[方法]采取Spearman秩次相关检验方法、累积距平法和双累积曲线法等对黄土丘陵沟壑区罗玉沟流域1986—2016年降水量、径流量和输沙量变化趋势和演变过程进行分析。[结果](1) 1986—2016年罗玉沟流域年降水量趋势性变化不显著;流域年径流量和年输沙量变化具有明显的阶段性,以1993年为界,年径流量和年输沙量呈显著降低趋势。(2)降水和水土保持措施影响罗玉沟流域年径流量和输沙量,不同降水水平年,降水和水土保持措施对流域水沙变化的影响不同,水土保持措施使流域枯水年份径流量和输沙量减少最为显著。(3)水土保持措施对年径流量和年输沙量的减少贡献率分别为83.50%和80.97%。[结论]降水量变化不显著的情况下,水土保持措施对罗玉沟流域年径流量和年输沙量的减少起主导作用,水土保持综合治理对枯水年径流量和输沙量的影响幅度最大。

基于可解释机器学习的黄河源区径流分期组合预报

黄河源区是黄河流域重要的产流区和我国重要的清洁能源基地,提高黄河源区径流预报准确率可为流域水资源科学调配和水风光清洁能源高效利用提供重要支撑。以黄河源区唐乃亥和玛曲水文站为研究对象,基于不同月份径流组分的差异,考虑积雪覆盖率及融雪水当量变化,构建了中长期径流分期组合机器学习预报模型及其可解释性分析框架。研究结果表明:1)年内的径流预报时段可划分为融雪影响期(3—6月)和非融雪主导(以降雨和地下水补给为主)期(7月—次年2月);2)与传统不分期模型相比,唐乃亥站和玛曲站分期组合预报模型的纳什效率系数分别达0.897、0.835,确定系数(R2)分别达0.897、0.839,均方根误差分别降低了10%、17%,提高了径流预报准确率,通过分位数映射校正,唐乃亥站和玛曲站预报模型的R2分别进一步提升至0.926和0.850;3)基于SHAP机器学习可解释性分析框架,辨识了预报因子对径流预报结果的贡献程度,由高到低依次为降水、前一个月流量、蒸发、气温、相对湿度、融雪水当量等,发现了不同预报因子之间交互作用散点分布具有拖尾式或阶跃式的特征。

1960—2020年青藏高原东部主要河流的径流和泥沙变化特征

青藏高原作为“亚洲水塔”,对下游地区用水具有重要作用,在气候变化和人类活动影响下,河流的径流和输沙量已发生显著变化。选取青藏高原东部的黄河、长江、澜沧江、怒江和雅鲁藏布江五大河流源区,利用站点实测数据和Pettitt突变点检验、线性回归等统计方法,系统性地分析了1960—2020年间(近60 a)径流和泥沙的年际和季节性变化特征。主要结论为:①近60 a来,长江和怒江流域的年径流显著上升,其余流域变化不显著,径流变化存在较强的空间变异性,在2006年左右存在突变点,大部分站点的年径流在此后的十几年中均呈上升趋势;年输沙量方面,长江流域呈不显著上升,黄河、澜沧江、雅鲁藏布江流域均呈不显著下降,年际变化趋势与径流变化总体一致,但变异性更大。②对于60 a尺度的季节径流,上游站点冬、春季径流均呈增加趋势,所有站点夏、秋季径流的变化趋势与年径流总体一致,且夏季径流在空间分布上存在“北增南减”的特征。③澜沧江下游的水电梯级开发已经对下游天然径流过程产生干扰,主要表现为径流年内分布的突变,季节差异显著减小,趋于均匀化。以上结论深化了对青藏高原径流和泥沙变化特征的认识,可为开展径流趋势预估和水资源规划等提供科学参考。

基于CMIP6的气候变化对南水北调西线工程水源区径流的影响

探讨未来气候变化对南水北调西线工程水源区水文过程的影响对其调水规划及开发利用具有重要意义。利用4种SSPs-RCPs情景下CMIP6中12个全球气候模式的输出结果,驱动DTVGM模型研究21世纪气候变化对西线工程水源区径流的影响。结果表明,在SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5情景下,2021—2100年南水北调西线工程3个水源区气温、降水量、径流量均较基准期呈现不断增加的趋势,增幅随着辐射强度的升高而增大,其中金沙江水源区年径流量及年代际径流量变化较大,对气候变化的响应更为明显。未来各月平均径流也呈现出随年代增加的趋势,但不同月份增幅存在差异,枯水期月径流量相对基准期的变化较小,丰水期增量较大。根据径流与气温、降水相关分析结果,径流量对降水的改变更为敏感,降水量的变化始终对径流具有显著影响,气温对径流量的影响程度随辐射强度的升高而逐渐变大。研究结果可为南水北调西线工程水源区适应气候变化影响及水资源综合管理提供参考依据。

汉江流域植被变化及对径流的影响

基于汉江流域的气象水文和归一化植被指数(normalized difference vegetation index,I_(NDV))数据,采用多元线性回归分析方法定量计算不同因素对汉江流域植被变化的影响程度,然后采用修正后的Budyko方程和弹性系数法定量计算植被变化对径流变化的影响程度。结果表明:1982—2015年的年均I_(NDV)呈显著的波动增长趋势(p<0.01),且空间异质性较明显,植被增长明显地区主要分布在中下游区域;气候条件和人为因素对I_(NDV)增长具有积极作用,贡献率分别为99.56%和0.44%,但其贡献率的时空分布存在显著差异;下垫面参数(ω)和I_(NDV)之间有较强相关性,表明流域产流能力对植被变化具有高度敏感性;气候因素引起的植被变化和人为因素引起的植被变化对径流量的贡献率分别为28.28%和0.12%,表明气候因素导致植被增长是致使汉江流域水资源减少的重要因素之一,因此,应高度重视植被生长导致流域水资源减少这一问题。

以树木年轮宽度重建渭河上游过去377 a来4-6月平均流量

目的 探讨渭河流域上游过去377 a的径流流量的变化历史,提供流域百年尺度水文要素波动的变率、强度、周期及其长期演变特征等水资源评估所需的关键信息。方法 使用相关系数法、线性回归法和功率谱法判定树轮年表与平均流量的响应关系、径流变化模型及水文要素周期信号。结果 基于贵清山油松年表与渭河南河川站月流量的相关结果,重建了1630-2006年当年4-6月的平均流量变化,建模期1945-1979年时段的方差解释量为34.9%。树轮流量序列确定了51个极端高流量年和54个低流量年,分别为过去377 a的13.53%和14.32%。其中连续3 a以上的极端高值年份出现在1647-1649年、1699-1701年、1843-1845年、1905-1907年和1909-1911年;极端低流量年份出现在1722-1727年、1747-1749年和1763-1765年。对基于11年滑动平均确定的流量偏高偏低时段水文要素进行量化,显著的低流量年份与区域大尺度干旱事件有较好的印证关系。重建流量具有50.400 a和2.290 a的周期信号。结论 树轮重建的377 a渭河天水段季节径流变化具有变率高幅度大的特点,流域水文气候变化与大尺度气候驱动,如太平洋年代际涛动、厄尔尼诺-南方涛动存在紧密关联。

大通水文站流量演变及其旱涝特征分析

在变化环境和上游水库调蓄影响下,分析长江中下游地区流量的演变规律和旱涝特征,对江苏省防洪安全和水资源利用至关重要。为定量评估长江中下游近50年来流量演变规律和旱涝变化特征,基于大通水文站1971~2020年逐月流量实测数据,采用Mann-Kendall检验、Morlet小波分析等方法综合分析大通站流量变化趋势、突变性、周期性和年内分布规律,并计算标准化流量指数分析其旱涝特征。结果表明,近50年来,大通水文站年均流量具有非显著增加趋势,年际变化较为稳定,但在2003年存在显著突变,突变后显著增加;年均流量序列主要存在12年的周期,且主要集中在1995年之前,具有3次丰枯交替震荡;大通站流量年内分配不均,主要集中在汛期,7、8月流量最大,但汛期各月流量序列变化不显著,而枯季各月如12月至次年3月流量具有显著增加趋势;不同时间尺度标准化降水指数反映干湿变化较为一致,2003年后干旱发生几率较高,年干旱发生频率为44.4%,而洪涝发生几率由37.5%减少到16.7%,发生率明显降低。从季节上看,2003年秋季和夏季干旱发生率偏高,而春季和冬季发生率明显降低。对各季标准化降水指数进行MK趋势检验可知,除在秋季减少外,其他各季增加,尤其是冬季增加趋势显著。

基于多层土壤湿度数据同化的小流域径流模拟

土壤水分状态在水文循环中起关键作用,对径流预报的准确性影响很大。以美国宾西法尼亚州中部的Shale Hills流域为例,研究多层实测土壤湿度数据同化对径流模拟的作用。首先利用新安江模型对研究区内2008~2012年的日径流进行模拟,同时通过集合卡尔曼滤波算法构建基于新安江模型的水文数据同化模型,并设置未同化的开环试验作为对比,研究模型同化土壤湿度对改善径流模拟和土壤水分的能力。模拟结果显示,新安江模型在该流域有较好的适用性;相比于同化前,模型同化多层土壤湿度后径流量相对误差降低了5.2%,纳什效率系数从0.62提高到0.66,土壤含水率的均方根误差从16.4 mm减小到3.0 mm。其次对单层土壤含水率同化结果进行比较,结果表明,下层土壤含水率同化比上层同化对径流模拟作用更明显,但仅加入上层实测土壤含水率同化能够降低下层土壤水模拟的不确定性。研究结果表明同化多层土壤湿度可提高径流模拟精度,同时也能够有效优化土壤水的模拟结果。

雅鲁藏布江流域奴下水文站极端降水与极端径流的关系研究

青藏高原是亚洲十多条大江大河的发源地,被称为“亚洲水塔”。随着全球变暖加剧,青藏高原极端水文和气象事件频发。然而,其复杂的气候和下垫面(冰川、积雪、和冻土等)特征,限制了对该地区极端径流对极端降水响应关系的理解。本文以青藏高原南部的雅鲁藏布江流域为例,利用逐日降水和径流数据,通过改进极端径流阈值法,并结合相关分析等方法,研究了不同发生概率下极端径流与极端降水之间的关系。研究结果表明:(1)雅鲁藏布江流域的极端径流事件通常持续1~2天,且随着发生概率的减小,事件持续时间相应缩短。(2)发生概率为10%、5%和1%的极端径流与极端降水的相关系数在0.07~0.28之间(P<0.01),且随着极端降水发生概率的增加,两者之间的响应关系逐渐加强。(3)在奴下水文站,极端径流对该水文站以上雅鲁藏布江流域极端降水的响应强度最大,而对奴下气象站和林芝气象站的响应相对较弱,同时,最高响应时间滞后一天。此外,极端径流对极端降水的响应关系也受到流域湿润程度、植被状况和土壤湿度等因素影响。本文改进的极端径流识别方法能够有效捕捉雅鲁藏布江流域受极端降水影响的极端径流事件,提高了径流组分复杂的高寒流域极端径流对极端降水响应关系的理解。与此同时,该研究还能够为气候变暖背景下雅鲁藏布江流域的水资源管理和区域经济发展提供重要的理论支撑和科学指导。

三江源地区春季流量与积雪的年际变化关系

基于三江源区站点流量资料、中国区域格点降水气温资料及遥感积雪资料,分析了长江、黄河和澜沧江源区春季流量的年际变化及其与降水、积雪、气温的关系。结果表明,1980~2020年三江源地区春季流量年际变化显著,其中5月流量的年际变率最大;三江源地区春季流量受春季雪深影响最大,其中4月、5月流量受雪深、积雪覆盖率、前期累积降水的影响显著,且流量与雪深的正相关性最强,5月流量与雪深相关系数可达0.7以上。进一步分析表明,高原积雪对流量的影响随源区不同流域、春季不同月份呈现一定的差异,其中黄河源区4月、5月流量受4月雪深影响最明显,澜沧江源区4月、5月流量与2~5月的雪深均存在较好关系,而长江源区5月流量则主要与4~5月雪深显著正相关。源区积雪对春季流量的影响也存在一定的海拔依赖性,其中低海拔区域积雪对流量的影响更多体现在春季前期,而黄河源区阿尼玛卿山、长江源区巴颜喀拉山脉及唐古拉山脉等高海拔区域的积雪对流量的影响可以持续到5月。气温作为春季融雪径流的关键因子,主要是通过影响源区前期积雪量、融雪产流过程,进而影响春季流量的异常。本研究结果在深入认识青藏高原气候变化对三江源地区春季流量的影响及过程等方面具有重要意义。

秃尾河流域河川径流组分变化及归因分析

[目的]探究秃尾河流域河川径流组分和基流指数(BFI)的变化特征,阐明环境因子对其变化的影响,识别主导因子,为流域水资源管理提供科学依据。[方法]以黄河中游的秃尾河流域为研究区,分析了1958—2017年流域地表径流、基流和基流指数(BFI)的变化,并通过相关性分析、弹性系数等方法定性定量分析了环境因子对它们的影响。[结果]地表径流和基流分别以4.55,9.73 mm/10 a的速度显著下降(p<0.01),BFI呈显著上升趋势(p<0.05)。地表径流(或基流、BFI)对潜在蒸散发、归一化植被指数(NDVI)和土壤含水量的变化高度敏感。NDVI和潜在蒸散发是导致地表径流(-34%,-29%)、基流(-39%,-29%)和BFI(33%,31%)变化的主要原因。[结论]植被恢复的直接及潜在水文效应对于河川径流组分减少发挥着重要作用。河川径流组分变化及其与环境变化之间的关系可为水资源和植被的可持续性提供理论依据。

黄土高原SRC参数的空间物理释义与流量估算应用

黄土高原地处我国西北旱区,降水稀少且潜在蒸发强烈,包气带较厚且空间异质性大等多种问题交织耦合导致区域河川径流演化机制极其复杂,影响区域生态保护与高质量发展的进程。对黄土高原不同生态分区的10个代表性流域的SRC参数进行空间分析,研究了SRC参数的异质性分布规律,探析了SRC参数在黄土高原地区的物理释义,并基于SRC建立了流域出口断面流量与陆地水储量的区域化响应关系,实现了通过GRACE重力卫星反演数据对流域出口断面流量的初步估算。研究表明,同一生态区内SRC参数α、β具有一定的稳定性,其中β和流域的干旱指数成负相关关系,与平均坡度成正相关关系,在地理位置中随纬度的减小和经度的增大而增大;而α则和土壤孔隙度存在正相关关系,与导水率衰减参数存在负相关关系。通过幂函数可建立黄土高原流域陆地水储量与出口断面流量间的拟合关系,并形成适用于平稳GRACE数据的流域流量过程估算方法。

不同降水产品及WRF-Hydro模式在黄河源区的适用性分析

黄河源区是黄河流域主要的产流区和水源涵养区,研究和探索该区域陆面水文过程对理解陆面过程及水文循环特征,揭示陆面—水文耦合过程具有重要的科学意义。本研究基于2009~2018年中国区域高时空分辨率地面气象要素驱动数据(China Meteorological Forcing Dataset,简称CMFD)、全球高分辨率降水数据集(Climate Prediction Center Morphing Technique,简称CMORPH)、热带降雨测量卫星(Tropical Rainfall Measuring Mission,简称TRMM)及全球陆地数据同化系统(Global Land Data Assimilation System,简称GLDAS)降水,评估了四类降水产品在黄河源区的降水精度,在此基础上,利用最优降水数据驱动独立运行的天气研究预报及水文耦合模型系统(Weather Research and Forecasting Model Hydrological modeling system,简称WRF-Hydro),探究该模式在黄河源区径流模拟的适用性。结果表明:四类降水产品均能够反映出降水的分布特征,但在量值及细节捕捉上存在显著差异。CMFD在不同时空尺度上都能很好地捕捉到降水的演变特征,其与日观测降水的相关系数达到0.99,均方根误差仅为0.25 mm。在表征降水能力方面,四类降水产品总体表现为CMFD>CMORPH>TRMM>GLDAS,CMFD的平均探测成功率(Critical Success Index,简称CSI)在0.93以上。经参数率定后的WRF-Hydro模式在黄河源区月径流模拟方面表现较好,率定期纳什系数(Nash-Sutcliffe efficiency coefficient,简称NSE)均在0.92以上,而验证期丰水年模拟结果明显好于枯水年(NSE=0.15),这与降水和径流的非线性程度有关。本研究方案和结果为亚寒带半干旱气候区大尺度流域水文模拟及径流预测提供了一定的参考价值。

极端干旱下长江口咸潮入侵成因及压咸补淡措施

2022年长江上游遭受极端干旱,长江口咸潮入侵加剧,区域用水安全受到严峻考验。为探究极端干旱天气下咸潮入侵成因和应对策略,基于长江口2019~2023年枯水期的实测盐度、潮水位、风速风向数据与同期上游大通站径流量数据,采用Spearman相关性分析、卡方分析等方法,分析了2022年9月起长江口咸潮入侵过程与长江口咸潮入侵的主要影响因素。分析结果显示:①2022年8月起长江干流枯水期提前,叠加台风和天文大潮情况,使得2022年9月至2023年4月长江口地区崇明洲头站、杨林站和六滧站的盐度大幅超标,分别达到1.922‰,0.585‰和2.134‰。②长江口3个主要盐度监测站点受上游来水影响时间存在前后差异,在大通站流量低于15000 m^(3)/s时,不同站点所属区域发生了不同程度的咸潮入侵情况。③2022年10月的压咸补淡措施在小潮期间作用于长江口地区,使得大通站流量高于12000 m^(3)/s,在南支上半段产生较好的压咸效果。研究成果可为长江口流域不同地区咸潮上溯成因分析和压咸补淡应对策略提供参考。

气候变化对青藏高原河流源区径流的影响

The Tibetan Plateau(TP)is the headwater of the Yangtze,Yellow,and the transboundary Yarlung Zangbo,Lancang,and Nujiang Rivers,providing essential and pristine freshwater to around 1.6 billion people in Southeast and South Asia.However,the temperature rise TP has experienced is almost three times that of the global warming rate.The rising temperature has resulted in glacier retreat,snow cover reduction,permafrost layer thawing,and so forth.Here we show,based on the longest observed streamflow data available for the region so far,that changing climatic conditions in the TP already had significant impacts on the streamflow in the headwater basins in the area.Our analysis indicated that the annual average temperature in the headwater basins of these five major rivers has been rising on a trend averaging 0.38℃-decade^(-1) since 1998,almost triple the rate before 1998,and the change of streamflow has been predominantly impacted by precipitation in these headwater basins.As a result,streamflow in the Yangtze,Yarlung Zangbo,Lancang,and Nujiang River headwater areas is on a decreasing trend with a reduction of flow ranging from 3.0-10^(9)-5.9-10^(9) m^(3)·decade^(-1)(-9.12%to-16.89%per decade)since 1998.The increased precipitation in the Tangnahai(TNH)and Lanzhou(LZ)Basins contributed to the increase of their streamflows at 8.04%and 14.29%per decade,respectively.Although the increased streamflow in the headwater basins of the Yellow River may ease some of the water resources concerns,the decreasing trend of streamflow in the headwater areas of the southeastern TP region since 1998 could lead to a water crisis in transboundary river basins for billions of people in Southeast and South Asia.

Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.