Query：
学者姓名：黄迎春
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Assimilating either soil moisture or streamflow individually has been well demonstrated to enhance the simulation performance of hydrological models. However, the runoff routing process may introduce a lag between soil moisture and outlet discharge, presenting challenges in simultaneously assimilating the two types of observations into a hydrological model. The asynchronous ensemble Kalman filter (AEnKF), an adaptation of the ensemble Kalman filter (EnKF), is capable of utilizing observations from both the assimilation moment and the preceding periods, thus holding potential to address this challenge. Our study first merges soil moisture data collected from field soil moisture monitoring sites with China Meteorological Administration Land Data Assimilation System (CLDAS) soil moisture data. We then employ the AEnKF, equipped with improved error models, to assimilate both the observed outlet discharge and the merged soil moisture data into the Xin'anjiang model. This process updates the state variables of the model, aiming to enhance real-time flood forecasting performance. Tests involving both synthetic and real-world cases demonstrates that assimilation of these two types of observations simultaneously substantially reduces the accumulation of past errors in the initial conditions at the start of the forecast, thereby aiding in elevating the accuracy of flood forecasting. Moreover, the AEnKF with the enhanced error model consistently yields greater forecasting accuracy across various lead times compared to the standard EnKF.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Gong, Junfu , Liu, Xingwen , Yao, Cheng et al. State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models [J]. | HYDROLOGY AND EARTH SYSTEM SCIENCES , 2025 , 29 (2) : 335-360 . |
| MLA | Gong, Junfu et al. "State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models" . | HYDROLOGY AND EARTH SYSTEM SCIENCES 29 . 2 (2025) : 335-360 . |
| APA | Gong, Junfu , Liu, Xingwen , Yao, Cheng , Li, Zhijia , Weerts, Albrecht H. , Li, Qiaoling et al. State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models . | HYDROLOGY AND EARTH SYSTEM SCIENCES , 2025 , 29 (2) , 335-360 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
For flood simulation in humid catchments, utilizing discharge observations to update the states of hydrological models may enhance performance. Asynchronous Ensemble Kalman Filter (AEnKF), an asynchronous variant of the Ensemble Kalman Filter (EnKF), holds substantial application potential in hydrological assimilation due to its ability to utilize more observations with almost no additional computational time. This study employs AEnKF to update the state variables of the Xin'anjiang model, necessitating the use of error models to perturb both model states and observations to generate ensemble spread. The Bias-corrected Gaussian Error Model (BGEM) is used to mitigate the systematic bias brought by perturbating soil moisture, and the Maximum a Posteriori Estimation Method (MAP) is employed for the estimation of hyperparameters of error models. Through synthetic and real- world data testing, it has been validated that the rectification of soil moisture perturbations using the BGEM significantly reduces the systematic bias induced by Gaussian perturbations. Moreover, the assimilation scheme introduced in this study, based on AEnKF with enhanced error models, outperforms the EnKF with those models. It substantially reduces the accumulation of past errors in the initial conditions at the start of the forecast, thereby aiding in elevating the accuracy of flood forecasting.
Keyword ：
Asynchronous Ensemble Kalman filtering Asynchronous Ensemble Kalman filtering Data assimilation Data assimilation Error model Error model Flood forecasting Flood forecasting Xin'anjiang model Xin'anjiang model
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Gong, Junfu , Yao, Cheng , Weerts, Albrecht H. et al. State updating in Xin'anjiang model by Asynchronous Ensemble Kalman filtering with enhanced error models [J]. | JOURNAL OF HYDROLOGY , 2024 , 640 . |
| MLA | Gong, Junfu et al. "State updating in Xin'anjiang model by Asynchronous Ensemble Kalman filtering with enhanced error models" . | JOURNAL OF HYDROLOGY 640 (2024) . |
| APA | Gong, Junfu , Yao, Cheng , Weerts, Albrecht H. , Li, Zhijia , Wang, Xiaoyi , Xu, Junzeng et al. State updating in Xin'anjiang model by Asynchronous Ensemble Kalman filtering with enhanced error models . | JOURNAL OF HYDROLOGY , 2024 , 640 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
为解决陆气双向耦合中气象与水文模型不易匹配的问题,基于质量守恒原理,以土壤含水量为纽带,构建高效的WRF/Grid-XAJ双向耦合(双耦)系统.通过屯溪流域2场洪水事件分析发现:模型权重参数可定量评估水文-气象模型耦合的相容性;降水同化后,双耦系统的降水峰值模拟精度稍好于WRF模型(误差在±5％内);双耦较单向耦合(单耦)系统能更准确地反映土壤含水量;降水同化前,双耦和单耦系统低估了洪水过程;同化后两者的模拟结果提升且与Grid-XAJ模型接近(三者纳什效率系数ENS＞0.85),其中双耦系统洪峰模拟效果最好(误差在±11％内),说明WRF/Grid-XAJ双耦系统在暴雨洪水模拟预报方面有较好的应用潜力,为水文-气象模型双向反馈建模提供了新思路.
Keyword ：
Grid-XAJ模型 Grid-XAJ模型 WRF模型 WRF模型 分布式水文气象模型 分布式水文气象模型 洪水模拟 洪水模拟 陆气双向耦合 陆气双向耦合
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 孙明坤 , 刘志雨 , 姚成 et al. WRF/Grid-XAJ双向耦合系统构建及其暴雨洪水模拟应用 [J]. | 水科学进展 , 2024 , 35 (5) : 752-762 . |
| MLA | 孙明坤 et al. "WRF/Grid-XAJ双向耦合系统构建及其暴雨洪水模拟应用" . | 水科学进展 35 . 5 (2024) : 752-762 . |
| APA | 孙明坤 , 刘志雨 , 姚成 , 李致家 , 李超群 , 李荣容 et al. WRF/Grid-XAJ双向耦合系统构建及其暴雨洪水模拟应用 . | 水科学进展 , 2024 , 35 (5) , 752-762 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
以位于不同水文气象分区的屯溪流域和绥德流域为研究对象,选取TIGGE(THORPEX Interactive Garnd Global Ensemble)数据集中NCEP(National Centers for Environmental Prediction)、ECMWF(European Centre for Medium-range Weather Forecasts)、CMA(China Meteorological Administration)3种预报产品的2010—2015年控制预报数据,基于分位数映射法中的QUANT(non-parametric quantile mapping using empirical quantiles)法和RQUANT(non-parametric quantile mapping using robust empirical quantiles)法进行预报降雨修正,并采用多分类预报检验、连续型预报检验和概率型预报检验等方法,对不同水文气象分区、不同预报产品和不同修正方法进行比较与适用性分析;同时,以屯溪流域实测降雨为例,通过增加噪声项对降雨重采样,基于新安江模型分析降雨不确定性对水文模拟结果的影响.结果表明:在研究流域,所选的预报产品对无雨和小雨期的预报精度都较高,但随着降雨量的增加,各产品的预报能力均出现较为明显的下降.多分类和连续型检验表明绥德流域的降雨预报效果更佳,NCEP和ECMWF在研究流域的整体预报精度较高,CMA的整体预报精度在研究流域略低于其他产品.各产品在修正后大部分检验指标预报精度提高,其中:ECMWF在绥德流域修正后预报精度最高,对两种修正方法都有很好的适用性;在屯溪流域,NCEP和ECMWF在不同修正方法后各指标预报精度各有高低,CMA在修正后仅在大雨量级的TS评分预报精度高于其他产品.降雨的不确定性会对水文模拟产生消极影响,并导致参数的不确定性和水文模拟精度的下降.
Keyword ：
TIGGE TIGGE 分位数映射法 分位数映射法 流域对比 流域对比 降雨不确定性 降雨不确定性 降雨评估 降雨评估
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 韦经豪 , 黄迎春 , 姚成 . 降水预报产品在不同水文气象分区中小流域的适应性评估 [J]. | 南水北调与水利科技（中英文） , 2022 , 20 (6) : 1208-1219 . |
| MLA | 韦经豪 et al. "降水预报产品在不同水文气象分区中小流域的适应性评估" . | 南水北调与水利科技（中英文） 20 . 6 (2022) : 1208-1219 . |
| APA | 韦经豪 , 黄迎春 , 姚成 . 降水预报产品在不同水文气象分区中小流域的适应性评估 . | 南水北调与水利科技（中英文） , 2022 , 20 (6) , 1208-1219 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
