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Application and effect evaluation of different optimization algorithms in distributed hydrological model

JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2025年 58卷

作者： Wu, Ming Gao, Yuqin Liu, Yunping Xu, Longsheng Gao, Li Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China

Study Region: The upper reaches of the Shaying River Basin (the USR Basin) in the Huai River Basin, China Study Focus: The calibration of model parameters is one of the key challenges in advancing the application of hydrological models. The study proposes a novel metaheuristic optimization algorithm, the chaotic particle swarm genetic algorithm (CPSGA), and compares its performance with four well-known optimization algorithms in the field of hydrological model calibration: GA, PSO, DE, and SCE-UA. The comparison focuses on effectiveness, stability, time consumption, and convergence characteristics. New hydrological Insights for the Region: During the parameter calibration process of runoff simulation in the USR Basin, CPSGA demonstrates strong effectiveness and convergence characteristics. It enhances the GA framework by integrating initial population chaotization, perturbation evolution, and sub-adaptation strategies, which improve individual diversity and facilitate targeted evolution, thereby increasing effectiveness and convergence. However, these enhancements compromise stability and increase time consumption compared to other algorithms. While PSO shows the best convergence characteristics, it suffers from reduced swarm diversity in later iterations, leading to local optima and poor effectiveness. The complex concept and competitive complex evolution (CCE) strategy of SCE-UA make it less effective for optimization problems with a considerable number of variables, limiting its suitability for calibrating fully distributed hydrological models. These results can provide reference for parameter calibration and uncertainty analysis in distributed hydrological models.

关键词： Optimization algorithm Chaotic particle swarm genetic algorithm distributed hydrological model Parameter calibration Performance evaluation

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Accurate simulation of extreme rainfall-flood events via an improved distributed hydrological model

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JOURNAL OF HYDROLOGY 2024年 645卷

作者： Li, Ji Liu, Jiao Xia, Zhiqiang Liu, Chenrun Li, Yuechen Southwest Univ Sch Geog Sci Chongqing Key Lab Karst Environm Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R Chongqing 400715 Peoples R China Chongqing Municipal Hydrol Monitoring Stn Chongqing 401120 Peoples R China

Recently, the high incidence of extreme rainfall and flood events worldwide has severely harmed regional economies and societies. Therefore, flood simulations and forecasts, which can provide key technical support for regional flood control and disaster reduction, are urgently needed. The Liuxihe model, as a fully distributed, physically based hydrological model, was improved in this study to simulate extreme rainfall flood events in the Beijiang River Basin. This basin is a famous rainstorm centre in Guangxi Province, China. In this work, the Liuxihe model is improved in two aspects: first, its structure and runoff generation and confluence algorithm are improved, and second, the parameter calibration method is optimised. These two adjustments improve the flood simulation performance of the model and reduce the uncertainty of the simulation results. The results revealed that the flood simulated by the improved Liuxihe model was strongly consistent with the measured values, and the index values of the Nash coefficient, correlation coefficient, process relative error, and flood peak flow error performed very well in the scheme evaluation;in particular, the mean process relative error, flood peak error, and peak time difference decreased by 62%, 63%, and 80%, respectively, after model improvement. The error indicators of the simulation were within the allowable error range from the Standard for hydrological Information and hydrological Forecasting (GB/T-22482-2008), which meets the accuracy requirements of flood forecasting in the local hydrological department and can be used as a practical operational plan for flood forecasting. These satisfactory flood simulation results showed that the model and algorithm were improved;thus, the improved Liuxihe model can provide important theoretical guidance for regional flood forecasting and flood disaster mitigation.

关键词： Extreme rainfall flood events distributed hydrological model Beijiang River Basin Flood forecast

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Remotely Sensed Soil Moisture Assimilation in the distributed hydrological model Based on the Error Subspace Transform Kalman Filter

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REMOTE SENSING 2023年第7期15卷 1852-1852页

作者： Li, Yibo Cong, Zhentao Yang, Dawen Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China

The data assimilation of remotely sensed soil moisture observations provides a feasible path of improving river flow simulation. In this work, we studied the performance of the error subspace transform Kalman filter (ESTKF) assimilation algorithm on the assimilation of remotely sensed soil moisture from SMAP, including the improvement of soil moisture and river flow in the hydrological model. Additionally, we discussed the advantages and added value of ESTKF compared to the ensemble Kalman filter (EnKF) in a hydrological model. To achieve this objective, we solved the spatial resolution gap between the remotely sensed soil moisture and the simulated soil moisture of the hydrological model. The remotely sensed soil moisture from SMAP was assimilated into the first layer soil moisture in the distributed hydrological model. The spatial resolution of the hydrological model was 600 m, while the spatial resolution of the SMAP remotely sensed soil moisture was 9 km. There is a considerable gap between the two spatial resolutions. By employing observation operators and observation localization based on geolocation, the distributed hydrological model assimilated multiple remotely sensed soil moisture values for each grid, thereby ensuring the consistent updates of soil moisture in the model. The results show the following: (1) In terms of improving soil moisture, we found that both ESTKF and EnKF were effective, and the ubRMSE of ESTKF was lower than that of EnKF. (2) ESTKF improved most cases where open-loop high river flow simulations were too low, but EnKF did not improve this situation. (3) In ESTKF, the relative error of flood volume was reduced on average to 2.52%, but the relative error of flood peak did not improve. The results provide evidence of the value of ESTKF in the hydrological model.

关键词： soil moisture data assimilation distributed hydrological model river flow

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State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation

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JOURNAL OF HYDROLOGY 2023年第PartA期620卷

作者： Gong, Junfu Weerts, Albrecht H. Yao, Cheng Li, Zhijia Huang, Yingchun Chen, Yuanfang Chang, Yifei Huang, Pengnian Hohai Univ Coll Hydrol & Water Resources Nanjing 210024 Peoples R China Deltares NL-2600 MH Delft Netherlands Wageningen Univ Hydrol & Quantitat Water Management Grp NL-6700 HB Wageningen Netherlands Fuzhou Univ Coll Civil Engn Fuzhou 350108 Peoples R China Shaanxi Prov Inst Water Resources & Elect Power In Xian 710001 Peoples R China Nanjing Univ Informat Sci & Technol Sch Hydrol & Water Resources Nanjing 210044 Peoples R China

For flood simulation in small-and medium-sized catchments, discharge observations may be used to update model states of a distributed hydrological model to improve performance. The ensemble Kalman filter (EnKF) has been widely used for hydrological assimilation due to its relative simplicity and robustness. An advantage of the EnKF is that it is easy to include different sources of uncertainty, therefore the choice of error model is crucial for the application of the EnKF assimilation. This paper describes an EnKF assimilation scheme for estimating error models using the maximum a posteriori estimation method (MAP). We test this scheme in two small and medium-sized catchments in China with different characteristics, and in addition compared the performance differences under two kinds of rainfall forcing. We show that MAP is beneficial in specifying error models and providing reliable ensemble spread. The assimilation scheme can effectively ameliorate the degradation of distributed hydrological model performance due to uncalibrated model parameters and/or poor quality of input data.

关键词： Flood hydrological assimilation Ensemble Kalman filtering Maximum a posteriori estimation distributed hydrological model

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GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed hydrological model

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SCIENCE OF THE TOTAL ENVIRONMENT 2023年第1期870卷 161852页

作者： Pan, Zihao Yang, Shengtian Ren, Xiaoyu Lou, Hezhen Zhou, Baichi Wang, Huaixing Zhang, Yujia Li, Hao Li, Jiekang Dai, Yunmeng Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China Beijing Key Lab Urban Water Cycle & Sponge City Te Beijing 100875 Peoples R China China Meteorol Adm Beijing Weather Modificat Off Beijing Key Lab Cloud Precipitat & Atmospher Water Field Expt Base Cloud & Precipitat Res North China Beijing 100089 Peoples R China

The coupling of multisource remote sensing data and the lack of measured runoff introduce input data and model parameters uncertainties to the remote sensing-driven distributed hydrological model (RS-DHM). The PB satellite remote sensing datasets of the Google Earth Engine (GEE) are widely used in RS-DHM and remote sensing runoff inversion research, but whether GEE can reduce the two abovementioned uncertainties is still unknown. To answer this question, twelve remote sensing data sources provided by GEE were used in this study to drive a typical RS-DHM called the remote sensing-driven distributed time-variant gain model (RS-DTVGM) and the remote sensing runoff inversion technology called remote sensing hydrological station (RSHS), and the contribution of GEE to the improving hydrological model uncertainties was quantitatively analyzed from 2001 to 2020. The results showed that (1) the GEE-based improved data preparation not only effectively reduced the uncertainty in the input data with better spatialtemporal continuity and a 6.20 % reduction in the total area occupied by invalid grids, but also enhanced the operational efficiency by reducing the image number, memory size and data processing time of the satellite remote sensing data by 83.63 %, 99.53 %, and 98.73 %, respectively;(2) the GEE-based RSHS technology provided sufficient data support for parameter adjustment and accuracy validation of the RS-DTVGM, which effectively reduced the uncertainty in the model parameters and increased the Nash efficiency coefficient (NSE) in the calibration and validation period from 0.67 to 0.87 and 0.75, respectively;and (3) the calibrated RS-DTVGM was more reliable and robust, and its runoff and evapotranspiration were consistent with the actual statistical data. In the future, GEE and RSHS technology should be

关键词： Google Earth Engine distributed hydrological model Efficiency and uncertainty Remote sensing Remote sensing hydrological station technology

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A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

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JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2024年 55卷

作者： Guo, Xiao Wu, Zhiyong Fu, Guobin He, Hai Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210024 Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210024 Peoples R China Hohai Univ Yangtze Inst Conservat & Dev Nanjing 210024 Peoples R China CSIRO Environm Floreat WA 6014 Australia

Study region: The Ganjiang River Basin, China Study focus: Parameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insights: Compared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

关键词： Multi-objective optimization Parameter calibration Merged evapotranspiration NSGA-II distributed hydrological model

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Recognition of the Interaction Mechanisms between Water and Land Resources Based on an Improved distributed hydrological model

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WATER 2023年第10期15卷 1946-1946页

作者： Wang, Jianwei Lv, Xizhi Qin, Tianling Ni, Yongxin Ma, Li Zhang, Qiufen Nie, Hanjiang Lv, Zhenyu Li, Chenhao Zhang, Xin Feng, Jianming Yellow River Inst Hydraul Res Henan Key Lab Yellow Basin Ecol Protect & Restorat Zhengzhou 450003 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Hohai Univ Coll Hydrol & Water Recourses Nanjing 210098 Peoples R China Key Lab Geog Proc Anal & Simulat Hubei Prov Wuhan 430079 Peoples R China Cent China Normal Univ Sch Urban & Environm Sci Wuhan 430079 Peoples R China China Three Gorges Corp Wuhan 430010 Peoples R China Capital Normal Univ Beijing Lab Water Resources Secur Beijing 100048 Peoples R China Capital Normal Univ Beijing Inst Hydrogeol Coll Resource Environm & Tourism Beijing 100048 Peoples R China Zhengzhou Univ Sch Water Conservancy & Civil Engn Zhengzhou 450001 Peoples R China

Conflicts between humans and land use in the process of using water and conflicts between humans and water resources in the process of using land have led to an imbalance between natural ecosystems and socio-economic systems. It is difficult to understand the impact of the processes of water production and consumption on land patches and their ecological effects. A grid-type, basin-distributed hydrological model was established in this study, which was based on land-use units and coupled with groundwater modules to simulate the water production and consumption processes in different units. By combining land use and net primary productivity, the runoff coefficient and the water use efficiency (NPP/ET) of different land units were used as indicators to characterize the interaction between water and land resources. The results showed that the average runoff coefficients of cultivated land, forest land and grassland were 0.7, 0.5 and 0.9, respectively. Moreover, the average runoff coefficients of hills, plains and basins were 0.7, 0.7 and 0.8, respectively. The NPP produced by the average unit, evapotranspiration, in cultivated land, forest land and grassland was 7 (gC/(m(2).a))/mm, 0.7 (gC/(m(2).a))/mm and 0.2 (gC/(m(2).a))/mm, respectively. These results provide quantitative scientific and technological support in favor of the comprehensive ecological management of river basins.

关键词： distributed hydrological model water and land resources evapotranspiration runoff coefficient Sihe River Basin

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Research on Parameter Regionalization of distributed hydrological model Based on Machine Learning

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WATER 2023年第3期15卷 518-518页

作者： Wang, Wenchuan Zhao, Yanwei Tu, Yong Dong, Rui Ma, Qiang Liu, Changjun North China Univ Water Resources & Elect Power Coll Water Resources Zhengzhou 450046 Peoples R China China Inst Water Resource & Hydropower Res Beijing 100038 Peoples R China Beijing Tianzhixiang Informat Technol Co Ltd Beijing 100191 Peoples R China

In the past decade, more than 300 people have died per year on average due to mountain torrents in China. Mountain torrents mostly occur in ungauged small and medium-sized catchments, so it is difficult to maintain high accuracy of flood prediction. In order to solve the problem of the low accuracy of flood simulation in the ungauged areas, this paper studies the influence of different methods on the parameter regionalization of distributed hydrological model parameters in hilly areas of Hunan Province. According to the terrain, landform, soil and land use characteristics of each catchment, we use Shortest Distance, Attribute Similarity, Support Vector Regression, Generative Adversarial Networks, Classification and Regression Tree and Random Forest methods to create parameter regionalization schemes. In total, 426 floods of 25 catchments are selected to calibrate the model parameters, and 136 floods of 8 catchments are used for verification. The results showed that the average values of the Nash-Sutcliffe coefficients of each scheme were 0.58, 0.64, 0.60, 0.66, 0.61 and 0.68, and the worst values were 0.27, 0.31, 0.25, 0.43, 0.35 and 0.59. The random forest model is the most stable solution and significantly outperforms other methods. Using the random forest model to regionalize parameters can improve the accuracy of flood simulation in ungauged areas, which is of great significance for flash flood forecasting and early warning.

关键词： mountain torrents distributed hydrological model parameters regionalization machine learning

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Impact of assimilating dam outflow measurements to update distributed hydrological model states: Localization for improving ensemble Kalman filter performance

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JOURNAL OF HYDROLOGY 2022年 608卷 127651-127651页

作者： Khaniya, Manoj Tachikawa, Yasuto Ichikawa, Yutaka Yorozu, Kazuaki Kyoto Univ Dept Civil & Earth Resources Engn Kyoto Japan

This paper presents an investigation on the effect of dam operation on the ensemble Kalman filter (EnKF) performance in a distributed hydrological model based on kinematic wave theory. For this purpose, two flood events in a mountainous catchment in Japan are selected, and the model error parameters for the EnKF implementation are first determined through a series of experiments by varying the input error magnitude, state variable perturbation, and its correlation length. Then, through the assimilation of dam outflow observations, it is shown that special treatment of such measurements is critical in ensuring the success of the filter when flood control operation significantly changes the flow hydrograph and when the covariances between distant grids are high. Localization methods based on Euclidean distance, flow accumulation values, and river distance are all able to limit the deleterious updates to model states at distant grids and improve filter efficacy. However, no single localization approach is found to be consistently optimal for varying noise configurations, number (and type) of assimilated stations, or flood events. Separate localization of dam outflows from streamflow measurements by assuming the dam grids to be only related to their downstream grids shows potential in improving prediction at locations near the dams themselves while minimizing disruptions at long distances.

关键词： distributed hydrological model Ensemble Kalman filter Data assimilation Localization Dam outflow measurements Human control

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Integration of Forest Growth Component in the FEST-WB distributed hydrological model: The Bonis Catchment Case Study

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FORESTS 2021年第12期12卷 1794页

作者： Feki, Mouna Ravazzani, Giovanni Ceppi, Alessandro Pellicone, Gaetano Caloiero, Tommaso Politecn Milan Dept Civil & Environm Engn DICA Piazza Leonardo da Vinci 32 I-20133 Milan Italy CNR Inst Agr & Forest Syst Mediterranean Natl Res Council ISAFOM I-87036 Arcavacata Di Rende Italy

In this paper, the FEST-FOREST model is presented. A FOREST module is written in the FORTRAN-90 programming language, and was included in the FEST-WB distributed hydrological model delivering the FEST-FOREST model. FEST-FOREST is a process-based dynamic model allowing the simulation at daily basis of gross primary production (GPP) and net primary production (NPP) together with the carbon allocation of a homogeneous population of trees (same age, same species). The model was implemented based on different equations from literature, commonly used in Eco-hydrological models. This model was developed within the framework of the INNOMED project co-funded under the ERA-NET WaterWorks2015 Call of the European Commission. The aim behind the implementation of the model was to simulate in a simplified mode the forest growth under different climate change and management scenarios, together with the impact on the water balance at the catchment. On a first application of the model, the results are considered very promising when compared to field measured data.

关键词： ecohydrological forest growth distributed hydrological model

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