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Application of developed Grid-GA distributed hydrologic model in semi-humid and semi-arid basin

Transactions of Tianjin University 2025年第3期16卷 209-215页

作者： Lili Wang Zhijia Li Hongjun Bao College of Hydrology and Water Resources Hohai University Nanjing China National Meteorological Center China Meteorological Administration Beijing China

A grid and Green-Ampt based(Grid-GA)distributed hydrologic physical model was developed for flood simulation and forecasting in semi-humid and semi-arid basin. Based on topographical information of each grid cell extracted from the digital elevation model (DEM) and Green-Ampt infiltration method, the Grid-GA model takes into consideration the redistribution of water content, and consists of vegetation and root interception, evapotranspiration, runoff generation via the excess infiltration mechanism, runoff concentration, and flow routing. The downslope redistribution of soil moisture is explicitly calculated on a grid basis, and water exchange among grids within runoff routing along the river drainage networks is taken into consideration. The proposed model and Xin’anjiang model were applied to the upper Lushi basin in the Luohe River, a tributary of the Yellow River, with an area of 4 716 km² for flood simulation. Results show that both models perform well in flood simulation and can be used for flood forecasting in semi-humid and semi-arid region.

关键词： distributed hydrologic model digital elevation model (DEM) Green-Ampt redistribution excess infiltration mechanism semi-humid and semi-arid basin

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Simulating the spatio-temporal dynamics of soil erosion, deposition, and yield using a coupled sediment dynamics and 3D distributed hydrologic model

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ENVIRONMENTAL modelLING & SOFTWARE 2016年第0期83卷 310-325页

作者： Zi, Tan Kumar, Mukesh Kiely, Gerard Lewis, Ciaran Albertson, John Duke Univ Dept Civil & Environm Engn Durham NC 27706 USA Duke Univ Nicholas Sch Environm Durham NC 27706 USA Univ Coll Cork Ctr Hydrol Micrometeorol & Climate Change Dept Civil & Environm Engn Cork Ireland Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA

Since soil erosion is driven by overland flow, it is fair to expect heterogeneity in erosion and deposition in both space and time. In this study, we develop and evaluate an open-source, spatially-explicit, sediment erosion, deposition and transport module for the distributed hydrological model, GEOtop. The model was applied in Dripsey catchment in Ireland, where it captured the total discharge volume and suspended sediment yield (SSY) with a relative bias of -1.2% and -22.4%, respectively. Simulation results suggest that daily SSY per unit rainfall amount was larger when the top soil was near saturation. Simulated erosion and deposition areas, which varied markedly between events, were also found to be directly influenced by spatial patterns of soil saturation. The distinct influence of soil saturation on erosion, deposition and SSY underscores the role of coupled surface-subsurface hydrologic interactions and a need to represent them in models for capturing fine resolution sediment dynamics. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： distributed hydrologic model Soil erosion Sediment spatio-temporal dynamics Coupled modeling Sediment transport model GEOtop model

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Flood inundation simulation in a river basin using a physically based distributed hydrologic model

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hydrologicAL PROCESSES 2000年第3期14卷 497-519页

作者： Dutta, D Herath, S Musiake, K Univ Tokyo INCEDE IIS Minato Ku Tokyo 1068558 Japan

A physically based distributed hydrologic model is developed in this study for flood inundation simulation combining newly developed overland and channel network flow simulation models with evapotranspiration, unsaturated zone and saturated zone models. The overland flow and river flow models are validated individually with test data, and then coupled with other models. The model can take fine resolution spatial data as input preserving spatial heterogeneity of physical characteristics of a river basin. River embankments play an important role in flood prevention. The model can incorporate river embankment data in flood inundation simulation. The model is applied in a river catchment in Japan to simulate a flood event in 1996. Outputs from the model show good agreements with observed flood hydrographs and surveyed flood inundation. Copyright (C) 2000 John Wiley & Sons, Ltd.

关键词： distributed hydrologic model simulation of flood inundation Ichinomiya river basin

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Quantitative evaluation of NEXRAD data and its application to the distributed hydrologic model BPCC

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Science China(Technological Sciences) 2012年第9期55卷 2617-2624页

作者： ZHANG HuiLan Li DanXun WANG XingKui JIANG XiaoMing State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing 100084China

The next-generation weather radar(NEXRAD) can generally capture the spatial variability of rainfall fields,but fails to provide accurate depth measurements.A systematic strategy to evaluate the accuracy of radar data in depth measurement and its performance in hydrologic model is *** evaluation coefficients are calculated by comparing NEXRAD data with individual raingauges as well as subbasin-averaged interpolations,and point-and surface-average factors are introduced to revise radar data *** simulations are then performed with a distributed hydrologic model,called basin pollution calculation center(BPCC) with both raingauge observations and revised NEXRAD estimates *** BPCC model is applied to Clear Creek Watershed,IA,USA,on an hourly scale,and the calibration and validation parameters are semi-automatically optimized to improve manual calibration *** show that hydrographs generated from both gauge and NEXRAD are in good agreement with observed flow *** statistics reveal that NEXRAD contributes to model performance,indicating that NEXRAD data has the potential to be used as an alternative source of precipitation data and improve the accuracy of hydrologic simulations.

关键词： NEXRAD rain gauge distributed hydrologic model evaluation calibration

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Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales

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JOURNAL OF HYDROLOGY 2006年第1-2期329卷 174-185页

作者： Carpenter, Theresa M. Georgakakos, Konstantine P. Hydrol Res Ctr San Diego CA 92130 USA Univ Calif San Diego Scripps Inst Oceanog La Jolla CA 92093 USA

distributed hydrologic models, with the capability to incorporate a variety of spatially-varying land characteristics and precipitation forcing data, are thought to have great potential for improving hydrologic forecasting. However, uncertainty in the high resolution estimates of precipitation and model parameters may diminish potential gains in prediction accuracy achieved by accounting for the inherent spatial variability. This paper develops a probabilistic methodology for comparing ensemble streamflow simulations from hydrologic models with high- and low-spatial resolution under uncertainty in both precipitation input and model parameters. The methodology produces ensemble streamflow simulations using well calibrated hydrologic models, and evaluates the distinctiveness of the ensembles from the high-and low-resolution models for the same simulation point. The study watersheds are of the scale for which operational streamflow forecasts are issued (order of a few 1000 km(2)), and the models employed are adaptations of operational models used by the US National Weather Service. A high-resolution (i.e., spatially distributed) model and a low-resolution (i.e., spatially lumped) model were used to simulate selected events for each of two study watersheds located in the southern Central Plains of the US using operational-quality data to drive the models. Ensemble streamflow simulations were generated within a Monte Carlo framework using models for uncertainty in radar-based precipitation estimates and in the hydrologic soil model parameters. The Kolmogorov-Smirnov test was then employed to assess whether the ensemble flow simulations at the time of observed peak flow from the high- and low-resolution models can be distinguished with high confidence. Further assessment evaluated the model performance in terms of reproducing the observed peak flow magnitude and timing. Most of the selected events showed the high- and low-resolution models produced statistically different

关键词： distributed hydrologic model streamflow simulation model comparison uncertainty ensemble simulation

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Ensemble flood forecasting with stochastic radar image extrapolation and a distributed hydrologic model

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hydrologicAL PROCESSES 2009年第4期23卷 597-611页

作者： Kim, Sunmin Tachikawa, Yasuto Sayama, Takahiro Takara, Kaoru Kyoto Univ Disaster Prevent Res Inst Kyoto 6110011 Japan Kyoto Univ Grad Sch Urban & Environm Engn Nishikyo Ku Kyoto 6158540 Japan

A new attempt oil ensemble flood forecasting is introduced by use of a radar image extrapolation along with a stochastic error field simulation, and a distributed hydrologic model. In contrast to the conventional ensemble simulation that uses initial condition control to obtain a statistical outcome, stochastic prediction en-or fields were externally generated to offer probable variations of deterministic predictions. Firstly, a radar extrapolation model provided deterministic rainfall prediction, and its prediction error structure was analysed by comparing the observed rainfall fields. Secondly, on the basis of the analysed error structure, spatially correlated error fields Were Simulated using a covariance matrix decomposition method oil a real-time basis. The simulated error fields enabled not only the production of probable rainfall field variations for ensemble simulation but also an improvement in prediction accuracy by offsetting the deterministic prediction error, Finally, the simulated error fields along with the deterministic fields were tested with a distributed hydrologic model to measure the validity of the ensemble runoff prediction. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： stochastic flood forecasting ensemble external error simulation radar image extrapolation distributed hydrologic model

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modelling the hydrologic effects of dynamic land-use change using a distributed hydrologic model and a spatial land-use allocation model

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hydrologicAL PROCESSES 2010年第18期24卷 2538-2554页

作者： Chu, Hone-Jay Lin, Yu-Pin Huang, Chun-Wei Hsu, Cheng-Yu Chen, Horng-Yng Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei 106 Taiwan

This study develops a novel approach for modelling and examining the impacts of time-space land-use changes on hydrological components. The approach uses an empirical land-use change allocation model (CLUE-s) and a distributed hydrological model (DHSVM) to examine various land-use change scenarios in the Wu-Tu watershed in northern Taiwan. The study also uses a generalized likelihood uncertainty estimation approach to quantify the parameter uncertainty of the distributed hydrological model. The results indicate that various land-use policies-such as no change, dynamic change and simultaneous change-have different levels of impact on simulating the spatial distributions of hydrological components in the watershed study. Peak flow rates under simultaneous and dynamic land-use changes are 5.71% and 2.77%, respectively, greater than the rate under the no land-use change scenario. Using dynamic land-use changes to assess the effect of land-use changes on hydrological components is more practical and feasible than using simultaneous land-use change and no land-use change scenarios. Furthermore, land-use change is a spatial dynamic process that can lead to significant changes in the distributions of ground water and soil moisture. The spatial distributions of land-use changes influence hydrological processes, such as the ground water level of whole areas, particularly in the downstream watershed. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： land-use change distributed hydrologic model DHSVM uncertainty analysis GLUE Taiwan

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Ensemble Kalman Filtering and Particle Filtering in a Lag-Time Window for Short-Term Streamflow Forecasting with a distributed hydrologic model

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JOURNAL OF hydrologic ENGINEERING 2013年第12期18卷 1684-1696页

作者： Noh, Seong Jin Tachikawa, Yasuto Shiiba, Michiharu Kim, Sunmin Korea Inst Construct Technol Water Resources & Environm Res Dept Water Resources Res Div Goyang Si 411712 Gyeonggi Do South Korea Kyoto Univ Dept Civil & Earth Resources Engn Kyoto 6158540 Japan

The performance of the ensemble Kalman filter (EnKF) and the particle filter (PF) is assessed for short-term streamflow forecasting with a distributed hydrologic model, namely, the water and energy transfer processes (WEP) model. To mitigate the drawbacks of conventional filters, the ensemble square root filter (EnSRF) and the regularized particle filter (RPF) are implemented. For both the EnSRF and the RPF, sequential data assimilation is performed within a lag-time window to consider the response times of internal hydrologic processes. The proposed methods are applied to two catchments in Japan and Korea to assess their performance. The model ensembles are perturbed by the noise of the soil moisture content and are assimilated with streamflow observations. The forecasting accuracy of both the EnSRF and the RPF is improved when sufficient lag-time windows are provided. The EnSRF is sensitive to the length of the lag-time window and has a limited ability to forecast within short lead times, whereas the RPF has a more stable forecasting capability for the entire range of lead times. Filtering with a limited number of ensembles also yields improved performance using a lag-time window.

关键词： Kalman filters Streamflow Forecasting hydrologic models Ensemble Kalman filter Particle filter Streamflow forecasting distributed hydrologic model Lag-time window

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Basin-Scale Water Resources Assessment in Oklahoma under Synthetic Climate Change Scenarios Using a Fully distributed hydrologic model

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JOURNAL OF hydrologic ENGINEERING 2010年第2期15卷 107-122页

作者： Liuzzo, Lorena Noto, Leonardo V. Vivoni, Enrique R. La Loggia, Goffredo Univ Palermo Dipartimento Ingn Idraul Applicaz Ambientali I-90128 Palermo Italy Arizona State Univ Sch Earth & Space Explorat Tempe AZ 85287 USA Arizona State Univ Sch Sustainable Engn & Built Environm Tempe AZ 85287 USA

Climate change resulting from the enhanced greenhouse effect is expected to have significant implications for the hydrological cycle. Several studies have pointed out the importance of basin-scale investigations for determining regional impacts on water resources, including the effects of floods and droughts. In this study, a fully distributed hydrologic model is used to assess the potential impacts of climate change on water availability in a basin in Oklahoma (United States). With this aim, the hydrologic model was applied for current conditions as well as under the hypotheses of climate variations represented by scenarios consistent with a climatic trend analysis generated using a stochastic weather model. hydrologic simulations indicate that streamflow and evapotranspiration reflect variations in precipitation differently. Positive trends in precipitation result in an increase in surface and groundwater resources, while evapotranspiration is only affected slightly due to the higher soil moisture in the basin. Sensitivity analyses of the evapotranspiration and runoff changes to precipitation variations confirm these results. Comparisons of the impacts of the precipitation trend on surface and groundwater resources showed that the increase of surface water resources is similar to 3 times greater, implying the groundwater system is affected less by climate change. The use of a distributed model also provided insight on the spatial variation of the water balance components. Results showed that the most significant increases of soil moisture (similar to 60%) are located along the river network and in the flat areas of the basin, characterized by a higher frequency of saturation excess runoff. In summary, climate change scenarios in this region produced an increase in water resources that can have beneficial impacts, but these positive effects are tempered by the increasing potential for flood risk. The increase of this risk has been evaluated as well in this analysis

关键词： Water resources management Climate change Trend analysis distributed hydrologic model Basin water balance

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Sequential data assimilation for streamflow forecasting using a distributed hydrologic model: particle filtering and ensemble Kalman filtering

Sequential data assimilation for streamflow forecasting usin...

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5th International Conference on Flood Management (ICFM5)

作者： Noh, Seong Jin Tachikawa, Yasuto Shiiba, Michiharu Kim, Sunmin Kyoto Univ Dept Urban & Env Eng Kyoto 6158540 Japan Korea Inst Construct Technol Water Resources & Environm Res Dept Gyeonggi 411712 South Korea Kyoto Univ Dept Civil & Earth Resources Engn Kyoto 6158540 Japan

ISBN: (纸本)9781907161353

Accurate streamflow predictions are crucial for mitigating flood damage and addressing operational flood scenarios. In recent years, sequential data assimilation methods have drawn attention due to their potential to handle explicitly the various sources of uncertainty in hydrologic models. In this study, we implement two ensemble-based sequential data assimilation methods for streamflow forecasting via the particle filters and the ensemble Kalman filter (EnKF). Among variations of filters, the ensemble square root filter (EnSRF) and the lagged regularized particle filter (LRPF) are implemented for a distributed hydrologic model. Two methods are applied for short-term flood forecasting in a small-sized catchment located in Japan (<1000 km(2)). Soil moisture contents are perturbed by process noises and model ensembles are updated by streamflow observation at the outlet. In the case of the LRPF, state updating is performed through a lag-time window to take into account the different response times of hydrologic processes. For different flood events and various forecast lead times, LRPF forecasts outperform EnSRF forecasts and deterministic cases. The EnSRF shows limited performance in both forecasting accuracy and probabilistic intervals, which require introduction of a lag-time window in the filtering processes.

关键词： sequential data assimilation flood forecasting particle filter ensemble Kalman filter distributed hydrologic model

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