HEC-4 Monthly Streamflow Simulation (User's Manual)

1971 ◽  
Author(s):  
HYDROLOGIC ENGINEERING CENTER DAVIS CA
Keyword(s):  
Monthly Streamflow ◽  
Streamflow Simulation

scholarly journals Evaluation of Precipitation Products by Using Multiple Hydrological Models over the Upper Yellow River Basin, China

2020 ◽  
Vol 12 (24) ◽  
pp. 4023
Author(s):  
Xiaoxiang Guan ◽  
Jianyun Zhang ◽  
Qinli Yang ◽  
Xiongpeng Tang ◽  
Cuishan Liu ◽  
...  
Keyword(s):  
River Basin ◽  
Yellow River ◽  
Yellow River Basin ◽  
The Other ◽  
Monthly Precipitation ◽  
Hydrological Models ◽  
Runoff Simulation ◽  
Monthly Streamflow ◽  
Upper Yellow River ◽  
Streamflow Simulation

In this study, 6 widely used precipitation products APHRODITE, CPC_UNI_PRCP, CN05.1, PERSIANN-CDR, Princeton Global Forcing (PGF), and TRMM 3B42 V7 (TMPA), were evaluated against gauge observations (CMA data) from 1998 to 2014, and applied to streamflow simulation over the Upper Yellow River basin (UYRB), using 4 hydrological models (DWBM, RCCC-WBM, GR4J, and VIC). The relative membership degree (u), as the comprehensive evaluation index in the hydrological evaluation, was calculated by the optimum fuzzy model. The results showed that the spatial pattern of precipitation from the CMA dataset and the other 6 precipitation products were very consistent with each other. The satellite-derived rainfall products (SDFE), like PSERSIANN-CDR and TMPA, depicted considerably finer and more detailed spatial heterogeneity. The SDFE and reanalysis (RA) products could estimate the monthly precipitation very well at both gauge and basin-average scales. The runoff simulation results indicated that the APHRODITE and TMPA were superior to the other 4 precipitation datasets, obtaining much higher scores, with average u values of 0.88 and 0.77. The precipitation estimation products tended to show better performance in streamflow simulation at the downstream hydrometric stations. In terms of performance of hydrological models, the RCCC–WBM model showed the best potential for monthly streamflow simulation, followed by the DWBM. It indicated that the monthly models were more flexible than daily conceptual or distributed models in hydrological evaluation of SDFE or RA products, and that the difference in precipitation estimates from various precipitation datasets were more influential in the GR4J and VIC models.

scholarly journals Impacts of swat weather generator statistics from high-resolution datasets on monthly streamflow simulation over Peninsular Spain

2021 ◽  
Vol 35 ◽  
pp. 100826
Author(s):  
Javier Senent-Aparicio ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
José Ginés Giménez ◽  
Julio Pérez-Sánchez ◽  
...  
Keyword(s):  
High Resolution ◽  
Weather Generator ◽  
Monthly Streamflow ◽  
Streamflow Simulation ◽  
Peninsular Spain

Assessing the performance of radar-based and satellite precipitation products in hydrological modelling with SWAT in Vils Basin, Germany

2020 ◽  
Author(s):  
Zheng Duan ◽  
Edward Duggan ◽  
Ye Qing ◽  
Ye Tuo
Keyword(s):  
Spatial Resolution ◽  
Swat Model ◽  
Rain Gauge ◽  
Hydrological Modelling ◽  
Satellite Precipitation ◽  
Precipitation Product ◽  
Monthly Streamflow ◽  
Precipitation Estimates ◽  
Temporal And Spatial ◽  
Streamflow Simulation

<p>Hydrological modelling is an important tool to improve our understanding of hydrological processes of river basins and to predict impacts of climate change and environmental change on water resources. Precipitation is a key component of the hydrological cycle, and the most important driver/input data for hydrological models. Accurate precipitation measurements at desirable temporal and spatial resolution are essential for achieving reasonable performance of hydrological modelling. Compared to the conventional measurements from point-based rain gauge stations, remote sensing of precipitation with satellite sensors and ground-based radar can expand observational coverage and provide regional precipitation at varying temporal and spatial resolutions. Radars can provide sampling at very high resolution but also tend to contain significant errors in precipitation estimates. The Deutscher Wetterdienst (DWD; German Weather Service) developed the RADOLAN (RADar-OnLine-ANeichung) method (a real-time, gauge-adjustment and correction procedure) to generate precipitation estimates (termed as RADOLAN product) from the German Doppler radar network. More recently (2017), the DWD published a reanalysis of radar data to generate RADKLIM (RADarKLIMatologie) precipitation product using upgraded correction algorithms and additional offline gauge adjustment. </p><p> </p><p>This study presents the first assessment of the performance of two high spatial resolution (1 km) radar-based precipitation products (RADOLAN and RADKLIM) in streamflow simulation using the hydrological model SWAT (Soil and Water Assessment Tool) in Germany. We also evaluate the performance of conventional point-based rain gauge data and a satellite precipitation product in driving SWAT for streamflow simulation. The selected satellite product is CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) because of its well reported good performance and the relative higher spatial resolution (0.05°). The Vils Basin located in Bavaria, Germany is chosen as the study area. Performance of investigated precipitation product is assessed by comparing simulated streamflow using calibrated SWAT model against measured streamflow at basin outlet at both daily and monthly time scales. The model calibration is performed using the SWAT-CUP program with measured streamflow. Different calibration procedures are also investigated to analyze the influence on model performance. This study presents and discusses the accuracy and uncertainty of using ground-based radar and satellite precipitation products in driving SWAT model for daily and monthly streamflow simulation. Our findings are expected to provide beneficial feedback to product developers for further improvements, and to inform local end-users about the quality of investigated precipitation products.</p>

Long memory monthly streamflow simulation by a broken line model

1972 ◽  
Vol 8 (4) ◽  
pp. 1100-1105 ◽  
Author(s):  
Luis E. Garcia ◽  
David R. Dawdy ◽  
Jose M. Mejia
Keyword(s):  
Long Memory ◽  
Line Model ◽  
Monthly Streamflow ◽  
Streamflow Simulation
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