scholarly journals Surface runoff modeling for Xitiaoxi catchment, Taihu Basin

2006 ◽  
Vol 18 (4) ◽  
pp. 401-406 ◽  
Author(s):  
ZHANG Qi ◽  
◽  
LI Hengpeng ◽  
XU Ligang
Keyword(s):  
Surface Runoff ◽  
Taihu Basin ◽  
Runoff Modeling ◽  
Xitiaoxi Catchment ◽  
Surface Runoff Modeling

scholarly journals Surface runoff modeling of Lake Fuxian catchment

2007 ◽  
Vol 19 (6) ◽  
pp. 718-726 ◽  
Author(s):  
XU Jintao ◽  
◽  
ZHANG Qi ◽  
XU Ligang
Keyword(s):  
Surface Runoff ◽  
Runoff Modeling ◽  
Lake Fuxian ◽  
Surface Runoff Modeling

scholarly journals GIS-based Approach to Estimate Surface Runoff in Small Catchments: A Case Study

2016 ◽  
Vol 35 (3) ◽  
pp. 97-116 ◽  
Author(s):  
Matej Vojtek ◽  
Jana Vojteková
Keyword(s):  
Surface Runoff ◽  
Daily Rainfall ◽  
Curve Number ◽  
Point Of View ◽  
Small Catchment ◽  
Gis And Remote Sensing ◽  
Runoff Modeling ◽  
Digital Elevation ◽  
Surface Runoff Modeling ◽  
Elevation Model

Abstract The issue of surface runoff assessment is one of the important and relevant topics of hydrological as well as geographical research. The aim of the paper is therefore to estimate and assess surface runoff on the example of Vyčoma catchment which is located in the Western Slovakia. For this purpose, SCS runoff curve number method, modeling in GIS and remote sensing were used. An important task was the creation of a digital elevation model (DEM), which enters the surface runoff modeling and affects its accuracy. Great attention was paid to the spatial interpretation of land use categories applying aerial imagery from 2013 and hydrological soil groups as well as calculation of maximum daily rainfall with N-year return periods as partial tasks in estimating surface runoff. From the methodological point of view, the importance of the paper can be seen in the use of a simple GIS-based approach to assess the surface runoff conditions in a small catchment.

Surface runoff modeling in Ketar watershed, Ethiopia

2020 ◽  
Vol 5 (1) ◽  
pp. 151-162 ◽  
Author(s):  
Chala Hailu Sime ◽  
Tamene Adugna Demissie ◽  
Fayera Gudu Tufa
Keyword(s):  
Surface Runoff ◽  
Runoff Modeling ◽  
Surface Runoff Modeling

scholarly journals Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1116 ◽  
Author(s):  
Won Chang ◽  
Xi Chen
Keyword(s):  
Comparative Study ◽  
Surface Runoff ◽  
Aridity Index ◽  
Hydrologic Model ◽  
Data Driven ◽  
Vegetation Coverage ◽  
Potential Evaporation ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Input Variables

Data-driven machine learning approaches have been rapidly developed in the past 10 to 20 years and applied to various problems in the field of hydrology. To investigate the capability of data-driven approaches in rainfall-runoff modeling in comparison to theory-driven models, we conducted a comparative study of simulated monthly surface runoff at 203 watersheds across the contiguous USA using a conceptual model, the proportionality hydrologic model, and a data-driven Gaussian process regression model. With the same input variables of precipitation and mean monthly aridity index, the two models showed similar performance. We then introduced two more input variables in the data-driven model: potential evaporation and the normalized difference vegetation index (NDVI), which were selected based on hydrologic knowledge. The modified data-driven model performed much better than either the conceptual or original data-driven model. A sensitivity analysis was conducted on all three models tested in this study, which showed that surface runoff responded positively to increased precipitation. However, a confounding effect on surface runoff sensitivity was found among mean monthly aridity index, potential evaporation, and NDVI. This confounding was caused by complex interconnections among energy supply, vegetation coverage, and climate seasonality of the watershed system. We also conducted an uncertainty analysis on the two data-driven models, which showed that both models had reasonable predictability within the 95% confidence interval. With the additional two input variables, the modified data-driven model had lower prediction uncertainty and higher prediction accuracy.

Hydrodynamic rainfall-runoff modeling using different approaches for runoff creation

2020 ◽  
Author(s):  
Karl Broich ◽  
Thomas Pflugbeil ◽  
Johannes Mitterer ◽  
Markus Disse
Keyword(s):  
Water Depth ◽  
Surface Runoff ◽  
Flash Flood ◽  
Flash Floods ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Hydrodynamic Models ◽  
Additional Advantage ◽  
Runoff Modeling ◽  
Scs Cn

<p>After extreme flash floods events 2016 in Bavaria, the cooperation project HiOS (reference map for surface runoff and flash floods) was started aiming at the detailed analysis of risk generated by flash floods using GIS methods as well as hydrological and hydrodynamic models. Part of the risk analysis is done using hydrodynamic rainfall-runoff modeling (HDRRM). HDRRM gets more and more popular since hydrodynamic models are able to accept rainfall as input. But most of the known hydrodynamic models have no integrated precipitation modules and therefore cannot be used uniquely for rainfall-runoff modeling. In this study, TELEMAC-2D is used for HDRRM because it already contains the SCS-CN-method and offers the possibility to implement new precipitation modules due to its open source license. An additional advantage of TELEMAC-2D is the good scaling on HPC cluster systems.</p><p>In this study, two different approaches for runoff creation will be compared. (1) The well-proven SCS-CN method calculates effective rain. Due to its simple structure, the process of runoff generation is completely decoupled from runoff concentration. Consequently, SCS-CN cannot account for re-infiltration due to surface runoff. (2) However, the Green-Ampt infiltration (GAI) is coupled to surface runoff as long as the water depth is non-zero. GAI is implemented recently and thus will be described in more detail. Both approaches are first tested using a simple model setup. The general model performance of the enhanced hydrodynamic rainfall-runoff modeling (EHDRRM) is verified using the case study Simbach/Triftern in Bavaria. This extreme flash flood event from 1<sup>st</sup> June 2016 hit the townships Simbach am Inn and Triftern. It is well documented and all necessary data is available in good quality. The main setup for the catchment area of 47 km² resp. 90 km² is built on a 1x1 m DEM, land use data, hydrological soil group data and 5 min-RADOLAN precipitation data. The calculated catchment outflow can be verified by measured data at the gauging stations in Simbach am Inn resp. Triftern. All comparisons include as reference results for precipitation without losses by infiltration.</p><p>The hydrodynamic precipitation runoff modeling HDRRM has proven to be a useful method for calculating flow paths, depths and velocities with a high spatial resolution during flash flood events. If the process of runoff generation is performed by the hydrodynamic model EHDRRM then the quality of results is improved significantly while keeping the modeling procedure simple. Concerning infiltration, EHDRRM allows for a physically correct representation taking the actual local water depth into consideration.</p>

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