Development of Storm Sewer-Network Extraction Tool(SS-NET) for Creating Pipe Network Input Data of Urban Rainfall-Runoff Model

2017 ◽  
Vol 17 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Jongpyo Park ◽  
◽  
Soyoung Lee ◽  
Byongju Lee ◽  
◽  
...  
2009 ◽  
Vol 40 (5) ◽  
pp. 433-444 ◽  
Author(s):  
David A. Post

A methodology has been derived which allows an estimate to be made of the daily streamflow at any point within the Burdekin catchment in the dry tropics of Australia. The input data requirements are daily rainfall (to drive the rainfall–runoff model) and mean average wet season rainfall, total length of streams, percent cropping and percent forest in the catchment (to regionalize the parameters of the rainfall–runoff model). The method is based on the use of a simple, lumped parameter rainfall–runoff model, IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data). Of the five parameters in the model, three have been set to constants to reflect regional conditions while the other two have been related to physio-climatic attributes of the catchment under consideration. The parameter defining total catchment water yield (c) has been estimated based on the mean average wet season rainfall, while the streamflow recession time constant (τ) has been estimated based on the total length of streams, percent cropping and percent forest in the catchment. These relationships have been shown to be applicable over a range of scales from 68–130,146 km2. However, three separate relationships were required to define c in the three major physiographic regions of the Burdekin: the upper Burdekin, Bowen and Suttor/lower Burdekin. The invariance of the relationships with scale indicates that the dominant processes may be similar across a range of scales. The fact that different relationships were required for each of the three major regions indicates the geographic limitations of this regionalization approach. For most of the 24 gauged catchments within the Burdekin the regionalized rainfall–runoff models were nearly as good as or better than the rainfall–runoff models calibrated to the observed streamflow. In addition, models often performed better over the simulation period than the calibration period. This indicates that future improvements in regionalization should focus on improving the quality of input data and rainfall–runoff model conceptualization rather than on the regionalization procedure per se.


2019 ◽  
Vol 24 (5) ◽  
pp. 04019011 ◽  
Author(s):  
Tero J. Niemi ◽  
Teemu Kokkonen ◽  
Nora Sillanpää ◽  
Heikki Setälä ◽  
Harri Koivusalo

2002 ◽  
Vol 45 (2) ◽  
pp. 27-33 ◽  
Author(s):  
M. Quirmbach ◽  
G.A. Schultz

This paper presents an application of radar data (DX-product of the German Weather Service) with a high resolution in space (1° × 1 km) and time (Δt = 5 minutes) in urban hydrology. The radar data and data of rain gauges with different locations in the test catchment were compared concerning their suitability as input into an urban rainfall-runoff model. In order to evaluate the accuracy of model simulation results, five evaluation criteria have been specified which are relevant for an efficient management of sewer systems and wastewater treatment plants. The results demonstrate that radar data should be used in urban hydrology if distances > 4 km between rain gauge and catchment exist and for catchments with a density of rain gauges smaller than 1 rain gauge per 16 km2.


2021 ◽  
Author(s):  
Jamie Lee Stevenson ◽  
Christian Birkel ◽  
Aaron J. Neill ◽  
Doerthe Tetzlaff ◽  
Chris Soulsby

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1226
Author(s):  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  
...  

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.


2012 ◽  
Vol 26 (26) ◽  
pp. 3953-3961 ◽  
Author(s):  
Jiangmei Luo ◽  
Enli Wang ◽  
Shuanghe Shen ◽  
Hongxing Zheng ◽  
Yongqiang Zhang

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