Progress in the use of drainage network indices for rainfall-runoff modelling and runoff prediction

1994 ◽  
Vol 18 (4) ◽  
pp. 539-557 ◽  
Author(s):  
Geraldene Wharton
Keyword(s):  
Drainage Basin ◽  
Dynamic Network ◽  
Network Routing ◽  
Future Research ◽  
Drainage Network ◽  
Rainfall Runoff ◽  
Widespread Application ◽  
Digital Format ◽  
Drainage Networks ◽  
Runoff Prediction

Traditional catchment-based approaches to runoff prediction suffer from the problem that it is difficult to interpret the collective physical significance of a large number of intercorrelated drainage basin variables. This has highlighted the need for a sensitive and meaningful index to relate the basin character to the discharge produced. Network routing models also require an appropriate descriptor of drainage basin form to relate to hydrologic response characteristics. An index of the drainage network is potentially the most valuable because it responds to precipitation, reflects the characteristics of the basin and affects runoff. Although a large number of drainage network indices have been developed they have proved inadequate in their failure to describe the dynamic nature of drainage networks. Future research into the use of drainage networks for rainfall- runoff modelling and runoff prediction needs to have as its central aim the development of a dynamic network index which has physical meaning for drainage basins of all sizes and which is quick and easy to calculate from data that are rapidly obtainable. Despite the improved resolution of satellite imagery its high cost still prevents the widespread application of satellite remote sensing techniques to monitoring storm-specific drainage network changes. However, the increased availability of topographic data in digital format and the recent developments in digital elevation models (DEMs) have demonstrated the potential for the rapid derivation of both perennial and extended drainage networks from which network expansion potential can be calculated.

Network Routing Mechanism Simulation Research Based on VANETs

2014 ◽  
Vol 651-653 ◽  
pp. 1868-1874
Author(s):  
Li Zhu ◽  
Lan Bai
Keyword(s):  
Routing Protocol ◽  
Vehicular Networks ◽  
Routing Algorithm ◽  
Dynamic Network ◽  
Network Routing ◽  
On Demand ◽  
Routing Mechanism ◽  
Demand Routing ◽  
On The Road ◽  
Low Efficiency

VANETs is a rapid developed wireless mobile MANETs network with special challenge nature. It is a distributed and self-organized communication network based on moving vehicle. This network has characteristics like limited bandwidth, strong mobility, strong dynamic network topology, limited node degrees freedom, equipment capacity constraints and weak physical security. These characteristics usually make typical routing protocol in mobile MANET show a low efficiency in mobile VANETs, even fail. MANET routing algorithm is roughly divided into two categories, namely table driven routing protocol and on-demand routing protocol. It is due to the particularity of driving cars on the road. So how to improve network routing protocol in the performance is now a challenging problem. The purpose of this article studies network routing mechanism based on wireless vehicular networks simulation method. On the basis of analyzing the performance of typical routing protocols in MANET, such as DSDV, AODV and DSR, the improved scheme of AODV on-demand routing algorithm is put forward.

Metal Ions on the Membrane Bioreactor in the Slow Progress of Membrane Fouling Studies

2014 ◽  
Vol 926-930 ◽  
pp. 158-161
Author(s):  
Wan You Zhang ◽  
Sheng Chao Ji ◽  
Hai Feng Zhang ◽  
Xi Xin Zhang
Keyword(s):  
Metal Ions ◽  
Membrane Fouling ◽  
Membrane Bioreactor ◽  
Future Research ◽  
Widespread Application ◽  
Slow Progress ◽  
Flocculation Process

Membrane bioreactor (MBR) has developed rapidly in recent years; however, the membrane fouling problems are affecting its further widespread application. This paper discusses the role of metal ions in the mixture in the mechanism; also on the high-priced metal ions coexist in the mixture when the effects of biological flocculation process; finally, the direction of future research in this area are summarized.

Role of computer simulation in the application of knowledge to animal industries

1993 ◽  
Vol 44 (3) ◽  
pp. 541 ◽  
Author(s):  
JL Black ◽  
GT Davies ◽  
JF Fleming
Keyword(s):  
Management Strategies ◽  
Research Priorities ◽  
Simulation Models ◽  
Relative Price ◽  
Biological Efficiency ◽  
Future Research ◽  
Mechanistic Models ◽  
Animal Performance ◽  
Financial Return ◽  
Widespread Application

The net financial return of an enterprise depends on the interaction among a great many factors. Some of these factors relate to the animal, some to its diet, some to its environment, some to the prevalence of disease and some to circumstances outside the production enterprise such as the premiums paid for products of different quality, the relative price structure of feeds and products, and the availability and cost of capital, labour, breeding stock and other resources. Although there has been a great deal of research into many of these factors, the complexity of the interactions between them makes it virtually impossible for the human mind to assess accurately the consequences of alternative management strategies on either the efficiency of production or the long-term profitability of a livestock enterprise. By transforming the concepts and knowledge into mathematical equations and integrating them in computer programs using simulation modelling techniques, this vast store of information can be applied directly to improving the management of commercial animal enterprises. Models are also valuable for defining research priorities. These simulation models should, as far as possible, be based on descriptions of the mechanisms perceived to determine animal function, not on empirical relationships of correlation and association. This need for mechanistic models has major implications for the direction and nature of future research into animal function. Mechanistic models of animal performance alone are unlikely to result in the widespread application of knowledge to the animal industries. Models must be integrated with other modules that cover the major areas of an enterprise determining its profitability, as well as with programming features that make the whole Decision Support Software System easy to use and interpret by industry personnel. The animal model is likely to represent less than 20% of a commercially useful package. A major factor limiting the application of animal growth models is lack of an adequate description of the conditions within commercial enterprises. Collection of such data is difficult and frequently regarded as unattractive by scientists and funding organisations, but it is essential for effective application of existing knowledge through simulation models. Furthermore, industry must make frequent measurements of factors determining animal performance and enterprise profitability if the significance of predictions from animal models is to be evaluated fully. An example is presented illustrating how simulation models can improve the biological efficiency and profitability of a commercial animal enterprise when this information is available.

Research for Combined Drainage Networks in Chuanfang River Basin of Kunming City Based on SWMM

2012 ◽  
Vol 170-173 ◽  
pp. 2380-2385
Author(s):  
Xiao Min Zhu ◽  
Bing Huang ◽  
Shu Dong Wang ◽  
Jin Long Zheng ◽  
Bo Yao ◽  
...  
Keyword(s):  
River Basin ◽  
Drainage System ◽  
Sewage Water ◽  
Management Model ◽  
Peak Time ◽  
Drainage Network ◽  
Efficiency Coefficient ◽  
Storm Water Management Model ◽  
Drainage Networks ◽  
Water Base

A model for simulating combined drainage networks in Chuangfang river basin of Kunming City based on the Storm Water Management Model was established. The type and period of using water base on residential area, marketplace, school area, and guesthouse area Kunming city were introduced into the model, and their infection for drainage system was research. The results show that simulation results of two outlets flow have coherence with monitoring data based two typical rainfall in Kunming, the Nash-Sutcliffe efficiency coefficient is 0.71-0.82. And the model can be using analyze ‘bottleneck’ nodes and restricting conduits, simulating the running status of drainage network of combined drainage at raining and draining peak time of sewage water. The research provide strong technical support for rebuild drainage network in Kunming or other city.

scholarly journals Assessing the Performance of SuDS in the Mitigation of Urban Flooding: The Industrial Area of Sesto Ulteriano (MI)

Proceedings ◽  
2019 ◽  
Vol 48 (1) ◽  
pp. 5
Author(s):  
Roberta D’Ambrosio ◽  
Anacleto Rizzo ◽  
Alessandro Balbo ◽  
Antonia Longobardi
Keyword(s):  
Urban Areas ◽  
Treatment Strategies ◽  
Industrial Area ◽  
Drainage Network ◽  
Urban Centers ◽  
Project Proposal ◽  
Widespread Application ◽  
Event Scale ◽  
Current Configuration ◽  
Effective Manner

Recent development dynamics of urban centers forced administrations to deal more frequently with problems linked to the inability of traditional sewer systems to manage rainwater in a sustainable and effective manner. Currently, several laws require compliance with the quantitative and qualitative stormwater limits to be discharged into watercourses but, in parallel with a “regulatory” approach, integrated strategies are increasingly being developed. A fundamental role is carried out by Sustainable Drainage Systems (SuDS), whose basic principle is the management of rainwater at the source, through the implementation of prevention, mitigation and treatment strategies. This study, starting from a project proposal made by different Italian firms and funded by PoliS-Lombardia, aims to assess the benefits deriving from the widespread application of SuDS in the Sesto Ulteriano industrial area, through a comparison between a scenario that represents the current configuration of the drainage network, and an ideal scenario where SuDS are taken into consideration. SWMM5 software was used, in order to simulate the behavior of the drainage network in contexts without and with SuDS, after the construction of the synthetic rainfall data sets. Although only event scale simulations have been conducted so far, the encouraging results suggest that these systems really contribute can to mitigating the effects of flooding in urban areas.

scholarly journals Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1556 ◽  
Author(s):  
Daeeop Lee ◽  
Giha Lee ◽  
Seongwon Kim ◽  
Sungho Jung
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Change Scenario ◽  
Swat Model ◽  
Mekong River ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
Runoff Prediction ◽  
Runoff Analysis

In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.

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