Rainfall-Runoff Modeling Using MIKE 11 NAM Model for Vinayakpur Intercepted Catchment, Chhattisgarh

2014 ◽  
Vol 29 (2) ◽  
pp. 1 ◽  
Author(s):  
Ashutosh Singh ◽  
Surjeet Singh ◽  
A.K. Nema ◽  
Gaurav Singh ◽  
Anshu Gangwar
Keyword(s):  
Rainfall Runoff ◽  
Mike 11 ◽  
Runoff Modeling

scholarly journals Rainfall Runoff Modeling Using MIKE 11 Nam Model

2019 ◽  
Vol 14 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Pushpendra Kumar ◽  
A.K. Lohani ◽  
A.K. Nema
Keyword(s):  
River Basin ◽  
Efficiency Index ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Discharge Data ◽  
Mike 11 ◽  
Runoff Modeling ◽  
Basin Scale ◽  
Development And Management

River basin planning and management are primarily based on the accurate assessment and prediction of catchment runoff. A continuous effort has been made by the various researchers to accurately assess the runoff generated from precipitation by developing various models. In this paper conceptual hydrological MIKE 11 NAM approach has been used for developing a runoff simulation model for Arpasub-basin of Seonath river basin in Chhattisgarh, India. NAM model has been calibrated and validated using discharge data at Kota gauging site on Arpa basin. The calibration and validation results show that this model is capable to define the rainfall runoff process of the basin and thus predicting daily runoff. The ability of the NAM model in rainfall runoff modelling of Arpa basin was assessed using Nash–Sutcliffe Efficiency Index (EI), coefficient of determination (R2) and Root Mean Square Error (RMSE). This study demonstrates the usefulness of the developed model for the runoff prediction in the Arpa basin which acts as a useful input for the integrated water resources development and management at the basin scale.

scholarly journals Intra-Storm Pattern Recognition through Fuzzy Clustering

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 57
Author(s):  
Konstantinos Vantas ◽  
Epaminondas Sidiropoulos
Keyword(s):  
Fuzzy Clustering ◽  
A Priori ◽  
Temporal Distribution ◽  
Distribution Patterns ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Domain Specific ◽  
Runoff Modeling ◽  
National Bank ◽  
Rainfall Patterns

The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former.

Physics-Based Distributed Rainfall-Runoff Modeling of Urbanized Areas with CASC2D

2001 ◽  
Author(s):  
Fred L. Ogden ◽  
Ehab A. Meselhe ◽  
Justin Niedzialek ◽  
Ben Smith
Keyword(s):  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Urbanized Areas

Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships

2021 ◽  
pp. 127043
Author(s):  
Kang Xie ◽  
Pan Liu ◽  
Jianyun Zhang ◽  
Dongyang Han ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Extreme Events ◽  
Rainfall Runoff ◽  
Runoff Modeling

scholarly journals Rainfall-Runoff Modeling Using NRCS-CN method and GIS approach

2020 ◽  
Author(s):  
Rekha Verma ◽  
Azhar Husain ◽  
Mohammed Sharif
Keyword(s):  
Hydrological Modeling ◽  
Curve Number ◽  
Geographical Information ◽  
Rainfall Runoff ◽  
Moisture Condition ◽  
Natural Resource Conservation ◽  
Antecedent Moisture ◽  
Natural Resource Conservation Service ◽  
Runoff Modeling ◽  
Runoff Depth

Rainfall-Runoff modeling is a hydrological modeling which is extremely important for water resources planning, development, and management. In this paper, Natural Resource Conservation Service-Curve Number (NRCS-CN) method along with Geographical Information System (GIS) approach was used to evaluate the runoff resulting from the rainfall of four stations, namely, Bilodra, Kathlal, Navavas and Rellawada of Sabarmati River basin. The rainfall data were taken for 10 years (2005-2014). The curve number which is the function of land use, soil and antecedent moisture condition (AMC) was generated in GIS platform. The CN value generated for AMC- I, II and III were 57.29, 75.39 and 87.77 respectively. Using NRCS-CN method, runoff depth was calculated for all the four stations. The runoff depth calculated with respect to the rainfall for Bilodra, Kathlal, Navavas and Rellawada shows a good correlation of 0.96. The computed runoff was compared with the observed runoff which depicted a good correlation of 0.73, 0.70, 0.76 and 0.65 for the four stations. This method results in speedy and precise estimation of runoff from a watershed.

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