Rainfall-runoff modeling: a modification of the EBA4SUB framework for ungauged and highly impervious urban catchments

2021 ◽  
pp. 127371
Author(s):  
Andrea Petroselli ◽  
Andrzej Wałęga ◽  
Dariusz Młyński ◽  
Artur Radecki-Pawlik ◽  
Agnieszka Cupak ◽  
...  
Keyword(s):  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Urban Catchments

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.

scholarly journals Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks

2021 ◽  
Author(s):  
Kei Ishida ◽  
Masato Kiyama ◽  
Ali Ercan ◽  
Motoki Amagasaki ◽  
Tongbi Tu
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Time Series Data ◽  
Series Data ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Input Time ◽  
Target Data ◽  
Resolution Data

Abstract This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series data to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, the finer temporal resolution data are utilized to learn the fine temporal scale behavior of the target data. Then, coarser temporal resolution data are expected to capture long-duration dependencies between the input and target variables. The proposed approaches were implemented for hourly rainfall–runoff modeling at a snow-dominated watershed by employing a long short-term memory network, which is a type of RNN. Subsequently, the daily and hourly meteorological data were utilized as the input, and hourly flow discharge was considered as the target data. The results confirm that both of the proposed approaches can reduce the required computational time for the training of RNN significantly. Lastly, one of the proposed approaches improves the estimation accuracy considerably in addition to computational efficiency.

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