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.

scholarly journals Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 49-56
Author(s):  
S.JOSEPHINE VANAJA ◽  
B.V. MUDGAL ◽  
S.B. THAMPI
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Runoff ◽  
Cyclonic Storm ◽  
Rain Gauges ◽  
Runoff Modeling

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

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 Temporal and Spatial Coexistence of Archaeal and BacterialamoAGenes and Gene Transcripts in Lake Lucerne

Archaea ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Elisabeth W. Vissers ◽  
Flavio S. Anselmetti ◽  
Paul L. E. Bodelier ◽  
Gerard Muyzer ◽  
Christa Schleper ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Water Layer ◽  
Distribution Patterns ◽  
Freshwater Ecosystems ◽  
Ammonia Oxidizers ◽  
Lake Lucerne ◽  
Ammonia Oxidizing Archaea ◽  
Gene Transcripts ◽  
Temporal And Spatial

Despite their crucial role in the nitrogen cycle, freshwater ecosystems are relatively rarely studied for active ammonia oxidizers (AO). This study of Lake Lucerne determined the abundance of bothamoAgenes and gene transcripts of ammonia-oxidizing archaea (AOA) and bacteria (AOB) over a period of 16 months, shedding more light on the role of both AO in a deep, alpine lake environment. At the surface, at 42 m water depth, and in the water layer immediately above the sediment, AOA generally outnumbered AOB. However, in the surface water during summer stratification, when both AO were low in abundance, AOB were more numerous than AOA. Temporal distribution patterns of AOA and AOB were comparable. Higher abundances ofamoAgene transcripts were observed at the onset and end of summer stratification. In summer, archaealamoAgenes and transcripts correlated negatively with temperature and conductivity. Concentrations of ammonium and oxygen did not vary enough to explain theamoAgene and transcript dynamics. The observed herbivorous zooplankton may have caused a hidden flux of mineralized ammonium and a change in abundance of genes and transcripts. At the surface, AO might have been repressed during summer stratification due to nutrient limitation caused by active phytoplankton.

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