scholarly journals Comparison and Analysis of Different Discrete Methods and Entropy-Based Methods in Rain Gauge Network Design

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1357
Author(s):  
Yanyan Huang ◽  
Hongli Zhao ◽  
Yunzhong Jiang ◽  
Xin Lu ◽  
Zheng Hao ◽  
...  
Keyword(s):  
Network Design ◽  
Rainy Season ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Network Information ◽  
Discretization Methods ◽  
Network Layout ◽  
Development And Utilization ◽  
Discrete Methods ◽  
Rain Gauge Network

A reasonable rain gauge network layout can provide accurate regional rainfall data and effectively support the monitoring, development and utilization of water resources. Currently, an increasing number of network design methods based on entropy targets are being applied to network design. The discretization of data is a common method of obtaining the probability in calculations of information entropy. To study the application of different discretization methods and different entropy-based methods in the design of rain gauge networks, this paper compares and analyzes 9 design results for rainy season rain gauge networks using three commonly used discretization methods (A1, SC and ST) and three entropy-based network design algorithms (MIMR, HT and HC) from three perspectives: the joint entropy, spatiality, and accuracy of the network, as evaluation indices. The results show that the variation in network information calculated by the A1 and ST methods for rainy season rain gauge data is too large or too small compared to that calculated by the SC method, and also that the MIMR method performs better in terms of spatiality and accuracy than the HC and HT methods. The comparative analysis results provide a reference for the selection of discrete methods and entropy-based objectives in rain gauge network design, and provides a way to explore a more suitable rain gauge network layout scheme.

Rule-based rain gauge network design in urban areas aided by spatial kernel density

2016 ◽  
Vol 11 (1) ◽  
pp. 166-175 ◽  
Author(s):  
Changfeng Jing ◽  
Jianjun Yu ◽  
Peipei Dai ◽  
Haiyang Wei ◽  
Mingyi Du
Keyword(s):  
Network Design ◽  
Urban Areas ◽  
Residential Buildings ◽  
Scientific Evidence ◽  
Kernel Density ◽  
Rain Gauge ◽  
Signal Interference ◽  
Site Location ◽  
Rule Based ◽  
Rain Gauge Network

An algorithm of rule-based rain gauge network design in urban areas was proposed in this study. We summarized three general criteria to select the sites of rain gauges, including: (i) installment in open space; (ii) priority consideration of important regions and even distribution; and (iii) keep strong signal and avoid weak interference. Aided by spatial kernel density, the candidate locations were determined through clustering the residential buildings at first. Secondly, the overlay and buffer spatial analyses were carried out to optimize the candidate sites to avoid signal interference. Finally, the quality of site location was evaluated by cross-validation in using observed historical rainfall and ranked by mean square error for final consideration. A study case in Xicheng district, Beijing, China was selected to demonstrate the proposed method. The result showed that it could be well applied in urban areas with the capability of considering complex urban features through defining rules. It thus could provide scientific evidence for decision making in rain gauge site selection.

scholarly journals On CMORPH Rainfall for Streamflow Simulation in a Small, Hortonian Watershed

2011 ◽  
Vol 12 (3) ◽  
pp. 456-466 ◽  
Author(s):  
Dawit A. Zeweldi ◽  
Mekonnen Gebremichael ◽  
Charles W. Downer
Keyword(s):  
Peak Flow ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Rain Gauge Data ◽  
Small Watersheds ◽  
Hydrologic Analysis ◽  
Comparable Performance ◽  
Climate Prediction Center ◽  
Rain Gauge Network ◽  
Streamflow Simulation

Abstract The objective is to assess the use of the Climate Prediction Center morphing method (CMORPH) (~0.073° latitude–longitude, 30 min resolution) rainfall product as input to the physics-based fully distributed Gridded Surface–Subsurface Hydrologic Analysis (GSSHA) model for streamflow simulation in the small (21.4 km2) Hortonian watershed of the Goodwin Creek experimental watershed located in northern Mississippi. Calibration is performed in two different ways: using rainfall data from a dense network of 30 gauges as input, and using CMORPH rainfall data as input. The study period covers 4 years, during which there were 24 events, each with peak flow rate higher than 0.5 m3 s−1. Streamflow simulations using CMORPH rainfall are compared against observed streamflows and streamflow simulations using rainfall from a dense rain gauge network. Results show that the CMORPH simulations captured all 24 events. The CMORPH simulations have comparable performance with gauge simulations, which is striking given the significant differences in the spatial scale between the rain gauge network and CMORPH. This study concludes that CMORPH rainfall products have potential value for streamflow simulation in such small watersheds. Overall, the performance of CMORPH-driven simulations increases when the model is calibrated with CMORPH data than when the model is calibrated with rain gauge data.

scholarly journals A GIS methodology for the analysis of weather radar precipitation data

2003 ◽  
Vol 5 (2) ◽  
pp. 113-126 ◽  
Author(s):  
M. A. Gad ◽  
I. K. Tsanis
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Gis Environment ◽  
Arcview Gis ◽  
The City ◽  
Rain Gauge Network

A GIS multi-component module was developed within the ArcView GIS environment for processing and analysing weather radar precipitation data. The module is capable of: (a) reading geo-reference radar data and comparing it with rain-gauge network data, (b) estimating the kinematics of rainfall patterns, such as the storm speed and direction, and (c) accumulating radar-derived rainfall depths. By bringing the spatial capabilities of GIS to bear this module can accurately locate rainfall on the ground and can overlay the animated storm on different geographical features of the study area, making the exploration of the storm's kinematic characteristics obtained from radar data relatively simple. A case study in the City of Hamilton in Ontario, Canada is used to demonstrate the functionality of the module. Radar comparison with rain gauge data revealed an underestimation of the classical Marshal & Palmer Z–R relation to rainfall rate.

Evaluation of information transfer and data transfer models of rain-gauge network design based on information entropy

2019 ◽  
Vol 178 ◽  
pp. 108686 ◽  
Author(s):  
Wenqi Wang ◽  
Dong Wang ◽  
Vijay P. Singh ◽  
Yuankun Wang ◽  
Jichun Wu ◽  
...  
Keyword(s):  
Network Design ◽  
Information Entropy ◽  
Information Transfer ◽  
Data Transfer ◽  
Rain Gauge ◽  
Rain Gauge Network ◽  
Transfer Models

Optimal rain gauge network design based on multi-objective optimization approach

2020 ◽  
Author(s):  
Claudia Bertini ◽  
Elena Ridolfi ◽  
Leonardo Alfonso ◽  
Francesco Napolitano
Keyword(s):  
Network Design ◽  
Rain Gauge ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Rain Gauge Network

scholarly journals A Scheme for Rain Gauge Network Design Based on Remotely Sensed Rainfall Measurements

2017 ◽  
Vol 18 (2) ◽  
pp. 363-379 ◽  
Author(s):  
Qiang Dai ◽  
Michaela Bray ◽  
Lu Zhuo ◽  
Tanvir Islam ◽  
Dawei Han
Keyword(s):  
Network Design ◽  
Principal Component ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Storm Events ◽  
Remotely Sensed Data ◽  
Grid Network ◽  
Rain Gauges ◽  
Rain Gauge Network ◽  
Insight Into

Abstract A remarkable decline in the number of rain gauges is being faced in many areas of the world, as a compromise to the expensive cost of operating and maintaining rain gauges. The question of how to effectively deploy new or remove current rain gauges in order to create optimal rainfall information is becoming more and more important. On the other hand, larger-scaled, remotely sensed rainfall measurements, although poorer quality compared with traditional rain gauge rainfall measurements, provide an insight into the local storm characteristics, which are sought by traditional methods for designing a rain gauge network. Based on these facts, this study proposes a new methodology for rain gauge network design using remotely sensed rainfall datasets that aims to explore how many gauges are essential and where they should be placed. Principal component analysis (PCA) is used to analyze the redundancy of the radar grid network and to determine the number of rain gauges while the potential locations are determined by cluster analysis (CA) selection. The proposed methodology has been performed on 373 different storm events measured by a weather radar grid network and compared against an existing dense rain gauge network in southwestern England. Because of the simple structure, the proposed scheme could be easily implemented in other study areas. This study provides a new insight into rain gauge network design that is also a preliminary attempt to use remotely sensed data to solve the traditional rain gauge problems.

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