scholarly journals Spatial-Temporal Evaluation of Rain-Fauge Network Based on Entropy Theory

10.29007/1kc9 ◽  
2018 ◽  
Author(s):  
Wenqi Wang ◽  
Dong Wang ◽  
Vijay P. Singh ◽  
Yuankun Wang
Keyword(s):  
Water Management ◽  
Information Theory ◽  
Network Design ◽  
Spatial Model ◽  
Rain Gauge ◽  
Entropy Theory ◽  
Precipitation Data ◽  
D Model ◽  
Rain Gauge Network

Ground-based rain-gauge stations are the most direct sources of precipitation data. The evaluation of rain-gauge network is essential and important for water management. One of the most popular methods for design of hydrometric network including rain- gauge network is information theory. Entropy concepts from information theory has been widely adopted and applied in rain-gauge network design. In this paper, spatial- temporal evaluation of rain-gauge network located in Shanghai, China will be performed based on entropy theory. The transinformation-distance (T-D) spatial model is applied under three different sampling frequencies. Weekly precipitation data fits the T-D model best. In addition, the representative network is evaluated to be suitable according to the result.

scholarly journals A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

2020 ◽  
Vol 12 (1) ◽  
pp. 194
Author(s):  
Yanyan Huang ◽  
Hongli Zhao ◽  
Yunzhong Jiang ◽  
Xin Lu
Keyword(s):  
Remote Sensing ◽  
Network Design ◽  
Spatial Information ◽  
Measurement Data ◽  
Remote Sensing Data ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Satellite Precipitation ◽  
Sensing Data ◽  
Rain Gauge Network

A well-designed rain gauge network can provide precise and detailed rainfall data for earth science research; meanwhile, satellite precipitation data has been developed to generate more real spatial features, which provides new data support for the improvement of ground station network design methods. In this paper, satellite precipitation data are introduced into the design of a rain gauge network and an optimized method for designing a rain gauge network that comprehensively considers the information content, spatiotemporality, and accuracy (ISA) of the data is proposed. After screening the potential stations, the average spatial information index of the rain gauge network, which is calculated from remote sensing data, is used to address the shortcomings of applying spatial information from single-use measurement data. Then, the greedy ranking algorithm is used to rank the order in which the rain gauges are added to the network. The results of the rain gauge network design in the upper reaches of the Chaobai river show that compared with two methods that do not consider spatiality or use only measured data to consider spatiality, the proposed method performs better in terms of the spatial layout and accuracy verification. This study provides new ideas and references for the design of hydrological station networks and explores the use of remote sensing data for the layout of ground-based station networks.

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 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.

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
Sign in / Sign up

Export Citation Format

Share Document