Design of rain gauge network using radar and road network

2021 ◽  
Author(s):  
Taeyong Kwon ◽  
Sanghoo Yoon
Keyword(s):  
Nuclear Power ◽  
Road Network ◽  
Conditional Entropy ◽  
Radar Data ◽  
Rain Gauge ◽  
Large Area ◽  
The Road ◽  
Basin Area ◽  
Rain Gauge Network ◽  
Small Basin

<p>Uncertainty in the gauged network can lead to inaccuracies in dam operations. Entropy is a well-known measurement of uncertainty. Goesan Dam has a small basin area and is affected by a small amount of precipitation, and Hwacheon Dam is contained outside the territory of South Korea, making it difficult to observe the water flow. The observed gauged precipitation and radar data on rainy days were considered between 2018 and 2019. Choosing appropriate radar were performed based on the priority of the rainfall gauge network using conditional entropy. This is because the rainfall gauge network is the actual precipitation and it can only cover certain points. However, the radar is the cloud reflectivity of a large area. Therefore the location of additional rain spots was selected through conditional entropy of highly consistent radar data. Nevertheless, there might be difficulties in installing gauged equipment in reality. So the optimal rainfall network was designed in consideration of the road network. As a result, the uncertainty of precipitation in Goesan Dam and Hwachoen Dam could be decreased by 63.3% and 67.9% respectively when three additional potential rain points were operated without any restriction. The uncertainty in the Goesan Dam basin and Hwachoen Dam would be reduced up to 55.3% and 65.0% when three additional potential rain points were installed nearby the road network. Therefore, through the proposed method, an optimal rainfall network can be designed by balancing cost and uncertainty.</p><p>This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2018-Tech-20)</p>

scholarly journals Design of Optimal Rainfall Monitoring Network Using Radar and Road Networks

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 378
Author(s):  
Taeyong Kwon ◽  
Seongsim Yoon ◽  
Sanghoo Yoon
Keyword(s):  
Road Networks ◽  
Monitoring Network ◽  
Conditional Entropy ◽  
Radar Data ◽  
Disaster Risk ◽  
Water Levels ◽  
The Road ◽  
Dam Operation ◽  
A Site ◽  
High Degree

Uncertainty in the rainfall network can lead to mistakes in dam operation. Sudden increases in dam water levels due to rainfall uncertainty are a high disaster risk. In order to prevent these losses, it is necessary to configure an appropriate rainfall network that can effectively reflect the characteristics of the watershed. In this study, conditional entropy was used to calculate the uncertainty of the watershed using rainfall and radar data observed from 2018 to 2019 in the Goesan Dam and Hwacheon Dam watersheds. The results identified radar data suitable for the characteristics of the watershed and proposed a site for an additional rainfall gauge. It is also necessary to select the location of the additional rainfall gauged by limiting the points where smooth movement and installation, for example crossing national borders, are difficult. The proposed site emphasized accessibility and usability by leveraging road information and selecting a radar grid near the road. As a practice result, the uncertainty of precipitation in the Goesan and Hwacheon Dam watersheds could be decreased by 70.0% and 67.9%, respectively, when four and three additional gauge sites were installed without any restriction. When these were installed near to the road, with five and four additional gauge sites, the uncertainty in the Goesan Dam and Hwacheon Dam watersheds were reduced by up to 71.1%. Therefore, due to the high degree of uncertainty, it is necessary to measure precipitation. The operation of the rainfall gauge can provide a smooth site and configure an appropriate monitoring network.

scholarly journals Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone

2019 ◽  
Author(s):  
Yao Liu ◽  
Jianmai Shi ◽  
Zhong Liu ◽  
Jincai Huang ◽  
Tianren Zhou
Keyword(s):  
High Voltage ◽  
Road Network ◽  
Inspection System ◽  
Large Area ◽  
Technical Improvement ◽  
Routing Problem ◽  
Ground Vehicle ◽  
The Road ◽  
Battery Capacity ◽  
On The Road

A novel high-voltage powerline inspection system is investigated, which consists of the cooperated ground vehicle and drone. The ground vehicle acts as a mobile platform that can launch and recycle the drone, while the drone can fly over the powerline for inspection within limited endurance. This inspection system enables the drone to inspect powerline networks in a very large area. Both vehicle’ route in the road network and drone’s routes along the powerline network have to be optimized for improving the inspection efficiency, which generates a new two-layer point-arc routing problem. Two constructive heuristics are designed based on “Cluster First, Rank Second” and “Rank First, Split Second”. Then local search strategies are developed to further improve the quality of the solution. To test the performance of the proposed algorithms, practical cases with different-scale are designed based on the road network and powerline network of Ji’an, China. Sensitivity analysis on the parameters related with the drone’s inspection speed and battery capacity is conducted. Computational results indicate that technical improvement on the inspection sensor is more important for the cooperated ground vehicle and drone system.

scholarly journals A New Land Surface Hydrology within the Noah-WRF Land-Atmosphere Mesoscale Model Applied to Semiarid Environment: Evaluation over the Dantiandou Kori (Niger)

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
B. Decharme ◽  
C. Ottlé ◽  
S. Saux-Picart ◽  
N. Boulain ◽  
B. Cappelaere ◽  
...  
Keyword(s):  
Land Surface ◽  
Mesoscale Model ◽  
West African Monsoon ◽  
Radar Data ◽  
Rain Gauge ◽  
Surface Energy Budget ◽  
Surface Hydrology ◽  
Land Surface Hydrology ◽  
Rain Gauge Network

Land-atmosphere feedbacks, which are particularly important over the Sahel during the West African Monsoon (WAM), partly depend on a large range of processes linked to the land surface hydrology and the vegetation heterogeneities. This study focuses on the evaluation of a new land surface hydrology within the Noah-WRF land-atmosphere-coupled mesoscale model over the Sahel. This new hydrology explicitly takes account for the Dunne runoff using topographic information, the Horton runoff using a Green-Ampt approximation, and land surface heterogeneities. The previous and new versions of Noah-WRF are compared against a unique observation dataset located over the Dantiandou Kori (Niger). This dataset includes dense rain gauge network, surfaces temperatures estimated from MSG/SEVIRI data, surface soil moisture mapping based on ASAR/ENVISAT C-band radar data and in situ observations of surface atmospheric and land surface energy budget variables. Generally, the WAM is reasonably reproduced by Noah-WRF even if some limitations appear throughout the comparison between simulations and observations. An appreciable improvement of the model results is also found when the new hydrology is used. This fact seems to emphasize the relative importance of the representation of the land surface hydrological processes on the WAM simulated by Noah-WRF over the Sahel.

scholarly journals Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

2017 ◽  
Author(s):  
Igor Paz ◽  
Bernard Willinger ◽  
Auguste Gires ◽  
Laurent Monier ◽  
Christophe Zobrist ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Radar Data ◽  
Rain Gauge ◽  
Distributed Hydrological Model ◽  
Space Resolution ◽  
Rain Gauges ◽  
X Band ◽  
Distributed Simulations ◽  
Rain Gauge Network ◽  
Catchment Model

This paper presents a comparison between rain gauges, C-band and X-band radar data over an instrumented and regulated catchment of the Paris region, as well as their respective hydrological impacts with the help of flow observations and a semi-distributed hydrological model. Both radars confirm the high spatial variability of the rainfall down to their space resolution (respectively one kilometer and 250 m) and therefore underscore limitations of semi-distributed simulations. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, neither calibration was performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (below called ENPC X-band radar), nor any optimization of its scans. In spite of that and the non-negligible fact that the catchment was much closer to the C-band radar than to the X-band radar (20 km vs. 40 km), the latter seems to perform at least as well as the former, but with a higher scale resolution. This characteristic was best highlighted with the help of a multifractal analysis of the respective radar data, which also shows that the X-band radar was able to pick up a few extremes that were smoothed out by the C-band radar.

scholarly journals Comparison of Entropy Methods for an Optimal Rain Gauge Network: A Case Study of Daegu and Gyeongbuk Area in South Korea

2020 ◽  
Vol 10 (16) ◽  
pp. 5620
Author(s):  
Taeyong Kwon ◽  
Junghyun Lim ◽  
Seongsim Yoon ◽  
Sanghoo Yoon
Keyword(s):  
Conditional Entropy ◽  
Rain Gauge ◽  
Optimal Number ◽  
Severe Drought ◽  
Prediction Errors ◽  
Joint Entropy ◽  
Spatio Temporal ◽  
Rain Gauge Network ◽  
Better Than

To reduce hydrological disasters, it is necessary to operate rain gauge stations at locations where the spatio-temporal characteristics of rainfall can be reflected. Entropy has been widely used to evaluate the designs and uncertainties associated with rain gauge networks. In this study, the optimal rain gauge network in the Daegu and Gyeongbuk area, which requires the efficient use of water resources due to low annual precipitation and severe drought damage, was determined using conditional and joint entropy, and the selected network was quantitatively evaluated using the root mean square error (RMSE). To consider spatial distribution, prediction errors were generated using kriging. Four estimators used in entropy calculations were compared, and weighted entropy was calculated by weighting the precipitation. The optimal number of rain gauge stations was determined by calculating the RMSE reduction and the reduction ratio according to the number of selected rain gauge stations. Our findings show that the results of conditional entropy were better than those of joint entropy. The optimal rain gauge stations showed a tendency wherein peripheral rain gauge stations were selected first, with central stations being added afterward.

scholarly journals Models and spatial patterns of deforestation in the Jeneberang watershed

2021 ◽  
Vol 886 (1) ◽  
pp. 012082
Author(s):  
Syamsu Rijal ◽  
Tirza Tirsyayu ◽  
A Chairil ◽  
Munajat Nursaputra ◽  
Andi Nurul Mukhlisa
Keyword(s):  
Spatial Pattern ◽  
Road Network ◽  
Forest Cover ◽  
Binary Logistic Regression ◽  
Analysis Model ◽  
Large Area ◽  
River Population ◽  
Deforestation Rate ◽  
The Road ◽  
Logistic Regression Method

Abstract Deforestation is an event of permanent land cover change from forest cover to non-forest cover. Deforestation events are very influential on the condition of a watershed area. One of the watersheds on the island of Sulawesi that has become a concern is the Jeneberang watershed because of its influence on the city of Makassar and is a priority watershed in Indonesia. This study aims to analyze the model and spatial pattern of deforestation in the Jeneberang watershed. The deforestation analysis model uses the binary logistic regression method by including factors such as a river, population density, road, count, and slope. Analysis of the spatial pattern of deforestation using Fragstat software based on three indices to describe the spatial pattern, namely the Clumpiness Index, Contiguity Mean Index, and Patch Density. The model of deforestation in the Jeneberang watershed shows the road network factor that has the most influence on the occurrence of deforestation. The road network is quite high in all areas in the Jeneberang watershed including the upstream part as a protection zone. The road network serves as community access between villages and sub-districts in Gowa Regency and connects other regencies such as Sinjai, Takalar, and Jeneponto. The spatial pattern of deforestation in the Jeneberang watershed is grouping, the level of connectivity is high, and it is not fragmented. This pattern shows that deforestation occurs in groups, is interconnected with previously deforested areas, and has a fairly large area. This pattern occurs at a relatively low rate and remains the same when the deforestation rate increases or decreases.

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.

Comprehensive evaluation of precipitation analyses using a very dense rain gauge network in southeast Austria

2020 ◽  
Author(s):  
Esmail Ghaemi ◽  
Ulrich Foelsche ◽  
Alexander Kann ◽  
Gottfried Kirchengast ◽  
Juergen Fuchsberger
Keyword(s):  
Temporal Resolution ◽  
Comprehensive Evaluation ◽  
Analysis Data ◽  
Radar Data ◽  
Rain Gauge ◽  
Accurate Estimation ◽  
Wet Season ◽  
Rainfall Events ◽  
Rainfall Analysis ◽  
Rain Gauge Network

<p>Precipitation is one of the most important inputs of meteorological and hydrological models and also flood warning systems. Thus, accurate estimation of rainfall is essential for improving the reliability of the models and systems. Although remote sensing (RS) techniques for rainfall estimation (e.g., weather radars and satellite microwave imagers) have improved significantly over the last decades, rain gauges are still more reliable and widely used for this purpose and also for the evaluation of RS estimates. Since the characteristics of a rainfall event can change rapidly in space and time, the accuracy of rain gauge estimation is highly dependent on the spatial and temporal resolution of the gauge network.</p><p>The main aim of this study is to evaluate the ability of the Integrated Nowcasting through Comprehensive Analysis (INCA) of the Central Institute for Meteorology and Geodynamics (ZAMG) to detect and estimate rainfall events. This is done by using 12 years of data from a very dense rain gauge network, the WegenerNet Feldbach region, as a reference, and comparing its data to the INCA analyses. INCA rainfall analysis data are based on a combination of ZAMG ground station data, weather radar data, and high-resolution topographic data. The system provides precipitation rate data with a 1 km spatial grid resolution and 15 minutes temporal resolution. The WegenerNet includes 155 ground stations, almost uniformly spread over a moderate hilly orography area of about 22 km × 16 km.</p><p>After removing outliers and scale WegenerNet data to 1 km, the accuracy of INCA to detect and estimate rainfall events was investigated using 12 years of the dataset. The results show that INCA can detect rainfall events relatively well. It was found that INCA overestimates the rainfall amount between 2012 and 2014, and generally overestimates precipitation for light rainfall events. For heavy rainfall events, however, an underestimation of INCA is prominent in most events. Based on the results, the difference between INCA and WegenerNet estimates is relatively higher during the wet season in the summer half-year (May-September). It is worth pointing out that INCA performs better in detecting and estimating rainfall around the two ZAMG stations located within the study area.</p>

The recommended location of rain gauge stations based on radar

2020 ◽  
Author(s):  
Taeyong Kwon ◽  
Sanghoo Yoon
Keyword(s):  
High Resolution ◽  
Water Resources ◽  
Nuclear Power ◽  
Conditional Entropy ◽  
Rain Gauge ◽  
Entropy Theory ◽  
High Entropy ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Precipitation Estimates

<p>The characteristics of the watershed are important to reduce hydrologic disasters, such as the risk of dam flooding. In other words, quantitative precipitation estimation(QPE) is important to manage water resources in large regions. Both radar and rain gauged data are used to improve QPE. This study is dealt with suggesting the best location of additional rain gauged stations to be installed in order to improve QPE as entropy theory. Conditional entropy is used to quantitatively evaluate the location of additional gauged stations to be installed given the existing rainfall network. Because radar produces high-resolution precipitation estimates, it can be used to identify the high entropy points to reduce rainfall uncertainty. The data were collected from May 2018 to August 2019 in the Bukhan river dam basin. Road networks were also considered for the establishment for a practical approach.</p><p> </p><p>This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD</p><p>(No. 2018-Tech-20)</p>

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