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.

scholarly journals Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates

2012 ◽  
Vol 13 (6) ◽  
pp. 1784-1798 ◽  
Author(s):  
Emad Habib ◽  
Alemseged Tamiru Haile ◽  
Yudong Tian ◽  
Robert J. Joyce
Keyword(s):  
Rain Gauge ◽  
Random Errors ◽  
Event Scale ◽  
Entire Study ◽  
Rain Gauges ◽  
Fine Resolution ◽  
Rain Rates ◽  
Climate Prediction Center ◽  
Rain Gauge Network ◽  
Insight Into

Abstract This study focuses on the evaluation of the NOAA–NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based rainfall product at fine space–time resolutions (1 h and 8 km). The evaluation was conducted during a 28-month period from 2004 to 2006 using a high-quality experimental rain gauge network in southern Louisiana, United States. The dense arrangement of rain gauges allowed for multiple gauges to be located within a single CMORPH pixel and provided a relatively reliable approximation of pixel-average surface rainfall. The results suggest that the CMORPH product has high detection skills: the probability of successful detection is ~80% for surface rain rates >2 mm h−1 and probability of false detection <3%. However, significant and alarming missed-rain and false-rain volumes of 21% and 22%, respectively, were reported. The CMORPH product has a negligible bias when assessed for the entire study period. On an event scale it has significant biases that exceed 100%. The fine-resolution CMORPH estimates have high levels of random errors; however, these errors get reduced rapidly when the estimates are aggregated in time or space. To provide insight into future improvements, the study examines the effect of temporal availability of passive microwave rainfall estimates on the product accuracy. The study also investigates the implications of using a radar-based rainfall product as an evaluation surface reference dataset instead of gauge observations. The findings reported in this study guide future enhancements of rainfall products and increase their informed usage in a variety of research and operational applications.

scholarly journals Geographic segmentation, spatial dependencies, and evaluation of the relative position of rain-gauges based on gridded data of mean monthly precipitation: application in Nigeria

2016 ◽  
Vol 49 (1) ◽  
pp. 107-122 ◽  
Author(s):  
V. G. Aschonitis ◽  
G. O. Awe ◽  
T. P. Abegunrin ◽  
K. A. Demertzi ◽  
D. M. Papamichail ◽  
...  
Keyword(s):  
Relative Position ◽  
Principal Component ◽  
Spatiotemporal Analysis ◽  
Rain Gauge ◽  
Spatiotemporal Variability ◽  
Monthly Precipitation ◽  
Rain Gauges ◽  
Spatial Dependencies ◽  
The Mean ◽  
Rain Gauge Network

Abstract The aim of the study is to present a combination of techniques for (a) the spatiotemporal analysis of mean monthly gridded precipitation datasets and (b) the evaluation of the relative position of the existing rain-gauge network. The mean monthly precipitation (P) patterns of Nigeria using ∼1 km2 grids for the period 1950–2000 were analyzed and the position of existing rain-gauges was evaluated. The analysis was performed through: (a) correlations of P versus elevation (H), latitude (Lat) and longitude (Lon); (b) principal component analysis (PCA); (c) Iso-Cluster and maximum likelihood classification (MLC) analysis for terrain segmentation to regions with similar temporal variability of mean monthly P; (d) use of MLC to create reliability classes of grid locations based on the mean clusters’ characteristics; and (e) analysis to evaluate the relative position of 33 rain-gauges based on the clusters and their reliability classes. The correlations of mean monthly P versus H, Lat, Lon, and PCA highlighted the spatiotemporal effects of the Inter Tropical Discontinuity phenomenon. The cluster analysis revealed 47 clusters, of which 22 do not have a rain-gauge while eight clusters have more than one rain-gauge. Thus, more rain-gauges and a better distribution are required to describe the spatiotemporal variability of P in Nigeria.

scholarly journals Climatology of daily rainfall semi-variance in The Netherlands

2011 ◽  
Vol 15 (1) ◽  
pp. 171-183 ◽  
Author(s):  
C. Z. van de Beek ◽  
H. Leijnse ◽  
P. J. J. F. Torfs ◽  
R. Uijlenhoet
Keyword(s):  
Daily Rainfall ◽  
Rain Gauge ◽  
Time Variability ◽  
Rain Gauges ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
One Year ◽  
Rain Gauge Network ◽  
Cosine Functions ◽  
Insight Into

Abstract. Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted.

scholarly journals Climatology of daily rainfall semivariance in The Netherlands

2010 ◽  
Vol 7 (2) ◽  
pp. 2085-2120 ◽  
Author(s):  
C. Z. van de Beek ◽  
H. Leijnse ◽  
P. J. J. F. Torfs ◽  
R. Uijlenhoet
Keyword(s):  
Daily Rainfall ◽  
Rain Gauge ◽  
Time Variability ◽  
Rain Gauges ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
One Year ◽  
Rain Gauge Network ◽  
Cosine Functions ◽  
Insight Into

Abstract. Rain gauges can offer high quality rainfall measurements at their location. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate accurate variograms. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, closely following simple cosine functions allowing for applications in catchment hydrology and rainfall field generation. Semivariances at short ranges during winter and spring tend to be underestimated, but summer and autumn are well predicted. This climatological semivariance can be employed to estimate the accuracy of the rainfall input to a hydrological model even with only few gauges in a given catchment area.

scholarly journals Influence of the rain gauge network on the performance of a hydrological lumped model applied at different basin scales

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Stefany Correia de Paula ◽  
Rutineia Tassi ◽  
Daniel Gustavo Allasia Piccilli ◽  
Francisco Lorenzini Neto
Keyword(s):  
Spatial Distribution ◽  
Monitoring Network ◽  
Rain Gauge ◽  
Network Density ◽  
Low Density ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Rain Gauges ◽  
The Poor ◽  
Rain Gauge Network

ABSTRACT In this study was evaluated the influence of the rainfall monitoring network density and distribution on the result of rainfall-runoff daily simulations of a lumped model (IPH II) considering basins with different drainage scales: Turvo River (1,540 km2), Ijuí River (9,462 km2), Jacuí River (38,700 km2) and Upper Uruguay (61,900 km2). For this purpose, four rain gauge coverage scenarios were developed: (I) 100%; (II) 75%; (III) 50% and (IV) 25% of the rain gauges of the basin. Additionally, a scenario considering the absence of monitoring was evaluated, in which the rainfall used in the modeling was estimated based on the TRMM satellite. Was verified that, in some situations, the modeling produced better results for scenarios with a lower rain gauges density if the available gauges presented better spatial distribution. Comparatively to the simulations performed with the rainfall estimated by the TRMM, the results obtained using rain gauges’ data were better, even in scenarios with low rain gauges density. However, when the poor spatial distribution of the rain gauges was associated with low density, the satellite’s estimation provided better results. Thus, was conclude that spatial distribution of the rain gauge network is important in the rainfall representation and that estimates obtained by the TRMM can be presented as alternatives for basins with a deficient monitoring network.

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 Estimation of convective precipitation: the meteorological radar versus an automatic rain gauge network

2005 ◽  
Vol 2 ◽  
pp. 103-109 ◽  
Author(s):  
M. C. Llasat ◽  
T. Rigo ◽  
M. Ceperuelo ◽  
A. Barrera
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Space Distribution ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Convective Rainfall ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Meteorological Radar ◽  
Rain Gauge Network

Abstract. The estimation of convective precipitation and its contribution to total precipitation is an important issue both in hydrometeorology and radio links. The greatest part of this kind of precipitation is related with high intensity values that can produce floods and/or damage and disturb radio propagation. This contribution proposes two approaches for the estimation of convective precipitation, using the β parameter that is related with the greater or lesser convective character of the precipitation event, and its time and space distribution throughout the entire series of the samples. The first approach was applied to 126 rain gauges of the Automatic System of Hydrologic Information of the Internal Basins of Catalonia (NE Spain). Data are series of 5-min rain rate, for the period 1996-2002, and a long series of 1-min rain rate starting in 1927. Rainfall events were classified according to this parameter. The second approach involved using information obtained by the meteorological radar located near Barcelona. A modified version of the SCIT method for the 3-D analysis and a combination of different methods for the 2-D analysis were applied. Convective rainfall charts and β charts were reported. Results obtained by the rain gauge network and by the radar were compared. The application of the β parameter to improve the rainfall regionalisation was demonstrated.

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.

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