scholarly journals A Field Experiment on the Small-Scale Variability of Rainfall Based on a Network of Micro Rain Radars and Rain Gauges

2015 ◽  
Vol 54 (1) ◽  
pp. 243-255 ◽  
Author(s):  
Yong Chen ◽  
Huizhi Liu ◽  
Junling An ◽  
Ulrich Görsdorf ◽  
Franz H. Berger
Keyword(s):  
Spatial Scales ◽  
Rainfall Variability ◽  
Summer Rainfall ◽  
Small Scale ◽  
Precipitation Radar ◽  
River Catchment ◽  
Rain Gauges ◽  
Straight Line ◽  
Areal Rainfall ◽  
Xilin River Catchment

AbstractSmall-scale summer rainfall variability in a semiarid zone was studied by deploying five vertically pointing Micro Rain Radars (MRRs) along a nearly straight line and by using 12 rain gauges in the study area of the Xilin River catchment in China. The spatial scales of 4 and 9 km correspond to the resolution of precipitation radar and rainfall products from satellites. The dataset of the MRRs and rain gauges covers two months in the summer of 2009. Three parameters, that is, spatial correlation, intermittency, and the coefficient of variation (CV), were used to describe the rainfall variability as based on the data from the MRRs and rain gauges. The probability of partial beamfilling in a 4-km (9 km) pixel over a 30-min temporal scale was 17%–20% (28%–37%). More accurate equipment can measure lower rainfall intermittency. For scales of 4 and 9 km, the median CV of the accumulation times that were longer than 3 h with rainfall > 1 mm was 0.17–0.42. The accuracy of areal rainfall measured by different quantities of equipment was also evaluated. One MRR was sufficient for measuring the daily areal rainfall at a 4-km scale, with a fraction of prediction within a factor of 2 of observations of 1.0 and a correlation coefficient of ≥0.58 when daily mean rainfall was >1 mm.

scholarly journals Rainfall spatio-temporal correlation and intermittency structure from micro-γ to meso-β scale in the Netherlands

2021 ◽  
Author(s):  
Thomas C. van Leth ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
Remko Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Temporal Structure ◽  
Temporal Correlation ◽  
Exponential Model ◽  
Rain Gauges ◽  
Wide Range ◽  
Spatio Temporal ◽  
Two Parameter

AbstractWe investigate the spatio-temporal structure of rainfall at spatial scales from 7m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatio-temporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

Commercial microwave links instead of rain gauges: fiction or reality?

2014 ◽  
Vol 71 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Petr Sýkora ◽  
David Stránský ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Variability ◽  
A Priori ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Spatio Temporal ◽  
Areal Rainfall ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links ◽  
Unique Dataset

Commercial microwave links (MWLs) were suggested about a decade ago as a new source for quantitative precipitation estimates (QPEs). Meanwhile, the theory is well understood and rainfall monitoring with MWLs is on its way to being a mature technology, with several well-documented case studies, which investigate QPEs from multiple MWLs on the mesoscale. However, the potential of MWLs to observe microscale rainfall variability, which is important for urban hydrology, has not been investigated yet. In this paper, we assess the potential of MWLs to capture the spatio-temporal rainfall dynamics over small catchments of a few square kilometres. Specifically, we investigate the influence of different MWL topologies on areal rainfall estimation, which is important for experimental design or to a priori check the feasibility of using MWLs. In a dedicated case study in Prague, Czech Republic, we collected a unique dataset of 14 MWL signals with a temporal resolution of a few seconds and compared the QPEs from the MWLs to reference rainfall from multiple rain gauges. Our results show that, although QPEs from most MWLs are probably positively biased, they capture spatio-temporal rainfall variability on the microscale very well. Thus, they have great potential to improve runoff predictions. This is especially beneficial for heavy rainfall, which is usually decisive for urban drainage design.

scholarly journals Subpixel-Scale Rainfall Variability and the Effects on Separation of Radar and Gauge Rainfall Errors

2007 ◽  
Vol 8 (6) ◽  
pp. 1348-1363 ◽  
Author(s):  
Yu Zhang ◽  
Thomas Adams ◽  
James V. Bonta
Keyword(s):  
Spatial Correlation ◽  
Rainfall Variability ◽  
Domain Size ◽  
Error Variance ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Size Number ◽  
Proportional Contribution ◽  
Error Terms ◽  
Areal Rainfall

Abstract This paper presents an extended error variance separation method (EEVS) that allows explicit partitioning of the variance of the errors in gauge- and radar-based representations of areal rainfall. The implementation of EEVS demonstrated in this study combines a kriging scheme for estimating areal rainfall from gauges with a sampling method for determining the correlation between the gauge- and radar-related errors. On the basis of this framework, this study examines scale- and pixel-dependent impacts of subpixel-scale rainfall variability on the perceived partitioning of error variance for four conterminous Hydrologic Rainfall Analysis Project (HRAP) pixels in central Ohio with data from Next-Generation Weather Radar (NEXRAD) stage III product and from 11 collocated rain gauges as input. Application of EEVS for 1998–2001 yields proportional contribution of two error terms for July and October for each HRAP pixel and for two fictitious domains containing the gauges (4 and 8 km in size). The results illustrate the importance of considering subpixel variation of spatial correlation and how it varies with the size of domain size, number of gauges, and the subpixel locations of gauges. Further comparisons of error variance separation (EVS) and EEVS across pixels results suggest that accounting for structured variations in the spatial correlation under 8 km might be necessary for more accurate delineation of domain-dependent partitioning of error variance, and especially so for the summer months.

A dense network of rain gauges within an urban area: Rainfall uncertainty and variability

2021 ◽  
Author(s):  
Luc Neppel ◽  
Pierre Marchand ◽  
Pascal Finaud-Guyot ◽  
Vincent Guinot ◽  
Christian Salles
Keyword(s):  
Spatial Variability ◽  
Urban Area ◽  
Urban Areas ◽  
Rainfall Variability ◽  
Temporal Structure ◽  
Drainage System ◽  
Spatiotemporal Variability ◽  
Small Scale ◽  
Rain Gauges ◽  
Rain Gauges Network

<p>This study presents a new high density rain gauges network installed in urban area to study spatio-temporal structure and variability of precipitation at small scales. The preliminary results concerning gauges calibration and characterization of the rainfall spatial variability at fine scale are discussed.</p><p>In urban areas, the impervious surfaces connected to the drainage system leads to highly dynamic flows. The flood and runoff risk characterization requires  fine spatiotemporal scale to describe hydrological model input data :rainfall within spatial scale of less than 1km and temporal scale close to 1minis necessary for urban hydrological applications and risk assessment. In order to characterize small-scale rainfall spatiotemporal variability, a dense rain gauges network is deployed at Montpellier (France) with inter-gauges distances from 100m to 1km. Currently, 9 tipping bucket rain gauges  associated with 9 anemometers are acquiring rainfall and wind norm intensity every minutes. The network density and extension will be increased soon.</p><p>The first year measurements highlight a spatial variability of the 1-minute rainfall at the subkilometer scale. This observed variability is analyzed in view of the measurement uncertainty (gauge calibration, gauge error, bias due to the gauge location) to identify the natural rainfall variability.</p><p>This contribution presents the new densely extensive rainfall  network measurement, the typing bucket raingauge calibration and highlights that the observed 1-minute rainfall intensity variability  is significant and cannot be only explained by the measurement uncertainties.</p>

scholarly journals Assessing the frequency of drought/flood severity in the Luvuvhu River catchment, Limpopo Province, South Africa

Water SA ◽  
2021 ◽  
Vol 47 (2 April) ◽  
Author(s):  
SM Mazibuko ◽  
G Mukwada ◽  
ME Moeletsi
Keyword(s):  
High Frequency ◽  
Catchment Area ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Small Scale ◽  
Severe Drought ◽  
River Catchment ◽  
The North ◽  
Standardised Precipitation Index ◽  
North And South

The Luvuvhu River catchment experiences rainfall variability with a high frequency of extremely dry and wet conditions. Understanding the frequency of drought and floods in this catchment area is important to the agriculture sector for managing the negative impacts of these natural hazards. This study was undertaken to investigate the frequency and severity of drought/floods and linkages with the El Niño Southern Oscillation (ENSO) phenomenon. Poor and resource-limited small-scale farmers in the Luvuvhu River catchment area struggle to adjust due to decreasing crop yields and livestock mortality caused by drought and floods. Monthly rainfall data from 15 grid points (0.5° × 0.5°) was used to compute the Standardised Precipitation Index (SPI) for the period between 1979 and 2016. The 3-month SPI was calculated for the December–January–February (DJF) period. The second half of the agricultural season was selected because the influence of ENSO is high during the late summer season (DJF) in the catchment. The SPI results indicate that the agricultural seasons 1982/83, 1991/92 and 2015/16 were characterised by extreme drought. Conversely, the SPI values also show that the wettest seasons were recorded in 1998/99 and 1999/00. The catchment experiences a high frequency of moderate to severe drought in the north and north-eastern parts. Spatially, the occurrence of moderate to severe dry conditions covers large areas in the north and south-western parts. Severe to extreme wet conditions cover large areas in the north and south-eastern parts of the catchment. The SST index (Niño 3.4) shows a strong influence on rainfall variability in the catchment, resulting in either dry or wet conditions. Therefore, this study recommends further research focusing on more climatic modes that influence rainfall variability, as well as further development of drought and flood forecasting to improve farmers’ adaptations options and reliability of weather forecasts used as a tool to manage crop production.

scholarly journals Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network

2013 ◽  
Vol 17 (6) ◽  
pp. 2195-2208 ◽  
Author(s):  
N. Peleg ◽  
M. Ben-Asher ◽  
E. Morin
Keyword(s):  
Temporal Variability ◽  
Rainfall Variability ◽  
Rain Gauge ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Daily Scale ◽  
Rain Gauge Network ◽  
Hydrological Applications

Abstract. Runoff and flash flood generation are very sensitive to rainfall's spatial and temporal variability. The increasing use of radar and satellite data in hydrological applications, due to the sparse distribution of rain gauges over most catchments worldwide, requires furthering our knowledge of the uncertainties of these data. In 2011, a new super-dense network of rain gauges containing 14 stations, each with two side-by-side gauges, was installed within a 4 km2 study area near Kibbutz Galed in northern Israel. This network was established for a detailed exploration of the uncertainties and errors regarding rainfall variability within a common pixel size of data obtained from remote sensing systems for timescales of 1 min to daily. In this paper, we present the analysis of the first year's record collected from this network and from the Shacham weather radar, located 63 km from the study area. The gauge–rainfall spatial correlation and uncertainty were examined along with the estimated radar error. The nugget parameter of the inter-gauge rainfall correlations was high (0.92 on the 1 min scale) and increased as the timescale increased. The variance reduction factor (VRF), representing the uncertainty from averaging a number of rain stations per pixel, ranged from 1.6% for the 1 min timescale to 0.07% for the daily scale. It was also found that at least three rain stations are needed to adequately represent the rainfall (VRF < 5%) on a typical radar pixel scale. The difference between radar and rain gauge rainfall was mainly attributed to radar estimation errors, while the gauge sampling error contributed up to 20% to the total difference. The ratio of radar rainfall to gauge-areal-averaged rainfall, expressed by the error distribution scatter parameter, decreased from 5.27 dB for 3 min timescale to 3.21 dB for the daily scale. The analysis of the radar errors and uncertainties suggest that a temporal scale of at least 10 min should be used for hydrological applications of the radar data. Rainfall measurements collected with this dense rain gauge network will be used for further examination of small-scale rainfall's spatial and temporal variability in the coming years.

Combined rainfall estimates from personal weather station and commercial microwave link data in Germany

2021 ◽  
Author(s):  
Maximilian Graf ◽  
Abbas El Hachem ◽  
Micha Eisele ◽  
Jochen Seidel ◽  
Christian Chwala ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Hydrological Cycle ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Rain Gauge ◽  
Data Sets ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Rainfall Estimates

&lt;p&gt;Rain gauges and weather radars are the default sources of rainfall information. Rainfall estimates from these sensors improve our understanding of the hydrological cycle and are vital for water-resource management, agriculture, urban planning, as well as for weather, climate, and hydrological modelling. Still, due to the high spatio-temporal variability of rainfall and the specific drawbacks of the individual rainfall sensors, the rainfall variability cannot be captured completely. In the last decade, the number and availability of opportunistic rainfall sensors increased rapidly. These sensors are initially not meant to measure rainfall for scientific or operational purposes, but, if processed carefully, can be used for these cases . Here we present an analysis of two years of data from two opportunistic rainfall sensors, namely personal weather stations (PWS) and commercial microwave links (CMLs). We evaluate the performance of rainfall maps derived from these sensors on different spatial and temporal scales in Germany.&lt;/p&gt;&lt;p&gt;The data from around 15000 PWS tipping bucket-style rain gauges from the Netatmo network were accessed via Netatmos API. The data from around 4000 CMLs, which can be used to derive rainfall estimates from the rain-induced attenuation of the CMLs&amp;#8217; signal, were obtained from Ericsson. As both, PWS and CML data, can suffer from various error sources e.g. from unfavourable positioning and poor maintenance of PWS and from non-rain induced attenuation of the CMLs signal, we used a strict filtering routine. A total of seven gridded rainfall products were derived from different combinations of PWS, CML, and rain gauge data from the German Weather Service (DWD) with a geostatistical interpolation approach. This approach incorporates the uncertainty of the opportunistic sensors and the path-averaging characteristic of the CML observations.&lt;/p&gt;&lt;p&gt;To evaluate the resulting rainfall maps, we used three rain gauge data sets with different temporal and spatial scales covering the whole of Germany, the state of Rhineland-Palatinate and the city of Reutlingen, respectively. For all three reference data sets, rainfall maps from opportunistic sensors provided good agreement, with best results being derived from the combinations with PWS. Rainfall maps including CML data had the lowest bias. In a comparison with gauge adjusted radar products from the DWD, the radar products yielded better results than the rainfall maps from opportunistic sensors for the country-wide comparison of daily rainfall sums, which was carried out using the DWD&amp;#8217;s independent network of manual rain gauges. But for the hourly references covering Rhineland-Palatinate and Reutlingen, the rainfall maps derived from opportunistic sensors outperformed the radar products. These results highlight the capabilities of opportunistic rainfall sensors which could be used in many hydrometeorological applications.&lt;/p&gt;

scholarly journals Lidar-Based Estimation of Small-Scale Rainfall: Empirical Evidence

2009 ◽  
Vol 26 (3) ◽  
pp. 656-664 ◽  
Author(s):  
Piotr A. Lewandowski ◽  
William E. Eichinger ◽  
Anton Kruger ◽  
Witold F. Krajewski
Keyword(s):  
Empirical Evidence ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Small Scale ◽  
Lidar Signal ◽  
Inversion Algorithm ◽  
Long Distance ◽  
Radar Rainfall ◽  
Optical Extinction ◽  
Rain Rates

Abstract A significant scale gap between radar and in situ measurements of rainfall using rain gauges and disdrometers indicates a pressing need for improved knowledge of rainfall variability at the spatial scales below those of today’s operational radar rainfall products, that is, ∼1–4 km. Lidar technology has the potential to fulfill this need, but there has been inconsistency in the literature pertaining to quantitative observations of rain using lidar. Several publications have stated that light scattering properties of raindrops could not be correlated with rain rates, while other papers have demonstrated the existence of such relationships. This note provides empirical evidence in support of the latter claim. The authors conducted a simple experiment using a near-horizontal-pointing elastic lidar to observe rain in Iowa City, Iowa, in the fall of 2005. The lidar signal was used to estimate rainfall quantities that were subsequently compared with independent estimates of the same quantities obtained from an optical disdrometer that was placed about 370 m from the lidar, ∼10 m below the lidar beam. To perform the conversion from the raw lidar signal, the authors used an optical geometry-based procedure to estimate optical extinction data. A theoretical relationship between extinction coefficients and rain rates was derived based on a theoretical drop size distribution. The parameters of the relationship were found through a best-fit procedure using lidar and disdrometer data. The results show that the lidar-derived rain rates correspond to those obtained from the optical disdrometer with a root-mean-square difference of 55%. The authors conclude that although a great deal remains to be done to improve the inversion algorithm, lidar measurements of rain are possible and warrant further studies. Lidars deployed in conjunction with disdrometers can provide high spatial (&lt;5 m) and temporal (&lt;1 min disdrometer, ∼1 s lidar) resolution data over a relatively long distance for rainfall measurements (1–2 km in the case of the University of Iowa lidar).

scholarly journals Spatial Diagnosis of Rain Gauges’ Distribution and Flood Impacts: Case Study in Itaperuna, Rio de Janeiro—Brazil

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1120 ◽  
Author(s):  
Priscila Celebrini de Oliveira Campos ◽  
Igor Paz
Keyword(s):  
Fractal Dimension ◽  
Rio De Janeiro ◽  
Rainfall Variability ◽  
Probability Of Detection ◽  
Extreme Weather Events ◽  
Small Scale ◽  
Essential Information ◽  
Rain Gauges ◽  
Flood Susceptibility ◽  
Susceptibility Maps

The global increase of urban areas highlights the need to improve their adaptation to extreme weather events, in particular heavy rainfall. This study analyzes the impacts of in-situ rain gauges’ distribution (by applying the fractal dimension concept) associated with a spatial diagnosis of flood occurrences in the municipality of Itaperuna, Rio de Janeiro–Brazil, performing an investigation of flood susceptibility maps based on transitory (considering precipitation) and on permanent factors (natural flood susceptibility). The fractal analysis results pointed out that the rain gauges’ distribution presented a scaling break behavior with a low fractal dimension ( 0.416 ) at the small-scale range, highlighting the incapacity of the local instrumentation to capture the spatial rainfall variability. Thereafter, the cross-tabulation method was used to validate both predictive maps with recorded data of the major January 2020 event, which indicated that the transitory factors’ flood map presented an unsatisfactory Probability of Detection of floods ( P O D = 0.552 ) when compared to the permanent factors’ map ( P O D = 0.944 ) . These issues allowed to consider the hydrological uncertainties associated with the sparse gauge network distribution and its impacts on the use of flood susceptibility maps. Such methodology enables the evaluation of other municipalities and regions, constituting essential information in aid of territorial management.

Präzisions-Forstwirtschaft – was ist das? | Precision forestry – what's that?

2007 ◽  
Vol 158 (8) ◽  
pp. 235-242 ◽  
Author(s):  
Hans Rudolf Heinimann
Keyword(s):  
Business Processes ◽  
Spatial Scales ◽  
Supply Chain Coordination ◽  
Sensor Technology ◽  
Small Scale ◽  
Soil Physics ◽  
Coordination Problem ◽  
Precision Engineering ◽  
Precision Forestry ◽  
And Control

The term «precision forestry» was first introduced and discussed at a conference in 2001. The aims of this paper are to explore the scientific roots of the precision concept, define «precision forestry», and sketch the challenges that the implementation of this new concept may present to practitioners, educators, and researchers. The term «precision» does not mean accuracy on a small scale, but instead refers to the concurrent coordination and control of processes at spatial scales between 1 m and 100 km. Precision strives for an automatic control of processes. Precision land use differs from precision engineering by the requirements of gathering,storing and managing spatio-temporal variability of site and vegetation parameters. Practitioners will be facing the challenge of designing holistic, standardized business processes that are valid for whole networks of firms,and that follow available standards (e.g., SCOR, WoodX). There is a need to educate and train forestry professionals in the areas of business process re-engineering, computer supported management of business transactions,methods of remote sensing, sensor technology and control theory. Researchers will face the challenge of integrating plant physiology, soil physics and production sciences and solving the supply chain coordination problem (SCCP).

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