Importance of no‐rain measurements on the comparison of radar and rain gauge rain rate

This study evaluated four possible cases of comparing radar and rain gauge rain rate for the detection of mean‐field bias. These four cases, or detection designs, consider in this study are: (1) design 1‐uses all the data sets available, including zero radar rain rate and zero rain gauge rain rate, (2) design 2—uses the data sets of positive radar rain rate and zero or positive rain gauge rain rate, (3) design 3—uses the data sets of zero or positive radar rain rate and positive rain gauge rain rate and (4) design 4—uses the data sets of positive radar rain rate and positive rain gauge rain rate. A theoretical review of these four detection designs showed that only the design 1 causes no design bias, but designs 2, 3 and 4 can cause positive, negative and negative design biases, respectively. This theoretical result was also verified by applying these four designs to the rain rate field generated by a multi‐dimensional rain rate model, as well as to that of the Mt Gwanak radar in Korea. The results from both applications showed that especially the design 4, which is generally used for the detection of mean‐field bias of radar rain rate, causes a serious design bias; therefore, is inappropriate as a design for detecting the mean‐field bias of radar rain rate. Copyright © 2009 John Wiley & Sons, Ltd.     

The Use of Radar and Gauge Measurements to Estimate Areal Precipitation for Several Czech River Basins

Two methods estimating areal precipitation for selected river basins in the Czech Republic are compared. The methods use radar precipitation (the radar-derived precipitation estimate based on column maximum reflectivity) and data from 81 on-line rain gauges routinely provided by the Czech Hydrometeorological Institute. Data from a dense network of climatological rain gauges (the average inter-station distance is approximately 8 km), the measurements of which are not available in real time, are utilized for the verification. The mean areal precipitation, which is used as the ground truth, is obtained by the weighted interpolation of the dense rain gauge network. The accuracy of the methods is evaluated by the root-mean-square-error.The first, pixel-related method merges radar precipitation with rain gauge data to obtain adjusted pixel values. The adjusting procedure combines radar and gauge values in one variable that is interpolated into all radar pixels. The adjusted pixel precipitation is calculated from radar precipitation and from the value of the combined variable. The areal estimates are determined by adding the corresponding pixel values. The second method applies a linear regression model to describe the relationship between the areal precipitation (dependent variable) and its estimates, which are determined from (i) non-adjusted radar precipitation and (ii) on-line rain gauge measurements interpolated into pixels. Classical linear regression, ridge regression and robust regression models are tested.Both the methods decrease the average areal error in comparison with the reference method, which uses the on-line rain gauge data only. The decrease is about 10% and 15% for the pixel-related and regression methods, respectively. When the estimates of the pixel-related method are included as predictors into the regression method then the improvement of accuracy is almost 25%.     

Detection of mean-field bias of the radar rain rate using rain gauges available within a small portion of radar umbrella: a case study of the Donghae (East Sea) radar in Korea

Observation of a storm approaching from the ocean to the in-land area is very important for the flood forecasting. Radar is generally used for this purpose. However, as rain gauges are mostly located within the in-land area, detection of the mean-field bias of radar rain rate cannot be easily made. This problem is obviously different from that with evenly-spaced rain gauges over the radar umbrella. This study investigated the detection problem of mean-field bias of radar rain rate when rain gauges are available within a small portion of radar umbrella. To exactly determine the mean-field bias, i.e. the difference between the radar rain rate and the rain gauge rain rate, the variance of the difference between two observations must be small; thus, a sufficient number of observations are indispensable. Therefore, the problem becomes determining the number of rain gauges that will satisfy the given accuracy, that being the variance of the difference between two observations. The dimensionless error variance derived by dividing the expected value of the error variance by the variance of the areal average rain rate was introduced as a criteria to effectively detect the mean field bias. Here, the variance of the areal average rain rate was assumed to be the climatological one and the expectation for the error variance could be changed depending one the sampling characteristics. As an example, this study evaluated the rainfall observation over the East Sea by the Donghae radar. About 6.8 % of the entire radar umbrella covered in-land areas, where the rain gauges were available. It was found that, to limit the dimensionless error variance to 2 %, a total of 26 rain gauges are required for the entire radar umbrella; whereas, a total of 24 rain gauges would be required within the in-land area with available for the rain gauge data.     

Adjustment of wind-drift effect for real-time systematic error correction in radar rainfall data

An effective bias correction procedure using gauge measurement is a significant step for radar data processing to reduce the systematic error in hydrological applications. In these bias correction methods, the spatial matching of precipitation patterns between radar and gauge networks is an important premise. However, the wind-drift effect on radar measurement induces an inconsistent spatial relationship between radar and gauge measurements as the raindrops observed by radar do not fall vertically to the ground. Consequently, a rain gauge does not correspond to the radar pixel based on the projected location of the radar beam. In this study, we introduce an adjustment method to incorporate the wind-drift effect into a bias correlation scheme. We first simulate the trajectory of raindrops in the air using downscaled three-dimensional wind data from the weather research and forecasting model (WRF) and calculate the final location of raindrops on the ground. The displacement of rainfall is then estimated and a radar–gauge spatial relationship is reconstructed. Based on this, the local real-time biases of the bin-average radar data were estimated for 12 selected events. Then, the reference mean local gauge rainfall, mean local bias, and adjusted radar rainfall calculated with and without consideration of the wind-drift effect are compared for different events and locations. There are considerable differences for three estimators, indicating that wind drift has a considerable impact on the real-time radar bias correction. Based on these facts, we suggest bias correction schemes based on the spatial correlation between radar and gauge measurements should consider the adjustment of the wind-drift effect and the proposed adjustment method is a promising solution to achieve this.     

Validation and use of rainfall radar data to simulate water flows in the Rio Escondido basin

This paper presents a combined validation method of radar-sensed rainfall, using rain gauge data and hydrologic closure, with an application to the Rio Escondido basin (North-East of Mexico). The space–time scaling behavior of rainfall between rain gauge and radar scales is compared with the intrinsic variability of rainfall, for a statistical validation of space–time variability. For hydrological validation purposes, the CEQUEAU model is used to perform rainfall-runoff routing. It provides a basin-wide water balance, to be compared with the measured water flow at the Villa de Fuentes hydrometric station, for mean-value gauging closure. A good qualitative agreement in terms of hydrograph shape and timing is obtained between the simulated and the observed water flows, and a multiplicative correction factor of an initially proposed Z–R relationship is adopted for the watershed under study, which agrees approximately with other authors’ findings about that relationship. The results are considered particularly useful as a validation-and-correction methodology of radar rainfall estimates for areas sparsely covered by rain gauges.     

Flood simulation using the gauge‐adjusted radar rainfall and physics‐based distributed hydrologic model

This paper reports the results of an investigation into flood simulation by areal rainfall estimated from the combination of gauged and radar rainfalls and a rainfall–runoff model on the Anseong‐cheon basin in the southern part of Korea. The spatial and temporal characteristics and behaviour of rainfall are analysed using various approaches combining radar and rain gauges: (1) using kriging of the rain gauge alone; (2) using radar data alone; (3) using mean field bias (MFB) of both radar and rain gauges; and (4) using conditional merging technique (CM) of both radar and rain gauges. To evaluate these methods, statistics and hyetograph for rain gauges and radar rainfalls were compared using hourly radar rainfall data from the Imjin‐river, Gangwha, rainfall radar site, Korea. Then, in order to evaluate the performance of flood estimates using different rainfall estimation methods, rainfall–runoff simulation was conducted using the physics‐based distributed hydrologic model, Vflo^?. The flood runoff hydrograph was used to compare the calculated hydrographs with the observed one. Results show that the rainfall field estimated by CM methods improved flood estimates, because it optimally combines rainfall fields representing actual spatial and temporal characteristics of rainfall. Copyright © 2008 John Wiley & Sons, Ltd.     

Orographic controls on rain water isotope distribution in the Mount Lofty Ranges of South Australia

Hydrogen and oxygen isotopes of water are common environmental tracers used to investigate hydrological processes, such as evaporation, vegetation water use, surface water–groundwater interaction, and groundwater recharge. The water isotope signature in surface water and groundwater evolves from the initial rain signature. In mountain terrain, rain water stable isotope composition spatially varies due to complex orographic precipitation processes. Many studies have examined the isotope–elevation relationships, while few have quantitatively investigate the terrain aspect and slope effect on rain isotope distribution. In this paper, we examine the orographic effects more completely, including elevation, terrain slope and aspect, on stable isotope distribution in the Mount Lofty Ranges (MLR) of South Australia, using a multivariate regression model. The regression of precipitation isotope composition suggests that orographic effects are the dominant controls on isotope spatial variability. About 75% of spatial variability in δ¹⁸O and deuterium excess is represented by the regression using solely orography-related variables (elevation, terrain aspect and slope), with about 25% of δ¹⁸O spatial variability attributed to the terrain aspect and slope effect. The lapse rate is about −0.25‰ for every 100 m at both windward and leeward slopes. However, at the same elevation, δ¹⁸O at the leeward slope (eastern MLR) is 0.5‰ larger than that at the windward slope. The difference can be explained by different mechanisms – continuous rain-out processes on the windward side and sub-cloud evaporation on the leeward side. Both δ¹⁸O and deuterium excess maps (1 km resolution) are constructed based on the regression results for the MLR. Both maps are consistent with groundwater of local precipitation origin, and useful to examine groundwater recharge.     

Spatial variability in precipitation within the Hilton Experimental Site,Shropshire, UK (1982–2006)

Accurate precipitation measurements are essential for many hydrological and hydrogeological management strategies. Precipitation at the Hilton Experimental Site has been regularly measured since 1982. This paper summarises 157 rain gauge years of precipitation data, recorded between 1982 and 2006, using 11 rain gauges on the 0·5 hectare site. Precipitation varied markedly within the site. Precipitation totals were notably different between two adjacent rain gauges, the mean difference being 0·3% of the total. Variations in mean annual precipitation within the site were ?8%. Spatial variations in wind turbulence appeared to be the main factor influencing intra‐site variability. Precipitation totals varied with gauge exposure, with surface level gauges receiving ?5·9% more precipitation than standard rain gauges, the difference being less lower down the slope. On a steep (～15° ) slope, basal sections had 2·5–7·9% more precipitation. Upper gauges received less, probably due to turbulence as increased exposure on the top of the slope resulted in precipitation being carried over the gauge orifice. Results confirm that due attention must be given to the inherent variability of precipitation amounts when calculating precipitation inputs. Copyright © 2008 John Wiley & Sons, Ltd.     

A multi‐source precipitation estimation approach to fill gaps over a radar precipitation field: a case study in the Colorado River Basin

The present work develops an approach to seamlessly blend satellite, available radar, climatological and gauge precipitation products to fill gaps in ground‐based radar precipitation field. To mix different precipitation products, the error of any of the products relative to each other should be removed. For bias correction, the study uses an ensemble‐based method that aims to estimate spatially varying multiplicative biases in SPEs using a radar precipitation product. A weighted successive correction method (SCM) is used to make the merging between error corrected satellite and radar precipitation estimates. In addition to SCM, we use a combination of SCM and Bayesian spatial model for merging the rain gauges (RGs) and climatological precipitation sources with radar and SPEs. We demonstrated the method using a satellite‐based hydro‐estimator; a radar‐based, stage‐II; a climatological product, Parameter‐elevation Regressions on Independent Slopes Model and a RG dataset for several rain events from 2006 to 2008 over an artificial gap in Oklahoma and a real radar gap in the Colorado River basin. Results show that: the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements. The study implies that using the available radar pixels surrounding the gap area, RG, Parameter‐elevation Regressions on Independent Slopes Model and satellite products, a radar‐like product is achievable over radar gap areas that benefit the operational meteorology and hydrology community. Copyright © 2013 John Wiley & Sons, Ltd.     

Quantitative correction of influence of rain on well water level

In this paper a new method of the quantitative correction of influence of the rain on the water level in a well is given. Using the faltung filtering and polynary regression, considering the effect of the rain on the well water level with the lag “memory”, the correction of the influence of the rain on the well water level is made. The result of correction of the water level in the well Lu-08 by the rain shows that the correction result of this method is better. The Chinese version of this paper appeared in the Chinese edition ofActa Seismologica Sinica,15, 202–207, 1993. This study was supported by the Chinese Joint Seismological Science Foundation. Thank The Seismic Office of Victory Oil Field for their help in gathering data of the well Lu-08.     

Rain event properties in nature and in rainfall simulation experiments: a comparative review with recommendations for increasingly systematic study and reporting

In hydrology and geomorphology, less attention has been paid to rain event properties such as duration, mean and peak rain rate than to rain properties such as drop size or kinetic energy. A literature review shows a lack of correspondence between natural and simulated rain events. For example, 26 studies that report event statistics from substantial records of natural rain reveal a mean rain rate of just 3·47 mm h^?1 (s.d. 2·38 mm h^?1). In 17 comparable studies dealing with extreme rain rates including events in cyclonic, tropical convective, and typhoon conditions, a mean maximum rain rate (either hourly or mean event rain rate) of 86·3 mm h^?1 (s.d. 57·7 mm h^?1) is demonstrated. However, 49 studies using rainfall simulation involve a mean maximum rain rate of 103·1 mm h^?1 (s.d. 81·3 mm h^?1), often sustained for > 1 h, exceeding even than of extreme rain events, and nearly 30 times the mean rain rate in ordinary, non‐exceptional, rain events. Thus rainfall simulation is often biased toward high rain rates, and many of the rates employed (in several instances exceeding 150 mm h^?1) appear to have limited relevance to ordinary field conditions. Generally, simulations should resemble natural rain events in each study region. Attention is also drawn to the raindrop arrival rate at the surface. In natural rain, this is known to vary from < 100 m^?2 s^?1 to > 5000 m^?2 s^?1. Arrival rate may need to be added to the list of parameters that must be reproduced realistically in rainfall simulation studies. Copyright © 2008 John Wiley & Sons, Ltd.     

Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales

Precipitation is a key control on watershed hydrologic modelling output, with errors in rainfall propagating through subsequent stages of water quantity and quality analysis. Most watershed models incorporate precipitation data from rain gauges; higher‐resolution data sources are available, but they are associated with greater computational requirements and expertise. Here, we investigate whether the Multisensor Precipitation Estimator (MPE or Stage IV Next‐Generation Radar) data improve the accuracy of streamflow simulations using the Soil and Water Assessment Tool (SWAT), compared with rain gauge data. Simulated flows from 2002 to 2010 at five timesteps were compared with observed flows for four nested subwatersheds of the Neuse River basin in North Carolina (21‐, 203‐, 2979‐, and 10 100‐km² watershed area), using a multi‐objective function, informal likelihood‐weighted calibration approach. Across watersheds and timesteps, total gauge precipitation was greater than radar precipitation, but radar data showed a conditional bias of higher rainfall estimates during large events (>25–50 mm/day). Model parameterization differed between calibrations with the two datasets, despite the fact that all watershed characteristics were the same across simulation scenarios. This underscores the importance of linking calibration parameters to realistic processes. SWAT simulations with both datasets underestimated median and low flows, whereas radar‐based simulations were more accurate than gauge‐based simulations for high flows. At coarser timesteps, differences were less pronounced. Our results suggest that modelling efforts in watersheds with poor rain gauge coverage can be improved with MPE radar data, especially at short timesteps. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.     

A comparison of gauge and radar precipitation data for simulating an extreme hydrological event in the Severn Uplands,UK

This paper provides a comparison of gauge and radar precipitation data sources during an extreme hydrological event. November–December 2006 was selected as a time period of intense rainfall and large river flows for the Severn Uplands, an upland catchment in the United Kingdom. A comparison between gauge and radar precipitation time‐series records for the event indicated discrepancies between data sources, particularly in areas of higher elevation. The HEC‐HMS rainfall‐runoff model was selected to assess the accuracy of the precipitation to simulate river flows for the extreme event. Gauge, radar and gauge‐corrected radar rainfall were used as model inputs. Universal cokriging was used to geostatistically interpolate gauge data with radar and elevation data as covariates. This interpolated layer was used to calculate the mean‐field bias and correct the radar composites. Results indicated that gauge‐ and gauge‐corrected radar‐driven models replicated flows adequately for the extreme event. Gauge‐corrected flow predictions produced an increase in flow prediction accuracy when compared with the raw radar, yet predictions were comparative in accuracy to those using the interpolated gauge network. Subsequent investigations suggested this was due to an adequate spatial and temporal resolution of the precipitation gauge network within the Severn Uplands. Results suggested that the six rain gauges could adequately represent precipitation variability of the Severn Uplands to predict flows at an approximately equal accuracy to that obtained by radar. Temporally, radar produced an increase in flow prediction accuracy in mountainous reaches once the gauge time step was in excessive of an hourly interval. Copyright © 2010 John Wiley & Sons, Ltd.     

Investigation of the scale-dependent variability of radar-rainfall and rain gauge error covariance

There is a significant spatial sampling mismatch between radar and rain gauge data. The use of rain gauge data to estimate radar-rainfall error variance requires partitioning of the variance of the radar and rain gauge difference to account for the sampling mismatch. A key assumption in the literature pertaining to the error variance separation method used to partition the variance is that the covariance between radar-rainfall error and the error of rain gauges in representing radar sampling domain is negligible. Our study presents the results of an extensive test of this assumption. The test is based on empirical data and covers temporal scales ranging from 0.25 to 24 h and spatial scales ranging from 1 to 32 km. We used a two-year data set from two high quality and high density rain gauge networks in Oklahoma and excluded the winter months. The results obtained using a resampling procedure show that covariance can be considerable at large scales due to the significant variability. As the variability of the covariance rapidly increases with larger spatial and shorter temporal scales, applications of the error variance separation method at those scales require more caution. The variability of the covariance and one of its constituting variables, the variance ratio of radar and gauge errors, shows simple scaling behavior well characterized by a power-law.     

First Results of DSD Measurement by Videodistrometer in the Czech Republic in 1998-1999

The first results of the almost one year drop size distribution (DSD) measurement in the Czech Republic are summarised in this study. The ESA-ESTEC 2D videodistrometer was used to measure the rain drop parameters. The average DSD is shown to be of the gamma type. One minute DSDs were evaluated to test the accuracy of analytical DSD models. Parameters of gamma distribution and exponential distribution functions were evaluated for the whole data set as well as for the various rain rate intervals. Regression technique and the method of moments were applied to estimate the parameters of DSD. It is shown that the parameter value strongly depends on the method of computation as well as on the rain type. Its average value is about 0.59 for the average (smooth) one minute DSD while an average value of un-smoothed DSD is 11.0 (moment method) or 5.4 (regression technique). The Joss's shape parameter and the Tokay-Short's parameter CS estimating roughly the rain type are also discussed (if CS>1, the event should be convective). The tendency of increasing numerical value of the CS parameter with the increasing rain rate was observed (the DSDs were distributed into classes respecting the rain rate value) and thus the idea of the convectivity occurrence bounded with the higher CS parameter value was supported. The study also compares the parameters of the average DSD with the averages of parameter values of all 4 183 one minute DSDs.     

Inter-station correlation and estimation errors of areal average rain rate

In this study an equation for estimating the error involved in the areal average rain rate considering the inter-station correlation was derived and applied for two cases: the first compared two storm events with different inter-station correlations, and the second evaluated the seasonal variation of estimation error of monthly rainfall. Similar cases, but without considering the rainfall seasonality, were also investigated for the comparison. This study was applied to the Geum River Basin with 28 rain gauge measurements, each having more than 30 years of rainfall data. A summary of the application results follows: (1) When considering the inter-station correlation, the estimation error involved in the areal average rain rate became significantly decreased proportional to the inter-station correlation. (2) The estimation error of monthly areal average rainfall showed strong seasonality with high ones during the wet season and lower ones during the dry season. (3) The estimation error was well proportional to the areal average rain rate as well as to its standard deviation. The ratio of estimation error to the areal average rain rate itself was estimated to be about 0.1 for the case of assuming no inter-station correlations, but decreased to 0.06 for the case of considering the inter-station correlations between measurements. (4) The relation between the standard deviation of areal average rain rate and the estimation error became much stronger than that between the areal average rain rate itself and the estimation error. The ratio of estimation error to the standard deviations of rain rate amount was estimated to be about 0.2 for the case of assuming no inter-station correlations, but decreased to 0.1 for the case of considering the inter-station correlations. This relation was found to be valid for any case of accumulation time such as in daily, monthly, or annual rainfall data.     

Rainfall spatial variability observed by X-band weather radar and its implication for the accuracy of rainfall estimates

The main objective of this paper is to estimate the error in the rainfall derived from a polarimetric X-band radar, by comparison with the corresponding estimate of a rain gauge network. However the present analysis also considers the errors inherent to rain gauge, in particular instrumental and representativeness errors. A special emphasis is addressed to the spatial variability of the rainfall in order to appreciate the representativeness error of the rain gauge with respect to the 1 km square average, typical of the radar derived estimate. For this purpose the spatial correlation function of the rainfall is analyzed.     

Hydrological impact and potential flooding of convective rain cells in a semi-arid environment

Abstract

t Spatio-temporal storm properties have a large impact on catchment hydrological response. The sensitivity of simulated flash floods to convective rain-cell characteristics is examined for an extreme storm event over a 94 km² semi-arid catchment in southern Israel. High space–time resolution weather radar data were used to derive and model convective rain cells that then served as input into a hydrological model. Based on alterations of location, direction and speed of a major rain cell, identified as the flooding cell for this case, the impacts on catchment rainfall and generated flood were examined. Global sensitivity analysis was applied to identify the most important factors affecting the flash flood peak discharge at the catchment outlet. We found that the flood peak discharge could be increased three-fold by relatively small changes in rain-cell characteristics. We assessed that the maximum flash flood magnitude that this single rain cell can produce is 175 m³/s, and, taking into account the rest of the rain cells, the flash flood peak discharge can reach 260 m³/s.

Editor Z.W. Kundzewicz; Guest editor R.J. Moore

Citation Morin, E. and Yakir, H., 2013. Hydrological impact and potential flooding of convective rain cells in a semi-arid environment. Hydrological Sciences Journal, 59 (7), 1275–1284. http://dx.doi.org/10.1080/02626667.2013.841315     

Comparison of three types of laser optical disdrometers under natural rainfall conditions

ABSTRACT

Optical disdrometers can be used to estimate rainfall erosivity; however, the relative accuracy of different disdrometers is unclear. This study compared three types of optical laser-based disdrometers to quantify differences in measured rainfall characteristics and to develop correction factors for kinetic energy (KE). Two identical PWS100 (Campbell Scientific), one Laser Precipitation Monitor (Thies Clima) and a first-generation Parsivel (OTT) were collocated with a weighing rain gauge (OTT Pluvio²) at a site in Austria. All disdrometers underestimated total rainfall compared to the rain gauge with relative biases from 2% to 29%. Differences in drop size distribution and velocity resulted in different KE estimates. By applying a linear regression to the KE–intensity relationship of each disdrometer, a correction factor for KE between the disdrometers was developed. This factor ranged from 1.15 to 1.36 and allowed comparison of KE between different disdrometer types despite differences in measured drop size and velocity.     

Ability of a dual polarized X-band radar to estimate rainfall

The aim of this study is to assess rainfall estimates by a dual polarized X-band radar. This study was part of the European project FRAMEA (Flood forecasting using Radar in Alpine and Mediterranean Areas). Two radars were set up near the small town of Collobrières in South Eastern France. The first radar was a dual polarized X-band radar (Hydrix^®) associated with a ZPHI^® algorithm while the second one was an S-band radar (Météo France). We compared radar rainfall data with measurements obtained by two rain gauge networks (Météo France and Cemagref). During the experiments from February 2006 to June 2007, four significant rainfall events occurred. The accuracy of the rain rate obtained with both S-band and X-band radars decreased significantly beyond 60 km, in particular for the X-band radar. At closer ranges, such as 30–60 km from the radars, the X-band and the S-band radar retrievals showed similar performance with Nash criteria around 0.80 for the X-band radar and 0.75 for the S-band radar. Furthermore, the X-band radar did not require calibration on rainfall records, which tends to make it a useful method to assess rainfall in areas without a rain gauge network.