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1.
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.  相似文献   

2.
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.  相似文献   

3.
In the quantitative evaluation of radar-rainfall products (maps), rain gauge data are generally used as a good approximation of the true ground rainfall. However, rain gauges provide accurate measurements for a specific location, while radar estimates represent areal averages. Because these sampling discrepancies could introduce noise into the comparisons between these two sensors, they need to be accounted for. In this study, the spatial sampling error is defined as the ratio between the measurements by a single rain gauge and the true areal rainfall, defined as the value obtained by averaging the measurements by an adequate number of gauges within a pixel. Using a non-parametric scheme, the authors characterize its full statistical distribution for several spatial (4, 16 and 36 km2) and temporal (15 min and hourly) scales.  相似文献   

4.
将雷达测雨数据与分布式水文模型相耦合进行径流过程模拟,分析雷达测雨误差及其径流过程模拟效果,研究雷达测雨误差对径流过程模拟的影响效应.在对淮河流域气象中心业务化的5种淮河流域雷达测雨数据进行误差分析的基础上,采用雷达测雨数据驱动HEC-HMS水文模型,模拟分析淮河息县水文站以上流域2007年7月1-10日强降雨集中期的径流过程.结果表明:利用雷达测雨数据的径流模拟结果与实测资料的模拟结果基本吻合,各种雷达测雨数据误差经过HEC-HMS水文模型传递后,误差明显减小.联合校准法对应的模拟效果最好,过程流量相对误差NBs'和洪峰流量相对误差Z'分别为-20.2%和-13.3%.  相似文献   

5.
It is well acknowledged that there are large uncertainties associated with radar-based estimates of rainfall. Numerous sources of these errors are due to parameter estimation, the observational system and measurement principles, and not fully understood physical processes. Propagation of these uncertainties through all models for which radar-rainfall are used as input (e.g., hydrologic models) or as initial conditions (e.g., weather forecasting models) is necessary to enhance the understanding and interpretation of the obtained results. The aim of this paper is to provide an extensive literature review of the principal sources of error affecting single polarization radar-based rainfall estimates. These include radar miscalibration, attenuation, ground clutter and anomalous propagation, beam blockage, variability of the Z–R relation, range degradation, vertical variability of the precipitation system, vertical air motion and precipitation drift, and temporal sampling errors. Finally, the authors report some recent results from empirically-based modeling of the total radar-rainfall uncertainties. The bibliography comprises over 200 peer reviewed journal articles.  相似文献   

6.
There are large uncertainties associated with radar estimates of rainfall, including systematic errors as well as the random effects from several sources. This study focuses on the modeling of the systematic error component, which can be described mathematically in terms of a conditional expectation function. The authors present two different approaches: non-parametric (kernel-based) and parametric (copula-based). A large sample (more than six years) of rain gauge measurements from a dense network located in south-west England is used as an approximation of the true ground rainfall. These data are complemented with rainfall estimates by a C-band weather radar located at Wardon Hill, which is about 40 km from the catchment. The authors compare the results obtained using the parametric and non-parametric schemes for four temporal scales of hydrologic interest (5 and 15 min, hourly and three-hourly) by means of several different performance indices and discuss the strengths and weaknesses of each approach.  相似文献   

7.
Rainfall data in continuous space provide an essential input for most hydrological and water resources planning studies. Spatial distribution of rainfall is usually estimated using ground‐based point rainfall data from sparsely positioned rain‐gauge stations in a rain‐gauge network. Kriging has become a widely used interpolation method to estimate the spatial distribution of climate variables including rainfall. The objective of this study is to evaluate three geostatistical (ordinary kriging [OK], ordinary cokriging [OCK], kriging with an external drift [KED]), and two deterministic (inverse distance weighting, radial basis function) interpolation methods for enhanced spatial interpolation of monthly rainfall in the Middle Yarra River catchment and the Ovens River catchment in Victoria, Australia. Historical rainfall records from existing rain‐gauge stations of the catchments during 1980–2012 period are used for the analysis. A digital elevation model of each catchment is used as the supplementary information in addition to rainfall for the OCK and kriging with an external drift methods. The prediction performance of the adopted interpolation methods is assessed through cross‐validation. Results indicate that the geostatistical methods outperform the deterministic methods for spatial interpolation of rainfall. Results also indicate that among the geostatistical methods, the OCK method is found to be the best interpolator for estimating spatial rainfall distribution in both the catchments with the lowest prediction error between the observed and estimated monthly rainfall. Thus, this study demonstrates that the use of elevation as an auxiliary variable in addition to rainfall data in the geostatistical framework can significantly enhance the estimation of rainfall over a catchment.  相似文献   

8.
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%.  相似文献   

9.
Rainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A well‐designed rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging‐based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.  相似文献   

10.
Estimating accurate spatial distribution of precipitation is important for understanding the hydrologic cycle and various hydro‐environmental applications. Satellite‐based precipitation data have been widely used to measure the spatial distribution of precipitation over large extents, but an improvement in accuracy is still needed. In this study, three different merging techniques (Conditional Merging, Geographical Differential Analysis and Geographical Ratio Analysis) were used to merge precipitation estimations from Communication, Ocean and Meteorological Satellite (COMS) Rainfall Intensity data and ground‐based measurements. Merged products were evaluated with varying rain‐gauge network densities and accumulation times. The results confirmed that accuracy of detecting quantitative rainfall was improved as the accumulation time and network density increased. Also, the impact of spatial heterogeneity of precipitation on the merged estimates was investigated. Our merging techniques reproduced accurate spatial distribution of rainfall by adopting the advantages of both gauge and COMS estimates. The efficacy of the merging techniques was particularly pronounced when the spatial heterogeneity of hourly rainfall, quantified by variance of rainfall, was greater than 10 mm2/accumulation time2. Among the techniques analysed, Conditional Merging performed the best, especially when the gauge density was low. This study demonstrates the utility of the COMS Rainfall Intensity product, which has a shorter latency time (1 h) and higher spatio‐temporal resolution (hourly, 4 km by 4 km) than other widely used satellite precipitation products in estimating precipitation using merging techniques with ground‐based point measurements. The outcome has important implications for various hydrologic modelling approaches, especially for producing near real‐time products. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

11.
In this study, an EnKF-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using the coupled LSP–DSSAT model during a growing season of corn. Experiments using both synthetic and field observations were conducted to understand effects of simultaneous state–parameter estimation, spatial and temporal update frequency, and forcing uncertainties on RZSM estimates. Estimating the state–parameters simultaneously with every 3-day assimilation of volumetric soil moisture (VSM) observations at 5 depths lowered the average standard deviation (ASD) and the root mean square error (RMSE) for RZSM by approximately 1.77% VSM (78%) and 2.18% VSM (93%), respectively, compared to the open-loop ASD where as estimating only states lowered the ASD by approximately 1.26% VSM (56%) and the RMSE by 1.66% VSM (71%). The synthetic case obtained RZSM estimates closer to the observations than the MicroWEX-2 case, particularly after precipitation/irrigation events. The differences in EnKF performance between MicroWEX-2 and synthetic observations may indicate other sources of errors in addition to those in parameters and forcings, such as errors in model biophysics.  相似文献   

12.
Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data.  相似文献   

13.
The seismogenic layer thickness correlates with surface heat flow beneath the Japanese islands. However, this correlation is shown at restricted area, where seismic activity is high. In order to overcome this spatial limitation, we used another approach to estimate the regional thermal structure in the crust beneath the Japanese islands with more uniform coverage. The bottom depths of the magnetized crust determined from the spectral analysis of residual magnetic anomalies is generally interpreted as the level of the Curie point isotherm. We applied this method to estimate the crustal thermal structure in square windows of 2.125° × 2.125°. The obtained depths ranging from 11 to 30 km with average value of 18 km, correlate with the seismogenic layer thickness. It suggests that the Curie point depth is a useful indicator of the crustal thermal structure in these regions.  相似文献   

14.
Issues associated with microwave link rainfall estimation such as the effects of spatial and temporal variation in rain, the nonlinearity of R–kR–k relations, temporal sampling, power resolution, and wet antenna attenuation are investigated using more than 1.5 years of data from a high-resolution X-band weather radar. Microwave link signals are generated for different link frequencies and lengths from these radar data, so that retrieved path-averaged rainfall intensities can be compared to true path-averaged values. Results of these simulations can be linked to the space–time structure of rain. A frequency-dependent relation between the rainfall intensity at an antenna and the attenuation caused by its wetting is derived using microwave link and rain gauge data. It is shown that if the correct temporal sampling strategy is chosen, the effects of the degradation of power resolution and of wet antenna attenuation (if a correction is applied) are minor (i.e., MBE and bias-corrected RMSE are >−20% and <20% of the mean rainfall intensity, respectively) for link frequencies and lengths above ∼20 GHz and ∼2 km, respectively.  相似文献   

15.
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.  相似文献   

16.
司伟  包为民  瞿思敏  石朋 《湖泊科学》2018,30(2):533-541
空间集总式水文模型的洪水预报精度会受到面平均雨量估计误差的严重影响.点雨量监测值的误差类型、误差大小以及流域的雨量站点密度和站点的空间分布都会影响到面平均雨量的计算.为提高实时洪水预报精度,本文提出了一种基于降雨系统响应曲线洪水预报误差修正方法.通过此方法估计降雨输入项的误差,从而提高洪水预报精度.此方法将水文模型做为输入和输出之间的响应系统,用实测流量和计算流量之间的差值做为信息,通过降雨系统响应曲线,使用最小二乘估计原理,对面平均雨量进行修正,再用修正后的面平均雨量重新计算出流过程.将此修正方法结合新安江模型使用理想案例进行检验,并应用于王家坝流域的16场历史洪水以及此流域不同雨量站密度的情况下,结果证明均有明显修正效果,且在雨量站密度较低时修正效果更加明显.该方法是一种结构简单且不增加模型参数和复杂度的实时洪水修正的新方法.  相似文献   

17.
High resolution radar rainfall fields and a distributed hydrologic model are used to evaluate the sensitivity of flood and flash flood simulations to spatial aggregation of rainfall and soil properties at catchment scales ranging from 75 to 983 km2. Hydrologic modeling is based on a Hortonian infiltration model and a network-based representation of hillslope and channel flow. The investigation focuses on three extreme flood and flash flood events occurred on the Sesia river basin, North Western Italy, which are analysed by using four aggregation lengths ranging from 1 to 16 km. The influence of rainfall spatial aggregation is examined by using the flow distance as a spatial coordinate, hence emphasising the role of river network in the averaging of space–time rainfall. The effects of reduced and distorted rainfall spatial variability on peak discharge have been found particularly severe for the flash flood events, with peak errors up to 35% for rainfall aggregation of 16 km and at 983 km2 catchment size. Effects are particularly remarkable when significant structured rainfall variability combines with relatively important infiltration volumes due to dry initial conditions, as this emphasises the non-linear character of the rainfall–runoff relationship. In general, these results confirm that the correct estimate of rainfall volume is not enough for the accurate reproduction of flash flood events characterised by large and structured rainfall spatial variability, even at catchment scales around 250 km2. However, accurate rainfall volume estimation may suffice for less spatially variable flood events. Increasing the soil properties aggregation length exerts similar effects on peak discharge errors as increasing the rainfall aggregation length, for the cases considered here and after rescaling to preserve the rainfall volume. Moreover, peak discharge errors are roughly proportional to runoff volume errors, which indicates that the shape of the flood wave is influenced in a limited way by modifying the detail of the soil property spatial representation. Conversely, rainfall aggregation may exert a pronounced influence on the discharge peak by reshaping the spatial organisation of the runoff volumes and without a comparable impact on the runoff volumes.  相似文献   

18.
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19.
Extreme rainfall events are of particular importance due to their severe impacts on the economy, the environment and the society. Characterization and quantification of extremes and their spatial dependence structure may lead to a better understanding of extreme events. An important concept in statistical modeling is the tail dependence coefficient (TDC) that describes the degree of association between concurrent rainfall extremes at different locations. Accurate knowledge of the spatial characteristics of the TDC can help improve on the existing models of the occurrence probability of extreme storms. In this study, efficient estimation of the TDC in rainfall is investigated using a dense network of rain gauges located in south Louisiana, USA. The inter-gauge distances in this network range from about 1 km to 9 km. Four different nonparametric TDC estimators are implemented on samples of the rain gauge data and their advantages and disadvantages are discussed. Three averaging time-scales are considered: 1 h, 2 h and 3 h. The results indicate that a significant tail dependency may exist that cannot be ignored for realistic modeling of multivariate rainfall fields. Presence of a strong dependence among extremes contradicts with the assumption of joint normality, commonly used in hydrologic applications.  相似文献   

20.
This paper compares two Monte Carlo sequential data assimilation methods based on the Kalman filter, for estimating the effect of measurements on simulations of state error variance made by a one-dimensional hydrodynamic model. The first method used an ensemble Kalman filter (EnKF) to update state estimates, which were then used as initial conditions for further simulations. The second method used an ensemble transform Kalman filter (ETKF) to quickly estimate the effect of measurement error covariance on forecast error covariance without the need to re-run the simulation model. The ETKF gave an unbiased estimate of EnKF analysed error variance, although differences in the treatment of measurement errors meant the results were not identical. Estimates of forecast error variance could also be made, but their accuracy deteriorated as the time from measurements increased due in part to model non-linearity and the decreasing signal variance. The motivation behind the study was to assess the ability of the ETKF to target possible measurements, as part of an adaptive sampling framework, before they are assimilated by an EnKF-based forecasting model on the River Crouch, Essex, UK. The ETKF was found to be a useful tool for quickly estimating the error covariance expected after assimilating measurements into the hydrodynamic model. It, thus, provided a means of quantifying the ‘usefulness’ (in terms of error variance) of possible sampling schemes.  相似文献   

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