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1.
Rainfall network design using kriging and entropy   总被引：4，自引：0，他引：4  
The spatial distribution of rainfall is related to meteorological and topographical factors. An understanding of the weather and topography is required to select the locations of the rain gauge stations in the catchment to obtain the optimum information. In theory, a well‐designed rainfall network can accurately represent and provide the needed information of rainfall in the catchment. However, the available rainfall data are rarely adequate in the mountainous area of Taiwan. In order to provide enough rainfall data to assure the success of water projects, the rainfall network based on the existing rain gauge stations has to be redesigned. A method composed of kriging and entropy that can determine the optimum number and spatial distribution of rain gauge stations in catchments is proposed. Kriging as an interpolator, which performs linear averaging to reconstruct the rainfall over the catchment on the basis of the observed rainfall, is used to compute the spatial variations of rainfall. Thus, the rainfall data at the locations of the candidate rain gauge stations can be reconstructed. The information entropy reveals the rainfall information of the each rain gauge station in the catchment. By calculating the joint entropy and the transmitted information, the candidate rain gauge stations are prioritized. In addition, the saturation of rainfall information can be used to add or remove the rain gauge stations. Thus, the optimum spatial distribution and the minimum number of rain gauge stations in the network can be determined. The catchment of the Shimen Reservoir in Taiwan is used to illustrate the method. The result shows that only seven rain gauge stations are needed to provide the necessary information. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

2.
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   

3.
Conditional bias-penalized kriging (CBPK)   总被引：1，自引：1，他引：0  
Simple and ordinary kriging, or SK and OK, respectively, represent the best linear unbiased estimator in the unconditional sense in that they minimize the unconditional (on the unknown truth) error variance and are unbiased in the unconditional mean. However, because the above properties hold only in the unconditional sense, kriging estimates are generally subject to conditional biases that, depending on the application, may be unacceptably large. For example, when used for precipitation estimation using rain gauge data, kriging tends to significantly underestimate large precipitation and, albeit less consequentially, overestimate small precipitation. In this work, we describe an extremely simple extension to SK or OK, referred to herein as conditional bias-penalized kriging (CBPK), which minimizes conditional bias in addition to unconditional error variance. For comparative evaluation of CBPK, we carried out numerical experiments in which normal and lognormal random fields of varying spatial correlation scale and rain gauge network density are synthetically generated, and the kriging estimates are cross-validated. For generalization and potential application in other optimal estimation techniques, we also derive CBPK in the framework of classical optimal linear estimation theory.  相似文献   

4.
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   

5.
The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4×4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.  相似文献   

6.
The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4×4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.  相似文献   

7.
An adequate and reliable raingauge network is essential for observing rainfall data in hydrology and water resource applications. A raingauge network developed for a catchment area is commonly extended periodically to increase data accuracy. Due to financial constraints, the network is reviewed for the optimal number of stations. A new optimization approach is developed in this study by coupling a cross-validation technique with a geostatistical method for raingauge network optimization to prioritize raingauge stations. The spatial interpolation error of the spatial rainfall distribution, measured as the root mean square error (Erms) optimization criterion is applied to a raingauge network in a tropical urban area. The results indicate that this method can successfully optimize the number of rainfall stations in an existing raingauge network, as the stations are prioritized based on their importance in the network.  相似文献   

8.
Rain‐gauge networks are often used to provide estimates of area average rainfall or point rainfalls at ungauged locations. The level of accuracy a network can achieve depends on the total number and locations of gauges in the network. A geostatistical approach for evaluation and augmentation of an existing rain‐gauge network is proposed in this study. Through variogram analysis, hourly rainfalls are shown to have higher spatial variability than annual rainfalls, with hourly Mei‐Yu rainfalls having the highest spatial variability. A criterion using ordinary kriging variance is proposed to assess the accuracy of rainfall estimation using the acceptance probability defined as the probability that estimation error falls within a desired range. Based on the criterion, the percentage of the total area with acceptable accuracy Ap under certain network configuration can be calculated. A sequential algorithm is also proposed to prioritize rain‐gauges of the existing network, identify the base network, and relocate non‐base gauges. Percentage of the total area with acceptable accuracy is mostly contributed by the base network. In contrast, non‐base gauges provide little contribution to Ap and are subject to removal or relocation. Using a case study in northern Taiwan, the proposed approach demonstrates that the identified base network which comprises of approximately two‐thirds of the total rain‐gauges can achieve almost the same level of performance (expressed in terms of percentage of the total area with acceptable accuracy) as the complete network for hourly Mei‐Yu rainfall estimation. The percentage of area with acceptable accuracy can be raised from 56% to 88% using an augmented network. A threshold value for the percentage of area with acceptable accuracy is also recommended to help determine the number of non‐base gauges which need to be relocated. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

9.
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   

10.
Compared to other estimation techniques, one advantage of geostatistical techniques is that they provide an index of the estimation accuracy of the variable of interest with the kriging estimation standard deviation (ESD). In the context of radar–raingauge quantitative precipitation estimation (QPE), we address in this article the question of how the kriging ESD can be transformed into a local spread of error by using the dependency of radar errors to the rain amount analyzed in previous work. The proposed approach is implemented for the most significant rain events observed in 2008 in the Cévennes-Vivarais region, France, by considering both the kriging with external drift (KED) and the ordinary kriging (OK) methods. A two-step procedure is implemented for estimating the rain estimation accuracy: (i) first kriging normalized ESDs are computed by using normalized variograms (sill equal to 1) to account for the observation system configuration and the spatial structure of the variable of interest (rainfall amount, residuals to the drift); (ii) based on the assumption of a linear relationship between the standard deviation and the mean of the variable of interest, a denormalization of the kriging ESDs is performed globally for a given rain event by using a cross-validation procedure. Despite the fact that the KED normalized ESDs are usually greater than the OK ones (due to an additional constraint in the kriging system and a weaker spatial structure of the residuals to the drift), the KED denormalized ESDs are generally smaller the OK ones, a result consistent with the better performance observed for the KED technique. The evolution of the mean and the standard deviation of the rainfall-scaled ESDs over a range of spatial (5–300 km2) and temporal (1–6 h) scales demonstrates that there is clear added value of the radar with respect to the raingauge network for the shortest scales, which are those of interest for flash-flood prediction in the considered region.  相似文献   

11.
Regional models of extreme rainfall must address the spatial variability induced by orographic obstacles. However, the proper detection of orographic effects often depends on the availability of a well‐designed rain gauge network. The aim of this study is to investigate a new method for identifying and characterizing the effects of orography on the spatial structure of extreme rainfall at the regional scale, including where rainfall data are lacking or fail to describe rainfall features thoroughly. We analyse the annual maxima of daily rainfall data in the Campania region, an orographically complex region in Southern Italy, and introduce a statistical procedure to identify spatial outliers in a low order statistic (namely the mean). The locations of these outliers are then compared with a pattern of orographic objects that has been a priori identified through the application of an automatic geomorphological procedure. The results show a direct and clear link between a particular set of orographic objects and a local increase in the spatial variability of extreme rainfall. This analysis allowed us to objectively identify areas where orography produces enhanced variability in extreme rainfall. It has direct implications for rain gauge network design criteria and has led to promising developments in the regional analysis of extreme rainfall. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

12.
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   

13.
Abstract

Rainfall is the most important input parameter for water resource planning and hydrological studies because flood risk assessment, rainfall harvesting and runoff estimation depend on the rainfall distribution within a region. Due to practical and economic factors, it is not possible to site rainfall stations everywhere, so representative rainfall stations are sited at specific locations. Rainfall distribution is then estimated from such stations. In this study, rainfall distribution in the southwestern region of Saudi Arabia was estimated using kriging, co-kriging and inverse distance weighted (IDW) methods. Historical records of rainfall from 47 stations for the period 1965–2010 and the altitude of these stations were used. The study shows that co-kriging is a better interpolator than the kriging and IDW methods, with a better correlation between actual and estimated monthly average rainfall for the region.  相似文献   

14.
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   

15.
Rainfall is a phenomenon difficult to model and predict, for the strong spatial and temporal heterogeneity and the presence of many zero values. We deal with hourly rainfall data provided by rain gauges, sparsely distributed on the ground, and radar data available on a fine grid of pixels. Radar data overcome the problem of sparseness of the rain gauge network, but are not reliable for the assessment of rain amounts. In this work we investigate how to calibrate radar measurements via rain gauge data and make spatial predictions for hourly rainfall, by means of Monte Carlo Markov Chain algorithms in a Bayesian hierarchical framework. We use zero-inflated distributions for taking zero-measurements into account. Several models are compared both in terms of data fitting and predictive performances on a set of validation sites. Finally, rainfall fields are reconstructed and standard error estimates at each prediction site are shown via easy-to-read spatial maps.  相似文献   

16.
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.  相似文献   

17.
Influence of SOI, DMI and Niño3.4 on South Australian rainfall   总被引：1，自引：0，他引：1  
The influences of climate drivers (SOI, DMI and Niño3.4) on South Australian (SA) rainfall are investigated in this study. Recent records of monthly rainfall and climate driver index values from 1981 to 2010 were analysed for 53 rainfall stations, located across eight SA natural resources management (NRM) regions. The Pearson, Kendall and Spearman correlation tests were applied between rainfall and climate drivers and between the climate drivers themselves. Both SA summer (December to February) and autumn (March to May) rainfalls were found not significantly influenced by climate indices. Winter rainfall in the south and east parts of SA was found strongly influenced by both SOI and DMI, particularly in July and August. Both SOI and DMI are inter-correlated in winter. Spring rainfall was found significantly influenced by DMI in the south and east parts of SA, particularly in September and October. In terms of ENSO phenomena, whilst both SOI and Niño3.4 are correlated, SOI was found more to be influential than Niño3.4 for SA winter and spring rainfall. Outcomes of the study are useful for stochastic rainfall generation and for developing downscaling techniques to generate rainfall projections in the region.  相似文献   

18.
Understanding the variability in monthly rainfall amounts is important for the management of water resources. We use entropy, a measure of variability, to quantify the rainfall variability in Australia. We define the entropy of stable rainfall (ESR) to measure the long‐term average rainfall variability across the months of the year. The stations in northern Australia observe substantially more variability in rainfall distributions and stations in southern Australia observe less variability in rainfall distribution across the months of the year. We also define the consistency index (CI) to compare the distribution of the monthly rainfall for a given year with the long‐term average monthly rainfall distribution. Higher value of the CI indicates the rainfall in the year is consistent with the overall long‐term average rainfall distribution. Areas close to the coastline in northern, southern and eastern Australia observe more consistent rainfall distribution in individual years with the long‐term average rainfall distribution. For the studied stations, we categorize the years into different potential water resource availability on the basis of annual rainfall amount and CI. For almost all Australian rainfall stations, El Niño years have a greater risk of having below median and relatively inconsistent rainfall distribution than La Niña years. The results may be helpful for developing area‐specific water usage strategies. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

19.
A variety of spatially continuous rainfall products are available but little evaluation of their accuracy has been published for areas with high spatial variability in rainfall. Five gridded rainfall products (PRISM, RTMA, and the interpolated Florida Automated Weather Network, FAWN, rainfall layers based on three interpolated methods) were assessed for Florida State. Point-to-pixel and pixel-to-pixel comparisons were performed to compare the five products. On average, the PRISM and RTMA products resulted in a better fit with the daily FAWN rainfall datasets, while FAWN-based interpolated products resulted in a better fit with the monthly FAWN rainfall datasets based on point-to-pixel analysis. Inverse distance weighting and ordinary kriging methods performed slightly better than the thin plate spline method in predicting daily rainfall. In general, monthly and seasonal rainfall amounts from PRISM and RTMA products were higher and lower, respectively, than reference rainfall amounts from FAWN gauge stations and FAWN-based interpolated products.  相似文献   

20.
The project captured a subset of the hydrological cycle for the tropical island of O'ahu, linking precipitation to groundwater recharge and aquifer storage. We determined seasonal storm events contributed more to aquifer recharge than year-round baseline orographic trade wind rainfall. Hydrogen and oxygen isotope values from an island-wide rain collector network with 20 locations deployed for 16 months and sampled at 3-month intervals were used to create the first local meteoric water line for O'ahu. Isotopic measurements were influenced by the amount effect, seasonality, storm type, and La Niña, though little elevation control was noted. Certain groundwater compositions from legacy data showed a strong similarity with collected precipitation from our stations. The majority of these significant relationships were between wet season precipitation and groundwater. A high number of moderate and heavy rainfall days during the dry season, large percentage of event-based rainfall, and wind directions outside of the typical NE trade wind direction were characteristics of the 2017–2018 wet season. This indicates that the majority of wet season precipitation is from event-based storms rather than typical trade wind weather. The deuterium-excess values provided the strongest evidence of a relationship between groundwater and different precipitation sources, indicating that this may be a useful metric for determining the extent of recharge from different rain events and systems.  相似文献   

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