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1.
Geospatial blending to improve spatial mapping of precipitation with high spatial resolution by merging satellite‐based and ground‐based data 
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下载免费PDF全文 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
2.
AbstractGiven that radar-based rainfall has been broadly applied in hydrological studies, quantitative modelling of its uncertainty is critically important, as the error of input rainfall is the main source of error in hydrological modelling. Using an ensemble of rainfall estimates is an elegant solution to characterize the uncertainty of radar-based rainfall and its spatial and temporal variability. This paper has fully formulated an ensemble generator for radar precipitation estimation based on the copula method. Each ensemble member is a probable realization that represents the unknown true rainfall field based on the distribution of radar rainfall (RR) error and its spatial error structure. An uncertainty model consisting of a deterministic component and a random error factor is presented based on the distribution of gauge rainfall conditioned on the radar rainfall (GR|RR). Two kinds of copulas (elliptical and Archimedean copulas) are introduced to generate random errors, which are imposed by the deterministic component. The elliptical copulas (e.g. Gaussian and t-copula) generate the random errors based on the multivariate distribution, typically of decomposition of the error correlation matrix using the LU decomposition algorithm. The Archimedean copulas (e.g. Clayton and Gumbel) utilize the conditional dependence between different radar pixels to obtain random errors. Based on those, a case application is carried out in the Brue catchment located in southwest England. The results show that the simulated uncertainty bands of rainfall encompass most of the reference raingauge measurements with good agreement between the simulated and observed spatial dependences. This indicates that the proposed scheme is a statistically reliable method in ensemble radar rainfall generation and is a useful tool for describing radar rainfall uncertainty.
Editor D. Koutsoyiannis; Associate editor S. Grimaldi 相似文献
3.
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
4.
Rainfall threshold (RT) method is one of the evolving flood forecasting approaches. When the cumulative rainfall depth for a given initial soil moisture condition intersects the threshold rainfall curve, the peak discharge is expected to be equal or greater than the threshold discharge for flooding at the target site. Besides the total rainfall depth, spatial and temporal distribution of rainfall impacts the flood peak discharge and the time to peak. To revisit a previous study conducted by the authors, in which spatially independent rainfall pattern was assumed, the spatial distribution of rainfall was simulated following a Monte Carlo approach. The structure of the spatial dependence among sub‐watersheds' rainfalls was taken into account under three different scenarios, namely independent, bivariate copula (2copula) and multivariate Gaussian copula (MGC). For each set of generated random dimensionless rainfalls, the probabilistic RT curves were derived for dry moisture condition. Results were evaluated with both historical and simulated events. For the simulated events, threshold curves were assessed by means of categorical statistics, such as hit rate, false rate and critical success index (CSI). Results revealed that the best performance based on the CSI criterion corresponded to 50% curve in 2copula and MGC scenarios as well as 90% curve in the independent scenario. The recognition of 50% curve in 2copula and MGC scenarios is in agreement with our expectations that the mean probable curve should have the best performance. Moreover, the proposed inclusion of spatially dependent rainfall scenario improved the performance of RT curves by about 25% in comparison with the presumed spatially uniform rainfall scenario. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
5.
A stochastic framework to assess the performance of flood warning systems based on rainfall‐runoff modeling 下载免费PDF全文
下载免费PDF全文 Nowadays, Flood Forecasting and Warning Systems (FFWSs) are known as the most inexpensive and efficient non‐structural measures for flood damage mitigation in the world. Benefit to cost of the FFWSs has been reported to be several times of other flood mitigation measures. Beside these advantages, uncertainty in flood predictions is a subject that may affect FFWS's reliability and the benefits of these systems. Determining the reliability of advanced flood warning systems based on the rainfall–runoff models is a challenge in assessment of the FFWS performance which is the subject of this study. In this paper, a stochastic methodology is proposed to provide the uncertainty band of the rainfall–runoff model and to calculate the probability of acceptable forecasts. The proposed method is based on Monte Carlo simulation and multivariate analysis of the predicted time and discharge error data sets. For this purpose, after the calibration of the rainfall–runoff model, the probability distributions of input calibration parameters and uncertainty band of the model are estimated through the Bayesian inference. Then, data sets of the time and discharge errors are calculated using the Monte Carlo simulation, and the probability of acceptable model forecasts is calculated by multivariate analysis of data using copula functions. The proposed approach was applied for a small watershed in Iran as a case study. The results showed using rainfall–runoff modeling based on real‐time precipitation is not enough to attain high performance for FFWSs in small watersheds, and it seems using weather forecasts as the inputs of rainfall–runoff models is essential to increase lead times and the reliability of FFWSs in small watersheds. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
6.
Qiang Dai Dawei Han Miguel A. Rico‐Ramirez Lu Zhuo Nergui Nanding Tanvir Islam 《水文研究》2015,29(7):1704-1716
Radar‐based estimates of rainfall are affected by many sources of uncertainties, which would propagate through the hydrological model when radar rainfall estimates are used as input or initial conditions. An elegant solution to quantify these uncertainties is to model the empirical relationship between radar measurements and rain gauge observations (as the ‘ground reference’). However, most current studies only use a fixed and uniform model to represent the uncertainty of radar rainfall, without consideration of its variation under different synoptic regimes. Wind is such a typical weather factor, as it not only induces error in rain gauge measurements but also causes the raindrops observed by weather radar to drift when they reach the ground. For this reason, as a first attempt, this study introduces the wind field into the uncertainty model and designs the radar rainfall uncertainty model under different wind conditions. We separate the original dataset into three subsamples according to wind speed, which are named as WDI (0–2 m/s), WDII (2–4 m/s) and WDIII (>4 m/s). The multivariate distributed ensemble generator is introduced and established for each subsample. Thirty typical events (10 at each wind range) are selected to explore the behaviours of uncertainty under different wind ranges. In each time step, 500 ensemble members are generated, and the values of 5th to 95th percentile values are used to produce the uncertainty bands. Two basic features of uncertainty bands, namely dispersion and ensemble bias, increase significantly with the growth of wind speed, demonstrating that wind speed plays a considerable role in influencing the behaviour of the uncertainty band. On the basis of these pieces of evidence, we conclude that the radar rainfall uncertainty model established under different wind conditions should be more realistic in representing the radar rainfall uncertainty. This study is only a start in incorporating synoptic regimes into rainfall uncertainty analysis, and a great deal of more effort is still needed to build a realistic and comprehensive uncertainty model for radar rainfall data. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
7.
Subimal Ghosh 《水文研究》2010,24(24):3558-3567
The rainfall patterns of neighbouring meteorological subdivisions of India are similar because of similar climatological and geographical characteristics. Analysing the rainfall pattern separately for these meteorological subdivisions may not always capture the correlation and tail dependence. Furthermore, generating the multivariate rainfall data separately may not preserve the correlation. In this study, copula method is used to derive the bivariate distribution of monsoon rainfall in neighbouring meteorological subdivisions. Different Archimedean copulas are used for this purpose and the best copula is selected based on nonparametric test and tail dependence coefficient. The fitted copula is then applied to derive the bivariate distribution, joint return period and conditional distribution. Bivariate rainfall data is generated with the fitted copula and it is observed with the increase of sample size, the generated data is able to capture the correlation as well as tail dependence. The methodology is demonstrated with the case study of two neighbouring meteorological subdivisions of North‐East India: Assam and Meghalaya meteorological subdivision and Nagaland, Manipur, Mizoram and Tripura meteorological subdivision. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
8.
High spatial and temporal resolution of precipitation data is critical input for hydrological budget estimation and flash flood modelling. This study evaluated four methods [Bias Adjustment (BA), Simple Kriging with varying Local Means (SKlm), Kriging with External Drift (KED), and Regression Kriging (RK)] for their performances in incorporating gauge rainfall measurements into Next Generation Weather Radar (NEXRAD) multi‐sensor precipitation estimator (MPE; hourly and 4 × 4 km2). Measurements from a network of 50 gauges at the Upper Guadalupe River Basin, central Texas and MPE data for the year 2004 were used in the study. We used three evaluation coefficients percentage bias (PB), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) to examine the performance of the four methods for preserving regional‐ and local‐scale characteristics of observed precipitation data. The results show that the two Kriging‐based methods (SKlm and RK) are in general better than BA and KED and that the PB and NSE criteria are better than the R2 criterion in assessing the performance of the four methods. It is also worth noting that the performance of one method at regional scale may be different from its performance at local scale. Critical evaluation of the performance of different methods at local or regional scale should be conducted according to the different purposes. The results obtained in this study are expected to contribute to the development of more accurate spatial rainfall products for hydrologic budget and flash flood modelling. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
9.
This study uses elliptical copulas and transition probabilities for uncertainty modeling of categorical spatial data. It begins by discussing the expressions of the cumulative distribution function and probability density function of two major elliptical copulas: Gaussian copula and t copula. The basic form of spatial copula discriminant function is then derived based on Bayes’ theorem, which consists of three parts: the prior probability, the conditional marginal densities, and the conditional copula density. Finally, three kinds of parameter estimation methods are discussed, including maximum likelihood estimation, inference functions for margins and canonical maximum likelihood (CML). To avoid making assumptions on the form of marginal distributions, the CML approach is adopted in the real-world case study. Results show that the occurrence probability maps generated by these two elliptical copulas are similar to each other. However, the prediction map interpolated by Gaussian copula has a relatively higher classification accuracy than t copula. 相似文献
10.
Qiang Dai Dawei Han Lu Zhuo Jun Zhang Tanvir Islam Prashant K. Srivastava 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(1):27-38
Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of the possible uncertainty associated with the rainfall products. Long-term empirical modeling of the relationship between radar and gauge measurements is an efficient and practical method to describe the radar rainfall uncertainty. However, complicated variation of synoptic conditions makes the radar-rainfall uncertainty model based on historical data hard to extend in the future state. A promising solution is to integrate synoptic regimes with the empirical model and explore the impact of individual synoptic regimes on radar rainfall uncertainty. This study is an attempt to introduce season, one of the most important synoptic factor, into the radar rainfall uncertainty model and proposes a seasonal ensemble generator for radar rainfall using copula and autoregressive model. We firstly analyze the histograms of rainfall-weighted temperature, the radar-gauge relationships, and Box and Whisker plots in different seasons and conclude that the radar rainfall uncertainty has strong seasonal dependence. Then a seasonal ensemble generator is designed and implemented in a UK catchment under a temperate maritime climate, which can fully model marginal distribution, spatial dependence, temporal dependence and seasonal dependence of radar rainfall uncertainty. To test its performance, 12 typical rainfall events (4 for each season) are chosen to generate ensemble rainfall values. In each time step, 500 ensemble members are produced and the values of 5th to 95th percentiles are used to derive the uncertainty bands. Except several outliers, the uncertainty bands encompass the observed gauge rainfall quite well. The parameters of the ensemble generator vary considerably for each season, indicating the seasonal ensemble generator reflects the impact of seasons on radar rainfall uncertainty. This study is an attempt to simultaneously consider four key features of radar rainfall uncertainty and future study will investigate their impacts on the outputs of hydrological models with radar rainfall as input or initial conditions. 相似文献
11.
The infrared‐microwave rainfall algorithm (IMRA) was developed for retrieving spatial rainfall from infrared (IR) brightness temperatures (TBs) of satellite sensors to provide supplementary information to the rainfall field, and to decrease the traditional dependency on limited rain gauge data that are point measurements. In IMRA, a SLOPE technique (ST) was developed for discriminating rain/no‐rain pixels through IR image cloud‐top temperature gradient, and 243K as the IR threshold temperature for minimum detectable rainfall rate. IMRA also allows for the adjustment of rainfall derived from IR‐TB using microwave (MW) TBs. In this study, IMRA rainfall estimates were assessed on hourly and daily basis for different spatial scales (4, 12, 20, and 100 km) using NCEP stage IV gauge‐adjusted radar rainfall data, and daily rain gauge data. IMRA was assessed in terms of the accuracy of the rainfall estimates and the basin streamflow simulated by the hydrologic model, Sacramento soil moisture accounting (SAC‐SMA), driven by the rainfall data. The results show that the ST option of IMRA gave accurate satellite rainfall estimates for both light and heavy rainfall systems while the Hessian technique only gave accurate estimates for the convective systems. At daily time step, there was no improvement in IR‐satellite rainfall estimates adjusted with MW TBs. The basin‐scale streamflow simulated by SAC‐SMA driven by satellite rainfall data was marginally better than when SAC‐SMA was driven by rain gauge data, and was similar to the case using radar data, reflecting the potential applications of satellite rainfall in basin‐scale hydrologic modelling. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
12.
Under a climate change, the physical factors that influence the rainfall regime are diverse and difficult to predict. The selection of skilful inputs for rainfall forecasting models is, therefore, more challenging. This paper combines wavelet transform and Frank copula function in a mutual information‐based input variable selection (IVS) for non‐linear rainfall forecasting models. The marginal probability density functions (PDFs) of a set of potential rainfall predictors and the rainfall series (predictand) were computed using a wavelet density estimator. The Frank copula function was applied to compute the joint PDF of the predictors and the predictand from their marginal PDFs. The relationship between the rainfall series and the potential predictors was assessed based on the mutual information computed from their marginal and joint PDFs. Finally, the minimum redundancy maximum relevance was used as an IVS stopping criterion to determine the number of skilful input variables. The proposed approach was applied to four stations of the Nigerien Sahel with rainfall series spanning the period 1950–2016 by considering 24 climate indices as potential predictors. Adaptive neuro‐fuzzy inference system, artificial neural networks, and random forest‐based forecast models were used to assess the skill of the proposed IVS method. The three forecasting models yielded satisfactory results, exhibiting a coefficient of determination between 0.52 and 0.69 and a mean absolute percentage error varying from 13.6% to 21%. The adaptive neuro‐fuzzy inference system performed better than the other models at all the stations. A comparison made with KDE‐based mutual information showed the advantage of the proposed wavelet–copula approach. 相似文献
13.
Drought hotspot identification requires continuous drought monitoring and spatial risk assessment. The present study analysed drought events in the agriculture‐dominated mid‐Mahanadi River Basin in Odisha, India, using crop water stress as a drought indicator. This drought index incorporated different factors that affect crop water deficit such as the cropping pattern, soil characteristics, and surface soil moisture. The drought monitoring framework utilized a relevance vector machine model‐based classification that provided the uncertainty associated with drought categorization. Using the proposed framework, drought hotspots are identified in the study region and compared with indices based on precipitation and soil moisture. Further, a bivariate copula is employed to model the agricultural drought characteristics and develop the drought severity–duration–frequency (S–D–F) relationships. The drought hotspot maps and S–D–F curves are developed for different locations in the region. These provided useful information on the site‐specific drought patterns and the characteristics of the devastating droughts of 2002 and 2012, characterized by an average drought duration of 7 months at several locations. The site‐specific risk of short‐ and long‐term agricultural droughts are then investigated using the conditional copula. The results suggest that the conditional return periods and the S–D–F curves are valuable tools to assess the spatial variability of drought risk in the region. 相似文献
14.
In recent decades, copula functions have been applied in bivariate drought duration and severity frequency analysis. Among several potential copulas, Clayton has been mostly used in drought analysis. In this research, we studied the influence of the tail shape of various copula functions (i.e. Gumbel, Frank, Clayton and Gaussian) on drought bivariate frequency analysis. The appropriateness of Clayton copula for the characterization of drought characteristics is also investigated. Drought data are extracted from standardized precipitation index time series for four stations in Canada (La Tuque and Grande Prairie) and Iran (Anzali and Zahedan). Both duration and severity data sets are positively skewed. Different marginal distributions were first fitted to drought duration and severity data. The gamma and exponential distributions were selected for drought duration and severity, respectively, according to the positive skewness and Kolmogorov–Smirnov test. The results of copula modelling show that the Clayton copula function is not an appropriate choice for the used data sets in the current study and does not give more drought risk information than an independent model for which the duration and severity dependence is not significant. The reason is that the dependence of two variables in the upper tail of Clayton copula is very weak and similar to the independent case, whereas the observed data in the transformed domain of cumulative density function show high association in the upper tail. Instead, the Frank and Gumbel copula functions show better performance than Clayton function for drought bivariate frequency analysis. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
15.
Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data 总被引：1，自引：0，他引：1
A. Azimi-Zonooz W. F. Krajewski D. S. Bowles D. J. Seo 《Stochastic Environmental Research and Risk Assessment (SERRA)》1989,3(1):51-67
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
16.
Multivariate drought characteristics using trivariate Gaussian and Student t copulas 总被引：4，自引：0，他引：4
This study aims to investigate the changing properties of drought events in Weihe River basin, China, by modeling the multivariate joint distribution of drought duration, severity and peak using trivariate Gaussian and Student t copulas. Monthly precipitations of Xi'an gauge are used to illustrate the meta‐elliptical copula‐based methodology for a single‐station application. Gaussian and Student t copulas are found to produce a better fit comparing with other six symmetrical and asymmetrical Archimedean copulas, and, checked by the goodness‐of‐fit tests based on a modified bootstrap version of Rosenblatt's transformation, both of them are acceptable to model the multivariate joint distribution of drought variables. Gaussian copula, the best fitting, is employed to construct the dependence structures of positively associated drought variables so as to obtain the multivariate joint and conditional probabilities of droughts. A Kendall's return period (KRP) introduced by Salvadori and De Michele (2010) is then adopted to assess the multivariate recurrent properties of drought events, and its spatial distributions indicate that prolonged droughts are likely to break out with rather short recurrence intervals in the whole region, while drought status in the southeast seems to be severer than the northwest. The study is of some merits in terms of multivariate drought modeling using a preferable copula‐based method, the results of which could serve as a reference for regional drought defense and water resources management. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
17.
A. Azimi-Zonooz W. F. Krajewski D. S. Bowles D. J. Seo 《Stochastic Hydrology and Hydraulics》1989,3(1):51-67
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
18.
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
19.
Addressing uncertainty in reflectivity‐rainfall relations in mountain watersheds during summer convection 下载免费PDF全文
下载免费PDF全文 The use of precipitation estimates from weather radar reflectivity has become widespread in hydrologic predictions. However, uncertainty remains in the use of the nonlinear reflectivity–rainfall (Z‐R) relation, in particular for mountainous regions where ground validation stations are often lacking, land surface data sets are inaccurate and the spatial variability in many features is high. In this study, we assess the propagation of rainfall errors introduced by different Z‐R relations on distributed hydrologic model performance for four mountain basins in the Colorado Front Range. To do so, we compare spatially integrated and distributed rainfall and runoff metrics at seasonal and event time scales during the warm season when convective storms dominate. Results reveal that the basin simulations are quite sensitive to the uncertainties introduced by the Z‐R relation in terms of streamflow, runoff mechanisms and the water balance components. The propagation of rainfall errors into basin responses follows power law relationships that link streamflow uncertainty to the precipitation errors and streamflow magnitude. Overall, different Z‐R relations preserve the spatial distribution of rainfall relative to a reference case, but not the precipitation magnitude, thus leading to large changes in streamflow amounts and runoff spatial patterns at seasonal and event scales. Furthermore, streamflow errors from the Z‐R relation follow a typical pattern that varies with catchment scale where higher uncertainties exist for intermediate‐sized basins. The relatively high error values introduced by two operational Z‐R relations (WSR‐57 and NEXRAD) in terms of the streamflow response indicate that site‐specific Z‐R relations are desirable in the complex terrain region, particularly in light of other uncertainties in the modelling process, such as model parameter values and initial conditions. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
20.
Gridded climate data products are an alternative to instrumental measurements as inputs to rainfall–runoff models 下载免费PDF全文
下载免费PDF全文 Rainfall–runoff models are widely used to predict flows using observed (instrumental) time series of air temperature and precipitation as inputs. Poor model performance is often associated with difficulties in estimating catchment‐scale meteorological variables from point observations. Readily available gridded climate products are an underutilized source of temperature and precipitation time series for rainfall–runoff modelling, which may overcome some of the performance issues associated with poor‐quality instrumental data in small headwater monitoring catchments. Here we compare the performance of instrumental measured and E‐OBS gridded temperature and precipitation time series as inputs in the rainfall–runoff models “PERSiST” and “HBV” for flow prediction in six small Swedish catchments. For both models and most catchments, the gridded data produced statistically better simulations than did those obtained using instrumental measurements. Despite the high correspondence between instrumental and gridded temperature, both temperature and precipitation were responsible for the difference. We conclude that (a) gridded climate products such as the E‐OBS dataset could be more widely used as alternative input to rainfall–runoff models, even when instrumental measurements are available, and (b) the processing applied to gridded climate products appears to provide a more realistic approximation of small catchment‐scale temperature and precipitation patterns needed for flow simulations. Further research on this issue is needed and encouraged. 相似文献