共查询到20条相似文献，搜索用时 31 毫秒
1.
Post‐processing of medium‐range probabilistic hydrological forecasting: impact of forcing,initial conditions and model errors 
下载免费PDF全文
下载免费PDF全文 The impact of errors in the forcing, errors in the model structure and parameters, and errors in the initial conditions is investigated in a simple hydrological ensemble prediction system. The hydrological model is based on an input nonlinearity connected with a linear transfer function and forced by precipitation forecasts from the European Centre for Medium‐Range Weather Forecast (ECMWF) Ensemble Prediction System (EPS). The post‐processing of the precipitation and/or the streamflow using information from the reforecasts performed by ECMWF is tested. For this purpose, hydrological reforecasts are obtained by forcing the hydrological model with the precipitation from the reforecast data. In the present case study, it is found that the post‐processing of the hydrological ensembles with a statistical model fitted on the hydrological reforecasts improves the verification scores better than the use of post‐processed precipitation ensembles. In the case of large biases in the precipitation, combining the post‐processing of both precipitation and streamflow allows for further improvements. During the winter, errors in the initial conditions have a larger impact on the scores than errors in the model structure as designed in the experiments. Errors in the parameter values are largely corrected with the post‐processing. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
2.
Aizhong Ye Qingyun Duan John Schaake Jing Xu Xiaoxue Deng Zhenhua Di Chiyuan Miao Wei Gong 《水文研究》2015,29(10):2438-2453
For water supply, navigational, ecological protection or water quality control purposes, there is a great need in knowing the likelihood of the river level falling below a certain threshold. Ensemble streamflow prediction (ESP) based on simulations of deterministic hydrologic models is widely used to assess this likelihood. Raw ESP results can be biased in both the ensemble means and the spreads. In this study, we applied a modified general linear model post‐processor (GLMPP) to correct these biases. The modified GLMPP is built on the basis of regression of simulated and observed streamflow calculated on the basis of canonical events, instead of the daily values as is carried out in the original GLMPP. We conducted the probabilistic analysis of post‐processed ESP results falling below pre‐specified low‐flow levels at seasonal time scale. Raw ESP forecasts from the 1980 to 2006 periods by four different land surface models (LSMs) in eight large river basins in the continental USA are included in the analysis. The four LSMs are Noah, Mosaic, variable infiltration capacity and Sacramento models. The major results from this study are as follows: (1) a modified GLMPP was proposed on the basis of canonical events; (2) post‐processing can improve the accuracy and reduce the uncertainty of hydrologic forecasts; (3) post‐processing can help deal with the effect of human activity; and (4) raw simulation results from different models vary greatly in different basins. However, post‐processing can always remove model biases under different conditions. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
3.
4.
Ramesh S. V. Teegavarapu 《水文研究》2014,28(11):3789-3808
Spatial interpolation methods used for estimation of missing precipitation data generally under and overestimate the high and low extremes, respectively. This is a major limitation that plagues all spatial interpolation methods as observations from different sites are used in local or global variants of these methods for estimation of missing data. This study proposes bias‐correction methods similar to those used in climate change studies for correcting missing precipitation estimates provided by an optimal spatial interpolation method. The methods are applied to post‐interpolation estimates using quantile mapping, a variant of equi‐distant quantile matching and a new optimal single best estimator (SBE) scheme. The SBE is developed using a mixed‐integer nonlinear programming formulation. K‐fold cross validation of estimation and correction methods is carried out using 15 rain gauges in a temperate climatic region of the U.S. Exhaustive evaluation of bias‐corrected estimates is carried out using several statistical, error, performance and skill score measures. The differences among the bias‐correction methods, the effectiveness of the methods and their limitations are examined. The bias‐correction method based on a variant of equi‐distant quantile matching is recommended. Post‐interpolation bias corrections have preserved the site‐specific summary statistics with minor changes in the magnitudes of error and performance measures. The changes were found to be statistically insignificant based on parametric and nonparametric hypothesis tests. The correction methods provided improved skill scores with minimal changes in magnitudes of several extreme precipitation indices. The bias corrections of estimated data also brought site‐specific serial autocorrelations at different lags and transition states (dry‐to‐dry, dry‐to‐wet, wet‐to‐wet and wet‐to‐dry) close to those from the observed series. Bias corrections of missing data estimates provide better serially complete precipitation time series useful for climate change and variability studies in comparison to uncorrected filled data series. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
5.
Forecast ensembles of hydrological and hydrometeorologial variables are prone to various uncertainties arising from climatology, model structure and parameters, and initial conditions at the forecast date. Post‐processing methods are usually applied to adjust the mean and variance of the ensemble without any knowledge about the uncertainty sources. This study initially addresses the drawbacks of a commonly used statistical technique, quantile mapping (QM), in bias correction of hydrologic forecasts. Then, an auxiliary variable, the failure index (γ), is proposed to estimate the ineffectiveness of the post‐processing method based on the agreement of adjusted forecasts with corresponding observations during an analysis period prior to the forecast date. An alternative post‐processor based on copula functions is then introduced such that marginal distributions of observations and model simulations are combined to create a multivariate joint distribution. A set of 2500 hypothetical forecast ensembles with parametric marginal distributions of simulated and observed variables are post‐processed with both QM and the proposed multivariate post‐processor. Deterministic forecast skills show that the proposed copula‐based post‐processing is more effective than the QM method in improving the forecasts. It is found that the performance of QM is highly correlated with the failure index, unlike the multivariate post‐processor. In probabilistic metrics, the proposed multivariate post‐processor generally outperforms QM. Further evaluation of techniques is conducted for river flow forecast of Sprague River basin in southern Oregon. Results show that the multivariate post‐processor performs better than the QM technique; it reduces the ensemble spread and is a more reliable approach for improving the forecast. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
6.
Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
7.
This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model. The evolutionary data assimilation (EDA) method uses multi-objective evolutionary strategy to continuously evolve ensemble of model states and parameter sets where it adaptively determines the model error and the penalty function for different assimilation time steps. The assimilation was determined by applying the penalty function to merge background information (i.e., model forecast) with perturbed observation data. The assimilation was based on updated estimates of the model state and its parameterizations, and was complemented by a continuous evolution of competitive solutions.The EDA was illustrated in an integrated assimilation approach to estimate model state using soil moisture, which in turn was incorporated into the soil and water assessment tool (SWAT) to assimilate streamflow. Soil moisture was independently assimilated to allow estimation of its model error, where the estimated model state was integrated into SWAT to determine background streamflow information before they are merged with perturbed observation data. Application of the EDA in Spencer Creek watershed in southern Ontario, Canada generates a time series of soil moisture and streamflow. Evaluation of soil moisture and streamflow assimilation results demonstrates the capability of the EDA to simultaneously estimate model state and parameterizations for real-time forecasting operations. The results show improvement in both streamflow and soil moisture estimates when compared to open-loop simulation, and a close matching between the background and the assimilation illustrates the forecasting performance of the EDA approach. 相似文献
8.
Evaluation of monthly precipitation forecasting skill of the National Multi‐model Ensemble in the summer season 下载免费PDF全文
下载免费PDF全文 As a test bed, the National Multi‐model Ensemble (NMME) comprises seven climate models from different sources, including the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration, the National Center for Atmospheric Research and the International Research Institute for Climate and Society. It provides 89 ensemble members of precipitation forecasts at different lead times. Precipitation forecasting from climate models has been applied to provide streamflow forecasts, and its utility in water resource system operation has been demonstrated in the literature. In this study, 1‐month‐ahead precipitation forecasts from NMME are evaluated for 945 grid points of 1°‐by‐1° resolution over the continental USA using mean square error and rank probability score. The temporal and spatial variabilities of the forecasting skill over different months of the summer season are discussed. The relation between forecasting uncertainty and observed precipitation is investigated. Such analyses have implications for monthly operational forecasts and water resource management at the watershed scale. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
9.
Caleb DechantHamid Moradkhani 《Advances in water resources》2011,34(3):351-364
Estimation of seasonal snowpack, in mountainous regions, is crucial for accurate streamflow prediction. This paper examines the ability of data assimilation (DA) of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center (NWSRFC) are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the ensemble Kalman filter (EnKF) and the particle filter (PF), is made using a coupled SNOW17 and the microwave emission model for layered snow pack (MEMLS) model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the advanced microwave scanning radiometer-earth observing system (AMSR-E) at the 36.5 GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. 相似文献
10.
A statistic–stochastic multi‐fractal downscaling technique was evaluated from a hydrologic point of view. Ensemble hydrologic forecasts with a time step of 3 h were performed for original and disaggregated ensemble rainfall forecasts issued by the Canadian Global Ensemble Prediction System in its 2009 operational version. This hydro‐meteorological operational forecasting chain was conducted using the hydrological model SWMM5. The model was implemented on a small 6‐km2 urban catchment located in the Québec City region. The hydrological evaluation was based on the comparison of forecasted flows to the observed ones, calculating several deterministic and probabilistic scores, and drawing rank histograms and reliability diagrams. Disaggregated products led to a better representation of the ensemble members' dispersion. This disaggregation technique represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
11.
Ajay Kalra William P. Miller Kenneth W. Lamb Sajjad Ahmad Thomas Piechota 《水文研究》2013,27(11):1543-1559
In a water‐stressed region, such as the western United States, it is essential to have long lead times for streamflow forecasts used in reservoir operations and water resources management. Current water supply forecasts provide a 3‐month to 6‐month lead time, depending on the time of year. However, there is a growing demand from stakeholders to have forecasts that run lead times of 1 year or more. In this study, a data‐driven model, the support vector machine (SVM) based on the statistical learning theory, was used to predict annual streamflow volume with a 1‐year lead time. Annual average oceanic–atmospheric indices consisting of the Pacific decadal oscillation, North Atlantic oscillation (NAO), Atlantic multidecadal oscillation, El Niño southern oscillation (ENSO), and a new sea surface temperature (SST) data set for the ‘Hondo’ region for the period of 1906–2006 were used to generate annual streamflow volumes for multiple sites in the Gunnison River Basin and San Juan River Basin, both located in the Upper Colorado River Basin. Based on the performance measures, the model showed very good forecasts, and the forecasts were in good agreement with measured streamflow volumes. Inclusion of SST information from the Hondo region improved the model's forecasting ability; in addition, the combination of NAO and Hondo region SST data resulted in the best streamflow forecasts for a 1‐year lead time. The results of the SVM model were found to be better than the feed‐forward, back propagation artificial neural network and multiple linear regression. The results from this study have the potential of providing useful information for the planning and management of water resources within these basins. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
12.
In this study, we investigate the impact of the spatial variability of daily precipitation on hydrological projections based on a comparative assessment of streamflow simulations driven by a global climate model (GCM) and two regional climate models (RCMs). A total of 12 different climate input datasets, that is, the raw and bias‐corrected GCM and raw and bias‐corrected two RCMs for the reference and future periods, are fed to a semidistributed hydrological model to assess whether the bias correction using quantile mapping and dynamical downscaling using RCMs can improve streamflow simulation in the Han River basin, Korea. A statistical analysis of the daily precipitation demonstrates that the precipitation simulated by the GCM fails to capture the large variability of the observed daily precipitation, in which the spatial autocorrelation decreases sharply within a relatively short distance. However, the spatial variability of precipitation simulated by the two RCMs shows better agreement with the observations. After applying bias correction to the raw GCM and raw RCMs outputs, only a slight change is observed in the spatial variability, whereas an improvement is observed in the precipitation intensity. Intensified precipitation but with the same spatial variability of the raw output from the bias‐corrected GCM does not improve the heterogeneous runoff distributions, which in turn regulate unrealistically high peak downstream streamflow. GCM‐simulated precipitation with a large bias correction that is necessary to compensate for the poor performance in present climate simulation appears to distort streamflow patterns in the future projection, which leads to misleading projections of climate change impacts on hydrological extremes. 相似文献
13.
Yeonsang Hwang Martyn P. Clark Balaji Rajagopalan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(7):957-972
Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested.
The authors tested impact of uncertainty in daily precipitation on streamflow forecasts. In order to test the impact, a distributed
hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin
at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble streamflows. The uncertainty in model
inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations
in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences
in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider
range of streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time
lags between rainfall and runoff in this rainfall dominated basin. This ensemble streamflow generation framework was also
applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method. 相似文献
14.
《Advances in water resources》2005,28(2):135-147
Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. 相似文献
15.
Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self‐organizing map 下载免费PDF全文
下载免费PDF全文 During typhoons or storms, accurate forecasts of hourly streamflow are necessary for flood warning and mitigation. However, hourly streamflow is difficult to forecast because of the complex physical process and the high variability in time. Furthermore, under the global warming scenario, events with extreme streamflow may occur that leads to more difficulties in forecasting streamflows. Hence, to obtain more accurate hourly streamflow forecasts, an improved streamflow forecasting model is proposed in this paper. The computational kernel of the proposed model is developed on the basis of support vector machine (SVM). Additionally, self‐organizing map (SOM) is used to analyse observed data to extract data with specific properties, which are capable of providing valuable information for streamflow forecasting. After reprocessing, these extracted data and the observed data are used to construct the SVM‐based model. An application is conducted to clearly demonstrate the advantage of the proposed model. The comparison between the proposed model and the conventional SVM model, which is constructed without SOM, is performed. The results indicate that the proposed model is better performed than the conventional SVM model. Moreover, as regards the extreme events, the result shows that the proposed model reduces the forecasting error, especially the error of peak streamflow. It is confirmed that because of the use of data extracted by SOM, the improved forecasting performance is obtained. The proposed model, which can produce accurate forecasts, is expected to be useful to support flood warning systems. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
16.
Precipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high‐resolution models. 相似文献
17.
ABSTRACTAn appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations. 相似文献
18.
Automation in baseflow separation procedures allowed fast and convenient baseflow and baseflow index (BF and BFI) estimation for studies including multiple watersheds and covering large spatio‐temporal scales. While most of the existing algorithms are developed and tested extensively for rainfall‐ and baseflow‐dominated systems, little attention is paid on their suitability for snowmelt‐dominated systems. Current publishing practice in regional‐scale studies is to omit BF and BFI uncertainty evaluation or sensitivity analysis. Instead, “standard” and “previously recommended” parameterizations are transferred from rainfall/BF to snowmelt‐dominated systems. We believe that this practice should be abandoned. First, we demonstrate explicitly that the three most popular heuristic automated BF separation methods—Lyne–Hollick and Eckhardt recursive digital filters, and the U.K. Institute of Hydrology smoothed minima method—produce a wide range of annual BF and BFI estimates due to parameter sensitivity during the annual snowmelt period. Then, we propose a solution for cases when BF and BFI calibration is not possible, namely excluding the snowmelt‐dominated period from the analysis. We developed an automated filtering procedure, which divides the hydrograph into pre‐snowbelt, post‐snowmelt, and snowmelt periods. The filter was tested successfully on 218 continuous water years of daily streamflow data for four snowmelt‐dominated headwater watersheds located in Wyoming (60–837 km2). The post‐snowmelt BF and BFI metric can be used for characterizing summer low‐flows for snowmelt‐dominated systems. Our results show that post‐snowmelt BF and BFI sensitivity to filter parameterization is reduced compared with the sensitivity of annual BF and BFI and is similar to the sensitivity levels for rainfall/baseflow systems. 相似文献
19.
Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate.Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts’ availability and accuracy raises the question on how to use them for operational management.The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance.TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods. 相似文献