共查询到20条相似文献，搜索用时 31 毫秒
1.
Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making. 相似文献
2.
ABSTRACTThis study focused on the performance of the rotated general regression neural network (RGRNN), as an enhancement of the general regression neural network (GRNN), in monthly-mean river flow forecasting. The study of forecasting of monthly mean river flows in Heihe River, China, was divided into two steps: first, the performance of the RGRNN model was compared with the GRNN model, the feed-forward error back-propagation (FFBP) model and the soil moisture accounting and routing (SMAR) model in their initial model forms; then, by incorporating the corresponding outputs of the SMAR model as an extra input, the combined RGRNN model was compared with the combined FFBP and combined GRNN models. In terms of model efficiency index, R2, and normalized root mean squared error, NRMSE, the performances of all three combined models were generally better than those of the four initial models, and the RGRNN model performed better than the GRNN model in both steps, while the FFBP and the SMAR were consistently the worst two models. The results indicate that the combined RGRNN model could be a useful river flow forecasting tool for the chosen arid and semi-arid region in China.
Editor D. Koutsoyiannis; Associate editor not assigned 相似文献
3.
AbstractThe rating curve model (RCM) proposed by Moramarco and co-authors is modified here for flood forecasting purposes without using rainfall information. The RCM is a simple approach for discharge assessment at a river site of interest based on relating the local recorded stage and the remote discharge monitored at an upstream gauged river site located some distance away. The proposed RCM for real-time application (RCM-RT), involves only two parameters and can be used for river reaches where significant lateral flows occur. The forecast lead time depends on the mean wave travel time of the reach. The model is found to be accurate for a long reach of the Po River (northern Italy) and for two branches of the Tiber River (central Italy) characterized by different intermediate drainage areas and wave travel times. Moreover, the assessment of the forecast uncertainty coming from the model parameters is investigated by performing a Monte Carlo simulation. Finally, the model capability to accurately forecast the exceedence of fixed hydrometric thresholds is analysed.Editor D. Koutsoyiannis; Associate editor C. Perrin 相似文献
4.
Abstract Four different error-forecast updating models are investigated in terms of their capability of providing real-time river flow forecast accuracy superior to that of rainfall-runoff models applied in the simulation (nonupdating) mode. The first and most widely used is the single autoregressive (AR) model, the second being an elaboration of that model, namely the autoregressive-threshold (AR-TS) updating model. A fuzzy autoregressive-threshold (FU-AR-TS) updating model is proposed as the third form of model, the fourth and final error-forecast updating model applied being the artificial neural network (ANN) model. In the application of these four updating models, the lumped soil moisture accounting and routing (SMAR) conceptual model has been selected to simulate the observed discharge series on 11 selected test catchments. As expected, it is found that all of these four updating models are very successful in improving the flow forecast accuracy, when operating in real-time forecasting mode. A less expected, but nonetheless welcome, result is that the three updating models having the most parameters, i.e. AR-TS, FU-AR-TS, and ANN, do not show any considerable advantages in improving the real-time flow forecast efficiency over that of the simple standard AR model. Thus it is recommended that, in the context of real-time river flow forecasting based on error-forecast updating, modellers should continue to use the AR model. 相似文献
5.
《水文科学杂志》2013,58(4)
Abstract The global climate change may have serious impacts on the frequency, magnitude, location and duration of hydrological extremes. Changed hydrological extremes will have important implications on the design of future hydraulic structures, flood-plain development, and water resource management. This study assesses the potential impact of a changed climate on the timing and magnitude of hydrological extremes in a densely populated and urbanized river basin in southwestern Ontario, Canada. An ensemble of future climate scenarios is developed using a weather generating algorithm, linked with GCM outputs. These climate scenarios are then transformed into basin runoff by a semi-distributed hydrological model of the study area. The results show that future maximum river flows in the study area will be less extreme and more variable in terms of magnitude, and more irregular in terms of seasonal occurrence, than they are at present. Low flows may become less extreme and variable in terms of magnitude, and more irregular in terms of seasonal occurrence. According to the evaluated scenarios, climate change may have favourable impacts on the distribution of hydrological extremes in the study area. 相似文献
6.
Prabin Rokaya Luis Morales-Marín Barrie Bonsal Howard Wheater Karl-Erich Lindenschmidt 《水文科学杂志》2013,58(11):1265-1278
ABSTRACTIn cold region environments, any alteration in the hydro-climatic regime can have profound impacts on river ice processes. This paper studies the implications of hydro-climatic trends on river ice processes, particularly on the freeze-up and ice-cover breakup along the Athabasca River in Fort McMurray in western Canada, which is an area very prone to ice-jam flooding. Using a stochastic approach in a one-dimensional hydrodynamic river ice model, a relationship between overbank flow and breakup discharge is established. Furthermore, the likelihood of ice-jam flooding in the future (2041–2070 period) is assessed by forcing a hydrological model with meteorological inputs from the Canadian regional climate model driven by two atmospheric–ocean general circulation climate models. Our results show that the probability of ice-jam flooding for the town of Fort McMurray in the future will be lower, but extreme ice-jam flood events are still probable. 相似文献
7.
ABSTRACT This paper attempts to design statistical models to forecast annual precipitation in the Neuquen and Limay river basins in the Comahue region of Argentina. These forecasts are especially useful as they are used to better organize the operation of hydro-electric dams, the agriculture in irrigated valleys and the safety of the population. In this work, multiple linear regression statistical models are built to forecast mean annual rainfall over the two river basins. Since the maximum precipitation occurs in the winter (June–August), forecasting models have been developed for the beginning of March and for the beginning of June, just before the rainy season starts. The results show that the sea-surface temperatures of the Indian and Pacific oceans are good predictors for March models and explain 42.8% of the precipitation index variance. The efficiency of the models increases in June, adding more predictors related to the autumn circulation. 相似文献
8.
K. Y. Raneesh 《水文科学杂志》2013,58(6):946-965
Abstract Climate change is recognized to be one of the most serious challenges facing mankind today. Driven by anthropogenic activities, it is known to be a direct threat to our food and water supplies and an indirect threat to world security. Increase in the concentration of carbon dioxide and other greenhouse gases in the atmosphere will certainly affect hydrological regimes. The consequent global warming is expected to have major implications on water resources management. The objective of this research is to present a general approach for evaluating the impacts of potential climate change on streamflow in a river basin in the humid tropical zone of India. Large-scale global climate models (GCMs) are the best available tools to provide estimates of the effect of rising greenhouse gases on rainfall and temperature. However the spatial resolution of these models (250 km?×?250 km) is not compatible with that of watershed hydrological models. Hence the outputs from GCMs have to be downscaled using regional climate models (RCMs), so as to project the output of a GCM to a finer resolution (50 km?×?50 km). In the present work, the projections of a GCM for two scenarios, A2 and B2 are downscaled by a RCM to project future climate in a watershed. Projections for two important climate variables, viz. rainfall and temperature are made. These are then used as inputs for a physically-based hydrological model, SWAT, in order to evaluate the effect of climate change on streamflow and vegetative growth in a humid tropical watershed. Citation Raneesh, K. Y. & Santosh, G. T. (2011) A study on the impact of climate change on streamflow at the watershed scale in the humid tropics. Hydrol. Sci. J. 56(6), 946–965. 相似文献
9.
ABSTRACTThe problem of estimation of suspended load carried by a river is an important topic for many water resources projects. Conventional estimation methods are based on the assumption of exact observations. In practice, however, a major source of natural uncertainty is due to imprecise measurements and/or imprecise relationships between variables. In this paper, using the Multivariate Adaptive Regression Splines (MARS) technique, a novel fuzzy regression model for imprecise response and crisp explanatory variables is presented. The investigated fuzzy regression model is applied to forecast suspended load by discharge based on two real-world datasets. The accuracy of the proposed method is compared with two well-known parametric fuzzy regression models, namely, the fuzzy least-absolutes model and the fuzzy least-squares model. The comparison results reveal that the MARS-fuzzy regression model performs better than the other models in suspended load estimation for the particular datasets. This comparison is done based on four goodness-of-fit criteria: the criterion based on similarity measure, the criterion based on absolute errors and the two objective functions of the fuzzy least-absolutes model and the fuzzy least-squares model. The proposed model is general and can be used for modelling natural phenomena whose available observations are reported as imprecise rather than crisp.
Editor D. Koutsoyiannis; Associate editor H. Aksoy 相似文献
10.
Abstract This paper presents four different approaches for integrating conventional and AI-based forecasting models to provide a hybridized solution to the continuous river level and flood prediction problem. Individual forecasting models were developed on a stand alone basis using historical time series data from the River Ouse in northern England. These include a hybrid neural network, a simple rule-based fuzzy logic model, an ARMA model and naive predictions (which use the current value as the forecast). The individual models were then integrated via four different approaches: calculation of an average, a Bayesian approach, and two fuzzy logic models, the first based purely on current and past river flow conditions and the second, a fuzzification of the crisp Bayesian method. Model performance was assessed using global statistics and a more specific flood related evaluation measure. The addition of fuzzy logic to the crisp Bayesian model yielded overall results that were superior to the other individual and integrated approaches. 相似文献
11.
River temperature models play an increasingly important role in the management of fisheries and aquatic resources. Among river temperature models, forecasting models remain relatively unused compared to water temperature simulation models. However, water temperature forecasting is extremely important for in-season management of fisheries, especially when short-term forecasts (a few days) are required. In this study, forecast and simulation models were applied to the Little Southwest Miramichi River (New Brunswick, Canada), where water temperatures can regularly exceed 25–29°C during summer, necessitating associated fisheries closures. Second- and third-order autoregressive models (AR2, AR3) were calibrated and validated using air temperature as the exogenous variable to predict minimum, mean and maximum daily water temperatures. These models were then used to predict river temperatures in forecast mode (1-, 2- and 3-day forecasts using real-time data) and in simulation mode (using only air temperature as input). The results showed that the models performed better when used to forecast rather than simulate water temperatures. The AR3 model slightly outperformed the AR2 in the forecasting mode, with root mean square errors (RMSE) generally between 0.87°C and 1.58°C. However, in the simulation mode, the AR2 slightly outperformed the AR3 model (1.25°C < RMSE < 1.90°C). One-day forecast models performed the best (RMSE ~ 1°C) and model performance decreased as time lag increased (RMSE close to 1.5°C after 3 days). The study showed that marked improvement in the modelling can be accomplished using forecasting models compared to water temperature simulations, especially for short-term forecasts.
EDITOR M.C. Acreman ASSOCIATE EDITOR S. Huang 相似文献
12.
《水文科学杂志》2013,58(1):66-82
Abstract An adaptive model for on-line stage forecasting is proposed for river reaches where significant lateral inflow contributions occur. The model is based on the Muskingum method and requires the estimation of four parameters if the downstream rating curve is unknown; otherwise only two parameters have to be determined. As the choice of the forecast lead time is linked to wave travel time along the reach, to increase the lead time, a schematization of two connected river reaches is also investigated. The variability of lateral inflow is accounted for through an on-line adaptive procedure. Calibration and validation of the model were carried out by applying it to different flood events observed in two equipped river reaches of the upper-middle Tiber basin in central Italy, characterized by a significant contributing drainage area. Even if the rating curve is unknown at the downstream section, the forecast stage hydrographs were found in good agreement with those observed. Errors in peak stage and time to peak along with the persistence coefficient values show that the model has potential as a practical tool for on-line flood risk management. 相似文献
13.
ABSTRACTUnderstanding streamflow patterns by incorporating climate signal information can contribute remarkably to the knowledge of future local environmental flows. Three machine learning models, the multivariate adaptive regression splines (MARS), the M5 Model Tree and the least squares support vector machine (LSSVM) are established to predict the streamflow pattern over the Mediterranean region of Turkey (Besiri and Baykan stations). The structure of the predictive models is built using synoptic-scale climate signal information and river flow data from antecedent records. The predictive models are evaluated and assessed using quantitative and graphical statistics. The correlation analysis demonstrates that the North Pacific (NP) and the East Central Tropical Pacific Sea Surface Temperature (Niño3.4) indices have a substantial influence on the streamflow patterns, in addition to the historical information obtained from the river flow data. The model results reveal the utility of the LSSVM model over the other models through incorporating climate signal information for modelling streamflow. 相似文献
14.
《水文科学杂志》2013,58(2)
Abstract In ice forecasting, a key problem is the forecast of freeze-up and break-up dates. Ice-water mechanics and the principle of heat-exchange were mainly adopted in previous research. However, the mathematical models in these studies are complex and many parameters are required in relation to upstream and/or downstream gauging stations. Moreover, too many assumptions or simplifications for these parameters and constraints directly lead to low accuracy of the models and limitations as to their practical applications. This paper develops a fuzzy optimization neural network approach for the forecast of freeze-up date and break-up date. The Inner Mongolia reach lies in the top north of the Yellow River, China. Almost every year ice floods occur because of its special geographical location, hydrometeorological conditions and river course characteristics. Therefore, it is of particular importance for ice flood prevention to forecast freeze-up date and break-up date accurately. A case study in this region shows that the proposed methodology may allow obtaining useful results. 相似文献
15.
ABSTRACTAssessment of forecast precipitation is required before it can be used as input to hydrological models. Using radar observations in southeastern Australia, forecast rainfall from the Australian Community Climate Earth-System Simulator (ACCESS) was evaluated for 2010 and 2011. Radar rain intensities were first calibrated to gauge rainfall data from four research rainfall stations at hourly time steps. It is shown that the Australian ACCESS model (ACCESS-A) overestimated rainfall in low precipitation areas and underestimated elevated accumulations in high rainfall areas. The forecast errors were found to be dependent on the rainfall magnitude. Since the cumulative rainfall observations varied across the area and through the year, the relative error (RE) in the forecasts varied considerably with space and time, such that there was no consistent bias across the study area. Moreover, further analysis indicated that both location and magnitude errors were the main sources of forecast uncertainties on hourly accumulations, while magnitude was the dominant error on the daily time scale. Consequently, the precipitation output from ACCESS-A may not be useful for direct application in hydrological modelling, and pre-processing approaches such as bias correction or exceedance probability correction will likely be necessary for application of the numerical weather prediction (NWP) outputs.
EDITOR M.C. Acreman ASSOCIATE EDITOR A. Viglione 相似文献
16.
Z. Sokol 《Studia Geophysica et Geodaetica》2006,50(1):105-120
A statistical post-processing methodology for application to numerical weather prediction (NWP) model outputs for precipitation
forecast is proposed. The post-processing is based on the model output statistics approach. The statistical relationships
are described by the multiple linear regression model, which is complemented by an iteration procedure to further correct
the regression outputs. Prognostic fields of the ALADIN/LACE (Aire Limitée Adaptation Dynamique Développement InterNational/Limited
Area Modelling in Central Europe) NWP model are used for the forecast of 6-hourly areal precipitation amounts at 15 river
basins. The NWP model integration starts at 00UTC and forecasts are calculated for lead times of +12, +18, +24 and +30 hours.
The post-processing models are developed separately for each lead time and for separate warm (April to September) and cool
(October to March) seasons. The forecasts are focused on large precipitation amounts. Using all the combinations, data from
four years (1999–2002) are divided into calibration data (3 years), where the models are developed, and verification data.
The models are evaluated by examining the root-mean-square error (RMSE), bias, and correlation coefficient (CC) on the verification
data samples.
The results show that the additional iteration procedure increases the forecast accuracy for a given range of precipitation
amounts and simultaneously does not deteriorate the bias, a situation which can arise when negative regression outputs are
set to zero. The post-processing method improves the forecast of the NWP model in terms of RMSE and CC. For large precipitation
amounts during the summer season, the decrease of RMSE reaches 10% to 20% depending upon the applied method of verification.
For the cool season, the decrease is somewhat smaller (7% to 15%). 相似文献
17.
Confidence interval of real‐time forecast stages provided by the STAFOM‐RCM model: the case study of the Tiber River (Italy) 
下载免费PDF全文
下载免费PDF全文 Silvia Barbetta Tommaso Moramarco Luca Brocca Marco Franchini Florisa Melone 《水文研究》2014,28(3):729-743
This study proposes a statistically based procedure to quantify the confidence interval (CI) to be associated to the stages forecast by a simple model called STAge FOrecasting Model‐Rating Curve Model (STAFOM‐RCM). This model can be used for single river reaches characterized by different intermediate drainage areas and mean wave travel times when real‐time stage records, cross section surveys and rating curves are available at both ends. The model requires, at each time of forecast, an estimate of the lateral contribution qfor between the two sections delimiting the reach. The CI of the stage is provided by analyzing the statistical properties of model output in terms of lateral flow, and it is derived from the CI of the lateral contribution qfor which, in turn, is set up by associating to each qfor the qopt which allows STAFOM‐RCM to reproduce the exact observed stage. From an operative point of view, the qfor values are ranked in order of magnitude and subdivided in classes where the qopt values can be represented through normal distributions of proper mean and variance from which an interval of selected confidence level for qfor is computed and transferred to the stage. Three river reaches of the Tiber river, in central Italy, are used as case study. A sensitivity analysis is also performed in order to identify the minimum calibration set of flood events. The CIs obtained are consistent with the level of confidence selected and have practical utility. An interesting aspect is that different CI widths can be produced for the same forecast stage since they depend on the estimate of qfor made at the time of forecast. Overall, the proposed procedure for CI estimate is simple and can be conveniently adapted for other forecasting models provided that they have physically based parameters which need to be updated during the forecast. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
18.
AbstractSteep mountainous areas account for 70% of all river catchments in Japan. To predict river discharge for the mountainous catchments, many studies have applied distributed hydrological models based on a kinematic wave approximation with surface and subsurface flow components (DHM-KWSS). These models reproduce observed river discharge of catchments in Japan well; however, the applicability of a DHM-KWSS to catchments with different geographical and climatic conditions has not been sufficiently examined. This research applied a DHM-KWSS to two river basins that have different climatic conditions from basins in Japan to examine the transferability of the DHM-KWSS model structure. Our results show that the DHM-KWSS model structure explained flow regimes for a wet river basin as well as a large flood event in an arid basin; however, it was unable to explain long-term flow regimes for the arid basin case study. 相似文献
19.
Abstract Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore, researchers try to devise alterative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only marginally in terms of prediction accuracy in normal and extreme events. Citation Londhe, S. & Charhate, S. (2010) Comparison of data-driven modelling techniques for river flow forecasting. Hydrol. Sci. J. 55(7), 1163–1174. 相似文献
20.
《水文科学杂志》2013,58(6):1006-1020
AbstractThis paper aims to compare the shift in frequency distribution and skill of seasonal climate forecasting of both streamflow and rainfall in eastern Australia based on the Southern Oscillation Index (SOI) Phase system. Recent advances in seasonal forecasting of climate variables have highlighted opportunities for improving decision making in natural resources management. Forecasting of rainfall probabilities for different regions in Australia is available, but the use of similar forecasts for water resource supply has not been developed. The use of streamflow forecasts may provide better information for decision-making in irrigation supply and flow management for improved ecological outcomes. To examine the relative efficacy of seasonal forecasting of streamflow and rainfall, the shift in probability distributions and the forecast skill were evaluated using the Wilcoxon rank-sum test and the linear error in probability space (LEPS) skill score, respectively, at three river gauging stations in the Border Rivers Catchment of the Murray-Darling Basin in eastern Australia. A comparison of rainfall and streamflow distributions confirms higher statistical significance in the shift of streamflow distribution than that in rainfall distribution. Moreover, streamflow distribution showed greater skill of forecasting with 0–3 month lead time, compared to rainfall distribution. 相似文献