共查询到20条相似文献，搜索用时 34 毫秒
1.
Jens Christian Refsgaard Jeroen P. van der Sluijs James Brown Peter van der Keur 《Advances in water resources》2006
Although uncertainty about structures of environmental models (conceptual uncertainty) is often acknowledged to be the main source of uncertainty in model predictions, it is rarely considered in environmental modelling. Rather, formal uncertainty analyses have traditionally focused on model parameters and input data as the principal source of uncertainty in model predictions. The traditional approach to model uncertainty analysis, which considers only a single conceptual model, may fail to adequately sample the relevant space of plausible conceptual models. As such, it is prone to modelling bias and underestimation of predictive uncertainty. 相似文献
2.
The relationship between the monsoon rainfall throughout all India, northwest India and peninsular India as well as the onset dates of the monsoon and two indices of southern oscillation (SOI), namely Isla de Pascua minus Darwin (I-D) and Tahiti minus Darwin (T-D) pressure anomaly have been studied for different periods. The study indicates that the monsoon rainfall shows a strong and significant direct relationship with SOI for the concurrent, succeeding autumn and succeeding winter seasons. The magnitude of the direct correlation coefficient for the SOI using (I-D) is enhanced over all India and peninsular India if the above seasons happen to be associated with an easterly phase of the QBO (Quasi-Biennial Oscillation) at 50 mb. The result indicates that the strength of the monsoon plays an important role in the following southern oscillation events in the Pacific Ocean. The premonsoon tendency of the SOI anomaly spring minus winter SOI shows a significant positive correlation with monsoon rainfall over all India, northwest India and peninsular India. The absolute value of the positive correlation coefficient becomes highly enhanced over all India, northwest India as well as peninsular India if the 6-month period from December to March is associated with the westerly phase of the QBO. Hence, the premonsoon SOI tendency parameter can be a useful predictor of Indian monsoon rainfall especially if it happens to be associated with the westerly QBO. Significant negative association is also found between the anomaly of monsoon onset dates and SOI of the previous spring season, the absolute value being higher for SOI (T-D) than for SOI (I-D). The negative correlation coefficient becomes enhanced if the previous springs are associated with a westerly phase of the QBO. It shows that the previous spring SOI has some predictive value for the onset date of Indian monsoon, a positive SOI followed by an early onset of monsoon, andvice versa, especially if it is associated with a westerly phase of the QBO. 相似文献
3.
新疆北部降水的气候分布特片及其对ENSO的响应 总被引：1，自引：2，他引：1
分析研究了新疆北部地区近50年（1951-2000年）全年各月降水的气候分布特征和各季降水的年际变化规律，重点揭示了北疆多雨季节（4-7月）及其各月降水量对赤道东太平洋的海温SST和南方涛动指数SOI的显著响应关系，并用前期SST和SOI作为预报因子，建立了北疆地区雨季水量的预报方程。该方程可用于北疆地区雨季降水量的长期预报。 相似文献
4.
The revised empirical model for in- and outflow calculation of Upper Lake Constance has provided satisfying results supported by measured values. The given model was implemented to simulate total water inputs of the lake during the period from 1941 to 2000 with emphasis on the flood conditions of 1999. Analysis of annual water input development reveals a tendency toward slight increases until the 1960s. Thereafter, a reduction in inputs can be noted. This trend probably continues to hold true to present. Weather conditions of given individual years have caused distinct fluctuations to the water budget.Unusual meteorological conditions led to extreme flooding in early May of 1999. Daily water inputs of up to 200 mio m3 generated the highest water levels ever observed for this time of the year. Continual extraordinarily high water inputs occurring from February until July and then again from September until the end of 1999 resulted in the second largest annual total water input recorded since 1941. 相似文献
5.
During the latest several decades, there has been considerable interest in revealing the relationship between El Niño–southern oscillation (ENSO) and hydro‐meteorological variables. The oscillation is characterized by a simple index, the southern oscillation index (SOI). However, thus far, there is little evidence for the influence of ENSO in Korea and Japan. The influence of ENSO has also been studied in South Korea, but the estimated results are still qualitative and show an indirect relationship between ENSO and hydro‐meteorological variables. In this study we use simple approaches to reveal the quantitative and direct correlation between SOI and the monthly precipitation at five stations distributed over South Korea. The monthly precipitation data are transformed into nonexceedance probability time series because the data cannot be normally distributed by applying the usual transformations. The SOI is classified into five categories according to their values. Additionally, to detect the nonlinear relationship between categorized SOI and nonexceedance probability of the monthly precipitation, we use Kendall's τ, a nonparametric test. Significant correlations between the categorized SOI and the transformed precipitation are detected. Generally, the monthly precipitation is influenced by a La Niña event with a lag time of 4 months for southern coastal areas and a lag time of 5 months for middle to high regions in South Korea. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
6.
Due to the increasing popularity of analyzing empirical Green’s functions obtained from ambient seismic noise, more and more
regional tomographical studies based on short-period surface waves are published. Results could potentially be biased in mountainous
regions where topography is not small compared to the wavelength and penetration depth of the considered waves. We investigate
the effect of topography on the propagation of short-period Rayleigh waves empirically by means of synthetic data using a
spectral element code and a 3-D model with real topography. We show that topography along a profile through the studied area
can result in an underestimation of phase velocities of up to about 0.7% at the shortest investigated period (3 s). Contrary
to the expectation that this bias results from the increased surface distance along topography, we find that this error can
be estimated by local topographic contrasts in the vicinity of the receiver alone. We discuss and generalize our results by
considering topographic profiles through other mountain ranges and find that southern Norway is a good proxy to assess the
topography effect. Nevertheless, topographic bias on phase velocity measurements is in general not large enough to significantly
affect recovered velocity variations in the ambient noise frequency range. 相似文献
7.
A. T. M. Sakiur Rahman Takahiro Hosono Ozgur Kisi Boateng Dennis A. H. M. Rahmatullah Imon 《水文科学杂志》2020,65(12):1994-2006
ABSTRACT This study aimed to evaluate the potential of the recently introduced Prophet model for estimating reference evapotranspiration (ETo). A comparative study was conducted for benchmarking the model results with support vector regression (SVR) and temperature-based empirical models (Thornthwaite and Hargreaves) in southern Japan. The performance of the Prophet, SVR and temperature-based empirical models was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2). The results indicate that temperature-based Prophet and SVR models have greater accuracy than the empirical models. The Prophet model with sole input of relative humidity, sunshine hours or windspeed showed acceptable accuracy (NSE > 0.80; R2 > 0.80), while SVR models with similar inputs showed greater errors. Accuracy improved with increasing number of input parameters, giving excellent performance (NSE > 0.95; R2 > 0.95) with all input parameters. Hence, the Prophet model is a new promising approach for modelling ETo with limited input variables. 相似文献
8.
Hydrological model and observation errors are often non-Gaussian and/or biased, and the statistical properties of the errors are often unknown or not fully known. Thus, determining the true error covariance matrices is a challenge for data assimilation approaches such as the most widely used Kalman filter (KF) and its extensions, which assume Gaussian error nature and need fully known error statistics. This paper introduces H-infinite filter (HF) to hydrological modeling and compares HF with KF under various model and observation error conditions. HF is basically a robust version of KF. When model performance is not well known, or changes unpredictably, HF may be preferred over KF. HF is especially suitable for the cases where the estimation performance in the worst error case needs to be guaranteed. Through the application of HF to a hypothetical hydrologic model, this paper shows that HF is less sensitive to the uncertainty in the initial condition, corrects system bias more effectively, and converges to true state faster after interruptions than KF. In particular, HF performs better in dealing with instant human inputs (irrigation is used as an example), which are characterized by non-stationary, non-Gaussian and not fully known errors. However HF design can be more difficult than KF design due to the sensitivity of HF performance to design parameters (weights for model and observation error terms). Through sensitivity analysis, this paper shows the existence of a certain range of those parameters, in which the “best” value of the parameters is located. The tuning of HF design parameters, which can be based on users’ prior knowledge on the nature of model and observation errors, is critical for the implementation of HF. 相似文献
9.
Spatial uncertainty in bias corrected climate change projections and hydrogeological impacts 
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下载免费PDF全文 The question of which climate model bias correction methods and spatial scales for correction are optimal for both projecting future hydrological changes as well as removing initial model bias has so far received little attention. For 11 climate models (CMs), or GCM/RCM – Global/Regional Climate Model pairing, this paper analyses the relationship between complexity and robustness of three distribution‐based scaling (DBS) bias correction methods applied to daily precipitation at various spatial scales. Hydrological simulations are forced by CM inputs to assess the spatial uncertainty of groundwater head and stream discharge given the various DBS methods. A unique metric is devised, which allows for comparison of spatial variability in climate model bias and projected change in precipitation. It is found that the spatial variability in climate model bias is larger than in the climate change signals. The magnitude of spatial bias seen in precipitation inputs does not necessarily correspond to the magnitude of biases seen in hydrological outputs. Variables that integrate basin responses over time and space are more sensitive to mean spatial biases and less so on extremes. Hydrological simulations forced by the least parameterized DBS approach show the highest error in mean and maximum groundwater heads; however, the most highly parameterised DBS approach shows less robustness in future periods compared with the reference period it was trained in. For hydrological impacts studies, choice of bias correction method should depend on the spatial scale at which hydrological impacts variables are required and whether CM initial bias is spatially uniform or spatially varying. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
10.
The monthly values of the southern atmospheric oscillation indices (SOI), the corresponding values of the Nino-3.4 index, the data on the onsets of intense volcanic eruptions from 1870 to 2002, the daily values of the Ap and AE indices and the IMF B z component, and the data on cloudiness and wind characteristics at 14 Antarctic stations have been considered. The beginning of the warm El Nino current is observed after an increase in the amplitude of the Ap magnetic indices, which continues for more than five months. The beginning of the cold period of the La Nina southern atmospheric oscillation is as a rule related to a decrease in Ap. A change in atmospheric transparency caused by volcanic eruptions is often followed by the beginning of the cold period of the southern atmospheric oscillation (ENSO). A change in the wind system in the Antarctic Regions, related to a change in the temperature balance caused by variations in the solar wind parameters in the winter season, promotes a short-term disturbance of the circumpolar vortex and the beginning of the El Nino warm period. 相似文献
11.
In this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2-year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute-balance approach and (2) a water age-based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age-based model than the solute-balance model; 4. using time-variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS-TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute-balance model, the rainfall concentration Crain and in the age-based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute-balance model. Little change in parameter sensitivity over time was found for the age-based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute-balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions. 相似文献
12.
Interannual variability in western US precipitation 总被引：6，自引：0，他引：6
Low-frequency (interannual or longer period) climatic variability is of interest, because of its significance for the understanding and prediction of protracted climatic anomalies. Since precipitation is one of the key variables driving various hydrologic processes, it is useful to examine precipitation records to better understand long-term climate dynamics. Here, we use the multi-taper method of spectral analysis to analyze the monthly precipitation time series (both occurrence and amount) at a few stations along a meridional transect from Priest River, ID to Tucson, AZ. We also examine spectral coherence between monthly precipitation and widely used atmospheric indices, such as the central Northern Pacific (CNP) and southern oscillation index (SOI). This analysis reveals statistically significant ‘signals' in the time series in the 5–7 and 2–3 year bands. These interannual signals are consistent with those related to El-Niño southern oscillation (ENSO) and quasi-biennial variability identified by others. 相似文献
13.
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
14.
Mean annual rates of tritium input into the ocean averaged over 5° latitude bands are presented for the major oceans, for the period 1952–1975. The rates are obtained by converting tritium concentrations in marine precipitation into net oceanic tritium input, by means of a hydrological model. The tropospheric tritium pattern is specified on the basis of available observations, and climatological means from the literature are used for the rates of evaporation and precipitation and for the relative humidity in ship's height, that enter the model. Tritium input by water vapor exchange exceeds that by precipitation about three-fold. Tritium input by river runoff and by net tropospheric tritium outflow from the continents is also accounted for. This contribution is small except for the northern Indian Ocean and the North Atlantic.The inputs have hemispheric maxima near 50° latitude. The northern hemisphere inputs were strongly peaked in 1963–1964, whereas temporal changes in the southern hemisphere were much more gradual. By 1972, about 75% of the total oceanic input had been received by the northern ocean. For the Pacific, the computed total input agrees with the actual tritium inventory within the limits of uncertainty (about ±20%). The global tritium inventory is estimated at 1.9 GCi in 1972, which corresponds to an average tritium yield of 0.9 kg tritium per megaton TNT equivalent of nuclear fusion. 相似文献
15.
16.
This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R2 parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R2), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models. 相似文献
17.
In this study, uncertainty in model input data (precipitation) and parameters is propagated through a physically based, spatially distributed hydrological model based on the MIKE SHE code. Precipitation uncertainty is accounted for using an ensemble of daily rainfall fields that incorporate four different sources of uncertainty, whereas parameter uncertainty is considered using Latin hypercube sampling. Model predictive uncertainty is assessed for multiple simulated hydrological variables (discharge, groundwater head, evapotranspiration, and soil moisture). Utilizing an extensive set of observational data, effective observational uncertainties for each hydrological variable are assessed. Considering not only model predictive uncertainty but also effective observational uncertainty leads to a notable increase in the number of instances, for which model simulation and observations are in good agreement (e.g., 47% vs. 91% for discharge and 0% vs. 98% for soil moisture). Effective observational uncertainty is in several cases larger than model predictive uncertainty. We conclude that the use of precipitation uncertainty with a realistic spatio‐temporal correlation structure, analyses of multiple variables with different spatial support, and the consideration of observational uncertainty are crucial for adequately evaluating the performance of physically based, spatially distributed hydrological models. 相似文献
18.
The paper presents a novel approach to the setup of a Kalman filter by using an automatic calibration framework for estimation of the covariance matrices. The calibration consists of two sequential steps: (1) Automatic calibration of a set of covariance parameters to optimize the performance of the system and (2) adjustment of the model and observation variance to provide an uncertainty analysis relying on the data instead of ad-hoc covariance values. The method is applied to a twin-test experiment with a groundwater model and a colored noise Kalman filter. The filter is implemented in an ensemble framework. It is demonstrated that lattice sampling is preferable to the usual Monte Carlo simulation because its ability to preserve the theoretical mean reduces the size of the ensemble needed. The resulting Kalman filter proves to be efficient in correcting dynamic error and bias over the whole domain studied. The uncertainty analysis provides a reliable estimate of the error in the neighborhood of assimilation points but the simplicity of the covariance models leads to underestimation of the errors far from assimilation points. 相似文献
19.
海表温度系统性观测偏差的订正是开展长历史序列网格化海表温度气候数据产品研制的关键.本文在引入美国SR02海表温度偏差订正方法的基础上,结合国家气象信息中心自主研发的全球海表观测定时值数据集,进行了相关参数的优化改进,从而研制了1901-2016年印度洋-太平洋核心海域月平均2°×2°分辨率的海表温度偏差订正数据集.对海温偏差订正量的时空分布特征分析表明,基于自主研制的基础数据和优化改进的方法求解的偏差订正量能有效反映海表温度观测手段的历史变迁,以及海表温度系统性偏差随季节变化的规律.同时,与ERSST订正量的对比表明,由于优化改进后的方法其阈值计算随空间样本而变,因而其局地变化特征的表现能力更强,且其订正量在观测手段转型期的变化更为明显.相较订正前的海表温度距平(SSTA)场,订正后的SSTA资料与ERSSTv5SSTA间的偏差误差和均方根误差均有明显降低.其中,偏差误差的缩减比例在37.7%~87.9%之间,均方根误差可降低0.06℃.此外,与国际同类产品的对比表明,本文发展的SSTA订正数据集与国际同类SSTA产品序列的相关系数不低于0.97,且变化趋势类似.从差异对比上看,除中高纬东亚大陆近海区域外,本文的偏差订正数据集与国际上同类产品的SSTA差异基本在-0.2~0.2℃之间. 相似文献
20.
The values of parameters in a groundwater flow model govern the precision of predictions of future system behavior. Predictive precision, thus, typically depends on an ability to infer values of system properties from historical measurements through calibration. When such data are scarce, or when their information content with respect to parameters that are most relevant to predictions of interest is weak, predictive uncertainty may be high, even if the model is "calibrated." Recent advances help recognize this condition, quantitatively evaluate predictive uncertainty, and suggest a path toward improved predictive accuracy by identifying sources of predictive uncertainty and by determining what observations will most effectively reduce this uncertainty. We demonstrate linear and nonlinear predictive error/uncertainty analyses as applied to a groundwater flow model of Yucca Mountain, Nevada, the United States' proposed site for disposal of high-level radioactive waste. Linear and nonlinear uncertainty analyses are readily implemented as an adjunct to model calibration with medium to high parameterization density. Linear analysis yields contributions made by each parameter to a prediction's uncertainty and the worth of different observations, both existing and yet-to-be-gathered, toward reducing this uncertainty. Nonlinear analysis provides more accurate characterization of the uncertainty of model predictions while yielding their (approximate) probability distribution functions. This article applies the above methods to a prediction of specific discharge and confirms the uncertainty bounds on specific discharge supplied in the Yucca Mountain Project License Application. 相似文献