共查询到20条相似文献，搜索用时 463 毫秒
1.
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
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Modeling the spread of subsurface contaminants requires coupling a groundwater flow model with a contaminant transport model. Such coupling may provide accurate estimates of future subsurface hydrologic states if essential flow and contaminant data are assimilated in the model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data assimilation into the transport model. This is, however, a crude assumption as flow models can be subject to many sources of uncertainty. If the flow is not accurately simulated, contaminant predictions will likely be inaccurate even after successive Kalman updates of the contaminant model with the data. The problem is better handled when both flow and contaminant states are concurrently estimated using the traditional joint state augmentation approach. In this paper, we introduce a dual estimation strategy for data assimilation into a one-way coupled system by treating the flow and the contaminant models separately while intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals with the estimation of state variables but it can also be used for state and parameter estimation problems. This EnKF-based dual state-state estimation procedure presents a number of novel features: (i) it allows for simultaneous estimation of both flow and contaminant states in parallel; (ii) it provides a time consistent sequential updating scheme between the two models (first flow, then transport); (iii) it simplifies the implementation of the filtering system; and (iv) it yields more stable and accurate solutions than does the standard joint approach. We conducted synthetic numerical experiments based on various time stepping and observation strategies to evaluate the dual EnKF approach and compare its performance with the joint state augmentation approach. Experimental results show that on average, the dual strategy could reduce the estimation error of the coupled states by 15% compared with the joint approach. Furthermore, the dual estimation is proven to be very effective computationally, recovering accurate estimates at a reasonable cost. 相似文献
4.
Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation 总被引：1，自引：0，他引：1
Haishen Lü Zhongbo Yu Yonghua ZhuSam Drake Zhenchun HaoEdward A. Sudicky 《Advances in water resources》2011,34(3):395-406
With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards’ equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect. 相似文献
5.
Tidal flow forecasting using reduced rank square root filters 总被引：1，自引：0，他引：1
M. Verlaan A. W. Heemink 《Stochastic Environmental Research and Risk Assessment (SERRA)》1997,11(5):349-368
The Kalman filter algorithm can be used for many data assimilation problems. For large systems, that arise from discretizing
partial differential equations, the standard algorithm has huge computational and storage requirements. This makes direct
use infeasible for many applications. In addition numerical difficulties may arise if due to finite precision computations
or approximations of the error covariance the requirement that the error covariance should be positive semi-definite is violated.
In this paper an approximation to the Kalman filter algorithm is suggested that solves these problems for many applications.
The algorithm is based on a reduced rank approximation of the error covariance using a square root factorization. The use
of the factorization ensures that the error covariance matrix remains positive semi-definite at all times, while the smaller
rank reduces the number of computations and storage requirements. The number of computations and storage required depend on
the problem at hand, but will typically be orders of magnitude smaller than for the full Kalman filter without significant
loss of accuracy.
The algorithm is applied to a model based on a linearized version of the two-dimensional shallow water equations for the prediction
of tides and storm surges.
For non-linear models the reduced rank square root algorithm can be extended in a similar way as the extended Kalman filter
approach. Moreover, by introducing a finite difference approximation to the Reduced Rank Square Root algorithm it is possible
to prevent the use of a tangent linear model for the propagation of the error covariance, which poses a large implementational
effort in case an extended kalman filter is used. 相似文献
6.
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
7.
J. Sénégas H. Wackernagel W. Rosenthal T. Wolf 《Stochastic Environmental Research and Risk Assessment (SERRA)》2001,15(1):65-86
The efficiency of a sequential data assimilation scheme relies on the capability to describe the error covariance. This aspect
is all the more relevant if one needs accurate statistics on the estimation error. Frequently an ad hoc function depending
on a few parameters is proposed, and these parameters are tuned, estimated or updated. This usually requires that the covariance
is second-order stationary (i.e. depends only on the distance between two points). In this paper, we discuss this feature
and show that even in simple applications (such as one-dimensional hydrodynamics), this assumption does not hold and may lead
to poorly described estimation errors. We propose a method relying on the analysis of the error term and the use of the hydrodynamical
model to generate one part of the covariance function, the other part being modeled using a second-order stationary approach.
This method is discussed using a twin experiment in the case where a physical parameter is erroneous, and improves significantly
the results: the model bias is strongly reduced and the estimation error is well described. Moreover, it enables a better
adaptation of the Kalman gain to the actual estimation error. 相似文献
8.
研究利用无线电掩星探测数据反演大气水汽参数,文中使用一维非线性方法,反演对流层水汽压廓线.首先,定义了一维目标函数,并以几何光学假设为前提,反演出电波弯曲角;将电波弯曲角作为掩星探测量,大气模式输出的温、湿参量作为初始场,代入目标函数,对目标函数求最优反演出水汽压廓线.文中给出了部分反演结果,经讨论分析认为:非线性方法反演水汽压对初始场的精度依赖较小,能够同时反演出大气温度\水汽压廓线及相应的误差分析,对数值天气预报的应用及数据同化研究具有重要意义. 相似文献
9.
High-resolution models and realistic boundary conditions are necessary to reproduce the mesoscale dynamics of the Gulf of Mexico (GOM). In order to achieve this, we use a nested configuration of the Hybrid Coordinate Ocean Model (HYCOM), where the Atlantic TOPAZ system provides lateral boundary conditions to a high-resolution (5 km) model of the GOM . However, such models cannot provide accurate forecasts of mesoscale variability, such as eddy shedding event, without data assimilation. Eddy shedding events involve the rapid growth of nonlinear instabilities that are difficult to forecast. The known sources of error are the initial state, the atmospheric condition, and the lateral boundary condition. We present here the benefit of using a small ensemble forecast (10 members) for providing confidence indices for the prediction, while using a data assimilation scheme based on optimal interpolation. Our set of initial states is provided by using different values of a data assimilation parameter, while the atmospheric and lateral boundary conditions are perturbed randomly. Changes in the data assimilation parameter appear to control the main position of the large features of the GOM in the initial state, whereas changes in the boundary conditions (lateral and atmospheric) appears to control the propagation of cyclonic eddies at their boundary. The ensemble forecast is tested for the shedding of Eddy Yankee (2006). The Loop Current and eddy fronts observed from ocean color and altimetry are almost always within the estimated positions from the ensemble forecast. The ensemble spread is correlated both in space and time to the forecast error, which implies that confidence indices can be provided in addition to the forecast. Finally, the ensemble forecast permits the optimization of a data assimilation parameter for best performance at a given forecast horizon. 相似文献
10.
Evaluating the utility of the ensemble transform Kalman filter for adaptive sampling when updating a hydrodynamic model 总被引：1，自引：0，他引：1
This paper compares two Monte Carlo sequential data assimilation methods based on the Kalman filter, for estimating the effect of measurements on simulations of state error variance made by a one-dimensional hydrodynamic model. The first method used an ensemble Kalman filter (EnKF) to update state estimates, which were then used as initial conditions for further simulations. The second method used an ensemble transform Kalman filter (ETKF) to quickly estimate the effect of measurement error covariance on forecast error covariance without the need to re-run the simulation model. The ETKF gave an unbiased estimate of EnKF analysed error variance, although differences in the treatment of measurement errors meant the results were not identical. Estimates of forecast error variance could also be made, but their accuracy deteriorated as the time from measurements increased due in part to model non-linearity and the decreasing signal variance. The motivation behind the study was to assess the ability of the ETKF to target possible measurements, as part of an adaptive sampling framework, before they are assimilated by an EnKF-based forecasting model on the River Crouch, Essex, UK. The ETKF was found to be a useful tool for quickly estimating the error covariance expected after assimilating measurements into the hydrodynamic model. It, thus, provided a means of quantifying the ‘usefulness’ (in terms of error variance) of possible sampling schemes. 相似文献
11.
Accuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km2 mixed-forest basin in the Great Lakes region of central Ontario, Canada. Sub-basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi-objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi-objective calibration or data assimilation, as demonstrated in this study. 相似文献
12.
《Journal of Atmospheric and Solar》2000,62(12):1057-1070
Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models, thus producing a “best” estimate of current conditions that is consistent with both information sources. The four major challenges in data assimilation are: (1) to generate an initial state for a computer forecast that has the same mass-wind balance as the assimilating model, (2) to deal with the common problem of highly non-uniform distribution of observations, (3) to exploit the value of proxy observations (of parameters that are not carried explicitly in the model), and (4) to determine the statistical error properties of observing systems and numerical model alike so as to give each information source the proper weight. Variational data assimilation is practiced at major meteorological centers around the world. It is based upon multivariate linear regression, dating back to Gauss, and variational calculus. At the heart of the method is the minimization of a cost function, which guarantees that the analyzed fields will closely resemble both the background field (a short forecast containing a priori information about the atmospheric state) and current observations. The size of the errors in the background and the observations (the latter, arising from measurement and non-representativeness) determine how close the analysis is to each basic source of information. Three-dimensional variational (3DVAR) assimilation provides a logical framework for incorporating the error information (in the form of variances and spatial covariances) and deals directly with the problem of proxy observations. 4DVAR assimilation is an extension of 3DVAR assimilation that includes the time dimension; it attempts to find an evolution of model states that most closely matches observations taken over a time interval measured in hours. Both 3DVAR and, especially, 4DVAR assimilation require very large computing resources. Researchers are trying to find more efficient numerical solutions to these problems. Variational assimilation is applicable in the upper atmosphere, but practical implementation demands accurate modeling of the physical processes that occur at high altitudes and multiple sources of observations. 相似文献
13.
A multi‐storage groundwater concept for the SWAT model to emphasize nonlinear groundwater dynamics in lowland catchments 
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下载免费PDF全文 Hydrological models are useful tools to analyze present and future conditions of water quantity and quality. The integrated modelling of water and nutrients needs an adequate representation of the different discharge components. In common with many lowlands, groundwater contribution to the discharge in the North German lowlands is a key factor for a reasonable representation of the water balance, especially in low flow periods. Several studies revealed that the widely used Soil and Water Assessment Tool (SWAT) model performs poorly for low flow periods. This paper deals with the extension of the groundwater module of the SWAT model to enhance low flow representation. The current two‐storage concept of SWAT was further developed to a three‐storage concept. This was realized due to modification of the groundwater module by splitting the active groundwater storage into a fast and a slow contributing aquifer. The results of this study show that the groundwater module with three storages leads to a good prediction of the overall discharge especially for the recession limbs and the low flow periods. The improved performance is reflected in the signature measures for the mid‐segment (percent bias ?2.4% vs ?15.9%) and the low segment (percent bias 14.8% vs 46.8%) of the flow duration curve. The three‐storage groundwater module is more process oriented than the original version due to the introduction of a fast and a slow groundwater flow component. The three‐storage version includes a modular approach, because groundwater storages can be activated or deactivated independently for subbasin and hydrological response unit level. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
14.
Uncertainty forecast from 3-D super-ensemble multi-model combination: validation and calibration 总被引：1，自引：1，他引：0
Measurements collected during the Recognized Environmental Picture 2010 experiment (REP10) in the Ligurian Sea are used to
evaluate 3-D super-ensemble (3DSE) 72-hour temperature predictions and their associated uncertainty. The 3DSE reduces the
total Root-Mean-Square Difference by 12 and 32% respectively with reference to the ensemble mean and the most accurate of
the models when comparing to regularly distributed surface temperature data. When validating against irregularly distributed
in situ observations, the 3DSE, ensemble mean and most accurate model lead to similar scores. The 3DSE temperature uncertainty
estimate is obtained from the product of a posteriori model weight error covariances by an operator containing model forecast
values. This uncertainty prediction is evaluated using a criterion based on the 2.5th and 97.5th percentiles of the error
distribution. The 3DSE error is found to be on average underestimated during the forecast period, reflecting (i) the influence
of ocean dynamics and (ii) inaccuracies in the a priori weight error correlations. A calibration of the theoretical 3DSE uncertainty
is proposed for the REP10 scenario, based on a time-evolving amplification coefficient applied to the a posteriori weight
error covariance matrix. This calibration allows the end-user to be confident that, on average, the true ocean state lies
in the −2/+2 3DSE uncertainty range in 95% of the cases. 相似文献
15.
An ensemble Kalman filter (EnKF) is developed to identify a hydraulic conductivity distribution in a heterogeneous medium by assimilating solute concentration measurements of solute transport in the field with a steady‐state flow. A synthetic case with the mixed Neumann/Dirichlet boundary conditions is designed to investigate the capacity of the data assimilation methods to identify a conductivity distribution. The developed method is demonstrated in 2‐D transient solute transport with two different initial instant solute injection areas. The influences of the observation error and model error on the updated results are considered in this study. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating solute concentration measurements. The larger area of the initial distribution and the more observed data obtained, the better the calculation results. When the standard deviation of the observation error varies from 1% to 30% of the solute concentration measurements, the simulated results by the data assimilation method do not change much, which indicates that assimilation results are not very sensitive to the standard deviation of the observation error in this study. When the inflation factor is more than 1.0 to enlarge the model error by increasing the forecast error covariance matrix, the updated results of the hydraulic conductivity by the data assimilation method are not good at all. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
16.
Integrated hydrologic models characterize catchment responses by coupling the subsurface flow with land surface processes. One of the major areas of uncertainty in such models is the specification of the initial condition and its influence on subsequent simulations. A key challenge in model initialization is that it requires spatially distributed information on model states, groundwater levels and soil moisture, even when such data are not routinely available. Here, the impact of uncertainty in initial condition was explored across a 208 km2 catchment in Denmark using the ParFlow.CLM model. The initialization impact was assessed under two meteorological conditions (wet vs dry) using five depth to water table and soil moisture distributions obtained from various equilibrium states (thermal, root zone, discharge, saturated and unsaturated zone equilibrium) during the model spin‐up. Each of these equilibrium states correspond to varying computation times to achieve stability in a particular aspect of the system state. Results identified particular sensitivity in modelled recharge and stream flow to the different initializations, but reduced sensitivity in modelled energy fluxes. Analysis also suggests that to simulate a year that is wetter than the spin‐up period, an initialization based on discharge equilibrium is adequate to capture the direction and magnitude of surface water–groundwater exchanges. For a drier or hydrologically similar year to the spin‐up period, an initialization based on groundwater equilibrium is required. Variability of monthly subsurface storage changes and discharge bias at the scale of a hydrological event show that the initialization impacts do not diminish as the simulations progress, highlighting the importance of robust and accurate initialization in capturing surface water–groundwater dynamics. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
17.
Roberta-Serena Blasone Jasper A. Vrugt Henrik Madsen Dan Rosbjerg Bruce A. Robinson George A. Zyvoloski 《Advances in water resources》2008
In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit estimate of the associated uncertainty. This uncertainty arises from incomplete process representation, uncertainty in initial conditions, input, output and parameter error. The generalized likelihood uncertainty estimation (GLUE) framework was one of the first attempts to represent prediction uncertainty within the context of Monte Carlo (MC) analysis coupled with Bayesian estimation and propagation of uncertainty. Because of its flexibility, ease of implementation and its suitability for parallel implementation on distributed computer systems, the GLUE method has been used in a wide variety of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models that require significant computational time to run and produce the desired output. In this paper we improve the computational efficiency of GLUE by sampling the prior parameter space using an adaptive Markov Chain Monte Carlo scheme (the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm). Moreover, we propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic and real-world streamflow data from two catchments with different hydrologic regimes. 相似文献
18.
Haishen Lü Zhongbo Yu Robert Horton Yonghua Zhu Zhenlong Wang Zhenchun Hao Long Xiang 《水文研究》2011,25(20):3158-3172
When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions. In this paper, we present and demonstrate a new multi‐scale assimilation method by combining the direct insertion assimilation method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone soil water with a multi‐scale assimilation method by using observed in situ data from the Wudaogou experiment station, Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi‐scale assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition is similar to the actual situation, the multi‐scale assimilation method can well represent the root zone soil water content. The results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
19.
This paper, based on a real world case study (Limmat aquifer, Switzerland), compares inverse groundwater flow models calibrated with specified numbers of monitoring head locations. These models are updated in real time with the ensemble Kalman filter (EnKF) and the prediction improvement is assessed in relation to the amount of monitoring locations used for calibration and updating. The prediction errors of the models calibrated in transient state are smaller if the amount of monitoring locations used for the calibration is larger. For highly dynamic groundwater flow systems a transient calibration is recommended as a model calibrated in steady state can lead to worse results than a noncalibrated model with a well-chosen uniform conductivity. The model predictions can be improved further with the assimilation of new measurement data from on-line sensors with the EnKF. Within all the studied models the reduction of 1-day hydraulic head prediction error (in terms of mean absolute error [MAE]) with EnKF lies between 31% (assimilation of head data from 5 locations) and 72% (assimilation of head data from 85 locations). The largest prediction improvements are expected for models that were calibrated with only a limited amount of historical information. It is worthwhile to update the model even with few monitoring locations as it seems that the error reduction with EnKF decreases exponentially with the amount of monitoring locations used. These results prove the feasibility of data assimilation with EnKF also for a real world case and show that improved predictions of groundwater levels can be obtained. 相似文献
20.
Assimilating recent observations improves model outcomes for real-time assessments of groundwater processes. This is demonstrated in estimating time-varying recharge to a shallow fractured-rock aquifer in response to precipitation. Results from estimating the time-varying water-table altitude (h) and recharge, and their error covariances, are compared for forecasting, filtering, and fixed-lag smoothing (FLS), which are implemented using the Kalman Filter as applied to a data-driven, mechanistic model of recharge. Forecasting uses past observations to predict future states and is the current paradigm in most groundwater modeling investigations; filtering assimilates observations up to the current time to estimate current states; and FLS estimates states following a time lag over which additional observations are collected. Results for forecasting yield a large error covariance relative to the magnitude of the expected recharge. With assimilating recent observations of h, filtering and FLS produce estimates of recharge that better represent time-varying observations of h and reduce uncertainty in comparison to forecasting. Although model outcomes from applying data assimilation through filtering or FLS reduce model uncertainty, they are not necessarily mass conservative, whereas forecasting outcomes are mass conservative. Mass conservative outcomes from forecasting are not necessarily more accurate, because process errors are inherent in any model. Improvements in estimating real-time groundwater conditions that better represent observations need to be weighed for the model application against outcomes with inherent process deficiencies. Results from data assimilation strategies discussed in this investigation are anticipated to be relevant to other groundwater processes models where system states are sensitive to system inputs. 相似文献