Search Results (106)

The production performances of fractured tight gas wells are closely related to several complex and unknown factors, including the formation properties, fracture parameters, gas–water two-phase flow, and other nonlinear flow mechanisms. The rate transient analysis (RTA) results have significant uncertainties, which should be quantified to evaluate the formation and fracturing treatment better. This paper provides an efficient method for uncertainty quantification in the RTA of fractured tight gas wells with multiple unknown factors incorporated. The theoretical model for making forward predictions is based on a trilinear flow model, which incorporates the effects of two-phase flow and other nonlinear flow mechanisms. The normalized rates and material balance times of both water and gas phases are regarded as observations and matched with the theoretical model. The unknowns in the model are calibrated using the ensemble Kalman filter (EnKF), which applies an ensemble of multiple realizations to match the observations and updates the unknown parameters step by step. Finally, a comprehensive field case from Northwestern China is implemented to benchmark the proposed method. The results show that the parameters and rate transient responses have wide ranges and significant uncertainties before history matching, while all the realizations in the ensemble can have good matches to the field data after calibration. The posterior distribution of each unknown parameter in the model can be obtained after history matching, which can be used to quantify the uncertainties in the RTA of the fractured tight gas wells. The ranges and uncertainties of the parameters are significantly narrowed down, but the parameters are still with significant uncertainties. The main contribution of the paper is the provision of an efficient integrated workflow to quantify the uncertainties in RTA. It can be readily used in field applications of multi-fractured horizontal wells from tight gas reservoirs. Full article

The retrieval of continuous snow water equivalent (SWE) directly from passive microwave observations is hampered by ambiguity, which can potentially be mitigated by incorporating knowledge on snow hydrological processes. In this paper, we present a data assimilation (DA)-based SWE retrieval framework coupling the QCA-Mie scattering (DMRT-QMS) model (a dense medium radiative transfer (RT) microwave scattering model) and a one-dimensional column-based multiple-layer snow hydrology model. The snow hydrology model provides realistic estimates of the snowpack physical parameters required to drive the DMRT-QMS model. This paper devises a strategy to specify those internal parameters in the snow hydrology and RT models that lack observational records. The modeled snow depth is updated by assimilating brightness temperatures (Tbs) from the X, Ku, and Ka bands using an ensemble Kalman filter (EnKF). The updated snow depth is then used to predict the SWE. The proposed framework was tested using the European Space Agency’s Nordic Snow Radar Experiment (ESA NoSREx) dataset for a snow field experiment from 2009 to 2012 in Sodankylä, Finland. The achieved SWE retrieval root mean square error of 34.31 mm meets the requirements of NASA and ESA snow missions and is about 70% less than the open-loop SWE. In summary, this paper introduces a novel SWE retrieval framework that leverages the combined strengths of a snow hydrology model and a radiative transfer model. This approach ensures physically realistic retrievals of snow depth and SWE. We investigated the impact of various factors on the framework’s performance, including observation time intervals and combinations of microwave observation channels. Our results demonstrate that a one-week observation interval achieves acceptable retrieval accuracy. Furthermore, the use of multi-channel and multi-polarization Tbs is preferred for optimal SWE retrieval performance. Full article

Most Penman-Monteith-Leuning (PML) evapotranspiration (ET) modeling studies are dominated by consideration of meteorological, energy, and land use information, etc., but the dynamic coupling of soil moisture content (SM), especially in terms of improving accuracy through assimilation, lacks sufficient attention. This paper proposes a research framework for the dynamic coupling simulation of PML model and SM based on data assimilation, i.e., the remote sensing monitored SM is combined with soil evaporation of PML to obtain high-precision time-continuous SM data through data assimilation; simultaneously, dynamical soil evaporation coefficients are generated based on the assimilated SM to improve the simulation accuracy of the PML model. The new scheme was validated at a typical irrigation zone in north China and showed obvious improvements in both SM and ET simulations. Moreover, the effect of the assimilation of SM on the simulation accuracy of ET for different crop growth periods is further analyzed. This research provides a new idea for the coupling simulation of the SM and PML models. Full article

The potential of satellite soil moisture (SM) in improving hydrological modeling has been addressed in synthetic experiments, but it is less explored in real data cases. Here, we investigate the added value of Soil Moisture and Passive (SMAP) and Advanced Scatterometer (ASCAT) SM data to distributed hydrological modeling with the soil and water assessment tool (SWAT) in a highly human disturbed catchment (126, 486 km²) featuring a network of SM and streamflow observations. The investigation is based on the ensemble Kalman filter (EnKF) considering SM errors from satellite data using the triple collocation. The assimilation of SMAP and ASCAT SM improved the surface (0–10 cm) and rootzone (10–30 cm) SM at >70% and > 50% stations of the basin, respectively. However, the assimilation effects on distributed streamflow simulation of the basin are un-significant and not robust. SM assimilation improved the simulated streamflow at two upstream stations, while it deteriorated the streamflow at the remaining stations. This can be largely attributed to the poor vertical soil water coupling of SWAT, suboptimal model parameters, satellite SM data quality, humid climate, and human disturbance to rainfall-runoff processes. This study offers strong evidence of integrating satellite SM into hydrological modeling in improving SM estimation and provides implications for achieving the added value of remotely sensed SM in streamflow improvement. Full article

Handling the challenge of missing measurements in nonlinear systems is a difficult problem in various scientific and engineering fields. Missing measurements, which can arise from technical faults during observation, diffusion channel shrinking, or the loss of specific metrics, can bring many challenges when estimating the state of nonlinear systems. To tackle this issue, this paper proposes a technique that utilizes a robust cubature Kalman filter (RCKF) by integrating Huber’s M-estimation theory with the standard conventional cubature Kalman filter (CKF). Although a CKF is often used for solving nonlinear filtering problems, its effectiveness might be limited due to a lack of knowledge regarding the nonlinear model of the state and noise-related statistical information. In contrast, the RCKF demonstrates an ability to mitigate performance degradation and discretization issues related to track curves by leveraging covariance matrix predictions for state estimation and output control amidst dynamic disruption errors—even when noise statistics deviate from prior assumptions. The performance of extended Kalman filters (EKFs), unscented Kalman filters (UKFs), CKFs, and RCKFs was compared and evaluated using two numerical examples involving the Univariate Non-stationary Growth Model (UNGM) and bearing-only tracking (BOT). The numerical experiments demonstrated that the RCKF outperformed the EKF, EnKF, and CKF in effectively handling anomaly errors. Specifically, in the UNGM example, the RCKF achieved a significantly lower ARMSE (4.83) and ANCI (3.27)—similar outcomes were observed in the BOT example. Full article

Flood modeling and forecasting are crucial for managing and preparing for extreme flood events, such as those in the Tropical Andes. In this context, assimilating streamflow data is essential. Data Assimilation (DA) seeks to combine errors between forecasting models and discharge measurements through the updating of model states. This study aims to assess the applicability and performance of streamflow DA in a sub-daily forecasting system of the Peruvian Tropical Andes using the Ensemble Kalman Filter (EnKF) and Particle Filter (PF) algorithms. The study was conducted in a data-sparse Andean basin during the period February–March 2022. For this purpose, the lumped GR4H rainfall–runoff model was run forward with 100 ensemble members in four different DA experiments based on IMERG-E and GSMaP-NRT precipitation sources and assimilated real-time hourly discharges at the basin outlet. Ensemble modeling with EnKF and PF displayed that perturbation introduced by GSMaP-NRT’-driven experiments reduced the model uncertainties more than IMERG-E’ ones, and the reduction in high-flow subestimation was more notable for the GSMaP-NRT’+EnKF configuration. The ensemble forecasting framework from 1 to 24 h proposed here showed that the updating of model states using DA techniques improved the accuracy of streamflow prediction at least during the first 6–8 h on average, especially for the GSMaP-NRT’+EnKF scheme. Finally, this study benchmarks the application of streamflow DA in data-sparse basins in the Tropical Andes and will support the development of more accurate climate services in Peru. Full article

The simultaneous consideration of a numerical model and of different observations can be achieved using data-assimilation methods. In this contribution, the ensemble Kalman filter (EnKF) is applied to obtain the system-state development and also an estimation of unknown model parameters. An extension of the Kalman filter used is presented for the case of uncertain model parameters, which should not or cannot be estimated due to a lack of necessary measurements. It is shown that incorrectly assumed probability density functions for present uncertainties adversely affect the model parameter to be estimated. Therefore, the problem is embedded in a multilayered uncertainty space consisting of the stochastic space, the interval space, and the fuzzy space. Then, we propose classifying all present uncertainties into aleatory and epistemic ones. Aleatorically uncertain parameters can be used directly within the EnKF without an increase in computational costs and without the necessity of additional methods for the output evaluation. Epistemically uncertain parameters cannot be integrated into the classical EnKF procedure, so a multilayered uncertainty space is defined, leading to inevitable higher computational costs. Various possibilities for uncertainty quantification based on probability and possibility theory are shown, and the influence on the results is analyzed in an academic example. Here, uncertainties in the initial conditions are of less importance compared to uncertainties in system parameters that continuously influence the system state and the model parameter estimation. Finally, the proposed extension using a multilayered uncertainty space is applied on a multi-degree-of-freedom (MDOF) laboratory structure: a beam made of stainless steel with synthetic data or real measured data of vertical accelerations. Young’s modulus as a model parameter can be estimated in a reasonable range, independently of the measurement data generation. Full article

As an important ecological hinterland in Hunan Province, the Dongting Lake area has an irreplaceable role in regional socioeconomic development. However, owing to rapid environmental changes and complex land use relationships, land use/land cover (LULC) changes are actively occurring in the region. Therefore, assessment of the current LULC status and the future development trend for sustainable economic development is of considerable importance. In this study, the driving mechanisms of spatiotemporal evolution for land use conflicts (LUCF) in Dongting Lake from 2000 to 2020 were analyzed by constructing a LUCF model. Additionally, a new model, EnKF-PLUS, which couples ensemble Kalman filtering (EnKF) with patch-generating land use simulation (PLUS), was developed to predict the LULC changes and LUCF in 2030 under different scenarios. The results provide three insights. First, during the period of 2000–2020, high LUCF values were concentrated in highly urbanized and densely populated areas, whereas low LUCF values were centered in hilly regions. Secondly, the impacts of static factors (topographical factors) and dynamic factors (population, GDP, and climate factors) on changes in LUCF were regionally differentiated. Thirdly, our results indicate that the implementation of land use strategies of cropland conservation and ecological conservation can effectively mitigate the degree of LUCF changes in the region and contribute to the promotion of the rational allocation of land resources. Full article

Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R²) and a root mean square error (RMSE) of 0.80 and 0.65 m²/m², respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production. Full article

In 1960, R.E. Kalman published his famous paper describing a recursive solution, the Kalman filter, to the discrete-data linear filtering problem. In the following decades, thanks to the continuous progress of numerical computing, as well as the increasing demand for weather prediction, target tracking, and many other problems, the Kalman filter has gradually become one of the most important tools in science and engineering. With the continuous improvement of its theory, the Kalman filter and its derivative algorithms have become one of the core algorithms in optimal estimation. This paper attempts to systematically collect and sort out the basic principles of the Kalman filter and some of its important derivative algorithms (mainly including the Extended Kalman filter (EKF), the Unscented Kalman filter (UKF), the Ensemble Kalman filter (EnKF)), as well as the scope of their application, and also to compare their advantages and limitations. In addition, because there are a large number of applications based on the Kalman filter in data assimilation, this paper also provides examples and classifies the applications of both the Kalman filter and its derivative algorithms in the field of data assimilation. Full article

In arid and semi-arid areas, soil moisture (SM) plays a crucial role in land-atmosphere interactions, hydrological processes, and ecosystem sustainability. SM data at large scales are critical for related climatic, hydrological, and ecohydrological research. Data fusion based on satellite products and model simulations is an important way to obtain SM data at large scales; however, little has been reported on the comparison of the data fusion methods in different categories. Here, we compared the performance of two widely used data fusion methods, the Ensemble Kalman Filter (EnKF) and the Back-Propagation Artificial Neural Network (BPANN), in the degraded grassland site (DGS) and the alpine grassland site (AGS). The SM data from the Community Land Model 5.0 (CLM5.0) and the Soil Moisture Active and Passive (SMAP) were fused and validated against the observations of the Cosmic-Ray Neutron Sensor (CRNS) to avoid the impacts of scale-mismatch. Results show that compared with the original data sets at both sites, the RMSE of the fused data by BPANN (FD-BPANN) and EnKF (FD-EnKF) had improved by more than 50% and 31%, respectively. Overall, the FD-BPANN performs better than the FD-EnKF because the BPANN method assigned higher weights to input data with better performance and the EnKF method is affected by the strong variabilities of both the fused CLM5.0 and SMAP data and the CRNS data. However, in terms of the percentile range, the FD-BPANN showed the worst performance, with overestimations in the low SM range of 25th percentile (<Q25), because the BPANN method tends to be trapped in a local minimum. The BPANN method performed better in humid areas, then followed by semi-humid areas, and finally arid and semi-arid areas. Moreover, compared with the previous studies in arid and semi-arid areas, the BPANN method in this study performed better. Full article

The experimental rapid-cycling Ensemble Kalman Filter (EnKF) in the convection-allowing ensemble-based Warn-on-Forecast System (WoFS) at the National Severe Storms Laboratory (NSSL) is used to assimilate all-sky infrared radiance observations from the GOES-16 7.3 μm water vapor channel in combination with radar wind and reflectivity observations to improve the analysis and subsequent forecast of severe thunderstorms (which occurred in Oklahoma on 2 May 2018). The method for radiance data assimilation is based primarily on the version used in WoFS. In addition, the methods for adaptive observation error inflation and background error inflation and the method of time-expanded sampling are also implemented in two groups of experiments to test their effectiveness and examine the impacts of radar observations and all-sky radiance observations on ensemble analyses and predictions of severe thunderstorms. Radar reflectivity observations and brightness temperature observations from the GOES-16 6.9 μm mid-level troposphere water vapor channel and 11.2 μm longwave window channel are used to evaluate the assimilation statistics and verify the forecasts in each experiment. The primary findings from the two groups of experiments are summarized: (i) Assimilating radar observations improves the overall (heavy) precipitation forecast up to 5 (4) h, according to the improved composite reflectivity forecast skill scores. (ii) Assimilating all-sky water vapor infrared radiance observations from GOES-16 in addition to radar observations improves the brightness temperature assimilation statistics and subsequent cloud cover forecast up to 6 h, but the improvements are not significantly affected by the adaptive observation and background error inflations. (iii) Time-expanded sampling can not only reduce the computational cost substantially but also slightly improve the forecast. Full article

The data assimilation of remotely sensed soil moisture observations provides a feasible path of improving river flow simulation. In this work, we studied the performance of the error subspace transform Kalman filter (ESTKF) assimilation algorithm on the assimilation of remotely sensed soil moisture from SMAP, including the improvement of soil moisture and river flow in the hydrological model. Additionally, we discussed the advantages and added value of ESTKF compared to the ensemble Kalman filter (EnKF) in a hydrological model. To achieve this objective, we solved the spatial resolution gap between the remotely sensed soil moisture and the simulated soil moisture of the hydrological model. The remotely sensed soil moisture from SMAP was assimilated into the first layer soil moisture in the distributed hydrological model. The spatial resolution of the hydrological model was 600 m, while the spatial resolution of the SMAP remotely sensed soil moisture was 9 km. There is a considerable gap between the two spatial resolutions. By employing observation operators and observation localization based on geolocation, the distributed hydrological model assimilated multiple remotely sensed soil moisture values for each grid, thereby ensuring the consistent updates of soil moisture in the model. The results show the following: (1) In terms of improving soil moisture, we found that both ESTKF and EnKF were effective, and the ubRMSE of ESTKF was lower than that of EnKF. (2) ESTKF improved most cases where open-loop high river flow simulations were too low, but EnKF did not improve this situation. (3) In ESTKF, the relative error of flood volume was reduced on average to 2.52%, but the relative error of flood peak did not improve. The results provide evidence of the value of ESTKF in the hydrological model. Full article

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects on the performance of yield estimation. This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. To address these issues, we proposed an improved crop data-model assimilation (CDMA) framework, which integrates LAI and SM, as retrieved from remotely sensed data, into the World Food Studies (WOFOST) model using the ensemble Kalman filter (EnKF) approach for winter wheat yield estimation. The results showed that the yield estimation at a 10 m grid size outperformed that at a 500 m grid size, using the same assimilation strategy. Additionally, the winter wheat yield estimation accuracy was higher when using the bivariate data assimilation method (R² = 0.46, RMSE = 756 kg/ha) compared to the univariate method. In conclusion, our study highlights the advantages of joint assimilating LAI and SM for crop yield estimation and emphasizes the importance of finer spatial resolution in remotely sensed observations for crop yield estimation using the CDMA framework. The proposed approach would help to develop a high-accuracy crop yield monitoring system using optical and SAR retrieved parameters. Full article

The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance. Full article