scholarly journals Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
M. Khaki ◽  
H.-J. Hendricks Franssen ◽  
S. C. Han
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Area Index ◽  
Mission Satellite ◽  
Model Predictions

Abstract Satellite remote sensing offers valuable tools to study Earth and hydrological processes and improve land surface models. This is essential to improve the quality of model predictions, which are affected by various factors such as erroneous input data, the uncertainty of model forcings, and parameter uncertainties. Abundant datasets from multi-mission satellite remote sensing during recent years have provided an opportunity to improve not only the model estimates but also model parameters through a parameter estimation process. This study utilises multiple datasets from satellite remote sensing including soil moisture from Soil Moisture and Ocean Salinity Mission and Advanced Microwave Scanning Radiometer Earth Observing System, terrestrial water storage from the Gravity Recovery And Climate Experiment, and leaf area index from Advanced Very-High-Resolution Radiometer to estimate model parameters. This is done using the recently proposed assimilation method, unsupervised weak constrained ensemble Kalman filter (UWCEnKF). UWCEnKF applies a dual scheme to separately update the state and parameters using two interactive EnKF filters followed by a water balance constraint enforcement. The performance of multivariate data assimilation is evaluated against various independent data over different time periods over two different basins including the Murray–Darling and Mississippi basins. Results indicate that simultaneous assimilation of multiple satellite products combined with parameter estimation strongly improves model predictions compared with single satellite products and/or state estimation alone. This improvement is achieved not only during the parameter estimation period ($$\sim $$ ∼  32% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.66 to $$\sim $$ ∼  0.85) but also during the forecast period ($$\sim $$ ∼  14% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.69 to $$\sim $$ ∼  0.78) due to the effective impacts of the approach on both state and parameters.

China land soil moisture EnKF data assimilation based on satellite remote sensing data

2011 ◽  
Vol 54 (9) ◽  
pp. 1430-1440 ◽  
Author(s):  
ChunXiang Shi ◽  
ZhengHui Xie ◽  
Hui Qian ◽  
MiaoLing Liang ◽  
XiaoChun Yang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data

Updating the soil hydraulic parameters in a 3D subsurface model

2021 ◽  
Author(s):  
Natascha Brandhorst ◽  
Insa Neuweiler
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Parameter Uncertainty ◽  
Unsaturated Flow ◽  
Model Parameters ◽  
Hydraulic Parameters ◽  
Model Errors ◽  
Model Predictions ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

<p>Soil moisture is an important variable for land surface processes. To make good model predictions of soil moisture, the flow processes in the subsurface need to be captured well. Flow in the subsurface strongly depends on the soil hydraulic parameters. Information about model parameters is often not available, at least not for the entire domain of interest. The resulting parameter uncertainty needs to be accounted for in the applied model. Data assimilation can account for parameter and model errors as well as for all other possible sources of uncertainty if observations are available that can be used to condition the model states. Thus, the parameter uncertainty might be reduced and model predictions improved. However, including the parameters increases the size of the state vector and thus the computational burden. Especially for large models, this can be a problem. Furthermore, the updates can produce unphysical parameter combinations which in unsaturated zone models often lead to numerical problems.</p><p>In this work, we test the effect of updating the soil hydraulic parameters along with soil moisture in a 3D subsurface hillslope model. We use the ensemble Kalman filter for data assimilation and synthetic observations of soil moisture. In a similar study using a 1D unsaturated flow model, parameter updates were found to be the best way to handle parameter uncertainty. Updating parameters resulted in improved predictions of soil moisture, although not necessarily in more realistic model parameters. The parameter updates should rather be considered a method of treating parameter uncertainty than a method for parameter identification. In the 1D settings, updating all uncertain parameters led to the best results. Whether this still holds and is feasible for a more complex 3D model is the question addressed in this presentation.</p>

The Role of Different Sources of Uncertainty in Data Assimilation with Coupled Land Surface - Subsurface Models

2021 ◽  
Author(s):  
Eduardo Emilio Sanchez-Leon ◽  
Natascha Brandhorst ◽  
Bastian Waldowski ◽  
Ching Pui Hung ◽  
Insa Neuweiler ◽  
...  
Keyword(s):  
Plant Physiology ◽  
Data Assimilation ◽  
Land Surface ◽  
Reference Model ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Sources Of Uncertainty

<p>The success of data assimilation systems strongly depends on the suitability of the generated ensembles. While in theory data assimilation should correct the states of an ensemble of models, especially if model parameters are included in the update, its effectiveness will depend on many factors, such as ensemble size, ensemble spread, and the proximity of the prior ensemble simulations to the data. In a previous study, we generated an ensemble-based data-assimilation framework to update model states and parameters of a coupled land surface-subsurface model. As simulation system we used the Terrestrial Systems Modeling Platform TerrSysMP, with the community land-surface model (CLM) coupled to the subsurface model Parflow. In this work, we used the previously generated ensemble to assess the effect of uncertain input forcings (i.e. precipitation), unknown subsurface parameterization, and/or plant physiology in data assimilation. The model domain covers a rectangular area of 1×5km<sup>2</sup>, with a uniform depth of 50m. The subsurface material is divided into four units, and the top soil layers consist of three different soil types with different vegetation. Streams are defined along three of the four boundaries of the domain. For data assimilation, we used the TerrsysMP PDAF framework. We defined a series of data assimilation experiments in which sources of uncertainty were considered individually, and all additional settings of the ensemble members matched those of the reference. To evaluate the effect of all sources of uncertainty combined, we designed an additional test in which the input forcings, subsurface parameters, and the leaf area index of the ensemble were all perturbed. In all these tests, the reference model had homogenous subsurface units and the same grid resolution as all models of the ensemble. We used point measurements of soil moisture in all data assimilation experiments. We concluded that precipitation dominates the dynamics of the simulations, and perturbing the precipitation fields for the ensemble have a major impact in the performance of the assimilation. Still, considerable improvements are observed compared to open-loop simulations. In contrast, the effect of variable plant physiology was minimal, with no visible improvement in relevant fluxes such as evapotranspiration. As expected, improved ensemble predictions are propagated longer in time when parameters are included in the update.</p>

scholarly journals Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

2020 ◽  
Vol 13 (3) ◽  
pp. 1267-1284 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Y. Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Lake Ecosystem ◽  
Sensing Data ◽  
Localization Scheme ◽  
Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

scholarly journals Simultaneous state-parameter estimation of rainfall-induced landslide displacement using data assimilation

2019 ◽  
Author(s):  
Jing Wang ◽  
Guigen Nie ◽  
Shengjun Gao ◽  
Changhu Xue
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Prediction Method ◽  
State Parameter ◽  
Joint Estimation ◽  
Model Parameters ◽  
Displacement Estimation ◽  
Landslide Stability ◽  
Landslide Displacement Prediction ◽  
Using Data

Abstract. Landslide displacement prediction has great practical engineering significance to landslide stability evaluation and early warning. The evolution of landslide is a complex dynamic process, applying classical prediction method will result in significant error. Data assimilation method offers a new way to merge multi-source data with the model. However, data assimilation is still deficient in the ability to meet the demand of dynamic landslide system. In this paper, simultaneous state-parameter estimation (SSPE) using particle filter-based data assimilation is applied to predict displacement of the landslide. Landslide SSPE assimilation strategy can make use of time-series displacements and hydrological information for the joint estimation of landslide displacement and model parameters, which can improve the performance considerably. We select Xishan Village, Sichuan province, China as experiment site to test SSPE assimilation strategy. Based on the comparison of actual monitoring data with prediction values, results strongly suggest the effectiveness and feasibility of SSPE assimilation strategy in short-term landslide displacement estimation.

scholarly journals Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling

2018 ◽  
Vol 25 (4) ◽  
pp. 731-746 ◽  
Author(s):  
Sangeetika Ruchi ◽  
Svetlana Dubinkina
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Particle Filter ◽  
Reservoir Modeling ◽  
Model Parameters ◽  
Uncertain Parameters ◽  
Initial Ensemble ◽  
Leading Mode ◽  
Ensemble Transform

Abstract. Over the years data assimilation methods have been developed to obtain estimations of uncertain model parameters by taking into account a few observations of a model state. The most reliable Markov chain Monte Carlo (MCMC) methods are computationally expensive. Sequential ensemble methods such as ensemble Kalman filters and particle filters provide a favorable alternative. However, ensemble Kalman filter has an assumption of Gaussianity. Ensemble transform particle filter does not have this assumption and has proven to be highly beneficial for an initial condition estimation and a small number of parameter estimations in chaotic dynamical systems with non-Gaussian distributions. In this paper we employ ensemble transform particle filter (ETPF) and ensemble transform Kalman filter (ETKF) for parameter estimation in nonlinear problems with 1, 5, and 2500 uncertain parameters and compare them to importance sampling (IS). The large number of uncertain parameters is of particular interest for subsurface reservoir modeling as it allows us to parameterize permeability on the grid. We prove that the updated parameters obtained by ETPF lie within the range of an initial ensemble, which is not the case for ETKF. We examine the performance of ETPF and ETKF in a twin experiment setup, where observations of pressure are synthetically created based on the known values of parameters. For a small number of uncertain parameters (one and five) ETPF performs comparably to ETKF in terms of the mean estimation. For a large number of uncertain parameters (2500) ETKF is robust with respect to the initial ensemble, while ETPF is sensitive due to sampling error. Moreover, for the high-dimensional test problem ETPF gives an increase in the root mean square error after data assimilation is performed. This is resolved by applying distance-based localization, which however deteriorates a posterior estimation of the leading mode by largely increasing the variance due to a combination of less varying localized weights, not keeping the imposed bounds on the modes via the Karhunen–Loeve expansion, and the main variability explained by the leading mode. A possible remedy is instead of applying localization to use only leading modes that are well estimated by ETPF, which demands knowledge of which mode to truncate.

scholarly journals Investigating temporal field sampling strategies for site-specific calibration of three soil moisture–neutron intensity parameterisation methods

2015 ◽  
Vol 19 (7) ◽  
pp. 3203-3216 ◽  
Author(s):  
J. Iwema ◽  
R. Rosolem ◽  
R. Baatz ◽  
T. Wagener ◽  
H. R. Bogena
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Satellite Remote Sensing ◽  
Cosmic Ray ◽  
Sampling Strategies ◽  
Site Specific ◽  
Neutron Intensity ◽  
Soil Wetness ◽  
Semi Arid

Abstract. The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modelling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results while investigating actual neutron intensity measurements, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland, and a temperate forest. When calibrated with 1 year of data, both COSMIC and the modified N0 method performed better than HMF. The performance of COSMIC was remarkably good at the semi-arid site in the USA, while the N0mod performed best at the two temperate sites in Germany. The successful performance of COSMIC at all three sites can be attributed to the benefits of explicitly resolving individual soil layers (which is not accounted for in the other two parameterisations). To better calibrate these parameterisations, we recommend in situ soil sampled to be collected on more than a single day. However, little improvement is observed for sampling on more than 6 days. At the semi-arid site, the N0mod method was calibrated better under site-specific average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. Errors in actual neutron intensities were translated to average errors specifically to each site. At the semi-arid site, these errors were below the typical measurement uncertainties from in situ point-scale sensors and satellite remote sensing products. Nevertheless, at the two humid sites, reduction in uncertainty with increasing sampling days only reached typical errors associated with satellite remote sensing products. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.

scholarly journals The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France

2017 ◽  
Vol 21 (4) ◽  
pp. 2015-2033 ◽  
Author(s):  
David Fairbairn ◽  
Alina Lavinia Barbu ◽  
Adrien Napoly ◽  
Clément Albergel ◽  
Jean-François Mahfouf ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Land Surface ◽  
River Discharge ◽  
Surface Soil ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index

Abstract. This study evaluates the impact of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations into a land surface model using the SAFRAN–ISBA–MODCOU (SIM) hydrological suite. SIM consists of three stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the three-layer ISBA land surface model, which then provides drainage and runoff inputs to (3) the MODCOU hydro-geological model. The drainage and runoff outputs from ISBA are validated by comparing the simulated river discharge from MODCOU with over 500 river-gauge observations over France and with a subset of stations with low-anthropogenic influence, over several years. This study makes use of the A-gs version of ISBA that allows for physiological processes. The atmospheric forcing for the ISBA-A-gs model underestimates direct shortwave and long-wave radiation by approximately 5 % averaged over France. The ISBA-A-gs model also substantially underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess runoff flowing into the rivers and aquifers contributes to an overestimation of the SIM river discharge. Two experiments attempted to resolve these problems: (i) a correction of the minimum LAI model parameter for grasslands and (ii) a bias-correction of the model radiative forcing. Two data assimilation experiments were also performed, which are designed to correct random errors in the initial conditions: (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the median SIM Nash scores by about 9 % and 18 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the median discharge Nash efficiency by about 7 %. The poor performance of the SEKF originates from the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage and/or runoff and to insufficient corrections during winter, respectively. Possible ways to improve the model are discussed, including a new multi-layer diffusion model and a more realistic response of photosynthesis to temperature in mountainous regions. The data assimilation should be advanced by accounting for model and forcing uncertainties.

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