scholarly journals Correcting Satellite Precipitation Data and Assimilating Satellite-Derived Soil Moisture Data to Generate Ensemble Hydrological Forecasts within the HBV Rainfall-Runoff Model

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2138
Author(s):  
Maurycy Ciupak ◽  
Bogdan Ozga-Zielinski ◽  
Jan Adamowski ◽  
Ravinesh C Deo ◽  
Krzysztof Kochanek
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Snow Water Equivalent ◽  
Model Updating ◽  
Rainfall Runoff ◽  
Satellite Precipitation ◽  
Hbv Model

An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a diverse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models).

scholarly journals Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments

2011 ◽  
Vol 15 (8) ◽  
pp. 2437-2457 ◽  
Author(s):  
S. Nie ◽  
J. Zhu ◽  
Y. Luo
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Estimation Method ◽  
State Parameter ◽  
Hydraulic Parameters ◽  
Constrained Estimation ◽  
Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

Unscented weighted ensemble Kalman filter for soil moisture assimilation

2020 ◽  
Vol 580 ◽  
pp. 124352 ◽  
Author(s):  
Xiaolei Fu ◽  
Zhongbo Yu ◽  
Yongjian Ding ◽  
Yu Qin ◽  
Lifeng Luo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter

scholarly journals Evaluation of Satellite Based Precipitations and Their Applicability for Rainfall Runoff Modelling in Narayani Basin of Nepal

2016 ◽  
Vol 8 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Sunil Ghaju ◽  
Knut Alfredsen
Keyword(s):  
Bias Correction ◽  
Rain Gauge ◽  
Extensive Study ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Satellite Precipitation ◽  
Precipitation Estimates ◽  
Set Up ◽  
Rain Gauge Network ◽  
Precipitation Station

High spatial variability of precipitation over Nepal demands dense network of rain-gauge stations. But to set-up a dense rain gauge network is almost impossible due to mountainous topography of Nepal. Also the dense rain gauge network will be very expensive and some time impossible for timely maintenance. Satellite precipitation products are an alternative way to collect precipitation data with high temporal and spatial resolution over Nepal. In this study, the satellite precipitation products TRMM and GSMaP were analyzed. Precipitation was compared with ground based gauge precipitation in the Narayani basin, while the applicability of these rainfall products for runoff simulation were tested using the LANDPINE model for Trishuli basin which is a sub-basin within Narayani catchment. The Nash-Sutcliffe efficiency calculated for TRMM and GSMaP from point to pixel comparison is negative for most of stations. Also the estimation bias for both the products is negative indicating under estimation of precipitation by satellite products, with least under estimation for the GSMaP precipitation product. After point to pixel comparison, satellite precipitation estimates were used for runoff simulation in the Trishuli catchment with and without bias correction for each product. Among the two products, TRMM shows good simulation result without any bias correction for calibration and validation period with scaling factor of 2.24 for precipitation which is higher than that for gauge precipitation. This suggests, it could be used for runoff simulation to the catchments where there is no precipitation station. But it is too early to conclude by just looking into one catchment. So extensive study need to be done to make such conclusion.Journal of Hydrology and Meteorology, Vol. 8(1) p.22-31

scholarly journals Dynamic Calibration with an Ensemble Kalman Filter Based Data Assimilation Approach for Root-Zone Moisture Predictions

2007 ◽  
Vol 8 (4) ◽  
pp. 910-921 ◽  
Author(s):  
Nicola Montaldo ◽  
John D. Albertson ◽  
Marco Mancini
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Root Zone ◽  
Sensitivity Analyses ◽  
Surface Model ◽  
Early Model ◽  
Model Errors ◽  
Model Bias ◽  
Near Surface

Abstract In the presence of uncertain initial conditions and soil hydraulic properties, land surface model (LSM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the near-surface soil moisture (θg), as observed from a remote platform. In this paper the possibility of merging observations and the model optimally for providing robust predictions of root-zone soil moisture (θ2) is demonstrated. An assimilation approach that assimilates θg through the ensemble Kalman filter (EnKF) and provides a physics-based update of θ2 is developed. This approach, as with other common soil moisture assimilation approaches, may fail when a key LSM parameter, for example, the saturated hydraulic conductivity (ks), is estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach is developed that accepts this violation in the early model run times and dynamically calibrates all the components of the ks ensemble as a function of the persistent bias in root-zone soil moisture, allowing one to remove the model bias, restore the fidelity to the EnKF requirements, and reduce the model uncertainty. The robustness of the proposed approach is also examined in sensitivity analyses.

scholarly journals Ensemble Kalman filter for model updating – A special issue

2011 ◽  
Vol 15 (2) ◽  
pp. 223-224 ◽  
Author(s):  
Geir Nævdal ◽  
Remus G. Hanea ◽  
Dean S. Oliver ◽  
Brice Vallès
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Model Updating ◽  
Special Issue

scholarly journals Optimal ensemble size of ensemble Kalman filter in sequential soil moisture data assimilation

2015 ◽  
Vol 42 (16) ◽  
pp. 6710-6715 ◽  
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Youfei Zheng ◽  
Christopher R. Hain ◽  
Jicheng Liu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Ensemble Size ◽  
Soil Moisture Data ◽  
Optimal Ensemble
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