Integration Frameworks for Merging Satellite Remote Sensing Observations with Hydrological Model Outputs (original) (raw)
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The study presents a compatibility analysis of gravimetric observations with passive microwave observations. Monitoring the variability of soil water content is one of the essential issues in climate-related research. Total water storage changes (ΔTWS) observed by Gravity Recovery and Climate Experiment (GRACE), enables the creation of many applications in hydrological monitoring. Soil moisture (SM) is a critical variable in hydrological studies. Advanced Microwave Scanning Radiometer (AMSR-E) satellite products provided unique observations on this variable in near-daily time resolutions. The study used maximum covariance analysis (MCA) to extract principal components for ΔTWS and SM signals. The analysis was carried out for the global area, dividing the discussion into individual continents. The amplitudes of gravimetric and microwave signals were computed via the complex empirical orthogonal function (EOF) and the complex conjugate EOF* to determine the regions for detailed compar...
Scientific Reports
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 constr...
Hydrology and Earth System Sciences, 2014
During the last few decades, satellite measurements have been widely used to study the continental water cycle, especially in regions where in situ measurements are not readily available. The future Surface Water and Ocean Topography (SWOT) satellite mission will deliver maps of water surface elevation (WSE) with an unprecedented resolution and provide observation of rivers wider than 100 m and water surface areas greater than approximately 250 × 250 m over continental surfaces between 78 • S and 78 • N. This study aims to investigate the potential of SWOT data for parameter optimization for large-scale river routing models. The method consists in applying a data assimilation approach, the extended Kalman filter (EKF) algorithm, to correct the Manning roughness coefficients of the ISBA (Interactions between Soil, Biosphere, and Atmosphere)-TRIP (Total Runoff Integrating Pathways) continental hydrologic system. Parameters such as the Manning coefficient, used within such models to describe water basin characteristics, are generally derived from geomorphological relationships, which leads to significant errors at reach and large scales. The current study focuses on the Niger Basin, a transboundary river. Since the SWOT observations are not available yet and also to assess the proposed assimilation method, the study is carried out under the framework of an observing system simulation experiment (OSSE). It is assumed that modeling errors are only due to uncertainties in the Manning coefficient. The true Manning coefficients are then supposed to be known and are used to generate synthetic SWOT observations over the period [2002][2003]. The impact of the assimilation system on the Niger Basin hydrological cycle is then quantified. The optimization of the Manning coefficient using the EKF (extended Kalman filter) algorithm over an 18month period led to a significant improvement of the river water levels. The relative bias of the water level is globally improved (a 30 % reduction). The relative bias of the Manning coefficient is also reduced (40 % reduction) and it converges towards an optimal value. Discharge is also improved by the assimilation, but to a lesser extent than for the water levels (7 %). Moreover, the method allows for a better simulation of the occurrence and intensity of flood events in the inner delta and shows skill in simulating the maxima and minima of water storage anomalies, especially in the groundwater and the aquifer reservoirs. The application of the assimilation method in the framework of an observing system simulation experiment allows evaluating the skill of the EKF algorithm to improve hydrological model parameters and to demonstrate SWOT's promising potential for global hydrology issues. However, further studies (e.g., considering multiple error sources and the difference between synthetic and real observations) are needed to achieve the evaluation of the method.
Science of The Total Environment, 2019
With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of the state of the hydrological processes via data assimilation. Observations can be assimilated into numerical models using dynamics and datadriven approaches. The present study aims to assess these assimilation frameworks for integrating different sets of satellite measurements in a hydrological context. To this end, we implement a traditional data assimilation system based on the Square Root Analysis (SQRA) filtering scheme and the newly developed data-driven Kalman-Takens technique to update the water components of a hydrological model with the Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage (TWS), and soil moisture products from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) in a 5-day temporal scale. While SQRA relies on a physical model for forecasting, the Kalman-Takens only requires a trajectory of the system based on past data. We are particularly interested in testing both methods for assimilating different combination of the satellite data. In most of the cases, simultaneous assimilation of the satellite data by either standard SQRA or Kalman-Takens achieves the largest improvements in the hydrological state, in terms of the agreement with independent in-situ measurements. Furthermore, the Kalman-Takens approach performs comparably well to dynamical method at a fraction of the computational cost.
Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges
Water Resources Research
The scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state-of-the-art terrestrial water storage product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low-flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM-simulated groundwater correlates strongly with 12-month precipitation anomalies in low-latitude and midlatitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional-scale drought indicators, with discrepancies mainly in their estimated drought severity.
Technical Note: Calibration and validation of geophysical observation models
Biogeosciences, 2012
We present a method to calibrate and validate observational models that interrelate remotely sensed energy fluxes to geophysical variables of land and water surfaces. Coincident sets of remote sensing observation of visible and microwave radiations and geophysical data are assembled and subdivided into calibration (Cal) and validation (Val) 5 data sets. Each Cal/Val pair is used to derive the coefficients (from the Cal set) and the accuracy (from the Val set) of the observation model. Combining the results from all Cal/Val pairs provides probability distributions of the model coefficients and model errors. The method is generic and demonstrated using comprehensive matchup sets from two very different disciplines, soil moisture and water quality. The results demon-10 strate that the method provides robust model coefficients and quantitative measure of the model uncertainty. This approach can be adopted for the calibration/validation of satellite products of land and water surfaces and the resulting uncertainty can be used as input to data assimilation schemes.
Bias correction of satellite soil moisture through data assimilation
Journal of Hydrology, 2022
Soil moisture exhibits great spatio-temporal heterogeneity and plays a critical part in land surface energy and water cycles, being identified as a terrestrial essential climate variable. Thus, it is urgently needed in a wide variety of environmental processes such as hydrology, meteorology, agriculture, and ecology. Microwave remote sensing has the potential to provide near real-time soil moisture estimates on large spatial scales according to the distinctive contrast between dielectric properties of water and dry soils. Thus, many space-borne microwave sensors have been launched for retrieving soil moisture. Especially, SMOS and SMAP at L-band frequency (1.4 GHz) supply an unprecedented opportunity for retrieving surface soil moisture due to their deeper penetration than instruments at other bands. However, these satellite soil moisture products need bias correction before application such as data assimilation. Two common correction methods require reliable land surface soil moisture simulations. However, the quality of these simulations relies heavily on model parameters, such as soil porosity and texture, which are almost unavailable in remote regions such as the Tibetan Plateau. In this study, a dual-cycle assimilation algorithm is taken to make on-line bias correction when assimilating SMAP soil moisture products. During the assimilation, a linear bias correction scheme is regarded as the observation operator to link the simulated soil moisture values and the satellite retrievals. In the inner cycle, a sequentially based assimilation algorithm is run with both model parameters and bias correction coefficients, which are provided by the outer cycle. At the same time, both the analyzed soil moisture and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of both model parameters and correction coefficients through an optimization algorithm. A series of numerical experiments are designed and conducted, indicating that the soil moisture estimates by the presented algorithm are superior to those with the existing bias correction schemes.
Global analysis of approaches for deriving total water storage changes from GRACE satellites
Water Resources Research, 2015
Increasing interest in use of GRACE satellites and a variety of new products to monitor changes in total water storage (TWS) underscores the need to assess the reliability of output from different products. The objective of this study was to assess skills and uncertainties of different approaches for processing GRACE data to restore signal losses caused by spatial filtering based on analysis of 1 3 1 grid-scale data and in 60 river basins globally. Results indicate that scaling factors from six LSMs, including GLDAS-1 four models (Noah2.7, Mosaic, VIC, and CLM 2.0), CLM 4.0, and WGHM, are similar over most of humid, subhumid, and high-latitude regions but can differ by up to 100% over arid and semiarid basins and areas with intensive irrigation. Temporal variability in scaling factors is generally minor at the basin scale except in arid and semiarid regions, but can be appreciable at the 1 3 1 grid scale. Large differences in TWS anomalies from three processing approaches (scaling factor, additive, and multiplicative corrections) were found in arid and semiarid regions, areas with intensive irrigation, and relatively small basins (e.g., 200,000 km 2). Furthermore, TWS anomaly products from gridded data with CLM4.0 scaling factors and the additive correction approach more closely agree with WGHM output than the multiplicative correction approach. This comprehensive evaluation of GRACE processing approaches should provide valuable guidance on applicability of different processing approaches with different climate settings and varying levels of irrigation.