Integrating remote sensing information into a distributed hydrological model for improved water budget predictions in large - scale basins through data assimilation

This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems

Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model

This paper aims to investigate how surface soil moisture data assimilation affects each hydrologic process and how spatially varying inputs affect the potential capability of surface soil moisture assimilation at the watershed scale. The Ensemble Kalman Filter (EnKF) is coupled with a watershed scale, semi-distributed hydrologic model, the Soil and Water Assessment Tool (SWAT), to assimilate surface (5 cm) soil moisture. By intentionally setting inaccurate precipitation with open loop and EnKF scenarios in a synthetic experiment, the capability of surface soil moisture assimilation to compensate for the precipitation errors were examined. Results show that daily assimilation of surface soil moisture for each HRU improves model predictions especially reducing errors in surface and profile soil moisture estimation. Almost all hydrological processes associated with soil moisture are also improved with decreased root mean square error (RMSE) values through the EnKF scenario. The EnKF does not produce as much a significant improvement in streamflow predictions as compared to soil moisture estimates in the presence of large precipitation errors and the limitations of the infiltration–runoff model mechanism. Distributed errors of the soil water content also show the benefit of surface soil moisture assimilation and the influences of spatially varying inputs such as soil and landuse types. Thus, soil moisture update through data assimilation can be a supplementary way to overcome the errors created by inaccurate rainfall. Even though this synthetic study shows the potential of remotely sensed surface soil moisture measurements for applications of watershed scale water resources management, future studies are necessary that focus on the use of real-time observational data

Assimilating high resolution remotely sensed soil moisture into a distributed hydrologic model to improve runoff prediction

The susceptibility of a catchment to flooding during an extreme rainfall event is affected by its soil moisture condition prior to the event. A study to improve the state vector of a distributed hydrologic model by assimilating high resolution remotely sensed soil moisture is described. The launch of Sentinel-1 has stimulated interest in measuring soil moisture at high resolution suitable for hydrological studies using Synthetic Aperture Radars (SARs). The advantages of using SAR soil moisture in conjunction with land cover data are considered. These include the ability to reduce contamination of the surface soil signal due to vegetation, radar artefacts, mixed pixels and land cover classes not providing meaningful soil moistures. Results for 2008 using ASAR data showed that the assimilation of ASAR soil moisture values improved the predicted flows for all images. The improvement was less marked for 2007, probably because the antecedent soil moisture conditions were of reduced importance during the extreme flooding that occurred then. Particularly for 2008, the higher resolution of ASAR data improved predicted flows compared to low resolution ASCAT data that were not disaggregated and limited to the temporal frequency of ASAR. The method is likely to give better results with Sentinel-1 rather than ASAR data due to its higher temporal resolution

Assessment of assimilating SMOS soil moisture information into a distributed hydrologic model

The role that soil moisture plays in terms of modulating hydrologic processes including infiltration and runoff generation makes it an essential component to capture for hydrologic modeling. This work aims to leverage information gained from SMOS to improve surface soil moisture simulations in the Russian River Basin (California, U.S.A). The basin's complex terrain offers a rigorous testing ground for SMOS soil moisture products. Data from seven in situ observation sites are used to assess model performance after assimilating SMOS-based soil saturation ratios. For a comparison of "best case" scenarios, the in situ observations themselves are assimilated. Results show that SMOS assimilated simulations shows modest improvement at most in situ locations. Despite the observed decrease in model performance at some locations, overall performance of simulations assimilated with SMOS-based saturation ratios remains high. Findings suggest that even in a complex environment, useful information may be extracted from SMOS estimates for hydrologic modeling

Assimilating SAR-derived water level data into a hydraulic model: a case study

Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction in the uncertainty at the analysis step. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data

Synergistic calibration of a hydrological model using discharge and remotely sensed soil moisture in the Paraná river basin

Hydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components of the water cycle. This has led the hydrologic community to look for ways to constrain models with multiple variables. Remote sensing estimates of soil moisture are very promising in this sense, especially in large areas for which field observations may be unevenly distributed. However, the use of such data to calibrate hydrological models in a synergistic way is still not well understood, especially in tropical humid areas such as those found in South America. Here, we perform multiple scenarios of multiobjective model optimization with in situ discharge and the SMOS L4 root zone soil moisture product for the Upper Paraná River Basin in South America (drainage area > 900,000 km2), for which discharge data for 136 river gauges are used. An additional scenario is used to compare the relative impacts of using all river gauges and a small subset containing nine gauges only. Across the basin, the joint calibration (CAL-DS) using discharge and soil moisture leads to improved precision and accuracy for both variables. The discharges estimated by CAL-DS (median KGE improvement for discharge was 0.14) are as accurate as those obtained with the calibration with discharge only (median equal to 0.14), while the CAL-DS soil moisture retrieval is practically as accurate (median KGE improvement for soil moisture was 0.11) as that estimated using the calibration with soil moisture only (median equal to 0.13). Nonetheless, the individual calibration with discharge rates is not able to retrieve satisfactory soil moisture estimates, and vice versa. These results show the complementarity between these two variables in the model calibration and highlight the benefits of considering multiple variables in the calibration framework. It is also shown that, by considering only nine gauges inst

Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter : proof of concept

With the onset of new satellite radar constellations (e.g. Sentinel-1) and advances in computational science (e.g. grid computing) enabling the supply and processing of multimission satellite data at a temporal frequency that is compatible with real-time flood forecasting requirements, this study presents a new concept for the sequential assimilation of Synthetic Aperture Radar (SAR)-derived water stages into coupled hydrologic-hydraulic models. The proposed methodology consists of adjusting storages and fluxes simulated by a coupled hydrologic-hydraulic model using a Particle Filterbased data assimilation scheme. Synthetic observations of water levels, representing satellite measurements, are assimilated into the coupled model in order to investigate the performance of the proposed assimilation scheme as a function of both accuracy and frequency of water level observations. The use of the Particle Filter provides flexibility regarding the form of the probability densities of both model simulations and remote sensing observations. We illustrate the potential of the proposed methodology using a twin experiment over a widely studied river reach located in the Grand-Duchy of Luxembourg. The study demonstrates that the Particle Filter algorithm leads to significant uncertainty reduction of water level and discharge at the time step of assimilation. However, updating the storages of the model only improves the model forecast over a very short time horizon. A more effective way of updating thus consists in adjusting both states and inputs. The proposed methodology, which consists in updating the biased forcing of the hydraulic model using information on model errors that is inferred from satellite observations, enables persistent model improvement. The present schedule of satellite radar missions is such that it is likely that there will be continuity for SAR-based operational water management services. This research contributes to evolve reactive flood management into systematic or quasi-systematic SAR-based flood monitoring services

Predictability of seasonal runoff in the Mississippi River basin

Recent advances in climate prediction and remote sensing offer the potential to improve long-lead streamflow forecasts and to provide better land surface state estimates at the time of forecast. We characterize predictability of runoff at seasonal timescales in the Mississippi River basin due to climatic persistence (represented by El Niño-Southern Oscillation and the Arctic Oscillation) and persistence related to the initial land surface state (soil moisture and snow). These climate and land surface state indicators, at varying lead times, are then used in a multiple linear regression to explain the variance of seasonal average runoff. Soil moisture dominates runoff predictability for lead times of 1 1/2 months, except in summer in the western part of the basin, where snow dominates. For the western part of the basin, the land surface state has a stronger predictive capability than climate indicators through leads of two seasons; climate indicators are more important in the east at lead times of one season or greater. Modest winter runoff predictability exists at a lead time of 3 seasons due to both climate and soil moisture, but this is in areas producing little runoff and is therefore of lessened importance. Local summer runoff predictability is limited to the western mountainous areas (generating high runoff) through a lead of 2 seasons. This could be useful to water managers in the western portion of the Mississippi River basin, because it suggests the potential to provide skillful forecast information earlier in the water year than currently used in operational forecasts