Abstract
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.
Highlights
Because water is becoming the limiting factor for development in many parts of the world, more systematic approaches are needed to analyze the uses, depletion, and productivity of water
Surface Energy Balance System (SEBS) estimates are aggregated into 11752 hydrologic response units (HRU) (see Figure 7(b)) in order to make the data series matching for data assimilation
Some spatial variability information could be missed in the process of data format conversion, SEBS ET distribution remains more spatially variable than model derived ET distribution
Summary
Because water is becoming the limiting factor for development in many parts of the world, more systematic approaches are needed to analyze the uses, depletion, and productivity of water. An improved knowledge of the land surface hydrologic states and fluxes, and of their spatial and temporal variability across different scales, is urgently needed in many hydrologic studies and water resources management [1,2]. Combining and integrating the capabilities and information within an integrated framework from simulation and remote sensing techniques is both appealing and necessary for improving our knowledge of fundamental hydrological processes and for supporting water resources management in the catchment or basin scale. The work presented in this paper is motivated by the need to develop an improved data assimilation system that use remote sensed ET to improve the predictive performance of a distributed hydrological model in a large river basin. Pan et al [3] proposed and tested a data assimilation system that consisted of a combination of two filters - a particle filter (PF) [24,25] and an ensemble Kalman filter (EnKF) [26,27] to estimate the water budget using a MODIS based estimate of surface evapotranspiration (ET) over the spatial domain of Red-Arkansas river basin
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