title: Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation.
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.
authors.affiliation: Department of Water Resources, Institute of Water Resources and Hydropower Research (IWHR), Beijing, 100038, China. qin@itc.nl.|International Institute for Geo-Information Science and Earth Observation (ITC), 7500AA Enschede, The Netherlands. qin@itc.nl.|The Center for Clean Technology and Environmental Policy (CSTM), University of Twente, 7500AE Enschede, The Netherlands. qin@itc.nl.
authors.forename: Changbo
authors.initials: C
authors.lastname: Qin
authors.affiliation: Department of Water Resources, Institute of Water Resources and Hydropower Research (IWHR), Beijing, 100038, China. jiayw@iwhr.com.
authors.forename: Yangwen
authors.initials: Y
authors.lastname: Jia
authors.affiliation: International Institute for Geo-Information Science and Earth Observation (ITC), 7500AA Enschede, The Netherlands. b_su@itc.nl.
authors.forename: Z
authors.initials: Z
authors.lastname: Su
authors.affiliation: Department of Water Resources, Institute of Water Resources and Hydropower Research (IWHR), Beijing, 100038, China.
authors.forename: Zuhao
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: Department of Water Resources, Institute of Water Resources and Hydropower Research (IWHR), Beijing, 100038, China.
authors.forename: Yaqin
authors.initials: Y
authors.lastname: Qiu
authors.affiliation: Department of Water Resources, Institute of Water Resources and Hydropower Research (IWHR), Beijing, 100038, China.
authors.forename: Shen
authors.initials: S
authors.lastname: Suhui
keywords: Data assimilation; Distributed hydrological model; Evapotranspiration; Extended Kalman Filter.; SEBS; WEP