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have also been applied to describe soil moisture observations in regions with either strong or weak seasonality in forcing (e.g., Calanca 2004 ; Teuling et al. 2005 ; Miller et al. 2007 ; Teuling et al. 2007 ). The stochastic models are described in section 2 . Soil parameters are taken from three LSMs used within the European Land Data Assimilation System project (ELDAS). For more information on the ELDAS soil moisture, we refer to Jacobs et al. (2008) and van den Hurk et al. (2008) . The
have also been applied to describe soil moisture observations in regions with either strong or weak seasonality in forcing (e.g., Calanca 2004 ; Teuling et al. 2005 ; Miller et al. 2007 ; Teuling et al. 2007 ). The stochastic models are described in section 2 . Soil parameters are taken from three LSMs used within the European Land Data Assimilation System project (ELDAS). For more information on the ELDAS soil moisture, we refer to Jacobs et al. (2008) and van den Hurk et al. (2008) . The
–Meuse delta (low elevation and peat and clay of the last 4000 yr; Berendsen and Stouthamer 2000 ), which onlaps coversands and sandur outwash deposits in front of a Saalian ice-pushed ridge (high elevation and 150 000 yr old; Busschers et al. 2007 ). Figure 1b shows the elevation together with the location of the rain gauges and the soil moisture measurements, land use, and soil types of the Langbroekerwetering. For a description of the soil types, we refer to Table 1 . At the higher elevations
–Meuse delta (low elevation and peat and clay of the last 4000 yr; Berendsen and Stouthamer 2000 ), which onlaps coversands and sandur outwash deposits in front of a Saalian ice-pushed ridge (high elevation and 150 000 yr old; Busschers et al. 2007 ). Figure 1b shows the elevation together with the location of the rain gauges and the soil moisture measurements, land use, and soil types of the Langbroekerwetering. For a description of the soil types, we refer to Table 1 . At the higher elevations
the interaction of microwaves with the earth’s surface, retrieval methods have been mostly experimental and limited to certain climatic regions. One of the long-term global remotely sensed soil moisture datasets available today is the dataset derived from European Remote Sensing Satellites 1 and 2 ( ERS-1 ) and ( ERS-2 ) scatterometers (SCATs; coarse-resolution radar instruments with superior radiometric accuracy), using a soil moisture retrieval algorithm developed at the Vienna University of
the interaction of microwaves with the earth’s surface, retrieval methods have been mostly experimental and limited to certain climatic regions. One of the long-term global remotely sensed soil moisture datasets available today is the dataset derived from European Remote Sensing Satellites 1 and 2 ( ERS-1 ) and ( ERS-2 ) scatterometers (SCATs; coarse-resolution radar instruments with superior radiometric accuracy), using a soil moisture retrieval algorithm developed at the Vienna University of
ratio of total upper-leaf surface of vegetation divided by the surface area of land on which the vegetation grows. Remote sensing fractional vegetation cover and LAI data have been widely used in distributed hydrological models. Andersen et al. (2002) used LAI time series data derived from Advanced Very High Resolution Radiometer (AVHRR) in a distributed hydrological model and found remote sensed LAI can better represent the spatial heterogeneity in model simulations and improve simulated
ratio of total upper-leaf surface of vegetation divided by the surface area of land on which the vegetation grows. Remote sensing fractional vegetation cover and LAI data have been widely used in distributed hydrological models. Andersen et al. (2002) used LAI time series data derived from Advanced Very High Resolution Radiometer (AVHRR) in a distributed hydrological model and found remote sensed LAI can better represent the spatial heterogeneity in model simulations and improve simulated
properties via derived land surface temperature (LST). For over two decades, models of varying sophistication have been used to relate top-of-canopy LST to evaporative fluxes ( Mecikalski et al. 1999 ; Boni et al. 2001 ; Caparrini et al. 2004 ; Sobrino et al. 2007 ). More challenging, however, is the diagnosis of profile soil moisture from LST using data assimilation methods ( Crow et al. 2008 ), which requires explicit knowledge of the relationships between soil moisture, canopy resistance, and the
properties via derived land surface temperature (LST). For over two decades, models of varying sophistication have been used to relate top-of-canopy LST to evaporative fluxes ( Mecikalski et al. 1999 ; Boni et al. 2001 ; Caparrini et al. 2004 ; Sobrino et al. 2007 ). More challenging, however, is the diagnosis of profile soil moisture from LST using data assimilation methods ( Crow et al. 2008 ), which requires explicit knowledge of the relationships between soil moisture, canopy resistance, and the
mean of the perturbed state variables are compared with unperturbed state variables. However, owing to nonlinear processes imbedded in land surface models, it is unavoidable that ensemble perturbation using Gaussian noise will lead to biased model forecasts ( De Lannoy et al. 2006 ). As a consequence, the mere ensembling of the model during implementation of the EnKF can introduce systematic error into its flux and state predictions. Examples of nonlinear model processes potentially responsible for
mean of the perturbed state variables are compared with unperturbed state variables. However, owing to nonlinear processes imbedded in land surface models, it is unavoidable that ensemble perturbation using Gaussian noise will lead to biased model forecasts ( De Lannoy et al. 2006 ). As a consequence, the mere ensembling of the model during implementation of the EnKF can introduce systematic error into its flux and state predictions. Examples of nonlinear model processes potentially responsible for
the representation of the land surface characteristics, and the lack of a continuous and reliable observation network. Mature techniques for generating ensemble members or quantifying the uncertainties associated with the initial conditions and the model are also nonexistent or are still in their infancy. In many cases, an ensemble of meteorological forecasts is used to drive the same hydrological integration, with the same model and from the same initial conditions. For example, Pappenberger et
the representation of the land surface characteristics, and the lack of a continuous and reliable observation network. Mature techniques for generating ensemble members or quantifying the uncertainties associated with the initial conditions and the model are also nonexistent or are still in their infancy. In many cases, an ensemble of meteorological forecasts is used to drive the same hydrological integration, with the same model and from the same initial conditions. For example, Pappenberger et
could be beneficial ( Romanowicz et al. 2006 ). Here, in situ soil moisture profiles in a small agricultural field [Optimizing Production Inputs for Economic and Environmental Enhancement (OPE 3 )] are simulated by individual, unconnected soil columns in the Community Land Model, version 2.0 (CLM2.0). Ensembles are used to quantify the variables’ uncertainty in each individual profile. Then, the ensemble a priori error variance is adapted, and the error correlation between the profiles is sought for
could be beneficial ( Romanowicz et al. 2006 ). Here, in situ soil moisture profiles in a small agricultural field [Optimizing Production Inputs for Economic and Environmental Enhancement (OPE 3 )] are simulated by individual, unconnected soil columns in the Community Land Model, version 2.0 (CLM2.0). Ensembles are used to quantify the variables’ uncertainty in each individual profile. Then, the ensemble a priori error variance is adapted, and the error correlation between the profiles is sought for
(from steps 1 and 2) to seasonal predictions of discharge, we need a hydrological model. Here we use the global hydrological model PCRaster Global Water Balance (PCR-GLOBWB) ( van Beek 2007 ). PCRaster ( Wesseling et al. 1996 ) is the scripting language in which the model is coded. The model simulates soil water storage, groundwater storage, and specific runoff (local runoff per unit land area) globally for daily time steps at 0.5° × 0.5° spatial resolution. Specific runoff is accumulated along a
(from steps 1 and 2) to seasonal predictions of discharge, we need a hydrological model. Here we use the global hydrological model PCRaster Global Water Balance (PCR-GLOBWB) ( van Beek 2007 ). PCRaster ( Wesseling et al. 1996 ) is the scripting language in which the model is coded. The model simulates soil water storage, groundwater storage, and specific runoff (local runoff per unit land area) globally for daily time steps at 0.5° × 0.5° spatial resolution. Specific runoff is accumulated along a
