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  • Author or Editor: Valentijn R. N. Pauwels x
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Valentijn R. N. Pauwels and Eric F. Wood

Abstract

One of the governing scientific objectives of the Boreal Ecosystem–Atmosphere Study (BOREAS) is the development of methods for applying process models over large spatial scales using remote sensing and other integrative modeling techniques. This paper presents the first step in a modeling strategy that focuses on scaling a point model up to the BOREAS regional scale. The objective of this paper is to compare the effect of differences in spatial resolution of land cover data to land–atmosphere model results relative to the effect of differences in land cover sensors and classification schemes. The analysis suggests that the uncertainty in model results arises mainly from the uncertainty in the land cover classification and that the lack of spatial resolution has a lower effect. Overall, an uncertainty of approximately 15% in modeled energy and water balance fluxes and states has to be assigned because of the uncertainty in land cover classification.

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Valentijn R. N. Pauwels and Gabriëlle J. M. De Lannoy

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The objective of this paper is to improve the performance of a hydrologic model through the assimilation of observed discharge. Since an observation of discharge at a certain time is always influenced by the catchment wetness conditions and meteorology in the past, the assimilation method will have to modify both the past and present soil wetness conditions. For this purpose, a bias-corrected retrospective ensemble Kalman filter has been used as the assimilation algorithm. The assimilation methodology takes into account bias in the forecast state variables for the calculation of the optimal estimates. A set of twin experiments has been developed, in which it is attempted to correct the model results obtained with erroneous initial conditions and strongly over- and underestimated precipitation data. The results suggest that the assimilation of observed discharge can correct for erroneous model initial conditions. When the precipitation used to force the model is underestimated, the assimilation of observed discharge can reduce the bias in the modeled turbulent fluxes by approximately 50%. This is due to a correction of the modeled soil moisture. In the case of an overestimation of the precipitation, an improvement in the modeled wetness conditions is also obtained after data assimilation, but this does not lead to a significant improvement in the modeled energy balance. The results in this paper indicate that there is potential to improve the estimation of hydrologic states and fluxes through the assimilation of observed discharge data.

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Bruno Samain, Willem Defloor, and Valentijn R. N. Pauwels

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A large aperture scintillometer (LAS) observes the intensity of the atmospheric turbulence across large distances, which is related to the path-averaged sensible heat flux H. In this paper, two problems in the derivation of continuous series of H from LAS data are investigated and the importance of nighttime H fluxes is assessed. First, as an LAS is unable to determine the sign of H, the transition from unstable to stable conditions is evaluated in order to make continuous H series. Therefore, different algorithms to judge the atmospheric stability for an LAS installed over a distance of 9.5 km have been tested. The diurnal cycle of the refractive index structure parameter, , results in the best suitable, operational algorithm. A second issue is the humidity correction for LAS data, which is performed by using the Bowen ratio (β). As β is taken from ground-based measurements with data gaps, the number of resulting H values is reduced. Not including this humidity correction results in a marginal error in H, but increases the completeness of the H series. Applying these conclusions to the 2-yr time series of the LAS results in an almost-continuous H time series. As the majority of the time steps has been found to be under stable conditions, there is a clear impact of H stable on H 24h—the 24-h average of H. For stable conditions, H stable values are mostly negative, and hence lower than the H = 0 W m−2 assumption as mostly adopted. For months where stable conditions prevail (winter), H 24h is overestimated using this assumption, and calculation of H stable is recommended.

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Ashley J. Wright, Jeffrey P. Walker, and Valentijn R. N. Pauwels

Abstract

An increased understanding of the uncertainties present in rainfall time series can lead to improved confidence in both short- and long-term streamflow forecasts. This study presents an analysis that considers errors arising from model input data, model structure, model parameters, and model states with the objective of finding a self-consistent set that includes hydrological models, model parameters, streamflow, remotely sensed (RS) soil moisture (SM), and rainfall. This methodology can be used by hydrologists to aid model and satellite selection. Taking advantage of model input data reduction and model inversion techniques, this study uses a previously developed methodology to estimate areal rainfall time series for the study catchment of Warwick, Australia, for multiple rainfall–runoff models. RS SM observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) satellites were assimilated into three different rainfall–runoff models using an ensemble Kalman filter (EnKF). Innovations resulting from the observed and predicted SM were analyzed for Gaussianity. The findings demonstrate that consistency between hydrological models, model parameters, streamflow, RS SM, and rainfall can be found. Joint estimation of rainfall time series and model parameters consistently improved streamflow simulations. For all models rainfall estimates are less than the observed rainfall, and rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model are the most consistent with gauge-based observations. The SAC-SMA model simulates streamflow that is most consistent with observations. EnKF innovations obtained when SMOS RS SM observations were assimilated into the SAC-SMA model demonstrate consistency between SM products.

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Gabriëlle J. M. De Lannoy, Paul R. Houser, Niko E. C. Verhoest, and Valentijn R. N. Pauwels

Abstract

Data assimilation aims to provide an optimal estimate of the overall system state, not only for an observed state variable or location. However, large-scale land surface models are typically column-based and purely random ensemble perturbation of states will lead to block-diagonal a priori (or background) error covariance. This facilitates the filtering calculations but compromises the potential of data assimilation to influence (unobserved) vertical and horizontal neighboring state variables. Here, a combination of an ensemble Kalman filter and an adaptive covariance correction method is explored to optimize the variances and retrieve the off-block-diagonal correlations in the a priori error covariance matrix. In a first time period, all available soil moisture profile observations in a small agricultural field are assimilated into the Community Land Model, version 2.0 (CLM2.0) to find the adaptive second-order a priori error information. After that period, only observations from single individual soil profiles are assimilated with inclusion of this adaptive information. It is shown that assimilation of a single profile can partially rectify the incorrectly simulated soil moisture spatial mean and variability. The largest reduction in the root-mean-square error in the soil moisture field varies between 7% and 22%, depending on the soil depth, when assimilating a single complete profile every two days during three months with a single time-invariant covariance correction.

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Gabriëlle J. M. De Lannoy, Rolf H. Reichle, and Valentijn R. N. Pauwels

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A zero-order (tau-omega) microwave radiative transfer model (RTM) is coupled to the Goddard Earth Observing System, version 5 (GEOS-5) catchment land surface model in preparation for the future assimilation of global brightness temperatures (Tb) from the L-band (1.4 GHz) Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions. Simulations using literature values for the RTM parameters result in Tb biases of 10–50 K against SMOS observations. Multiangular SMOS observations during nonfrozen conditions from 1 July 2011 to 1 July 2012 are used to calibrate parameters related to the microwave roughness h, vegetation opacity τ and/or scattering albedo ω separately for each observed 36-km land grid cell. A particle swarm optimization is used to minimize differences in the long-term (climatological) mean values and standard deviations between SMOS observations and simulations, without attempting to reduce the shorter-term (seasonal to daily) errors. After calibration, global Tb simulations for the validation year (1 July 2010 to 1 July 2011) are largely unbiased for multiple incidence angles and both H and V polarization [e.g., the global average absolute difference is 2.7 K for TbH(42.5°), i.e., at 42.5° incidence angle]. The calibrated parameter values depend to some extent on the specific land surface conditions simulated by the GEOS-5 system and on the scale of the SMOS observations, but they also show realistic spatial distributions. Aggregating the calibrated parameter values by vegetation class prior to using them in the RTM maintains low global biases but increases local biases [e.g., the global average absolute difference is 7.1 K for TbH(42.5°)].

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Gabriëlle J. M. De Lannoy, Rolf H. Reichle, Paul R. Houser, Kristi R. Arsenault, Niko E. C. Verhoest, and Valentijn R. N. Pauwels

Abstract

Four methods based on the ensemble Kalman filter (EnKF) are tested to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of passive microwave satellite retrievals) into finescale (1 km) land model simulations. Synthetic coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (regridding) to the finescale model resolution prior to data assimilation. In either case, observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated finescale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the finescale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

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