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Dingchen Hou, Kenneth Mitchell, Zoltan Toth, Dag Lohmann, and Helin Wei

Abstract

Hydrological processes are strongly coupled with atmospheric processes related, for example, to precipitation and temperature, and a coupled atmosphere–land surface system is required for a meaningful hydrological forecast. Since the atmosphere is a chaotic system with limited predictability, ensemble forecasts offer a practical tool to predict the future state of the coupled system in a probabilistic fashion, potentially leading to a more complete and informative hydrologic prediction. As ensemble forecasts with coupled meteorological–hydrological models are operationally running at major numerical weather prediction centers, it is currently possible to produce a gridded streamflow prognosis in the form of a probabilistic forecast based on ensembles. Evaluation and improvement of such products require a comprehensive assessment of both components of the coupled system.

In this article, the atmospheric component of a coupled ensemble forecasting system is evaluated in terms of its ability to provide reasonable forcing to the hydrological component and the effect of the uncertainty represented in the atmospheric ensemble system on the predictability of streamflow as a hydrological variable. The Global Ensemble Forecast System (GEFS) of NCEP is evaluated following a “perfect hydrology” approach, in which its hydrological component, including the Noah land surface model and attached river routing model, is considered free of errors and the initial conditions in the hydrological variables are assumed accurate. The evaluation is performed over the continental United States (CONUS) domain for various sizes of river basins. The results from the experiment suggest that the coupled system is capable of generating useful gridded streamflow forecast when the land surface model and the river routing model can successfully simulate the hydrological processes, and the ensemble strategy significantly improves the forecast. The expected forecast skill increases with increasing size of the river basin. With the current GEFS system, positive skill in short-range (one to three days) predictions can be expected for all significant river basins; for the major rivers with mean streamflow more than 500 m3 s−1, significant skill can be expected from extended-range (the second week) predictions. Possible causes for the loss of skills, including the existence of systematic error and insufficient ensemble spread, are discussed and possible approaches for the improvement of the atmospheric ensemble forecast system are also proposed.

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Cheng-Hsuan Lu, Masao Kanamitsu, John O. Roads, Wesley Ebisuzaki, Kenneth E. Mitchell, and Dag Lohmann

Abstract

This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture characteristics well in all aspects. Sometimes R-1 provides a better soil moisture analysis on monthly time scales, which is likely a consequence of the deficiencies in the R-2 surface water balance.

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Bart Nijssen, Greg M. O'Donnell, Dennis P. Lettenmaier, Dag Lohmann, and Eric F. Wood

Abstract

The ability to simulate coupled energy and water fluxes over large continental river basins, in particular streamflow, was largely nonexistent a decade ago. Since then, macroscale hydrological models (MHMs) have been developed, which predict such fluxes at continental and subcontinental scales. Because the runoff formulation in MHMs must be parameterized because of the large spatial scale at which they are implemented, some calibration of model parameters is inevitably necessary. However, calibration is a time-consuming process and quickly becomes infeasible when the modeled area or the number of basins increases. A methodology for model parameter transfer is described that limits the number of basins requiring direct calibration. Parameters initially were estimated for nine large river basins. As a first attempt to transfer parameters, the global land area was grouped by climate zone, and model parameters were transferred within zones. The transferred parameters were then used to simulate the water balance in 17 other continental river basins. Although the parameter transfer approach did not reduce the bias and root-mean-square error (rmse) for each individual basin, in aggregate the transferred parameters reduced the relative (monthly) rmse from 121% to 96% and the mean bias from 41% to 36%. Subsequent direct calibration of all basins further reduced the relative rmse to an average of 70% and the bias to 12%. After transferring the parameters globally, the mean annual global runoff increased 9.4% and evapotranspiration decreased by 5.0% in comparison with an earlier global simulation using uncalibrated parameters. On a continental basis, the changes in runoff and evapotranspiration were much larger. A diagnosis of simulation errors for four basins with particularly poor results showed that most of the error was attributable to bias in the Global Precipitation Climatology Project precipitation products used to drive the MHM.

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