Journal of Hydrometeorology

Bertoldi, Giacomo; Rigon, Riccardo; Over, Thomas M.

doi: 10.1175/JHM500.1pmid: N/A

The GEOtop model makes it possible to analyze the short- and long-term effects of geomorphic variation on the partitioning of the lateral surface and subsurface water and surface energy fluxes. The topography of the Little Washita basin (Oklahoma) and of the Serraia basin (Trentino, Italy) have been used as base topographies from which virtual topographies with altered slopes and elevations have been created with corresponding modifications of the soil thickness and the extension of the channel network, according to applicable geomorphological theories, in order to quantify the contribution of these topographic features to the spatial and temporal variability of energy and water fluxes. Simulation results show that both a more extended channel network and more accentuated slopes cause an increase in the discharge balanced by a diminution of the evapotranspiration. The diminution of the latent heat flux is balanced by the increase in the sensible heat flux. Net radiation shows a minor sensitivity to topography. Evaporative fraction, on the contrary, is shown to be strongly dependent on geomorphic characteristics. The results confirm the importance of including an adequate treatment of topography in large-scale land surface models.

Bosilovich, Michael G.; Chern, Jiun-Dar

doi: 10.1175/JHM501.1pmid: N/A

An atmospheric general circulation model simulation for 1948––97 of the water budgets for the MacKenzie, Mississippi, and Amazon River basins is presented. In addition to the water budget, passive tracers are included to identify the geographic sources of water for the basins, and the analysis focuses on the mechanisms contributing to precipitation recycling in each basin. While each basin’’s precipitation recycling has a strong dependency on evaporation during the mean annual cycle, the interannual variability of the recycling shows important relationships with the atmospheric circulation. The MacKenzie River basin recycling has only a weak interannual correspondence with evaporation, where the variations in zonal moisture transport from the Pacific Ocean can affect the basin water cycle. On the other hand, the Mississippi River basin precipitation and recycling have strong interannual correlation on evaporation. The evaporation is related to the moist and shallow planetary boundary layer that provides moisture for convection at the cloud base. At global scales, high precipitation recycling is also found to be partly correlated to warm SSTs in the tropical Pacific Ocean. The Amazon River basin evaporation exhibits small interannual variations, so the interannual variations of precipitation recycling are related to atmospheric moisture transport from the tropical South Atlantic Ocean. Increasing SSTs over the 50-yr period are causing increased easterly transport across the basin. As moisture transport increases, the Amazon precipitation recycling decreases (without real-time varying vegetation changes). In addition, precipitation recycling from a bulk diagnostic method is compared to the passive tracer method used in the analysis. While the mean values of the different recycling methods are different, the interannual variations are comparable between each method. The methods also exhibit similar relationships to the terms of the basin-scale water budgets.

Hasan, Shaakeel; Troch, Peter A.; Boll, J.; Kroner, C.

doi: 10.1175/JHM488.1pmid: N/A

A superconducting gravimeter has observed with high accuracy (to within a few nm s −−2 ) and high frequency (1 Hz) the temporal variations in the earth’’s gravity field near Moxa, Germany, since 1999. Hourly gravity residuals are obtained by time averaging and correcting for earth tides, polar motion, barometric pressure variations, and instrumental drift. These gravity residuals are significantly affected by hydrological processes (interception, infiltration, surface runoff, and subsurface redistribution) in the vicinity of the observatory. In this study time series analysis and distributed hydrological modeling techniques are applied to understand the effect of these hydrological processes on observed gravity residuals. It is shown that the short-term response of gravity residuals to medium- to high-rainfall events can be efficiently modeled by means of a linear transfer function. This transfer function exhibits an oscillatory behavior that indicates fast redistribution of stored water in the upper layers (interception store, root zone) of the catchment surrounding the instrument. The relation between groundwater storage and gravity residuals is less clear and varies according to the season. High positive correlation between groundwater and gravity exists during winter months when the freezing of the upper soil layers immobilizes water stored in the unsaturated zone of the catchment. To further explore the spatiotemporal dynamics of the relevant hydrological processes and their relation to observed gravity residuals, a GIS-based distributed hydrological model is applied for the Silberleite catchment. Driven by observed atmospheric forcings (precipitation and potential evapotranspiration), the model allows the authors to compute the variation of water storage in three different layers: the interception store, the snow cover store, and the soil moisture store. These water storage dynamics are then converted to predicted gravity variation at the location of the superconducting gravimeter and compared to observed gravity residuals. During most of the investigated period (January 2000 to January 2004) predictions are in good agreement with the observed patterns of gravity dynamics. However, during some winter months the distributed hydrological model fails to explain the observations, which supports the authors’’ conclusion that groundwater variability dominates the hydrological gravity signal in the winter. More hydrogeological research is needed to include groundwater dynamics in the hydrological model.

Wóójcik, R.; Troch, Peter A.; Stricker, H.; Torfs, P.; Wood, E.; Su, H.; Su, Z.

doi: 10.1175/JHM491.1pmid: N/A

This paper proposes a new probabilistic approach for describing uncertainty in the ensembles of latent heat flux proxies. The proxies are obtained from hourly Bowen ratio and satellite-derived measurements, respectively, at several locations in the southern Great Plains region in the United States. The novelty of the presented approach is that the proxies are not considered separately, but as bivariate samples from an underlying probability density function. To describe the latter, the use of Gaussian mixture density models——a class of nonparametric, data-adaptive probability density functions——is proposed. In this way any subjective assumptions (e.g., Gaussianity) on the form of bivariate latent heat flux ensembles are avoided. This makes the estimated mixtures potentially useful in nonlinear interpolation and nonlinear probabilistic data assimilation of noisy latent heat flux measurements. The results in this study show that both of these applications are feasible through regionalization of estimated mixture densities. The regionalization scheme investigated here utilizes land cover and vegetation fraction as discriminatory variables.

Haddeland, Ingjerd; Lettenmaier, Dennis P.; Skaugen, Thomas

doi: 10.1175/JHM496.1pmid: N/A

Hydrological model predictions are sensitive to model forcings, input parameters, and the parameterizations of physical processes. Analyses performed for the Variable Infiltration Capacity model show that the resulting moisture fluxes are sensitive to the time step and energy balance closure assumptions. In addition, the model results are sensitive to the method of spatial and temporal disaggregation of precipitation. For parameter estimation purposes, it is desirable to do parameter searches in water balance mode (meaning that the effective surface temperature is assumed equal to the surface air temperature; hence no iteration for energy balance closure is performed) at daily time steps. However, transferring these parameters directly to other model modes (e.g., energy balance, in which an iteration for effective surface temperature is performed, and/or different model time steps) results in changes in the simulated moisture fluxes. The simulated differences in moisture fluxes are mainly a result of the parameterization of evapotranspiration at different time steps and model modes. A simple scheme that calculates correction factors for some model parameters is developed. The scheme is used to match simulated moisture fluxes in hourly and 3-hourly energy balance mode to the daily water balance simulation results, and to match hourly energy balance runs using spatially and temporally disaggregated precipitation to 3-hourly energy balance runs using uniformly disaggregated precipitation. For both approaches, the corrected simulations match the baseline simulations quite closely, both over transects across much of the continental United States and for test applications in the Ohio and Arkansas––Red River basins.

Wilker, Henning; Drusch, Matthias; Seuffert, Gisela; Simmer, Clemens

doi: 10.1175/JHM498.1pmid: N/A

The impact of model and observation errors in the European Land Data Assimilation System (ELDAS) data assimilation system on the analyzed surface variables has been studied using the Southern Great Plains Hydrology Experiment (SGP) 1997 and 1999 datasets. The model error for soil moisture was derived from an error propagation experiment based on perturbed rainfall forcing data. It was found that the errors for the top three model layers are 0.010, 0.010, and 0.0015 m 3 m −−3 , respectively. Data assimilation experiments based on screen-level variables (2-m temperature and humidity) and L-band brightness temperature observations from SGP97 with this error distribution result in improved soil moisture forecasts when compared to model runs with a vertically constant model error of 0.005 m 3 m −−3 . In the second part of this study, the effect of the vertical soil moisture distribution——which can hardly be resolved by large-scale hydrological models——in the assimilation system has been quantified using SGP99 data. The vertical profile has a significant impact on the modeled brightness temperatures. Based on the time elapsed between a rainfall event and the observation, a correction scheme has been developed that can be applied in observation space. The assimilation of brightness temperatures led to more accurate predictions of soil moisture and surface fluxes when the correction scheme was used.

Slater, Andrew G.; Clark, Martyn P.

doi: 10.1175/JHM505.1pmid: N/A

A snow data assimilation study was undertaken in which real data were used to update a conceptual model, SNOW-17. The aim of this study is to improve the model’’s estimate of snow water equivalent (SWE) by merging the uncertainties associated with meteorological forcing data and SWE observations within the model. This is done with a view to aiding the estimation of snowpack initial conditions for the ultimate objective of streamflow forecasting via a distributed hydrologic model. To provide a test of this methodology, the authors performed experiments at 53 stations in Colorado. In each case the situation of an unobserved location is mimicked, using the data at any given station only for validation; essentially, these are withholding experiments. Both ensembles of model forcing data and assimilated data were derived via interpolation and stochastic modeling of data from surrounding sources. Through a process of cross validation the error for the ensemble of model forcing data and assimilated observations is explicitly estimated. An ensemble square root Kalman filter is applied to perform assimilation on a 5-day cycle. Improvements in the resulting SWE are most evident during the early accumulation season and late melt period. However, the large temporal correlation inherent in a snowpack results in a less than optimal assimilation and the increased skill is marginal. Once this temporal persistence is removed from both model and assimilated observations during the update cycle, a result is produced that is, within the limits of available information, consistently superior to either the model or interpolated observations.

Rigon, Riccardo; Bertoldi, Giacomo; Over, Thomas M.

doi: 10.1175/JHM497.1pmid: N/A

This paper describes a new distributed hydrological model, called GEOtop. The model accommodates very complex topography and, besides the water balance, unlike most other hydrological models, integrates all the terms in the surface energy balance equation. GEOtop uses a discretization of the landscape based on digital elevation data. These digital elevation data are preprocessed to allow modeling of the effect of topography on the radiation incident on the surface, both shortwave (including shadowing) and longwave (accounting for the sky view factor). For saturated and unsaturated subsurface flow, GEOtop makes use of a numerical solution of the 3D Richards’’ equation in order to properly model, besides the lateral flow, the vertical structure of water content and the suction dynamics. These characteristics are deemed necessary for consistently modeling hillslope processes, initiation of landslides, snowmelt processes, and ecohydrological phenomena as well as discharges during floods and interstorm periods. An accurate treatment of radiation inputs is implemented in order to be able to return surface temperature. The motivation behind the model is to combine the strengths and overcome the weaknesses of flood forecasting and land surface models. The former often include detailed spatial description and lateral fluxes but usually lack appropriate knowledge of the vertical ones. The latter are focused on vertical structure and usually lack spatial structure and prediction of lateral fluxes. Outlines of the processes simulated and the methods used to simulate them are given. A series of applications of the model to the Little Washita basin of Oklahoma using data from the Southern Great Plains 1997 Hydrology Experiment (SGP97) is presented. These show the model’’s ability to reproduce the pointwise energy and water balance, showing that just an elementary calibration of a few parameters is needed for an acceptable reproduction of discharge at the outlet, for the prediction of the spatial distribution of soil moisture content, and for the simulation of a full year’’s streamflow without additional calibration.

Crow, Wade T.; Van Loon, Emiel

doi: 10.1175/JHM499.1pmid: N/A

Data assimilation approaches require some type of state forecast error covariance information in order to optimally merge model predictions with observations. The ensemble Kalman filter (EnKF) dynamically derives such information through a Monte Carlo approach and the introduction of random noise in model states, fluxes, and/or forcing data. However, in land data assimilation, relatively little guidance exists concerning strategies for selecting the appropriate magnitude and/or type of introduced model noise. In addition, little is known about the sensitivity of filter prediction accuracy to (potentially) inappropriate assumptions concerning the source and magnitude of modeling error. Using a series of synthetic identical twin experiments, this analysis explores the consequences of making incorrect assumptions concerning the source and magnitude of model error on the efficiency of assimilating surface soil moisture observations to constrain deeper root-zone soil moisture predictions made by a land surface model. Results suggest that inappropriate model error assumptions can lead to circumstances in which the assimilation of surface soil moisture observations actually degrades the performance of a land surface model (relative to open-loop assimilations that lack a data assimilation component). Prospects for diagnosing such circumstances and adaptively correcting the culpable model error assumptions using filter innovations are discussed. The dual assimilation of both runoff (from streamflow) and surface soil moisture observations appears to offer a more robust assimilation framework where incorrect model error assumptions are more readily diagnosed via filter innovations.

McLaughlin, Dennis; Zhou, Yuhua; Entekhabi, Dara; Chatdarong, Virat

doi: 10.1175/JHM493.1pmid: N/A

Land surface data assimilation problems are often limited by the high dimensionality of states created by spatial discretization over large high-resolution computational grids. Yet field observations and simulation both confirm that soil moisture can have pronounced spatial structure, especially after extensive rainfall. This suggests that the high dimensionality of the problem could be reduced during wet periods if spatial patterns could be more efficiently represented. After prolonged drydown, when spatial structure is determined primarily by small-scale soil and vegetation variability rather than rainfall, the original high-dimensional problem can be effectively replaced by many independent low-dimensional problems that can be solved in parallel with relatively little effort. In reality, conditions are continually varying between these two extremes. This is confirmed by a singular value decomposition of the replicate matrix (covariance square root) produced in an ensemble forecasting simulation experiment. The singular value spectrum drops off quickly after rainfall events, when a few leading modes dominate the spatial structure of soil moisture. The spectrum is much flatter after a prolonged drydown period, when spatial structure is less significant. Deterministic reduced-rank Kalman filters can achieve significant computational efficiency by focusing on the leading modes of a system with large-scale spatial structure. But these methods are not well suited for land surface problems with complex uncertain inputs and rapidly changing spectra. Local ensemble Kalman filters are suitable for such problems during dry periods but give less accurate results after rainfall. The most promising option for achieving computational efficiency and accuracy is to develop generalized localization methods that dynamically aggregate states, reflecting structural changes in the ensemble.