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[1] Micrometeorological measurements of evapotranspiration (ET) can be difficult to interpret and use for validating model calculations in the presence of land cover heterogeneity. Land surface fluxes, soil moisture (q), and surface temperatures (T s ) data were collected by an eddy correlation-based tower located at the Orroli (Sardinia) experimental field (covered by woody vegetation, grass, and bare soil) from April 2003 to July 2004. Two Quickbird high-resolution images (summer 2003 and spring 2004) were acquired for depicting the contrasting land cover components. A procedure is presented for estimating ET in heterogeneous ecosystems as the residual term of the energy balance using T s observations, a two-dimensional footprint model, and the Quickbird images. Two variations on the procedure are successfully implemented: a proposed two-source random model (2SR), which treats the heat sources of each land cover component separately but computes the bulk heat transfer coefficient as spatially homogeneous, and a common two-source tile model. For 2SR, new relationships between the interfacial transfer coefficient and the roughness Reynolds number are estimated for the two bare soil-woody vegetation and grass-woody vegetation composite surfaces. The ET versus q relationships for each land cover component were also estimated, showing that that the woody vegetation has a strong tolerance to long droughts, transpiring at rates close to potential for even the driest conditions. Instead, the grass is much less tolerant to q deficits, and the switch from grass to bare soil following the rainy season had a significant impact on ET.

Abstract. A large eddy simulation (LES) code of the atmospheric boundary layer (ABL)has been developed and applied to study the effect of spatially variable surface properties on the areally averaged surface shear stress at the land-atmosphere interface. The LES code simulates the space and time evolution of the large-scale turbulent eddies and their transport effects in the ABL. We report here on simulations of flow over spatially variable roughness fields. The dynamics are simulated, and the resulting space-time fields are averaged to explore the effects of the surface variability length scales on the average surface shear stress, as used in large-scale models to estimate scalar fluxes, such as evaporation. We observe asymmetrical response of the smooth-to-rough and rough-tosmooth transitions, such that the effects of the transitions accumulate rather than cancel.It is shown that the presence of abrupt changes in surface roughness and the atmosphere's response to these patches create a marked dependence of the statistical structure of surface shear stress on the length scale of the surface patches. An increase in regionally averaged surface stress for decreasing horizontal patch length scale is found. IntroductionSuccessful modeling of surface hydrologic and atmospheric processes hinges on the ability to describe the exchange of water, heat, and momentum across the land-atmosphere interface. A major concern is how to account for the effect of the spatial variability of surface conditions on scales smaller than the model grid cell. The actual total grid cell exchange is an integration of small-scale exchange processes over the area of the cell, where the physical integration is provided by the turbulent mixing in the atmospheric boundary layer (ABL).

[1] The structure and function of vegetation regulate fluxes across the biosphereatmosphere interface with large effects in water-limited ecosystems. Vegetation dynamics are often neglected in hydrological modeling except for simple prescriptions of seasonal phenology. However, changes in vegetation densities, influencing the partitioning of incoming solar energy into sensible and latent heat fluxes, can result in long-term changes in both local and global climates with resulting feedbacks on vegetation growth. This paper seeks a simple vegetation dynamics model (VDM) for simulation of the leaf area index (LAI) dynamics in hydrologic models. Five variants of a VDM are employed, with a range of model complexities. The VDMs are coupled to a land surface model (LSM), with the VDM providing the LAI evolution through time and the LSM using this to compute the land surface fluxes and update the soil water contents. We explore the models through case studies of water-limited grass fields in California (United States) and North Carolina (United States). Results show that a simple VDM, simulating only the living aboveground green biomass (i.e., with low parameterization), is able to accurately simulate the LAI. Results also highlight the importance of including the VDM in the LSM when studying the climate-soil-vegetation interactions over moderate to long timescales. The inclusion of the VDM in the LSM is demonstrated to be essential for assessing the impact of interannual rainfall variability on the water budget of a water limited region.

[1] A variety of surface roughness characterizations have emerged from nineteenth and twentieth century studies of channel hydraulics. When the water depth h is much larger than the characteristic roughness height k s , roughness formulations such as Manning's n and the friction factor f can be explicitly related to the momentum roughness height z o in the log-law formulation for turbulent boundary layers, thereby unifying roughness definitions for a given surface. However, when h is comparable to (or even smaller than) k s , the log-law need not be valid. Using a newly proposed mixing layer analogy for the inflectional velocity profile within and just above the roughness layer, a model for the flow resistance in shallow flows is developed. The key model parameter is the characteristic length scale describing the depth of the Kelvin-Helmholtz wave instability. It is shown that the new theory, originally developed for canopy turbulence, recovers much of the earlier roughness results for flume experiments and shallow gravel streams. This study is the first to provide such a unifying framework between canopy atmospheric turbulence and shallow gravel stream roughness characterization. The broader implication of this study is to support the merger of a wealth of surface roughness characterizations independently developed in nineteenth and twentieth century hydraulics and atmospheric sciences and to establish a connection between roughness formulations across traditionally distinct boundary layer types.INDEX TERMS: 1860 Hydrology: Runoff and streamflow; 1824 Hydrology: Geomorphology (1625); 3379 Meteorology and Atmospheric Dynamics: Turbulence; KEYWORDS: Manning's roughness, momentum roughness height, friction factor, mixing layer analogy, shallow gravel bed, canopy turbulence Citation: Katul, G., P. Wiberg, J. Albertson, and G. Hornberger, A mixing layer theory for flow resistance in shallow streams,

Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate-carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO 2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid-and high-latitudes, (2) a strong function of dryness at mid-and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45 • N). The sensitivity of NEE to mean annual temperature breaks down at ∼16 • C (a threshold value of mean annual temperature), above which no further increase of CO 2 uptake with temperature was observed and dryness influence overrules temperature influence.

[1] Coarse soil moisture fields and nonlinear relationships between fluxes and soil moisture combine to yield errors in both diagnostic and predictive estimates of largescale mass and energy fluxes. Efforts to empirically define the dynamics of subgrid spatial variance of soil moisture have led to contradictory results. Moreover, most reports of soil moisture variability range from qualitative to descriptively quantitative, owing to the lack of a robust theoretical framework for moisture variance dynamics. In this paper we derive a conservation equation for the spatial variance of subgrid root zone soil moisture, based on first principles of statistical fluid mechanics. We arrive at a variance budget in which explicit covariances between moisture fields and land surface flux fields act to produce or destroy variance through time (according to the sign of the correlation between the flux and state fields). A series of examples are used to explore how simple forms of soil, vegetation, precipitation, topography, and initial moisture variability lead to evolving covariances between spatial fields of soil moisture and particular land surface fluxes and how these covariances relate to the temporal trajectory of the spatial variance of soil moisture. We isolate a set of processes and conditions that demonstrate variance production through time and a set that demonstrate variance destruction. Of particular interest is the tendency for transpiration and infiltration-runoff processes to either produce or destroy variance, depending on the background wetness regime. Field data are also employed and shown to demonstrate a temporal behavior of the spatial variance that is readily described by the proposed approach. Ultimately, this work should aid field data interpretation and, when supplemented with a closure model for the variance budget, lead to improved land surface flux predictability over coarse grids.

Above forest canopies, eddy covariance (EC) measurements of mass (CO 2 , H 2 O vapor) and energy exchange, assumed to represent ecosystem fluxes, are commonly made at one point in the roughness sublayer (RSL). A spatial variability experiment, in which EC measurements were made from six towers within the RSL in a uniform pine plantation, quantified large and dynamic spatial variation in fluxes. The spatial coefficient of variation (CV) of the scalar fluxes decreased with increasing integration time, stabilizing at a minimum that was independent of further lengthening the averaging period (hereafter a 'stable minimum'). For all three fluxes, the stable minimum (CV 5 9-11%) was reached at averaging times (s p ) of 6-7 h during daytime, but higher stable minima (CV 5 46-158%) were reached at longer s p (412 h) during nighttime. To the extent that decreasing CV of EC fluxes reflects reduction in micrometeorological sampling errors, half of the observed variability at s p 5 30 min is attributed to sampling errors. The remaining half (indicated by the stable minimum CV) is attributed to underlying variability in ecosystem structural properties, as determined by leaf area index, and perhaps associated ecosystem activity attributes. We further assessed the spatial variability estimates in the context of uncertainty in annual net ecosystem exchange (NEE). First, we adjusted annual NEE values obtained at our long-term observation tower to account for the difference between this tower and the mean of all towers from this experiment; this increased NEE by up to 55 g C m À2 yr À1 . Second, we combined uncertainty from gap filling and instrument error with uncertainty because of spatial variability, producing an estimate of variability in annual NEE ranging from 79 to 127 g C m À2 yr À1 . This analysis demonstrated that even in such a uniform pine plantation, in some years spatial variability can contribute $ 50% of the uncertainty in annual NEE estimates.