[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.

[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.

In order to assess the retrieval of soil moisture from ERS 1 (European Remote Sensing Satellite) synthetic aperture radar (SAR) data, an inversion procedure based on the integral equation model (IEM) [Fung et al., 1992] is developed. First, the IEM is used to analyze the sensitivity of radar echoes (in terms of the backscattering coefficient tr ø) to the surface parameters (roughness and dielectric constant) under ERS 1 SAR configuration. Results obtained for random rough bare soil fields show that the effect of surface roughness is very strong, particularly in the case of smooth surfaces, and that the sensitivity of tr ø to dielectric constant is independent of the radar configuration and the roughness conditions. This means that the range of variation of backscattering with respect to the dielectric constant variation of dry to wet soil remains the same (about 5 dB) for any roughness condition and radar configuration. The possibility of applying the inversion procedure to retrieve soil moisture is investigated using a set of data collected in a test site situated near Naples, Italy, during the Sele Synthetic Aperture Radar experiment (SESAR) campaign (November 1993). Simultaneous with ERS 1 overpasses, dielectric constant and roughness measurements were taken over two flat bare fields. From this analysis it is found that the inversion of backscattering from ERS 1 SAR into soil moisture is not reliable without accurate information on roughness if the surface is smooth. In this case it is observed that the sensitivity to the roughness parameters is much higher than the sensitivity to dielectric constant, so that even a small error in the measurement of this parameter can affect the retrieved value of soil moisture significantly. The inversion procedure provides more reliable soil moisture estimates when surfaces rougher than those analyzed in the field experiment are considered.

Abstract. Mediterranean ecosystems are commonly heterogeneous savanna-like ecosystems, with contrasting plant functional types (PFTs, e.g. grass and woody vegetation) competing for water. Mediterranean ecosystems are also commonly characterized by strong inter-annual rainfall variability, which influences the distributions of PFTs that vary spatially and temporally. An extensive field campaign in a Mediterranean setting was performed with the objective to investigate interactions between vegetation dynamics, soil water budget and land-surface fluxes in a water-limited ecosystem. Also a vegetation dynamic model (VDM) is coupled to a 3-component (bare soil, grass and woody vegetation) Land surface model (LSM). The case study is in Orroli, situated in the mid-west of Sardegna within the Flumendosa river basin. The landscape is a mixture of Mediterranean patchy vegetation types: trees, including wild olives and cork oaks, different shrubs and herbaceous species. Land surface fluxes, soil moisture and vegetation growth were monitored during the May 2003-June 2006 period. Interestingly, hydrometeorological conditions of the monitored years strongly differ, with dry and wet years in turn, such that a wide range of hydrometeorological conditions can be analyzed. The coupled VDM-LSM model is successfully tested for the case study, demonstrating high model performance for the wide range of eco-hydrologic conditions. Results demonstrate also that vegetation dynamics are strongly influenced by the inter-annual variability of atmospheric forcing, with grass leaf area index changing significantly each spring season according to seasonal rainfall amount.

ABSTRACT:In order to investigate the behaviour of climatic and hydrological variables, several statistical and stochastic techniques are currently applied to time series. In the present study a statistical analysis of annual and seasonal precipitation has been performed over 109 cumulated rainfall series with more than 50 years of data observed in a region of Southern Italy (Calabria). Trend analyses have been made by using both nonparametric (Mann-Kendall test) and parametric (linear regression analysis) procedures. The long historical series of monthly rainfall data employed in this work have been previously processed through a pre-whitening (PW) technique in order to reduce the autocorrelation of rainfall series and its effects on outcomes of trend detection. The application of the above mentioned procedures has shown a decreasing trend for annual and winter-autumn rainfall and an increasing trend for summer precipitation. Moreover the Mann-Whitney test has been used to evidence the possible change points in the data. The higher percentages of rainfall series show possible year changes during decade [1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970] for almost all of the temporal aggregation rainfall.