"A conceptual-stochastic approach to short time runoff data modelling is proposed, according to the aim of reproducing the hydrological aspects of the streamflow process and of preserving as much as possible the dynamics of the process itself. This latter task implies preservation of streamflow characteristics at higher scales of aggregation and, within a conceptual framework, involves compatibility with models proposed for the runoff process at those scales. At a daily time scale the watershed response to the effective rainfall is considered as deriving from the response of three linear reservoirs, respectively representing contributions to streamflows of large deep aquifers, with over-year response lag, of aquifers which run dry by the end of the dry season and of subsurface runoff. The surface runoff component is regarded as an uncorrelated point process. Considering the occurrences of effective rainfall events as generated by an independent Poisson process, the output of the linear system represents a conceptually-based multiple shot noise process. Model identification and parameter estimation are supported by information related to the aggregated runoff process, in agreement to the conceptual framework proposed, and this allows parameter parsimony, efficient estimation and effectiveness of the streamflow reproduction. Good performances emerged from the model application and testing made with reference to some daily runoff series from Italian basins.
Diffuse cerebellar meningeal carcinomatosis secondary to haematogenous dissemination from an anaplastic solid mammary carcinoma was diagnosed in an old German shepherd dog suffering from seizures and rapidly progressing to stupor. A single computed tomography cerebellar scan identified an unusual homogeneous density area that was considered to be associated with a vascular disorder, in the absence of space-occupying lesions. At necropsy, nodular masses were observed in the mammary gland, lungs, tracheobronchial lymph nodes and adrenals. Cerebellar leptomeninges were affected by diffuse blood effusion. Histology showed a solid mammary tumour, characterised by anaplastic cells with a cytoplasmic keratin-positive and vimentin-negative immunoreaction. The tumour had spread to the lungs, tracheobronchial lymph nodes and adrenals. Cerebellar leptomeninges were diffusely infiltrated by the cytokeratin-positive neoplastic cells. Even though computed tomography scan gave no evidence of meningeal carcinomatosis, it was considered that a cerebellar vascular disorder might be present. This was subsequently confirmed by neuropathological investigation and seen to be associated with a cerebellar leptomeningeal carcinomatosis.
"The statistical and phenomenological aspects of the runoff process observed on different scales of aggregation are taken as a priori information for the conceptually based stochastic modeling of seasonal runoff. Runoff is considered as the sum of two groundwater components, with over-year and subannual response lag, and of a purely random component representing the direct runoff. This scheme is equivalent to a linear system, with two parallel linear reservoirs plus a zero lag linear channel. The system output is the runoff, and the input is the effective rainfall, considered proportional to the direct runoff. Assuming the effective rainfall as a non-Gaussian periodic independent process and considering nonseasonal groundwater parameters, this conceptualization leads to an autoregressive and moving average (2, 2) stochastic process with periodic independent residual. Stochastic model parameters are directly related to the linear system coefficients, and the effective rainfall structure can be determined from the estimated model residual. In order to obtain parameter estimates consistent with the conceptual constraints, two estimation stages, on an annual and a seasonal basis, and an iterative procedure are needed. The model was applied to a number of time series of monthly streamflows in the Apennine regions of Italy with promising results.
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