1,721,261 research outputs found
Un modello di trasferimento a parametri ‘fisici’ per la stima dei flussi di ricarica alla falda
No full textNella memoria si affronta il problema della propagazione dei flussi di percolazione al di sotto dello strato radicato del suolo, fino alla superficie della falda acquifera. L’approccio dei modelli fisicamente basati adottato per la descrizione di questi flussi non è facilmente applicabile a scale ampie. Infatti, tali modelli richiedono un elevato numero di parametri, a fronte di una conoscenza generalmente inadeguata delle caratteristiche del sottosuolo, oltre a comportare un rilevante onere di calcolo. D’altra parte i processi di trasferimento nella zona insatura, sia in termini di tempi di percorrenza che di rimodulazione dei flussi, possono influenzare in modo significativo la calibrazione dei modelli di flusso negli acquiferi e devono quindi essere adeguatamente descritti.
La metodologia proposta per la stima dei flussi di ricarica alla falda considera la forma linearizzata dell’equazione di Richards. Pertanto, il comportamento idraulico del suolo al di sotto dello strato radicato è descritto attraverso una funzione di trasferimento, i cui parametri sono derivati dalle sole proprietà idrauliche del suolo, valutate in corrispondenza del valore di contenuto idrico adottato per la linearizzazione. Tale valore viene individuato in modo automatico a prescindere dagli andamenti effettivi di contenuto idrico nel suolo, in considerazione solo delle caratteristiche di variabilità dei flussi di infiltrazione e delle proprietà del suolo
Application of the changepoint detection to daily temperature series with the MAC-D procedure
No full textInvestigation of spatial and temporal variability of saturated soil hydraulic conductivity at the field-scale
No full textThough soil hydrologists agree that field saturated
conductivity (Ks) is a key parameter in modelling the dynamics of water flow and solute transport in soils, they also recognize that its variability in space and time is far from being completely understood. In order to highlight the variability of Ks at the plot scale we performed ten measurement campaigns in three parcels within a 10 ha maize field during two subsequent crop seasons and in the fallow periods following them, in uniform conditions of crop, agricultural practices and, to a large extent, of pedological characteristics. This paper reports the
outcomes of the measurements, conducted with the Guelph permeameter (GP) and with the tension infiltrometer (TI), along with detailed information on the data and a thorough description of the experimental field and of the measurement techniques. Based on a careful statistical analysis of the dataset and an extensive discussion of the results, the following conclusions were reached.
GP Ks show changes in time and space, both between and within the parcels, with a different temporal behaviour for the different parcels, and no evident seasonal cycle. Mean and standard deviation of the transformed GP data samples are shown to be linearly related. This allowed the definition of a model of Ks statistical distribution that elucidates the distinct contributions of soil matrix and macropores, and provides a validation of the Morales et al. (2010) concept of
biologically-driven macropore dynamics.
TI estimations of Ks vary in space in agreement with the soil texture and
show a stable seasonal pattern. However, in presence of macropores, they
are not representative of the actual values of the saturated conductivity. On the other hand, TI Ks could provide an estimate of the conductivity of the soil matrix. The comparison with the soil Matrix conductivity values deriving from the proposed model of Ks statistical distribution seem to support this possibility.
These results, that shall be corroborated by further experiments, support the importance of thoroughly investigating the interactions between soil biota, vegetation and the soil hydraulic properties
Changepoint detection in case of step change and inhomogeneous segments (platform-like inhomogeneities)
No full textUN NUOVO TEST STATISTICO PER LA RICERCA DI ANOMALIE NELLE SERIE TEMPORALI
No full textLe serie temporali di misure delle grandezze idro-meteorologiche risentono spesso di imperfezioni, quali dati mancanti, outliers e discontinuità nei valori medi. Se per le elaborazioni statistiche si opta spesso per concentrare le analisi sui soli dati validi, per gli scopi modellistici è in genere indispensabile affrontare e risolvere i problemi menzionati, al fine di ottenere serie complete di tutte le grandezze necessarie per la calibrazione e validazione dei modelli. Le discontinuità nella media, su cui questo lavoro si concentra, possono essere effetto di offset strumentali e delle loro correzioni, di modifiche nella stazione di rilevamento (posizione, tipologia degli strumenti) o anche di alterazioni dell’ambiente che circonda la stazione. Queste discontinuità si presentano come gradini o trend all’interno delle serie; come per gli outliers, la registrazione indica un valore differente dal valore reale della grandezza misurata ma, a differenza di questi, l’errore è sistematico e modifica i dati di una quantità che persiste per un certo periodo, mantenendo approssimativamente stabili le altre caratteristiche della serie, quali ad esempio la differenza di passo uno e parte degli eventuali andamenti stagionali. Accade sovente che gli offset non siano rilevati o che, quando lo sono, le azioni correttive non vengano documentate. E’ quindi consigliabile, prima di utilizzare le serie di misure, sottoporle ad un test statistico per la verifica dell’eventuale presenza di punti di discontinuità. In letteratura sono stati proposti diversi test di questo tipo; generalmente essi sono efficaci soprattutto quando la serie contiene un singolo punto di discontinuità, mentre riducono notevolmente le loro prestazioni quando l’intervallo di valori anomali si colloca all’interno della serie o quando i punti di discontinuità sono molteplici. Nella memoria viene presentato un nuovo test (Rienzner & Gandolfi, 2009), che supera in larga misura queste limitazioni, e viene illustrato un esempio di applicazione a serie di grandezze meteorologiche di alcune stazioni della rete agrometeorologica della Regione Lombardia
A procedure for the detection of undocumented multiple abrupt changes in the mean value of daily temperature time series of a regional network
No full textThis paper presents the new procedure MAC-D for the automated detection of undocumented Multiple Abrupt Changes in the mean value of Daily temperature series, recorded in a network of meteorological stations. MAC-D can be applied to series containing seasonality, multiple change points, outliers, and with a noise component that can be autocorrelated and non-normally distributed. The main novelties of the procedure are (1) the pretreatment of the observed series, to derive a series of daily values that complies with the theoretical requirements of the change point detection tests and in (2) the combined use of the reference series and pairwise comparison approaches. MAC-D consists of three phases in sequence. In phase 1, the seasonal and climatic fluctuations are estimated and removed, using the reference series approach. Phase 2 combines a linear filtering with a change point detection test in an iterative algorithm, which runs until full compliance between the characteristics of the filtered series and the test requirements is achieved. Phase 3 is aimed at removing the false change points, due to error propagation in the reference series analysis, by double checking the detected change points with the pairwise comparison approach. Monte Carlo estimations of the actual significance and overall performance of the procedure for different series features and test resolutions are provided. Results demonstrate that MAC-D performs very well with daily series having a wide range of different characteristics
A composite statistical method for the detection of multiple undocumented abrupt changes in the mean value within a time series
No full textThe time series of measurements of hydro-meteorological variables often suffer from imperfections such as missing data, outliers and discontinuities in the mean values. The discontinuity in the mean can be the effect of: instrumental offsets and of their corrections, of changes in the monitoring station or in the surrounding environment. If the discontinuities can be identified with a reasonable precision, a correction of the erroneous data can be made. Several authors have put their great effort into developing techniques to identify non-climatic inhomogeneities; the resulting statistical methods are especially effective when the series contains a single change point, while their performances decline when the series contains multiple change points or inhomogeneous segments (a portion of the series bounded by two complementary shifts). These limitations also affect the standard normal homogeneity test (SNHT), one of the most effective and widely applied tests. We present a composite method of homogeneity testing, standard normal homogenization composite method (SNHCM), including the SNHT as one component, which improves the SNHT performances with multiple change points
and inhomogeneous segments. A number of comparisons among the new method, the SNHT and a powerful optimal segmentation method (OSM-CM), are illustrated in the paper. SNHCM demonstrates their performances in change-point detection similar to, or better than, the SNHT and very close to the OSM-CM. The SNHCM is effective in recognizing complex patterns of discontinuities, especially inhomogeneous segments, which represent a severe problem for SNHT; on the contrary, SNHT performs slightly better only when the series contains a single change point, but the difference
between the two methods is negligible. Compared to the OSM-CM, SNHCM provides very similar performances, with some favourable features deriving from the fact that it is computationally lighter, simpler to implement, can easily handle very long series and is based on statistical hypothesis tests with a well-defined and adjustable significance level
INFLUENCE OF THE DRAINAGE NETWORK IDENTIFICATION METHOD ON GEOMORPHOLOGICAL PROPERTIES AND HYDROLOGICAL RESPONSE
No full textThe influence of the method of identification of the drainage network on its geomorphological characteristics and on its hydrological response is analysed. Blue lines, photo-interpreted networks and networks generated from digital elevation models (DEMs) by an automatic algorithm are compared with field observations for two small alpine catchments. The comparisons are carried out in quantitative terms by using several geomorphological indices and functions and by calculating the hydrological response of the networks as represented by their geomorphologic instantaneous unit hydrograph (GIUH). The results show that the effect of the identification method on the geomorphological indices and on the hydrological response is significant, and that the threshold area for channel initiation is not constant. Moreover, the available data show a poor correlation between local slope and threshold area. Finally, the influence of the threshold area on the shape of the GIUH is larger when the residence time on the hillslopes is of the same order as the residence time in the network. In the opposite case, the variability of the flow velocity along the network seems to play an important role
Modelling of spatial controls on denitrification at the landscape scale
No full textA simple model for estimating likely spatial patterns in landscape-scale denitrification rates is described. In the absence of limiting nitrate concentration, denitrification is assumed to be controlled principally by the soil water regime and the amount of available soil carbon. A formulation of TOPMODEL is used to estimate the spatial distribution of water table depths. Soil carbon concentration is assumed to decrease exponentially with depth. The spatial distribution of carbon concentrations at the soil surface is assumed to be imperfectly correlated with the topographic index used in TOPMODEL. Monte Carlo simulation techniques were used to introduce a stochastic element to the spatial distribution of soil carbon. This allowed estimates of the uncertainty in model outputs, resulting from uncertainties in the distribution and variability of soil carbon to be made. The model predicted spatial and temporal patterns of nitrate-non-limiting denitrification for a 15 year period in the Slapton Wood catchment in southwest England. Predicted denitrification followed a slight seasonal pattern with a winter maximum. Total annual denitrification losses tended to be positively correlated with total annual precipitation in the catchment. Highest rates tended to be predicted near to the stream. The modelling approach provides a means of assessing the proximity of local-scale field measurements to probable landscape-scale denitrification fluxes. Combining a deterministic model core with a stochastic generation of model parameters or state variables provides an attractive way of embracing variability and uncertainty whilst maintaining a conceptual description of the system dynamics. Copyrigh
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